ISSN 0288-4534

CODEN: KPPJAD

KONA

Powder andParticle Journal

No.33 (2016)

Hosokawa Powder Technology Foundation
available online-www.kona.or.jp

KONA Powder and Particle Journal No. 33 (2016)

http://www.kona.or.jp

About this Journal

Editorial Board

KONA Powder and Particle Journal is a refereed scientificjournal that publishes articles on powder and particle scienceand technology. This journal has been published annually
since 1983 and is distributed free of charge to researchers,
members of the scientific communities, universities and research libraries throughout the world, by Hosokawa Powder
Technology Foundation (http://www.kona.or.jp/) establishedby Mr. Masuo Hosokawa in 1991.

Editor-in-Chief
J. Hidaka(Doshisha Univ., Japan)
Asian / Oceanian Editorial Board
S. Biggs(Univ. of Queensland, Australia)
X.S. Cai(Univ. of Shanghai for Sci. & Tech., China)
T. Charinpanitkul(Chulalongkorn Univ., Thailand)
Y. Fukumori(Kobe Gakuin Univ., Japan)
K. Higashitani(Kyoto Univ., Japan)
H. Kage(Kyushu Institute of Technology, Japan)
Y. Kang(Chungnam National Univ., Korea)
D.J. Lee(National Taiwan Univ., Taiwan)
J.H. Li(Chinese Academy of Science, China)
H. Makino(CRIEPI, Japan)
S. Matsusaka(Kyoto Univ., Japan)
M. Naito(Osaka Univ., Japan)
K. Nogi(Osaka Univ., Japan)
K. Okuyama(Hiroshima Univ., Japan)
Pradip(TRDDC, India)
M. Rhodes(Monash Univ., Australia)
M. Takahashi(Nagoya Institute of Technology, Japan)
H. Takeuchi(Gifu Pharmaceutical Univ., Japan)
W. Tanthapanichakoon (Tokyo Institute of Technology, Japan)
Y. Tsuji(Osaka Univ., Japan)
S. Watano(Osaka Pref. Univ., Japan)
Secretariat
T. Yokoyama(Hosokawa Powder Tech. Foundation, Japan)
L. Cui(Hosokawa Micron Corp., Japan)

The Chinese character “ 粉 ” in the cover is pronounced“KONA” in Japanese, and means “Powder”. The hand writ” is after the late Mr. Eiichi Hosokawa, founder often “
the Hosokawa Micron Corporation (http://www.hosokawamicron.co.jp).
About the Cover:
Introduction of numerical simulation technologies is desired
to optimize the design of industrial devices and/ or the operational conditions. The discrete element method (DEM) is
often employed in the simulation of the granular flow. At
present, the DEM has critical problems form a viewpoint of
the industrial application. Specifically, modeling of arbitrary
shaped wall boundary became problematic since solid particlewall interaction algorithm was much complex. Signed distance function (SDF) based wall boundary model was newly
developed to simulate the granular flow in an industrial system. The DEM/SDF method has been applied to actual industrial systems such as the die-filling, ribbon mixer andtwin-screw kneader so far. For front cover, a new die-filling
simulation was performed, where the die shape was “ 粉 ”(see the video at Doi:10.14356/kona.2016023). Please readthe paper on pp.169–178.

European / African Editorial Board
Chairman
S.E. Pratsinis(ETH Zürich, Switzerland)
B. Biscans(Univ. de Toulouse, France)
M.-O. Coppens(Univ. College London, UK)
L. Gradon(Warsaw Univ. of Technology, Poland)
G.M.H. Meesters(Univ. of Delft, Netherlands)
W. Peukert(Univ. Erlangen, Germany)
J.M. Valverde(Univ. of Seville, Spain)
Secretariat
S. Sander(Hosokawa Alpine AG, Germany)
L. Kneisl(Hosokawa Alpine AG, Germany)
American Editorial Board
Chairman
B.M. Moudgil(Univ. of Florida., USA)
D.W. Fuerstenau(Univ. of California, USA)
A.J. Hickey(Univ. of North Carolina, USA)R. Hogg(Pennsylvania State Univ., USA)
R.K. Rajamani(Univ. of Utah, USA)
A.D. Rosato(New Jersey Institute of Technology, USA)
L.M. Tavares(UFRJ, Brazil)
Secretariat
C.C. Huang(Hosokawa Micron Intl. Inc., USA)

■ Publication Office

Headquarters of Hosokawa Micron Corporation

Hosokawa Powder Technology Foundation (http://www.kona.or.jp)(in the headquarters building of Hosokawa Micron Corporation)
1-9, Shodaitajika, Hirakata-shi, Osaka 573-1132, Japan
E-mail: contact_zainq@hmc.hosokawa.com

KONA Powder and Particle Journal No. 33 (2016) <Editorial>
1

Editor's Preface<Review Papers>
3

Viscosity Reduction of Heavy Oil Using
Nanocatalyst in Aquathermolysis Reaction

Ferry Iskandar, Erlandy Dwinanto, Mikrajuddin
Abdullah, Khairurrijal and Oki Muraza
17

Surface Chemistry and Rheology of Slurriesof Kaolinite and Montmorillonite fromDifferent Sources

Pek-Ing Au and Yee-Kwong Leong
33

Controlled Magnetic Properties of Iron
Oxide-Based Nanoparticles for Smart
Therapy

Dung The Nguyen and Kyo-Seon Kim
48

Analysis of Particle Size Distributions of
Quantum Dots: From Theory to Application

Doris Segets

Contents

KONA Powder and Particle Journal No. 33 (2016) 

63

Aerosol Delivery of siRNA to the Lungs.
Part 1: Rationale for Gene Delivery Systems

Susanne R. Youngren-Ortiz, Nishant S. Gandhi,
Laura España-Serrano and Mahavir B. Chougule
86

Effect of Process Conditions on Fluidization

Paola Lettieri and Domenico Macrì
109

Transport Properties and Segregation
Phenomena in Vibrating Granular Beds

Chun-Chung Liao and Shu-San Hsiau
127

Mixing and Fluid Dynamics Effects in
Particle Precipitation Processes

Jerzy Bałdyga

Contents

KONA Powder and Particle Journal No. 33 (2016) 

150

Granular Temperature and Segregation in
Dense Sheared Particulate Mixtures

Kimberly M. Hill and Yi Fan
169 How Should the Discrete Element Method Be
Applied in Industrial Systems?: A Review

Mikio Sakai
179

Developments of Blocking Filtration Modelin Membrane Filtration

Eiji Iritani and Nobuyuki Katagiri

Contents

KONA Powder and Particle Journal No. 33 (2016) <Original Research Papers>
203

Preparation of LaB6 Powders via
Calciothermic Reduction using
Mechanochemistry and Acid Leaching

Duygu Ağaoğulları, Özge Balcı, M. LütfiÖveçoğlu and İsmail Duman
219

Precipitation Process of Calcium Phosphatefrom Calcium Carbonate Suspension

Nayane Macedo Portela da Silva, Fabienne
Espitalier and Ange Nzihou
228

A Novel Fluorescent Chemosensor Based on
β-(2-Pyridyl)acrolein-Rhodamine B
Derivative: Polymer Particle Interaction withan Enhanced Sensing Performance

Kyo-Sun Ku, Ji-Yong Hwang, Palanisamy
Muthukumar and Young-A Son
239 Controlling of Segregation in Rotating
Drums by Independent End Wall Rotations

Hsiu-Po Kuo, Wei-Ting Tseng and An-Ni Huang

Contents

KONA Powder and Particle Journal No. 33 (2016) 

249

Consolidation of Non-Colloidal Spherical
Particles at Low Particle Reynolds Numbers

Kevin P. Galvin, Marveh Forghani, Elham
Doroodchi and Simon M. Iveson
264

Effect of Drag Models on Residence Time
Distributions of Particles in a Wurster
Fluidized Bed: a DEM-CFD Study

Liang Li, Johan Remmelgas, Berend G.M. van
Wachem, Christian von Corswant, Staffan
Folestad, Mats Johansson and Anders Rasmuson
278

Reduction Kinetics of Zinc Powder from
Brass Converter Slag by Pyrometallurgical
Method Using Hydrogen Gas

Tae-Hyuk Lee, Sin-Hyeong Joo, Hayk H.
Nersisyan, Man-Sik Kong, Jae-Woo Lee,Ki-Won Park and Jong-Hyeon Lee
287

Mechanical Properties of Agglomerates
Produced by the Mechanical Vibration of
Cohesive Powders

Hamid Salehi Kahrizsangi, Diego Barletta andMassimo PolettoContents

KONA Powder and Particle Journal No. 33 (2016) 

296

Effects of Mixing Ratio of Binary Fine
Particles on the Packing Density andFiltration Characteristics

Kuo-Jen Hwang and Iou-Liang Lin
304

Improving Dispersion of Bacterial
Endospores for Enumeration

Vijay B. Krishna, Jue Zhao, Smithi Pumprueg,
Ben L. Koopman and Brij M. Moudgil
310

Optical Characterization of Industrial
Slurries

Marco Potenza, Tiziano Sanvito and Giuseppe
Fazio
322

Decoration of Carbon Nanotubes by
Semiconducting or Metallic Nanoparticles
using Fluidized Bed Chemical Vapour
Deposition

Pierre Lassègue, Nicolas Coppey, Laure Noé,
Marc Monthioux and Brigitte Caussat

Contents

KONA Powder and Particle Journal No. 33 (2016) 

333

Nanostructure Design on Porous Carbon
Powders under Chemical Process and Their
Physical Properties

Tomoya Nakazono and Takahiro Morishita
340

Preparation of Monodispersed Nanoparticles
of Transparent Conductive Oxides

Atsushi Muramatsu, Kiyoshi Kanie, Takafumi
Sasaki and Masafumi Nakaya<Information Articles>
354

The 49th Symposium on Powder Technology

356

The 22nd KONA Award(Awardee: Prof. Dr. Yoshio Otani)

357

General Information

Some achievements of the research for the 22nd KONA Award: (a) Illustration of centrifugal filter.
(b) Fractional collection efficiency of centrifugal filter at various rotation speed. (p.356)

Contents

KONA Powder and Particle Journal No. 33 (2016) /Doi:10.14356/kona.2016026

Acknowledgement

Reviewer Appreciation
Jusuke Hidaka, Editor-in-Chief
Doshisha University, Japan

I express our gratitude to the reviewers for the publication of this KONA Powder and Particle Journal. Thanks to
the generous contributions of the volunteer reviewers as well as the interest of researchers in our journal, the number of submission to this journal is increasing after the registration on the major international journal platforms andits level is further improving. Because of the limited space of this annual journal, the number of papers publishedthere is regrettably rather restricted. Therefore the papers of good quality need to be selected for the journal andfurther improved and refined by the reviewing.
The editorial team of KONA Journal would like to take this opportunity to gratefully acknowledge the reviewers
here and deeply appreciate their valuable time and professional contributions to the KONA Journal. We apologize
to any reviewer who may have inadvertently been omitted from this list. We are sincerely grateful to all who havebeen of any assistance to the publication of this journal.
BISCANS, Béatrice
COPPENS, Marc-Olivier
DOBASHI, Ritsu
FUERSTENAU, Douglas
FUJI, Masayoshi
FUKUI, Kunihiro
FUKUMORI, Yoshinobu
GOTOH, Kuniaki
GRADOŃ, Leon
HICKEY, Anthony
HIDAKA, Jusuke
HIGASHITANI, Ko
HIROTA, Ken
HOGG, Richard
HUANG, Ching-Chung
ICHIKAWA, Hideki
IIMURA, Kenji
ITO, TAKEO
IWASAKI, Tomohiro
IWATA, Masashi
KAJISHIMA, Takeo
KAMIYA, Hidehiro
KANAOKA, Chikao
KANO, Junya
KAWASAKI, Akira
KIMATA, Mitsumasa

LENGGORO, Wuled
MAENOSONO, Shinya
MAKINO, Hisao
MASUDA, Hiroaki
MATSUSAKA, Shuji
MEESTERS, Gabriel
MIYAHARA, Minoru
MIYAMOTO, Yoshinari
MORI, Yasushige
MORIMOTO, Yasuo
MOUDGIL, Brij
MURAMATSU, Atsushi
NAITO, Makio
NAKAHIRA, Atsushi
NAKAZATO, Tsutomu
NOGI, Kiyoshi
NOMURA, Toshiyuki
OGI, Takashi
OKUYAMA, Kikuo
OTANI, Yoshio
PEUKERT, Wolfgang
PRATSINIS, Sotiris
PURI, Virendra
RAJAMANI, Raj
ROSATO, Anthony
SAITO, Fumio

SAKAI, Mikio
SATOH, Munetaka
SHARMA, Parvesh
SHINTO, Hiroyuki
SHIRAKAWA, Yoshiyuki
SUZUKI, Hisao
SUZUKI, Michitaka
TAKEDA, Shin-ichi
TAKEUCHI, Hirofumi
TAKEUCHI, Manabu
TANAKA, Satoshi
TATAMI, Junichi
TAVARES, Luis Marcelo
TOKORO, Chiharu
TOYODA, Masahiro
TOZUKA, Yuichi
TSUJI, Yutaka
TSUKAGOSHI, Kazuhiko
VALVERDE, José Manuel
WAKAI, Fumihiro
WATANO, Satoru
WINDOWS-YULE, Kit
YASUDA, Kouichi
YOKOYAMA, Toyokazu
YOSHIDA, Hideto
YUU, Shinichi

November 10, 2015©2016 Hosokawa Powder Technology Foundation

KONA Powder and Particle Journal No. 33 (2016) 1–2/Doi:10.14356/kona.2016027

Editorial

Editorʼs Preface
Brij M. Moudgil
Chairman of American Editorial Board
University of Florida, USA

As Chair of the American Editorial Board, I am pleased to introduce the KONA Powder & Particle Journal No.
33, and to also share my thoughts on powder and particle technology opportunities and challenges.
Globally, powder and particle technology remains strong, primarily because the quality of life improvements all
across are leading to higher demand for powder and particle mediated products. Powder technology is recognized
as an important business with great global opportunity.
Demand for nonrenewable resources continues to be the driver for powder and particle technology advances inCentral and South America and Canada; whereas in the USA, nanotechnology applications in life sciences, healthcare, microelectronics, and food and agriculture are leading particle technology innovations. Advances in simulation and modeling at practical scales are on the horizon, and are expected to result in new powder and particle mediated products with enhanced performance. It is likely that more people are working in particle science andtechnology than before albeit without recognizing the field as such. Sustainable production and utilization of powders and particles with minimal environmental footprint is gaining attention as society becomes more appreciativeof such products.
However, training the next generation of powder and particle industry professionals is becoming our most significant challenge and opportunity. Particle technology knowledge now seems to be spread across departments anddisciplines, especially in the USA with a very limited number of single departments offering a comprehensive
training in powder and particle production, characterization and processing. Expertise continues to be lost in industry due to mergers and reorganizations, whereas in academia, powder and particle technology champions havebeen lost to retirement. New leadership is stepping up to the plate but more champions in industry and academe are
required to carry the subject forward. Additionally, students also have more majors to choose from as seemingly
more glamorous and rewarding professional degree options become available.
Industry primarily hires scientists and engineers who have the depth and rigor in a traditional discipline, and in
addition are willing to undertake interdisciplinary and collaborative team research endeavors. Additionally, young
professionals with effective communication skills, and teamwork and leadership experience have competitive advantage over those who lack such skills.
Although existing academic structure would continue to provide education and training in the traditional fields,
which are in demand by industry in a large number of graduates on a continuing basis; training a smaller andhighly specialized field workforce requires new economic models at the university level. A number of options canbe envisioned for providing customized training to a smaller number of graduates needed by a specific industry
sector.
Establishing university based education program in close cooperation with industry (or professors of practice) in
designing and delivery of the curriculum is one option. In this regard, a one year Master’s degree program in particulate engineering at the University of Delaware was recently launched, and another master’s degree program tomeet pharmaceutical industry needs was designed by the University of Massachusetts, Amherst. Another option isto design a 5 (3 + 2) year MS degree program (post-high school) that requires 1 year internship in a company; orallows students to acquire business and/or finance skills. This model would be successful only if the financial andcareer growth opportunity benefits outweigh an additional year’s investment of time and financial resources.
These approaches can involve multiple departments across universities so that no single university has to offer all©2016 Hosokawa Powder Technology Foundation

1

Brij M. Moudgil / KONA Powder and Particle Journal No. 33 (2016) 1–2

the required courses.
Technology Bootcamps can be another option. In the IT industry sector, for profit organizations are offering
technology boot camps for training in specific fields within a period of a few months. Although tuition for bootcamps is reported to be in tens of thousands of dollars for a three month course, graduates are promised high paying jobs upon graduation. This model can be a valuable tool, especially for acquiring specific skills to avail new career opportunities.
At the same time, it is recognized that we cannot teach what we cannot understand, and acquiring deep understanding requires sustained research. Fortunately, as other economies grow, a lot of good research is being conducted around the globe. A golden opportunity exists to mine the new knowledge, and organize it in a fashion thatenhances the existing curriculum and also develops new courses to train the next generation of powder and particle
technology professionals.
Overall, I believe it is a time of unique opportunity for revitalizing powder and particle technology education for
training a diverse workforce. International partnership opportunities in research and education are available to provide the platform for capitalizing on global teaching excellence, practice expertise and research facilities.
Current issue of KONA contains a number of excellent invited research and review articles from groups acrossthe globe working on emerging powder and particle technology applications. I sincerely hope you enjoy this issueof KONA. I am certain KONA will continue to serve as an important and meaningful platform for dialog andknowledge dissemination in the years to come.

Brij M. Moudgil
Chairman of American Editorial Board
November 9, 2015

2

KONA Powder and Particle Journal No. 33 (2016) 3–16/Doi:10.14356/kona.2016005

Review Paper

Viscosity Reduction of Heavy Oil Using Nanocatalyst
in Aquathermolysis Reaction †Ferry Iskandar 1,2*, Erlandy Dwinanto 1, Mikrajuddin Abdullah 1,
Khairurrijal 1 and Oki Muraza 3
1

Department of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Indonesia
National Research Center for Nanotechnology (NRCN), Institut Teknologi Bandung, Indonesia
3
Chemical Engineering Department and Center of Excellence in Nanotechnology,
King Fahd University of Petroleum and Minerals, Saudi Arabia
2

Abstract
Enhanced oil recovery (EOR) in the last several years has become an important factor in oil production due to theshortfall in high quality crude oil. Heavy oil as one of the unconventional hydrocarbons is still vastly abundant innature and is hence frequently explored with EOR. The viscous characteristic of heavy oil necessitates further
in-situ upgrading processes to be executed before extraction. An interesting upgrading method is throughaquathermolysis under the addition of catalyst. This review focuses on presenting nanoparticle catalysts, such as
nickel-, iron- and cobalt-based nanocatalyst. The explanation covers topics from synthesis methods andcharacterization up to the effect of reducing the viscosity of heavy oil. Lastly, concluding remarks and future
perspectives are highlighted regarding the visibility and available approaches of developing nanofluids for EOR.
Keywords: aquathermolysis, catalyst, enhanced oil recovery, heavy oil, nanoparticles

1. Introduction
Global energy demands increase quite considerably
every year due to the ever-increasing population of mankind. Therefore, oil and gas acting as main sources ofenergy up until now (IEA, 2014), need to be exploredeven further to meet those demands. Recently, in its annual “Statistical Review of World Energy 2014”, British
Petroleum (BP) in 2014 reported that global oil consumption increased above the average, by 1.4 % or equivalent
to 1.4 million barrels per day (b/d) (Petroleum B., 2014).
However, since the predicted scarcity of high quality
crude oil supply is starting to take effect, interest is shifting towards an abundant natural resource that has rarely
been developed extensively before, which is unconventional hydrocarbon in the presence of heavy oil. As shownin Fig. 1, heavy oil and bitumen are predicted to accountfor about 70 % of the total remaining hydrocarbon resources (Alboudwarej H. et al., 2006). Several well-known
fields of heavy oil are found scattered among many oil

fields in different countries, such as Orinoco, Venezuela(Martinez A.R., 1987), Alberta, Canada (Semple K. andWestlake D., 1987), Liaohe and Shengli, China (Wen S. etal., 2007; Wu C. et al., 2010), Mariner, United Kingdom(Knudsen M.T. and Hill D., 2012), and Duri, Indonesia(Arfie M. et al., 2005).
The main characteristic of heavy oil is its density or
specific gravity that is defined to be less than 20° API(Dusseault M., 2001) and its high viscosity due to the highaverage molecular weight of its structure, as stated byHyne (Hyne J. et al., 1982). Therefore heavy oil has a poor†

Received 17 April 2015; Accepted 10 June 2015
J-STAGE Advance published online 10 July 2015
1,2
Jl. Ganesha 10, Bandung 40132, Indonesia
3
Dhahran 31261, Saudi Arabia* Corresponding author: Ferry Iskandar;
E-mail: ferry@fi.itb.ac.id
TEL: +62-22-2500834 FAX: +62-22-2506452©2016 Hosokawa Powder Technology Foundation

Fig. 1

Column chart of total world oil reserves where unconventional hydrocarbon resources hold 70 % of the totalreserves.

3

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

flow rate and hence is very difficult to transport. Heavy
oil is also known for its low quality because of highamounts of asphaltenes and resin, whereas the saturated
and aromatic hydrocarbon contents are relatively low.
Moreover, higher contents of asphaltenes and resin, bothof which have a high average molecular weight, will consequently cause difficulties in exploitation and induce
high production costs. Another constituent of unconventional hydrocarbon is extra heavy oil, defined to have agravity below 10° API and a viscosity of more than
100 Pa·s by the World Energy Council in 2010, with acontribution of around 25 % to total world oil reserves.

2. Aquathermolysis reaction
Several reaction methods have been developed and adopted in order to enhance heavy oil recovery and reduce
its viscosity. A variety of methods exist, ranging fromchemical flooding to water flooding and gas drive fornon-thermal treatment, while steam injection, hot water
flooding and in-situ combustion for thermal treatment are
popular and frequently used (Owen J. and Floyd J., 1981).
Hyne et al. have proposed another reaction, called
aquathermolysis, which is a promising way to reduce theviscosity of heavy oil (Hyne J. et al., 1982). During theaquathermolysis reaction, heavy oil will be added withwater or steam at 200–280 °C with the reaction time varied from a few hours up to several days.
Laboratory-scale aquathermolysis can be executed in ahigh-pressure reactor, usually an autoclave, which is capable to withstand pressures up to 3 MPa. Meanwhile insitu aquathermolysis frequently uses the “Huff and Puff”
process, which takes place deep inside the well and consist of three main steps, i.e. injection of steam, well shutdown, and finally production of oil as depicted in Fig. 2(Maity S.K. et al., 2010). Later, it was discovered thataquathermolysis reaction solely using water hydrolysis
did not proceed as well as predicted since the hetero at-

Fig. 2

4oms S, N, and O in the heavy oil molecule can interact
with each other by means of hydrogen bonding or Van der
Waals forces, and polymerize to form larger molecules.
Hence, this is not a very effective way to reduce heavy oil
viscosity (Chen Y. et al., 2008).
Based on the problems described above, a better solution is needed to address the problems arising in theaquathermolysis reaction. Usage of catalyst, either a mineral, water-soluble, oil-soluble, or nanoparticle catalyst, is
proposed to have the necessary effects to overcome theproblems. This is due to the fact that a catalyst only acts
as an intermediary substance between the reactant andthe product in order to lower the activation energy of thereaction and therefore does not alter the general reaction
during the process.
Clark et al. studied the addition of aqueous metal tofurther reduce the viscosity of heavy oil compared to thesteam-only aquathermolysis method (Clark P. and Hyne J.,
1984). The usage of catalyst in aquathermolysis proved to
be a success, since it enables prevention of regression due
to polymerization while also promoting pyrolysis to breaklarger molecules of the heavy oil into smaller ones andvarious other synergistic reactions like hydrogenation andring opening as illustrated in Fig. 3. Below is the chemical reaction for aquathermolysis (Hyne J. et al., 1982):
RCH2CH2SCH3 + 2H2O
→ RCH3 + CO2 + H2 + H2S + CH4(1)

The cleavage of the C-S bonds results in a reduction of
the average molecular weight, bringing along the reduction of viscosity, improvement of flow properties and simplifying the exploitation and transportation of heavy oil.
Actually, not only the C-S bonds but also the C-N and
C-O bonds are broken in a catalytic aquathermolysis reaction. However, due to the lower bond energy of C-S, thedecrease of sulfur content in heavy oil after catalytic
aquathermolysis is more significant. Overall, with thehelp of a catalyst in the aquathermolysis reaction, it is

A simplified flow diagram of “Huff and Puff” process used commercially for recovery of heavy crude
oils. Reprinted with permission from Maity, S.K. et al. Copyright © 2010, American Chemical Society.

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

Fig. 3

Schematic representation of reactions that occur in catalytic aquathermolysis. A: pyrolysis, B: hydrogenation,
and C: ring opening. Reprinted with permission from
Wang, Y. et al. Copyright © 2010, American Chemical
Society.

noticeable that the amount of asphaltenes and resins will
decrease and, in contrast, the amount of aromatic hydrocarbon and saturated hydrocarbons will increase (Chao K.
et al., 2012). The whole process of aquathermolysis with
the introduction of a catalyst is called catalytic aquathermolysis.
The catalytic aquathermolysis process can also cause
domino effects like increasing the H/C ratio of the oil due
to hydrogenation during aquathermolysis reaction. In fact,
Hamedi Shokrlu and Babadagli found that the presence of
metal nanocatalyst improved the heat transfer capabilities
during the process of steam injection (Hamedi Shokrlu Y.
and Babadagli T., 2010). This thermal promotion effectoccurs because metals known for high thermal conductivity can improve the thermal conductivity of hydrocarbon
or the porous medium inside the reservoir.
The presence of hydrogen donor additives during theaquathermolysis process can have a favorable effect on
the general reaction. As stated by Liu and Fan, a hydrogen
donor, represented by tetralin in their work, can improve
the stability of viscosity reduction of heavy oil (Liu Y.
and Fan H., 2002). When the hydrogen donor was added,
besides the additional viscosity reduction of the heavy oil,there was also the noticeable benefit of the diminution ofviscosity regression after the reaction. The reason this regression occurs under the absence of a hydrogen donor isthat active chains produced as a by-product of theaquathermolysis reaction can react with each other tocreate a high molecular weight chain. On the other hand,under a sufficient amount of active H2, the active chain
will instead react with the active H2 to produce a low molecular weight chain.
Another source of hydrogen donor can be found in zeo-

lite. Silicate and aluminate, both having the shape of atetrahedral, are the main building units of zeolite. The
main source of hydrogen donor inside zeolite is the presence of Brønsted acid site, usually located in the vicinityof Si-O-Al bonds. With the possibility of seed-assisted ororganic structure-directing agent (OSDA)-free synthesis(Kamimura Y. et al., 2011; Purnomo C.W. et al., 2012),
valuable synthetic zeolite is now easier to produce. Because of this, synthetic zeolite is often employed as catalyst in the field of oil processing (Degnan T.F. et al., 2000;
Van Donk S. et al., 2003) with MFI, FAU, Beta and MORtype zeolite being the most frequently used due to theirlarger pore size that is well fitted with the corresponding
molecular size (Xu B. et al., 2006).
However, the percentage of viscosity reduction in
static/laboratory experiments is usually higher comparedwith dynamic/in-situ experiments. This difference appears because in-situ conditions are more complicatedand complex, such as the gradual drop of steam temperature with the increase of depth from the surface. Thiscondition causes a decreasing amount of energy which is
required for the reaction to take place and provides lessmomentum to make contact with the heavy oil. Anothercondition that contributes to reduced viscosity reduction
in dynamic experiments is the adsorption of catalyst byrock or minerals inside the well. Accordingly, recent developments in the synthesis of catalyst are aimed at overcoming these problems by designing a low-temperature
activated catalyst by using ultrasonic or elastic wave stimulation to assist the catalytic aquathermolysis reaction.
Wave stimulated aquathermolysis, started back in 1994,
when Beresnev and Johnson noticed that elastic wave
stimulation (by an earthquake for example) implies somechanges, the most noticeable one being an increase of water level and oil production in the reservoir (Beresnev I.A.
and Johnson P.A., 1994). This notion was later validated
in a number of related works, such as Guo, et al. and Prideet al., based on high-frequency vibration and numericalsimulation theory, respectively (Guo X. et al., 2004; Pride
S.R. et al., 2008). Integration of either high-frequency or
low-frequency vibration in catalytic aquathermolysis has
recently been performed (Xu H. et al., 2012; Xu H.X. andPu C.S., 2011). They reported that both types of vibration
assisted catalytic aquathermolysis, producing synergistic
effects on the performance of heavy oil viscosity reduction
as well as the composition of saturated, aromatic, resin
and asphaltenes (SARA) of the heavy oil. Compared to acatalytic aquathermolysis-only process, low-frequency
vibration-assisted catalytic aquathermolysis produces
better viscosity reduction, up to 6.6 %, with lower averagemolecular weight crude oil.

5

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

3. Nanocatalysts
Depending on the catalyst-reactant phase, catalysts canbe grouped into two different kinds: homogeneous, if thecatalyst and reactant are in the same phase (e.g. liquid-­liquid), and heterogeneous, if the catalyst and reactant are
in a different phase (e.g. solid-liquid). Meanwhile basedon their solubility, catalysts can be grouped into mineral,water-soluble, oil-soluble, and dispersed catalyst. In thisreview, we will limit the discussion to catalyst based ondispersed nanoparticles in the form of powder.
One of the advantages of nanoparticle catalysts is theirrelatively high surface area, which makes them ideal forcatalytic aquathermolysis, so as to increase the light fraction of crude oil and hence improve the overall quality of
the heavy oil. In addition, nanocatalysts have the advantage of easier separation process in the catalytic aquathermolysis process and, after proper thermal treatment, they
are available to be re-used in the process with adequate
performance (Junaid A.S.M. et al., 2014).

3.1 Nickel-based nanocatalysts
Nickel (Ni) is a chemical element with atomic number
28, which belongs to the first row transition metals. Because of this, nickel has a partially filled 3d orbital electron band and is very reactive to chemical reactions, inparticular when in the powder form, having a larger surface area. On the other hand, in an ambient air environment, nickel will slowly react with oxygen to form nickel
oxide compound. Nickel is quite abundant on earth, but it
is rarely found in pristine condition. It has been proposedthat nickel-iron is one of the components that form theearth’s core.
Lenggoro et al. reported their effort to synthesize
nickel oxide via spray pyrolysis using several precursors,
such as nickel chloride, nitrate and formate (Lenggoro
I.W. et al., 2003). The resulting particles have a broad
range of sizes, with only the nickel nitrate precursor resulting in the nanoparticle range, whereas the chloride
and formate precursor result in submicron particles. In
another attempt, Ai et al. studied the synthesis of nickel
oxide nanoparticles via a wet chemical method using ethylene glycol (EG) as solvent (Ai D. et al., 2004). Apartfrom being the solvent, the role of ethylene glycol can beextended to agglomeration-preventing agent, forming aprotective layer around the nickel particle, thus creating awell-dispersed nanoparticle.
Meneses et al. further developed nickel oxide nanoparticles using gelatin instead of EG as the organic precursor(Meneses C.T. et al., 2007). Although very small particles
sizes (3.2 nm) were successfully obtained, the polydispersed characteristic of the nanoparticles still persisted asa major problem until Li, et al. (2007) found a better way

6

to synthesize nano-nickel using the micro emulsion
method. In the attempted method, oil phase (e.g. cyclohexane, methyl cyclohexane) was used to prepare nano-­nickel catalyst that could act as hydrogen donor to further
improve the heavy oil characteristics via some mechanisms already explained in the section about the aquathermolysis reaction. The nano-nickel particles obtained had
a spherical shape and an average diameter of 6.3 nm. Li etal, further applied nano-nickel as catalyst in an aquathermolysis reaction of Liaohe extra heavy oil at 280 °C (LiW. et al., 2007). As predicted, the best viscosity reductionof heavy oil as measured at 50 °C, was achieved under thepresence of nano-nickel catalyst, with up to 98 % viscosity reduction. Aside from the viscosity reduction, an increase in H/C ratio and reduced amount of asphaltenes,
resin, and sulfur content was also noticed in the catalytic
aquathermolysis-reacted heavy oil. The nano-nickel via
microemulsion experiment was again attempted by Wu etal., this time under much lower reaction temperature of
200 °C, yet high catalytic performance with more than
90 % viscosity reduction was obtained (Wu C. et al.,2013).
It has been reported that there is the possibility of ametal catalytic reaction of heavy oil without the additionof water or steam or non-aquathermolysis reaction. However, the reduction of viscosity would be more significant
after water or steams is added into a system (Hamedi
Shokrlu Y. and Babadagli T., 2010).
The development of nano-nickel-based catalyst for use
in aquathermolysis was further reported by Wu et al.,who utilized San56-13-19 extra heavy oil as the subject of
an aquathermolysis reaction at 200 °C (Wu C. et al.,2013). The nano-nickel catalyst was successfully preparedby the microemulsion methods according to (Wu Z. et al.,2008). Transmission electron microscopy (TEM) characterization indicated that the size of the nano-nickel catalyst was around 4.2 nm. Viscosity reduction was around90.36 % as measured by Bruker Avance-500 NMR spectrometer, and group composition after reaction alsochanged, with a decrease in asphaltene and resin of
5.28 % and 6.48 %, whereas saturated and aromatic hydrocarbons increased by 6.73 % and 5.03 %.
Based on these findings, Noorlaily et al. have tried
to synthesize nickel oxide nanoparticles employing
microwave-assisted co-precipitation methods (Noorlaily P.
et al., 2013). Using NiCl2·6H2O as nickel source, with
NH4HCO3 and ethylene glycol as reductant and solvent
respectively, spherical morphology, uniform size distribution around 65 nm, and a large surface area particle of
158.4 m2/gram were obtainable. Fig. 4 shows the x-ray
diffraction patterns of the nickel oxide nanoparticles produced. Applying Scherrer’s equation to the strongest
peak, the crystal sizes of NiO were predicted to be
4.77 nm, 9.08 nm and 15.26 nm, respectively, at forFerry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

prove the overall reaction rates since they will fluctuateand create more contact with the reactant molecules (BeraA. and Babadagli T., 2015). Because usually the optimum
amount of additional Fe as catalyst range only around
0.1–1 wt% (Greff J.H. and Babadagli T., 2012), theamount of Fe ions could well be insignificant comparedwith the amount of other minerals inside the reservoir.
Consequently, there is only slight chance of corrosion
caused by the addition of Fe-based catalyst, although it
does not completely rule out the possibility that there
would arise some minor contributing factor.

3.2.1 Hematite (α-Fe2O3) and maghemite (γ-Fe2O3)

Fig. 4

X-ray diffraction patterns of prepared NiO at calcination temperature of (a) 500 °C, (b) 400 °C, (c) 300 °C.
Reprinted with permission from Noorlaily, P. et al.,copyright © 2013, Trans Tech Publications Inc.

300 °C, 400 °C and 500 °C calcination temperature. The
effect of NiO nanoparticles on the viscosity reduction ofheavy oil after catalytic aquathermolysis reaction wasreported to be 22 %.

3.2 Iron-based nanocatalysts
Iron (Fe) is one of the metal elements that compose
the earth’s crust, therefore it is the fourth most abundantelement in terms of weight, at around 5 wt%. Iron has
an atomic number of 26 with electron configuration [Ar]3d6 4s2. In general, there are two forms of iron based-­
nanocatalyst, which are often encountered as specified
by the oxidation states of the iron: hematite and magnetite (α-Fe2O3 and γ-Fe2O3) and magnetite (Fe3O4). Ironin general is classified as magnetic materials, with therelationship of magnetic properties of materials to theircatalytic activity has been long investigated in the field ofmagneto-chemistry. As early as 1946, Selwood P.W.
described the close relationship of magnetic properties to adsorption phenomena, whereas adsorption of several strongly
paramagnetic compound is found to be more prominent
due to the strong tendency in forming electron pairing
arising from the unpaired electrons spin (Selwood P.,
1946). Therefore, magnetic materials in general, indicatedby magnetic moment, are good adsorbent and suitable tobe used as catalyst, just like the one applied by Khamarui,
et al. (Khamarui S. et al., 2015). Another advantage of
having magnetic nano metal-oxides particles is theirsynergistic to be implemented in the electromagnetic-­solvent heating. In an extensive review paper written by
Bera and Babadagli, magnetic nanoparticles in the radio
frequency electromagnetic heating will increase and im-

Fe2O3 is a polymorph material with two naturally occurring and hence commonly found substances of iron.
The two naturally occurring forms are the α phase andthe γ phase, with the difference between those two arisingfrom the most fundamental crystal structure.
While α-Fe2O3 (hematite) has a rhombohedral crystal
structure like corundum (α-Al2O3), γ-Fe2O3 (maghemite)
has a cubic crystal structure. The magnetic characteristicsof hematite and maghemite also show some distinction.
Hematite is found to be weakly ferromagnetic in between
260 K and 950 K, while maghemite shows ferromagnetic
properties, making it often being used for recording tapecomponent. The characteristics described above are restricted to bulk form; there is a discrepancy between
nanoparticle and bulk form, especially regarding magnetic properties. Magnetic nanoparticles usually show superparamagnetic phenomena due to the finite- size limitthat is distinctive for every material. However, there issimilarity between hematite and maghemite, with bothconsisting of Fe2+ divalent iron compound, usually known
as ferrous ion. In contrast, Fe3O4 (magnetite) consists ofboth ferrous and ferric ion.
As a matter of fact, all three iron oxides—hematite,maghemite, and magnetite—are connected and therefore
can be produced by transformation into each other. Fe3O4
can be produced by α-Fe2O3 reduction, while when appropriately annealed and re-oxidized, Fe3O4 will results inthe formation of γ-Fe2O3 (Aharoni A. et al., 1962). Finally,
to complete the cycle of transformation, γ-Fe2O3 can beheated to the required temperature to create hematite.
Shimomura et al. have suggested that γ-Fe2O3 could also
be formed directly as an intermediate phase during the
reduction of α-Fe2O3 (Shimomura Y. et al., 1954). Thiswas proven afterwards by Randrianantoandro, et al., asthey succeeded to create γ-Fe2O3 directly from α-Fe2O3
employing high-energy ball milling with the aid of ethanol (Randrianantoandro N. et al., 2001). In another attempt to produce γ-Fe2O3 nanoparticles, some researchers
are using a microemulsion method that utilizes a surfactant to stabilize the mixture as well as a precipitating
agent (e.g. NH3, NaOH)(Sahoo S. et al., 2010; Tueros M.J.

7

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

et al., 2003).
Hematite, meanwhile, reportedly can be synthesizedusing various methods, such as co-precipitation (Zhang H.
et al., 2008), hydrothermal (Giri S. et al., 2005), sol-gel(Woo K. et al., 2003) and forced hydrolysis (Musić S. etal., 1997). In his work, Music used precursors such as
FeCl3·6H2O and 10 % solution of tetramethylammoniumhydroxide (TMAH). Later on, in 2003, Musić et al. published a paper explaining the factors influencing forcedhydrolysis of FeCl3solution (Musić S. et al., 2003). Another way to synthesize α-Fe2O3 is through hydrothermal
methods. Giri et al. (2005) explained that hematite, withsize and nature depending strongly on pH was successfully created under a complex microemulsion system. A
narrow-distribution ultrafine dispersed Fe2O3 nanoparticle
was reported by Zhang H., et al., where a co-precipitation
method was employed with NaOH as the precipitating
agent (Zhang H. et al., 2008). NaCl salts produced as aby-product of the reaction can help encapsulating the precursor, so that in the end the nanoparticle size varies only
from 1.9 nm to 2.6 nm.
Wang et al., then tested the influence of Fe3+ ion catalyst under the synthesizing method of Chen et al. which
utilizes Fe2O3 or Fe and aromatic sulfonic acid to prepare
what is called amphiphilic metallic chelate catalyst (Chen
Y. et al., 2008; Wang Y. et al., 2010). The catalytic
aquathermolysis experiments were conducted using an
oil/water ratio of 7:3 and 0.3 g of catalyst. The viscosityof the heavy oil after the reaction showed a decrease ofaround 95 % under a recorded ambient temperature of
50 °C. Apart from the viscosity reduction, Wang et al.
also characterized the composition and content level of
SARA before and after the reaction. All parameters
showed improved quality of the heavy oil, since the levelof saturated and aromatic hydrocarbons and the H/C ratio
increased, while resin and asphaltenes decreased under arecorded ambient temperature of 50 °C.
Nurhayati et al., have reported the synthesis of α-Fe2O3
nanoparticles using a microwave-assisted calcination
method (Nurhayati T. et al., 2013). They reported that themethod could reduce the preparation time needed, while
keeping the heat distributed evenly throughout the process because of resonance and relaxation from the electromagnetic radiation, as explained by Lagashetty, et al.
(Lagashetty A. et al., 2007). The precursors used were
FeCl3·6H2O, with NaOH as a precipitating agent, which
was later titrated into the FeCl3 solution. The morphology
and size of the nanoparticles characterized using SEM
can be seen in Fig. 5. Firstly, the overall stoichiometric
reactions that occurred during the course of this experiment can be represented by:
FeCl3·6H2O(aq) + 3 NaOH(aq)→ Fe(OH)3(s) + 3 NaCl(aq) + 6 H2O(l)8(2)

Fe(OH)3(s) → FeOOH(s) + H2O(l)(3)

2 FeOOH(s) [heated] → Fe2O3(s) + H2O(l)(4)

Furthermore, the microwave heating process would givethe initial stage of crystal growth. The crystal growthmechanism may be a combination of O-R (Ostwald-­
ripening) and O-A (oriented-attachment) pathways,
whereas the secondary crystal growth occurred during
the calcination process.
The development of nanocomposites in order to combine the strength of two or more materials has gained
popularity since a nanocomposite of hematite with silicon
was studied thoroughly back in 1988, when Ishikawa andMatijevic attempted to synthesize it (Ishikawa T. andMatijević E., 1988). The goal of composing another material with hematite is to prevent agglomeration due to its
magnetic surface interaction. As already studied by
Vasquez-Mansilla et al. (Vasquez-Mansilla M. et al., 1999)
and by Willis, et al. (Willis A.L. et al., 2005), magnetic
interaction on the surface of antiferromagnetic hematite

Fig. 5Comparison between two SEM images of Fe2O3
nanoparticle catalyst. Top: sample calcined at 300 °C.
Bottom: sample calcined at 350 °C. Reprinted withpermission from Nurhayati, et al., copyright © 2013,
Trans Tech Publications Inc.

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

arises because of magnetic moment as well as anisotropy
energy originating within the surface. It is therefore necessary to passivate the surface by a ligand coating ofoleic acid. However, problems emerge since oleic acid asa ligand coating suffers a structural change during high-­temperature synthesis. Consequently, carbon coating likethe one used by Zhang Y., et al. became imminent (Zhang
Y. et al., 2011).
Swardhani et al. have reported a preparation of carbon-­coated α-Fe2O3 nanoparticles using microwave-assisted
calcination method (Swardhani A.P. et al., 2014). It resulted
in creating nanoparticles with a mean size of 199 nm fromscanning electron microscopy (SEM) characterization,with 33 % carbon content giving the optimum result. It isinteresting to note that the presence of carbon will give asmaller internal crystal size of hematite and on the other
hand an increase of the surface area, therefore increasing
its effectiveness and making it more suitable to be used as
catalyst. Fig. 6 presented the SEM image of the resulting
nanocomposite, indicating that the carbon coated aroundthe shape of Fe2O3 nanorod. Another work in α-Fe2O3 nanocomposite synthesis was done using zeolite (Iskandar F.
et al., 2014). Using the same methods as for the α-Fe2O3/C
nanocomposite, it was observed that the nanoparticles
size grows larger as the microwave heating time and calcination temperature increase, with optimum calcination
temperature thus giving better α-Fe2O3/zeolite crystallinity, at 450 °C. This pattern of improving crystallinity withincreasing temperature is something that has to do withthe sintering process that comes from excessive thermalenergy, as explained more comprehensively by Wang, etal. (Wang Y. et al., 2009).

3.2.2 Magnetite (Fe3O4)

Fe3O4, usually called magnetite, is another type of ironoxide that is commonly encountered. It consists of both

Fe2+ and Fe3+ ions that form both the octahedral and tetrahedral of the inverse spinel crystal structure. Fe3O4 shows
ferrimagnetic phenomena at room temperature, havingsome magnetic domains that oppose each other. However,there is net magnetization in the direction of the dominantdomain with magnitude relatively the same as in ferromagnetic materials. Interest in studying Fe3O4 nanoparticles appear from the broad domains it can be applied in,such as magnetic recording, biomedics, etc (Ito A. et al.,
2004; Mornet S. et al., 2004).
As usual, the number of variations of the method tosynthesize Fe3O4 is incredibly vast. Jiang, et al. tried tosynthesize nano-Fe3O4 particles using co-precipitation, a
simple and relatively high-yield method. FeSO4·7H2O and
FeCl3·6H2O were used as Fe source, while NH4OH, produced by mixing ammonia with aqueous solution, wastitrated until the pH of the mixture reached 10-11 (Jiang
W. et al., 2004). Control of pH is very critical during thesynthesis process, since it was proven to affect the size
distribution of the nanoparticles created. In his work, Jiang
successfully synthesized Fe3O4 nanoparticles with relatively small average size and narrow particle distribution,i.e. 25.3 nm and 5.9 nm, respectively. In other works,
some researchers carried out hydrothermal methods inorder to synthesize Fe3O4 nanoparticles (Daou T. et al.,2006; Mizutani N. et al., 2008). Although both discuss thesame methods, the interesting point is having two authors
using a different approach. Daou’s report emphasized thatmagnetite nanoparticles with a mean size of 39 nm andgood dispersity were correctly synthesized, as proven using XRD and other characterization techniques. Meanwhile, Mizutani emphasized the challenge of the effect offerrous/ferric ions molar ratio, with a higher ions molar
ratio resulting in more crystallinity and larger particle
size. The full reaction regarding the transformation mechanism can be written as follow:
Fe(OH)2 + 2FeOOH → Fe3O4 + 2H2O

Fig. 6

SEM images of carbon-coated Fe2O3 nanoparticle withrod morphology, under carbon content of 33 %. Reprinted with permission from Swardhani, et al, copyright © 2013, Trans Tech Publications Inc.

(5)

with the FeOOH transformed from ferric hydroxide(Fe(OH)3). Other methods performed to synthesize Fe3O4
are chemical vapor deposition (Chao L.T. et al., 2006),
which resulted in Fe3O4 thin film deposited on Si substrate,
and microemulsion (Koutzarova T. et al., 2006), by which
nanopowder of polydispersed size was obtained.
Nugraha et al. have reported the synthesis of Fe3O4
nanoparticles (Nugraha M.I. et al., 2013). During theexperiment, the co-precipitation approach to synthesize
NixFe3-xO4 was chosen due to its simplicity. The result
showed that the average particle size was 43 nm and thatthe best composition for enhancing oil recovery is when
there is no nickel impurity, with reduction of heavy oil
viscosity approaching 30 % after the catalytic aquathermolysis reaction. Like Fe2O3, nanocomposite of Fe3O4

9

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

was also studied in the hope of modifying the surface
characteristics of the nanoparticles and stabilizing thenanoparticles dispersion so that catalytic performance
could be improved. Lan, et al. tried to coat Fe3O 4nanoparticles with oleic acid (Lan Q. et al., 2007). Employing a co-precipitation method followed by hydrothermal synthesis, the resulting colloidal system indicated no
precipitate that showed its stability. Recently, Haryono etal. have tried a modification of this method using a threeneck flask (Haryono A. et al., 2013). They obtained asmaller particle size, in the order of around 2 nm. However, as we already knew, oleic acid suffers from detrimental structural change in high temperature conditions,which is typical in hydrothermal synthesis methods. Thus,an alternative was given by Stjerndahl, et al. (Stjerndahl
M. et al., 2008), making use of SiO2 or silica instead ofoleic acid. Under a water-in-oil microemulsion system,
the loading of iron oxide crystals, thickness of silica shells
and particle size are tunable. More detailed picture ofphase influence is given in Fig. 7, with darker component
indicating the superparamagnetic iron oxides (SPIOs).
Iskandar et al. have reported a first attempt of magnetite nanocomposite Fe3O4/Zeolite synthesis by employing
simple microwave heating methods (Iskandar F. et al.,2014). The reason behind the selection of zeolite as a matrix phase in the nanocomposite was that zeolite is wellknown for its ability to act as a hydrogen donor that isessential in improving the quality of the heavy oil duringcatalytic aquathermolysis and is also easy to find since it
is abundant in quantity, either as heulandite or as clipnotilolite (Merissa S. et al., 2013). However, in order to be

Fig. 7

10

ready for use, natural zeolite requires thermal treatment
to activate and reduce the amount of metal content whileat the same time increasing the surface area as well as theratio of Si/Al. In terms of catalytic performance, i.e. therate of heavy oil viscosity reduction, the Fe3O4/zeolite
nanocomposite we produced, with heulandite type ofnatural zeolite, reached levels of up to 92 % for a Fe3O4:
zeolite ratio of 20:80.

3.3 Cobalt-based nanocatalysts
Cobalt oxide is composed of both Co2+ and Co3+ ions,just like Fe3O4. Co3O4 is an inorganic compound that has
antiferromagnetic properties at room temperature. Approaches that have been used to synthesize Co3O4 arethermal decomposition (He T. et al., 2004), mechanochemical (Yang H. et al., 2004) and hydrothermal methods (Dong Y. et al., 2007). Using thermal decomposition,it has been detected that an intermediate product is produced, with the final Co3O4 nanocrystals created werelater separated by centrifugation and washed with cyclohexane three times. In another study, Yang stated that
via mechanochemical milling of Co(NO3)2·6H 2O with
NH4HCO3 and continued by thermal treatment, approximately 38 nm crystal size was obtained. The reaction
stoichiometry of both the milling and calcination process
is given by:
2Co(NO3)2·6H2O + 5 NH4HCO3 → Co2(OH)2CO3+ 4 NH4NO3 + NH3 + 4 CO2 + 14 H2O

TEM micrographs of shell-coated Fe3O4/SiO2 nanocomposites under different synthesis conditions, (a–c)
within microemulsion region, (d) in the two-phase region. Reprinted with permission from Stjerndahl, etal. Copyright © 2008, American Chemical Society.

(6)Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

3 Co2(OH)2CO3 + O2 → 2 Co3O4 + 3 H2O + 3 CO2 (7)Nassar, et al. reported that using Co3O4 nanoparticles
with a surface area of 41 m2 based on BET characterization, the reduction of asphaltenes content of heavy oil
could reach 32 % at 300 °C (Nassar N.N. et al., 2011).
Further analysis on asphaltenes cracking activation energy, based on the Coats—Redfern method, indicated asignificant decrease of the activation energy in the presence of nanoparticles catalyst, making the reaction possible to undergo at lower temperature.

4. Concluding remarks and future perspective
Based on the above reviews, all discussed materials arefound to have synergistic effects when used as catalyst
during catalytic aquathermolysis and hence are able togreatly improve the reduction of heavy oil viscosity andenhance the heavy crude oil recovery and quality. Table 1
summarizes several characteristic features of nanoparticles and their catalytic performance during enhanced oil
recovery through aquathermolysis. Although no specificreport regarding the optimum size of catalyst to be used

Table 1in catalytic aquathermolysis, small nanoparticles and porous materials are certainly ideal and becoming the main
subject of interest as they inherited high surface area
which indicate excellent catalytic performance. As for porous materials, pores in the range of mesopores (2–50 nm)
is more suitable in heavy oil conversion as reported by
Van Donk, et al. due to better accessibility of large molecules (Van Donk S. et al., 2003). There are many othernanoparticles, including their variations and derivatives,that have not yet been synthesized due to the enormous
amount of methods and complexities in this field, therefore further study in exploring new discoveries is still
very much wide open.
In the future, we expect to see that the development of
nanocatalysts in enhanced oil recovery will be heading
towards colloidal mixtures of nanoparticles with basefluid, known as nanofluids, as first described comprehensively by Wang et al. for the purpose of increasing theheat transfer (Wang Y. et al., 2009). The superiority of using nano-sized particles in base fluids is due to the factthat they would increase the contact between the reactant
molecule and the catalyst, such as the water-oil-catalyst
surface in the aquathermolysis reaction, which leads to amore progressive reaction. Moreover, nanofluids offer

Summary of various nanoparticles used in enhanced oil recovery

Nanoparticle

Ni

Synthesis Method

Particle Size

Catalytic performanceRef.

6.3 nmviscosity reduction: 98.2 % H/C:
1.46; MW = 422

Li, et al.
(2007)

4.2 nmviscosity reduction: 90.36 % H/C:
2.09; 28.06 % decreased of asphaltene MWWu, et al.
(2013)

viscosity reduction: 22 %microemulsion

NiO

co-precipitation

60–70 nmNiO

unknown (purchased)

12 nm% conversion of asphaltene at(crystal size) 300 °C: 37 %α-Fe2O3

mixing Fe2O3 with aromatic
sulfonic acid

Noorlaily, et al.
(2013)
Nassar, et al.
(2011)—(1) maximum viscosity reduction
up to 93.3 %.

Chen, et al.
(2008)—(2) viscosity reduction: 95.6 %;decrease in asphaltene and resin,increase in saturated and aromatic.

Wang Y., et al.
(2010)

α-Fe2O3/zeolite

microwave assisted co-­precipitation + calcination

135 nmviscosity reduction: 89 % (forNurhayati, et al.
α-Fe2O3:zeolite composition of 1:3)
(2013)

Fe3O4

microwave assisted co-­precipitation

43 nm

viscosity reduction: 30 %Fe3O4

obtained from nanostructured
22 nm% conversion of asphaltene at
and amorphous materials(crystal size) 300 °C: 33 %Nassar, et al.
(2011)

Fe3O4/zeolite

microwave assisted co-­precipitation

Iskandar, et al.
(2014)

Co3O4

unknown (purchased)

96 nm

viscosity reduction: 92 % (forFe3O4:zeolite composition of 1:4)

22 nm
% conversion of asphaltene at(crystal size) 300 °C: 20 %Nugraha, et al.
(2013)

Nassar, et al.
(2011)

11

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

more stability compared to the use of micron- or millimeter-­dimension particles, which can cause severe abrasion andclogging problems. Another prospect of nanofluids, observed by Eastman, et al., is that nanofluids consisting of
nano-copper particles dispersed in ethylene glycol haveup to 40 % better thermal conductivity properties compared to those of pure fluid (Eastman J.A. et al., 2001).
Based on this fact, nanofluids show great potential forvarious industries, one of which is the oil and gas industry where in-situ steam flooding is customarily used toenhance oil recovery. To find out what causes the increasein thermal conductivity, Keblinski, et al. proposed four
possible mechanisms of heat flow in nanofluids, i.e.
Brownian motion of the particles, molecular layering of
the liquid at the liquid-particles interface, the nature ofheat transport in nanoparticles, and effects of nanoparticle
clustering (Keblinski P. et al., 2001).
Related to the preparation method, simple liquid-solidinterfusion will not work towards achieving good nanofluids. Special methods, for example using surface activators and dispersants, or ultrasonic vibration, are available
to create stable nanofluids that produce no precipitate overlonger times (Xuan Y. and Li Q., 2000). Zeta potential
analysis is one of the most effective ways to predict nanofluids stability. Generally, nanofluids preparation methods
can be divided into two groups: single-step methods andtwo-step methods, as discussed comprehensively in thereview paper by Li et al. and Haddad et al. (Haddad Z. etal., 2014; Li W. et al., 2007). One important aspect to noteis the necessity to compromise the optimum value ofnanoparticles inside the base fluid so as to get the best
thermal conductivity, yet still a very stable colloidal suspension.
Another way to achieve this objective is to use beads
milling, with which a stable colloidal nanoparticle suspension can be prepared. An illustration of the beads millapparatus can be seen in Inkyo et al., which used beadparticles with a diameter of only 15 μm, the smallest at
the time, and employed centrifugal force in the upper partof the apparatus to separate the beads from the nanoparticle suspension (Inkyo M. et al., 2006). As a result,
nanoparticles with a size of only 15 nm dominated thecomposition after 180 minutes of milling and low contamination from zirconia (ZrO2), the beads’ constituent
material, was achieved.
Our preliminary work of water-based Fe3O4 nanofluids
preparation showed initial success in increasing the viscosity reduction of heavy oil compared to the use of
nanoparticles (Iskandar F. et al., 2015). The result also reflected that the presence of surfactant under the additionof 20 wt% ethylene glycol had a negligible effect on catalytic performance degradation.

12

Acknowledgements
This scientific work was partly financed from “DanaRiset Inovasi ITB 2015” and was also supported by“TWAS Research Grants Programme in Basic Sciences
2014–2015” No. 14-236 RG/PHYS/AS_I.

References
Aharoni A., Frei E.H., Schieber M., Some properties of γ-Fe2O3
obtained by hydrogen reduction of α-Fe2O3, Journal of
Physics and Chemistry of Solids, 23 (1962) 545–554.
Ai D., Dai X., Li Q., Deng C., Kang S., Synthesis of NiO nano­particles in ethylene glycol, China Particuology, 2 (2004)
157–159, DOI:http://dx.doi.org/10.1016/S1672-2515(07)60047-5.
Alboudwarej H., Felix J., Taylor S., Badry R., Bremner C.,
Brough B., Skeates C., Baker A., Palmer D., Pattison K.,
Beshry M., Krawchuk P., Brown G., Calvo R., Triana J.
A.C., Hathcock R., Koerner K., Hughes T., Kundu D., De
Cárdenas J.L., West C., Highlighting heavy oil, Oilfield
Review, 18 (2006) 34–53.
Arfie M., Marika E., Woodard H.A., Purbodiningrat E.S.,
Implementation of slurry fracture injection technology for
E&P wastes at duri oilfield, Society of Petroleum Engineers, 2005, DOI:10.2118/96543-MS.
Bera A., Babadagli T., Status of electromagnetic heating forenhanced heavy oil/bitumen recovery and future prospects:
A review, Applied Energy, 151 (2015) 206–226, DOI:http://dx.doi.org/10.1016/j.apenergy.2015.04.031.
Beresnev I.A., Johnson P.A., Elastic-wave stimulation of oil
production: a review of methods and results, Geophysics,
59 (1994) 1000–1017.
Chao K., Chen Y., Li J., Zhang X., Dong B., Upgrading and visbreaking of super-heavy oil by catalytic aquathermolysis
with aromatic sulfonic copper, Fuel Processing Technology,
104 (2012) 174–180, DOI:http://dx.doi.org/10.1016/j.fuproc.
2012.05.010.
Chao L.T., Wei M., MacManus-Driscoll J.L., Synthesis andcharacterisation of nanocrystalline iron oxides via ultrasonic spray assisted chemical vapour deposition, Journalof Physics: Conference Series, 26 (2006) 304–307,
DOI:10.1088/1742-6596/26/1/073.
Chen Y., Wang Y., Wu C., Xia F., Laboratory experiments andfield tests of an amphiphilic metallic chelate for catalytic
aquathermolysis of heavy oil, Energy and Fuels, 22 (2008)
1502–1508, DOI:10.1021/ef8000136.
Clark P., Hyne J., Steam-oil chemical reactions: mechanisms forthe aquathermolysis of heavy oils, AOSTRA Journal ofresearch, 1 (1984) 15–20.
Daou T., Pourroy G., Begin-Colin S., Greneche J., Ulhaq-Bouillet
C., Legare P., Bernhardt P., Leuvrey C., Rogez G., Hydrothermal synthesis of monodisperse magnetite nanoparticles,
Chemistry of Materials, 18 (2006) 4399–4404.
Degnan T.F., Chitnis G.K., Schipper P.H., History of ZSM-5
fluid catalytic cracking additive development at Mobil,
Microporous and Mesoporous Materials, 35–36 (2000)

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

245–252, DOI:10.1016/S1387-1811(99)00225-5.
Dong Y., He K., Yin L., Zhang A., A facile route to controlled
synthesis of Co3O4 nanoparticles and their environmental
catalytic properties, Nanotechnology, 18 (2007) 1–6,
DOI:10.1088/0957-4484/18/43/435602.
Dusseault M., Comparing Venezuelan and Canadian heavy oil
and tar sands. Paper presented at the Canadian International
Petroleum Conference, Calgary, Alberta, Canada, (2001)
061.
Eastman J.A., Choi S.U.S., Li S., Yu W., Thompson L.J.,
Anomalously increased effective thermal conductivitiesof ethylene glycol-based nanofluids containing copper
nanoparticles, Applied Physics Letters, 78 (2001) 718–720,
DOI:10.1063/1.1341218.
Giri S., Samanta S., Maji S., Ganguli S., Bhaumik A., Magnetic
properties of α-Fe2O3 nanoparticle synthesized by a newhydrothermal method, Journal of Magnetism and Magnetic Materials, 285 (2005) 296–302, DOI:10.1016/j.jmmm.
2004.08.007.
Greff J.H., Babadagli T., Use of nano-metal particles as catalyst
under electromagnetic heating for viscosity reduction ofheavy oil. Paper presented at the Society of Petroleum
Engineers—International Petroleum Technology Conference 2012, IPTC 2012.
Guo X., Du Z., Li G., Shu Z., High frequency vibration recovery enhancement technology in the heavy oil fields ofChina, Society of Petroleum Engineers, 2014, DOI:10.2118/
86956-MS.
Haddad Z., Abid C., Oztop H.F., Mataoui A., A review on how
the researchers prepare their nanofluids, International Journal of Thermal Sciences, 76 (2014) 168–189, DOI:10.1016/
j.ijthermalsci.2013.08.010.
Hamedi Shokrlu Y., Babadagli T., Effects of nano-sized metals
on viscosity reduction of heavy oil/bitumen during thermal
applications, Society of Petroleum Engineers, 2010, DOI:
10.2118/137540-MS.
Haryono A., Harmami B., Sondari D., Preparation of magnetite
nanoparticles by thermal decomposition of iron (III) acetylacetonate with oleic acid as capping agent, Materials
Science Forum, 737 (2013) 153–158, DOI:10.4028/www.
scientific.net/MSF.737.153.
He T., Chen D., Jiao X., Controlled Synthesis of Co3O4
Nanoparticles through Oriented Aggregation, Chemistry ofMaterials, 16 (2004) 737–743, DOI:10.1021/ cm0303033.
Houbiers M., Wiarda E., Mispel J., Nikolenko D., Vigh D.,
Knudsen B.-E., Thompson M., Hill D., 3D full-­waveform
inversion at mariner — a shallow north sea reservoir,(Eds.), SEG technical program expanded abstracts (2012)
1–5, DOI:10.1190/segam2012-0242.1
Hyne J., Clark P., Clarke R., Koo J., Greidanus J., Tyrer J.,
Verona D., Aquathermolysis of heavy oils, Revista Tecnica
Intevep, 2 (1982) 87–94.
IEA (International Energy Agency‌), 2014, World Energy Outlook 2014—Executive Summary <www.iea.org> accessed
29.06.2015.
Inkyo M., Tahara T., Iwaki T., Iskandar F., Hogan Jr C.J.,
Okuyama K., Experimental investigation of nanoparticle
dispersion by beads milling with centrifugal bead separa-tion, Journal of Colloid and Interface Science, 304 (2006)
535–540, DOI:10.1016/j.jcis.2006.09.021.
Ishikawa T., Matijević E., Formation of monodispersed pureand coated spindle-type iron particles, Langmuir, 4 (1988)
26–31.
Iskandar F., Asbahri A., Dwinanto E., Abdullah M., Khairurrijal
K., Synthesis of Fe3O4 Nanoparticles Using Co-Precipitation
Method and Its Development into Nanofluids as Catalyst in
Aquathermolysis Reaction, Advanced Materials Research,1112 (2015) 205–208.
Iskandar F., Fitriani P., Merissa S., Mukti R.R., Khairurrijal,
Abdullah M., Fe3O4/zeolite nanocomposites synthesized by
microwave assisted coprecipitation and its performance in
reducing viscosity of heavy oil, AIP Conference Proceedings, 1586 (2014) 132-135.
Ito A., Takizawa Y., Honda H., Hata K.I., Kagami H., Ueda M.,
Kobayashi T., Tissue engineering using magnetite nanoparticles and magnetic force: Heterotypic layers of cocultured
hepatocytes and endothelial cells, Tissue Engineering, 10(2004) 833–840, DOI:10.1089/ 1076327041348301.
Jiang W., Yang H.C., Yang S.Y., Horng H.E., Hung J.C., Chen
Y.C., Hong C.Y., Preparation and properties of superparamagnetic nanoparticles with narrow size distribution andbiocompatible, Journal of Magnetism and Magnetic Materials, 283 (2004) 210–214, DOI:10.1016/j.jmmm.2004.
05.022.
Junaid A.S.M., Rahman M.M., Rocha G., Wang W., Kuznicki T.,
McCaffrey W.C., Kuznicki S.M., On the role of water innatural-zeolite-catalyzed cracking of athabasca oilsands
bitumen, Energy and Fuels, 28 (2014) 3367–3376, DOI:
10.1021/ef500532w.
Kamimura Y., Tanahashi S., Itabashi K., Sugawara A., WakiharaT., Shimojima A., Okubo T., Crystallization behavior ofzeolite beta in OSDA-free, seed-assisted synthesis, Journalof Physical Chemistry C, 115 (2011) 744–750, DOI:10.1021/
jp1098975.
Keblinski P., Phillpot S.R., Choi S.U.S., Eastman J.A., Mechanisms of heat flow in suspensions of nano-sized particles(nanofluids), International Journal of Heat and Mass Transfer, 45 (2001) 855–863, DOI:10.1016/S0017-9310(01)00175-2.
Khamarui S., Saima Y., Laha R.M., Ghosh S., Maiti D.K., Functionalised MnVI-nanoparticles: An advanced high-valentmagnetic catalyst, Scientific Reports, 5 (2015) 8636, DOI:
10.1038/srep08636.
Koutzarova T., Kolev S., Ghelev C., Paneva D., Nedkov I.,
Microstructural study and size control of iron oxide
nanoparticles produced by microemulsion technique,
Physica Status Solidi C: Conferences, 3 (2006) 1302–1307,
DOI:10.1002/pssc.200563115.
Lagashetty A., Havanoor V., Basavaraja S., Balaji S.D.,
Venkataraman A., Microwave-assisted route for synthesisof nanosized metal oxides, Science and Technology ofAdvanced Materials, 8 (2007) 484–493, DOI:10.1016/j.
stam.2007.07.001.
Lan Q., Liu C., Yang F., Liu S., Xu J., Sun D., Synthesis ofbilayer oleic acid-coated Fe3O4 nanoparticles and theirapplication in pH-responsive Pickering emulsions, Journalof Colloid and Interface Science, 310 (2007) 260–269,

13

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

DOI:10.1016/j.jcis.2007.01.081.
Lenggoro I.W., Itoh Y., Iida N., Okuyama K., Control of size
and morphology in NiO particles prepared by a low-­pressure spray pyrolysis, Materials Research Bulletin, 38(2003) 1819–1827, DOI:10.1016/j.materresbull.2003.08. 005.
Li W., Zhu J.h., Qi J.h., Application of nano-nickel catalyst in
the viscosity reduction of Liaohe extra-heavy oil byaqua-thermolysis, Ranliao Huaxue Xuebao/Journal of Fuel
Chemistry and Technology, 35 (2007) 176–180, DOI:
10.1016/S1872-5813(07)60016-4.
Liu Y., Fan H., The effect of hydrogen donor additive on theviscosity of heavy oil during steam stimulation, Energy andFuels, 16 (2002) 842–846, DOI:10.1021/ef010247x.
Maity S.K., Ancheyta J., Marroquín G., Catalytic aquathermolysis used for viscosity reduction of heavy crude oils: A
review, Energy and Fuels, 24 (2010) 2809–2816, DOI:
10.1021/ef100230k.
Martinez A.R., The Orinoco Oil Belt, Venezuela, Journal of
Petroleum Geology, 10 (1987) 125–134.
Meneses C.T., Flores W.H., Garcia F., Sasaki J.M., A simple
route to the synthesis of high-quality NiO nanoparticles,
Journal of Nanoparticle Research, 9 (2007) 501–505,
DOI:10.1007/s11051-006-9109-2.
Merissa S., Fitriani P., Iskandar F., Abdullah M., Khairurrijal,
Preliminary study of natural zeolite as catalyst for decreasing the viscosity of heavy oil, AIP Conference Proceedings,
1554 (2013) 131–134.
Mizutani N., Iwasaki T., Watano S., Yanagida T., Tanaka H.,
Kawai T., Effect of ferrous/ferric ions molar ratio on reaction mechanism for hydrothermal synthesis of magnetite
nanoparticles, Bulletin of Materials Science, 31 (2008)
713–717.
Mornet S., Vasseur S., Grasset F., Duguet E., Magnetic
nanoparticle design for medical diagnosis and therapy,
Journal of Materials Chemistry, 14 (2004) 2161–2175,
DOI:10.1039/b402025a.
Musić S., Czakó-Nagy I., Salaj-Obelić I., Ljubešić N., Formation of α-Fe2O3 particles in aqueous medium and theirproperties, Materials Letters, 32 (1997) 301–305.
Musić S., Krehula S., Popović S., Skoko Ž., Some factors
influencing forced hydrolysis of FeCl3 solutions, Materials
Letters, 57 (2003) 1096–1102.
Nassar N.N., Hassan A., Pereira-Almao P., Application of nanotechnology for heavy oil upgrading: Catalytic steam
gasification/cracking of asphaltenes, Energy & Fuels, 25(2011) 1566–1570.
Noorlaily P., Nugraha M.I, Khairurrijal, Abdullah M., Iskandar
F., Ethylene glycol route synthesis of nickel oxide nanoparticles as a catalyst in aquathermolysis, Materials Science
Forum, 737 (2013) 93–97, DOI:10.4028/www.scientific.net/
MSF.737.93.
Nugraha M.I., Noorlaily P., Abdullah M., Khairurrijal, IskandaraF., Synthesis of NixFe3-xO4 nanoparticles by microwave-­assisted coprecipitation and their application on viscosity
reduction of heavy oil, Materials Science Forum, 737 (2013)
204–208, DOI:10.4028/www. scientific.net/MSF.737.204.
Nurhayati T., Iskandar F., Abdullah M., Khairurrijal, Synthesesof hematite (α-Fe2O3) nanoparticles using microwave-­

14

assisted calcination method, Materials Science Forum,
737 (2013) 197–203, DOI:10.4028/ www.scientific.net/MSF.
737.197.
Owen J., Floyd J., Enhanced oil recovery methods, Oil Gas
Petrochem Equip, 27 (1981) 11.
Petroleum B. (2014). BP Statistical Review of World Energy
2014, 1 June 2014.
Pride S.R., Flekkøy E.G., Aursjø O., Seismic stimulation forenhanced oil recovery, Geophysics, 73 (2008) O23–O35.
Purnomo C.W., Salim C., Hinode H., Synthesis of pure Na-X
and Na-A zeolite from bagasse fly ash, Microporous andMesoporous Materials, 162 (2012) 6–13, DOI: 10.1016/j.
micromeso.2012.06.007.
Randrianantoandro N., Mercier A.M., Hervieu M., Grenèche
J.M., Direct phase transformation from hematite tomaghemite during high energy ball milling, Materials
Letters, 47 (2001) 150–158, DOI:10.1016/S0167-577X(00)00227-5.
Sahoo S., Agarwal K., Singh A., Polke B., Raha K., Characterization of γ- and α-Fe2O3 nano powders synthesized byemulsion precipitation-calcination route and rheologicalbehaviour of α-Fe2O3, International Journal of Engineering,
Science and Technology, 2 (2010) 118–126.
Selwood P., Magnetism and catalysis, Chemical reviews, 38(1946) 41–82.
Semple K., Westlake D., Characterization of iron-reducing
Alteromonas putrefaciens strains from oil field fluids,
Canadian journal of microbiology, 33 (1987) 366–371.
Shimomura Y., Tsubokawa I., Kojima M., On Nickel Oxides ofHigh Oxygen Content, Journal of the Physical Society ofJapan, 9 (1954) 521–524.
Stjerndahl M., Andersson M., Hall H.E., Pajerowski D.M.,
Meisel M.W., Duran R.S., Superparamagnetic Fe3O4/SiO2
nanocomposites: enabling the tuning of both the iron oxideload and the size of the nanoparticles, Langmuir, 24 (2008)
3532–3536.
Swardhani A.P., Iskandar F., Abdullah M., Khairurrijal, Synthesis of Fe2O3/C Nanocomposite Using Microwave Assisted
Calcination Method, Advanced Materials Research, 896(2014) 100–103.
Tueros M.J., Baum L.A., Borzi R.A., Stewart S.J., Mercader
R.C., Marchetti S.G., Bengoa J.F., Mogni L.V., Characterization of Nanosized Maghemite Particles Prepared byMicroemulsion Using an Ionic Surfactant, Hyperfine Interactions, 148–149 (2003) 103–108, DOI:10.1023/B:HYPE.
0000003770.22742.e7.
Van Donk S., Janssen A.H., Bitter J.H., De Jong K.P., Generation, characterization, and impact of mesopores in zeolite
catalysts, Catalysis Reviews—Science and Engineering, 45(2003) 297–319, DOI:10.1081/CR-120023908.
Vasquez-Mansilla M., Zysler R.D., Arciprete C., Dimitrijewits
M.I., Saragovi C., Greneche J.M., Magnetic interaction evidence in α-Fe2O3 nanoparticles by magnetization andMossbauer measurements, Journal of Magnetism andMagnetic Materials, 204 (1999) 29–35, DOI:10.1016/S03048853(99)00404-7.
Wang Y., Chen Y., He J., Li P., Yang C., Mechanism of catalytic
aquathermolysis: Influences on heavy oil by two types ofFerry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

efficient catalytic ions: Fe3+ and Mo6+, Energy and Fuels, 24(2010) 1502–1510, DOI:10.1021/ ef901339k.
Wang Y., Du W., Xu Y., Effect of sintering temperature on thephotocatalytic activities and stabilities of hematite andsilica-dispersed hematite particles for organic degradation
in aqueous suspensions, Langmuir, 25 (2009) 2895–2899,
DOI:10.1021/la803714m.
Wen S., Zhao Y., Liu Y., Hu S., A study on catalytic aquathermolysis of heavy crude oil during steam stimulation, Society ofPetroleum Engineers, DOI:10.2118/106180-MS.
Willis A.L., Turro N.J., O’Brien S., Spectroscopic characterization of the surface of iron oxide nanocrystals, Chemistry ofMaterials, 17 (2005) 5970–5975, DOI:10.1021/cm051370v.
Woo K., Lee H.J., Ahn J.P., Park Y.S., Sol-Gel Mediated Synthesis of Fe2O3 Nanorods, Advanced Materials, 15 (2003)
1761–1764, DOI:10.1002/adma.200305561.
Wu C., Lei G., Yao C., Jia X., In situ upgrading extra-heavy oil
by catalytic aquathermolysis treatment using a new catalyst based anamphiphilic molybdenum chelate, Society ofPetroleum Engineers, DOI:10.2118/130333-MS.
Wu C., Su J., Zhang R., Lei G., Cao Y., The use of a nano-nickel
catalyst for upgrading extra-heavy oil by an aquathermolysis treatment under steam injection conditions, Petroleum
Science and Technology, 31 (2013) 2211–2218, DOI:10.1080/10916466.2011.644016.
Wu Z., Zhang M., Zhao Z., Li W., Tao K., Synthesis of a Pd on
Ni-B nanoparticle catalyst by the replacement reaction
method for hydrodechlorination, Journal of Catalysis, 256(2008) 323–330, DOI:10.1016/j.jcat.2008.03.026.
Xu B., Sievers C., Hong S.B., Prins R., van Bokhoven J.A.,
Catalytic activity of Brønsted acid sites in zeolites: Intrinsic activity, rate-limiting step, and influence of the local
structure of the acid sites, Journal of Catalysis, 244 (2006)
163–168, DOI:10.1016/j.jcat.2006.08.022.
Xu H., Pu C., Wu F., Low frequency vibration assisted catalytic
aquathermolysis of heavy crude oil, Energy and Fuels, 26(2012) 5655–5662, DOI:10.1021/ef301057t.
Xu H.X., Pu C.S., Experimental study of heavy oil undergroundaquathermolysis using catalyst and ultrasonic, Ranliao
Huaxue Xuebao/Journal of Fuel Chemistry and Technology,
39 (2011) 606–610.
Xuan Y., Li Q., Heat transfer enhancement of nanofluids, International Journal of Heat and Fluid Flow, 21 (2000) 58–64,
DOI:http://dx.doi.org/10.1016/S0142-727X(99)00067-3.
Yang H., Hu Y., Zhang X., Qiu G., Mechanochemical synthesis ofcobalt oxide nanoparticles, Materials Letters, 58 (2004) 387–
389, DOI:http://dx.doi.org/10.1016/S0167-577X(03)00507-X.
Zhang H., Wang W., Li H., Meng S., Li D., A strategy to prepare
ultrafine dispersed Fe2O3 nanoparticles, Materials Letters,
62 (2008) 1230–1233, DOI:http://dx.doi.org/10.1016/j.matlet.
2007.08.026.
Zhang Y., Liu X., Nie J., Yu L., Zhong Y., Huang C., Improvethe catalytic activity of α-Fe2O3 particles in decompositionof ammonium perchlorate by coating amorphous carbon on
their surface, Journal of Solid State Chemistry, 184 (2011)
387–390, DOI:http://dx.doi.org/10.1016/j.jssc.2010.12.004.

Author’s short biography
Ferry Iskandar
Dr. Ferry Iskandar is a researcher and lecturer in Department of Physics, Institut
Teknologi Bandung (ITB). He received his Bachelor and Master Degree at Kanazawa
University, Japan before completing his Doctoral Degree at Hiroshima University, Japan.
He began his teaching career as Assistant Professor in Chemical Engineering Department, Graduate School of Engineering, Hiroshima University before joined Departmentof Physics, Institut Teknologi Bandung (ITB) in 2010. His research and interest include
Material Physics, Filtration and Separation Engineering (Aerosol and Colloids), Powder
and Nanomaterials, Chemical Reaction Engineering and Advanced Materials. He has
published more than 130 research papers and 14 patents.

15

Ferry Iskandar et al. / KONA Powder and Particle Journal No. 33 (2016) 3–16

Author’s short biography
Erlandy DwinantoErlandy Dwinanto holds his Bachelor Degree from Department of Physics, Institut
Teknologi Bandung (ITB). He began his research in Energy and Environmental Materials Laboratory ITB on topics regarding nanoparticles synthesis and application in thefield of enhanced oil recovery (EOR) of heavy crude oil, under the supervision of Dr.
Ferry Iskandar. His research interests include catalysts, adsorbents and related materials.
He is currently preparing for his Master course at Graduate School of Engineering,Tokyo Institute of Technology on International Graduate Program.

Mikrajuddin AbdullahProf. Mikrajuddin Abdullah is a Professor at the Faculty of Mathematics and Natural
Sciences Institut Teknology Bandung (ITB). He received his Bachelor Degree and Master
Degree at Institut Teknologi Bandung (ITB), Indonesia and Doctoral Degree at Hiroshima
University, Japan. His research interests are related to synthesis of nanomaterials (nanoparticles, carbon nanotubes, nanocomposites), applications of nanostructured materials (display, luminescent nano-ink, nanocatalyst, cosmetics, etc.), and other interests in Physical
Education.

Khairurrijal
Prof. Khairurrijal is a Professor from Departement of Physics, Institut Teknologi
Bandung, Indonesia. He is a member of Physics of Electronic Materials Research Division, Faculty of Mathematics and Natural Sciences, ITB. He received his Bachelor Degree and Master Degree at Institut Teknologi Bandung (ITB), Indonesia and Doctoral
Degree at Hiroshima University, Japan. He is working on research topics in Physics,
Materials Science and Engineering, Instrumentation and Measurement Science, Automatic Control, Educational Technology, and Science Education.

Oki Muraza
Dr. Oki Muraza is a researcher at Center of Research Excellence in Nano Technology(CENT) and a faculty in the Chemical Engineering Department at King Fahd Universityof Petroleum & Minerals. He received his PhD in Chemical Engineering at Technische
Universiteit Eindhoven (TU Eindhoven). His research interest mainly are nanostructured catalysts and nanoparticles such as nanostructured catalytic thin films in (micro)
structured reactors, zeolite nanocrystals in catalysis, and nanoparticles for heavy oil
upgrading.

16

KONA Powder and Particle Journal No. 33 (2016) 17–32/Doi:10.14356/kona.2016007

Review Paper

Surface Chemistry and Rheology of Slurries of Kaolinite
and Montmorillonite from Different Sources †Pek-Ing Au and Yee-Kwong Leong *1

School of Mechanical and Chemical Engineering, The University of Western Australian, Australia

Abstract
Zeta potential (ζ)-pH and yield stress (τy)-pH behaviour of a number of kaolinite and montmorillonite slurries,
including CMS-sourced materials, were compared. The CMS KGa-2 and Crown kaolin with very similarelemental composition displayed almost identical ζ-pH and τy-pH behaviour. Both displayed a pHζ=0 at 3–4 where
the maximum τy was located. This pHζ=0 was higher at higher ionic strength as the pH-dependent charge increased
with ionic strength and the permanent structural charge being invariant. The other kaolinite slurries (Reidel andUnimin) with different composition showed different behaviour. The surface chemistry and rheological properties
of CMS-sourced SWy-2 (Na-) and STx-1b (Ca-) montmorillonite and bentonite slurries were also compared. They
all displayed a negative zeta potential that was insensitive to pH. STx-1b slurries required a much higher solid
concentration to form a gel. Maximum τy occurred over a broad range of pH. This pH is ~5 for SWy-2, 8 for STx1b, <2 for bentonite and 12 for API bentonite. Their difference in clay mineral composition such as impurities andexchangeable cations were highlighted. The point of zero charge (pzc) of kaolinite and montmorillonite slurries
obtained via Mular-Roberts pH-salt addition method did not correlate well with the pHζ=0 except for the KGa-2
and Crown kaolin.
Keywords: drilling fluids, yield stress-volume fraction, zeta potential, clay mineralogy, fractal dimension, MularRoberts, KGa-2, SWy-2, STx-1b

1. Introduction
Kaolin mineral slurries from different sources or deposits display different rheological and surface chemical
properties (Au P. and Leong Y., 2013; Au P. et al., 2014;
Lagaly G., 1989; Rand B. and Melton I.E., 1977; Teh E. etal., 2009). Kaolin is a very important commodity mined
all over the world. The ability to use its mineral and elemental composition to predict its slurry rheological behaviour from its surface properties would be extremely
useful and beneficial when processing this clay mineral.
However not all of the important factors responsible forthe variation in rheological behaviour have been identified
and their effects understood. Attempts have been made to
identify these factors (Rand B. and Melton I.E., 1977; Teh
E. et al., 2009). Factors such as Ca(II) or CaO content or
Ca/Na ratio (Avadiar L. et al., 2014; Avadiar L. et al.,2015; Lagaly G., 1989) have been identified. The presenceof a relatively small amount of smectite content can also
have a very significant effect on the rheological behaviour†

Received 26 May 2015; Accepted 29 June 2015
J-STAGE Advance published online 25 July 2015
1
35 Stirling Highway, Crawley WA 6009, Perth, Australia* Corresponding author: Yee-Kwong Leong;E-mail: yeekwong.leong@uwa.edu.au
TEL: +61-8-6488 3602 FAX: +61-8-6488 1084

©2016 Hosokawa Powder Technology Foundationof kaolinite slurries (Au P. and Leong Y., 2013).
Bentonite composed of mainly sodium montmorillonite(Na+Mt) is another important commercial clay mineralbecause of its many uses, for example in paper coating,catalysts, pharmaceutical products, drilling muds, as animpermeable slurry wall and nuclear waste storage barrier. Slurries of bentonite displayed highly complex
time-dependent rheological properties. Na+Mt in waterswelled considerably. Factors affecting its rheological behaviour are thus more numerous such as impurities, exchangeable cations and salt concentration (Chang W. andLeong Y., 2014; Goh R. et al., 2011; van Olphen H., 1955).
However, despite the numerous studies, the knowledge
available in the open literature is still quite confusing.
Gels of Na+Mt from different sources displayed different
rheological properties. According to Lagaly G. (1989), theimportant factors are particle structure and texture. Uponswelling different Na+Mt will disintegrate to different extents giving rise to stack-layered particles of different
thickness, different particle concentration and different
shape flow units producing gels. A better evaluation of thedifference would be to compare the rheological properties
over a wide pH range. Like kaolinite, the comparison
should also include zeta potential-pH behaviour. Such a
comparison would allow for a more effective evaluationof the factors responsible for the rheological variation.

17

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Rheological and surface properties data from slurries prepared from standard or well-characterised Na+Mt andkaolinite are required for comparison and evaluation.
Such clay minerals can be sourced from the Clay Mineral
Society USA (CMS) and will have well-characterised
compositional, physical and surface properties data (CMS,2015). The surface chemistry and rheological property
data obtained for these clay mineral slurries will form thebaseline or benchmark data for other kaolin and montmorillonite slurries to compare in this search to identify theimportant factors and understand their effects. CMSsourced kaolin KGa-2 and Na+Mt SWy-2 and Ca2+Mt
STx-1b were used in this study.
The complete characterization of the surface propertiesof the clay minerals may require the value of several components: point of zero charge (pzc) or isoelectric point(IEP) (Pradip et al., 2015), point of zero net proton charge,
zeta potential-pH-ionic strength behaviour, charges (layer,
tetrahedral, octahedral, unbalanced and extra Si) andcation exchanged capacity or CEC. For CMS-sourced
clay minerals, most of these parameters are known andlisted on their database (CMS, 2015). For correlation withrheological properties, normally only the zeta potential,
including pzc, is required because the magnitude of therheological parameters such as yield stress and viscosity,
is governed by the nature and strength of the predominantinterparticle force. Zeta potential is a measure of thestrength of the interparticle repulsive force and is often
used to define the state of the slurries; flocculated or dispersed. A high magnitude is normally associated with noor a very low yield stress. This is often not the case withsome clay mineral slurries (Leong Y. et al. 2012; Au P.
and Leong Y. 2013). Many kaolin slurries do obey theyield stress-DLVO model (Hunter R. and Nicol S., 1968;
Teh E. et al., 2009; Au P. and Leong Y, 2013).
Among the many yield stress τy-DLVO force or interaction energy models (Hunter R. and Nicol S., 1968; LeongY. and Ong B., 2003; Teh E. et al., 2010) is one based on
constant surface potential for interactions between spherical particles and is given by (Scales P., et al., 1998):

τy ≈φs 2  AH

−κ Do

2 κe
εξ
−
2πε
o


D
−
κ
2
o
a  12Do
1+ e


(1)

The number of particles per unit area is scaled to ϕs2/a2.
The model predicts a linear relationship between τy andzeta potential squared ξ2 at a fixed ionic strength. The
critical zeta potential ξcri characterizes the point of transition from flocculated to dispersed state where τy = 0(Leong Y.K. and Ong B.C., 2003). Thus Eqn. 1 is reduced
to giveξ cri =AH (1 + e −κ Do )24Do 2πε oεκ e −κ Do(2)ξcri is proportional to the square root of the Hamaker

18

constant AH and is independent of the particle size a andconcentration of the dispersion. This equation has beenused to determine the Hamaker constant of pristine oxide
in water (Leong Y. and Ong B., 2003; Teh E. et al., 2010).
Laxton P. and Berg J. (2006), however, observed a positive slope for the linear relationship between τy and ξ2 ofkaolinite–bentonite composite slurries. They attributed
this to positive–negative charge attraction between theclay platelets. In their study, the zeta potential was varied
by ionic strength instead of pH.
Apart from zeta potential, there are several techniques
for characterizing the pzc of a mineral powder. One suchtechnique is the Mular–Roberts (MR) salt addition–pH
method (Mular A. and Roberts R., 1966). This method
has been shown to accurately determine the pzc of pure
inorganic oxides such as, Fe2O3, Al2O3, SiO2 and TiO2
(Alvarez-Silva M. et al., 2010; Mular A.L. and Roberts
R.B., 1966). Its accuracy with clay minerals such as serpentine and chlorite phyllosilicate minerals is still questionable (Alvarez-Silva M. et al., 2010). For a pristine
inorganic oxide such as rutile TiO2, the pzc is independentof the ionic strength of an indifferent electrolyte (Hunter
R., 2003). The MR method exploited this property to determine the pzc. At pzc, the pH of the mineral suspended
in 0.001 M sodium chloride concentration will remainunchanged when the salt concentration is increased to
0.01 M. At other pH levels, a difference in the pH between these two ionic strength states will be present.
With pure oxides, such as alumina and silica, the surface
charges are all pH-dependent. In contrast, clay minerals
contain permanent structural negative charges which are
pH-independent (Bolland M. et al., 1980).
Alvarez-Silva M. et al. (2010) found that the MR
method for pzc determination was suitable for serpentine
but not for chlorite phyllosilicate mineral. In this study,
we extended their work to other clay minerals and included rheological data to correlate with the surface
chemistry data. In addition, we also aim to develop anunderstanding of the applications and limitations of the
MR method for kaolinite and montmorillonite.

2. Materials and methods
2.1 Materials and mineral characterisation
The CMS KGa-2 kaolin was sourced from a deposit
located in Warren County, Georgia, USA. KGa-2 is a highdefect or low crystallinity kaolin with a crystallinity index of only 0.16. It was composed of 96 % kaolinite (with
3 % anatase, 1 % crandallite + trace mica and/or illite)(Chipera S. and Bish D., 2001). It has a BET surface areaof 23.5 ± 0.06 m 2 g–1 and a cation exchange capacity(CEC) of only 3.3 meq/100 g. The large BET area is anPek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Table 1Elemental composition of kaolinite from different
sources expressed in oxide content (wt%).
KGa-2

Crown

ReidelUnimin

SiO2

43.9

45.1

48.5

46.8

Al2O3

38.5

37.7

38.2

TiO2

2.08

1.9

Fe2O3

0.98

1.4

FeO

0.15

Na2O

< 0.005

0.03

n.d.

K 2O

0.065

0.05

CaO

n.d.

MgO

0.03

MnO

n.d.

F

0.02

P2O5

0.045

0.147

S

0.02

0.04

Table 2Elemental composition of montmorillonite from different sources in terms of oxide content (wt%)
Swy-2

Bent (B3378)

STx-1b

API bent

SiO2

62.9

60.5

70.1

63

36.6

Al2O3

19.6

21

16

1.49

0.06

0.8

TiO2

0.09

0.153

0.22

0.975

0.93

1.02

Fe2O3

3.35

3.88

0.65

0.76

FeO

0.32

0.36

Na2O

1.53

2.11

0.27

2.18

2.064

0.22

K 2O

0.53

0.318

0.078

0.737

0.06

0.028

0.495

CaO

1.68

1.02

1.59

1.78

0.06

0.4

0.3

MgO

3.05

2.76

3.69

1.6

0.007

0.01

MnO

0.006

0.009

0.009

0.54

F

0.11

0.011

P2O5

0.049

0.04

0.026

0.08

<0.01

SO3

0.05 (S)0.74

0.04 (S)

0.02

0.15

0.084

n.d.: not detectableindication of its high fine particle content. The elemental
compositions of KGa-2 and other kaolinites, expressed inoxide content, are contained in Table 1 (CMS, 2015). The
air-floated Crown kaolin (sourced from a deposit inGeorgia) was provided by Active-Minerals Inc (USA)
who indicated a median particle size of 0.3 μm and the
crystallinity index of ~0.5. It composed 96–100 % kaolin,with 0–2 % quartz and 0–2 % TiO2 (as anatase).
KGa-2 displayed a point of zero charge (pzc) at pH 3.5
and a point of zero net proton charge (pznpc) at 5.4(Schroth B.K. and Sposito G., 1997). Zeta potential characterization of this kaolinite slurry produced a similar pzc
at pH 3–4 (Du J. et al., 2010).
The other clay minerals used in this study were Uniminkaolin (Unimin Corp.), Riedel kaolin (Sigma-Aldrich),
SWy-2 Na+Mt and STx-1b Ca2+Mt (both from CMS), bentonite from USA (B3378, Sigma-Aldrich) and an API
bentonite (Rheochem Ltd). This API bentonite is used in
the formulation of drilling fluids. The BET surface area ofUnimin and Riedel kaolin were 19.9 and 9.9 m2 g–1 respectively (Avadiar L. et al., 2014). The Reidel displayed avery low TiO2 content. XRD data showed the presence ofquartz (< 5 %) in the Unimin and—of both quartz andmica/illite (both < 5 %) in the Reidel (thus the high K 2O
content).
The elemental composition of the montmorillonites
used in this study is given in Table 2. The CEC of SWy-2
and STx-1b were 76.4 and 84.4 meq/100 g, and their specific areas were 31.82 and 83.79 m2 g–1 respectively, The
principal exchange cations were Na+ and Ca2+ for SWy-2
and Ca2+ for STx-1b. The SWy-2 was from a deposit in

Crook County, Wyoming, USA. It was composed of 75 %smectite, 8 % quartz, 16 % feldspar, 1 % gypsum, andtraces of mica and/or illite and kaolinite and/or Chlorite(Chipera S.J. and Bish D.L., 2001). The STx-1b comprised
63 % smectite, 33 % opal-CT (SiO2) and 3 % quartz (trace
amount of feldspar and kaolinite) (Humphries S.D. et al.,2011).
Powder characterization included particle sizing andparticle morphology examination. The particle size distribution was measured with a laser Malvern Mastersizer
Microplus. The morphology of the particles was imaged
with a Zeiss 1555 VP-FESEM scanning electron microscope(SEM). The median size of KGa-2, Unimin, Riedel kaolin, Swy-2 and bentonite were found to be 1.7, 3.26, 5.70,
2.09, and 2.77 μm respectively. The SEM images of thevarious clay mineral powders are shown in Fig. 1(a)–(f).
All four kaolin tested were poorly crystalline. Fig. 1(a)shows that the KGa-2 possessed a complex surface structure on its basal surface, including micro-pits, the highfrequency occurrence of micro-islands, and ragged andbroken edges. This was also observed in the poorly crystallised Unimin, Riedel and Crown kaolin. The overall
particle shape of these high defect kaolinites should leadto higher aspect ratios (Żbik M. and Smart R., 1998). The
kaolinite crystals in SEM micrographs appear as rigid
particles. However the particles of Swy-2 Na+Mt and bentonite appeared as undulated flexible sheets, as seen in
Figs. 1(e) and 1(f).The difference in microstructure between kaolinite and smectite is due to the relatively stronghydrogen bonding between hydroxyl groups on the 1:1
kaolinite sheets (Żbik M. et al., 2010).

19

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Fig. 1 SEM images of various kaolin (a) KGa-2, (b) Unimin, (c) Riedel and (d) crown. SEM images of (e) SWy-2
sodium montmorillonite and (f) bentonite.

2.2 Rheological charaterization
The rheological parameter used was the yield stress,
which was measured directly using a vane technique(Nguyen Q. and Boger D., 1983). Clay suspensions wereprepared at high pHs by homogenising a mixture of clay
powder and solution with a Branson Sonifier which breakups the particle aggregates by sonic vibration. Dilute suspensions were prepared in the same manner for zeta potential measurement.
Brookfield vane viscometers with different spring constants, LVDV-II + Pro and RVDV-II + Pro, were used. A
range of four-blade vanes were used to cover yield stressvalues ranging from < 1 Pa to 1000 Pa. The suspension pH
was varied by using a 10 M potassium hydroxide and a
6.61 M nitric acid solution in order to avoid excessive

20

dilution of sample during pH change in the experiment.
Yield stress measurement was conducted in the direction
from high to low pH so as to avoid the dissolution of clay
minerals and the precipitation of the hydrolysis productsof metal ions.

2.3 Zeta potential and pzc determination
Zeta potential was measured using a Colloidal Dynamics ZetaProbe (Appel C. et al., 2003). The pzc or IEP usually corresponded to the pH of zero zeta potential. The
pzc determination via the MR method of pH-salt addition
measurement required preparation of a 2 wt% slurry in a
0.001 M NaCl solution at a specific pH ranging from 3 to
11. In the case of kaolinite, 1 g of material was placed in a
50 mL of a 0.001 M NaCl solution prepared and the initial

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

pH (pHini) was measured. Dry NaCl solids was thenadded to increase the ionic strength from 0.001 to 0.1 M
and the final pH (pH fin) was recorded. When ΔpH(= pHfin – pHini) is zero, the pHfin is the pzc. The clay particle is positively charged when ΔpH is positive (Ndlovu
B. et al., 2014). For smectite swelling clay, 0.5 g wasadded to a 50 ml 0.001 M NaCl solution. Measurement forswelling clay was performed 24 hours after preparation.
Goh R. et al. (2011) reported that freshly prepared bentonite gels required about a day to reach surface chemical
equilibrium.

3. Results and discussion
3.1 Kaolinite slurries
The zeta potential–pH behaviour of KGa-2 and Crown
kaolin slurries was compared. They displayed almost
identical zeta potential–pH behaviour at an ionic strengthof 0.1 M NaCl (Fig. 2). An isoelectric point (IEP) or pzc
at pH 3–4 was observed. Both contained a very low
amount of CaO and other mineral impurities such as
quartz, mica and the like.
The pzc of Crown kaolin at lower ionic strength of
0.001 M NaCl occurred at a lower pH of 3.0 as shown in
Fig. 2. Kaolin possesses both permanent structural (negative)
charge due to isomorphic substitution and pH-dependent
charge. Unlike pH-dependent charge, the amount of
permanent negative charge is not dependent upon theionic strength. For pristine oxides such as TiO2 with only
pH-dependent charges, its surface charge density at agiven pH increased with ionic strength—except at the pzc(Hunter R., 2003). At the pzc, the amount of positive
charges must be equal to the negative charges. With kaolinslurries, at a given pH (below pznpc) away from pznpc,
the positive pH-dependent positive charge density increased with ionic strength. At low ionic strength, the pH
must be further away from the pznpc for the kaolin particles to acquire sufficient positive charge to balance thepermanent negative charge (Tombácz E. and Szekeres M.,
2006). Thus for Crown kaolin, a lower pH is required toacquire a sufficient amount of pH-dependent positive charge
to balance the permanent negative charge at low ionicstrength. This is the likely explanation for the lower pzc.
The yield stress–pH behaviour shown in Fig. 3(a) for
KGa-2 and 3(b) for Crown kaolin were also similar,
namely, high yield stress at low pH and low yield stress athigh pH. The maximum yield stress was observed to belocated at or close to pzc or IEP.
The yield stress–solids volume fraction relationships
shown in Fig. 4 at low pH of ~3 were almost identical. A
power law model described this relationship. An exponent
value of ~3 was obtained. A similar exponent value wasFig. 2 Zeta potential–pH behaviour of 3.76 wt% KGa-2 andCrown kaolin slurries (pzc of 3.4) and the effect ofionic strength on Crown kaolin slurries.

Fig. 3

Yield stress–pH behaviour of a) KGa-2 kaolin and (b)air-floated crown kaolin slurries.

observed at a higher pH of 7 for the Crown kaolin. The
low CaO Reidel kaolin slurries also displayed a similar
exponent value of 3.1 (Au P.I. et al., 2014). This exponent
value was related to the fractal dimension of the microstructure in the slurries (Au P. and Leong Y., 2015; Au P.I.
et al., 2015). Kranenburg C. (1994) derived the following
relationship between yield stress τy and ϕs from scaling
theory:21

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Fig. 4 The yield stress–solid volume fraction relationship of
KGa-2 and Crown kaolin dispersions.

Fig. 5 The cyro-SEM image of 15 wt% KGa-2 slurries at the
maximum yield stress of pH 4.2.

τ y ~ φs2/(3− Df )(3)

and applied it to clay sediment and suspensions. The same
equation was derived earlier by Wessel R. and Ball R.
(1992) for creeping shear flow of fractal aggregates andgels. Using Eqn (3), the fractal dimension D f of thesekaolin slurries was determined to have a value of 2.3. The
predominant particle interaction configuration is face–
face or stair–step (Au P. et al., 2014) with some face–faceaggregates interacting via edge–face configurations(Gupta V. et al., 2011).
The cryo-SEM image of microstructure of KGa-2 slurries at the maximum yield stress with a fractal dimensionof 2.3 is shown in Fig. 5. The image shows that dense aggregates formed predominantly by face–face and stairstep interactions participating in a mixture of interaction
configurations—between aggregates such as edge–face,edge–edge and face–face. Many of the edge–face interactions involved a particle face resting on the edge of anotherparticle at a very acute angle.
Fig. 6 shows the MR salt addition-pH result for KGa-2

22

Fig. 6The Mular–Roberts pH-salt addition results for ~2 wt%
KGa-2 and Crown kaolin slurries.

and Crown kaolin. For KGa-2, the experiment was repeated with a fresh sample. The two sets of data fittedwith a third order polynomimal. For both KGa-2 andCrown kaolin the ΔpH = 0 occurred at pH 4.2. This value
is similar to the pH of zero zeta potential of 4 obtained forboth at the same ionic strength of 0.1 M NaCl. It seemedthat good agreement in value between the two methods
was achieved. Different mixing methods—spatula mixing
and sonication—were evaluated for their effect on the pzc
value for Crown kaolin. No significant effect was observed.
The pzc of KGa-2 obtained here was in agreement withthe value in the literature (Schroth B. and Sposito G.,
1997; Du F. et al., 2010). Point of zero salt effect, point ofzero net proton charge (pznpc) and point of zero net
charge (pzc) of KGa-2 kaolin were reported to have avalue of pH 2.8, 5.4 and ~4 respectively (Schroth B. andSposito G., 1997). The good correlation between the
maximum yield stress and IEP or pzc suggested that thesurface chemistry–yield stress result can act as a goodreference point for comparison by other kaolinites.
The low CaO Riedel kaolin also displayed similar yieldstress–pH behaviour (Fig. 7), low yield stress at high pH
and high yield stress at low pH. Complete dispersion(τy = 0) of the slurries with 30 and 40 wt% solids was observed at pH 9. These slurries’ maximum yield stress, τymax,
was located at pH 3. Its zeta potential–pH behaviour,
however, did not display a pzc or IEP at low pH (Fig. 8).
The trend of a decreasing negative zeta potential with decreasing pH was similar to others such as KGa-2 andCrown kaolin. The absence of pzc is probably due to thepresence of mica/illite and quartz, as its main mineral impurities. Both these minerals have a very low IEP of ~2.0.
The MR method, however, gave a pzc value of 4.0 for thiskaolin (Fig. 9). Note that not all low CaO kaolin slurries
displayed complete dispersion at high pH (Teh E. et al.,2009).
Unimin kaolin has a relatively high CaO content. Its

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Fig. 7 Yield stress–pH behaviour of Riedel and Unimin kaolinslurries at 30 and 40 wt% solids.

Fig. 8

Zeta potential–pH behaviour of 1 wt% low Ca(II) Riedel
and high Ca(II) Unimin kaolin suspensions.

slurries displayed very different yield stress–pH behaviour (Fig. 7). The τymax was located at pH 7–9 where
the negative zeta potential is relatively large (Fig. 8). The
addition of CaSO4 was found to increase the Bingham
yield stress of homoionic kaolinite slurries at alkaline pH(Lagaly G., 1989). The decreasing negative zeta potential
with decreasing pH was similar to all others—Reidel,
Fluka and Sigma kaolin (Teh E. et al., 2009). The magnitude of the zeta potential was smaller for the Unimin. No
pH of zero zeta potential was observed. However the result of the MR method gave a high pzc value of pH 8.8(see Fig. 9), which coincidentally was the location of theτymax.
The value of the τymax for the Unimin was much larger
than that of Reidel at the same solid loading. At 30 wt%
solids, the τymax was 40 Pa for the Unimin compared toonly 20 Pa for the Riedel. This is probably due to thesmaller particle size of Unimin (3.26 μm) compared to
Riedel (5.70 μm) which meant a higher particle concentration in the Unimin—which should result in more particles

Fig. 9The MR pH–salt titration curves of ~2 wt% Riedel andUnimin kaolin slurries showing the effect of calcium
content in kaolinite.

participating in attractive interaction forming a stronger
network structure (Leong Y. et al., 1995).
From the elemental composition data in Table 1, it canbe seen that all four kaolin—KGa-2, Crown, Reidel andUnimin—possessed similar iron content in terms of Fe2O3
and FeO. So, Fe content does not account for the differences in the yield stress–pH, zeta potential–pH and MR
pzc results. The Ti content was very similar for KGa-2,
Crown and Unimin. But it was very low in Reidel, 10
times smaller in concentration, and yet its yield stress-pH
behaviour is very similar to that of KGa-2 and Crown
kaolin. In addition, this Reidel kaolin has a very highcontent of K (from mica/illite impurities), at least 10 times
larger than the other three kaolin. Only Unimin slurries
displayed very different yield stress–pH behaviour. The
only element significantly different to the others is the
Ca(II) content quoted in terms of CaO. Avadiar L. et al.
(2012; 2104; 2105) have conducted extensive studies on
the effect of Ca(II) on the yield stress–pH and zeta potential–pH of kaolin, silica and alumina particles. They
found that the addition of Ca(II) produced similar yieldstress–pH behaviour in low CaO kaolin and that it also
depressed the magnitude of the negative zeta potential.
Their studies were quite conclusive in showing that CaO
was the cause of the difference in yield stress–pH andzeta potential–pH behaviour.
Ca(II) shifts the maximum yield stress and pH of zerozeta potential to a higher pH (Avadiar L. et al. 2015). The
adsorption of calcium ions on kaolinite surface increasedin the high pH region in the form of positively charged ofcalcium complexes and precipitate in solution to provide
positive charges to the negatively charged kaolinite surfaces (Atesok G. et al., 1988; Heidmann I. et al., 2005).
Apart from that, the complex surface structure of poorly
crystalline kaolinite produces a higher concentration ofhydroxyl groups on the basal surfaces which provide ad-23

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

ditional sites for the adsorption of Ca2+ ions. At low pH,
Ca2+ ions adsorption has reduced as a result of increasingionic strength and the competition with the dissolved
Al3+, Si4+, Fe3+ and Cu2+ ions (Au P. et al. 2014; Heidmann
I. et al., 2005; Zhou Z. and Gunter W., 1992). Therefore,
Ca2+ is the cause of the shift in the location of the maximum yield stress and the increase in the yield strength of
the kaolinite suspensions.
The excellent correlation between yield stress and zeta
potential observed in KGa-2 and Crown kaolinite was notobserved with Unimin. Although the zeta potential–pH
behaviour does not explain the yield stress behaviour for
the Unimin, it clearly demonstrated the effect of calcium
content. The difference in magnitude of negative zeta potential between high Ca(II) Unimin and low Ca(II) Riedel
kaolin was considerably larger at the high pH region. The
smaller negative zeta potential of Unimin at high pH wasattributed to the adsorption of calcium ions and its positively charged hydrolysis product (Avadiar L. et al., 2014;
McFarlane A.J. et al., 2006). At low pH, and where calcium ions adsorption is not important, the zeta potentialof both Riedel and Unimin kaolin was similar in value.
This confirms the adsorption of calcium species affecting
the properties and behaviour of kaolinites.
MR Salt–pH addition results in Fig. 9 showed the
ΔpH = 0 was located at a particular pH (pzc for purematerial) for both Riedel and Unimin kaolin. For Riedel
kaolin, the ΔpH = 0 was located at a much lower pH of 4,
which was close to the location of the maximum yieldstress. The addition of 0.5 dwb% CaO to Reidel shifted
ΔpH = 0 to a higher pH of 8.7, a value similar to that displayed by Unimin. This result showed that CaO was responsible for the high pH of ΔpH = 0.
Kaolin slurries with low CaO content such as Reidel,
Sigma and Fluka, obeyed the yield stress–DLVO model(Au P. and Leong Y., 2013; Teh E. et al., 2009). High CaO
content Unimin slurries were found to obey this modelFig. 1024

The yield stress–DLVO model fit to the yield stresszeta potential data of Crown kaolin.

only after treatment with phosphate-based additives(Leong Y. et al., 2012; Shankar P. et al., 2010). The linear
decrease of τy with ξ2 illustrated in Fig. 10 showed that
the Crown kaolin slurries also obeyed the model. The ξ2cri
which characterises the state of transition from flocculated to disperse state (Leong Y.K. and Ong B.C., 2003;
Teh E.J. et al., 2010) is very high, 6000–7000 mV2. This
does not mean its Hamaker constant in water is very high.
Rather, positive edge-negative face interactions in kaolinslurries contributed to this high value.

3.2 Montmorillonite slurries
The complex rheological behaviour of Na+Mt slurries iswell-known and has been the subject of numerous studies
with surface chemistry effects being the main focus(Lagaly G., 1989; Liang H. et al., 2010; Packter A., 1956;
Paineau E. et al., 2011; Ramos-Tejada M. et al., 2001;
Sakairi N. et al., 2005). The surface chemistry parameters
evaluated were pH (Kelessidis V. and Maglione R., 2008;
Lagaly G., 1989; Tombácz E. and Szekeres M., 2004), salt
type and concentration (Abend S. and Lagaly G., 2000;
Abu-Jdayil B., 2011; Brandenburg U. and Lagaly G., 1988;
Yildiz N. et al., 1999), and a range of adsorbed additives
such as pyro- and poly-phosphates (Goh R. et al., 2011;
Lagaly G., 1989; Yoon J. and El Mohtar C., 2015), polymers (Dolz M. et al., 2007), and surfactants (Luckham P.
and Rossi S., 1999; Permien T. and Lagaly G., 1994). Theinterpretation of the results was complicated by the pronounced swelling and thixotropic behaviour of these slurries (Abend S. and Lagaly G., 2000; Galindo-Rosales F.
and Rubio-Hernández F., 2006; Lagaly G., 1989; Lee C. etal., 2012; van Olphen H., 1955, Yoon J. and El Mohtar C.,2013). Even the sequence of reagent addition such as KOH
during gel preparation has a dramatic effect on the yieldstress–pH behaviour (Au P. and Leong Y., 2013). Despite
more 50 years of research on this material, there is still noconsensus on the nature of the predominant interparticle
forces operating in the Na+Mt gels. Van Olphen H. (1951)
suggested that the space-filling gel was due to attractive
interaction between the positively charged edge and thenegative face forming a card-house structure andmany others agreed (Brandenburg U. and Lagaly G.,
1988; Lockhart N., 1980). Norrish K. (1954) and others(Callaghan I. and Ottewill R., 1974; Paineau E. et al.,2011) believed that the electrostatic double layer (repulsive) force was responsible. Reliable information on theeffect of surface chemistry such as pH and ionic strength
on surface forces which could be evaluated in terms ofrheological properties such as yield stress, shear modulus
and viscosity, may help to settle this issue. However thiswas not helped by its (negative) zeta potential being quite
pH-insensitive (Callaghan I. and Ottewill R., 1974; Goh R.
et al., 2011) while its rheological properties such as yieldPek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

stress or intrinsic viscosity showing strong pH-dependence(Packter A., 1956; Goh R. et al., 2011). High concentrations (> 0.1 M) of common salts such as LiCl, NaCl, KCl
and CsCl, were found to weaken the gel structure of thebentonite slurries which appeared to contradict the DLVO
theory (Chang W. and Leong Y., 2014).
Like the kaolinite slurries, evaluation of the trend of
the yield stress–pH behaviour should be included in establishing differences among the Na+Mt slurries from difference sources rather than just comparing the rheologicalproperties alone. Such evaluation should be accompanied
by information on their zeta potential–pH behaviour. The
evaluation of both the yield stress and zeta potential behaviour across a broad pH range was completed for bentonite and bentonite–kaolin composite slurries (Goh R. etal., 2011; Au P. and Leong Y., 2013). Past evaluations ofrheological properties have been conducted at its naturalpH, over a narrow pH range and at a small number of pH
values. Packter A. (1956) evaluated the intrinsic viscosityof very dilute Na+Mt sols over a very narrow pH range
from 7 to 11.5. Here we present yield stress and zeta potential data across a wide pH range from 2 to 13 for arange of montmorillonite slurries. These slurries contained a significant amount of other mineral impurities
and different exchangeable cations.
The time-dependent behaviour can complicate thecomparison of the rheological properties of Na+Mt gelsfrom different sources. The ageing gels can have manyrheological states. Comparison should be conducted at
the same surface chemical and structural state (Chang W
and Leong Y., 2014; Sehly K. et al., 2015). Ideally therheological characterization should be conducted after
the montmorillonite gels have attained surface chemical
equilibrium and their flocculated network structure broken down to an equilibrium state. Doing this allowed therheological data from different sources measured at thisspecific state which was easily attainable, to be quantitatively comparable.
The particle interaction configuration in the montmorillonite gel is crucial in understanding the relationshipbetween surface forces and rheological behaviour. However, clear SEM/TEM images of distinct montmorillonite
particles interacting are not possible to capture. According to Lagaly G. (2006) Na+Mt particles are strictly nottrue crystals and the particles are an assemblage of disordered and bent silicate layers that do not have the regularshape of real crystals. In Ca2+Mt particles, only a few silicate layers formed the coherent domains. These silicate
layers are separated by two layers of water molecules.
The effect of mineral additives such as barite andkaolin on the ageing and rheological behaviour of bentonite slurries have been recently investigated (Yap J. et al.,2011; Au P. and Leong Y., 2015). Bentonite displayed avery large influence on the rheological and ageing be-

haviour of the composite slurries. To identify mineral
impurities with a significant impact on rheological properties, more montmorillonite from various sources need
to be evaluated. This paper will present surface chemistry
and rheological results of well-characterised CMS source
montmorillonite (Swy-2 and STx-1b) in terms of mineralogy, impurities and elemental compositions, and exchangeable cations in addition to surface properties suchas BET area, CEC, layer and structural charges and others. In addition, similar types of results for other bentonite slurries with different mineral impurities are presented
for comparison. One of these is an API bentonite containing a very high content of mineral impurities and having
the following mineralogy: 24.5 % quartz (SiO2), 1.3 %cristobalite (SiO2), 1.5 % calcite (CaCO3), 16.3 % kaolinite, 16.0 % sepiolite (Mg4Si6O15(OH)2·6H2O) and 40.4 %smectite (amorphous). Table 2 lists its elemental composition which shows a relatively high content of iron compounds.
The yield stress–pH behaviour for (a) SWy-2 Na+Mt
and (b) for bentonite and API bentonite slurries is shownin Fig. 11. A characteristic feature of these clay mineralslurries is the very high yield stress of several hundred Pa
at very low solid loading of a few wt%. The shape of theFig. 11The yield stress–pH behaviour of various a) montmorillonite and b) bentonite slurries.

25

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Fig. 12

Zeta potential–pH behaviour of montmorillonite andbentonite slurries.

yield stress–pH curve of both the SWy-2 and bentonite
slurries differed slightly. The Swy-2 displayed a yieldstress decreasing with pH. At low pH the high yield stressshowed a plateauing trend. The yield stress of the bentonite slurries displayed a minimum at pH 8 and an increasing yield stress that showed no plateau trend at low
pH. Packter A. (1956) observed similar behaviour with
Wyoming Na+Mt sols with its intrinsic viscosity increasing with pH from pH 7 to 11.5. Similarly, Brandenburg U.
and Lagaly G. (1988) observed a minimum shear stress at
a fixed shear rate at pH ~ 8 for Wyoming bentonite andhomoionic Na+Mt slurries. The yield stress–pH behaviourof the API bentonite slurry is very different. It displayed
almost no yield stress in the low pH region, between 3 to6, and a maximum yield stress at pH 12. Its zeta potential
was more pH–sensitive as shown in Fig. 12. The mineralcomposition showed that the montmorillonite content was
relatively low at 40 % and the kaolinite and sepiolite content being relatively high, at 16.3 and 16 % respectively.
Sepiolite is a magnesium silicate smectite with a fibrousmorphology (Aznar A. et al., 1992). It formed stable
thixotropic gel in water and was used as a rheologicalmodifier. It is a common ingredient of drilling muds because of its rheological behaviour being insensitive tosalt concentration and it also imparts high temperaturestability to the muds (Galan E., 1996).
The dominant exchange cation in these clay minerals
was Na+ which resided in the interlayer of the clay particles. In the presence of water molecules, hydration of theexchangeable Na+ ions occurred in the interlayer, bringing
about surface charge development, which caused swellingand delamination of the particles. The delamination ofsmectite layered particles increased the number of primaryparticles and the concentration of these particles participating in attractive interactions. This is the likely cause of
the much higher yield stress observed (Leong Y. et al.,1995).

26

Fig. 13

MR results for Swy-2 and bentonite.

The zeta potential–pH behaviour of SWy-2 Na+Mt andbentonite slurries was very similar (Fig. 12). The insensitivity of the negative zeta potential to pH ranging from 2
to 12, was clearly displayed by these two clay minerals.
This insensitivity is due to the pH–insensitive permanent
negative charge accounting for 90–95 % of the total
charges (Duc M. et al., 2005). There was no correlation
between zeta potential and the yield stress for these clay
mineral slurries. This gives the impression that the DLVO
particle interaction theory is being violated. As can beseen in Fig. 12, the zeta potential–pH behaviour of STx-1b
Ca2+ Mont also showed insensitivity to pH. The magnitude of the negative zeta potential is much lower than thatof SWy-2 Na+Mont slurries. Addition of Ca(II) ions does
reduce the magnitude of the negative zeta potential ofboth smectite and kaolin slurries (McFarlane A. et al.,2006; Avadiar L. et al., 2014).
As Fig. 13 illustrates, the MR method for SWy-2 andbentonite resulted in ΔpH = 0 for both, at pH ~ 9.0. Both
had a relatively high CaO content of 1.68 % (SWy-2) and1.02 % (bentonite) as shown in Table 2. A similar pH
value for ΔpH = 0 was observed for kaolin with a highcontent of CaO (Fig. 9). Smectite (2:1) clay minerals normally possessed a higher SiO2 content (Table 2). As thepzc of SiO2 is located at a very low pH of 2.0, the smectite
particles should also have a low pzc value. According toReymond J. and Kolenda F. (1999) the pzc of mixed alumina and silica oxide would progress towards a lower pH
as the silica content increases. For this SWy-2 slurry,
Tombácz E. and Szekeres M. (2004) found no common
pzc at different salt contents or no pzse (pH of zero salt
effect). Even so, the edge pzc was attributed a pH value of~ 7 (Tombácz E. and Szekeres M., 2004; Durán J. et.al.,
2000; Heath D. and Tadros T., 1983)
Kosmulski M. (2011), however, attributed a pzc of
pH 6.4–7.2 to Swy-2, which was obtained by potentiometric titration at different salt concentrations and a pzc ofPek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

pH 8.1, using the method of the common intersection pointof potentiometric titration curves. He based these attributions on the data obtained by Tombácz E. and Szekeres M.
(2004).
With Na+Mt and bentonite suspensions, there is no obvious correlation between yield stress and zeta potential.
Similarly, there is no correlation between the maximum
yield stress and MR “pzc”. This lack of correlation between surface properties and rheological properties is notuncommon with thixotropic swelling clay. This may be
due to the rheological properties being determined by theswelling behaviour. The very high yield stress at low solid
concentrations suggested a very high concentration ofparticles being released by the clay swelling participating
in attractive particle interactions forming the very strong
gel structure. Many studies have determined the particle
interaction configurations (EE, FF and EF) of these smectite particles in water to be a function of pH, salt concentration and Ca(II) concentration (Stawinski J. et al., 1990;Żbik M. et al., 2008). However, clear SEM or TEM images of delaminated smectite particles interactions havenot been available.
The trend of an increasing yield stress with decreasing
pH of SWy-2 and bentonite is a reflection of a strengthening gel structure. This enhancement could be due to astronger attractive interaction between particles or astronger repulsion of the overlapping double layers. The
enhancement at low pH was accompanied by 3 times theincrease in the conductivity for the SWy-2 slurries. This
meant a thinner double layer at this pH. The double layer
repulsion responsible for the caging effect Norrish K.,
1954; Callaghan I. and Ottewill R., 1974; Paineau E. etal., 2011) should be weaker and thus cannot be responsible
for the yield stress enhancement. A stronger particle–particle attraction can come about by the following means:i) the same particle interaction configuration but astronger van der Waals or negative–positive charge attraction or both,ii) a change to a stronger particle-particle interaction
configuration—face–face to edge–face interactions; oriii) more particles being release by the swelling and delamination process, increasing the number concentrationof attractive interactions at lower pH.
The yield stress at high pH is due to the smectite particle–particle attraction being mediated by other positively
charged particles and hydrolysis products of cations suchas Mg(II) and Ca(II). These smectite particles are negatively charged since 90–95 % of the total charges being
the permanent structural negative charge. A portion of
Ca(II) content was locked in the less soluble mineralssuch as gypsum and plagioclase. At low acidic pH, thesecations became hydrated and remained in solution, acting
as an indifferent electrolyte. The pH–dependent charge
density of the smectite particles increased at the sametime. This could have changed the particle interaction
configuration to a predominately EF interaction at low pH(van Olphen H., 1977; Heath D. and Tadros T., 1983).
The STx-1b montmorillonite suspension also displayeda pH insensitive zeta potential behaviour (Fig. 12). The
magnitude of the negative zeta potential was muchsmaller. The predominant exchangeable cation of STx-1b
was calcium (CMS, 2015). In contrast the main exchangeable cations of Swy-2 were sodium and calcium. A highproportion of these cations were the exchangeable interlayer cations. STx-1b can therefore be regarded as the
Ca-form montmorillonite. The rheological behaviour of
Ca2+Mt was known to be very different to the Na+Mt (vanOlphen, H., 1955; 1957). The layered particle structure of
Ca2+Mt remained intact and the particles retained theiraggregated behaviour (Lagaly, G., 1989; van Olphen H.,
1957).
The yield stress–pH behaviour of the STx-1b Ca2+Mt
suspension is shown in Fig. 14. A much higher loading ofsolids was required to achieve a gel or yield stress material. The solid loading required was at least 20 wt%, ~ 4times more concentrated than the lowest yield stress gelof SWy-2 Na+Mt. The pH and magnitude of the τymax increased with solid loading. The τymax was 52, 190, 399 and
716 Pa located at pH 7.6, 8.3, 9.7 and 10.1 and for solid
loadings of 20, 25, 30 and 40 wt% respectively. Uniminkaolin slurries with a relatively high CaO content also
displayed a high pH of maximum yield stress of ~9. The
yield stress of STx-1b slurries was smaller at low and highpH. The value was as low as zero for the 20 wt% slurries
at pH 10.5 and ~17 Pa at pH 11 for the 25 wt% slurries. At
pH 4 the yield stress was about 25 Pa for the 20 wt%
slurry and 76 Pa for the 25 wt% slurry. This marked variation of the yield stress with pH bore no correlation withzeta potential-pH behaviour. Again the DLVO theory appeared not to explain the yield stress behaviour despite its
lack of thixotropy. The higher solid loading required toform a gel is consistent with the earlier findings of H. van

Fig. 14 Yield stress–pH behaviour of STx-1b (Ca2+Mt) slurries.

27

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Olphen (1955; 1957) research on Ca2+Mt slurries.
The maximum yield stress–solids volume fraction relationships are shown in Fig. 15 for the montmorillonite
slurries. The value of the slope is 2.2 for SWy-2 and 3.8
for STx-1b. Hence the fractal dimension Df of Na+Mt slurries is 2.1 and that of Ca2+Mt slurries is 2.5. The microstructure of STx-1b is thus more compact and more
sphere-like in shape. A Cryo-SEM image of the STx-1b
microstructure at the maximum yield stress prepared byplunged freezing of a slurry droplet is shown in Fig. 16.
Connection of compacted domains in the microstructure
was clearly seen. An image with such distinct particles
was not available for SWy-2 slurries—a reflection of thedelaminated silicate layers being in the nano-size dimension. For Unimin kaolin with more soluble Ca(II) minerals, the value of the slope was 3.6 to 3.9 (Au P. et al., 2014)
similar to that obtained for the Ca2+Mt slurries. It was
found that 25 % of the CaO in the Unimin kaolin weresoluble at pH 4.

Fig. 15

The maximum yield stress–volume fraction relationship for Na+Mt and Ca2+Mt slurries.

Fig. 16

Cryo-SEM of STx-1b slurries at the maximum yieldstress (pH ~ 9.5).

28

4. Conclusion
All kaolinite slurries with a low CaO content displayed
similar yield stress–pH behaviour: high yield stress at low
pH and low yield stress at high pH. High CaO content
kaolinite slurries displayed maximum yield stress at highpH of ~9. Many of the mineral impurities at the level in
the as-received kaolinites played a small or insignificant
role in determining this yield stress-pH behaviour. It was
shown here that the difference in the trend of the yieldstress–pH result could be used to identify factors in the
kaolinite responsible for this difference.
For high purity Crown kaolinite, the pH of zero zeta
potential or pzc showed a slight dependence on ionicstrength. The pH-independent permanent structural
charge required a lower pH for the clay mineral to acquiresufficient pH-dependent positive charge to neutralise it atlower ionic strength.
This study reviewed that the Mular–Roberts salt addition–pH method was not suitable for determining thepoint of zero charge (pzc) of clay minerals. This is particularly true for clay minerals containing a high content ofbasic impurities such as CaO. The addition of a small
amount of CaO (0.5 %) to Reidel kaolin increased its pzc
from 4 to 8. All clay minerals such as kaolin, bentonite
and Na-montmorillonite, with high content of CaO, ranging from 0.5 to 1.7 %, displayed ΔpH sign reversal (“pzc”)
at pH 8–9.
For the two low CaO Crown and KGa-2 kaolin suspensions, the maximum yield stress is located at or near the
point of zero zeta potential or zero charge of pH ~4. In
addition to yield stress–pH behaviour both kaolinite slurries displayed similar zeta potential–pH behaviour and an
identical maximum yield stress–solid volume fraction relationship. No point of zero zeta potential was detected foranother low CaO Riedel kaolin slurry. The values of thepower law exponent representing the yield stress-volume
fraction relationship were similar: ~3 for all three low
CaO kaolin slurries. In contrast high CaO Unimin kaolin
displayed a maximum yield stress at pH ~9 and no pointof zero zeta potential.
The negative zeta potential of SWy-2 Na+Mt, STx-1b
Ca2+Mt and bentonite slurries was quite pH-insensitive.
No clear correlation between the yield stress and zeta potential was observed for these slurries. Maximum yieldstress was detected at a low pH < 3 for SWy-2 and bentonite and at a high pH > 9 for the STx-2b slurries. The swelling property of the SWy-2 and bentonite controlled its
rheological behaviour to a greater extent than surface
chemistry such as pH. The difficulty of imaging Na+Mt
particles via SEM or TEM suggests the swelling–liberated
particles are nano-size in dimension. The high concentration of these nanoparticles can explain the demonstrationof high yield stress at very low solid loading and thePek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

time-dependent rheological behaviour displayed by Na+Mt
and bentonite slurries.
Unimin Kaolin and STx-1b Ca2+Mt slurries displayed alarger power law exponent value of ~3.8. Both have a highcontent of soluble Ca(II) minerals or exchangeable Ca2+ .

Acknowledgment
The authors acknowledged the use of the facilities andthe scientific and technical assistance of the Australian
Microscopy & Microanalysis Research Facility at theCentre for Microscopy, Characterisation & Analysis, The
University of Western Australia, a facility funded by the
University, State and Commonwealth Governments. Visiting professor Weian Huang conducted the yield stresszeta potential-pH characterization of the API bentonite
during his stay at UWA.

Nomenclature
BET

Brunauer-Emmett-Teller

2+

Ca Mt

Calcium montomorillonite

CMS

Clay Mineral Society (USA)CEC

Cation Exchange Capacity (meq.g–1)

DLVO

Deryaquin Landau Verwey Overbeek

MR

Mular-Roberts

+

Na Mt

Sodium montomorillonite

IEP

isoelectric point

pzc

point of zero charge

pzse

pH of zero salt effectpznpc

point of zero net proton charge

pzse

point of zero salt effect

SEM

Scanning Electron Microscope

TEM

Transmission Electron Microscopeaparticle size (m)AHparticle Hamaker constant in water (J)Do

surface separation of interacting particles (m)Dffractal dimensionεthe relative dielectric constant of water (–)ε0

the permittivity of free space (C2.J–1.m–1)

ϕssolid volume fraction (–)κ

inverse of double layer thickness (m –1)

τyyield stress (Pa)τymaxmaximum yield stress (Pa)ξzeta potential (mV)

ξcricritical zeta potential (mV)

References
Abend S., Lagaly G., Sol–gel transitions of sodium montmorillonite dispersions, Applied Clay Science, 16 (2000) 201–
227.
Abu-Jdayil B., Rheology of sodium and calcium bentonite–water dispersions: Effect of electrolytes and aging time, Int.
J. Miner. Process., 98 (2011) 208–213.
Alvarez-Silva M., Uribe-Salas A., Mirnezami M., Finch J.A.,
The point of zero charge of phyllosilicate minerals using
the Mular–Roberts titration technique, Minerals Engineering, 23 (2010) 383–389.
Appel C., Ma L.Q., Dean Rhue R., Kennelley E., Point of zerocharge determination in soils and minerals via traditional
methods and detection of electroacoustic mobility, Geoderma, 113 (2003) 77–93.
Atesok G., Somasundaran P., Morgan L.J., Adsorption properties of Ca2+ on Na-kaolinite and its effect on flocculation
using polyacrylamides, Colloids and Surfaces, 32 (1988)
127–138.
Au P.I., Leong Y.K., Rheological and zeta potential behaviourof kaolin and bentonite composite slurries: Colloids Surfaces A: Physicochem. Eng. Aspects 436 (2013) 530–541.
Au P.I., Siow S.Y., Avadiar L., Lee E.M., Leong Y.K., Muscovite
mica and koalin slurries: Yield stress–volume fraction anddeflocculation point zeta potential comparison, Powder
Technology, 262 (2014) 124–130.
Au P.I., Leong Y.K., Surface chemistry and rheology of laponite
dispersions-zeta potential, yield stress, ageing, fractal
dimension and pyrophosphate, Applied Clay Science, 107(2015) 36–45.
Avadiar L., Leong Y.K., Fourie A., Nugraha T., Rheological
Response to Ca(II) Concentration—The Source of Kaolin
Slurry Rheological Variation, Chemeca 2012: The 42th
Australasian Chemical Engineering Conference, Engineer
Australia, (2012) 243.pdf.
Avadiar L., Leong Y.K., Fourie A., Nugraha T., Clode P.L.,
Source of Unimin kaolin rheological variation–Ca2+ concentration, Colloids and Surfaces A: Physicochemical andEngineering Aspects, 459 (2014) 90–99.
Avadiar L., Leong Y.K., Fourie A., Physicochemical behaviors
of kaolin slurries with and without cations—Contributionsof alumina and silica sheets, Colloids and Surfaces A:
Physicochemical and Engineering Aspects 468 (2015) 103–
113.
Aznar A.J., Casal B., Ruiz-Hitzky E., Lopez-Arbeloa I.,
Lopez-Arbeloa F., Santaren J., Alvarez A., Adsorption of
methylene blue on sepiolite gels: spectroscopic and rheological studies, Clay Minerals, 27 (1992) 101–108.
Bolland M.D.A., Posner A.M., Quirk J.P., pH-Independent andpH-dependent surface charges on kaolinite, Clays and Clayminerals, 28 (1980) 412–418.
Brandenburg U., Lagaly G., Rheological properties of sodium
montmorillonite dispersions, Applied Clay Science, 3(1988) 263–279. Zbik, M.S., Smart R.S.C., Morris G.E.,
Kaolinite flocculation structure, Journal Colloid Interface
Science, 328 (2008) 73–80.
Callaghan I.C., Ottewill R.H., Interparticle forces in montmo-29

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

rillonite gels, Faraday Discussion, 57 (1974) 110–118.
Chang, W.Z., Leong, Y.K., Ageing and collapse of bentonite
gels—effects of Li, Na, K and Cs ions, Rheological Acta,
53 (2014) 109–122.
Chipera S.J., Bish D.L., Baseline studies of the clay minerals
society source clays: powder X-ray diffraction analyses.
Clays and Clay Minerals, 49 (2001) 398–409.
CMS (Clay Mineral Society USA), 2015, SC Data <http://www.
clays.org/SOURCE%20CLAYS/SCdata.html> accessed
1.04.2015.
Dolz M., Jiménez J., Hernández M.J., Delegido J., Casanovas
A., Flow and thixotropy of non-contaminating oil drilling
fluids formulated with bent and sodium carboxymethyl cellulose, Journal of Petroleum Science and Engineering, 57(2007) 294–302.
Du J., Morris G., Pushkarova R.A., Smart R. St. C., Effect ofSurface Structure of Kaolinite on Aggregation, Settling
Rate, and Bed Density, Langmuir, 26 (2010) 13227–13235.
Duc M., Gaboriaud F., Thomas F., Sensitivity of the acid–baseproperties of clays to the methods of preparation and measurement: 1. Literature review, Journal of Colloid andInterface Science, 289 (2005) 139–147.
Durán J.D.G., Ramos-Tejada M.M., Arroyo F.J., González-­
Caballero F., Rheological and electrokinetic properties of
sodium montmorillonite suspensions: I. rheological properties and interparticle energy of interaction, Journal of
Colloid and Interface Science, 229 (2000) 107–117.
Galan E., Properties and application of palygorskite-sepiolite
clays, Clay Minerals, 31 (1996) 443–453.
Galindo-Rosales F.J., Rubio-Hernández F.J., Structural breakdown and build-up in bentonite dispersions, Applied Clay
Science, 33 (2006) 109–115.
Goh R., Leong Y.K., Lehane B., Bentonite slurries—zeta potential, yield stress, adsorbed additive and time-dependent
behaviour, Rheologica Acta, 50 (2011) 29–38.
Gupta V., Hampton M.A., Stokes J.R., Nguyen A.V., Miller J.D.,
Particle interactions in kaolinite suspensions and corresponding aggregate structures, Journal Colloid Interface
Science, 359 (2011) 95–103.
Heath D., Tadros T.F., Influence of pH, electrolyte, and poly
(vinyl alcohol) addition on the rheological characteristics ofaqueous dispersions of sodium montmorillonite, Journal of
Colloid and Interface Science, 93 (1983) 307–319.
Heidmann I., Christl I., Leu C., Kretzschmar R., Competitive
sorption of protons and metal cations onto kaolinite: experiments and modeling, Journal Colloid and Interface Science, 282 (2005) 270–282.
Humphries S.D., Vaniman D.T., Sharma S.K., Bates D.E., Misra
A.K., Wiens R.C., McInroy R.E., Clegg S.M., Investigationof Mars clay analogs by remote Laser Induced Breakdown
Spectroscopy (LIBS), 42nd Lunar and Planetary Science
Conference (2011) 1851.pdf
Hunter R.J. Introduction to Modern Colloid Science, OUP,
Oxford, p. 226 (2003).
Hunter R.J., Nicol S.K., The dependence of plastic flow
behaviour of clay suspensions on surface properties: J.
Colloid Interface Sci. 28 (1968) 250–259.
Kelessidis V.C. and Maglione R., Yield stress of water–bentonite

30

dispersions, Colloids and Surfaces A: Physicochemical andEngineering Aspects, 318 (2008) 217–226.
Kosmulski M., The pH-dependent surface charging and pointsof zero charge: V. Update, Journal of Colloid and Interface
Science, 353 (2011) 1–15.
Kranenburg C., On the fractal structure of cohesive sediment
aggregates, Estuarine Coast Shelf Science, 39 (1994) 451–
460.
Lagaly G., Principles of flow of kaolin and bentonite dispersions, Applied Clay Science, 4 (1989) 105–123.
Lagaly G., Colloid clay science, in: Bergaya F., Theng B.K.G.,
Lagaly G. (Eds.), Handbook of Clay Science, Elsevier,
Amsterdam, 2006, pp.141–142.
Laxton P.B., Berg J.C., Relating clay yield stress to colloidal
parameters, Journal of Colloid and Interface Science, 296(2006) 749–755.
Lee C.E., Chandra S., Leong Y.K., Structural recovery
behaviour of kaolin, bentonite and K-montmorillonite slurries. Powder Technology, 223 (2012) 105–109.
Leong Y.K., Ong B.C., Critical zeta potential and the Hamaker
constant of oxides in water, Powder Technology, 134 (2003)
249–254
Leong Y.K., Scales P.J., Healy T.W., Boger D.V., Effect of Particle Size on Colloidal Zirconia Rheology at the Isoelectric
Point, Journal of the American Ceramic Society, 78 (1995)
2209–2212.
Leong Y.K., Teo J., Teh E.J., Smith J., Widjaja J., Lee J.X.,
Fourie A., Fahey M., Chen R., Controlling attractive interparticle forces via small anionic and cationic additives inkaolin clay slurries, Chemical Engineering Research and
Design, 90 (2012) 658–666.
Liang H.N., Long Z., Zhang H., Yang S.H., Rheological properties of acid-activated bentonite dispersions. Clays and Clay
Minerals, 58 (2010) 311–317.
Lockhart N.C., Electrical properties and the surface characteristics and structure of clays I. swelling clays, Journal Colloid
Interface Science, 74 (1980) 509–519.
Luckham P.F., Rossi S., The colloidal and rheological propertiesof bentonite dispersions. Advanced Colloid Interface Science, 82 (1999) 43–92.
McFarlane, A.J., Addai-Mensah, J., Bremmell, K.E., Improved
dewatering behaviour of clay minerals dispersions via
interfacial chemistry and particle interactions optimization.
J. Colloid Interface Sci., 293(2006) 116–127.
Melton I.E., Rand B., Particle interactions in aqueous kaolinsuspensions II. Comparison of some laboratory and commercial kaolin samples, Journal Colloid Interface Science,
60 (1977) 321–330.
Mular A.L., Roberts R.B., A simplified method to determine
isoelectric points of oxides Transactions of the Canadian
Institute of Mining and Metallurgy, 69 (1966) 438–439.
Ndlovu B., Forbes E., Farrokhpay S., Becker M., Bradshaw D.,
Deglon D., A preliminary rheological classification of phyllosilicate group minerals, Minerals Engineering, 55 (2014)
190–200.
Nguyen Q.D., Boger D.V., Yield stress measurement for concentrated suspensions, Journal of Rheology 27 (1983) 321–349.
Norrish K., The swelling of montmorillonite, Discussion FaradayPek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Society, 18 (1954) 120–134.
Packter A., Studies in the rheology of clay-water systems,
Kolloid-Z, 149 (1956) 109–115.
Paineau E., Michot L.J., Bihannic I., Baravian C., Aqueous suspensions of natural swelling clay minerals. 2. rheologicalcharacterization, Langmuir, 27 (2011) 7806–7819.
Permien T., Lagaly G., The rheological and colloidal propertiesof bentonite dispersions in the presence of organic compounds IV. sodium montmorillonite and acids, Applied
Clay Science 9 (1994) 251–263.
Pradip, Li C.C.H., Fuerstenau D.W., Surface chemical characterization of bastnaesite through electrokinetics, KONA
Powder and Particle Journal, 32 (2015) 176–183.
Ramos-Tejada M.M., Arroyo F.J., Perea R., Duran J.D., Scaling
behavior of the rheological properties of montmorillonite
dispersions: correlation between interparticle interaction
and degree of flocculation, Journal Colloid Interface Science, 235 (2001) 251–259.
Rand B., Melton I.E., Particle interactions in aqueous kaolinite
suspensions: I. Effect of pH and electrolyte upon the modeof particle interaction in homoionic sodium kaolinite suspensions, Journal of Colloid Interface Science, 60 (1977)
308–320.
Reymond J.P., Kolenda F., Estimation of the point of zero chargeof simple and mixed oxides by mass titration, Powder Technology, 103 (1999) 30–36.
Sakairi N., Kobayashi M., Adachi Y., Effects of salt concentration on the yield stress of sodium montmorillonite suspension, Journal Colloid Interface Science, 283 (2005) 245–
250.
Scales P.J., Johnson S.B., Healy T.W., Kapur P.C., Shear yieldstress of partially flocculated colloidal suspensions, American Institute Chemical Engineering Journal, 44 (1998)
538–544.
Schroth B.K., Sposito G., Surface charge properties of kaolinite,
Clays and Clay minerals, 45 (1997) 85–91.
Sehly K., Chiew H.L., Li H., Song A., Leong Y.K., Huang W.,
Stability and ageing behaviour and the formulation ofpotassium-based drilling muds, Applied Clay Science, 104(2015) 309–317.
Shankar P., Teo J., Leong Y.K., Fourie A., Fahey M., Adsorbed
phosphate additives for interrogating the nature of interparticle forces in kaolin clay slurries via rheological yieldstress, Advanced Powder Technology, 21 (2010) 380–385.
Stawinski J., Wierzchos J., Garcia-Gonzalez M.T., Influence of
calcium and sodium concentration on the microstructure ofbentonite and kaolin, Clays and Clay Minerals, 38 (1990)
617–622.
Teh E.J., Leong Y.K., Liu Y., Fourie A,B., Fahey M., Differences in the rheology and surface chemistry of kaolin clay
slurries: the source of the variations, Chemical Engineering

Science, 64 (2009) 3817–3825.
Teh E.J., Leong Y.K., Liu Y., Ong B.C., Berndt C.C., Chen S.B.,
Yield stress and zeta potential of washed and highly spherical oxide dispersions—critical zeta potential and Hamaker
constant, Powder Technology, 198 (2010) 114–119.
Tombácz E., Szekeres M., Colloidal behavior of aqueous montmorillonite suspensions: the specific role of pH in the presence of indifferent electrolytes, Applied Clay Science, 27(2004) 75–94.
Tombácz E., Szekeres M., Surface charge heterogeneity ofkaolinite in aqueous suspension in comparison with montmorillonite, Applied Clay Science, 34 (2006) 105–124.
van Olphen H., Rheological phenomena of clay sols in connection with charge distribution on the micelles, Discussion
Faraday Society, 11 (1951) 82–84.
van Olphen, H., Forces between suspended bentonite particles.
Clays and Clay Minerals, 4 (1955) 204–224.
van Olphen, H., Forces between suspended bentonite particles
part II—calcium bentonite. Clays and Clay Minerals, 6(1957) 196–206.
van Olphen H., An Introduction to Clay Colloid Chemistry,
Wiley, New York, 1977.
Wessel R., Ball R.C., Fractal aggregates and gels in shear flow,
Physical Review A, 46 (1992) R3008–R3011.
Yap J., Leong Y.K., Liu J., Structural recovery behavior of
barite-loaded bentonite drilling muds, Journal Petroleum
Science and Engineering, 78 (2011) 552–558.
Yildiz N., Sarikaya Y., Çalimli A., The effect of the electrolyte
concentration and pH on the rheological properties of theoriginal and the Na2CO3-activated Kütahya bentonite,
Applied Clay Science, 14 (1999) 319–327.
Yoon J., El Mohtar C., Dynamic rheological properties of
sodium pyrophosphate modified bentonite dispersions forliquefaction mitigation, Clays and Clay Minerals 61 (2013)
319–327.
Yoon J., El Mohtar C., Constitutive model parameters of concentrated bentonite suspensions modified with sodium
pyrophosphate, Journal of Material Science, (2015) DOI
10.1007/s10853-015-9073-2.
Żbik M.S., Smart R.S.C., Nanomorphology of kaolinites: comparative SEM and AFM studies, Clays and Clay minerals,
46 (1998) 153–160.
Żbik M.S., Smart R. St.C., Morris G.E., Kaolinite flocculation
structure, Journal of Colloid Interface Science, 328 (2008)
73–80.
Żbik M.S., Raftery N.A., Smart R.S.C., Frost R.L., Kaolinite
platelet orientation for XRD and AFM applications,Applied Clay Science, 50 (2010) 299–304.
Zhou Z., Gunter W.D., The nature of the surface charge ofkaolinite, Clays and Clay minerals, 40 (1992) 365–368.

31

Pek-Ing Au et al. / KONA Powder and Particle Journal No. 33 (2016) 17–32

Author’s short biography
Pek-Ing Au
Pek-Ing Au is a PhD student in the School of Mechanical and Chemical Engineering,
The University of Western Australia. She obtained a first class honours in Chemical andProcess Engineering from the same university in 2012. She has several journal and conference publications.

Yee-Kwong Leong
Yee-Kwong Leong employed rheological yield stress technique to study surface forces
especially those arising from adsorbed additives, in colloidal suspensions. He has over
100 publications in this area. Some of his experimental results can be found in textbooks
such as the “Structure and Rheology of Complex fluid” and “Suspension Rheology”. Anageing or structural recovery model was named after him by his colleagues. He is a
professor at the School of Mechanical and Chemical Engineering, The University ofWestern Australia. He obtained his PhD in 1989 from the University of Melbourne.

32KONA Powder and Particle Journal No. 33 (2016) 33–47/Doi:10.14356/kona.2016010

Review Paper

Controlled Magnetic Properties of Iron Oxide-Based
Nanoparticles for Smart Therapy †Dung The Nguyen and Kyo-Seon Kim*
1

Department of Chemical Engineering, Kangwon National University, Republic of Korea

Abstract
Magnetic nanoparticles provide a unique nanosystem for various smart therapy applications because of theirbiocompatibility, their nanostructures which can be prepared controllably to match with the interest of study and,specifically, their responses to an external magnetic field. Interests in utilizing magnetic nanoparticles forbiomedical treatments originate from their external controllability of transportation and movement insidebiological objects and magnetic heat generation which provide tremendous advantages for targeted drug delivery
and controlled drug release as well as magnetic hyperthermia. Recent progress in synthesis and functionalization
has led to the formation of various functional magnetic nanoparticles with controlled magnetic properties basedon controlling their particle size, shape and composition. In this review, we focus on the synthesis, protection andfunctionalization of iron oxide-based magnetic nanoparticles in order to control magnetic properties ofnanostructured systems. We also highlight the recent advances in the development of multifunctional therapeutic
nanosystems combining magnetic nanoparticles and drugs as well as their superior efficacy in biomedical
treatments with smart performance by including therapy and modulated drug delivery and release throughmagnetic heating.
Keywords: magnetic nanoparticles, magnetic properties, controlled synthesis, therapy, hyperthermia, drug delivery

1. Introduction
Over the last decades, interest in magnetic nanoparticles has increased enormously. The magnetic nanomaterials are unique because of their magnetic behaviors withmagnetic fields and field gradients. Fundamental researches exploiting various nanostructures with corresponding magnetic characteristics have utilized magnetic
nanoparticles for various applications. The magnetic
nanoparticles with appropriate modifications have been
extensively investigated for biomedical and pharmaceutical applications such as drug delivery, hyperthermia,
magnetic resonance imaging, tissue engineering and repair, biosensing, biochemical separations, and bioanalysis
as widely reported and reviewed (Hervault and Thanh,
2014; Kumar and Mohammad, Laurent et al., 2008; Lu etal., 2007; 2011; Nguyen and Kim, 2014; Reddy et al.,2012). In the field of disease therapy, advances in magnetic nanoparticle technology resulted in the developmentof theranostic nanoparticles which consist of therapeutic†
1*

Received 30 June 2015; Accepted 21 July 2015
J-STAGE Advance published online 20 August 2015
Chuncheon, Kangwon-do, 200-701, Republic of Korea
Corresponding author: Kyo-Seon Kim;
E-mail: kkyoseon@kangwon.ac.kr
TEL: +82-33-250-6334©2016 Hosokawa Powder Technology Foundationand diagnostic agents within a single particle. These magnetic nanoparticles should possess very uniform morphology with high magnetization values which ensure theexternal controllability. They also must have good dispersion, stability and biocompatibility to prevent endocytosis
by macrophages as well as to extend their residence timein circulating blood. The developments and applicationsof these nanoparticles strongly require synthesis of magnetic nanoparticles of customized size and shape with appropriate surface engineering. Precise control over thesynthetic conditions and surface functionalization ofmagnetic nanoparticles is crucial because it governs theirmagnetic characteristics, colloidal stability, physicochemical properties, and biological responses. However, themagnetic nanoparticles obtained under different synthetic
conditions may display large differences regarding theirmagnetic properties due to formation of structural disorder and phase boundaries, or the existence of a magnetically dead layer at the particle surface. Therefore, tounderstand, exploit and improve applications of magnetic
nanoparticles as well as to develop new ones, careful
studies for controlled synthesis and surface modification
are utmost important.
Herein, we briefly discuss the backgrounds on physicalproperties of magnetic nanoparticles and then introduce
the design strategies for magnetic nanoparticles as a smart

33

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

platform for controlled drug delivery and hyperthermia
treatment. We will highlight the recent progress on thesynthesis of magnetic nanostructures with growth mechanisms in order to control their magnetic behavior. We
also introduce and discuss some typical developments ofmechanical actuation and manipulation of drug release.
Finally, we will highlight recent state-of-the-art uses ofmagnetic nanoparticles in hyperthermia treatment.

2. Magnetism and engineering of magnetic
nanoparticles for smart therapies
The properties of magnetic materials are characterized
by the alignment of atomic magnetic moments generated
by the spinning of electrons. The atomic magnetic moments can be aligned in a parallel or antiparallel fashion
with respect to the neighboring ones in the crystal lattice
and this type of interaction gives rise to the macroscopic
magnetic behaviors which are usually measured by magnetization, coercivity, and magnetocrystalline anisotropy
constant (Leslie-Pelecky, 1996). A large-sized magnetic
material usually consists of a number of domains in orderto minimize its internal energy and exhibits ferromagnetic characteristics. When an external magnetic field ofstrength is applied to a multidomain magnetic particle,
the magnetization increases with the increase of magnetic
field until a saturation value is reached because the magnetic moments tend to align themselves in the same direction of applied field. When these particles are removed
from the magnetic field, the magnetic domains do not return to their original orientations and cause a remnant
magnetization. The remnant magnetization can only beremoved by applying a coercive field in the opposite direction to the initially applied field. The coercivity isstrictly related to the magnetocrystalline anisotropy constant which reflects the energy required to change the direction of magnetic moments.
When the particle size decreases to a certain level, theformation of a domain wall structure is not thermodynamically favored and thus the magnetic nanoparticles
consist of all spins aligned in the same direction (Beanand Livingston, 1959). As the particle size decreases toward critical particle diameter for single domain structure, D sd , the formation of domain walls becomesenergetically unfavorable and the particles are called single domain. As the particle size continues to decrease andreaches a critical size where thermal energy exceeds themagnetic anisotropy energy barrier, the magnetization
measured in a finite time interval or that in a removal ofan external magnetic field is zero. This particle size represents the critical size for superparamagnetism behavior,Dsp, and such particles are superparamagnetic. The characteristic temperature which defines a transition from fer-34

romagnetic to a thermally unstable or superparamagnetic
behavior is called the blocking temperature, TB. The corresponding magnetization curve of the superparamagnetic
material exhibits a saturation of the magnetization andno hysteresis around the origin. The negligible remnant
magnetization of superparamagnetic nanoparticles is advantageous because the nanoparticles can avoid theiraggregation during preparation and storage and after administration. The magnetism of nanoparticles is mainly
determined by the type of material, the crystallinity of thestructures, and also the particle size. The critical size forsingle domain structure and that for superparamagnetic
behavior could be predicted as shown in Fig. 1 (Krishnan
et al., 2006 and Krishnan, 2010) by using the physicaltheories for domain stability in crystalline particles (Kittel
C., 1949) as well as the bulk properties available in theliterature. Analysis and calculation for the critical sizes
for magnetization behavior and that for single-domain behavior could be referred from Bean (1955) and Frei et al(1957), respectively.
The magnetic behavior of magnetic nanoparticles in a
specific system is the results of both the intrinsic properties of the nanoparticles and the interactions between
nanoparticles and surrounding materials. The biomedical
applications of magnetic nanoparticles depend on a number of factors related to the size, shape and magnetism of
the biocompatible nanoparticles as well as the physicochemical properties of the drug-loaded nanoparticles,
field strength and geometry, depth of target tissue, rate ofblood flow and so on. In order to maximize performanceof magnetic nanoparticles in biomedical applications, it isFig. 1

Predicted size ranges for single domain structure andsuperparamagnetism behavior of common magnetic
materials. For diameters (D) larger than maximum diameter for single domain structure (Dsd), D > Dsd, themagnetic materials split into multiple domains; for diameters (D) smaller than maximum diameter for superparamagnetism behavior (Dsp), D < Dsp, they exhibit
superparamagnetism; and for diameters in between,
Dsd > D > Dsp, they are single domain and ferromagnetic (Krishnan et al., 2006; Krishnan, 2010).

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

necessary to consider the change of magnetic characteristics in the presence of various biocompatible coating or
suspension media. In fact, surface modification and functionalization of magnetic nanoparticles is required to protect particle agglomeration and to provide functionalgroups for the conjugation of other materials such as
drugs, targeting ligands, proteins and DNA sequences(Cuyper et al., 2007; Gupta and Curtis, 2004; Liu et al.,2008; Ma et al., 2006). Such modification can be achievedby a diverse group of materials such as polymeric stabilizers/surfactants like dextran (Bulte et al., 1992), carboxydextran (Briley-Saebo et al., 2006), poly(ethylene
glycol) (PEG) (Butterworth et al., 2001; Lutz et al., 2006)
or poly(vinyl alcohol) (PVA) (Liu et al., 2008); or by alayer of oxide surfaces (SiO2) (Chastellain, et al., 2004),
or inorganic metals (gold) (Jeong et al., 2006).
When polymeric materials are employed as stabilizers,
the adsorption of polymers onto the magnetic nanoparticles provides protective steric repulsion and acts as a barrier against the interaction between the particles and thus
keeping the particles from aggregation (Duran et al.,2008). The polymeric coatings can also tailor the surface
properties by introducing new chemical functionality andsurface charge to the nanoparticles for further attachmentof other materials. The key characteristics of polymericmaterials should be considered including length and molecular weight, biodegradability and hydrophobic/hydrophilic nature, conformation, degree of surface coverage,
and attachment mechanism to the particle surface. Several
functional materials such as dimercaptosuccinic acid
(Fauconnier et al., 1997), bisphosphonates (Portet et al.,2001) may be employed to facilitate the efficient attachment of the polymers onto the surface of magnetic
nanoparticles.
Inorganic coatings such as gold and SiO2 are other useful coating materials. For instance, Au-Fe3O4 nanoparticles could be synthesized by controlling growth of Au on
the surface of Fe3O4 (Goon et al., 2009; Nguyen et al.,2011; Xu et al., 2007) and the Au could provide new surface to be functionalized with various ligands such asprotein, oligonucleotide, DNA sequences through Au–S
covalent bonding as reviewed (Nguyen et al., 2011).
Robinson et al. (2010) reported that surface coating of
Fe3O4 by Au nanoparticles and subsequent functionalization with thiolated DNA slightly reduced the magnetization saturation due to addition of nonmagnetic materials
but helped particles to remain stable in aqueous solution.
It was also discussed that gold coating could modify themagnetic properties by isolating the magnetic cores andthus could provide possibility of producing very efficient
nanostructures (Banerjee et al., 2011; Wang et al., 2005).
For example, a gold coating of approximately 0.4 to
0.5 nm thickness around superparamagnetic iron oxide
nanoparticles resulted in a four- to five-fold increase in

the amount of heat release in comparison with uncoatednanoparticles on application of low frequency oscillating
magnetic fields (Mohammad et al., 2010).
It should be noted that, in most cases, the presence ofpolymers or other nonmagnetic materials may induce theimperfections in crystal structure that, consequently, may
negatively inf luence the magnetic properties of thenanoparticles. However, it has also been observed that it
is possible to maintain the magnetic properties by coating
the magnetic nanoparticles with phosphonates (Daou etal., 2008) which are supposed to reduce the spin canting
in the particle surface. In fact, since the magnetic response of a system to the surface modification is complex
and system specific, no correlation between surface coating and magnetic response of a system has been clearly
established.

3. Controlled properties of nanoparticles
Many publications have described the efficient synthetic procedures to size- and shape-controlled, highly
stable, and monodisperse magnetic nanoparticles.
High-quality magnetic nanoparticles have been synthesized with a number of different methods such as chemical co-precipitation (Massart, 1981; Wu et al., 2007),thermal decomposition and/or reduction (Kimata et al.,2003), microemulsion synthesis (Chin and Yaacob, 2007),hydrothermal/solvothermal synthesis (Chen et al., 2008;
Khollam et al., 2002). Among these methods, the magnetic nanoparticles prepared from thermal decompositionand hydrothermal/solvothermal approaches exhibit highuniformity in both size, shape with excellent magnetic
properties and the preparation processes show high potential on a large scale fabrication. Herein, we highlight several typical synthesis processes to control magnetic
properties of nanoparticles with the corresponding particle formation mechanisms, focusing on the Fe3O4-basedmaterial.
Monodisperse and superparamagnetic iron oxide
nanoparticles have been widely synthesized by thermal decomposition of organometallic compounds in high-boilingorganic solvents containing stabilizing surfactants. Since
the system temperature is high, the magnetic nanoparticles are formed with high crystallinity and magnetization.
Sun et al. (2002 and 2004) reported a synthesis of 4 nm
monodisperse magnetite nanoparticles by refluxing a reaction mixture containing the iron (III) acetylacetonate,Fe(acac)3, 1,2-hexadecanediol, oleic acid, and oleylamine
in diphenyl ether solvent (boiling point of 265 °C), while
6-nm monodisperse Fe3O4 nanoparticles were obtained inbenzyl ether solvent (boiling point of 300 °C). The oleylamine acted as both reducing agent and stabilizer to obtain monodisperse nanoparticles. To make larger Fe3O4

35

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

nanoparticles, a seeded growth process was demonstrated
for generating nanoparticles up to 16 nm by controlling theratio of seed to additional precursor. The magnetic saturation values of 16 nm magnetite nanoparticles were experimentally determined to be excellent with a value of 83 emu
g-1. In another study, monodisperse magnetic nanoparticles
were synthesized by mixing iron oleate with octadecaneand oleic acid, followed by heating to 300 °C (Jana et al.,2004). The diameter of the Fe3O4 nanoparticles could becontrolled between 6 and 50 nm by changing the ratio of
ferric oleate to oleic acid from 0.1 to 10. Similar to that in
the synthesis of Fe3O4 nanoparticles, the high temperature
reaction of Fe(acac)3 and Mn(acac)2 gives monodisperse
MnFe2O4 nanoparticles with their size, composition, andshape controlled by the reactant concentrations (Zeng etal., 2004). It was reported that triethylene glycol directly
reacted with Fe(acac)3 at an elevated temperature to prepare non-agglomerated Fe3O4 nanoparticles with uniform
shape and narrow size distribution (Cai and Wan, 2007).
Hyeon’s group prepared monodisperse iron oxide
nanoparticles with sizes of 4, 8, and 11 nm by thermal decomposition of [Fe(CO)5] as iron precursor and oleic acidwith different molar ratios, followed by controlled chemical oxidation with trimethylamine N-oxide as a mild oxidant (Hyeon et al., 2001). In this synthesis, monodisperse
iron nanoparticles were initially generated, and thesewere further oxidized controllably by trimethylamine
N-oxide to produce iron oxide nanoparticles. The particle
size could be controlled by using different solvents withdifferent boiling points. For instance, 5-, 9-, 12-, 16-, and22-nm-sized iron oxide nanoparticles were synthesized byusing 1-hexadecene, octyl ether, 1-octadecene, 1-eicosene,
and trioctylamine which have the boiling point of 274 °C,
287 °C, 317 °C, 330 °C and 365 °C, respectively (Park etal., 2007). The current synthetic procedure is quite general, and nanoparticles of many transition-metal oxides,
such as MnO, CoO, and ZnO, have been synthesized by

Fig. 236

using a similar procedure. Hyeon’s group also reported
that sequential decomposition of iron pentacarbonyl andthe iron oleate complex at different temperatures resultsin the formation of monodisperse iron nanoparticles inthe size range of 6–15 nm (Park et al., 2005). The overall
synthetic procedure was similar to seed-mediated growth.
The thermal decomposition of [Fe(CO)5] at relatively low
temperature induced short burst of nucleation from a supersaturated solution, and the decomposition of the ironoleate complex at a higher temperature facilitated subsequent growth of particles without any significant additional
nucleation (Kwon et al., 2007). They also developed anultra-large-scale synthesis of 40-g monodisperse Fe3O4
nanoparticles using Fe-oleate complex prepared from hydrated iron chloride and sodium oleate as reactants asshown in Fig. 2 (Park et al., 2004). The magnetic nanoparticles are dispersible in various organic solvents including
hexane and toluene. The water-soluble magnetite nanoparticles could be prepared under similar reaction conditions
with addition of α,ω-dicarboxyl-terminated poly(ethylene
glycol) as a surface capping agent (Hu et al., 2006).
Zhao et al. (2008) reported the formation of nanoporous
Fe3O4 particles with excellent magnetic properties andvarious specific morphologies by simply changing thesolvent system and amount of KOH. In a typical process,the FeSO4 was dissolved in ethylene glycol or glycerol toform a homogeneous solution, followed by a quick addition of KOH at room temperature. The mixture was thentransferred into a Teflon lined stainless steel autoclave,sealed, and maintained at 200 °C for 24 h for the complete
formation of product particles. The KOH concentrationand solvent properties determined the growth of particles,
leading to the evolution of different particle morphologies
including cube, truncated octahedron, octahedron, sphere,truncated cube and equilateral octahedron. All particles
exhibited magnetic saturation values of around 90 emu g–1
with negligible remnant magnetization and coercivity.

The overall scheme for the ultra-large-scale synthesis of monodisperse magnetic nanoparticles (Park etal., 2004).

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

It is utmost important to synthesize monodisperse andsuperparamagnetic nanoparticles with high saturation
magnetization values. A metal dopant of ferrite nanoparticles has been pursued to achieve high and tunable
nanomagnetism (Beji et al., 2010; Hochepied and Pileni,
2000; Sun et al., 2004). Jang et al. (2009) synthesized
Zn 2+ doped ferrite nanoparticles ((Zn xMn1–x)Fe2O4 and(Zn xFe1–x)Fe2O4, with x = 0, 0.1, 0.2, 0.3, 0.4, and 0.8) bythermal decomposition of a metal chloride (MCl 2 ,M = Zn2+, Mn2+, and Fe2+) with Fe(acac)3. The Zn 2+ doping level was carefully controlled by varying the initialmolar ratio of the metal chloride precursors. The structure
investigation and magnetization saturation measurement
revealed that, in both cases, the magnetization saturation
value reaches its maximum of 175 emu g–1 (Zn + Mn + Fe)
and 161 emu g–1 (Zn + Fe) at x = 0.4, which far exceeds
the value of 127 emu g–1 (Fe) observed for undoped bulk
iron oxide (Fe3O4).
Formation of Fe3O4 nanoparticles with porous/hollow
structures is expected to integrate the valuable characteristics of porous/hollow structure and unique magnetic
property of Fe3O4 material in a single platform which canprovide opportunities to tune their properties for specificapplications. Porous hollow magnetic nanoparticles are
expected to have similar magnetic, chemical, and biological properties as the solid nanoparticles and their structures offer the additional opportunity to store and releasedrugs at a target. Hollow nanocapsules of either haematiteor magnetite were produced through a wrap-bake-peel
process, depending on the heat treatment conditions(Piao et al., 2008; Wu et al., 2011). The spindle-shaped
β-FeOOH nanoparticles were prepared and then coatedwith a thin layer of silica by esterification of tetraethoxysilane in a base solution. The silica coated spindle wassubject to a thermal treatment at 500 °C in air, leading to
the formation of porous hollow nanocapsule structurewith a rhombohedral haematite (α-Fe2O3). The haematite
α-Fe2O3 was converted to magnetite Fe3O4 under a flow ofhydrogen. Interestingly, the β-FeOOH nanorods andα-Fe2O3 nanocapsules were paramagnetic while the Fe3O4
nanocapsules were superparamagnetic at room temperature.
Recently, one template-free method to prepare Fe3O4
hollow nanoparticles has been developed based on theinside-out Ostwald ripening (Cheng et al., 2011; Hu et al.,2009; Lin et al., 2012; Liu et al., 2009; Zhu et al., 2008).
The plausible mechanism was supported by several experimental observations, considering the chemical conversion as an important factor for the hollowing process(Nguyen and Kim, 2013). The chemical conversions ofsolid material caused a little shrinkage of the grain size
and thus made more voids between the grains inside theaggregates and led to the formation of loose package ofaggregates. The inner grains would dissolve into the solu-

tion and then diffuse to the outer stable shell by the Ostwald ripening process, resulting in continuous expansionof cavity space inside the aggregates (Fig. 3). The Fe3O4
porous/hollow nanoparticles with tunable particle sizeand porosity were synthesized controllably by simply adjusting the initial concentrations of Fe precursor and additive or varying other process variables such as processing
time and temperature (Nguyen and Kim, 2013; Nguyen etal., 2014 and 2015).
It has been reported that a magnetic coating on a magnetic nanoparticle leads to new magnetic nanocomposites
containing exchange of anisotropy between core and shellmaterials which causes a specif ic magnetization(López-Ortega et al., 2015; Nogus et al., 2005). For example, Yoon et al. (2011) presented the formation of nanoparticles consisted of an elemental iron (Fe) core and a ferrite
shell (Fe@MFe2O4, M = Fe, Mn, Co) by a seed-mediatedgrowth approach. In the first step, the Fe cores were prepared by thermally decomposing iron complex [Fe(CO)5]in the presence of oleylamine under air-free conditions.
After that, by co-injecting Fe-oleate complexes with Mnoleate or Co-oleate complexes into Fe nanoparticle suspension then annealing the mixture at elevated temperatures,
the Fe@Fe3O4, Fe@MnFe2O4 or Fe@CoFe2O4 core-shellstructures were obtained. The core-sell nanoparticles displayed a hysteresis loop with high saturation magnetization of about 150 emu g–1 but with negligible coercivity
and remanence. Increasing the crystallinity of ferrite shells
could further improve the magnetization of such core-shell
nanoparticles. For example, highly crystalline Fe/Fe3O4
nanoparticles exhibit 50 % higher magnetization thanamorphous Fe/Fe3O4 nanoparticles (Lacroix et al., 2011).

4. Application of magnetic nanoparticles for
smart therapies
4.1 Magnetic nanoparticles for controlled drug
releaseControlled drug delivery by nanostructured functional
materials is attracting great attention because of the highopportunities in cancer therapy. The magnetic nanoparticles have been widely used for targeted delivery and controlled drug release due to their response to a magnetic
field. Design of magnetic nanoparticles for biomedical applications required careful considerations for the physicochemical stability, targeting ability, drug loading andrelease. The functionalized magnetic nanoparticles could
carry other active targeting moieties, drugs and imaging
agents by both physical interactions and covalent linkages(Gupta and Gupta, 2005; Mahmoudia et al., 2011; Oh andPark, 2011). A controlled drug delivery system should be
able to deliver drugs to a targeted location in the body,37

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

Fig. 3(a, b, c) TEM images and (d, e, f) corresponding SEM images of cross-section of Fe3O4 nanoparticles
prepared by solvothermal process for 6 h, 8 h and 12 h of processing times, respectively, showing theevolution of porous/hollow structures, and (g) schematic of the hollow structure development in solvothermal process (Nguyen and Kim, 2013).

and to maintain drug levels within the required concentration range for therapy. A high-gradient of external
magnetic fields could be used to guide and concentrate
the magnetic nanocarriers at targeted site in order to reduce the systemic distribution of cytotoxic compounds,
and to enhance drug uptake, resulting in more effective
treatment at lower doses (Chertok et al., 2011; Todd et al.,2014).
Magnetic nanoparticles embedded in a polymer matrix
which could be mechanically deformed upon applicationof a static magnetic field or generate heat under an alternating current field was used to deliver drug and control
drug release. For example, upon applying a static magnetic field, the magnetic particles in the polymer matrix
aggregated instantly and produced a bulk magnetic moment, leading to a “close” configuration and resulted in aslower drug release than that when the static magnetic
field was off (Hu et al., 2007; Liu et al., 2006). Thismagnetic-induced mechanical deformation of the polymer
could be utilized to sustain drug levels within the desired
therapeutic range for a long time. On the contrary withthe static magnetic field, drug release could be enhanced
under an alternating magnetic field. The thermal energy
from magnetic nanoparticles could be used as an external
and remotely controlled trigger for controlled drug release.
The magnetic nanoparticles conjugated with thermore-

38

sponsive polymers offered a class of “smart” nanocarriers
which have ability to respond to a change in surrounding
temperature. The poly(ethyleneoxide)-poly(propylene-­
oxide)-poly(ethylene-oxide) (PEO-PPO-PEO) block copolymers were used to squeeze out drugs with magnetic
nanoparticles and then was investigated for drug releaseat different temperatures (Liu et al., 2009). The profiles ofdrug release rate were very favorable: very slow at 4 °C
and 25 °C, modest at 37 °C, much faster at 45 °C andbursting upon magnetic heating. It has been revealed that,
under magnetic heating, burst-like release was observed
due to the irreversible and disruptive changes of the magnetic nanoparticle core (Liu et al., 2009).
Several systems have been developed to demonstrate
the remotely controlled pulsatile drug release for a number of different drugs as well as for different “on–off”
durations of an alternating magnetic fields. Hoare et al.
(2009) developed nanocomposite membranes based onpoly(N-isopropylacrylamide)-based nanogels and magnetite nanoparticles entrapped in ethyl cellulose to achieve“on-demand” drug delivery upon the application of an oscillating magnetic field as shown in Fig. 4. A small-scale
device made by gluing two 1 cm diameter membranedisks to the ends of a 1cm length of silicone tubing filledwith a sodium fluorescein solution was mounted inside aflow cell placed in a solenoid coil. Under an oscillating

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

Fig. 4(a) Picture of membrane capped capsules used for magnetic flux testing. (b) The concept of the“on-demand” drug delivery upon the application of an oscillating magnetic field. (c) Temperature profilein the sample chamber and differential flux of sodium fluorescein out of membrane-capped devices as afunction of time over four successive on/off cycles of the external magnetic field (Hoare et al., 2009).

applied field, heat generated by magnetite nanoparticles
caused the shrinkage of nanogels and permitted drug diffusion out of the device. When the magnetic field wasturned off, the nanogels reswelled and refilled the membrane pores, resulting in the return to a near-zero value ofdrug flux. This study demonstrated new development ofadaptable therapeutic tools which could provide smart
control of drug release over real time drug dosing. In another study, poly(D,L-lactic-co-glycolic acid) (PLGA)
microcapsules (1–3 mm in diameter) with a 250 nm thickmembrane containing a high density of iron oxide
nanoparticles loaded with doxorubicin were prepared andthen used in cancer treatment (Chiang et al., 2012). It wasdemonstrated that, for magnetic nanoparticles loading ofat least 25 wt%, an application of alternative magnetic
field of 100 kHz and 2.5 kA m –1 could induce heating
above the glass temperature of PLGA and thus could allow controlled pulsatile release of the drug.
The thermal energy from magnetic heating was utilized
to open the gates of drug carriers which contain drugs fortherapy as shown in Fig. 5. Thomas et al. (2010) synthesized 15 nm (Zn0.4Fe0.6)Fe2O4 nanoparticles and then incorporated these nanoparticles inside porous drug carrier
nanoparticles. The drug molecules were loaded and kept by
nanovalves consisted of N-(6-N-Aminohexyl) aminomethyl-­
triethoxysilane and cucurbit[6]uril capped onto the pores.
When an external alternating magnetic field was applied,heat generation and subsequent pressure buildup inside
the porous nanoparticles caused the rapid removal of molecular valves and rapid release of drug molecules. Thesemagnetic nanoparticles were considered as effective actuators for controlled drug release from carriers.

4.2 Magnetic nanoparticles for hyperthermia
treatment
The use of magnetic nanoparticles for hyperthermia
has shown great promise in the field of disease treatment.
By raising the temperature of tissues to between 42 °C
and 46 °C, the viability of the disease tissues is reduced
and their sensitivity to chemotherapy is increased (Chanet al.,1993; Jordan et al.,1993; Gilchrist et al.,1957;
Overgaard and Overgaard, 1972). In addition to selectively killing tumor cells, a potential of developing antitumor immunity after hyperthermia treatment has been alsosuggested (Ito et al., 2003; Sivasai et al., 2010; Suzuki etal., 2003). In fact, it should be considered that the impactof the alternative magnetic field on human body is connected with heating effects due to eddy currents inducedin the electrically conducting tissue. It has been suggested
that the upper limit for the magnetic field (the product ofamplitude, H, and frequency, f ) should not exceed
5 × 109 A/m s–1 (Hergt and Dutz, 2007).
As shown in Fig. 6 (Nguyen and Kim, 2014), when exposed magnetic nanoparticles to an alternating magnetic
field, the external magnetic field is dissipated with the relaxation of the magnetic moment to its equilibrium orientation, or so-called Néel relaxation for superparamagnetic
nanoparticles. A so-called hysteresis loss mechanism
dominates heat generation of ferromagnetic materials
whose sizes exceed the domain wall width. Under application of an alternative field, the magnetic moments oscillate and cause domain wall displacement which generates
heat. The heat generation depends not only on the appliedfield but also on the magnetic pre-history of material. It

39Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

Fig. 5a) TEM image of (Zn0.4Fe0.6)Fe2O4 nanoparticles. b) TEM image of (Zn0.4Fe0.6)Fe2O4 encapsulated silica
nanoparticles. c) Chemical structure of nanovalve. d) Schematic of nanoparticles formation (1), ecapsulation (2), drug loading and nanovalve assembly (3), pore opening by the local internal heating (4), anddrug release (5). e) Drug release profile monitored during the AC field application with multiple AC field
pulses, single initial pulse, and no pulse. The sample was kept at 0 °C to determine whether internal
heating alone causes the valve opening or not (Thomas et al., 2010).

Fig. 640

General mechanisms for heat generation of magnetic nanoparticles (Nguyen and Kim, 2014).

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

has been found that, when the applied field is small, thehysteresis loops differ from full hysteresis loops in thatthey are minor loops having relatively smaller areas andthe amount of heat generated is considered to be proportional to the frequency multiplied by the area of the loop
(Kita et al., 2010; Vallejo-Fernandez et al., 2013). Considerable heat generation can be expected if we apply a magnetic field with appropriate frequency which is sufficient
to saturate the magnetization of magnetic material. For all
types of magnetic nanoparticles, heat generation is also
caused by the rotational Brownian motion within a dispersed media because of the torque exerted on the magnetic moment by the external alternating magnetic field.
In this condition, the thermal energy is characterized by
the viscosity of medium and the global hydrodynamic diameter of particle. However, due to the high viscosity of
the intracellular medium, the Brownian relaxation mechanisms could be significantly hindered and consequently
the performance of the nanoparticles mainly depended on
their primitive mechanisms (Fortin et al., 2008; Rivière etal., 2007; Wilhelm et al., 2007).
Magnetic particles in hyperthermia treatment havebeen focused on the magnetite Fe3O4 and also on thenanoparticles related with cobalt, nickel, or other substitutions in a size range from several nanometers to a few
tenths of a micron. The magnetic iron oxide nanoparticles
are still the most studied magnetic material for hyperthermia applications, due to their low toxicity, good biocompatibility, ease of synthesis and surface functionalization.
The main parameter to determine the heating of tissue isthe specific loss power (SLP) or specific absorption rate(SAR) which can be calculated based on the following
equation.
SLP (SAR) 

C T
m t

(1)

where C represents the sample specific heat capacity, m isthe amount of magnetic material per mass or volume ofsample used in the experiment and (ΔT/Δt) is the initialslope of the time-dependent heating curve.
The hyperthermia efficiency which could be reflectedby heating rate or SLP values depended on various factors, including the intrinsic factors of material such as
particle structure, magnetic properties and the external
factors such as amplitude and frequency of applied magnetic field (Deatsch and Evans, 2014; Laurent et al., 2011;
Mornet et al., 2004). Rosensweig (2002) formulated andcomputed heating rates for various magnetic samples andreported that the highest heating rates depended on theparticle size. In this work, the very sharp maximum of theheating rate was observed for monodisperse Fe 3O 4nanoparticles of about 14 nm diameter. An increase of thesize distribution caused a very fast decrease of the heatingrate. In another study, by using available data for domain

Fig. 7Size-dependent volumetric SLP values for different
magnetic nanoparticles in single domain regime (Habib
et al., 2008).

magnetization and magnetic anisotropy constant, Habib
et al. (2008) predicted the dependence of SLP values onparticle size for various materials in single domain regime
as shown in Fig. 7 wherein the maximum heating rate ofmonodisperse Fe3O4 nanoparticles was achieved for particles of about 19 nm diameter.
Practically, effect of each significant parameter such as
particle size and shape, functionalization, magnetic properties on the hyperthermia efficiency has been independently investigated from study to study with different
particle synthesis methods and different magnetic-induced
heating systems. Ma et al. (2004) reported that the SLP
values depended on the particle size and the coercivity of
Fe3O4 nanoparticles. A maximum SLP was recorded forsample containing 46 nm diameter of Fe3O4 nanoparticles
which also has the highest coercivity under an appliedfield of 80 kHz frequency and 32.5 kA m–1 amplitude.
Gonzales-Weimuller et al. (2009) demonstrated that thehighest SLP was measured for 11.2 nm particles under anapplied field of 400 kHz frequency and 24.5 kA m–1 amplitude while Gonzalez-Fernandez et al. (2009) showed amaximum of SLP for particles of around 30 nm diameterby applying an alternative magnetic field of 260 kHz frequency and 100 Oe (about 8 kA m–1) amplitude to induce
heat generation.
The choices of the ligand and thickness of coating areimportant because it can modify the magnetic propertiesof the magnetic nanoparticles. Larumbe et al. (2012)
showed that silica coating on magnetite nanoparticles
resulted in the decreases of the magnetic properties andthe SLP values, due to an enhancement of spin disordercaused by the silica coating. In another recent study, theheating efficiency was observed to decrease with increasing polymer chain length. However, with a propercoating, a ferromagnetic nanoparticle could give a fast
heating rate for disease treatment. For instance, chitosan

41

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

oligosaccharide-stabilized iron oxide nanocubes (Chito-­
FIONs) were developed for cancer hyperthermia (Bae etal., 2012). The magnetic heating revealed that Chito-­
FIONs encapsulated with multiple 30-nm-sized iron oxide
nanocubes showed the higher hyperthermal efficiency
than single nanocube and more than 30 times higher thanthat of the Feridex particles as reference. In this study, it
should be noticed that an evolution of aggregation of thenanoparticles caused a modification of the SLP values dueto the effect of dipolar interparticle coupling with Néel
relaxation.
There are potential opportunities to enhance hyperthermia through manipulation of magnetic properties utilizing core-shell architecture. The combination of different
magnetic nanoparticles such as MnFe2O4/CoFe2O4 or
CoFe2O4/MnFe2O4 or CoFe2O4/NiFe2O4 has been developed to enhance hyperthermia efficiency compared tosingle phase nanoparticles (Lee et al., 2011). Remarkably,
further enhancement of the SLP values has been obtainedin cubic Zn0.4Fe2.6O4/CoFe2O4 core/shell nanoparticles forachieving SLP values 14 times higher than the spherical
single Fe3O4 nanoparticles (Noh et al., 2012).
The temperature control during heating is essential but
still remains challenges because heat conduction and energy adsorption in vivo are widely unknown and local
overheating may damage the healthy tissue and, therefore,
the magnetic materials with a maximum self-heating temperature are very attractive for hyperthermia applications.
Achieving self-controlled and self-regulated heating by
developing materials with Curie temperature (TC) slightly
above the therapeutic temperature has been considered as
an effective method to prevent such local overheatingproblem. The Curie temperature is the maximal temperature reachable by magnetic particles to maintain theirmagnetic properties. Above this temperature, the magnetic particles lose their magnetic properties, and thus
they do not convert electromagnetic energy into heat.
Syntheses of several materials including M1–xZn xFe2O4(with M = Ni, Cu, Co, Mn) and alloy such as Ni1–xCrx withadjustable TC were reported. It has been also reported that
the TC of Gd-substituted Zn-ferrite (ZnGdxFe2–xO4) increased with an increasing Gd content and was found to
be around 45 °C at x = 0.02 (Yao et al., 2009) while the TC
of Ni1–xCr x increased with decreasing Cr content and wasfound equal to 44 °C at x = 5.63 (Akin et al., 2009). Other
studies reported that the TC of 46 °C could be achievedfor Mn1+xTixFe2O4 with x = 0.55 (Barati et al., 2013) andfor Mg1+x Fe2–2xTixO4 with x = 0.37 (Ferk et al., 2014).
Certainly, an ideal material with optimal physical features
for self-controlled and self-regulated magnetic heating
has not been well developed yet but, indeed, this concept
offers a smart way to control hyperthermia treatment.
The combination of hyperthermia and traditional chemotherapeutic functions in the same system could utilize

42

the great potentials of individual hyperthermia and controlled drug delivery as well as could raise synergistic
effects in the treatment of cancer. Kulshrestha et al. (2012)
developed paclitaxel loaded magnetoliposomes andachieved a better cell killing efficacy than individual
magnetoliposomes or paclitaxel loaded liposomes. The
drug release behaviors were studied under an AC magnetic
field at 37 °C and 43 °C. At 37 °C, only 1.2 % of paclitaxel
were released compared to 55.6 % at 43 °C. The hyperthermia was performed on HeLa cells at 42.5 ± 1 °C for aduration of 30 min with magnetoliposomes containing
6 mg mL –1 of magnetic nanoparticles only or in combination with 100 nM paclitaxel. The cell viability was decreased by 37 % after the heat treatment with magnetic
particle alone and by about 50 % and 89 % for paclitaxel
loaded magnetoliposomes without and with application ofan alternative magnetic field, respectively. This study
demonstrated that the magnetoliposomes exhibit promising potential for combined thermo-chemotherapy due toits good biocompatibility, slow drug release at 37 °C,
burst drug release at 43 °C and synergistic cancer cellkilling effect.

5. Conclusion
Controlled drug delivery and hyperthermia treatment
by magnetic nanoparticles exhibited high opportunities inbiomedical treatments. High-gradient, external magnetic
fields could be utilized to guide and concentrate the magnetic nanocarriers at target locations where the therapy
has to act specifically. A combination of hyperthermia-­
based therapy and controlled drug release has strong potential to develop an intelligent therapy for cancer
treatment. The hyperthermia and traditional chemotherapeutic functions combined in the same system could utilize the great potential of individual hyperthermia andcontrolled drug delivery as well as could raise the synergistic effects in the treatment of cancer. It is still a greatchallenge to develop a system that minimizes nanoparticle dose for actual treatment. In order to conquer thischallenge, it is crucial to develop systematic strategies ofcontrolled synthesis and surface modification of magnetic
nanoparticles to obtain the nanoparticles of customized
size and shape with high homogeneity as well as sufficientmagnetization. However, the magnetic nanoparticles obtained under different synthetic conditions may display
large differences due to formation of structural disorderand antiphase boundaries, or the existence of a magnetically dead layer at the particle surface and, thus, the synthesis and functionalization of high-quality magnetic
nanoparticles in a controlled manner are still the criticalchallenges to be faced in the coming years. It will also be
desirable to develop the large-scale synthesis of highlyDung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

functional magnetic nanoparticles with the requirementof reproducible and industrial processes without any laborious purification step to ensure cost effective synthetic
procedures.

Acknowledgements
This research was supported by the Converging Research Center Program through the Ministry of Science,
ICT and Future Planning (Grant number 2014048827).

References
Akin Y., Obaidat I.M., Issa B., Haik Y., Ni1–xCr x alloy for self
controlled magnetic hyperthermia, Crystal Research andTechnology, 44 (2009) 386–390.
Bae K.H., Park M., Do M.J., Lee N., Ryu J.H., Kim G.W., Kim
C.G., Park T.G., Hyeon T., Chitosan oligosaccharide–stabilized ferrimagnetic iron oxide nanocubes for magnetically modulated cancer hyperthermia, ACS Nano, 6 (2012)
5266–5273.
Banerjee S., Raja S.O., Sardar M., Gayathri N., Ghosh B.,
Dasgupta A., Iron oxide nanoparticles coated with gold:
Enhanced magnetic moment due to interfacial effects,
Journal of Applied Physics,109 (2011) 123902.
Barati M.R., Suzuki K., Selomulya C., Garitaonandia J. S., New
T_c–tuned manganese ferrite-based magnetic implant for
hyperthermia therapy application, IEEE Transactions on
Magnetics, 49 (2013) 3460–3463.
Bean C.P., Hysteresis loops of mixtures of ferromagnetic micropowders, Journal of Applied Physics, 26 (1955) 1381–1383.
Bean C.P., Livingston J.D., Superparamagnetism, Journal ofApplied Physics, 30 (1959) 120S–129S.
Beji Z., Hanini A., Smiri L.S., Gavard J., Kacem K., Villain F.,
Greneche J.-M., Chau F., Ammar S., Magnetic properties ofZn-substituted MnFe2O4 nanoparticles synthesized inpolyol as potential heating agents for hyperthermia. Evaluation of their toxicity on Endothelial cells, Chemistry ofMaterials, 22 (2010) 5420–5429.
Briley-Saebo K.C., Johansson L.O., Hustvedt S.O., Haldorsen
A.G., Bjørnerud A., Fayad Z.A., Ahlstrom H.K., Clearanceof iron oxide particles in rat liver: effect of hydrated particle size and coating material on liver metabolism, Investigative Radiology, 41 (2006) 560–571.
Bulte J.W., Hoekstra Y., Kamman R.L., Magin R.L., Webb A.G.,
Briggs R.W., Go, K.G., Hulstaert, C.E., Miltenyi S., The
T.H., Leij L.D., Specific MR imaging of human lymphocytes by monoclonal antibody-guided dextran-magnetite
particles, Magnetic Resonance in Medicine, 25 (1992) 148–
157.
Butterworth M.D., Illum L., Davis S.S., Preparation of ultrafine
silica- and PEG-coated magnetite particles, Colloids andSurfaces A: Physicochemical and Engineering Aspects, 179(2001) 93–102.
Cai W., Wan J., Facile synthesis of superparamagnetic magne-

tite nanoparticles in liquid polyols, Journal of Colloid andInterface Science, 305 (2007) 366–370.
Chan D.C.F., Kirpotin D.B., Bunn P.A., Synthesis and evaluation of colloidal magnetic iron oxides for the site-specificradiofrequency-induced hyperthermia of cancer, Journal ofMagnetism and Magnetic Materials, 122 (1993) 374–378.
Chastellain M., Petri A., Gupta A., Rao K.V., Hofmann H.,
Superparamagnetic silica-iron oxide nanocomposites forapplication in hyperthermia, Advanced Engineering Materials, 6 (2004) 235–241.
Chen F., Gao Q., Hong G., Ni J., Synthesis and characterizationof magnetite dodecahedron nanostructure by hydrothermal
method, Journal of Magnetism and Magnetic Materials,
320 (2008) 1775–1780.
Cheng C., Xu F., Gu H., Facile synthesis and morphology evolution of magnetic iron oxide nanoparticles in different polyol
processes, New Journal Of Chemistry, 35 (2011) 1072–1079.
Chertok B., David A.E., Yang V.C., Brain tumor targeting ofmagnetic nanoparticles for potential drug delivery: Effectof administration route and magnetic field topography,
Journal of Controlled Release, 155 (2011) 393–399.
Chiang W.-L., Ke C.-J., Liao Z.-X., Chen S.-Y., Chen F.-R., TsaiC.-Y., Xia Y., Sung H.-W., Pulsatile drug release fromPLGA hollow microspheres by controlling the permeabilityof their walls with a magnetic field, Small, 8 (2012) 3584–
3588.
Chin A.B., Yaacob I.I., Synthesis and characterization of magnetic iron oxide nanoparticles via w/o microemulsion and
Massart’s procedure, Journal of Materials Processing Technology, 191 (2007) 235–237.
Cuyper M., Soenen S.J.H., Coenegrachts K., Beek L.T., Surfacefunctionalization of magnetoliposomes in view of improving iron oxide-based magnetic resonance imaging contrast
agents: Anchoring of gadolinium ions to a lipophilic chelate, Analytical Biochemistry, 367 (2007) 266–273.
Daou T.J., Greneche J.M., Pourroy G., Buathong S., Derory A.,
Ulhaq-Bouillet C., Donnio B., Guillon D., Begin-Colin S.,
Coupling agent effect on magnetic properties of functionalized magnetite-based nanoparticles, Chemistry of Materials, 20 (2008) 5869–5875.
Deatsch A.E., Evans B.A., Heating efficiency in magnetic
nanoparticle hyperthermia, Journal of Magnetism andMagnetic Materials, 354 (2014) 163–172.
Duran J.D.G., Arias J.L., Gallardo V., Delgado A.V.J., Magnetic
colloids as drug vehicles, Pharmaceutical Sciences, 97(2008) 2948–2983.
Fauconnier N., Pons J.N., Roger J., Bee A., Thiolation of
Maghemite Nanoparticles by Dimercaptosuccinic Acid,
Journal of Colloid and Interface Science, 194 (1997) 427–
433.
Ferk G., Drofenik M., Lisjak D., Hamler A., Jaglicic Z., Makovec
D., Synthesis and characterization of Mg1+xFe2–2xTixO4
nanoparticles with an adjustable Curie point, Journal ofMagnetism and Magnetic Materials, 350 (2014) 124–128.
Fortin J.P., Gazeau F., Wilhelm C., Intracellular heating of living cells through Néel relaxation of magnetic nanoparticles,
European Biophysics Journal, 37 (2008) 223–228.
Frei E.H., Shtrikman S, Treves D., Critical size and nucleation

43

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

field of ideal ferromagnetic particles, Physical Review, 106(1957) 446–455.
Gilchrist R.K., Medal R., Shorey W.D., Hanselman R.C., Parott
J.C., Taylor C.B., Selective inductive heating of lymph
nodes, Annals of Surgery, 146 (1957) 596–606.
Gonzalez-Fernandez M.A., Torres T.E., Andrés-Vergés M.,
Costo R., de la Presa P., Serna C.J., Moralesa M.P.,
Marquina C., Ibarra M.R., Goya G.F., Magnetic nanoparticles for power absorption: Optimizing size, shape and magnetic properties, Journal of Solid State Chemistry, 182(2009) 2779–2784.
Gonzales-Weimuller M., Zeisberger M., Krishnan K.M.,
Size-depend ant heating rates of iron oxide nanoparticles
for magnetic fluid hyperthermia, Journal of Magnetism andMagnetic Materials, 321 (2009) 1947–1950.
Goon I.Y., Lai L.M.H., Lim M., Munroe P., Gooding J.J., Amal
R., Fabrication and dispersion of gold-shell-protected magnetite nanoparticles: systematic control using polyethyleneimine, Chemistry of Materials, 21 (2009) 673–681.
Gupta A.K., Curtis A.S.G., Surface modified superparamagnetic nanoparticles for drug delivery: interaction studies
with human fibroblasts in culture, Journal of Materials Science: Materials in Medicine, 15 (2004) 493–496.
Gupta A.K., Gupta M., Synthesis and surface engineering ofiron oxide nanoparticles for biomedical applications, Biomaterials, 26 (2005) 3995–4021.
Habib A.H., Ondeck C.L., Chaudhary P., Bockstaller M.R.,
McHenry M.E., Evaluation of iron-cobalt/ferrite core-shell
nanoparticles for cancer thermotherapy, Journal of Applied
Physics, 103 (2008) 07A307.
Hergt R., Dutz S.J., Magnetic particle hyperthermia—biophysical limitations of a visionary tumour therapy, Journal ofMagnetism and Magnetic Materials, 311 (2007) 187–192.
Hervault A., Thanh N.T.K., Magnetic nanoparticle-based therapeutic agents for thermo-chemotherapy treatment of cancer,
Nanoscale, 6, 2014, 11553–11573.
Hoare T., Santamaria J., Goya G.F., Irusta S., Lin D., Lau S.,
Padera R., Langer R., Kohane D.S., A magnetically triggered composite membrane for on-demand drug delivery,
Nano Letters, 9 (2009) 3651–3657.
Hochepied J.F., Pileni M.P., Magnetic properties of mixed
cobalt-zinc ferrite nanoparticles, Journal of Applied Physics, 87 (2000) 2472–2478.
Hu F.Q., Wei L., Zhou Z., Ran Y.L., Li Z., Gao M.Y., Preparation of biocompatible magnetite nanocrystals for in vivo
magnetic resonance detection of cancer, Advanced Materials, 18 (2006) 2553–2556.
Hu S.-H., Liu T.-Y., Liu D.-M., Chen S.-Y., Nano-ferrosponges
for controlled drug release, Journal of Controlled Release,121 (2007) 181–189.
Hu P., Yu L., Zuo A., Guo C. Yuan F., Fabrication of monodisperse magnetite hollow spheres, Journal of Physical
Chemistry C, 113 (2009) 900–906.
Hyeon T., Lee S.S., Park J., Chung Y., Na H.B., Synthesis ofHighly Crystalline and Monodisperse Maghemite Nanocrystallites without a Size-Selection Process, Journal of theAmerican Chemical Society, 123 (2001) 12798–12801.
Ito A., Shinkai M., Honda H., Yoshikawa K., S. Saga, Wakabayashi

44T., Yoshida J., Kobayashi T., Heat shock protein 70 expression induces antitumor immunity during intracellular
hyperthermia using magnetite nanoparticles, Cancer
Immunology Immunotherapy, 52 (2003) 80–88.
Jana N.R., Chen Y.F., Peng X.G., Size- and shape-controlled
magnetic (Cr, Mn, Fe, Co, Ni) oxide nanocrystals via a
simple and general approach, Chemistry of Materials, 16(2004) 3931–3935.
Jang J.-T., Nah H., Lee J.-H., Moon S.H., Kim M.G., Cheon J.,
Critical Enhancements of MRI Contrast and Hyperthermic
Effects by Dopant-Controlled Magnetic Nanoparticles,
Angewandte Chemie International Edition, 48 (2009)
1234–1238.
Jeong J., Ha T.H., Chung B.H., Enhanced reusability ofhexa-arginine-tagged esterase immobilized on gold-coatedmagnetic nanoparticles, Analytica Chimica Acta, 569(2006) 203–209.
Jordan A., Wust P., Fahling H., John W., Hinz A., Felix R.,
Inductive heating of ferrimagnetic particles and magnetic
fluids: physical evaluation of their potential for hyperthermia, International Journal of Hyperthermia, 9 (1993)
51–68.
Khollam Y.B., Dhage S.R., Potdar H.S., Deshpande S.B.,
Bakare P.P., Kulkarni S.D., Date S.K., Microwave hydrothermal preparation of submicron-sized spherical magnetite(Fe3O4) powders, Materials Letters, 56 (2002) 571–577.
Kimata M., Nakagawa D., Hasegawa M., Preparation of monodisperse magnetic particles by hydrolysis of iron alkoxide,
Powder Technology, 132 (2003) 112–118.
Kita E., Oda T., Kayano T., Sato S., Minagawa M., Yanagihara
H., Kishimoto M., Mitsumata C., Hashimoto S., Yamada K.,
Ohkohchi N., Ferromagnetic nanoparticles for magnetic
hyperthermia and thermoablation therapy, Journal of Physics D Applied Physics, 43 (2010) 474011.
Krishnan K.M., Pakhomov A.B., Bao Y., Blomqvist P., Chun Y.,
Gonzales M., Griffin K., Ji X., Roberts B.K., Nanomagnetism and spin electronics: Materials, microstructure andnovel properties, Journal of Materials Science, 41 (2006)
793–815.
Krishnan K.M., Biomedical nanomagnetics: a spin throughpossibilities in imaging, diagnostics, and therapy, IEEE
Transactions on Magnetics, 46 (2010) 2523–2558.
Kulshrestha P., Gogoi M., Bahadur D., Banerjee R., In vitro
application of paclitaxel loaded magnetoliposomes for combined chemotherapy and hyperthermia, Colloids and Surfaces B: Biointerfaces, 96 (2012) 1–7.
Kumar C.S.S.R., Mohammad F., Magnetic nanomaterials for
hyperthermia-based therapy and controlled drug delivery,
Advanced Drug Delivery Reviews, 63 (2011) 789–808.
Kwon S.G., Piao Y., Park J., Angappane S., Jo Y., Hwang N.-M.,
Park J.-G., Hyeon T., Kinetics of monodisperse iron oxide
nanocrystal formation by “heating-up” process, Journal of
the American Chemical Society, 129 (2007) 12571–12584.
Lacroix L.-M., Huls N.F., Ho D., Sun X., Cheng K., Sun S., Stable Single-Crystalline Body Centered Cubic Fe Nanoparticles, Nano Letters, 11 (2011) 1641–1645.
Larumbe S., Gomez-Polo C., Perez-Landazabal J.I., Pastor J.M.,
Effect of a SiO2 coating on the magnetic properties of Fe3O4

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

nanoparticles, Journal of Physics: Condensed Matter, 24(2012) 266007.
Laurent S., Forge D., Port M., Roch A., Robic C., Elst L.V.,
Muller R.N., Magnetic iron oxide nanoparticles: synthesis,
stabilization, vectorization, physicochemical characterizations, and biological applications, Chemical Reviews, 108(2008) 2064–2110.
Laurent S., Dutz S., Häfeli U.O., Mahmoudi M., Magnetic fluid
hyperthermia: Focus on superparamagnetic iron oxide
nanoparticles, Advances in Colloid and Interface Science,
166 (2011), 8.
Lee J.H., Jang J.T., Choi J.S., Moon S.H., Noh S.H., Kim J.W.,
Kim J.G., Kim I.S., Park K.I., Cheon J.W., Exchange-­
coupled magnetic nanoparticles for efficient heat induction,
Nature Nanotechnology, 6 (2011) 418–422.
Leslie-Pelecky D.L., Magnetic properties of nanostructured
materials, Chemistry of Materials, 8 (1996) 1770–1783.
Lin X., Ji G., Liu Y., Huang Q., Yang Z., Du Y., Formation
mechanism and magnetic properties of hollow Fe3O4 nanospheres synthesized without any surfactant, CrystEngComm, 14 (2012) 8658–8663.
Liu H., Guo J., Jin L., Yang W., Wang C., Fabrication and functionalization of dendritic poly(amidoamine)-immobilized
magnetic polymer composite microspheres, Journal of
Physical Chemistry B 112 (2008) 3315–3321.
Liu S., Xing R., Lu F., Rana R.K., Zhu J.J., One-pot
template-free fabrication of hollow magnetite nanospheres
and their application as potential drug carriers, Journal of
Physical Chemistry C, 113 (2009) 21042.
Liu T.Y., Hu S.H., Liu T.Y., Liu D.M., Chen S.Y., Magnetic-­
sensitive behavior of intelligent ferrogels for controlled
release of drug, Langmuir, 22 (2006) 5974–5978.
Liu T.Y., Hu S.H., Liu K.H., Liu D.M., Chen S.Y., Study on controlled drug permeation of magnetic-sensitive ferrogels:effect of Fe3O4 and PVA, Journal of Controlled Release,
126 (2008) 228–236.
Liu T.Y., Hu S.H., Liu D.M., Chen S.Y., Chen I.W., Biomedical
nanoparticle carriers with combined thermal and magnetic
responses, Nano Today, 4 (2009) 52–65.
Liu T.Y., Liu K.H., Liu D.M., Chen S.Y., Chen I.W., Temperature-­sensitive nanocapsules for controlled drug release caused
by magnetically triggered structural disruption, Advanced
Functional Materials, 19 (2009) 616–623.
López-Ortega A. Estrader M., Salazar-Alvarez G., Roca A.G.,
Nogués J., Applications of exchange coupled bi-magnetic
hard/soft and soft/hard magnetic core/shell nanoparticles,
Physics Reports, 553 (2015) 1–32.
Lu A.-H., Salabas E.L., Schüth F., Magnetic nanoparticles: synthesis, protection, functionalization, and application, Angewandte Chemie International Edition, 46 (2007) 1222–
1244.
Lutz J.F., Stiller S., Hoth A., Kaufner L., Pison U., Cartier R.,
One-pot synthesis of pegylated ultrasmall iron-oxide
nanoparticles and their in vivo evaluation as magnetic resonance imaging contrast agents, Biomacromolecules, 7(2006) 3132–3138.
Ma D.L., Guan J.W., Normandin F., Denommee S., Enright G.,
Veres T., Simard B., Multifunctional Nano-Architecture for

Biomedical Applications, Chemistry of Materials, 18 (2006)
1920–1927.
Ma M., Wu Y., Zhou J., Sun Y., Zhang Y., Gu N., Size dependence of specific power absorption of Fe3O4 particles in AC
magnetic field, Journal of Magnetism and Magnetic Materials, 268 (2004) 33–39.
Mahmoudia M., Sant S., Wang B., Laurent S., Sen T.,
Superparamagnetic iron oxide nanoparticles (SPIONs):
Development, surface modification and applications inchemotherapy, Advanced Drug Delivery Reviews, 63 (2011)24–46.
Massart R., Preparation of aqueous magnetic liquids in alkaline
and acidic media, IEEE Transactions on Magnetics, 17(1981) 1247–1248.
Mohammad F., Balaji G., Weber A., Uppu R.M., Kumar C.
S.S.R., Influence of gold nanoshell on hyperthermia ofsuperparamagnetic iron oxide nanoparticles, Journal of
Physical Chemistry C 114 (2010) 19194–19201.
Mornet S., Vasseur S., Grasset F., Duguet E., Magnetic
nanoparticle design for medical diagnosis and therapy,
Journal of Materials Chemistry, 14 (2004) 2161.
Nguyen D.T., Kim D.-J., Kim K.-S., Controlled synthesis andbiomolecular probe application of gold nanoparticles,
Micron, 42 (2011) 207–227.
Nguyen D.T., Park D.-W., Kim K.-S., Seed-mediated synthesisof iron oxide and gold/iron oxide nanoparticles, Journal for
Nanoscience and Nanotechnology, 11 (2011) 7214–7217.
Nguyen D.T., Kim K.-S., Analysis on development of magnetite
hollow spheres through one-pot solvothermal process,
AIChE J., 59 (2013) 3594–3600.
Nguyen D.T., Kim K.-S., Template-free synthesis and characterization of monodisperse magnetite hollow nanoparticles
through solvothermal process, Journal for Nanoscience andNanotechnology 13 (2013) 5773–5776.
Nguyen D. T., Charinpanitkul T., Park D.-W., Kim K.-S., Preparation of magnetite hollow structure for drug delivery
application, Journal for Nanoscience and Nanotechnology,
14 (2014) 7995–7999.
Nguyen D.T., Kim K.-S., Functionalization of magnetic
nanoparticles for biomedical applications, Korean Journalof Chemical Engineering, 31 (2014) 1289–1305.
Nguyen D.T., Park D.-W., Kim T., Kim K.-S., Controlled synthesis of magnetite porous/hollow nanoparticles through atemplate-free solvothermal process, Journal for Nanoscience and Nanotechnology, 15 (2015) 591–594.
Nogus J., Sort J., Langlais V., Skumryev V., Surinach S., Munoz
J.S., Baro M.D., Exchange bias in nanostructures, Physics
Reports, 422 (2005) 65–117.
Noh S.H., Na W., Jang J.T., Lee J.H., Lee E.J., Moon S.H., Lim
Y., Shin J.S., Cheon J., Nanoscale magnetism control via
surface and exchange anisotropy for optimized ferrimagnetic hysteresis, Nano Letters, 12 (2012) 3716–3721.
Oh J.K., Park J.M., Iron oxide-based superparamagnetic polymeric nanomaterials: Design, preparation, and biomedical
application, Progress in Polymer Science, 36 (2011) 168–
189.
Overgaard K., Overgaard J., Investigations on the possibility ofa thermic tumour therapy. I. Short-wave treatment of a45

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

transplanted isologous mouse mammary carcinoma, European Journal of Cancer, 8 (1972) 65–78.
Park J., An K., Hwang Y., Park J.G., Noh H.J., Kim J.Y., Park
J.H., Hwang N.M,, Hyeon T., Ultra-large-scale syntheses ofmonodisperse nanocrystals, Nature Materials, 3 (2004)
891–985.
Park J., Lee E., Hwang N.M., Kang M., Kim S.C., Hwang Y.,
Park J.G., Noh H.J., Kim J.Y., Park J.H., Hyeon T.,
One-nanometer-scale size-controlled synthesis of monodisperse magnetic iron oxide nanoparticles, Angewandte
Chemie International Edition, 44 (2005) 2872–2877.
Park J., Joo J., Kwon S.G., Jang Y., Hyeon T., Synthesis of monodisperse spherical nanocrystals, Angewandte ChemieInternational Edition, 46 (2007) 4630–4660.
Piao Y., Kim J., Na H.B., Kim D., Baek J.S., Ko M.K., Lee J.H.,
Shokouhimehr M., Hyeon T., Wrap-bake-peel process fornanostructural transformation from β-FeOOH nanorods tobiocompatible iron oxide nanocapsules, Nature Materials, 7(2008) 242–247.
Portet D., Denizot B., Rump E., Lejeune J.-J., Jallet P., Nonpolymeric coatings of iron oxide colloids for biological use as
magnetic resonance imaging contrast agents, Journal of
Colloid and Interface Science, 238 (2001) 37–42.
Reddy L.H., Arias J.L., Nicolas J., Couvreur P., Magnetic
nanoparticles: design and characterization, toxicity andbiocompatibility, pharmaceutical and biomedical applications, Chemical Reviews, 112 (2012) 5818–5878.
Rivière C., Wilhelm C., Cousin F., Dupuis V., Gazeau F.,
Perzynski R., Internal structure of magnetic endosomes,
European Physical Journal E, 22 (2007) 1–10.
Robinson I., Tung L.D., Maenosono S., Walti C., Thanh N.T.K,
Synthesis of core-shell gold coated magnetic nanoparticles
and their interaction with thiolated DNA, Nanoscale, 2(2010) 2624–2630.
Rosensweig R.E., Heating magnetic fluid with alternating magnetic field, Journal of Magnetism and Magnetic Materials,
252 (2002) 370–374.
Sivasai B., Rajashekar R., Hongwang W., Thilani S.N., RajKumar
D., Marla P., Franklin K.O., Brandon W., Xiaoxuan L., Olga
K.B., Masaaki T., Viktor C., Stefan B.H., Deryl T.L., A/C
magnetic hyperthermia of melanoma mediated by iron(0)/iron oxide core/shell magnetic nanoparticles: a mousestudy, BMC Cancer, 10 (2010) 119.
Sun S., Zeng H., Size-controlled synthesis of magnetite
nanoparticles, J. Am. Chem. Soc., 124 (2002) 8204–8205.
Sun S., Zeng H., Robinson D.B., Raoux S., Rice P.M., Wang
S.X., Li G., Monodisperse MFe2O4 (M = Fe, Co, Mn)nanoparticles, Journal of the American Chemical Society,
126 (2004) 273–279.
Suzuki M., Shinkai M., Honda H., Kobayashi T., Anticancer
effect and immune induction by hyperthermia of malignant
melanoma using magnetite cationic liposomes, Melanoma
Research, 13 (2003) 129–135.
Thomas C.R., Ferris D.P., Lee J.H., Choi E., Cho M.H., Kim
E.S., Stoddart J.F., Shin J.S., Cheon J., Zink J.I., Noninva-

46

sive remote-controlled release of drug molecules in vitro
using magnetic actuation of mechanized nanoparticles,
Journal of the American Chemical Society, 132 (2010)
10623–10625.
Todd T., Zhen Z., Tang W., Chen H., Wang G., Chuang Y.-J.,
Deaton K., Pan Z., Xie J., Iron oxide nanoparticle encapsulated diatoms for magnetic delivery of small molecules totumors, Nanoscale, 6 (2014) 2073–2076.
Vallejo-Fernandez G., Roca A.G., Hussain S., Timmis J., Patel
V., O’Grady K., Mechanisms of hyperthermia in magnetic
nanoparticles, Journal of Physics D: Applied Physics, 46(2013) 312001.
Wang L.Y., Luo J., Fan Q., Suzuki M., Suzuki I.S., Engelhard
M.H., Lin Y.H., Kim N., Wang J.Q., Zhong C.J., Monodispersed core−shell Fe3O4@Au nanoparticles, Journal of
Physical Chemistry B, 109 (2005) 21593–21601.
Wilhelm C., Fortin J.P., Gazeau F., Tumour cell toxicity of
intracellular hyperthermia mediated by magnetic nanoparticles, Journal for Nanoscience and Nanotechnology, 7(2007) 2933–2937.
Wu J.H., Ko S.P., Liu H.L., Kim S., Ju J.S., Kim Y.K., Sub 5 nm
magnetite nanoparticles: Synthesis, microstructure, andmagnetic properties, Materials Letters, 61 (2007) 3124–
3129.
Wu H., Zhang S., Zhang J., Liu G., Shi J., Zhang L., Cui X.,
Ruan M., He Q., Bu W., A hollow-core, magnetic, and mesoporous double-shell nanostructure: in situ decomposition/
reduction synthesis, bioimaging, and drug-delivery properties, Advanced Functional Materials, 21 (2011) 1850–1862.
Xu Z., Hou Y., Sun S., Magnetic Core/Shell Fe3O4/Au andFe3O4/Au/Ag Nanoparticles with Tunable Plasmonic Properties, Journal of the American Chemical Society, 129(2007) 8698–8699.
Yao A.H., Ai F.R., Wang D.P., Huang W.H., Zhang X.C., Synthesis, characterization and in vitro cytotoxicity of self-­
regulating magnetic implant material for hyperthermia
application, Materials Science and Engineering C: Materials
for Biological Applications, 29 (2009) 2525–2529.
Yoon T.-J., Lee H., Shao H., Weissleder R., Highly magnetic
core-shell nanoparticles with a unique magnetization mechanism, Angewandte Chemie International Edition, 50 (2011)4663–4666.
Zeng, H., Rice, P. M., Wang, S. X., Sun, S. H. Shape-controlled
synthesis and shape induced texture of MnFe2O4 nanoparticles. Journal of the American Chemical Society, 2004, 126(37), 11458–11459.
Zhao L., Zhang H., Xing Y., Song S., Yu S., Shi W., Guo X.,
Yang J., Lei Y., Cao F., Morphology-controlled synthesis ofmagnetites with nanoporous structures and excellent magnetic properties, Chemistry of Materials, 20 (2008) 198–
204.
Zhu L.P., Xiao H.M., Zhang W.D., Yang G., Fu S.Y., One-pot
template-free synthesis of monodisperse and single-crystal
magnetite hollow spheres by a simple solvothermal route,
Crystal Growth & Design, 8 (2008) 957–963.

Dung The Nguyen et al. / KONA Powder and Particle Journal No. 33 (2016) 33–47

Author’s short biography
Dung The Nguyen
Dung The Nguyen received his BS degree in chemical engineering from VNU University of Science, Vietnam National University, Vietnam in 2006 and then obtained the
PhD degree in chemical engineering from Kangwon National University in 2014. He is
now working as a full time researcher in Department of Chemical Engineering, Kangwon
National University. His current research interests include controlled synthesis of multifunctional nanoparticles and engineering of chemical reactions and processes.

Kyo-Seon Kim
Kyo-Seon Kim is currently a Professor of Chemical Engineering Department at Kangwon National University, Chuncheon, Korea, where he has been working from 1989. Hereceived the BS, MS and PhD degrees all in Chemical Engineering from Seoul National
University, KAIST and University of Cincinnati, USA in 1979, 1981 and 1989, respectively. He worked at Korea Institute of Energy and Resources as a researcher for 1981–
1985. On his sabbatical leaves, he was a Visiting Professor at Mechanical Engineering
Research Laboratory, Hitachi, Ltd, Japan for 1993–1994 and at DuPont Central ResearchLaboratory, USA for 2003–2004. His research interests are mainly focused on preparation and modification of nanoparticles by the gas, liquid and plasma processes for the applications to medical devices, pollution control and energy harvest.

47

KONA Powder and Particle Journal No. 33 (2016) 48–62/Doi:10.14356/kona.2016012

Review Paper

Analysis of Particle Size Distributions of Quantum Dots:
From Theory to Application †Doris Segets
1

Institute of Particle Technology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany

Abstract
Small, quantum-confined semiconductor nanoparticles, known as quantum dots (QDs) are highly importantmaterial systems due to their unique optoelectronic properties and their pronounced structure-property
relationships. QD applications are seen in the emerging fields of thin films and solar cells. In this review, different
characterization techniques for particle size distributions (PSDs) will be summarized with special emphasis on
strategies developed and suggested in the past to derive data on the dispersity of a sample from optical absorbance
spectra. The latter use the assumption of superimposed individual optical contributions according to the relative
abundance of different sizes of a colloidal dispersion. In the second part, the high potential of detailed PSD
analysis to get deeper insights of typical QD processes such as classification by size selective precipitation (SSP)
will be demonstrated. This is expected to lead to an improved understanding of colloidal surface properties which
is of major importance for the development of assumption-free interaction models.
Keywords: particle size distribution, quantum dot, structure-property relationship, classification by size selective
precipitation, solubility parameters

1. Introduction
Quantum dots (QDs) are semiconductor nanoparticles
which are small enough that quantum confinement occurs
in all three dimensions. The first systematic dynamic investigations of interfacial electron and hole processes aswell as reports on the size-dependent optical properties of
QDs in solution date back to the early 1980s (Duonghong
D. et al., 1982; Kuczinski J. and Thomas J.K., 1982;
Rossetti R. et al., 1984). Therefrom motivated, Brus was
the first who developed a quantum mechanical model
based on the effective mass approximation (EMA) ofelectrons and wholes to predict the evolution of thesize-dependent band gap energy (Brus L.E., 1983, 1984;
Steigerwald M.L. and Brus L.E., 1990).
Afterwards, as recently summarized by Tyrakowski
and Snee (Tyrakowski C.M. and Snee P.T., 2014), morethan two decades of intense research on QDs passed, covering various aspects from quantum mechanics and optical properties (Alivisatos A.P., 1996), particle synthesis(Park J. et al., 2007), functionalization and ligand exchange (Alvarado S.R. et al., 2014; Lin W. et al., 2015).

†
1

48

Received 15 May 2015; Accepted 26 June 2015
J-STAGE Advance published online 10 September 2015
Cauerstraße 4, 91058 Erlangen, Germany
E-mail: Doris.Segets@fau.de
TEL: +49-9131-85-29404 FAX: +49-9131-85-29402

Nowadays—although not yet an established mass product—QDs are commercially available via online shops
and are highly relevant for the emerging fields of printed
solar cells (Debnath R. et al., 2011) and thin-film electronics (Choi J.-H. et al., 2012; Moynihan T., 2015). Moreover,they become increasingly attractive for medical and biological applications (Cottingham K., 2005).
The fine-tuning of a PSD is decisive for any kind oflater application. This is due to the aforementioned pronounced size-property relationships of those transient
structures situated at the interface between dissolved
molecules and solid particles. Thus, QDs are inherently
related to interdisciplinary research. Whereas the molecular, rather than the chemical and physical approach is thepoint where investigations of QDs have originally been
started, those nanoparticles provide many aspects that areof equal interest to the field of process engineering andparticle technology. Usually, QDs with tailored opticalproperties in terms of defined absorption onset and emission features in combination with a narrow size distribution are required (Nightingale A.M. and De Mello J.C.,
2010; Yen B.K. et al., 2005). This is not only a central
issue related to scale-up but equally demanding in termsof characterization—especially when keeping in mind
the small particle sizes of a few nanometers in maximum.
To quantitatively compare the narrowness of a PSD
throughout different samples, the relative standard deviation (RSD) or coefficient of variation, defined as the stan-©2016 Hosokawa Powder Technology Foundation

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

dard deviation divided by the mean particle size
(RSD = σ/xmean), is usually applied. Following the convention, “monodispersity” is achieved if RSD < 0.05, whereas“narrow” PSDs are found for RSD < 0.1. This fundamental requirement of narrow distributions raises the following central questions:i) How can QDs that cover particle sizes clearly below
10 nm, often even down to 1 nm, be accurately characterized with justifiable experimental effort?
ii) To what extent can narrow PSDs with RSDs below 0.1
or even below 0.05 be achieved?
iii) Can knowledge on i) and ii) be used to access particle-­particle interactions of small colloids?
Whereas the second issue has already recently been
discussed by Kowalczyk and Mori and the second issue atleast partly addressed by Mori (Kowalczyk B. et al., 2011;
Mori Y., 2015), the challenge of size characterization
remains. Therefore, in the following sections, different
strategies based on the deconvolution of absorbance measurements developed by different groups will be discussed and compared to standard and advanced methodsof small particle characterization found in the field of
QDs and/or particle technology. The latter will be donewith respect to accuracy, availability, experimental effort
and potential of the method to become a suitable online
technique. In the last part of this work, the high potentialof size analysis—aiming to make particle interactions accessible—will be demonstrated by a more recent example
on QD classification by size selective precipitation (SSP)(Segets D. et al., 2015). The study shows how careful particle characterization on an Å-level is of paramount importance in order to access colloidal surface properties,
and how those may be linked in the near future to permit
calculation of assumption-free interaction potentials.

2. Size characterization of QDs
In the following sections, methods for QD size characterization will be introduced. First, standard methods
such as (high-resolution (HR)) transmission electron microscopy (TEM) and dynamic light scattering (DLS) as
well as advanced techniques such as small-angle X-ray
scattering (SAXS), field-flow fractionation (FFF) and analytical ultracentrifugation (AUC) will be discussed
briefly. Then, special emphasis will be placed on different
approaches for the deconvolution of absorption data toderive a PSD beyond a mean particle size and a standard
deviation.

(High-resolution) transmission electron microscopy

At first glance, the most obvious way to analyze QDs is(HR)TEM. It not only gives access to the primary particle

size and shape—most of QDs are in good approximation
spherical, however, other shapes such as cubes are reported
as well (Pietryga J.M. et al., 2004)—but also allows a discrimination of isolated particles from aggregates or superstructures. Moreover, crystallinity and thus the inner
structure of the particles can be derived directly from
TEM data. Drawbacks are the necessity of an additional
drying step and poor statistics that need to be carefully
considered when conclusions based on a limited amountof particles (< 1000) are to be drawn for the ensemble(>> 1010). Finally, side effects such as ripening on the
TEM substrate induced by the electron beam need to beconsidered as well (Segets D. et al., 2013). Although overthe past years, reports on liquid cell TEM (Nielsen M.H.
et al., 2014; Zheng H. et al., 2009) and the evaluation oflarger datasets due to automatized image analysis (Segets
D. et al., 2012; Sun Y. et al., 2012) came more and more
into the focus of interest, electron microscopy is demanding and not (yet) suitable for larger sample numbers andonline analysis.

Dynamic light scattering

A frequently used technique for particle size analysis is
DLS. It is commercially available, applicable in the liquid
phase, and comparatively fast (~ min). It is based on the
dynamic change in the scattering of coherent, monochromatic laser light due to the diffusion of small particles ina fluid. An autocorrelation function for the analysis of
those fluctuations gives access to the diffusion coefficient
D. With known temperature T and viscosity of the fluid η,
the hydrodynamic particle diameter x h (particle core + adsorbate/ligand shell) is available via the Stokes-Einsteinequation:Dk BT
3π xh(1)

If the extinction-weighted distribution of hydrodynamic
particle sizes is to be transferred to a volume or number
PSD, a Mie correction is necessary to consider thesize-dependent extinction coefficient.
Regarding smallest nanoparticles such as QDs, it becomes obvious that a core size distribution is hardly accessible via DLS due to the fact that the ligand shell is nolonger negligible (Marczak R. et al., 2010; Oshima H.,
2009; Reindl A. and Peukert W., 2008). Additionally, thescattering intensity of particles below 10 nm is small,causing a low resolution of commercial devices in the size
range of interest between 1 and 10 nm.

Small-Angle X-ray Scattering

In addition to the already mentioned standard techniques, other methods also exist to characterize PSDs
with good or even outstanding accuracy. For instance,
SAXS can be applied in situ to characterize colloids with49

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

respect to primary particle size and, if required, gives access to agglomerates and even hierarchical structures
such as mesocrystals (Seto J. et al., 2012; Sun Y. and Ren
Y., 2013). The scattering signal correlates with the electron density distribution within the sample and is thus
sensitive to concentration, size and shape (Keshari A.K.
and Pandey A.C., 2008; Pauw B.R., 2013). As only one
superimposed scattering curve is recorded, data interpretation is challenging and cross-validation against other,
independent techniques is required. However, once established for a certain system, SAXS is a powerful techniquefor particle analysis (Abécassis B. et al., 2015; Caetano
B.L. et al., 2010; Polte J. et al., 2010).

Field-Flow Fractionation

A versatile method for the size characterization of not
only QDs but also of proteins, polymers, macromolecules,
cells and colloids in general is field-flow fractionation(FFF) (Baalousha M. et al., 2011). It is a chromatographic
technique, which means that it includes classification of
the solute during the measurement. This is seen as a clear
advantage with respect to resolution and also with respect
to detection (Kowalczyk B. et al., 2011; Mori Y., 1994,
2015). Regarding the measurement principle, no column
is needed but separation rather occurs in a laminar flow
channel which is superimposed with an orthogonal second field. This second field is often a flow field, but also
thermal, electric, magnetic and gravitational fields are
possible (Baalousha M. et al., 2011; Williams S.K.R. et al.,2011). For nanoparticles, asymmetric flow FFF (AsFlFFF
or A4F) is usually applied. A further advantage of FFF is
the flexible detection that can be realized online (e.g. UV/Vis, organic carbon, fluorescence) or offline (e.g. TEM,
atomic force microscopy, AUC). Thus, although the investment costs are comparatively high and solute concentrations are an issue, FFF is a promising technique,especially in combination with multiple detectors andwith other, complementary analytical methods for size
characterization (Dieckmann Y. et al., 2009; Hagendorfer
H. et al., 2012).

Analytical Ultracentrifugation

Another outstanding characterization technique is
AUC. Due to rotor speeds of up to 60 krpm, it has unrivaled resolution on an Å-level and can detect smallest
particles even below 1 nm. The sedimentation of the particles in a centrifugal field is recorded and—usually basedon extinction analysis over time—the sedimentation velocity is derived. From this, the particle size, shape andsurface properties in terms of the ligand shell are accessible, which makes AUC an excellent technique for thecharacterization of smallest colloids (Lees E.E. et al.,2008). In parallel, special emphasis has been devoted tothe development of advanced evaluation routines and al-

50

gorithms (Brookes E.H. and Demeler B., 2008; Walter J.
et al., 2015b). More recently, AUC has been developed
further to allow not only for the detection of a single
wavelength but to extend for multiwavelength (MWL)
data analysis (Balbo A. et al., 2005; Bhattacharyya S.K.
et al., 2006; Strauss H.M. et al., 2008; Walter J. et al.,2014). As a result, the optical properties of individual species in a mixture, such as the absorbance of discrete-sized
QDs, will become accessible.
Although the availability of AUC is still limited andonline analysis is not possible, it is expected to become
the gold standard for particle characterization due to its
unmatched resolution of multimodal size distributions
and the possibility to extend towards multidimensional
characterization. For instance, size and spectral properties, size and shape or size and density (Carney R.P. et al.,2011; Walter J. et al., 2014; Walter J. et al., 2015a; Walter J.
et al., 2015b).
At this point it should be briefly mentioned that thesame holds true for analytical centrifugation (AC) whichworks with lower gravitational fields but extends the measurement range to larger particle sizes (Detloff T. andLerche D., 2008; Jafarzadeh S. et al., 2011; Krause B. etal., 2010; Walter J. et al., 2015c). Noteworthy, offline information from AUC, like for instance the optical properties of discrete QD fractions, could be implemented in
evaluation routines based on UV/Vis spectroscopy. The
latter is widely available and well-suited for fast online
investigations which are needed for kinetic studies at
high-time resolution (~ ms). Hence, the technique will be
discussed in more detail in the following text with special
emphasis on different approaches of data evaluation.

2.1 Deconvolution of absorbance spectra
A rather simple and straightforward strategy for the inline PSD analysis of QDs is the use of optical properties
such as absorption and emission. This is possible due tothe pronounced structure-property relationships of thesematerials such as—one of the most prominent examples—the size-dependent band gap energy Eg(x). As exemplarily depicted in Fig. 1 for Cd-based materials, bothabsorbance and emission can be tuned over a wide wavelength range by adjusting the particle size (Yu W.W. et al.,2003).
Knowledge of Eg(x) allows correlation of different features of an absorbance spectrum such as i) wavelength of
the absorption onset, ii) wavelength of the absorption
maximum of the first peak or iii) point of inflexion withdiscrete particle diameters. This leads to a rough estimation of the particle sizes present in a sample. However, although QD size distributions are narrow, often with RSDs
below 0.1 or even below 0.05, Mićić et al. pointed out
more than 20 years ago that already small polydispersity

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

Fig. 2

UV/Vis and PL spectra of CdTe, CdSe and CdS nanocrystal samples. A typical TEM image of CdTe isshown in the upper right corner. Reprinted with permission from Yu et al., “Experimental Determinationof the Extinction Coefficient of CdTe, CdSe, and CdS
Nanocrystals” Chem. Mater. 2003 15(14), p. 2854–
2860. Copyright 2003 American Chemical Society.

PL spectra at 10 K for an ensemble of InP quantum
dots with a mean diameter of 3.2 nm for different excitation energies. The first absorption peak of the QD
ensemble is at 2.17 eV, so that the PL curves a–e result from excitation (1.895–2.070 eV) in the red tail ofthe onset region of the absorption spectrum and are
fluorescence-line-narrowing (FLN) spectra; curve f isa global PL spectrum since its excitation was at
2.14 eV and is well into the blue end of the first absorption peak. Reprinted with permission from Mićić
et al., “Size-Dependent Spectroscopy of InP Quantum
Dots” J. Phys. Chem. B 1997 101(25), p. 4904–4912.
Copyright 1997 American Chemical Society.

values of 0.1 noticeably affect the emission properties(Mićić O.I. et al., 1997). For instance, emission line widthstrongly depends on which particle sizes in a sample are
excited. As can be seen from Fig. 2, if the excitation energy at 10 K is small, only electrons in large particles are
excited and a small band width is monitored. If the excitation energy is gradually increased by using smaller
wavelengths (spectra a–e), the emission blue shifts tolarger photon energies and becomes broader. If a larger
excitation energy (spectrum f) is used that is shifted tothe blue end of the first absorption peak of the QD ensemble, a large fraction of all QDs in the sample is excited.
Therefore, the global emission is substantially broader
due to polydispersity.
With respect to applications such as displays, it is obvious that a narrow emission is required and that the development of synthesis protocols and theoretical models todecrease polydispersity has been strongly in the focus ofinterest (Embden J.V. et al., 2009; Rogach A.L. et al.,2002). However, on the one hand standard techniques forthe derivation of PSDs such as DLS and TEM are notconvincing with respect to size resolution and measurement time, whereas on the other hand, advanced techniques such as SAXS or AUC are either not directly
available or are only suitable for offline analysis. Therefore, several approaches were developed to deconvolutethe absorbance data of quantum-confined semiconductor
nanoparticles with a direct band gap to PSDs. Althoughpossibly not as accurate as SAXS, FFF and/or AUC, UV/
Vis absorbance spectroscopy is a standard technologythat is widely available and suitable for fast inline analysis(~ ms).
At this point it needs to be mentioned that photoluminescence (PL) spectroscopy is sometimes applied as wellto get an impression of the dispersity of a sample (Peng X.
et al., 1998). However, as already mentioned by the authors of this work, the assumptions of (i) a δ-function
emission and (ii) a size-independent emission intensity
lead to an overestimation of the width of the reported
PSDs. Moreover, as later pointed out by Viswanatha andSarma, the PL approach is restricted to fully passivatedsamples and thus would, e.g. fail in the case of ZnO QDs
that do not show a band edge tuning (Viswanatha R. andSarma D.D., 2006).
Therefore in the following text, only approaches basedon absorbance analysis will be discussed (Mićić O.I. etal., 1994; Pesika N.S. et al., 2003a; Pesika N.S. et al.,2003b; Segets D. et al., 2009; Segets D. et al., 2012;
Viswanatha R. and Sarma D.D., 2006). Despite differences in the exact deconvolution procedure, they all use
the same basic assumptions and restrictions, namelyi) semiconductor nanoparticles with a direct band gap
which are small enough that the scattering contribution to the total extinction is negligible and quantum
confinement occurs,ii) a monotone dependency between particle size andFig. 151

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

Fig. 3

Sketch of the correlation between polydispersity in particle size and absorbance that is finally accessible by
UV/Vis/NIR analysis (Segets D. and Peukert W., 2014).

band gap energy,iii) a linear superposition of the individual contributionsof the different particle size fractions as illustrated inFig. 3.
Within a polydisperse sample of quantum-confinedparticles, every size fraction has its specific absorption
spectrum. According to the relative abundance of the differently sized QDs in the macroscopic sample, the global
absorption is created. The challenge is to deduce from anabsorbance measurement the size of the underlying species together with their relative abundance and therefrom
to create a PSD.

Approach 1: Least squares fit and pre-assumption of
PSD shape

Mićić et al. were the first who derived PSDs from absorbance data. In doing so, they already followed a highly
innovative two-step approach (Mićić O.I. et al., 1994).
First, they synthesized rather polydisperse InP QDs anddetermined the underlying PSD by HRTEM. Then, they
derived by a fit to experimental data (Palik E.D., 1985) ananalytical equation for the absorption coefficient α as afunction of energy hν (or wavelength, respectively) andband gap Eg. The latter included the particle size x (orstrictly the particle radius r = x/2) and two fitting parameters n and p:

  f h , Eg r, n, p

(2)

Now assuming a PSD following the frequently observed shape of a lognormal distribution, a least squares
fit was performed to find a PSD that gives a best matchwith the measured absorption data (Fig. 4, dataset a).
Noteworthy is that after having calibrated parameters n
and p, they were kept constant throughout all further PSD
calculations that matched the expectations well (Fig. 4,
datasets b and c) (Mićić O.I. et al., 1994).
Disadvantages are the limited number of particles thatwere evaluated for the calibration PSD, the missing validation of Eg(x) against literature data and the predefined
shape of the PSD. However, the first two points can beclearly attributed to the time when the study was performed. In the early 90s, TEM capabilities were much
lower than nowadays and reference data did not yet exist

52

Fig. 4

Experimental absorption spectra (circles) for InP QD
colloids with fit to model shown as a solid line: (a) fit
made to yield the dependence of QD band gap on QD
size; (b and c) fit made to yield the size distribution
from the results of (a). Reprinted with permission from
Mićić et al., “Synthesis and Characterization of InP
Quantum Dots” J. Phys. Chem. 1994 98(19), p. 4966–
4969. Copyright 1994 American Chemical Society.

as QDs and related research had just started to come into
being. Thus, the work is pioneering in a way that it not
only allows the calculation of non-symmetric lognormal
PSDs, but even offers the possibility to derive Eg(x) as amaterial property.
More than a decade later, Viswanatha and Sarma confirmed the applicability of this concept to other material
systems by generalizing it to experimental data of ZnO,ZnS and CdSe QDs (Viswanatha R. and Sarma D.D.,
2006). In contrast to the earlier work of Mićić, they used
Gaussian instead of lognormal distributions and did not
calibrate Eg(x) but used fundamental literature data. For
instance, in the case of ZnO, a carefully validated
tight-binding model (TBM) was applied (Viswanatha R.
et al., 2004). Moreover, they proved the assumption of theparallel-shifted bulk absorption spectrum to higher energies for decreasing particle sizes. Therefore they compared the results of the original approach to the outcome
when a simple 0-1 step function was used for the shape of
the absorption coefficient α. It was found that the difference in the calculated PSDs was negligible. Finally, theapparent width of the experimental and simulated absorbance data of various material systems (ZnO, ZnS(e),CdS(e), GaAs, InAs, InP) on a particle size scale (Δdapp)
was compared to the true widths of the underlying PSDs
(ΔDactual). A material-independent, empiric relationshipwas thereby identified that allows a quick estimate of asample’s polydispersity from Δdapp. In addition to the absorption data, only knowledge of Eg(x) is required:
ΔDactual  0.0025 2
d app 0.524  d app  1.41nm (3)nm

However, for both approaches, the aforementioned is-Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

sue of a predefined shape of the underlying PSD remains.
Therefore, in the following sections, two alternatives thatare able to access multimodal distributions without any
assumptions on the PSD shape(s) will be discussed.

Approach 2: Wavelength-based deconvolution for
arbitrary shaped PSDs

In 2003, the first concept of deriving arbitrary shaped
PSDs from absorbance data was developed by Pesika etal. (Pesika N.S. et al., 2003a; Pesika N.S. et al., 2003b).
Although the first report of their method was rather restricted to ZnO QDs, they summarized the procedure in
the same year again with a more general point of view.
Based on absorbance data of a (0001) ZnO single crystal (Fig. 5), they came up with a comparatively simple but
useful assumption, namely that in the case of a direct
band gap semiconductor, the absorption coefficient α inthe vicinity of the onset is given by (Pesika N.S. et al.,2003a; Pesika N.S. et al., 2003b):

2003a; Pesika N.S. et al., 2003b):

π 3
x n  x dx
6x

A  x  (5)From Eq. 5 it becomes clear that (i) the absorbance of
QDs is a volume signal that (ii) can be used to calculate
the relative abundance of distinct sizes from the local
slope of the absorbance spectrum (boundary condition:n(x) = 0 for x → ∞):dAn  x   dx
π
x³
6

(6)

C is a constant, hν is the photon energy and Egbulk is thebulk band gap.
Using an effective mass model according to Brus (Brus
L., 1986) for the correlation between band gap energy andparticle size and further assuming spherical particles anda size-independent absorption coefficient, the following
connection between absorbance A, particle size x andrelative abundance n was derived (Pesika N.S. et al.,As becomes clear from Fig. 6, validation of the calculation results revealed an excellent agreement with thesize distributions determined by TEM. However, in contrast to the formerly discussed approaches, even bimodal
distributions of arbitrary shape could be derived without
any input parameters. This was proven by the evaluationof absorbance data of mixed suspensions containing controlled amounts of small and large particles. A nearly perfect match was found when the results from the bimodal
absorbance spectrum were compared to the expected distributions obtained from the individually evaluated absorption data of small and large particles. Anotheradvantage of the technique is its comparatively simple implementation as only a correlation for the size-dependent
band gap energy Eg(x) and the local slope of the absorbance spectrum are required. The only drawback, how-Fig. 5

Fig. 6C h  Egbulk 
h

1/2



(4)

(a) Absorbance spectrum for a (0001) ZnO single crystal and (b) absorbance spectrum for a suspension of
ZnO quantum particles after 2 h of growth at 65 °C.
The inset shows the spectrum for the ZnO single crystal plotted as (Ahν)² versus hν. Reprinted with permission from Pesika et al., “Relationship between
Absorbance Spectra and Particle Size Distributions for
Quantum-Sized Nanocrystals” J. Phys. Chem. B 2003
107(38), p. 10412–10415. Copyright 2003 American
Chemical Society.

Distribution results of ZnO QDs after 2 h of growth at
65 °C. The histogram was obtained from analysis ofhigh-resolution transmission electron microscope images of 125 particles. The solid line was obtained fromthe absorbance spectrum. Reprinted with permission
from Pesika et al., “Relationship between AbsorbanceSpectra and Particle Size Distributions for Quantum-­Sized Nanocrystals” J. Phys. Chem. B 2003 107(38),p. 10412–10415. Copyright 2003 American Chemical
Society.

53

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

ever, is the strong assumption of a step function for theabsorbance of monodisperse particles. Simply trying to
make a reconstruction of the original absorbance spectrum by using a known n(x) and applying Eq. 5, the minimum observed after the first absorbance peak is neverobtained—especially in the case of smaller nanoparticles.
To address this issue, Segets et al. developed a more
flexible approach with respect to the shape evolution of
the shifted absorption coefficient (Segets D. et al., 2009;
Segets D. et al., 2012). Instead of using a step function,
the bulk absorption coefficient was used as an assumption
for the shape of the monodisperse QD absorption evolution. The size dependency was simply addressed by a parallel shift of the bulk absorption to smaller wavelengths
according to Eg(x). Regarding the deconvolution of a measured absorbance spectrum A(λ) into partial spectra Ai’(λ),
each of these assumed to have the form of the bulk absorption α(λ), the algorithm is realized as follows (Segets
D. et al., 2009; Segets D. et al., 2012):(i) First, the bulk absorption α is parallel-shifted in a
way that its peak position matches the largest wavelength
under consideration (λi). The particle diameter that is related to this specific wavelength needs to be derived either
from quantum mechanics or from a separate calibration
by matching calculated PSDscalc against PSDsTEM, as suggested by Mićić et al. In the following text, this shifted
absorption coefficient will be denoted as αshift,i(λ).
(ii) Then, αshift,i(λ) is scaled in a way that it coincides
with the measurement value A(λi):

Ai '( ) shift,i  Ai i  shift,i i (7)This leads to the first partial absorbance spectrum thatexactly matches the measurement value at position λi.
(iii) Dividing Ai(λi) by the value of the shifted absorption coefficient at this wavelength (αshift,i(λi)), the particle
size interval Δx = xi+1–xi under consideration and the optical path length dc, the first value of a size distribution thatis still related to the total mass of solid QD material is obtained (Segets D. et al., 2009). Worthy of note is that xi+1
is the particle size which is linked with the next smaller
wavelength analyzed. Thus, Δxi depends on the measurement settings.
(iv) Then, the absorbance contribution of this known,largest particle size fraction is subtracted from the measurement spectrum according to:A A   Ai '( )(8)Thus, a new spectrum is created and steps (i–iv) are repeated until all relevant wavelengths are analyzed.
(v) Finally, the concentration-related distribution isconverted to a PSD that fulfills the requirement of a nor-

54

Fig. 7 Comparison between the calculated PSDs, DLS and
optical image analysis of two different samples of b)
small and d) large ZnO QDs and the related measured(black solid line) and reconstructed (blue and red solidlines) absorbance spectra for a) smaller and c) larger-­sized samples. Reprinted with permission from Segets
et al., “Analysis of Optical Absorbance Spectra for theDetermination of ZnO Nanoparticle Size Distribution,
Solubility, and Surface Energy” ACS Nano 2009 3(7),p. 1703–1710. Copyright 2009 American Chemical
Society.

malized density distribution:xmax

 q3( x)  x 1

xmin

(9)

As already expected from the analysis of Viswanatha
and Sarma (Viswanatha R. and Sarma D.D., 2006) using
Gaussian distributions, the final results are not strongly
affected by the assumed shape of the parallel-shifted bulk
absorption (Segets D. et al., 2012) also in the case of arbitrary PSDs. To illustrate this, Fig. 7 shows the deconvolution results of absorbance data derived for small (a, b) andlarge (c, d) ZnO QDs using two different datasets for thebulk absorption coefficient (Bergström L. et al., 1996;
Yoshikawa H. and Adachi S., 1997). For validation, TEM
and DLS results are presented as well.
It becomes clear that while the calculated PSDs are notaffected by the dataset used for the absorption coefficient,
the reconstructed absorbance measurements do dependon the optical properties chosen. Noteworthy is that theshifted bulk absorption is not yet expected to be the correct absorption evolution of monodisperse QDs. However,as soon as discrete optical properties are accessible, forinstance via AUC analysis, they are easily implemented
to the existing code. A proof of the correct optical properties would be obtained as soon as not only the forward
calculation of a PSDcalc is done correctly, but when also

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

Fig. 8a) Absorbance spectrum of the bimodal PSD after mixing the two suspensions in the same volume ratio (blackline) and its corresponding reconstructions (blue andred lines); b) bimodal PSD calculated from the bimodal
absorbance spectrum with the bulk properties from
Bergström and Yoshikawa (blue and red lines), expected bimodal PSD calculated from the PSDs of thepure unmixed suspensions (green dotted line), and bimodal PSD measured with DLS (black line). Reprinted
with permission from Segets et al., “Analysis of Optical
Absorbance Spectra for the Determination of ZnO
Nanoparticle Size Distribution, Solubility, and Surface
Energy” ACS Nano 2009 3(7), p. 1703–1710. Copyright2009 American Chemical Society.

the reconstructed absorbance spectrum based on PSDcalc
matches the measurement data. As already discussed, theanalysis of bimodal distributions without any input parameters is possible as well using this approach. This issummarized in Fig. 8.
Additionally, in a subsequent work, the original ideaof Mićić to derive Eg (x) by careful calibration was
picked up again (Segets D. et al., 2012). A study on
PbS(e) was performed to demonstrate the general applicability of the method originally developed for ZnO bymeans of a completely different QD system. In addition,in the case of PbS(e) QDs that were chosen for this study,
well-established and properly validated literature data on
Eg(x) is available. Small and large particles were synthesized and aberration-corrected TEM micrographs wereanalyzed by semi-automatized image analysis. In parallel,a power law with three empiric parameters a [nm], b [–]and c [nm] was used for description of the size-dependent
band gap energy ΔEi(xi):ΔE b
xi a  i   c
 eV 

(10)

Finally, a, b and c were modified until a best match was
obtained between PSDscalc derived by the algorithm and
PSDsTEM derived by image analysis. It was found to bepreferable to analyze two samples covering two different
particle size regimes in order to arrive at compromise values for a, b and c. This leads to a function of Eg(x) which
is valid over a wide size range. The results are shown in
Fig. 9.

Fig. 9Relationship between fundamental band gap energy
and particle size as derived in this work along withcomparison to literature data for a) PbS (Borrelli
N.F. and Smith D.W., 1994; Cademartiri L. et al.,2006; Kane R.S. et al., 1996; Moreels I. et al., 2009)and b) PbSe (Dai Q. et al., 2009; Koole R. et al., 2008;
Lipovskii A. et al., 1997; Liu Y. et al., 2010; Ma W. etal., 2011; Moreels I. et al., 2007; Pietryga J.M. et al.,
2004; Sashchiuk A. et al., 2001; Steckel J.S. et al.,2003) as indicated. Reprinted with permission from
Segets et al., “Determination of the Quantum Dot Band
Gap Dependence on Particle Size from Optical Absorbance and Transmission Electron Microscopy Measurements” ACS Nano 2012 6(10), p. 9021–9032.
Copyright 2012 American Chemical Society.

It becomes clear that an excellent agreement exists withliterature data collected over the past decades. This confirms the large potential of QD absorbance data analysis.
Thus, the usefulness of PSD calculation from absorbancemeasurements by various approaches with all of themhaving their specific advantages and disadvantages is sufficiently proven. Future directions are seen in the combination of different characterization methods (e.g. SAXS,FFF, AUC and UV/Vis analysis) to increase accuracy andresolution even further.
Especially the latter is somewhat limited by the absorbance approach as i) information on small particles might
be hidden in the tail of absorbance spectra of larger sizes
and as ii) the resolution is somewhat limited by the requirement of a noticeable change of Eg(x) for two different
particle diameters. Therefore, AUC that provides a maximum knowledge gain with respect to a PSD with sub-nm
resolution, composition and surface chemistry becomes
one of the most important techniques for QD characterization (Carney R.P. et al., 2011; Lees E.E. et al., 2008). In
combination with in-situ techniques such as UV/Vis or
SAXS, it is expected to provide new, mechanistic insights
of QD formation with unrivaled accuracy.

3. Application to classification
At this stage, some fundamental conclusions with respect to colloidal interactions are possible. This is demonstrated by applying the method to classification on a
sub-10 nm scale. Worthy of note is that various approaches

55

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

Fig. 10 Illustration of the experimental procedure of SSP. With kind permission from Springer Science+BusinessMedia: Segets et al., “Quantitative evaluation of size selective precipitation of Mn-doped ZnS quantum
dots by size distributions calculated from UV/Vis absorbance spectra” J. Nanopart. Res. 2013 15:1486,
Scheme 1.

for nanoparticle classification and purification exist that are
already described in excellent review papers (Kowalczyk
B. et al., 2011; Mori Y., 2015; Sapsford K.E. et al., 2011).
However, for QDs, size selective precipitation (SSP)—
which is the preferred flocculation of larger particles in a
non-solvent—is still the most important post-processing
strategy.
In the following sections, first the working principle of
SSP will be introduced. It will be discussed how SSP canbe described theoretically based on results obtained fromcareful PSD and mass balance analysis. Finally, things are
set into perspective by an outlook that shows how classification by SSP is linked to Hansen solubility parameters(HSP) which in turn give access to the Flory-Huggins
interaction parameter χ. Knowledge of χ is decisive forderivation of the osmotic interactions between the ligand
shells around QDs and thus represents an important parameter in the context of steric stabilization.

3.1 Size selective precipitation
The working principle of SSP first reported by thegroups of Murray and Weller is as rational as it is simple(Murray C.B. et al., 1993; Vossmeyer T. et al., 1994). Starting from QDs dispersed in a good solvent, a non-solvent
is added that induces the preferred, reversible flocculationof larger particles.
In the case of nanoparticles with polar surface termination, a non-solvent would be something non-polar such as
toluene or heptane, in the case of nanoparticles with hydrophobic surface properties, a non-solvent would be hydrophilic, e.g. ethanol or acetone. Due to the fact that theflocculates are in the size range of μm to mm, a solid-liquid
separation by centrifugation in a typical lab centrifuge
with up to ~10 000 g is sufficient to separate the flocculated larger particles from small QDs in the supernatant.
As illustrated in Fig. 10, the process can be repeated inseveral cycles and even redispersion of the QDs on a primary particle level after centrifugation is possible. In the56

past it was demonstrated in various works that SSP is ahighly efficient procedure that is able to substantially narrow PSDs and improve the emission properties of a sample(Komada S. et al., 2012; Nag A. et al., 2007). For instance,
Mastronardi et al. subdivided a starting suspension of Si
nanoparticles into the impressive amount of 14 differently
sized samples with all of them revealing substantial differences in their optical properties (Mastronardi M.L. etal., 2011).
However, in addition to the final outcome of SSP interms of samples with small RSD as well as varying absorbance and emission properties, the classification process itself can be analyzed by means of well-known
concepts from the field of process engineering and particle technology (Segets D. et al., 2013; Segets D. et al.,2015). This will be briefly summarized in the following
section.
Knowing the relative masses of the coarse fraction g(= mrecovered /min) and the fine fraction f (= msupernatant /min)after drying as well as the PSDs of the feed qF(x), thecoarse qg(x) and the fines qf(x) from UV/Vis data, the following parameters are accessible (Segets D. et al., 2015):(i) The cut size xt at the intersection between the mass
weighted density distribution of the coarse g·qg(x)and the fines f·qf(x).
(ii) The separation efficiency:
T  x 

gqg ( x)qF ( x)(11)(iii) The separation sharpness κ to characterize the steepness of the cut with x25,t and x75,t being the particle
sizes at which T(x) is 0.25 or 0.75, respectively:



x25,t
x75,t

(12)

(iv) The yields for coarse ηg and fines ηf to evaluate theefficiency of the process:Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62
xmax

 gqg x dx
 g  xx x qF x dx
t

max

(13)

t

xt

  fqf x dx
 f  xx x qF x dx
min
t

(14)

min

For more details the reader is referred to the literature(Leschonski K., 1977; Leschonski K. et al., 1974; Rhodes
M.J., 1999; Rumpf H., 1990), however, two important
aspects shall be stressed here: the intrinsic inclusion of
the mass balance in T(x) (note that f + g = 1 andf·qf(x) + g·qg(x) = 1) and its independency of the quantity(number, area, volume/mass) based on which the PSD isderived. Thus, T(x) is a perfect measure for the classification process whereas RSD is a perfect measure for theclassification result in terms of the derived product PSD.
Using Eqs. 11–14 and performing classification studies
on ZnS QDs with different feed distributions and various
good solvent/poor solvent combinations, Segets et al.
found the following analytical description of SSP that canbe understood as a law of mass action (Segets D. et al.,2015) with dissociation constant Kdiss:K
diss,i  xi 

fqf ( xi ) q F  xi   gqg ( xi ) 1  T ( xi )(15)
gqg ( xi )gqg ( xi )T ( xi )Thus, the cut of an SSP experiment neither depends onthe feed PSD nor on process parameters such as stirring,time of poor solvent exposure or dosing. Even the solids
concentration of the feed does not have any influence aslong as it does not clearly exceed a comparatively high,critical upper limit (e.g. >> 10 g L –1 in the case of ZnS).
SSP is determined in terms of the steepness and positionof the cut by the chemical structure of the good and thepoor solvent as well as their volume ratio. Comparing theeffect of different solvents, the relative permittivity of the
final mixture—which is of major importance for the vander Waals interaction—is not the sole influencing factor
on SSP. In fact, the specific chemistry of the applied solvents and their interaction with the stabilizing ligands adsorbed at the QD surface are significant as well. This
represents the connection between classification results,solubility parameters and steric stabilization and will be
described in the following section.

3.2 Link to Flory-Huggins interaction parameter
It was found that the best way of data representation isto plot the QD dispersibility of a specific size fraction in a
3-dimensional Hansen space normalized to the Hamaker
constant of the core material in the solvent mixture(Segets D. et al., 2015). Further, it turned out that the HSP
of decorated nanoparticles which are understood as theensemble of QD core plus ligand shell need to be clearly
distinguished from the HSP of the unbound, free ligands
on a molecular level. The balance of van der Waals attraction and HSP is in agreement with works of the groups ofKorgel and Roberts. They reported that in the case of SSP,a steric repulsive force comprised of an elastic and an osmotic interaction due to solvation of the ligand tail by thesolvent—the latter being related to solubility parameters—needs to be balanced vs. van der Waals adhesion(Kitchens C.L. et al., 2003; Saunders S.R. and Roberts
C.B., 2009; Shah P.S. et al., 2002a; Shah P.S. et al., 2002b;
Vincent B. et al., 1986). As recently pointed out by Mori
(Mori Y., 2015), this can be extended by electrostatic andBorn repulsion, possibly entropic effects such as depletion
might need to be considered as well (Mao Y. et al., 1995).
However, in line with the findings of Segets et al. (Segets
D. et al., 2015), especially the study of Mori revealed thatvan der Waals adhesion and osmotic interaction are themain contributors to the total interaction.
Analyzing the osmotic term in more detail, two quantities are decisive: the volume fraction of the ligand ϕ and
the Flory-Huggins interaction parameter χ12 (with index 1
referring to the solvent and index 2 referring to theligand). Whereas the former can be estimated by the surface coverage or is directly accessible via AUC or
small-angle neutron scattering (SANS) (Whitell G.V. andKitchens C.L., 2010), the latter is even more challenging.
It relates to the Hildebrand solubility parameter δi (or thecohesive energy density ci = δi ²):


12

Vm
1   2 2
RT

(16)

With Vm being the molar volume, R being the ideal gas
constant and T being the temperature.
Hansen subdivided the energy of cohesion into a polar
contribution δp, a disperse contribution δd and the ability
to exchange electrons δh according to the concept ofLewis acids and bases (Hansen C.M., Hansen C.M. andSkaarup K.J., 1967). Eq. 16 can be rewritten as (Hansen
C.M., 2007):


12

Vm 
2
 d2   d1 2  0.25  p2   p1
RT 2
0.25  h2   h1 (17)Thus, once having access to the HSP of small nanoparticles, χ12 can be derived. Together with additional knowledge of the surface coverage, the osmotic interaction
potential is accessible and steric effects can be balanced
vs. van der Waals adhesion.
By the careful analysis of PSDs established from absorbance spectra, the principal applicability of HSP to QDs

57

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

could already be demonstrated (Segets D. et al., 2015).
Now suitable measurement techniques for HSP, for instance by using analytical centrifugation in the liquid
phase (Lerche D. and Sobisch T., 2014), need to be established and validated against numeric data like it was already done in the case of polymers (Díaz I. et al., 2013) or
gas-phase results, e.g. obtained by inverse gas chromatography (Liu Y. and Shi B., 2008). Thus, the careful evaluation of QD dispersity not only gives important insights
into the classification process itself but can even make animportant contribution to a better understanding of thesteric stabilization of small colloids.

4. Conclusion and perspectives
Different methods for analyzing the particle size distribution (PSD) of quantum dots (QD) were discussed. In
addition to standard techniques such as high-resolution(HR) transmission electron microscopy (TEM) and dynamic light scattering (DLS) as well as advanced techniques such as small-angle X-ray scattering (SAXS),
Field-Flow Fractionation (FFF) and analytical ultracentrifugation (AUC), the focus was put on the conversion ofabsorbance spectra to PSDs. In this context four approaches were presented, with two of them (according toMićić et al. and Viswanatha et al.) using predefined PSD
shapes (lognormal and Gaussian) and two of them (according to Pesika et al. and Segets et al.) using a step-wisedeconvolution approach. The two latter are seen to bepreferred whenever the shape of the target PSD is notknown. The technique of Pesika et al. uses the local slopeof the absorbance spectrum. The approach of Segets et al.
uses a shifted bulk absorption coefficient that brings thepossibility to include the optical properties of discrete QD
size fractions as soon as they are available. Worthy ofnote is that data of such monodisperse fractions is becoming more and more available, e.g. by using AUC equippedwith multiwavelength (MWL) detection.
In the last part of this work, a clear perspective of exactly why precise PSD characterization on an Ångström
level matters was presented. A colloidal classification
technique that is widely applied in the field of QDs, theso-called size selective precipitation (SSP) was introduced. It is based on the precise tailoring of steric interactions vs. van der Waals adhesion and can be evaluatedwith established routines from the field of process engineering and particle technology. SSP is not only described
well by the separation efficiency T(x) following a law ofmass action, but classification results can be illustrated in a
3D Hansen space normalized to van der Waals attraction.
Based on this finding, knowledge on PSDs potentiallyallows the determination of Hansen solubility parameters(HSP). The latter give access to the osmotic interaction

58

potential originating from the ligand shells adsorbed at
the QD surface and thus lead to a better description ofsteric stabilization. This is of major importance not onlywith respect to tailored classification processes but alsowith respect to formulation issues.

Acknowledgments
The support of the Deutsche Forschungsgemeinschaft(DFG) is gratefully acknowledged through the Cluster of
Excellence “Engineering of Advanced Materials”. Moreover, I want to thank Johannes Walter and Wolfgang
Peukert for fruitful discussion.

References
Abécassis B., Bouet C., Garnero C., Constantin D., Lequeux N.,
Ithurria S., Dubertret B., Pauw B.R., Pontoni D., Real-timein situ probing of high-temperature quantum dots solution
synthesis, Nano Lett., 15 (2015) 2620–2626.
Alivisatos A.P., Semiconductor clusters, nanocrystals, andquantum dots, Science, 271 (1996) 933–937.
Alvarado S.R., Guo Y., Purnima T., Ruberu P.A., Tavasoli E.,
Vela J., Inorganic chemistry solutions to semiconductor
nanocrystal problems, Coordin. Chem. Rev., 263–264(2014) 182–196.
Baalousha M., Stolpe B., Lead J.R., Flow field-flow fractionation for the analysis and characterization of natural colloids and manufactured nanoparticles in environmental
systems: A critical review, J. Chromatogr. A, 1218 (2011)
4078–4103.
Balbo A., Minor K.H., Velikovsky C.A., Mariuzza R.A., Peterson
C.B., Schuck P., Studying multiprotein complexes by multisignal sedimentation velocity analytical ultracentrifugation, Proc. Natl. Acad. Sci. U.S.A., 102 (2005) 81–86.
Bergström L., Meurk A., Arwin H., Rowcliffe D.J., Estimationof Hamaker constants of ceramic materials from optical
data using Lifshitz theory, J. Am. Ceram. Soc., 79 (1996)
339–348.
Bhattacharyya S.K., Maciejewska P., Börger L., Stadler M.,
Gülsün A.M., Cicek H.B., Cölfen H., Development of a fast
fiber based UV-Vis multiwavelength detector for an ultracentrifuge, Prog. Coll. Pol. Sci. S, 131 (2006) 9–22.
Borrelli N.F., Smith D.W., Quantum confinement of PbS microcrystals in glass, J. Non.-Cryst. Solids, 180 (1994) 25–31.
Brookes E.H., Demeler B., Monte Carlo analysis of sedimentation experiments, Colloid Polym. Sci., 286 (2008) 138–148.
Brus L., Electronic wave functions in semiconductor clusters:
Experiment and theory, J. Phys. Chem., 90 (1986) 2555–
2560.
Brus L.E., A simple model for the ionization potential, electron
affinity, and aqueous redox potentials of small semiconductor crystallites, J. Chem. Phys., 79 (1983) 5566–5571.
Brus L.E., Electron-electron and electron-hole interactions insmall semiconductor crystallites: The size dependence ofDoris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

the lowest excited electronic state, J. Chem. Phys., 80 (1984)
4403–4409.
Cademartiri L., Montanari E., Calestani G., Migliori A.,
Guagliardi A., Ozin G.A., Size-dependent extinction coefficient of PbS quantum dots, J. Am. Chem. Soc., 128 (2006)
10337–10346.
Caetano B.L., Santilli C.V., Meneau F., Briois V., Pulcinelli S.H.,
In situ and simultaneous UV-Vis/SAXS and UV-Vis/XAFS
time-resolved monitoring of ZnO quantum dots formationand growth, J. Phys. Chem. C, 115 (2010) 4404–4412.
Carney R.P., Kim J.K., Qian H., Jin R., Mehenni H., Stellacci F.,
Bakr O.M., Determination of nanoparticle size distribution
together with density or molecular weight by 2D analyticalultracentrifugation, Nat. Commun., 2 (2011) 1–8.
Choi J.-H., Fafarman A.T., Oh S.J., Ko D.-K., Kim D.K., Diroll
B.T., Muramoto S., Gillen J.G., Murray C.B., Kagan C.R.,
Bandlike transport in strongly coupled and doped quantum
dot solids: A route to high-performance thin-film electronics, Nano Lett., 12 (2012) 2631–2638.
Cottingham K., Quantum dots leave the light on, Anal. Chem.,
77 (2005) 354A–357A.
Dai Q., Wang Y., Li X., Zhang Y., Pellegrino D.J., Zhao M., Zou
B., Seo J.T., Wang Y., Yu W.W., Size-dependent composition and molar extinction coefficient of PbSe semiconductor
nanocrystals, ACS Nano, 3 (2009) 1518–1524.
Debnath R., Bakr O.M., Sargent E.H., Solution-processed colloidal quantum dot photovoltaics: A perspective, Energy
Environ. Sci., 4 (2011) 4870–4881.
Detloff T., Lerche D., Centrifugal separation in tube and discgeometries: Experiments and theoretical models, Acta
Mech., 201 (2008) 83–94.
Díaz I., Díez E., Camacho J., León S., Ovejero G., Comparison
between three predictive methods for the calculation ofpolymer solubility parameters, Fluid Phase Equilib., 337(2013) 6–10.
Dieckmann Y., Cölfen H., Hofmann H., Petri-Fink A., Particle
size distribution measurements of manganese-doped ZnSnanoparticles, Anal. Chem., 81 (2009) 3889–3895.
Duonghong D., Ramsden J., Grätzel M., Dynamics of interfacial
electron-transfer processes in colloidal semiconductor systems, J. Am. Chem. Soc., 104 (1982) 2977–2985.
Embden J.V., Sader J.E., Davidson M., Mulvaney P., Evolutionof colloidal nanocrystals: Theory and modeling of their
nucleation and growth, J. Phys. Chem. C, 113 (2009)
16342–16355.
Hagendorfer H., Kaegi R., Parlinska M., Sinnet B., Ludwig C.,
Ulrich A., Characterization of silver nanoparticle products
using asymmetric flow field flow fractionation with a multidetector approach—a comparison to transmission electron
microscopy and batch dynamic light scattering, Anal.
Chem., 84 (2012) 2678–2685.
Hansen C.M., The official Hansen solubility parameter site,<www.hansen-solubility.com> (accessed Aug 8, 2014).
Hansen C.M., Hansen solubility parameters: A user’s handbook,2nd edition, CRC Press, Boca Raton, FL, USA, 2007.
Hansen C.M., Skaarup K.J., The three dimensional solubility
parameter—key to paint component affinities: III independent calculations of the parameter components, J. Paint

Technol., 39 (1967) 511–520.
Jafarzadeh S., Thormann E., Rönnevall T., Adhikari A., Sundell
P.-E., Pan J., Claesson P.M., Toward homogeneous nanostructured polyaniline/resin blends, ACS Appl. Mater.
Interfaces, 3 (2011) 1681–1691.
Kane R.S., Cohen R.E., Silbey R., Theoretical study of the electronic structure of PbS nanoclusters, J. Phys. Chem., 100(1996) 7928–7932.
Keshari A.K., Pandey A.C., Size and distribution: A comparisonof XRD, SAXS and SANS study of II–VI semiconductor
nanocrystals, J. Nanosci. Nanotech., 8 (2008) 1221–1227.
Kitchens C.L., McLeod M.C., Roberts C.B., Solvent effects on
the growth and steric stabilization of copper metallic
nanoparticles in AOT reverse micelle systems, J. Phys.
Chem. B, 107 (2003) 11331–11338.
Komada S., Kobayashi T., Arao Y., Tsuchiya K., Mori Y., Optical properties of manganese-doped zinc sulfide nanoparticles classified by size using poor solvent, Adv. Powder
Technol., 23 (2012) 872–877.
Koole R., Allan G., Delerue C., Meijerink A., VanmaekelberghD., Houtepen A.J., Optical investigation of quantum confinement in PbSe nanocrystals at different points in the
Brillouin zone, Small, 4 (2008) 127–133.
Kowalczyk B., Lagzi I., Grzybowski B.A., Nanoseparations:
Strategies for size and/or shape-selective purification, Curr.
Op. Coll. Int. Sci., 16 (2011) 135–148.
Krause B., Mende M., Pötschke P., Petzold G., Dispersability
and particle size distribution of CNTs in an aqueous surfactant dispersion as a function of ultrasonic treatment time,
Carbon, 48 (2010) 2746–2754.
Kuczinski J., Thomas J.K., Photochemistry at the surface of
colloidal cadmium sulfide, Chem. Phys. Lett., 88 (1982)
445–447.
Lees E.E., Gunzburg M.J., Nguyen T.-L., Howlett G.J.,
Rothacker J., Nice E.C., Clayton A.H.A., Mulvaney P.,
Experimental determination of quantum dot size distributions, ligand packing densities, and bioconjugation using
analytical ultracentrifugation, Nano Lett., 8 (2008) 2883–
2890.
Lerche D., Sobisch T., Evaluation of particle interactions by insitu visualization of separation behaviour Coll. Surf. A:
Physicochem. Aspects, 440 (2014) 122–130.
Leschonski K., Das Klassieren disperser Feststoffe in gasförmigen Medien, Chem.-Ing.-Tech., 49 (1977) 708–719.
Leschonski K., Alex. W., Koglin B., Teilchengrößenanalyse,
Chem.-Ing.-Tech., 46 (1974) 23–26.
Lin W., Walter J., Burger A., Maid H., Hirsch A., Peukert W.,
Segets D., A general approach to study the thermodynamics
of ligand adsorption to colloidal surfaces demonstrated bymeans of catechols binding to zinc oxide quantum dots,
Chem. Mater., 27 (2015) 358–369.
Lipovskii A., Kolobkova E., Petrikov V., Kang I., Olkhovets A.,
Krauss T., Thomas M., Silcox J., Wise F., Shen Q., Kycia S.,
Synthesis and characterization of PbSe quantum dots in
phosphate glass, Appl. Phys. Lett., 71 (1997) 3406–3408.
Liu Y., Gibbs M., Puthussery J., Gaik S., Ihly R., Hillhouse
H.W., Law M., Dependence of carrier mobility on nanocrystal size and ligand length in PbSe nanocrystal solids,

59

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

Nano Lett., 10 (2010) 1960–1969.
Liu Y., Shi B., Determination of Flory interaction parameters
between polyimide and organic solvents by HSP theory and
IGC, Polym. Bull., 61 (2008) 501–509.
Ma W., Swisher S.L., Ewers T., Engel J., Ferry V.E., Atwater
H.A., Alivisatos A.P., Photovoltaic performance of ultrasmall PbSe quantum dots, ACS Nano, 5 (2011) 8140–8147.
Mao Y., Cates M.E., Lekkerkerker H.N.W., Depletion force incolloidal systems, Physica A, 222 (1995) 10–24.
Marczak R., Segets D., Voigt M., Peukert W., Optimum between
purification and colloidal stability of ZnO nanoparticles,
Adv. Powder Technol., 21 (2010) 41–49.
Mastronardi M.L., Maier-Flaig F., Faulkner D., Henderson E.J.,
Kübel C., Lemmer U., Ozin G.A., Size-dependent absolute
quantum yields for size-separated colloidally-stable silicon
nanocrystals, Nano Lett., 12 (2011) 337–342.
Mićić O.I., Cheong H.M., Fu H., Zunger A., Sprague J.R.,
Mascarenhas A., Nozik A.J., Size-dependent spectroscopyof InP quantum dots, J. Phys. Chem. B, 101 (1997) 4904–
4912.
Mićić O.I., Curtis C.J., Jones K.M., Sprague J.R., Nozik A.J.,
Synthesis and characterization of InP quantum dots, J.
Phys. Chem., 98 (1994) 4966–4969.
Moreels I., Lambert K., De Muynck D., Vanhaecke F., PoelmanD., Martins J.C., Allan G., Hens Z., Composition andsize-dependent extinction coefficient of colloidal PbSe
quantum dots, Chem. Mater., 19 (2007) 6101–6106.
Moreels I., Lambert K., Smeets D., De Muynck D., Nollet T.,
Martins J.C., Vanhaecke F., Vantomme A., Delerue C.,
Allan G., Hens Z., Size-dependent optical properties of colloidal PbS quantum dots, ACS Nano, 3 (2009) 3023–3030.
Mori Y., Retention behavior of colloidal dispersions in sedimentation field-flow fractionation, Adv. Colloid Interface Sci.,
53 (1994) 129–140.
Mori Y., Size-selective separation techniques for nanoparticles
in liquid, KONA Powder and Particle Journal, 32 (2015)
102–114.
Moynihan T., What are quantum dots, and why do I want themin my TV? <www.Wired.Com/2015/01/primer-quantum-dot/>,
WIRED.com, 2015 (accessed April 8, 2015)
Murray C.B., Norris D.J., Bawendi M.G., Synthesis and characterization of nearly monodisperse CdE (E = S, Se, Te)
semiconductor nanocrystallites, J. Am. Chem. Soc., 115(1993) 8706–8715.
Nag A., Sapra S., Nagamani C., Sharma A., Pradhan N., Bhat
S.V., Sarma D.D., A study of Mn 2+ doping in CdS nanocrystals, Chem. Mater., 19 (2007) 3252–3259.
Nielsen M.H., Li D., Zhang H., Aloni S., Han T.Y.-J., FrandsenC., Seto J., Banfield J.F., Cölfen H., De Yoreo J.J., Investigating processes of nanocrystal formation and transformation via liquid cell TEM, Microsc. Microanal., 20 (2014)
425–436.
Nightingale A.M., de Mello J.C., Microscale synthesis of quantum dots, J. Mater. Chem., 20 (2010) 8454–8463.
Oshima H., Theory of electrostatics and electrokinetics of soft
particles, Sci. Technol. Adv. Mater., 10 (2009) 063001.
Palik E.D., Handbook of optical constants of solids, Academic
Press, London, 1985.

60

Park J., Joo J., Kwon S.G., Jang Y., Hyeon T., Synthesis of monodisperse spherical nanocrystals, Angew. Chem. Int. Ed.,46 (2007) 4630–4660.
Pauw B.R., Everything SAXS: Small-angle scattering patterncollection and correction, J. Phys.: Condens. Matter, 25(2013) 383201.
Peng X., Wickham J., Alivisatos A.P., Kinetics of II–VI and III–V colloidal semiconductor nanocrystal growth: “Focusing”
of size distributions, J. Am. Chem. Soc., 120 (1998) 5343–
5344.
Pesika N.S., Stebe K.J., Searson P.C., Determination of the particle size distribution of quantum nanocrystals from absorbance spectra, Adv. Mater., 15 (2003a) 1289–1291.
Pesika N.S., Stebe K.J., Searson P.C., Relationship between
absorbance spectra and particle size distributions forquantum-sized nanocrystals, J. Phys. Chem. B, 107 (2003b)
10412–10415.
Pietryga J.M., Schaller R.D., Werder D., Stewart M.H., Klimov
V.I., Hollingsworth J.A., Pushing the band gap envelope:Mid-infrared emitting colloidal PbSe quantum dots, J. Am.
Chem. Soc., 126 (2004) 11752–11753.
Polte J., Erler R., Thünemann A.F., Sokolov S., Ahner T.T.,
Rademann K., Emmerling S., Kraehnert R., Nucleation andgrowth of gold nanoparticles studied via in situ small angle
X-ray scattering at millisecond time resolution, ACS Nano,
4 (2010) 1076–1082.
Reindl A., Peukert W., Intrinsically stable dispersions of silicon
nanoparticles, J. Coll. Int. Sci., 325 (2008) 173–178.
Rhodes M.J., Introduction to particle technology, John Wiley &Sons Ltd., London, 1999.
Rogach A.L., Talapin D.V., Shevchenko E.V., Kornowski A.,
Haase M., Weller H., Organization of matter on different
size scales: monodisperse nanocrystals and their superstructures, Adv. Funct. Mater., 12 (2002) 653–664.
Rossetti R., Ellison J.L., Gibson J.M., Brus L.E., Size effects inthe excited states of small colloidal CdS crystallites, J.
Chem. Phys., 80 (1984) 4464–4469.
Rumpf H., Particle technology, Chapmann and Hall, London,
1990.
Sapsford K.E., Tyner K.M., Dair B.J., Deschamps J.R., Medintz
I.L., Analyzing material bioconjugates: A review of current
and emerging purification and characterization techniques,
Anal. Chem., 83 (2011) 4453–4488.
Sashchiuk A., Langof L., Chaim R., Lifshitz E., Synthesis andcharacterization of PbSe and PbSe/PbS core-shell colloidal
nanocrystals, J. Cryst. Growth, 240 (2001) 431–438.
Saunders S.R., Roberts C.B., Size-selective fractionation of
nanoparticles at an application scale using CO2 gas-expandedliquids, Nanotechn., 20 (2009) 475605.
Segets D., Gradl J., Klupp Taylor R., Vassilev V., Peukert W.,
Analysis of optical absorbance spectra for the determination of ZnO nanoparticle size distribution, solubility andsurface energy, ACS Nano, 3 (2009) 1703–1710.
Segets D., Komada S., Butz B., Spiecker E., Mori Y., Peukert
W., Quantitative evaluation of the size selective precipitation of Mn-doped ZnS quantum dots by size distributions
calculated from UV/Vis absorbance spectra, J. Nanopart.
Res., 15 (2013) 1486.

Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

Segets D., Lucas J.M., Klupp Taylor R.N., Scheele M., Zheng H.,
Alivisatos A.P., Peukert W., Determination of the quantum
dot bandgap dependence on particle size from opticalabsorbance and transmission electron microscopical measurements, ACS Nano, 6 (2012) 9021–9032.
Segets D., Lutz C., Yamamoto K., Komada S., Süß S., Mori Y.,
Peukert W., Classification of zinc sulfide quantum dots by
size: Insights into the particle surface-solvent interaction ofcolloids, J. Phys. Chem. C, 119 (2015) 4009–4022.
Segets D., Peukert W., UV/Vis-spektroskopie zur in-situ-­
teilchengrößenanalyse, Laborpraxis, (2014).
Seto J., Ma Y., Davis S.A., Meldrum F., Gourrier A., Kim Y.-Y.,
Schilde U., Sztucki M., Burghammer M., Maltsev S., Jäger
C., Cölfen H., Structure-property relationships of a biological mesocrystal in the adult sea urchin spine, Proc. Natl.
Acad. Sci. U.S.A., 109 (2012) 3699–3704.
Shah P.S., Holmes J.D., Johnston K.P., Korgel B.A., Size-selective
dispersion of dodecanethiol-coated nanocrystals in liquidand supercritical ethane by density tuning, J. Phys. Chem.
B, 106 (2002a) 2545–2551.
Shah P.S., Husain S., Johnston K.P., Korgel B.A., Role of stericstabilization on the arrested growth of silver nanocrystals
in supercritical carbon dioxide, J. Phys. Chem. B, 106(2002b) 12178–12185.
Steckel J.S., Coe-Sullivan S., Bulovic V., Bawendi M.G., 1.3 μm
to 1.55 μm tunable electroluminescence from PbSe quantum dots embedded within an organic device, Adv. Mater.,
15 (2003) 1862–1866.
Steigerwald M.L., Brus L.E., Semiconductor crystallites: A
class of large molecules, Acc. Chem. Res., 23 (1990) 183–
188.
Strauss H.M., Karabudak E., Bhattacharyya S., Kretzschmar A.,
Wohlleben W., Cölfen H., Performance of a fast fiber based
UV/Vis multiwavelength detector for the analytical ultracentrifuge, Colloid Polym. Sci., 286 (2008) 121–128.
Sun Y., Ren Y., In situ synchrotron X-ray techniques for realtime probing of colloidal nanoparticle synthesis, Part. Part.
Sys. Charact., 30 (2013) 399–419.
Sun Y., Welch G.C., Leong W.L., Takacs C.J., Bazan G.C.,
Heeger A.J., Solution-processed small-molecular solar cellswith 6.7 % efficiency, Nature Mater., 11 (2012) 44–48.
Tyrakowski C.M., Snee P.T., A primer on the synthesis,
water-solubilization, and functionalization of quantum
dots, their use as biological sensing agents, and present status, Phys. Chem. Chem. Phys., 16 (2014) 837–855.
Vincent B., Edwards J., Emmett S., Jones A., Depletion flocculation in dispersions of sterically-stabilised particles (“softspheres”), Colloid Surface, 18 (1986) 261–281.
Viswanatha R., Sapra S., Satpati B., Satyam P.V., Dev B.N.,
Sarma D.D., Understanding the quantum size effects in
ZnO nanocrystals, J. Mater. Chem., 14 (2004) 661–668.
Viswanatha R., Sarma D.D., Study of the growth of capped ZnOnanocrystals: A route to rational synthesis, Chem. Eur. J.,
12 (2006) 180–186.
Vossmeyer T., Katsikas L., Giersig M., Popovic I.G., Diesner K.,
Chemseddine A., Eychmüller A., Weller H., CdS nanoclusters: Synthesis, characterization, size dependent oscillator
strength, temperature shift of the excitonic transitionenergy, and reversible absorbance shift, J. Phys. Chem., 98(1994) 7665–7673.
Walter J., Löhr K., Karabudak E., Reis W., Mikhael J., Peukert
W., Wohlleben W., Cölfen H., Multidimensional analysis of
nanoparticles with highly disperse properties using multiwavelength analytical ultracentrifugation, ACS Nano, 8(2014) 8871–8886.
Walter J., Nacken T.J., Thajudeen T., Eigler S., Peukert W.,
Determination of the lateral dimension of graphene oxide
nanosheets using analytical ultracentrifugation, Small, 11(2015a) 814–825.
Walter J., Sherwood P.J., Lin W., Segets D., Stafford W.F.,
Peukert W., Simultaneous analysis of hydrodynamic andoptical properties using analytical ultracentrifugationequipped with multiwavelength detection, Anal. Chem., 87(2015b) 3396–3403.
Walter J., Thajudeen T., Süß S., Segets D., Peukert W., New
possibilities of accurate particle characterisation by applying direct boundary models to analytical centrifugation,
Nanoscale, 7 (2015c) 6574–6587.
Whitell G.V., Kitchens C.L., Small-angle neutron scattering ofsilver nanoparticles in gas-expanded hexane, J. Phys.
Chem. C, 114 (2010) 16285–16291.
Williams S.K.R., Runyon J.R., Ashames A.A., Field-flow fractionation: Addressing the nano challenge, Anal. Chem., 83(2011) 634–642.
Yen B.K., Günther A., Schmidt M.A., Jensen K.F., Bawendi
M.G., A microfabricated gas-liquid segmented flow reactor
for high-temperature synthesis: The case of CdSe quantum
dots, Angew. Chem. Int. Ed., 44 (2005) 5447–5451.
Yoshikawa H., Adachi S., Optical constants of ZnO, Jpn. J.
Appl. Phys., 36 (1997) 6237–6243.
Yu W.W., Qu L., Guo W., Peng X., Experimental determinationof the extinction coefficient of CdTe, CdSe, and CdS nanocrystals, Chem. Mater., 15 (2003) 2854–2860.
Zheng H., Smith R.K., Jun Y.-W., Kisielowski C., Dahmen U.,
Alivisatos A.P., Observation of single colloidal platinum
nanocrystal growth trajectories, Science, 324 (2009) 1309–
1311.

Nomenclature
Abbreviations
AC

analytical centrifugation

AsFlFFF asymmetric flow field-flow fractionation
AUC

analytical ultracentrifugation

A4F

asymmetric flow field-flow fractionation

DLS

dynamic light scattering

EMA

effective mass approximation

FFF

field-flow fractionation

FLN

fluorescence line narrowingHRhigh resolution

HSP

Hansen solubility parameters

61Doris Segets / KONA Powder and Particle Journal No. 33 (2016) 48–62

MWL

multiwavelength

Latin Symbols

PLphotoluminescenceAabsorbance (–)

PSD

particle size distributionA(λ)

measured absorbance (–)

QD

quantum dot

Ai’(λ)

partial absorbance (–)

RSDrelative standard deviationaempiric parameter (m)SANSsmall angle neutron scatteringb

empiric parameter (–)

SAXS

small angle X-ray scatteringcempiric parameter (m)SSPsize selective precipitation

C

constant (eV0.5 m–1)

TEM

transmission electron microscopycicohesive energy density (J m –3)

D

diffusion coefficient (m² s –1)

Greek Symbols
αabsorption coefficient (M–1 m–1)

αshift,i(λ)

shifted absorption coefficient (M–1 m–1)

χ

Flory Huggins parameter (–)ΔDactualtrue width of PSD (m)Δdappapparent width of absorbance data on a particle size
scale (m)δddisperse contribution to HSP ((J m –3)0.5)

δh

HSP for ability to exchange electrons ((J m –3)0.5)

δiHildebrand solubility parameter ((J m –3)0.5)

δppolar contribution to HSP ((J m –3)0.5)

ϕvolume fraction of ligand (–)σ

standard deviation (m)ηdynamic viscosity (Pa s)ηfyield of fines (–)ηgyield of coarse (–)κ

separation sharpness (–)λ

wavelength (m)dc

optical path length (m)Egband gap energy (eV)frelative mass of fine fraction (–)

grelative mass of coarse fraction (–)

hνenergy (J)Kdiss

dissociation constant (–)

mmass (kg)nfitting parameter (–)n(x)

relative abundance (–)

pfitting parameter (–)

q3

volume density (m –1)

R

ideal gas constant (J mol–1 K–1)

r

particle radius (m)Ttemperature (K)T(x)

separation efficiency (–)

Vmmolar volume (m³ mol–1)

xparticle diameter (m)xhhydrodynamic particle diameter (m)xt

cut size (m)Author’s short biography
Doris Segets
Dr. Segets is currently affiliated at the Institute of Particle Technology of theFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU). She received her PhD in2013 and her research interests include the characterization and tailoring of technically
relevant colloidal interfaces, the development of new concepts for process engineeringand the scale-up of colloidal nanoparticles, micro reaction technology and high-throughput
experimentation (HTE) as well as the application of well-defined nanoparticles for ultrafiltration and chromatography.

62

KONA Powder and Particle Journal No. 33 (2016) 63–85/Doi:10.14356/kona.2016014

Review Paper

Aerosol Delivery of siRNA to the Lungs.
Part 1: Rationale for Gene Delivery Systems †Susanne R. Youngren-Ortiz 1, Nishant S. Gandhi 1,
Laura España-Serrano 1 and Mahavir B. Chougule 1,2*
1
2

Department of Pharmaceutical Sciences, The Daniel K. Inouye College of Pharmacy, University of Hawaii at Hilo, USA
Natural Products and Experimental Therapeutics Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, USA

Abstract
This article reviews the pulmonary route of administration, aerosol delivery devices, characterization of
pulmonary drug delivery systems, and discusses the rationale for inhaled delivery of siRNA. Diseases withknown protein malfunctions may be mitigated through the use of siRNA therapeutics. The inhalation route ofadministration provides local delivery of siRNA therapeutics for the treatment of various pulmonary diseases,
however barriers to pulmonary delivery and intracellular delivery of siRNA exists. siRNA loaded nanocarriers
can be used to overcome the barriers associated with the pulmonary route, such as anatomical barriers,
mucociliary clearance, and alveolar macrophage clearance. Apart from naked siRNA aerosol delivery, previously
studied siRNA carrier systems comprise of lipidic, polymeric, peptide, or inorganic origin. Such siRNA delivery
systems formulated as aerosols can be successfully delivered via an inhaler or nebulizer to the pulmonary region.
Preclinical animal investigations of inhaled siRNA therapeutics rely on intratracheal and intranasal siRNA andsiRNA nanocarrier delivery. Aerosolized siRNA delivery systems may be characterized using in vitro techniques,
such as dissolution test, inertial cascade impaction, delivered dose uniformity assay, laser diffraction, and laser
Doppler velocimetry. The ex vivo techniques used to characterize pulmonary administered formulations include
the isolated perfused lung model. In vivo techniques like gamma scintigraphy, 3D SPECT, PET, MRI, fluorescenceimaging and pharmacokinetic/pharmacodynamics analysis may be used for evaluation of aerosolized siRNAdelivery systems. The use of inhalable siRNA delivery systems encounters barriers to their delivery, however
overcoming the barriers while formulating a safe and effective delivery system will offer unique advances to thefield of inhaled medicine.
Keywords: siRNA, nanocarrier, nanoparticle, aerosolization, pulmonary, lung

1. Introduction
RNA interference (RNAi) is a process in which RNA
molecules inhibit gene expression by causing the destruction of specific messenger RNA (mRNA). Small interfering RNA (siRNA) are double stranded RNA molecules
containing 20–25 nucleotides that are involved in the RNAi
pathway and interfere with the expression of a specific genewith complementary nucleotide sequences (Agrawal N. etal., 2003). As shown in Fig. 1, siRNA degrades mRNA after transcription, thereby preventing translation, or plays
a role in RNAi-related pathways (Agrawal N. et al., 2003).
siRNA has potential therapeutic applications in treating
†
1
2

*

Received 16 July 2015; Accepted 21 August 2015
J-STAGE Advance published online 30 September 2015
200 West Kawili Street, Hilo, Hawaii 96720, USA
Honolulu, Hawaii 96813, USA
Corresponding author: Mahavir B. Chougule;E-mail: mahavir@hawaii.edu
TEL: +1-808-981-4519 FAX: +1-808-981-4520©2016 Hosokawa Powder Technology Foundation‘undruggable’ diseases by downregulating the expressionof a target gene in a post-transcriptional manner. Since
the discovery of siRNA in 1998 by Fire and Mello et al.,the mechanisms of RNAi have been extensively reviewed(Fellmann C. and Lowe S.W., 2014; Fire A. et al., 1998;
Hannon G.J., 2002; Kim D.H. and Rossi J.J., 2007). The
siRNA possess a specific sequence that is complementarywith its target mRNA that induces site-specific cleavage
and subsequent inhibition of intracellular protein synthesis. The siRNA, once in the cytoplasm of the cell, canincorporate within the RNA-induced silencing complex (RISC) and activates this complex. The sense
strand is then removed from the duplex and is degradedby nucleases in the activated RISC complex. At the sametime, the antisense strand directs the RISC to the base-­complementary sequence of the target mRNA located within
the cell cytoplasm. Binding of mRNA to the antisense
strand in the activated RISC eventually induces cleavage
by the endonuclease Argonaute and post-transcriptionalsilencing of the target gene expression. Major advantages

63

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

of siRNA over small molecule drugs or protein therapeutics are that the sequences can be rapidly designed forhighly specific inhibition of the target of interest and thatthe synthesis of siRNAs is relatively simple because it
does not require a cellular expression system, complex
protein purification, or refolding schemes (Amarzguioui
M. et al., 2005).
Pulmonary diseases such as lung cancer, cystic fibrosis,
pulmonary hypertension, asthma, and chronic obstructive
pulmonary disorder (COPD) have potential siRNA therapeutic targets (Amarzguioui M. et al., 2005; Burnett J.C.
and Rossi J.J., 2012; Kanasty R. et al., 2013). Nevertheless, siRNA delivery systems are likely to have instability
issues that cause premature release of the nucleic acids,
especially with systems that incorporate their cargothrough electrostatic interactions. Due to the high negative charge density and relatively large size of the siRNA
molecules, naked siRNA molecules are not able to enter
cells efficiently (Reischl D. and Zimmer A., 2009). Pulmonary delivery of siRNA faces major challenges that involve a lack of correlation between in vitro and in vivo
experiments, difficulty in translation from animal models
to humans, and non-applicable administration routes usedin animal studies for human use (Lam J.K.-W. et al.,2012).
In Part I, we review the modes of pulmonary deliveryof siRNA, the evaluation of aerosol drug delivery systems, and the rationale for the use of nanocarriers to overcome the barriers of pulmonary delivery and cellular
uptake of siRNA. Part II focuses on the siRNA loaded
non-viral particulates for aerosolized delivery systems,

and preparation and characterization techniques for
siRNA loaded nanoparticles.

2. Pulmonary route of administration
To achieve pulmonary delivery, inhalable aerosols generated by an inhaler or nebulizer are the preferred option.
Before entering clinical trials, new therapeutic agents must
demonstrate preclinical efficacy in appropriate animalmodels that are translatable to humans (Laube B.L., 2014).
Pulmonary aerosols are usually administered via the inhalation, intratracheal, or intranasal routes. Intratracheal
and intranasal routes of administration are commonlyused to deliver therapeutic siRNA or other therapeutic
agents to the lungs of animals due to ease of experimental
setup and control (Driscoll K.E. et al., 2000). In preclinical studies, the very different lung anatomy of mice andhumans needs to be considered while selecting the routeof administration to assess delivery and efficacy. Formulations administered via the pulmonary route of administration are required to be nonirritating to reduce risk ofpharyngeal edema, bronchial spasm, anaphylaxis, peracute death, and chronic pulmonary fibrosis (Turner P.V.
et al., 2011). These factors are vital to the successful development of an orally or intranasally inhaled siRNA delivery system.

2.1 Inhalation route
The most non-invasive way to locally deliver therapeutics to the lungs is through inhalation. Four types of inhalation devices are currently available including pressurized
metered dose inhalers (pMDIs), dry powder inhalers(DPIs), nebulizers, and soft mist inhalers (SMIs). With
appropriate developmental optimization, these devices
may deliver siRNA to the lungs. During development, key
parameters should be considered for an optimum inhaler
system, as shown in Table 1.

2.1.1 Inhalation aerosol delivery devices

Fig. 164

siRNA pathway schematic. Reprinted with permission
from Ref. (De Fougerolles A. and Novobrantseva T.,
2008; De Fougerolles A. et al., 2007). Copyright:(2007) Nature Publishing Group.

pMDIs are currently the most commonly used inhalers.
The therapeutic agents within a pMDI are in either a suspended particulate state or dissolved within propellants,
such as chlorofluorocarbons (CFCs) and hydrofluoroalkanes (HFAs) (Lam J.K.-W. et al., 2012). The propellants
are an indispensable part of pMDIs as they supply the energy required to aerosolize the drug for inhalation. siRNAor siRNA loaded nanocarriers may not be compatible
with propellant vehicles which limit the formulation ofsiRNA into the pMDIs (Lam J.K.-W. et al., 2012). However, crosslinked chitosan-PEG1000 based nanocarriers
with particle size of less than 230 nm were found to bephysically stable within HFA-227, highly dispersible, andSusanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Table 1

Parameters to consider in siRNA formulation designfor inhalation

Property

Parameters

Aerosol properties

Mass median aerodynamic diameter
Geometric standard deviation
Fine particle fraction
Air/particle velocity

Particle properties

Volume diameter
Bulk density
Tap density
Shape
ChargePhysiochemical
properties

Solubility

Lung properties

Disease state on airway structure andbreathing pattern

Hygroscopicity

Disease state on surfactant production
Disease state on mucus production andmucociliary clearance
Age of patient, pulmonary or nasal
breathing
Airway structure and diameter

successfully delivered to the deep lung airways using
pMDIs (Sharma K. et al., 2012). Selecting a stable and appropriate formulation for use with the pMDI devices willallow for the development of siRNA containing pMDIs.
DPIs and their formulations allow for the inhalation ofaerosol clouds of dry particles. DPI device design has amajor impact on their performance. Advantages of DPIs
are that they have improved sterility and chemical stability of biomolecules compared to liquid aerosols (Lam J.
K.-W. et al., 2012). Drug deposition from inhaled DPIs is
dependent on the inspiration flow rate of the patient, andoften times the illness that warrants their use of the inhalation therapy causes their inspiration flow rates to be abnormally low (Feddah M.R. et al., 2000; Ross D.L. and
Schultz R.K., 1996). The device must be designed to minimize these variation with respect to patient disease state(Shoyele S.A. and Slowey A., 2006). Spray drying techniques are often used to prepare protein and peptide inhalable DPI formulations that have demonstrated
successful in vivo delivery of therapeutic macromolecules(Bai S. et al., 2010; Codrons V. et al., 2003; Mastrandrea
L.D. and Quattrin T., 2006; Rawat A. et al., 2008). The
DPIs formulation can be used for pulmonary delivery ofsiRNA. The issues and challenges that have risen fromthe formulation of biological macromolecules, such as

flowability, dispersibility, and biochemical stability needs
to be also considered for development of siRNA based
DPIs (Chan H.-K., 2003). Considerations for formulationof proteins as powder aerosols have been reviewed previously (Chan H.-K., 2003). Maintaining biochemical stability of siRNA during formulation limits the processes
that may be used to prepare siRNA based dry powder formulations.
Nebulizers generate liquid aerosols that can deliver
saline-based solutions or suspensions of drug product atlarge volumes via inhalation (Sharma K. et al., 2013).
Nebulizer treatments usually last over several minutes,
rather than single inhalation. Generated droplets haveaerodynamic diameters between 1–10 μm, depending on
the formulation and the type of nebulizer device. The four
major categories of nebulizers are jet (pneumatic), vibrating mesh/membrane, smart, and ultrasonic nebulizers (AriA., 2014; Dailey L.A. et al., 2003).
A jet, or pneumatic nebulizer, can be used to deliversuspension formulation delivery suspension formulation,
which makes them suitable for delivering siRNA or
siRNA carrier delivery systems. Breath-enhanced jet nebulizers release more aerosol during inhalation, whereas
breath-actuated jet nebulizers sense the patient’s inspiratory flow and deliver aerosol only during inhalation(Arunthari V. et al., 2012; Haynes J.M., 2012; Ho S.L. etal., 2001). In an air jet nebulizer, compressed gas draws
up bulk liquid to a jet by the Bernoulli effect and atomization takes place where the liquid emerging from the jetinteracts with the shear force set up by the gas flow.
Rayleigh dispersion of bulk liquid into droplets occurs
and baffles remove the coarse droplets, which fall backinto the reservoir, whereas droplets with aerodynamic diameters of < 10 μm are available for inhalation. It has
been estimated that 99 % of the generated aerosols are recycled back to the reservoir, thus shear stress is exerted
multiple times on the formulation and hence, the possible
degradation of naked siRNA during this process needs tobe evaluated (Agu R.U. et al., 2001).
Vibrating mesh nebulizers use micropumps to force
liquid through a mesh or aperture plate for aerosol generation. Suspensions, nanocarriers, and nucleic acids havebeen successfully delivered via mesh nebulizers (Elhissi
A. et al., 2006; Elhissi A. and Taylor K., 2005; Hibbitts A.
et al., 2014; Lentz Y.K. et al., 2006; Luo Y. et al., 2012;
Wagner A. et al., 2006; Yoshiyama Y. et al., 2002). Meshnebulizers have consistent and improved aerosol generation efficiency, large fine-particle fraction, low residual
volume, and the ability to nebulize low volumes (Ari A.,
2014; Dhand R., 2002; Dolovich M.B. and Dhand R.,
2011; Waldrep J. and Dhand R., 2008). Vibrating mesh/membrane nebulizers are more expensive than jet nebulizers because of the electronics involved with controllingthe vibration process. In addition, mesh nebulizers are65

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

difficult to clean. However, mesh nebulizers are more
efficient than jet nebulizers at providing higher drug
doses to patients (Ari A., 2014). The limitations of meshnebulizers include the blocking of the mesh or aperturesduring the nebulization of viscous suspensions or the precipitation and crystallization of drug or excipients (Ari A.,
2014; Najlah M. et al., 2014). This should be a consideration when formulating a siRNA nanocarrier suspension
for nebulization.
There are two types of vibrating mesh nebulizers,
termed active and passive. Active mesh nebulizers utilizea piezoelectrical element that expands and contracts upon
application of electrical currents which then vibrates amesh in contact with the medication to generate the aerosol. Passive mesh nebulizers use a transducer horn thatinduces passive vibrations on the mesh plate for aerosol
generation (Ari A., 2014).
Smart nebulizers incorporate adaptive aerosol delivery(AAD®) technology that analyzes the patient’s breathing
pattern in order to determine the timing of aerosol drugdelivery during inhalation (Denyer J. and Dyche T., 2010).
The device is able to adapt to the patients breathing pattern, therefore it is able to reduce the aerosol losses andthe variation in drug delivery. Smart nebulizers can provide effectiveness and end of dose feedback to the patient
during therapy, which can increase patient compliance(Denyer J. and Dyche T., 2010; Denyer J. et al., 2004).
Ultrasonic nebulizers utilize a piezoelectric crystal
vibrating at high frequencies of 1–3 MHz to produce
aerosols. These types of nebulizers have the limitations oflarge residual volumes, inability to aerosolize viscous
solutions, and degradation of heat sensitive materials (AriA., 2014; Ari A. et al., 2009; Ari A. and Restrepo R.D.,
2012). Therefore, ultrasonic nebulizers may not be usefulfor suspensions of naked siRNA or siRNA nanocarriers(Taylor K.M. and Mccallion O.N., 1997; Watts A.B. et al.,2008).
Chemical and physical stability of the naked siRNAduring the nebulization process is of high concern. Therefore, delivery of siRNA using nebulizers should involvethe development of a formulation which protects thesiRNA from physical degradation, shear stress, andchemical degradation mediated via hydrolysis and endogenous enzymes (Lam J.K.-W. et al., 2012).
SMIs are a recently introduced category of inhaler
delivery system marketed as Respimat ® produced byBoehringer-Ingelhem (Dalby R. et al., 2004). This technology provides a metered dose to the user who activatesthe inhaler and energy from a spring imposes pressure on
the liquid container. The SMI generates the aerosol byimpinging opposing droplet streams emerging from a
uniblock nozzle created using silicon wafer technology(Dalby R. et al., 2004). The advantages of the SMI arethat it has high lung deposition due to the low velocity of66

the aerosol cloud and thus minimizing the deposition in
the mouth and throat (Dalby R. et al., 2004; Hochrainer D.
et al., 2005). In addition, the patient is not required to exert excessive inspiratory force in generating the aerosol
clouds associated with DPIs. The effect on physical andchemical stability of siRNA must be considered while delivering a formulation via SMIs (Merkel O.M. et al., 2014;
Sharma K. et al., 2013). The feasibility of SMIs as delivery devices for non-viral siRNA delivery has not been explored, but it may be a viable option.

2.2 Intratracheal route
The intratracheal route of administration is used mostoften in animal studies for assessing the inhalation delivery of various drugs. However, the clinical application ofthis route is limited due to its invasive setup and uncomfortable delivery technique (Driscoll K.E. et al., 2000;
Sakagami M., 2006). The intratracheal route is not feasible for human clinical studies due to method invasivenessthat involves inserting a tube to dispense the formulation
within a surgical incision made within the tracheal rings(Lam J.K.-W. et al., 2012). The traditional method involves the animal to be anesthetized, undergo surgicaltracheotomy, and after the trachea is exposed, an endotracheal tube or needle is inserted into the incision between
the tracheal cartilaginous rings, with the tip projected at adefined position before the tracheal bifurcation, as shownin Fig. 2. The drug solution or suspension formulation isadministered through the tube using a microsyringe andinstilled into the airways. Bivas-Benita et al. described anon-invasive intratracheal instillation that did not involve
surgical resection of the trachea that is also known as theoro-tracheal route (Bivas-Benita M. et al., 2005). Aerosol isdelivered to the lungs by placing a microsprayer over the
anesthetized animals tongue and down to the trachea or the
animal is intubated and the drug is instilled in solution orsuspension form. This method has the drawbacks of diffi-

Fig. 2Intratracheal instillation and bronchoalveolar lavage on
rat. Reprinted with permission from Ref. (Patel G. etal., 2009).

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

culty in accurate placement of the microsprayer catheterand reduced mucociliary clearance due to anesthesia.
There have been a number of studies that utilized theintratracheal route for siRNA delivery to the lungs (Conde
J. et al., 2013; Garbuzenko O.B. et al., 2009; Merkel O.M.
et al., 2009; Moschos S.A. et al., 2007; Perl M. et al.,2005; Rosas-Taraco A.G. et al., 2009; Wang J.-C. et al.,2010). Drug deposition by this route is less uniform thanby the inhalation route of administration (Sakagami M.,
2006). Since this route avoids oropharynx deposition, theamount of drug loss is lower compared to the inhalationroute. However, this route of delivery does not allow for
determination of the effect of aerodynamic particle size on
the lung deposition, therefore the in vivo intratracheal instillation studies in the animals do not reflect the intricacies of lung deposition. The intratracheal instillation routeof delivery is suitable for providing proof-of-concept for
local lung delivery studies in animal models.

2.2.1 Intratracheal aerosol delivery devices

Intratracheal aerosolization devices consists of an atomizer located in the distal tip of a long, narrow, stainlesssteel or plastic tube used to deliver an aerosol directly tothe lungs when inserted down the trachea, above the carina or further down, in an anesthetized animal. Devices
for preclinical studies include commercially available
technologies for administration of solution and for dry
powder aerosol formulations. Intratracheal delivery canbe achieved through devices, such as the Penn-Century
microsprayer (Gutbier B. et al., 2010; Rosas-Taraco A.G.
et al., 2009), the Micro-Mist nebulizer (Zamora-Avila D. etal., 2009), or the Aeroprobe nebulizing catheter (Nielsen
E.J. et al., 2010). The Penn-Century, Inc. (Wyndmoor, PA,USA) offers an air-free liquid device and an air-driven
dry powder device for preclinical use (Bivas-Benita M. etal., 2005; Morello M. et al., 2009). The miniaturized nebulizing catheter system has shown success for targeted
gene delivery to the lungs (Koping-Hoggard M. et al.,2005; Tronde A. et al., 2002). The nebulizing catheter device (NCD) was first adapted for the delivery of peptide
therapeutics to the rat lungs (AeroprobeTM, Trudell Medical International, London, Ontario, Canada) (Tronde A. etal., 2002). This NCD delivers a liquid, which is transported down a tube through a central lumen and pressurized gas is pushed through the peripheral lumens from acompressed airway source at 50 psi. The close proximityof the liquid and gas lumens at the distal tip produces anaerosol in the particle size range of 20–40 μm (Tronde A.
et al., 2002).

2.3 Intranasal route
The intranasal route provides a straight forward animalmodel experimental setup for delivery of siRNA to thelungs, therefore it is another common siRNA delivery
route (Bitko V. et al., 2005; Fulton A. et al., 2009; Ghosn
B. et al., 2010; Gutbier B. et al., 2010; Hosoya K. et al.,2011; Howard K.A. et al., 2006; Li B.-J. et al., 2005;
Massaro D. et al., 2004; Senoo T. et al., 2010; Tompkins
S.M. et al., 2004; Zhang X. et al., 2004). The animals must
be anesthetized and siRNA formulations are then instilled
into the nasal cavity to be breathed in. Although this routehas shown success in siRNA delivery to the lungs inmouse animal models, the translation of these studies tohumans has been limited. Humans are not obligate nose
breathers and have a nasal cavity that filters out the majority of particulates, therefore human intranasal studies
do not see such a high lung deposition (Hickey A.J. andGarcia-Contreras L., 2001). Additionally, anesthetics cause
the animals to have reduced mucociliary action, therefore
the reduced mucociliary clearance overestimates thetransfection efficiency of the formulations (Hinchcliffe M.
and Illum L., 1999). A study by Heyder et al. found that
only 3 % of monodisperse powder particles with aerodynamic diameters of 1–5 μm were deposited within humanbronchial airways after intranasal administration (Heyder
J. et al., 1986). Since approximately 97 % of the monodisperse particles were deposited within the nasopharynx
area, the intranasal route of delivery is ideal for targeting
this site in humans. The ALN-RSV01 siRNA, which targets the mRNA encoding the N-protein of respiratorysyncytial virus (RSV), developed by Alnylam Pharmaceuticals (Cambridge, MA, USA), has completed phase II
clinical trials for the treatment of human RSB infections
using the intranasal route of administration (Devincenzo
J. et al., 2008; Devincenzo J. et al., 2010; Zamora M.R. etal., 2011). This naked siRNA was delivered via a nasal
spray to the upper respiratory tract.
The intranasal route may also be used to allow for systemic or central nervous system (CNS) delivery ofsiRNA. Protein and peptide biological macromolecules
have been previously administered via the intranasal
route to access the systemic circulation (Hinchcliffe M.
and Illum L., 1999). The nasal cavity has a relatively large
surface area and vascularization for facilitating rapid
absorption. The intranasal route can access the CNS by
bypassing the blood-brain barrier (Hanson L.R. and Frey
W.H., 2nd, 2008; Kamei N. and Takeda-Morishita M.,2015).

2.3.1 Intranasal aerosol delivery devices

Intranasal delivery devices for liquid solution or suspension include the rhinyle catheter and instillation tube.
This simple method, often used on anesthetized or sedated animals, involves the intranasal insertion of a finecatheter or micropipette followed by instilling the liquid
into the desired area to be aspirated into the airways
during breathing (Fernandes C.A. and Vanbever R.,

67

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

2009). The use of small volumes of 5 μL per nostril or
10 μL total limited drug deposition to the nasal cavity,
whereas larger volumes of 25 μL per nostril or 50 μL total
allowed for deposition with the lung upper airways(Minne A. et al., 2007). Intranasal instillation is limited
due to its hindered ability to reach the deep lung.
For the intranasal delivery of dry powders to the pulmonary route of administration, the Dry Powder InsufflatorTM for mice can be used to aerosolize the powder dose
(Sinsuebpol C. et al., 2013). This device is actuated using
either an air syringe or a Penn-Century air pump, which
generates 200 μL puffs of air that generates the powder
dose and carries it into the deep lung.

2.4 Passive inhalation exposure chambers foranimal studies
Aerosol exposure chambers may be classified as noseonly, head-only, and whole-body exposure chambers.
These systems are commonly used to deliver nebulized
formulations (Cryan S.-A. et al., 2007). An example of anose-only exposure chamber is the InExposeTM (SCIREQ®)
model as shown in Fig. 3. This device has been evaluatedfor use with inhalable drug carrier systems containing antisense oligonucleotides and siRNA for the treatment oflung cancer (Mainelis G. et al., 2013). Liposomes withparticle size and mass median diameter of 130 nm and
270 nm respectively, aerosolized by a jet Collison nebulizer were found to be stable over the continuous aerosolization and a higher lung dose and retention time compared
to intravenous route was observed (Mainelis G. et al.,2013). Other examples of nose-only exposure chambers
include the Oro-Nasal and Respiratory Exposure Systems(CH Technologies, Westwood, NJ) and the In-Tox Products Small Animal Exposure System (Intox Products,
Edgewood, NM). Nose-only and head-only exposure
chambers avoid exposure of the aerosol to other parts ofFig. 368

TM

InExpose®

(SCIREQ ) nose-only exposure system.

body. These set-ups can be stressful for the animal due tothe restraint and exposure facemasks placed on the animals face and neck (Cryan S.-A. et al., 2007).
The whole-body exposure setup represents a less invasive exposure chamber because rodents are free to move
during the passive aerosol delivery (Cryan S.-A. et al.,2007). Whole-body exposure can also simulate environmental exposures to aerosols (Mühlfeld C. et al., 2008).
These exposure chambers are usually composed of a chamber where the animal resides during the aerosol therapy,
and a port where aerosol can be delivered to the insideof the chamber (Adamcakova-Dodd A. et al., 2014;
O’shaughnessy P.T. et al., 2003; Pettis R.J. et al., 2000).
When delivering a therapeutic aerosol via a whole-body
exposure chamber, there is possible administration of significant amount of formulation to other routes, such as theoral route (Cryan S.-A. et al., 2007). Parameters that impact the variation of the study may include the size of thechamber, the animals size, lung capacity, the inspiratory
flow rate, and the formulation characteristics (Cryan S.-A.
et al., 2007).

3. Evaluation of pulmonary drug delivery
systems
Aerosolized siRNA formulations are characterized using
various in vitro, ex vivo, and in vivo techniques that are
Table 2

Techniques for evaluating pulmonary drug delivery
systems

Type

Technique

In vitro Dissolution test

Measurement determined
Dissolution rate

Inertial impaction
using cascade
impactor

Aerodynamic particle size
distribution (APSD)

Delivered dose
assay

Total delivered dose uniformity

Laser diffraction

Particle size and particle size
distribution

Laser Doppler
velocimetry

Aerosol velocity

Ex vivo Isolated perfusedlungMechanisms of drug transportand deposition

In vivo

Scintigraphy

Visualization and quantificationof aerosol deposition in respiratory tract

Pharmacokinetic(PK)-animalmodel/clinical trial

PK parameters

Pharmacodynamics Biochemical and physiological(PD)effects of a drugSusanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Table 3

USP and Ph.Eur. Cascade Impactors for orally inhaled dosage forms

Dosage Form

USP

Ph.Eur.

pMDI- Andersen 8-stage non-viable cascade impactor
configuration at 28.3 L/min flow rate
- NGI

- Single Stage Twin Impinger- Andersen 8-stage non-viable cascade impactor
configuration at 28.3 L/min flow rate
- NGI

DPI

- Marple-Miller impactor (model 160, MMI)
- Andersen 8-stage non-viable cascade impactor
configuration at flow rates other than 28.3 L/min- Multi-stage liquid impinge- NGI- Multi-stage liquid impinger- Andersen 8-stage non-viable cascade impactor
configuration at 28.3 L/min flow rate
- NGI

Nebulizer*- Harmonized with Ph.Eur.

- Single-stage twin impinger- NGI* operated at a flow rate of 15 L/min. Ph.Eur. 2.9.44 includes the NGI and is harmonized with <1601> of the USP

shown in Table 2 (Mansour H.M. et al., 2009; Newman S.P.
et al., 2000). The purpose of these performance tests isprimarily to establish the efficacy of the drug product anddevice delivery platform have been addressed. These tests
were elaborated in Chapter <601> of the USP (Chapters
U.G., 2009).

3.1 In vitro characterization
Aerosolized siRNA formulation dissolution tests may
be performed by adding the formulation within dissolution medium vessel under constant agitation (Shah V.P. etal., 2008; Son Y.J. and Mcconville J.T., 2009). The solution is then sampled at pre-determined time intervals tomeasure the amount of siRNA released from the nanoparticles. Since there is not a standard method developed for
determining siRNA dissolution rate, various media, suchas distilled water (Dickinson P.A. et al., 2001), phosphatebuffer (Murata N. et al., 2008), simulated lung fluid (SLF)
and modified SLF have been used. In addition, various
apparatus, such as the flow-through cell (Davies N.M. andFeddah M.R., 2003), standard USP dissolution apparatus,and stirring vessels have been used. Test conditions, suchas sink, and particle introduction into the medium being
either direct or by aerosolization have been used to characterize the dissolution rate of inhaled drugs.
Inertial impaction is a standard in vitro technique used
to predict aerosol deposition in the lungs (Padhi B.K. et al.,2009). The cascade impactor is the equipment of choicefor particle size analysis of most inhaled aerosols because
it allow assay for mass of drug, determined aerodynamic
size directly, and provide size information that be indicative of lung deposition. The inertial size-separation andfractionation in a cascade or sequence of single stages
within the cascade impactor apparatus measures aerodynamic particle size distribution (APSD) (Chapters U.G.,
2009; Hickey A.J., 2003). The jet stream is aimed towards
a flat impaction plate, upon which the flow of particles

having high inertia is disrupted and impact on the plate.
Meanwhile, particles with lower inertia remain within thestreamline airflow and do not impact onto the plate(Chapters U.G., 2009; Hickey A.J., 2003; Merkus H.G.,
2009). Several impactor plates separate aerosol particles
into size ranges according to their aerodynamic size.
The larger particles will impact on the plate, whereas thesmaller ones will remain airborne. The Stokes number,which is defined as the ratio of the stopping distance tothe orifice diameter, can be used to estimate the impaction
efficiency (Mitchell J.P. and Nagel M.W., 2003).
The available cascade impactors accepted by theUnited States Pharmacopeia (USP) and the European
Pharmacopeia (Ph.Eur.) are listed in Table 3 (Mitchell J.P.
et al., 2010; Nichols S.C. et al., 2013). The Andersen
Cascade Impactor (ACI) is a pharmacopeial method forpMDIs and DPIs and is the most commonly used inertial
impactor instrument (Fig. 4A). Other cascade impactors
include the Marple-Miller model 160 (Fig. 4B) and themultistage liquid impinger (Fig. 4C) for DPIs. Nebulizers
are suggested to be characterized by the USP <601>/
Ph.Eur. 2.9.44 using the Next-Generation Impactor (NGI)
at 15 L/min or with a suitable validated cascade impactor
that operates at ≤15 L/min (Nichols S.C. et al., 2013).
Cascade impactors are apparatuses for measuring
APSD of inhaled aerosols and are not lung simulators ormodels (Mitchell J.P. and Nagel M.W., 2003). Since cascade impactors operate at a constant flow rate, they do not
simulate the environment of the respiratory tract, such astemperature, relative humidity, and the size-selectivity ofthe various processes that govern the deposition in thehuman respiratory tract. The constant particle velocity
within a cascade impactor contrasts with the decreasing
air velocity with increasing airway generation number inthe lung. The cascade impactor stage selectivity is muchsteeper than selectivity for regional deposition (Dunbar C.
and Mitchell J., 2005).
Non-aerodynamic droplet or particle size distribution

69

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Fig. 4 Schematic representation of the (A) Andersen 8-stage
cascade impactor, (B) Marple-Miller impactor, and (C)Multi-stage liquid impinger. Reprinted with permission
from Ref. (Nahar K. et al., 2013). Copyright 2013.

was determined using laser diffraction (Chougule M. etal., 2008; De Boer A.H. et al., 2002; Padhi B.K. et al.,2009). This technique measures the low angle scatteringof monochromatic coherent light from every particle inthe measurement zone. These simultaneous measurements are combined to contribute to the angular scattered
light intensity profile. Laser diffraction has the advantages of that it is non-invasive, rapid, has high resolution,
wide-dynamic range, can measure volume weighted particle size distribution directly, and is an absolute tech-

70

nique that does not require calibration or verification
using optical reticles (Lee Black D. et al., 1996). During alaser diffraction particle size determination measurement,
particles of the same size scatter light to the same positionin the detector array, where smaller particles scatter lightto wider angles (De Boer A.H. et al., 2002). Efficient data
inversion algorithms ensure rapid convergence to a valid
solution. Laser diffraction method utilizes the interpretation of the relationship between scattered light intensity
and scattering angle, which is the distance from the central axis of the photodetector. The Fraunhofer approximation is a simplified description assuming particles absorb
light completely (Eshel G. et al., 2004). In this case, only
scattering at contour is considered and the particle refractive index (Np) is not needed. The Fraunhofer model
works best when particle size is much larger than the
wavelength of light, and when the particles are opaque,spherical, and when Np is different to the refractive index
of the surrounding medium (e.g. air) (Newman S.P. et al.,2001). The Lorenz-Mie theory is a complete descriptionof angular light scattering process that requires a full description of complex particle refractive index, which attimes is unknown (Eshel G. et al., 2004). The Lorenz-Mie
model applies to all sizes of particles, but assumes thatparticles are homogeneous, isotropic, spherical, and has aknown complex Np that incorporates both refraction andabsorption components. A time-averaged data presentation mode is useful to capture overall performance of apulmonary inhaled product. The size distribution can bepresented in both differential and cumulative formats andthe key metrics like particle size of the 10th, 50th, and 90th
volume percentiles can be obtained (De Boer A.H. et al.,2002). Laser diffraction equipment can either be an open
bench or a close cell. Open bench is simple to perform
and is widely used where the aerosol can be directedacross the measurement zone. Closed sampling is necessary for DPI testing and may be needed to control evaporation for some measurements, especially with continuous
nebulizers. The limitations for laser diffraction are thatthe method has no assay for drug mass, particles are assumed to be spherical, constant droplet density versus
size, vignetting, beam splitting, and droplet evaporation(De Boer A.H. et al., 2002).
The delivered dose uniformity assay is used to quantitatively determine the delivered dose and dose uniformity
using a dose uniformity sampling apparatus (DUSA)(Chapters U.G., 2009). The apparatus is connected to anaerosol delivery system through a mouthpiece adapter anda vacuum pump is connected at the other end to provide
the desired air flow. The aerosol released into the sampling apparatus is captured onto a filter that is then assayed to quantify the delivered dose. The dose uniformity
is determined from variability of the measured delivered
dose by the collection of the total mass of contents for theSusanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Fig. 5 Isolated perfused rat lung preparations: (a) horizontally positioned isolated perfused rat lung with ascheme of forced solution instillation, (b) vertically positioned isolated perfused rat lung with a scheme ofnebulization catheter dosing. Reprinted with permission from Ref. (Sakagami M., 2006). Copyright 2003.

actuation of the clinical dose on the label, excluding volatile propellant and low-volatile species.

3.2 Ex vivo characterization
Ex vivo studies are used to identify lung-specific PK
without interference from systemic absorption, distribution, metabolism, and excretion (ADME) that occur duringin vivo studies (Sakagami M., 2006). The isolated perfusedlung method includes the isolation of a lung from the bodyand keeping it within an artificial system under certain experimental conditions. These tests utilize isolated perfusedlung models that maintain lung tissue functionality andarchitecture (Cryan S.-A. et al., 2007; Sakagami M.,
2006). The isolated perfused lung model is often used toestablish the mechanisms of drug absorption and deposition in the lungs. The shortcoming of ex vivo methods include relatively shortened timeframes for data collections
due to the viability of the perfused lungs, complex experimental set up, and the absence of tracheo-bronchial circulation and mucociliary clearance (Cryan S.-A. et al.,2007).
The isolated perfused lung preparation has been used inrats, guinea pigs, rabbits, dogs, and monkeys (Sakagami
M., 2006). The preparation, as shown in Fig. 5A–B, consists of peristaltic pumps and a tubing assembly to carry

71

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

the perfusate to and from the lung, and a double-jacketed
artificial thorax to house the isolated perfused lung at
37 °C. Perfusate flow, which is normally at 12–15 mL/minin rat lungs, is pumped from the reservoir to the isolated
perfused lung through a central porthole of the thorax lid.
The bottom of the thorax has an opening, where the perfusate can return to the reservoir or be collected. The thorax is sealed, which enables negative or positive pressure
ventilation or the maintenance of pressure (Sakagami M.,
2006). While the animal is under anesthesia, the pulmonary circulation in cannulated via the pulmonary arteryand the lung is perfused with autologous whole blood or abuffered artificial medium at pH 7.4 (Sakagami M., 2006).
The surgical procedures involved in preparing the isolated
perfused lung model was discussed previously (Sakagami
M., 2006). Following perfusion, the formulations are administered via the tracheal port and the perfusate samples
may be collected at predetermined time points to establish
absorption profiles (Sakagami M., 2006).

3.3 In vivo characterization
Gamma scintigraphy is a non-invasive imaging technique that allows for visualization of drug deposition following pulmonary delivery in the respiratory tract,
predicts in vivo efficacy, and estimates mucociliary clearance (Newman S. and Wilding I., 1998; Newman S.P. andWilding I.R., 1999; Snell N. and Ganderton D., 1999).
Prior to pulmonary delivery, the drug formulation isradio-labelled with gamma-ray-emitting radiotracer, such
as Technetium 99m (99mTc). Alternatively, the radiolabel
can be incorporated or attached to the microparticle ornanocarrier instead of the drug (Nahar K. et al., 2013).
Radiolabeling could be simply achieved by mixing aradiotracer solution with the formulation (Newman S.P. etal., 2003). An appropriate in vitro validation is required toensure distribution of the radiotracer across particle size
range and that the radiotracer is not affecting the particle
size distribution (Newman S. and Wilding I., 1998). A
gamma camera is used to visualize and quantify the drug
deposition in regions of interest within the respiratory
tract following inhalation of the radiolabeled formulation.
Scintigraphy data are considered an equivalency assessment tool since the data have been demonstrated to correlate well with clinical efficacy data (Bondesson E. et al.,
2003; Newman S. and Wilding I., 1998). After pulmonary
delivery of the formulation, a gamma camera scans thethorax and radioactive counts can be digitalized to get a
2D image of the lungs, oropharynx, stomach, the inhalation device, and the exhalation filter (Majoral C. et al.,2014; Nahar K. et al., 2013). The periphery of the lungs isimaged by 80mKr ventilation or transmission scans, and issuperimposed to quantify the gamma-labelled particles toestablish distribution and permeation (Conway J., 2012;

72

Nahar K. et al., 2013). However, the two dimensional (2D)
nature of gamma scintigraphy images make it difficult todifferentiate the overlaid anatomical structures. The ratioof peripheral to central deposition, known as the penetration index (PI), measures the extent to which an aerosol
reached the lower airways in the lung periphery (Conway
J., 2012; Nahar K. et al., 2013).
Three dimensional (3D) imaging methods, such as
single photon emission computed tomography (SPECT)
overcome 2D imaging issues in distinguishing physiological landmarks (Perring S. et al., 1994). Radiolabeled formulations are tracked by a gamma camera that rotatescompletely around the subject to form a topographicalimage in all planes (Conway J., 2012; Fleming J. et al.,2011; Nahar K. et al., 2013). 3D images can be formed
using computer software (Snell N. and Ganderton D.,1999). A limitation of SPECT is the possible occurrenceof regional deposition, redistribution by mucociliary
clearance, coughing, and absorption to the bloodstream
during the 30 min method timeframe (Huchon G.J. et al.,
1987; Nahar K. et al., 2013). Other limitations are the requirement of a high dose and the use of radiation. Otherin vivo imaging methods have been applied to overcome
these issues, nevertheless the relatively lower cost andtechnical skills required are much lower for planar
gamma scintigraphy or SPECT imaging (Everard M.L.,
1994; Nahar K. et al., 2013). SPECT 3D imaging is suitable for correlating in vitro and in vivo data (Chan H.-K.
et al., 2007; Eberl S. et al., 2006; Snell N. and GandertonD., 1999).
Positron emission tomography (PET) utilizes positronlabeled drugs that emits two high-energy photons throughelectron annihilation (Cherry S.R. and Gambhir S.S., 2001;
Dolovich M.B., 2009; Nahar K. et al., 2013; Phelps M.E.,
2000). These photons are concurrently emitted at 180°from one another, then detected and counted as a single
event (Nahar K. et al., 2013). Accurate delineation regionsof interest are obtained by combining the 3D PET image
with MRI or spiral CT scan of the thorax (Frauenfelder T.
et al., 2015; Yoon S.H. et al., 2014). This allows for the
calculation of the percentage of the inhaled dose deposited in these regions (Nahar K. et al., 2013). The advantage of PET is that the drug can act as the radioactive
tracer by incorporating a positron emitting isotope (11C,15
O, 13N, 18F, 64Cu, and 124I) by isotopic substitution at
trace amounts (Carvalho T.C. et al., 2011; Chakravarty R.
et al., 2014; Conway J., 2012; Park C.-W. et al., 2012). PET
has been employed to analyze lung pathology, biochemistry, inflammation, transgene expression, and cellular responses in vivo (Park C.-W. et al., 2012). The utility of themethod is limited by the short half-life (11C t1/2 ~20 min)of radionuclides that necessitates the need to be close to a
cyclotron during the study (Saha G.B. et al., 1992). PET
has been suggested to have no advantage over planar

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

gamma scintigraphy and PK studies for the calculation of
total lung deposition of pulmonary inhaled formulations(Nahar K. et al., 2013; Snell N. and Ganderton D., 1999).
Magnetic resonance imaging (MRI) does not require
the radiolabeling of an aerosol to determine qualitativeand quantitative deposition of particles in the lungs, butrather is based on the Nuclear Magnetic Resonance phenomenon. This method utilizes non-ionizable radiation to
generate images that can be augmented through the use ofsoft tissue contrast mechanisms and orientations of 2D
and 3D images (Nahar K. et al., 2013). The interaction of
nuclear magnetic moments of the experimental sample
tissue with an external magnetic and electromagnetic
fields form spin density, T1 and T2 relaxation times, andmotion which ranges from diffusion to rapid produce thesignals the MRI equipment utilized to form images. T1
and T2 depend on the local nuclei environment which is
subject to disease state of the tissues, where T1 is the longitudinal relaxation time constant and T2 is the transverse
relaxation time constant (Nahar K. et al., 2013; Thompson
R.B. and Finlay W.H., 2012). Signals measured from thenuclei are either full or half integer values of spin in proportion to the angular momentum that results from an odd
number of protons or neutrons. The majority of MRIs
measure the signal from the ubiquitous hydrogen (1H)
atom, however lung imaging performed using helium-­based (3He) methods is now well established because thenon-lipid soluble properties of helium allow it to staywithin the airspace (Komlosi P. et al., 2015; Kruger S.J. etal., 2014; Nahar K. et al., 2013). Hyperpolarized 3He or
129
Xe gas MRI produce more polarization, which leads tobetter imaging (Mosbah K. et al., 2008; Taratula O. andDmochowski I.J., 2010; Thompson R.B. and Finlay W.H.,
2012). MRI has been employed within in vivo animalstudies and within in vitro human lung airway replicas(Martin A.R. et al., 2008; Sood B.G. et al., 2008; Sood
B.G. et al., 2010; Thompson R.B. and Finlay W.H., 2012;
Wang H. et al., 2015).
Fluorescence imaging tracks fluorescently tagged particles within an aerosol to monitor their deposition in thelungs (Nahar K. et al., 2013; Ntziachristos V. et al., 2008).
This method is limited due to the issues of the excitationand emission fluorescence signals are scattered, reflected,
and absorbed by the tissues (Nahar K. et al., 2013; Yi D.
et al., 2010). This affects the light captured by the detector
and the actual number of molecules. In addition, a heterogeneous background signal exists because biological tissue has the capability to auto-fluoresce (Yi D. et al., 2010).
The autofluorescence background can be measured using
untreated tissue and subtracted from the experimental
background or the use of near-infrared wavelengths canbe used to overcome this issue (Adams K.E. et al., 2007;
Comsa D.C. et al., 2008; Kovar J.L. et al., 2007; Murata
M. et al., 2014). Fluorescent dyes are aerosolized, deliv-

ered to the lungs via inhalation, intracheal, or intranasal
routes, and imaged using an in vivo imaging system(IVIS). The particle deposition in the lobes of the lungs
can be visualized and quantified using image analyzing
software (Nahar K. et al., 2013). Fluorescence imaging
eliminates the need for radioactive tags, therefore it
may be used to image deposition patterns in live animals(Ntziachristos V. et al., 2008).
PK studies investigate ADME of drugs by collecting
lung deposition and plasma concentration data from either
animal or human test subjects to determine the PK parameters. These parameters include drug absorption parameters such as the peak drug concentration (Cmax), thetime Cmax occurs (tmax), and the absorption rate constant(ka) to determine the absorption rate and the area undercurve (AUC) to estimate bioavailability. Also, drug distribution and elimination parameters such as clearance (CL),volume of distribution (Vd), and half-life of the drug (t1/2)
can be determined. Mice and rat animal models are commonly used in animal PK studies because of their development in modeling various respiratory diseases, smaller
drug requirement, and low cost (Cryan S.-A. et al., 2007;
Sakagami M., 2006). The drug can be administeredthrough intranasal or intratracheal routes or by using passive inhalation (Cryan S.-A. et al., 2007; Sakagami M.,
2006; Zhang J. et al., 2011). Intratracheal or intranasal
direct administration avoids oropharyngeal deposition,
enabling accurate and reproducible dosing (Chiang P.-C.
et al., 2009).

4. siRNA delivery to the lungs
For clinical use, siRNA needs to be delivered to thetarget region of the lung and be released within the cytoplasm after entering the target cells as shown in Fig. 6.
Successful gene therapy requires the effective levels oftransgene expression in specific cell types to enhance thetreatment efficacy and to avoid adverse side effects causedby the expression in inappropriate cell types.
siRNA is a highly negatively charged, hydrophilic, andlarge sized (approximately 13.3 kDa) macromolecule thatcannot cross biological membranes to reach their target
sites. Viral vectors have previously demonstrated cell uptake and siRNA efficacy, however, major limitations tohuman therapeutic delivery exist, such as uncontrolled
viral replication, immunogenicity, tumorigenicity, andtoxicity (Thomas C.E. et al., 2003). Due to these concerns, non-viral delivery systems have been developedand successfully used to deliver siRNA. An ideal siRNAdelivery system should (1) condense siRNA into nanosized particles, (2) protect siRNA from enzymatic degradation, (3) facilitate cellular uptake, (4) promoteendosomal escape to release siRNA to the cytoplasm

73

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Table 4

Pulmonary barriers to lung siRNA delivery (Merkel
O.M. et al., 2014)

Airway Defenses

Fig. 6

Aerosol siRNA delivery to the lungs. (A) pulmonary delivery of naked or siRNA nanocarriers, (B) Carrier-based
delivery of siRNA cellular uptake. The aerosolized formulation of siRNA and use of appropriate device delivers siRNA by inhalation, intratracheal, or intranasal
delivery to the human or animal lung. Once the siRNA
carriers reach the lower respiratory tract, they may
reach target cells and become internalized through endocytosis. Carriers capable of endosomal escape avoid
degradation in the lysosome and release free siRNAwithin the cytoplasm. The RNAi pathway may then be
initiated, ultimately leading to mRNA degradation andgene silencing.

where the RISC is located, (5) have negligible effects on
gene silencing activity or specificity and (6) have negligible toxicity (Lam J.K.-W. et al., 2012; Merkel O.M. et al.,2012). Non-viral delivery systems include naked siRNAdelivery, and delivery vectors such as lipids, polymers,
peptides, and inorganic materials.

4.1 Barriers of pulmonary delivery of siRNA
Delivering siRNA efficiently to the lungs via the pulmonary route requires understanding of the anatomicaland physiological characteristics of the respiratory tract.
The human respiratory tract has a highly efficient gas exchange capacity and to keep particulates out (Table 4) as
discussed previously (Merkel O.M. et al., 2014). Therefore, the development of siRNA aerosol delivery systems
should consider careful control of particle or droplet size
to bypass the lungs natural defense mechanisms.
The respiratory tract is divided into two regions, (1) the
conducting airways and (2) the respiratory region. The
conducting airways encompass the nasal cavity, pharynx,
trachea, bronchi, and bronchioles and the respiratory region consists of the respiratory bronchioles and alveoli(Lam J.K.-W. et al., 2012). The human respiratory tract
has evolved to be a size selective fractionator that prevents particulates from reaching the deep lung. For exam-74

Alveoli Defenses

Upper airway (nose, throat,and trachea)

Degradative Enzymes e.g.
proteases, RNase

Bifurcations

Neutrophils

Mucus

Opsonins

Mucociliary Clearance

Surfactant

Cough Clearance

Complement

Airway epithelium

Alveolar Macrophages

ple, the largest particles are removed at the naso- or
oropharynx and progressively finer particles deposit in
the proximal, intermediate, and distal airways. The mainfeature of the respiratory tract is the high degree of bifurcations that have been described according to Weibel’slung model to have 24 generation in total. These gradually narrowing airways from the carina to the alveolar
sacs cause increased particle velocity, which causes particles to impact the walls of airways instead of reaching thelower regions of the lungs. Therefore, for a therapeutic
agent to reach the target site of action, it must possess
aerodynamic properties to allow it to pass through thebranched airways to the deep lung.
Aerodynamic particle size distribution is an important
influence on the location of lung deposition of a formulation (Bisgaard H. et al., 2001). Three forces dominate particle motion within an aerosol cloud: gravitational
sedimentation, inertial impaction, and Brownian diffusion. Gravitational sedimentation and inertial impaction
are dominant when the aerodynamic diameter is > 2 μm.
Large particles with aerodynamic particle size of > 6 μm
deposit mostly on the airway walls at bifurcations in theupper airways due to their high momentum. Smaller particles of aerodynamic diameters of < 1 μm deposit due toBrownian diffusion (Morén F., 1993). If submicron sizedparticles are delivered without a larger carrier particle ordroplet, then they would be mostly exhaled in the breath.
A breath hold method may be used to increase deposition
within the lower respiratory tract, however, it has lower
efficiency. The most optimum aerodynamic particle sizesfor deposition within the lower airways are between 1 to
5 μm (Agu R.U. et al., 2001; Sakagami M., 2006). As particle size decreases below 1 μm, lung deposition increases
due to increased diffusional mobility (Yang W. et al.,2008). For particles less than 100 nm in size, approximately 50 % of the nanoparticles will deposit within thealveolar region of the lungs. These nanoparticles often enter the lungs as large agglomerates that break apart upon
deposition.
The elimination pathways for nanoparticles within theSusanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

lungs involve coughing, dissolution, clearance via the
mucociliary escalator, translocation from the airways,
phagocytosis by macrophages and neuronal uptake (Roy I.
and Vij N., 2010; Sanders N. et al., 2009; Zhang J. et al.,2011). Nanoparticle degradation is dependent on the composition of the delivery system and mediated through hydrolysis, oxidation, and reduction pathways (Roy I. and
Vij N., 2010; Rytting E. et al., 2008). After deposition in
the lower airways following inhalation, free siRNA will
be susceptible to degradation from RNase, whose enzymatic activity is very high in the lungs (Amatngalim G.D.
et al., 2015; Morita T. et al., 1986; Sorrentino S., 1998).
A major pulmonary delivery barrier is the mucociliary
clearance driven by the physical action of the ciliated epithelial cells, which move the mucus and alveolar fluid
towards the upper airways, and eventually, the throat.
Mucociliary clearance also includes the alveolar macrophages along the airways, which phagocytose foreign
particles and deposits their remains to be removed by the
mucociliary escalator. Mucus is present on the respiratory
epithelium from the nasal cavity to the terminal bronchioles (Knowles M.R. and Boucher R.C., 2002). Types ofglycosylated protein, known as mucins, are the main
component of mucus (Rose M.C. et al., 2001). Mucus is aphysical barrier as it increases in viscosity of the moist
surface of the lung epithelial cells, thereby reducing drug
penetration and diffusion rate (Hill D.B. et al., 2014;
Mellnik J. et al., 2014). Particles that are deposited on themucosal layer above the ciliated epithelial cells are rapidly removed by mucociliary clearance and are eventuallyeither coughed out or swallowed into the gastrointestinal
tract (Knowles M.R. and Boucher R.C., 2002; Rubin B.K.,
2002). The alveolar fluid is found on the surface of alveoli
epithelium as a thin layer of pulmonary surfactant whichis composed of phospholipids and surfactant-associatedproteins known as SP-A, SP-B, SP-C and SP-D (Bastacky
J. et al., 1995; Possmayer F. et al., 2001). Following delivery of foreign antigen or nanocarrier, pulmonary surfactant proteins SP-A and SP-D have been shown to play arole in macrophage uptake by acting as opsonins andscavenger molecules (Kishore U. et al., 2006). These pulmonary surfactants are located at the air-liquid interfaceof the alveoli, therefore they can interact with particulate
aerosols that reach the lower airways (Seaton B.A. et al.,2010). Other major opsonins of the airways and alveoli ofthe lungs include immunoglobulins (IgG or IgM), complement fragments (C3, C4, C5), fibronectin, and laminin(Kuroki Y. et al., 2007; Patel B. et al., 2015). The alveolar
macrophages located within the alveoli rapidly engulf
foreign particles by phagocytosis as a defense mechanism(Patton J.S., 1996; Weinberger B. et al., 2014; Zhao F. etal., 2011).
In addition to macrophages, the other major phagocytes
are the neutrophils, or polymorphonuclear neutrophilic leu-

kocytes (PMNs or polys), that are abundant in the blood
but are not present in normal healthy tissue (Kolaczkowska
E. and Kubes P., 2013). Like macrophages, neutrophils arephagocytic cells that have a role in the innate immunity because they recognize, ingest, and destroy many pathogens
without the adaptive immune response (Kolaczkowska E.
and Kubes P., 2013). When macrophages first encounter apathogen within the lung, their action is augmented by therecruitment of large number of neutrophils to the infection site (Burns A.R. et al., 2003; Kolaczkowska E. andKubes P., 2013). Pulmonary administered naked siRNA issusceptible to alveolar macrophage or neutrophil uptake
and are subsequently degraded within these cells, thus
reducing their therapeutic efficacy.
Considering disease state is an important aspect of pulmonary clearance since the physiological conditions of
the airways are altered and pose a threat on the efficiency
of an aerosolized siRNA delivery system. During infection and inflammation, the airways become congested because there is increased mucus secretion and decreased
mucociliary clearance (Groneberg D. et al., 2002; Patton
J.S. et al., 2010). The thickness, viscosity, viscoelasticity,and the composition of the mucus layer depend on thepathological condition and have inter-patient variability(Sanders N. et al., 2009). For different therapeutic applications, disease state of the patient should be considered
in order to design an effective aerosolized delivery system. In addition to using particles with small aerodynamic diameter suitable for deposition in the lower
airways, it has been reported that the use of large porous
particles can prolong retention time in the lungs by effectively avoiding alveolar macrophage phagocytosis (Arnold
M.M. et al., 2007; Ben-Jebria A. et al., 2000; Edwards
D.A. et al., 1997). Porous particles over 10 μm in geometric diameter have a smaller aerodynamic diameter due totheir mass-inertia relationship. Their smaller aerodynamic
diameters of 1–3 μm fall within the ideal aerodynamic
size range for effective lung deposition, while their actual
geometric size is larger than the size range of alveolar
macrophage uptake and removal.
The mucus layer may be partially deteriorated using
mucolytic agents that break down the three dimensionalgel network of mucus. A mucus inhibitor, such as glucopyrrolate, may also be used to overcome the mucus barrier(Ferrari S. et al., 2001). In patients with bronchiectasis or
cystic fibrosis, mannitol has been clinically proven to improve the hydration and viscoelastic surface properties ofthe sputum, which increased mucus clearance (Daviskas
E. et al., 2010). The deposition site of nucleic acid delivery
systems in the airways has been previously manipulated
and directed through the use of ultrasound and magnetic
field technologies (Dames P. et al., 2007; Xenariou S. etal., 2006; Xenariou S. et al., 2007). Another less-invasive
option to enhance the transport across the mucus layer is75

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

to coat the surface of nanoparticles with neutrally charged
molecules, like polyethylene glycol (PEG) (Schuster B.S.
et al., 2013).
Lung disease involving chronic lung inflammation, likecystic fibrosis, emphysema, or asthma, are characterized
by increased levels of pulmonary proteases (Greene C.M.
and Mcelvaney N.G., 2009). These proteases may degrade
peptide conjugated siRNA or polymeric nanocarriers ofsiRNA before the carrier gets to target specific cells ofinterest.

4.2 Barriers of intracellular siRNA delivery
To exert their effects, the siRNA must cross the cellular
membrane and be released within the cytoplasm of thetarget cells of the respiratory tract, and it must access theRISC, where their intracellular interaction takes place.
Due to the size of 13 kDa and a negative charge, thesiRNA do not readily cross biological membranes. Therefore, an appropriate delivery system should facilitatesiRNA cellular uptake.
The major cellular uptake pathway for non-viralsiRNA delivery systems that are under 150 nm in size is
endocytosis (Khalil I.A. et al., 2006). Endocytosis is themechanism by which cells engulf polar molecules or
nanocarriers that cannot readily pass through the hydrophobic cellular membrane. The four endocytosis pathways
are clathrin-mediated endocytosis, caveolae-mediated endocytosis, micropinocytosis, and phagocytosis (Marsh M.,2001). For the inhalation route of administration, a particle size of less than 150 nm also avoids macrophage uptake, thus slowing lung clearance (Lebhardt T. et al.,2010). Clathrin-mediated endocytosis is the major pathway that allows for particle uptake through enclosed
clathrin-coated vesicles (Khalil I.A. et al., 2006). Thesevesicles then fuse with early endosomes to form late endosomes, which subsequently form into lysosomes. As
this process progresses from early endosome to lysosome,the pH within the vesicles drop down to approximately
pH 5.0 and degradative DNAse and RNase are present
within the lysosome (Bally M.B. et al., 1999; Khalil I.A.
et al., 2006; Maxfield F.R. and Mcgraw T.E., 2004). For asiRNA to elicit therapeutic effects within the cell, it must
escape from the endosome and be released within the cytoplasm to avoid degradation by degradative enzymes. To
promote endosomal escape, the “proton sponge hypothesis” can be practiced by using a high buffering capacity
polymer with a large pH range, such as polyethylenimine(PEI), which will become protonated as the pH of the endosome drops (Boussif O. et al., 1995; Gunther M. et al.,2011). This protonation of the polymer causes an influx ofchloride ions, protons and water into the endosomes. The
building osmotic pressure causes the endosomes to burst,
thus releasing its contents into the cytoplasm (Gunther M.

76

et al., 2011). However, the use of PEI has been associatedwith toxicity issues (Seow W.Y. et al., 2013). An alternative strategy to promote endosomal escape is through theuse of pH sensitive fusogenic peptides. These peptides
undergo pH-dependent conformation changes at low pH
which cause a membrane disrupting conformationalchange that destabilizes endosomal membranes (Choi S.W.
et al., 2010; Kusumoto K. et al., 2013; Lee S.H. et al.,2007; Oliveira S. et al., 2007).
Another cellular uptake mechanism of siRNA delivery
systems is the caveolae-mediated endocytosis pathway(Khalil I.A. et al., 2006). The delivery system is internalized through a caveolin-coated vesicle known as a caveosome. The caveosomes are void of nucleases, degradative
enzymes, and have a non-acidic pH. The delivery systems
avoid lysosomal degradation because the contents of the
caveosomes are transported to the Golgi or endoplasmic
reticulum. Caveolin is expressed within lung tissues,
therefore it may be a more efficient route for inhalation
siRNA delivery systems compared to clathrin-mediatedendocytosis, especially if the delivery system doesn’t possess endosomal escape properties (Lam J.K.-W. et al.,2012; Parton R.G. and Richards A.A., 2003; Yacobi N.R.
et al., 2010).
Another cellular uptake mechanism of cells of the lungs
is phagocytosis (Khalil I.A. et al., 2006). This route canonly be performed by alveolar macrophages and otherspecialized cells. Unless the target cell type is alveolar
macrophages, then uptake of delivery systems by thispathway should be avoided since siRNA molecules taken
up are eventually degraded by the phagolysosomes of thecells.

5. Conclusion
Lower airway pulmonary delivery can be achieved
through the inhalation of an aerosol from delivery devices
such as pMDIs, DPIs, nebulizers, and SMIs. Preclinicalsafety and efficacy studies using animal models often deliver pulmonary aerosols via the inhalation, intratracheal,
and intranasal routes. The lung physiological and anatomical differences between human and animals are an integral
consideration when selecting the route of administration
to assess the efficacy of an aerosol. Intratracheal administration involves an invasive surgical setup and anesthesia.
The intranasal route of administration can be used toreach the airways in mouse models by instilling and aspirating the liquid solution or suspension into the nasal cavityof the animal. Dry powder formulations can be delivered
using a microsprayer that when actuated, generates theaerosol and propels it into the airways. The nose-only exposure chamber used in preclinical studies has provided arobust means of delivering aerosols to the deep lung.

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Aerosolized siRNA formulations are characterized using
in vitro, ex vivo, and in vivo techniques. In vitro techniques measure the dissolution rate, aerodynamic particle
size distribution, total delivery dose uniformity, particle
size, and aerosol velocity. Ex vivo techniques analyze themechanisms of drug transport, deposition, and absorption. In vivo techniques provide visualization and quantification of aerosol deposition in the respiratory tract, PK
parameters, and biochemical and physiological effects ofthe pulmonary delivered drug. Barriers for pulmonary delivery of siRNA exist due to the natural defenses of thelungs to keep exogenous particulates and substances out.
Delivering aerosolized siRNA effectively to the lungs andto their target site-of-action involves the understanding of
the anatomical and physiological characteristics of the respiratory tract. The barriers for aerosolized delivery ofsiRNA include degradation by RNase and mucociliary
clearance. Intracellular naked siRNA delivery is impeded
due to their large size, negative charge, and susceptibility
to degradation. These intracellular delivery constraints
may be mitigated through the use of siRNA nanocarriersystems for aerosol delivery including those of lipidic,
polymeric, peptide, or inorganic origin. Part II of this review article will discuss the preparation methods of various siRNA nanocarrier systems, accompanied by a seriesof examples.

Acknowledgements
The authors acknowledge the support of the National
Institute of General Medical Science of the National Institutes of Health under award number SC3GM109873. The
authors acknowledge Hawai’i Community Foundation,
Honolulu, HI 96813, USA, for research support on lungcancer, mesothelioma, and asthma projects (Leahi Fund)in 2015, 2013, and 2011, respectively. The authors would
like to acknowledge the 2013 George F. Straub Trust andRobert C. Perry Fund of the Hawai’i Community Foundation, Honolulu, HI 96813, USA, for research support onlung cancer. The authors also acknowledge a seed grantfrom the Research Corporation of the University of Hawai’iat Hilo, Hilo, HI 96720, USA, and The Daniel K. Inouye
College of Pharmacy, University of Hawai’i at Hilo, Hilo,
HI 96720, USA, for providing start-up financial support
to their research group. The authors acknowledge the donation from Dr. Robert S. Shapiro, MD, Dermatologist,
Hilo, HI, USA in support of development of nanotechnology based medicines.

References
Adamcakova-Dodd A., Stebounova L.V., Kim J.S., Vorrink S.U.,

Ault A.P., O’shaughnessy P.T., Grassian V.H., Thorne P.S.,
Toxicity assessment of zinc oxide nanoparticles using subacute and sub-chronic murine inhalation models, Part Fibre
Toxicol, 11 (2014) b1.
Adams K.E., Ke S., Kwon S., Liang F., Fan Z., Lu Y., Hirschi K.,
Mawad M.E., Barry M.A., Sevick-Muraca E.M., Comparisonof visible and near-infrared wavelength-excitable fluorescent dyes for molecular imaging of cancer, Journal of biomedical optics, 12 (2007) 024017-024017-024019.
Agrawal N., Dasaradhi P., Mohmmed A., Malhotra P., Bhatnagar
R.K., Mukherjee S.K., RNA interference: Biology, mechanism, and applications, Microbiology and molecular biology reviews, 67 (2003) 657–685.
Agu R.U., Ugwoke M.I., Armand M., Kinget R., Verbeke N.,
The lung as a route for systemic delivery of therapeuticproteins and peptides, Respiratory research, 2 (2001) 198.
Amarzguioui M., Rossi J.J., Kim D., Approaches for chemicallysynthesized siRNA and vector-mediated RNAi, FEBS Letters, 579 (2005) 5974–5981.
Amatngalim G.D., Van Wijck Y., De Mooij-Eijk Y., Verhoosel
R.M., Harder J., Lekkerkerker A.N., Janssen R.A., Hiemstra
P.S., Basal cells contribute to innate immunity of the airway epithelium through production of the antimicrobial
protein rnase 7, J Immunol, 194 (2015) 3340–3350.
Ari A., Jet, ultrasonic, and mesh nebulizers: An evaluation of
nebulizers for better clinical outcomes (2014).
Ari A., Hess D., Myers T., Rau J., A guide to aerosol delivery
devices for respiratory therapists, Dallas, Texas: American
Association for Respiratory Care (2009).
Ari A., Restrepo R.D., Aerosol delivery device selection for
spontaneously breathing patients: 2012, Respiratory care,
57 (2012) 613–626.
Arnold M.M., Gorman E.M., Schieber L.J., Munson E.J.,
Berkland C., NanoCipro encapsulation in monodisperse
large porous PLGA microparticles, Journal of Controlled
Release, 121 (2007) 100–109.
Arunthari V., Bruinsma R.S., Lee A.S., Johnson M.M., A prospective, comparative trial of standard and breath-actuatednebulizer: Efficacy, safety, and satisfaction, Respiratory
care, 57 (2012) 1242–1247.
Bai S., Gupta V., Ahsan F., Inhalable lactose-based dry powder
formulations of low molecular weight heparin, Journal ofaerosol medicine and pulmonary drug delivery, 23 (2010)
97–104.
Bally M.B., Harvie P., Wong F.M., Kong S., Wasan E.K.,
Reimer D.L., Biological barriers to cellular delivery of
lipid-based DNA carriers, Advanced drug delivery reviews,
38 (1999) 291–315.
Bastacky J., Lee C., Goerke J., Koushafar H., Yager D., Kenaga
L., Speed T.P., Chen Y., Clements J.A., Alveolar lining
layer is thin and continuous: Low-temperature scanningelectron microscopy of rat lung, Journal of Applied Physiology, 79 (1995) 1615–1628.
Ben-Jebria A., Eskew M.L., Edwards D.A., Inhalation systemfor pulmonary aerosol drug delivery in rodents using large
porous particles, Aerosol Science & Technology, 32 (2000)
421–433.
Bisgaard H., O’callaghan C., Smaldone G.C., Drug delivery to77Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

the lung, CRC Press, 2001.
Bitko V., Musiyenko A., Shulyayeva O., Barik S., Inhibition of
respiratory viruses by nasally administered siRNA, Nature
medicine, 11 (2005) 50–55.
Bivas-Benita M., Zwier R., Junginger H.E., Borchard G.,
Non-invasive pulmonary aerosol delivery in mice by theendotracheal route, European Journal of Pharmaceutics andBiopharmaceutics, 61 (2005) 214–218.
Bondesson E., Bengtsson T., Borgström L., Nilsson L.-E.,
Norrgren K., Trofast E., Wollmer P., Planar gamma scintigraphy—points to consider when quantifying pulmonary
dry powder aerosol deposition, International journal ofpharmaceutics, 258 (2003) 227–240.
Boussif O., Lezoualc’h F., Zanta M.A., Mergny M.D., Scherman
D., Demeneix B., Behr J.-P., A versatile vector for gene andoligonucleotide transfer into cells in culture and in vivo:
Polyethylenimine, Proceedings of the National Academy of
Sciences, 92 (1995) 7297–7301.
Burnett J.C., Rossi J.J., RNA-based therapeutics: Current progress and future prospects, Chemistry & biology, 19 (2012)
60–71.
Burns A.R., Smith C.W., Walker D.C., Unique structural features that influence neutrophil emigration into the lung,2003.
Carvalho T.C., Peters J.I., Williams Iii R.O., Influence of particle size on regional lung deposition—what evidence isthere?, International Journal of Pharmaceutics, 406 (2011)
1–10.
Chakravarty R., Hong H., Cai W., Positron emission tomography image-guided drug delivery: Current status and future
perspectives, Molecular pharmaceutics, 11 (2014) 3777–
3797.
Chan H.-K., Formulation challenges: Protein powders for inhalation, Drugs and the Pharmaceutical Sciences, 126 (2003)
879–890.
Chan H.-K., Eberl S., Glover W., Radiolabeling of pharmaceutical aerosols and gamma scintigraphic imaging for lungdeposition, Encyclopedia of Pharmaceutical Technology,
3rd ed. Informa Healthcare, New York, (2007) 3094–3107.
Chapters U.G., USP <601> Aerosols, nasal sprays, metered-dose
inhalers and dry powder inhalers, USP 32-NF (2009).
Cherry S.R., Gambhir S.S., Use of positron emission tomography in animal research, ILAR journal, 42 (2001) 219–232.
Chiang P.-C., Alsup J.W., Lai Y., Hu Y., Heyde B.R., Tung D.,
Evaluation of aerosol delivery of nanosuspension forpre-clinical pulmonary drug delivery, Nanoscale researchletters, 4 (2009) 254–261.
Choi S.W., Lee S.H., Mok H., Park T.G., Multifunctional siRNAdelivery system: Polyelectrolyte complex micelles of sixarm PEG conjugate of siRNA and cell penetrating peptide
with crosslinked fusogenic peptide, Biotechnology progress, 26 (2010) 57–63.
Chougule M., Padhi B., Misra A., Development of spray driedliposomal dry powder inhaler of dapsone, AAPS PharmSciTech, 9 (2008) 47–53.
Codrons V., Vanderbist F., Verbeeck R.K., Arras M., Lison D.,
Préat V., Vanbever R., Systemic delivery of parathyroid
hormone (1–34) using inhalation dry powders in rats, Jour-78

nal of pharmaceutical sciences, 92 (2003) 938–950.
Comsa D.C., Farrell T.J., Patterson M.S., Quantitative fluorescence imaging of point-like sources in small animals, Physics in medicine and biology, 53 (2008) 5797.
Conde J., Tian F., Hernandez Y., Bao C., Cui D., Janssen K.P.,
Ibarra M.R., Baptista P.V., Stoeger T., De La Fuente J.M., In
vivo tumor targeting via nanoparticle-mediated therapeuticsiRNA coupled to inflammatory response in lung cancer
mouse models, Biomaterials, 34 (2013) 7744–7753.
Conway J., Lung imaging—two dimensional gamma scintigraphy, SPECT, CT and PET, Advanced drug delivery reviews,
64 (2012) 357–368.
Cryan S.-A., Sivadas N., Garcia-Contreras L., In vivo animalmodels for drug delivery across the lung mucosal barrier,Advanced drug delivery reviews, 59 (2007) 1133–1151.
Dailey L.A., Schmehl T., Gessler T., Wittmar M., Grimminger
F., Seeger W., Kissel T., Nebulization of biodegradable
nanoparticles: Impact of nebulizer technology and nanoparticle characteristics on aerosol features, Journal of Controlled Release, 86 (2003) 131–144.
Dalby R., Spallek M., Voshaar T., A review of the developmentof respimat® soft mistTM inhaler, International journal ofpharmaceutics, 283 (2004) 1–9.
Dames P., Gleich B., Flemmer A., Hajek K., Seidl N., WiekhorstF., Eberbeck D., Bittmann I., Bergemann C., Weyh T.,
Trahms L., Rosenecker J., Rudolph C., Targeted delivery ofmagnetic aerosol droplets to the lung, Nat Nanotechnol, 2(2007) 495–499.
Davies N.M., Feddah M.R., A novel method for assessing dissolution of aerosol inhaler products, International journal ofpharmaceutics, 255 (2003) 175–187.
Daviskas E., Anderson S.D., Jaques A., Charlton B., Inhaledmannitol improves the hydration and surface properties of
sputum in patients with cystic fibrosis, CHEST Journal, 137(2010) 861–868.
De Boer A.H., Gjaltema D., Hagedoorn P., Frijlink H.W., Characterization of inhalation aerosols: A critical evaluation ofcascade impactor analysis and laser diffraction technique,
Int J Pharm, 249 (2002) 219–231.
De Fougerolles A., Novobrantseva T., SiRNA and the lung:Research tool or therapeutic drug?, Current opinion inpharmacology, 8 (2008) 280–285.
De Fougerolles A., Vornlocher H.-P., Maraganore J., LiebermanJ., Interfering with disease: A progress report on siRNA-­
based therapeutics, Nat Rev Drug Discov, 6 (2007) 443–
453.
Denyer J., Dyche T., The adaptive aerosol delivery (AAD) technology: Past, present, and future, Journal of aerosol medicine and pulmonary drug delivery, 23 (2010) S-1–S-10.
Denyer J., Nikander K., Smith N., Adaptive aerosol delivery(AAD®) technology, Expert opinion on drug delivery, 1(2004) 165–176.
Devincenzo J., Cehelsky J.E., Alvarez R., Elbashir S., HarborthJ., Toudjarska I., Nechev L., Murugaiah V., Van Vliet A.,
Vaishnaw A.K., Evaluation of the safety, tolerability andpharmacokinetics of ALN-RSV01, a novel RNAi antiviraltherapeutic directed against respiratory syncytial virus(RSV), Antiviral research, 77 (2008) 225–231.

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Devincenzo J., Lambkin-Williams R., Wilkinson T., CehelskyJ., Nochur S., Walsh E., Meyers R., Gollob J., Vaishnaw A.,
A randomized, double-blind, placebo-controlled study ofan RNAi-based therapy directed against respiratory syncytial virus, Proceedings of the National Academy of Sciences, 107 (2010) 8800–8805.
Dhand R., Nebulizers that use a vibrating mesh or plate withmultiple apertures to generate aerosol, Respiratory care, 47(2002) 1406–1416; discussion 1416–1408.
Dickinson P.A., Howells S.W., Kellaway I.W., Novel nanoparticles for pulmonary drug administration, Journal of drug
targeting, 9 (2001) 295–302.
Dolovich M.B., 18f-fluorodeoxyglucose positron emission tomographic imaging of pulmonary functions, pathology, anddrug delivery, Proceedings of the American Thoracic Society, 6 (2009) 477–485.
Dolovich M.B., Dhand R., Aerosol drug delivery: Developments
in device design and clinical use, The Lancet, 377 (2011)
1032–1045.
Driscoll K.E., Costa D.L., Hatch G., Henderson R., Oberdorster
G., Salem H., Schlesinger R.B., Intratracheal instillation as
an exposure technique for the evaluation of respiratory
tract toxicity: Uses and limitations, Toxicological Sciences,
55 (2000) 24–35.
Dunbar C., Mitchell J., Analysis of cascade impactor mass distributions, Journal of Aerosol Medicine-Deposition Clearance and Effects in the Lung, 18 (2005) 439–451.
Eberl S., Chan H.-K., Daviskas E., SPECT imaging for radioaerosol deposition and clearance studies, Journal of aerosol
medicine, 19 (2006) 8–20.
Edwards D.A., Hanes J., Caponetti G., Hrkach J., Ben-Jebria A.,
Eskew M.L., Mintzes J., Deaver D., Lotan N., Langer R.,
Large porous particles for pulmonary drug delivery, Science, 276 (1997) 1868–1872.
Elhissi A., Karnam K.K., Danesh-Azari M.R., Gill H.S., Taylor
K.M., Formulations generated from ethanol-based proliposomes for delivery via medical nebulizers, Journal of pharmacy and pharmacology, 58 (2006) 887–894.
Elhissi A., Taylor K., Delivery of liposomes generated fromproliposomes using air-jet, ultrasonic, and vibrating-meshnebulisers, Journal of drug delivery science and technology,
15 (2005) 261–265.
Eshel G., Levy G., Mingelgrin U., Singer M., Critical evaluationof the use of laser diffraction for particle-size distribution
analysis, Soil Science Society of America Journal, 68(2004) 736–743.
Everard M.L., Studies using radiolabelled aerosols in children,
Thorax, 49 (1994) 1259–1266.
Feddah M.R., Brown K.F., Gipps E.M., Davies N.M., In-vitro
characterisation of metered dose inhaler versus dry powder
inhaler glucocorticoid products: Influence of inspiratory
flow rates, J Pharm Pharm Sci, 3 (2000) 318–324.
Fellmann C., Lowe S.W., Stable RNA interference rules forsilencing, Nature cell biology, 16 (2014) 10–18.
Fernandes C.A., Vanbever R., Preclinical models for pulmonary
drug delivery, Expert opinion on drug delivery, 6 (2009)1231–1245.
Ferrari S., Kitson C., Farley R., Steel R., Marriott C., Parkins D.,

Scarpa M., Wainwright B., Evans M., Colledge W., Mucus
altering agents as adjuncts for nonviral gene transfer to airway epithelium, Gene therapy, 8 (2001) 1380–1386.
Fire A., Xu S.Q., Montgomery M.K., Kostas S.A., Driver S.E.,
Mello C.C., Potent and specific genetic interference by
double-stranded RNA in caenorhabditis elegans, Nature,391 (1998) 806–811.
Fleming J., Conway J., Majoral C., Tossici-Bolt L., Katz I.,
Caillibotte G., Perchet D., Pichelin M., Muellinger B.,
Martonen T., The use of combined single photon emission
computed tomography and X-ray computed tomography toassess the fate of inhaled aerosol, Journal of aerosol me­
dicine and pulmonary drug delivery, 24 (2011) 49–60.
Frauenfelder T., Kestenholz P., Hunziker R., Nguyen T.D.L.,
Fries M., Veit-Haibach P., Husmann L., Stahel R., Weder
W., Opitz I., Use of computed tomography and positronemission tomography/computed tomography for staging of
local extent in patients with malignant pleural mesothelioma, Journal of computer assisted tomography, 39 (2015)
160–165.
Fulton A., Peters S.T., Perkins G.A., Jarosinski K.W., Damiani
A., Brosnahan M., Buckles E.L., Osterrieder N., Van De
Walle G.R., Effective treatment of respiratory alphaherpesvirus infection using RNA interference, PLoS One, 4(2009) e4118.
Garbuzenko O.B., Saad M., Betigeri S., Zhang M., Vetcher
A.A., Soldatenkov V.A., Reimer D.C., Pozharov V.P.,
Minko T., Intratracheal versus intravenous liposomal delivery of siRNA, antisense oligonucleotides and anticancer
drug, Pharmaceutical Research, 26 (2009) 382–394.
Ghosn B., Singh A., Li M., Vlassov A.V., Burnett C., Puri N.,
Roy K., Efficient gene silencing in lungs and liver using
imidazole-modified chitosan as a nanocarrier for smallinterfering RNA, Oligonucleotides, 20 (2010) 163–172.
Greene C.M., Mcelvaney N.G., Proteases and antiproteases inchronic neutrophilic lung disease-relevance to drug discovery, British journal of pharmacology, 158 (2009) 1048–
1058.
Groneberg D., Eynott P., Lim S., Oates T., Wu R., Carlstedt I.,
Roberts P., Mccann B., Nicholson A., Harrison B., Expression of respiratory mucins in fatal status asthmaticus andmild asthma, Histopathology, 40 (2002) 367–373.
Gunther M., Lipka J., Malek A., Gutsch D., Kreyling W., Aigner
A., Polyethylenimines for RNAi-mediated gene targeting invivo and siRNA delivery to the lung, Eur J Pharm Biopharm, 77 (2011) 438–449.
Gutbier B., Kube S.M., Reppe K., Santel A., Lange C.,
Kaufmann J., Suttorp N., Witzenrath M., RNAi-mediatedsuppression of constitutive pulmonary gene expression bysmall interfering RNA in mice, Pulmonary pharmacology& therapeutics, 23 (2010) 334–344.
Hannon G.J., RNA interference, Nature, 418 (2002) 244–251.
Hanson L.R., Frey W.H., 2nd, Intranasal delivery bypasses the
blood-brain barrier to target therapeutic agents to the central nervous system and treat neurodegenerative disease,
BMC Neurosci, 9 Suppl 3 (2008) S5.
Haynes J.M., Randomized controlled trial of a breath-activatednebulizer in patients with exacerbation of COPD, Respira-79

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

tory care, 57 (2012) 1385–1390.
Heyder J., Gebhart J., Rudolf G., Schiller C.F., Stahlhofen W.,
Deposition of particles in the human respiratory tract in thesize range 0.005–15 μm, Journal of Aerosol Science, 17(1986) 811–825.
Hibbitts A., O’mahony A.M., Forde E., Nolan L., OgierJ., Desgranges S., Darcy R., Macloughlin R., O’driscoll
C.M., Cryan S.A., Early-stage development of novel
cyclodextrin-siRNA nanocomplexes allows for successful
postnebulization transfection of bronchial epithelial cells,
J Aerosol Med Pulm Drug Deliv, 27 (2014) 466–477.
Hickey A.J., Pharmaceutical inhalation aerosol technology,
CRC Press, 2003.
Hickey A.J., Garcia-Contreras L., Immunological and toxicological implications of short-term studies in animals ofpharmaceutical aerosol delivery to the lungs: Relevance tohumans, Critical ReviewsTM in Therapeutic Drug Carrier
Systems, 18 (2001).
Hill D.B., Vasquez P.A., Mellnik J., Mckinley S.A., Vose A.,
Mu F., Henderson A.G., Donaldson S.H., Alexis N.E.,
Boucher R.C., A biophysical basis for mucus solids concentration as a candidate biomarker for airways disease, PloS
one, 9 (2014) e87681.
Hinchcliffe M., Illum L., Intranasal insulin delivery and therapy, Adv Drug Deliv Rev, 35 (1999) 199–234.
Ho S.L., Kwong W.J., O’drowsky L., Coates A.L., Evaluation offour breath-enhanced nebulizers for home use, Journal ofaerosol medicine, 14 (2001) 467–475.
Hochrainer D., Hölz H., Kreher C., Scaffidi L., Spallek M.,
Wachtel H., Comparison of the aerosol velocity and spray
duration of respimat® soft mistTM inhaler and pressurized
metered dose inhalers, Journal of aerosol medicine, 18(2005) 273–282.
Hosoya K., Satoh T., Yamamoto Y., Saeki K., Igawa K., Okano
M., Moriya T., Imamura O., Nemoto Y., Yokozeki H., Genesilencing of STAT6 with siRNA ameliorates contact hypersensitivity and allergic rhinitis, Allergy, 66 (2011) 124–131.
Howard K.A., Rahbek U.L., Liu X., Damgaard C.K., Glud S.Z.,
Andersen M.O., Hovgaard M.B., Schmitz A., Nyengaard
J.R., Besenbacher F., Kjems J., RNA interference in vitro
and in vivo using a novel chitosan/siRNA nanoparticle system, Mol Ther, 14 (2006) 476–484.
Huchon G.J., Montgomery A.B., Lipavsky A., Hoeffel J.M.,
Murray J.F., Respiratory clearance of aerosolized radioactive solutes of varying molecular weight, Journal of nuclear
medicine: official publication, Society of Nuclear Medicine,28 (1987) 894–902.
Kamei N., Takeda-Morishita M., Brain delivery of insulin
boosted by intranasal coadministration with cell-penetrating
peptides, J Control Release, 197 (2015) 105–110.
Kanasty R., Dorkin J.R., Vegas A., Anderson D., Delivery
materials for siRNA therapeutics, Nature materials, 12(2013) 967–977.
Khalil I.A., Kogure K., Akita H., Harashima H., Uptake pathways and subsequent intracellular trafficking in nonviralgene delivery, Pharmacological reviews, 58 (2006) 32–45.
Kim D.H., Rossi J.J., Strategies for silencing human diseaseusing RNA interference, Nature Reviews Genetics, 880(2007) 173–184.
Kishore U., Greenhough T.J., Waters P., Shrive A.K., Ghai R.,
Kamran M.F., Bernal A.L., Reid K.B., Madan T.,
Chakraborty T., Surfactant proteins SP-A and SP-D: Structure, function and receptors, Mol Immunol, 43 (2006)
1293–1315.
Knowles M.R., Boucher R.C., Mucus clearance as a primaryinnate defense mechanism for mammalian airways, The
Journal of clinical investigation, 109 (2002) 571–577.
Kolaczkowska E., Kubes P., Neutrophil recruitment and function in health and inflammation, Nat Rev Immunol, 13(2013) 159–175.
Komlosi P., Altes T.A., Qing K., Mooney K.E., Miller G.W., Mata
J.F., De Lange E.E., Tobias W.A., Cates G.D., Brookeman
J.R., Regional anisotropy of airspace orientation in the lungas assessed with hyperpolarized helium-3 diffusion MRI,
Journal of Magnetic Resonance Imaging (2015).
Koping-Hoggard M., Issa M.M., Kohler T., Tronde A., Varum
K.M., Artursson P., A miniaturized nebulization catheter
for improved gene delivery to the mouse lung, J Gene Med,
7 (2005) 1215–1222.
Kovar J.L., Simpson M.A., Schutz-Geschwender A., Olive D.M.,
A systematic approach to the development of fluorescentcontrast agents for optical imaging of mouse cancer models, Analytical biochemistry, 367 (2007) 1–12.
Kruger S.J., Niles D.J., Dardzinski B., Harman A., Jarjour N.N.,
Ruddy M., Nagle S.K., Francois C.J., Sorkness R.L., Burton
R.M., Hyperpolarized helium-3 MRI of exercise-induced
bronchoconstriction during challenge and therapy, Journalof Magnetic Resonance Imaging, 39 (2014) 1230–1237.
Kuroki Y., Takahashi M., Nishitani C., Pulmonary collectins ininnate immunity of the lung, Cellular microbiology, 9(2007) 1871–1879.
Kusumoto K., Akita H., Ishitsuka T., Matsumoto Y., Nomoto T.,
Furukawa R., El-Sayed A., Hatakeyama H., Kajimoto K.,
Yamada Y., Kataoka K., Harashima H., Lipid envelope-type
nanoparticle incorporating a multifunctional peptide forsystemic siRNA delivery to the pulmonary endothelium,
ACS Nano, 7 (2013) 7534–7541.
Lam J.K.-W., Liang W., Chan H.-K., Pulmonary delivery oftherapeutic siRNA, Advanced drug delivery reviews, 64(2012) 1–15.
Laube B.L., The expanding role of aerosols in systemic drugdelivery, gene therapy and vaccination: An update, Translational Respiratory Medicine, 2 (2014) 1–12.
Lebhardt T., Roesler S., Beck-Broichsitter M., Kissel T., Polymeric nanocarriers for drug delivery to the lung, Journal of
Drug Delivery Science and Technology, 20 (2010) 171–180.
Lee Black D., Mcquay M.Q., Bonin M.P., Laser-based techniques for particle-size measurement: A review of sizing
methods and their industrial applications, Progress in
Energy and Combustion Science, 22 (1996) 267–306.
Lee S.H., Kim S.H., Park T.G., Intracellular siRNA delivery
system using polyelectrolyte complex micelles preparedfrom VEGF siRNA-PEG conjugate and cationic fusogenic
peptide, Biochemical and biophysical research communications, 357 (2007) 511–516.
Lentz Y.K., Anchordoquy T.J., Lengsfeld C.S., Rationale for theSusanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

selection of an aerosol delivery system for gene delivery,
Journal of aerosol medicine, 19 (2006) 372–384.
Li B.-J., Tang Q., Cheng D., Qin C., Xie F.Y., Wei Q., Xu J., Liu
Y., Zheng B.-J., Woodle M.C., Using siRNA in prophylactic
and therapeutic regimens against SARS coronavirus in rhesus macaque, Nature medicine, 11 (2005) 944–951.
Luo Y., Zhai X., Ma C., Sun P., Fu Z., Liu W., Xu J., An inhalable beta(2)-adrenoceptor ligand-directed guanidinylated
chitosan carrier for targeted delivery of siRNA to lung, J
Control Release, 162 (2012) 28–36.
Mainelis G., Seshadri S., Garbuzenko O., Han T., Wang Z.,
Minko T., Characterization and application of a nose-only
exposure chamber for inhalation delivery of liposomal
drugs and nucleic acids to mice, Journal of aerosol medicine and pulmonary drug delivery, 26 (2013) 345–354.
Majoral C., Fleming J., Conway J., Katz I., Tossici-Bolt L.,
Pichelin M., Montesantos S., Caillibotte G., Controlled,parametric, individualized, 2D and 3D imaging measurements of aerosol deposition in the respiratory tract ofhealthy human volunteers: In vivo data analysis, Journal ofaerosol medicine and pulmonary drug delivery, 27 (2014)
349–362.
Mansour H.M., Rhee Y.-S., Wu X., Nanomedicine in pulmonary
delivery, International Journal of Nanomedicine, 4 (2009)299–319.
Marsh M., Endocytosis (2001).
Martin A.R., Thompson R.B., Finlay W.H., MRI measurementof regional lung deposition in mice exposed nose-only tonebulized superparamagnetic iron oxide nanoparticles,
Journal of aerosol medicine and pulmonary drug delivery,
21 (2008) 335–342.
Massaro D., Massaro G.D., Clerch L.B., Noninvasive deliveryof small inhibitory RNA and other reagents to pulmonary
alveoli in mice, American Journal of Physiology-Lung Cellular and Molecular Physiology, 287 (2004) L1066–L1070.
Mastrandrea L.D., Quattrin T., Clinical evaluation of inhaledinsulin, Advanced drug delivery reviews, 58 (2006) 1061–
1075.
Maxfield F.R., Mcgraw T.E., Endocytic recycling, Nature
reviews Molecular cell biology, 5 (2004) 121–132.
Mellnik J., Vasquez P.A., Mckinley S.A., Witten J., Hill D.B.,
Forest M.G., Micro-heterogeneity metrics for diffusion insoft matter, Soft matter, 10 (2014) 7781–7796.
Merkel O.M., Beyerle A., Librizzi D., Pfestroff A., Behr T.M.,
Sproat B., Barth P.J., Kissel T., Nonviral siRNA delivery to
the lung: Investigation of PEG-PEI polyplexes and their invivo performance, Molecular pharmaceutics, 6 (2009)
1246–1260.
Merkel O.M., Rubinstein I., Kissel T., SiRNA delivery to thelung: What’s new?, Adv Drug Deliv Rev, 75 (2014) 112–
128.
Merkel O.M., Zheng M., Debus H., Kissel T., Pulmonary genedelivery using polymeric nonviral vectors, Bioconjug
Chem, 23 (2012) 3–20.
Merkus H.G., Particle size measurements: Fundamentals, practice, quality, Springer Science & Business Media, 2009.
Minne A., Louahed J., Mehauden S., Baras B., Renauld J.C.,
Vanbever R., The delivery site of a monovalent influenza

vaccine within the respiratory tract impacts on the immune
response, Immunology, 122 (2007) 316–325.
Mitchell J.P., Nagel M.W., Cascade impactors for the size characterization of aerosols from medical inhalers: Their uses
and limitations, Journal of Aerosol Medicine-Deposition
Clearance and Effects in the Lung, 16 (2003) 341–+.
Mitchell J.P., Nagel M.W., Doyle C.C., Ali R.S., Avvakoumova
V.I., Christopher J.D., Quiroz J., Strickland H., Tougas T.,
Lyapustina S., Relative precision of inhaler aerodynamic
particle size distribution (APSD) metrics by full resolution
and abbreviated andersen cascade impactors (ACIs): Part 1,
Aaps Pharmscitech, 11 (2010) 843–851.
Morello M., Krone C.L., Dickerson S., Howerth E., Germishuizen
W.A., Wong Y.-L., Edwards D., Bloom B.R., Hondalus
M.K., Dry-powder pulmonary insufflation in the mouse forapplication to vaccine or drug studies, Tuberculosis, 89(2009) 371–377.
Morén F., Aerosols in medicine: Principles, diagnosis, and therapy, Elsevier Science Limited, 1993.
Morita T., Niwata Y., Ohgi K., Ogawa M., Irie M., Distributionof two urinary ribonuclease-like enzymes in human organs
and body fluids, J Biochem, 99 (1986) 17–25.
Mosbah K., Ruiz-Cabello J., Berthezene Y., Cremillieux Y.,
Aerosols and gaseous contrast agents for magnetic resonance imaging of the lung, Contrast media & molecularimaging, 3 (2008) 173–190.
Moschos S.A., Jones S.W., Perry M.M., Williams A.E., Erjefalt
J.S., Turner J.J., Barnes P.J., Sproat B.S., Gait M.J., Lindsay
M.A., Lung delivery studies using siRNA conjugated to
TAT (48–60) and penetratin reveal peptide induced reduction in gene expression and induction of innate immunity,
Bioconjugate chemistry, 18 (2007) 1450–1459.
Mühlfeld C., Rothen-Rutishauser B., Blank F., Vanhecke D.,
Ochs M., Gehr P., Interactions of nanoparticles with pulmonary structures and cellular responses, American Journal of
Physiology-Lung Cellular and Molecular Physiology, 294(2008) L817–L829.
Murata M., Tahara K., Takeuchi H., Real-time in vivo imaging
of surface-modified liposomes to evaluate their behavior
after pulmonary administration, European Journal of Pharmaceutics and Biopharmaceutics, 86 (2014) 115–119.
Murata N., Takashima Y., Toyoshima K., Yamamoto M., Okada
H., Anti-tumor effects of anti-VEGF siRNA encapsulatedwith PLGA microspheres in mice, Journal of Controlled
Release, 126 (2008) 246–254.
Nahar K., Gupta N., Gauvin R., Absar S., Patel B., Gupta V.,
Khademhosseini A., Ahsan F., In vitro, in vivo and ex vivo
models for studying particle deposition and drug absorptionof inhaled pharmaceuticals, Eur J Pharm Sci, 49 (2013)
805–818.
Najlah M., Parveen I., Alhnan M.A., Ahmed W., Faheem A.,
Phoenix D.A., Taylor K.M., Elhissi A., The effects of suspension particle size on the performance of air-jet, ultrasonic and vibrating-mesh nebulisers, Int J Pharm, 461(2014) 234–241.
Newman S., Wilding I., Gamma scintigraphy: An in vivo technique for assessing the equivalence of inhaled products,
International journal of pharmaceutics, 170 (1998) 1–9.

81

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Newman S.P., Pitcairn G.R., Hirst P.H., A brief history of
gamma scintigraphy, Journal of Aerosol Medicine, 14(2001) 139–145.
Newman S.P., Pitcairn G.R., Hirst P.H., Rankin L., Radionuclide
imaging technologies and their use in evaluating asthma
drug deposition in the lungs, Advanced drug delivery
reviews, 55 (2003) 851–867.
Newman S.P., Wilding I.R., Imaging techniques for assessing
drug delivery in man, Pharmaceutical science & technology
today, 2 (1999) 181–189.
Newman S.P., Wilding I.R., Hirst P.H., Human lung deposition
data: The bridge between in vitro and clinical evaluations
for inhaled drug products?, International journal of pharmaceutics, 208 (2000) 49–60.
Nichols S.C., Mitchell J.P., Shelton C.M., Roberts D.L., Good
cascade impactor practice (GCIP) and considerations for“in-use” specifications, Aaps Pharmscitech, 14 (2013) 375–
390.
Nielsen E.J., Nielsen J.M., Becker D., Karlas A., Prakash H.,
Glud S.Z., Merrison J., Besenbacher F., Meyer T.F., Kjems
J., Howard K.A., Pulmonary gene silencing in transgenic
EGFP mice using aerosolised chitosan/siRNA nanoparticles, Pharm Res, 27 (2010) 2520–2527.
Ntziachristos V., Culver J.P., Rice B.W., Small-animal opticalimaging, Journal of biomedical optics, 13 (2008) 011001011001-011001.
O’shaughnessy P.T., Achutan C., O’neill M.E., Thorne P.S., A
small whole-body exposure chamber for laboratory use,
Inhalation toxicology, 15 (2003) 251–263.
Oliveira S., Van Rooy I., Kranenburg O., Storm G., Schiffelers
R.M., Fusogenic peptides enhance endosomal escape
improving siRNA-induced silencing of oncogenes, International journal of pharmaceutics, 331 (2007) 211–214.
Padhi B.K., Chougule M.B., Misra A., Aerosol performance oflarge respirable particles of amikacin sulfate produced byspray and freeze drying techniques, Curr Drug Deliv, 6(2009) 8–16.
Park C.-W., Rhee Y.-S., Vogt F.G., Hayes D., Zwischenberger
J.B., Deluca P.P., Mansour H.M., Advances in microscopy
and complementary imaging techniques to assess the fateof drugs ex vivo in respiratory drug delivery: An invited
paper, Advanced drug delivery reviews, 64 (2012) 344–356.
Parton R.G., Richards A.A., Lipid rafts and caveolae as portals
for endocytosis: New insights and common mechanisms,
Traffic, 4 (2003) 724–738.
Patel B., Gupta N., Ahsan F., Particle engineering to enhance orlessen particle uptake by alveolar macrophages and to
influence the therapeutic outcome, European Journal of
Pharmaceutics and Biopharmaceutics, 89 (2015) 163–174.
Patel G., Chougule M., Singh M., Misra A., Nanoliposomal dry
powder formulations, Methods in enzymology, 464 (2009)
167–191.
Patton J.S., Mechanisms of macromolecule absorption by thelungs, Advanced Drug Delivery Reviews, 19 (1996) 3–36.
Patton J.S., Brain J.D., Davies L.A., Fiegel J., Gumbleton M.,
Kim K.J., Sakagami M., Vanbever R., Ehrhardt C., Theparticle has landed-characterizing the fate of inhaled pharmaceuticals, J Aerosol Med Pulm Drug Deliv, 23 Suppl 282(2010) S71–87.
Perl M., Chung C.-S., Lomas-Neira J., Rachel T.-M., Biffl W.L.,
Cioffi W.G., Ayala A., Silencing of Fas, but not caspase-8,
in lung epithelial cells ameliorates pulmonary apoptosis,
inflammation, and neutrophil influx after hemorrhagic
shock and sepsis, The American journal of pathology, 167(2005) 1545–1559.
Perring S., Summers Q., Fleming J., Nassim M., Holgate S., A
new method of quantification of the pulmonary regional
distribution of aerosols using combined CT and SPECT and
its application to nedocromil sodium administered bymetered dose inhaler, The British journal of radiology, 67(1994) 46–53.
Pettis R.J., Hall I., Costa D., Hickey A.J., Aerosol delivery ofmuramyl dipeptide to rodent lungs, AAPS PharmSci, 2(2000) 53–61.
Phelps M.E., Positron emission tomography provides molecularimaging of biological processes, Proceedings of the
National Academy of Sciences, 97 (2000) 9226–9233.
Possmayer F., Nag K., Rodriguez K., Qanbar R., Schürch S.,
Surface activity in vitro: Role of surfactant proteins, Comparative Biochemistry and Physiology Part A: Molecular &Integrative Physiology, 129 (2001) 209–220.
Rawat A., Majumder Q.H., Ahsan F., Inhalable large porous
microspheres of low molecular weight heparin: In vitro andin vivo evaluation, Journal of Controlled Release, 128(2008) 224–232.
Reischl D., Zimmer A., Drug delivery of siRNA therapeutics:
Potentials and limits of nanosystems, Nanomedicine: Nanotechnology, Biology and Medicine, 5 (2009) 8–20.
Rosas-Taraco A.G., Higgins D.M., Sánchez-Campillo J., Lee
E.J., Orme I.M., González-Juarrero M., Intrapulmonary
delivery of XCL1-targeting small interfering RNA in micechronically infected with mycobacterium tuberculosis,
American journal of respiratory cell and molecular biology,
41 (2009) 136–145.
Rose M.C., Nickola T.J., Voynow J.A., Airway mucus obstruction: Mucin glycoproteins, MUC gene regulation and goblet
cell hyperplasia, American Journal of Respiratory Cell andMolecular Biology, 25 (2001) 533–537.
Ross D.L., Schultz R.K., Effect of inhalation flow rate on thedosing characteristics of dry powder inhaler (DPI) andmetered dose inhaler (MDI) products, Journal of aerosol
medicine, 9 (1996) 215–226.
Roy I., Vij N., Nanodelivery in airway diseases: Challenges andtherapeutic applications, Nanomedicine, 6 (2010) 237–244.
Rubin B.K., Physiology of airway mucus clearance, Respir
Care, 47 (2002) 761–768.
Rytting E., Nguyen J., Wang X., Kissel T., Biodegradable polymeric nanocarriers for pulmonary drug delivery, Expert
Opin Drug Deliv, 5 (2008) 629–639.
Saha G.B., Macintyre W.J., Go R.T., Cyclotrons and positronemission tomography radiopharmaceuticals for clinicalimaging, Seminars in nuclear medicine, Elsevier, 1992.
Sakagami M., In vivo, in vitro and ex vivo models to assess pulmonary absorption and disposition of inhaled therapeutics
for systemic delivery, Advanced drug delivery reviews, 58(2006) 1030–1060.

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Sanders N., Rudolph C., Braeckmans K., De Smedt S.C.,
Demeester J., Extracellular barriers in respiratory genetherapy, Advanced drug delivery reviews, 61 (2009) 115–
127.
Schuster B.S., Suk J.S., Woodworth G.F., Hanes J., Nanoparticle
diffusion in respiratory mucus from humans without lungdisease, Biomaterials, 34 (2013) 3439–3446.
Seaton B.A., Crouch E.C., Mccormack F.X., Head J.F., Hartshorn
K.L., Mendelsohn R., Structural determinants of patternrecognition by lung collectins, Innate Immunity (2010).
Senoo T., Hattori N., Tanimoto T., Furonaka M., Ishikawa N.,
Fujitaka K., Haruta Y., Murai H., Yokoyama A., Kohno N.,
Suppression of plasminogen activator inhibitor-1 by RNA
interference attenuates pulmonary fibrosis, Thorax, 65(2010) 334–340.
Seow W.Y., Liang K., Kurisawa M., Hauser C.A., Oxidation asa facile strategy to reduce the surface charge and toxicity ofpolyethyleneimine gene carriers, Biomacromolecules, 14(2013) 2340–2346.
Shah V.P., Smurthwaite M.J., Veranth J.M., Zaidi K., The inhalation ad hoc advisory panel for the USP performance tests
of inhalation dosage forms (2008).
Sharma K., Somavarapu S., Colombani A., Govind N., Taylor
K.M., Nebulised siRNA encapsulated crosslinked chitosan
nanoparticles for pulmonary delivery, Int J Pharm, 455(2013) 241–247.
Sharma K., Somavarapu S., Colombani A., Govind N., Taylor
K.M.G., Crosslinked chitosan nanoparticle formulations fordelivery from pressurized metered dose inhalers, European
Journal of Pharmaceutics and Biopharmaceutics, 81 (2012)
74–81.
Shoyele S.A., Slowey A., Prospects of formulating proteins/
peptides as aerosols for pulmonary drug delivery, International journal of pharmaceutics, 314 (2006) 1–8.
Sinsuebpol C., Chatchawalsaisin J., Kulvanich P., Preparationand in vivo absorption evaluation of spray dried powders
containing salmon calcitonin loaded chitosan nanoparticles
for pulmonary delivery, Drug Design, Development andTherapy, 7 (2013) 861–873.
Snell N., Ganderton D., Assessing lung deposition of inhaled
medications: Consensus statement from a workshop of the
British Association for Lung Research, held at the Instituteof Biology, London, U.K. on 17 April 1998, Respiratory
medicine, 93 (1999) 123–133.
Son Y.J., Mcconville J.T., Development of a standardized dissolution test method for inhaled pharmaceutical formulations,
Int J Pharm, 382 (2009) 15–22.
Sood B.G., Shen Y., Latif Z., Chen X., Sharp J., Neelavalli J.,
Joshi A., Slovis T.L., Haacke E., Aerosol delivery in ventilated newborn pigs: An MRI evaluation, Pediatric research,
64 (2008) 159–164.
Sood B.G., Shen Y., Latif Z., Galli B., Dawe E.J., Haacke E.M.,
Effective aerosol delivery during high-frequency ventilation in neonatal pigs, Respirology, 15 (2010) 551–555.
Sorrentino S., Human extracellular ribonucleases: Multiplicity,
molecular diversity and catalytic properties of the major
RNase types, Cell Mol Life Sci, 54 (1998) 785–794.
Taratula O., Dmochowski I.J., Functionalized 129Xe contrast

agents for magnetic resonance imaging, Current opinion inchemical biology, 14 (2010) 97–104.
Taylor K.M., Mccallion O.N., Ultrasonic nebulisers for pulmonary drug delivery, International journal of pharmaceutics,
153 (1997) 93–104.
Thomas C.E., Ehrhardt A., Kay M.A., Progress and problems
with the use of viral vectors for gene therapy, Nature
Reviews Genetics, 4 (2003) 346–358.
Thompson R.B., Finlay W.H., Using MRI to measure aerosol
deposition, Journal of aerosol medicine and pulmonary
drug delivery, 25 (2012) 55–62.
Tompkins S.M., Lo C.-Y., Tumpey T.M., Epstein S.L., Protection against lethal influenza virus challenge by RNA interference in vivo, Proceedings of the National Academy of
Sciences of the United States of America, 101 (2004) 8682–
8686.
Tronde A., Baran G., Eirefelt S., Lennernäs H., Bengtsson U.H.,
Miniaturized nebulization catheters: A new approach fordelivery of defined aerosol doses to the rat lung, Journal ofaerosol medicine, 15 (2002) 283–296.
Turner P.V., Brabb T., Pekow C., Vasbinder M.A., Administration of substances to laboratory animals: Routes of administration and factors to consider, Journal of the American
Association for Laboratory Animal Science: JAALAS, 50(2011) 600–613.
Wagner A., Vorauer-Uhl K., Katinger H., Nebulization of liposomal rh-Cu/Zn-SOD with a novel vibrating membrane
nebulizer, Journal of liposome research, 16 (2006) 113–125.
Waldrep J., Dhand R., Advanced nebulizer designs employing
vibrating mesh/aperture plate technologies for aerosol generation, Current Drug Delivery, 5 (2008) 114–119.
Wang H., Sebrié C., Ruaud J.P., Guillot G., Bouazizi-Verdier K.,
Willoquet G., Maître X., Darrasse L., De Rochefort L.,
Aerosol deposition in the lungs of spontaneously breathingrats using Gd-DOTA-based contrast agents and ultra-shortecho time MRI at 1.5 tesla, Magnetic Resonance in Medicine, (2015).
Wang J.-C., Lai S., Guo X., Zhang X., De Crombrugghe B.,
Sonnylal S., Arnett F.C., Zhou X., Attenuation of fibrosis invitro and in vivo with SPARC siRNA, Arthritis research &therapy, 12 (2010) R60.
Watts A.B., Mcconville J.T., Williams Iii R.O., Current therapies and technological advances in aqueous aerosol drugdelivery, Drug development and industrial pharmacy, 34(2008) 913–922.
Weinberger B., Sinko P.J., Laskin J.D., Laskin D.L., Interaction
with lung macrophages, Nanoparticles in the Lung: Environmental Exposure and Drug Delivery, (2014) 85.
Xenariou S., Griesenbach U., Ferrari S., Dean P., Scheule R.,
Cheng S., Geddes D., Plank C., Alton E., Using magnetic
forces to enhance non-viral gene transfer to airway epithelium in vivo, Gene therapy, 13 (2006) 1545–1552.
Xenariou S., Griesenbach U., Liang H., Zhu J., Farley R.,
Somerton L., Singh C., Jeffery P., Ferrari S., Scheule R.,
Use of ultrasound to enhance nonviral lung gene transfer invivo, Gene therapy, 14 (2007) 768–774.
Yacobi N.R., Malmstadt N., Fazlollahi F., Demaio L.,
Marchelletta R., Hamm-Alvarez S.F., Borok Z., Kim K.-J.,83

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Crandall E.D., Mechanisms of alveolar epithelial translocation of a defined population of nanoparticles, American
journal of respiratory cell and molecular biology, 42 (2010)
604–614.
Yang W., Peters J.I., Williams R.O., 3rd, Inhaled nanoparticles—a current review, Int J Pharm, 356 (2008) 239–247.
Yi D., Price A., Panoskaltsis-Mortari A., Naqwi A., Wiedmann
T.S., Measurement of the distribution of aerosols amongmouse lobes by fluorescent imaging, Analytical biochemistry, 403 (2010) 88–93.
Yoon S.H., Goo J.M., Lee S.M., Park C.M., Seo H.J., Cheon
G.J., Positron emission tomography/magnetic resonanceimaging evaluation of lung cancer: Current status andfuture prospects, Journal of thoracic imaging, 29 (2014)
4–16.
Yoshiyama Y., Yazaki T., Arai M., Asai K., Kanke M., The nebulization of budesonide suspensions by a newly designed
mesh nebulizer, Respiratory drug delivery VIII. Raleigh:Davis Horwood, (2002) 487–489.
Zamora-Avila D., Zapata-Benavides P., Franco-Molina M.,
Saavedra-Alonso S., Trejo-Avila L., Resendez-Perez D.,

84

Mendez-Vazquez J., Isaias-Badillo J., Rodriguez-Padilla C.,
WT1 gene silencing by aerosol delivery of PEI–RNAi complexes inhibits B16-F10 lung metastases growth, Cancer
gene therapy, 16 (2009) 892–899.
Zamora M.R., Budev M., Rolfe M., Gottlieb J., Humar A.,
Devincenzo J., Vaishnaw A., Cehelsky J., Albert G.,
Nochur S., Gollob J.A., Glanville A.R., RNA interference
therapy in lung transplant patients infected with respiratorysyncytial virus, Am J Respir Crit Care Med, 183 (2011)
531–538.
Zhang J., Wu L., Chan H.-K., Watanabe W., Formation, characterization, and fate of inhaled drug nanoparticles, Advanced
drug delivery reviews, 63 (2011) 441–455.
Zhang X., Shan P., Jiang D., Noble P.W., Abraham N.G.,
Kappas A., Lee P.J., Small interfering RNA targeting heme
oxygenase-1 enhances ischemia-reperfusion-induced lungapoptosis, Journal of Biological Chemistry, 279 (2004)
10677–10684.
Zhao F., Zhao Y., Liu Y., Chang X., Chen C., Zhao Y., Cellular
uptake, intracellular trafficking, and cytotoxicity of nanomaterials, Small, 7 (2011) 1322–1337.

Susanne R. Youngren-Ortiz et al. / KONA Powder and Particle Journal No. 33 (2016) 63–85

Author’s short biography
Susanne R. Youngren-Ortiz
Susanne R. Youngren-Ortiz is a Ph.D. candidate the Daniel K. Inouye College of
Pharmacy of the University of Hawai’i at Hilo. She obtained her Bachelor of Science
Degree in Pharmaceutical Sciences with specialization in Industrial and Physical Pharmacy and minors in Chemistry and Statistics from Purdue University. Her dissertation
research project investigates the optimal delivery and the evaluation of cellular effects oftherapeutic nanocarriers for the treatment of asthma. She has authored original researchand review articles. She is an active member of the American Association of Pharmaceutical Scientists and has chaired the University of Hawaii Student Chapter.

Nishant S. Gandhi
Nishant S. Gandhi is a Ph.D. candidate at the Daniel K. Inouye College of Pharmacy
UH Hilo. He has completed his B.Pharm and M.Pharm from University of Pune in India.
He has also worked as a chemist at Aristo Pharmaceuticals in Bhopal, India. He is currently working on developing novel nanotechnology based drug delivery systems for thetreatment of lung cancer. He has authored publications including original research articles, review articles and book chapters. He is currently serving as chair of American Association of Pharmaceutical Scientists (DKICP chapter) and is currently a member ofprofessional pharmacy fraternity “Phi Delta Chi”.

Laura España-Serrano
Dr. Laura España-Serrano is a research associate in the Translational Drug Delivery
Research laboratory in the department of Pharmaceutical Sciences of the Daniel K. Inouye College of Pharmacy at the UH Hilo. She obtained her PhD in Biochemistry andMolecular Biology from the Complutense University of Madrid, Spain. She has nine
years of experience in the fields of Cellular and Molecular Biology of Cancer and Psychiatric diseases. She has authored original research publications and review articles, andshe is a member of the National Postdoctoral Association and the American Associationof Pharmaceutical Scientists.

Mahavir B. Chougule
Mahavir B. Chougule, Associate Professor of the Department of Pharmaceutical Sciences, investigates the use of therapeutic agent, siRNA, and targeted nanotechnology
based formulations for treatment of lung cancer and asthma. Chougule has 25 publications, 8 patents, 3 review articles, and 5 book chapters. He delivered 16 invited talks and
63 scientific presentations at the international conferences. He served as a grant reviewer
on the NIH, DOD, and international review panels. He is also serving as a member ofeditorial board on 5 international journals. Dr. Chougule is a recipient of several awards
including recent American Association of Cancer Research Minority-Serving Institution
Faculty Scholar in Cancer Research Award. He is an active member of American Association of Cancer Research and American Association of Pharmaceutical Scientists.

85

KONA Powder and Particle Journal No. 33 (2016) 86–108/Doi:10.14356/kona.2016017

Review Paper

Effect of Process Conditions on Fluidization †Paola Lettieri * and Domenico Macrì
1

Department of Chemical Engineering, University College London, UK

Abstract
The influence of process conditions such as temperature and the presence of fines on the fluidization behaviour ofgas-fluidized beds is of major importance in industrial fluid bed processes, which are often operated attemperatures well above ambient and where it is common practice to add fine particles to improve the reactor
performance. Several works have demonstrated that process conditions can influence the role of the interparticle
forces in the fluidization behaviour of powders. In particular, the beneficial effect on fluidization of adding fines
to the bulk of the material is well known.
The objective of this paper is to review experimental and theoretical studies of gas-solids fluidized beds operated
at high temperature and the effect of fines and fines distribution within the bed. The survey begins with a reviewof the effect of temperature on fundamental fluidization parameters such as minimum fluidization, bed expansion
and deaeration, and the role of hydrodynamic and interparticle forces at ambient conditions and high temperature
is discussed. The effect of temperature and fines and fines size distribution on the dynamics of gas-fluidized beds
is considered next, highlighting areas of current gaps in knowledge. Given the complexity of the phenomena
involved, a direct quantification of the particle-particle interactions in fluidized beds and of their changes underprocess conditions is very difficult. The review concludes by touching upon powder rheology as a methodology toevaluate indirectly the effects of the IPFs on fluidization. This leads to a review of the work done at UCL on
linking rheological measurements to fluidization tests in the attempt to quantify the effect of process conditions,i.e. high temperature and the effect of fines on fluidization.
Keywords: fluidization, high temperature, fines particle fraction, powder rheology

1. Introduction
Fluidization is the operation by which solid particles
are transformed into a fluid-like state through suspension
in a gas or liquid (Kunii and Levenspiel, 1991). Fluidized
beds can be considered the most powerful method tohandle a variety of solid particulate materials in industry(Horio, 2013).
Fluidized beds are widely applied in industry for theirability to provide a high heat transfer rate and rapid solids
mixing which lead to isothermal conditions in the particle
bed, and high heat and mass transfer rates between gas
and particles. Whenever a chemical reaction employing aparticulate solid as a reactant or as a catalyst requires reliable temperature control, a fluidized bed reactor is often
the choice for ensuring nearly isothermal conditions bysuitable selection of the operating conditions.
Fluidization was created as an industrial operation in
†
1

*

86

Received 8 June 2015; Accepted 4 August 2015
J-STAGE Advance Published date: 10 October 2015
Torrington Place, London WC1E 7JE, UK
Corresponding author: Paola Lettieri;
E-mail: p.lettieri@ucl.ac.uk
TEL: +44 20 7679-7867 FAX: +44-20-7679-7092

the Winkler process for the gasification of lignite and the
Ignifluid process for the combustion of coal, but it was
only with introduction in the 1940s of the FCC processthat its true potential became apparent.
It was in 1942 when a desperate need for aviation fuelduring the Second World War brought together a group ofoil companies which included Standard Oil Indiana (later
Amoco and now BP Amoco), M.W. Kellogg, Shell and theStandard Oil Development Company (now Exxon) to design the first fluidized solids process for catalytic cracking. Fluidization started as an innovative and successful
oil refining technology, revealing the potential of thetechnique for gas-solids reaction engineering. Since thenit has been applied in many other areas, becoming afirmly established technique in the processing industries.
The applications are spread throughout the chemicals, environmental, energy, nuclear, petrochemical, pharmaceuticals, and process industries. Some examples of industrial
applications are presented in Table 1.
In addition to this, fluidized bed technology has witnessed a resurgence of interest particularly from the pointof view of clean energy generation over the last two decades. Fluidized beds are employed for the combustionand gasification of solid fuels (coal, wastes and biomass),©2016 Hosokawa Powder Technology Foundation

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Table 1

Some industrial applications of fluidized beds

Process

ExampleCracking of hydrocarbons

FCC

Process conditionsaEthylene and propylene
Chemical synthesis

Acrylonitrilebb

Melaminea
Maleic anhydride (Mitsubishi Chem. Ind.)

1–3

10–30

750

~1

10–30

400–500

1.5–3

20–40

400

>1

—

410–420

1–5

25–55
—

220–245

2.5–6

30

Phthalic anhydridec

345–3852.7

28

75–105

20–25

—

850

3.5

—

530

1

—

800–1200

1

0–50

770

1

Variable

650

1

—

650–700

1—

660–9201—

650–11001<3

60–1101

Variable60

1

Variable

300

1

100

600–800

~1

—

450

—

—

Iron

c

a

AlF3 synthesiscAlumina calcinationc,dLimestone calcinationdcGold roastingSO2 from sulphide ore roastingb

Pyrite roastingbd

FeS2 from sulphide ore roasting
Inorganic materials
PharmaceuticalsddSiHCl3 productionb,dSilicon production

Nuclear industry

480–550<5Polyethylene

Semiconductor industry

Fines Content [%]360–420Ethylene dichloride

Drying of solids

P [atm]c

Maleic anhydride (DuPont)

Metallurgical industrycc

T [°C]

cb

Separation of U-235 from U-238

aWinter and Schratzer (2013)
Kunii and Levenspiel (1991)c
Jazayeri (2003)dBruni (2005)b

to generate steam for boilers, syngas, chemicals or fuels(Newby, 2003; Arena, 2013; Basu, 2013), including therecent development of a dual-stage fluid-bed plasma gasification process in UK for the production of electricity
and biosubstitute natural gas from waste gasification(Materazzi et al., 2013, 2015); fluidized beds are also employed in the waste incineration of solids and sludge(Horio, 2013). Another development gaining prominencein this area and aimed at new processes for clean energy
production is the use of fluidized beds for chemical looping combustion, Fan (2010). A review of fluid bed industrial applications can be found in Kunii and Levenspiel,
1991; Jazayeri, 2003; Winter and Schratzer, 2013.
In parallel with industrial developments, fluidization
also became the focus of a great deal of academic effort
aimed at providing a theoretical framework to underpin

the subject.
The first examples are the pioneering study of bubble
motion in fluidized beds by Davidson (1961), later developed by Davidson and Harrison (1963), Jackson (1963)
and Murray (1965), and Geldart’s empirical classification(Geldart, 1973) of fluidizable powders into four groups, A,
B, C and D, according to their particle size and density
and gas density (see Yates, 1996).
It is important to note that both bubble motion theories
and Geldart’s classification are based on observations
made under ambient conditions, hence it is not immediately obvious how accurate their predictions would be under process conditions such as high temperature andpressure.
For these reasons, fluid beds are commonly used whenhigh thermal efficiency, excellent temperature control and87

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

intimate gas-solids contact are desired. Despite this, theeffects of process conditions on fluidization are still notentirely understood. Design criteria and performance predictions for fluid bed units working at high temperature
have been largely based on fluid-dynamic models andcorrelations established from tests developed at ambient
temperature. However, extrapolating the results and relationships available at ambient conditions to elevated temperatures can lead to misleading predictions of the fluid
bed performance at high temperature. Drastic changes
can occur in the fluidization behaviour between low andhigh temperatures, due to possible modifications inducedby the temperature in the structure of the fluidized bed. Inorder to understand the factors responsible for suchchanges in fluidization behaviour, the role of the interparticle forces (IPFs) and hydrodynamic forces (HDFs) has
been studied, but much controversy still remains to define
their relative importance.
This work reviews experimental and theoretical research on the effect of process conditions such as hightemperature, fines content and fines size distribution on
fluidization. The review will begin by considering the influence of temperature on fundamental parameters ofgas-solids fluidization such as minimum fluidization, bed
expansion, bed voidage and deaeration rates.
The review will then address the effect of fines andfines size distribution on the dynamics of gas-fluidized
beds. This will lead to a review of the main techniques
used for the characterization and prediction of the flow
properties of powders. These include the standard bed
collapse test and rheological tests. The latter coveringboth stationary measurements (angle of repose, Hausnerratio, etc.) as well as dynamic tests (shear-cell-type tests
and viscometer-type tests). The paper will conclude by reviewing the work done at the University College London
on linking rheological measurements to fluidization tests,
in the attempt to quantify the role of the interparticle
forces on the fluidization behaviour of different industrial
powders.

2. Effect of temperature on minimum
fluidization conditions
Although several studies have been carried out on theinfluence of operating conditions on fluidization, the findings are still controversial and a satisfactory understanding of the phenomena which cause differences between
ambient conditions and high temperature processes has
not yet been achieved (Grewal and Gupta, 1989; Geldart,
1990; Fletcher et al., 1992; Knowlton, 1992; Yates, 1996;
Lettieri et al., 2000, 2001, 2002; Coltters and Rivas,
2004).
The operating conditions influence the operation of88

fluid-particle systems because they affect gas density andviscosity. Increasing temperature causes the gas density
to decrease and gas viscosity to increase. Most predictions of fluidization behaviour at high temperatures havebeen based solely on considering such changes in the gas
properties. However, this approach is valid under the condition that only hydrodynamic forces control the fluidization behaviour. Temperature can have a considerable
effect on particle adhesion, enhancing the role of the interparticle forces (IPFs) on the fluidization quality, if thesystem is operated at temperatures close to the minimum
sintering temperature of the particles. In addition, the increase of temperature may enhance the Hamaker constantand therefore the van der Waals’ attractive forces.
As reported by Lettieri et al. (2000), the effect of temperature on a fluidized bed is also strongly dependent on
the particle size, which in turn defines the type of particle-­particle and fluid-particle interaction, thus determining
the stronger or weaker role of the IPFs. Much theoreticaldebate is reported in the literature on the role of the interparticle forces on the fluid-bed behaviour and a soundunderstanding of the phenomena which control changes
in fluidization behaviour at high temperature has not yet
been achieved.
In this section we report some of the most significant
findings of the effect of temperature on the fluid-bed behaviour, outlining some of the most important aspects.
The minimum fluidization velocity, umf, is a fundamental parameter when designing fluidized-bed systems. The
minimum fluidization velocity may be found by measuring the pressure drop across a bed of particles as a function of the gas velocity. At umf, the weight of the bed isfully supported by the flow and the pressure drop becomes constant. umf may also be calculated on the basis of
the Ergun equation for the pressure drop through a packed
bed.
Various correlations can be found in the literature to
predict umf at high temperature (see Table 2), as for example the correlation given by Wu and Baeyens (1991). However, this approach is uncertain and not necessarily reliable,
because it is often necessary to extrapolate results for theconditions of interest and, in addition, it overlooks possible changes induced by temperature on the structure of
the fluidized bed that can in turn cause drastic alterationsin the flow behaviour and powder stability (Yang et al.,
1985; Lettieri et al., 2000).
Despite this, the Ergun equation (Eqn. 1) is one of themost frequently equations used in order to evaluate theeffect of temperature on minimum fluidization conditions.
Although the unknown voidage at minimum conditions
may be a problem in applying this equation, numerous
predictive correlations for umf are based on a modified
Ergun equation, as developed by Wen and Yu (1966).
Eqn. 1 can be rewritten using Archimedes dimensionlessPaola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Table 2 Selected equations for the calculation of minimum fluidization velocity
Authors

Equation

Ergun (1952)

150

Carman (1937)

u mf 

Miller and Logwinuk (1951)

u mf 

Leva et al. (1956)

u mf 

Goroshko et al. (1958)

u mf 

Leva (1959)

u mf 

Broadhurst and Becker (1975)





g 
Ar

u mf 
0.13
 g d p  p 

5 Ar 0.85
2.42×10
37.7

 



g

 


Riba et al. (1978)

u mf 

 d 3 2 g g 1.54×10 2  p g 2
 g d p  g 

Doichev and Akhmakov (1979)

u mf 

g1.08×10 3 Ar 0.947
gd p

(9)

g
7.33×105 10 8.24log10 Ar 8.81  gd p(10)

Wu and Baeyens (1991)


u mf

 gu mf 1   mf  u2 1
 1.75 g mf 3 
g  p   g 23
 d p  mf
 d p   mf
3
 d p 2  p   g  g   mf

180

1.25×10 3 d p2  p   g 

Ar 150

3
 2 mf

Remf 

1.75
3
 mf

2
Remf

7.39 d p1.82  p   g 3
 2 mf

1
3
 mf

 11

 14

or

0.94

(4)
(5)

8.1×10 3 d p2  p   g  g

(6)

g

(11)

(12)

(13)

(14)

(15)

0.66

0.5

  p  g  g 

Remf  a 2  b  Ar  awhere a 

That can be rearranged to the general formulas:2

Ar A Remf  B Remf

(3)

g 
Ar



 g d p  1400+5.2 Ar 

Leading to a modified form of Eqn. 11:2

Ar 1650 Remf  24.5 Remf

 g0.1g

 g0.06

Wen and Yu (1966) showed that the voidage and shapefactor functions in both the viscous and inertial term ofEqn. 11 can be approximated as:

1   mf

0.9

(2)

g

number (Ar) and Reynolds number at minimum fluidization conditions as follows:(1   mf )
 1   mf 

g

(1)

(7)

0.7






(8)

(16)

A
1
and b 2B
B

Values reported in the literature for the constants a andb are listed in Table 3. However, predictions using Eqn.
14 do not take into account possible changes in thevoidage which may occur with increasing temperature, as
discussed later.
Pattipati and Wen (1981) reported that the Wen and Yu
correlation is capable of predicting changes in umf whentemperature increases for sand material. They did not observe important changes with temperature in the voidage
at minimum fluidization and they found good matching
between experimental and predicted umf values.
The effect of temperature on the minimum fluidization
velocity has been reported by several other authors. At
first, we review work that is specific to large particles
falling within Geldart’s Group B and D particles. Generally, the experimental findings confirmed the trend predicted by the Wen and Yu equation. Nevertheless,89

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Table 3

Values for the constants a and b in Eqn. 16.

Authors

Remf  a 2  b Ar  aab

Wen and Yu (1966)

33.7

0.0408Bourgeois and Grenier (1968)

25.46

0.03824

Saxena and Vogel (1977)

25.28

0.0571Babu et al. (1978)

25.25

0.0651

Richardson and Jerónimo (1979) 25.7

0.0365Grace and Hetsroni (1982)

27.2

0.0408Chitester and Kornosky (1984)

28.7

0.0494

Thonglimp et al. (1984)

31.6

0.0425

Lucas and Arnaldos (1986)

25.2

0.0672Bin (1993)

27.31

0.0386

Reina et al. (2000)

48

0.045

absolute values did not always match the experiments.
Knowlton (1992) stated that one of the reasons lies in theevaluation of the correct mean particle size and shape factor. He suggested that this could be back-calculated fromthe Ergun equation using previously measured values ofumf. Doing so, an effective value for the particle size andshape factor would be found.
Botterill et al. (1982) reported experimental verificationof the temperature effect on umf for some Group B and D
powders. They observed a decrease of umf with increasing
temperature for Group B materials due to the consequent
increase in gas viscosity. They compared their resultswith predictions from the Wen and Yu equation and notedthat the decrease was less than that predicted since achange in εmf had occurred. This is not in agreement withthe results of Pattipati and Wen (1981). For Group D powders, Botterill et al. (1982) observed an increase in umf,
because of the decrease of gas density. They also foundthat εmf for the Group D materials did not change withtemperature. Their experimental data matched predictions
obtained from the Ergun equation, using the values of εmf
measured at the corresponding operating temperatures,and using also an appropriate value of the shape factor,
back-calculated from the Ergun equation for a given experiment (Lettieri, 1999).
Fletcher et al. (1992) reported that applying Eqn. 16 canintroduce a significant error into the prediction of umf,
mainly because the a and b constants are a function of the
shape factor (ϕ) and voidage at minimum fluidization
conditions, which are difficult to measure experimentally.
They proposed different correlations for the prediction ofRemf at ambient temperature on the basis of the shape of
the particles, see Table 4.
A decade later, Coltters and Rivas (2004), proposed a

90

Table 4

Correlations for the prediction of Remf at ambient
temperature (Fletcher et al., 1992).

Type of sands
Remf 

Ar
1400+5 Ar

Remf 

Ar
1400

Moderately sharp

Remf 

Ar
1170+ Ar

(18)

Very angular, coked

Remf 

Ar
790+7 Ar

(19)

Round

( Ar  1480) (17a)( Ar <1480)(17b)

new equation to estimate the minimum fluidization velocity without having to experimentally determine the bed
voidage and shape factor:
 d p2  p   g  g   p 1.23
u mf  K 

 


  g  



(20)

where K and α are constants and are functions of thesolids-gas system. They tested their correlation against
189 experiments reported in the literature on about 90
different materials, and their results showed good agreement with the experimental data. In parallel, Delebarre(2004), proposed the following revisited Wen and Yu
equations for the prediction of umf without the dependency on the voidage and shape factor:
2  29400  3 (1  
Ar 
24.5 Remfmf ) Remf
mf(21)

The relative role of the hydrodynamic and interparticle
forces on the minimum fluidization conditions at elevatedtemperature for Group A and C powders has been analysed by various authors. Lucas et al. (1986) reported thatchanges with temperature in ε mf can be explained on ahydrodynamic basis, contrary to Raso et al. (1992) andFormisani et al. (1998) who later related such changes toa variation of IPFs with temperature.
Lucas et al. (1986) explained the variation of εmf withtemperature in terms of a change in the flow pattern inside the bed. They analysed the variation of εmf as a function of Remf and observed that under flow regimes withRemf, less than 0.75, εmf remains constant while it decreases when this value is exceeded, becoming constantagain at an Remf higher than 2. They explained that at low
Remf a sucking effect is produced in the particle boundary
layer which increases the closeness of the particles andcauses εmf to decrease. At higher values of Remf, the attractive forces between the particles decrease and εmf
gradually increases up to an approximately constantvalue. From this theory, a powder classification scheme
based on the nature of the boundary layer around a parti-

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

cle was elaborated by Mathur and Saxena (1986), and validated with data on umf and εmf obtained by Botterill et al.
(1982) and Lucas et al. (1986) at high temperatures.
In agreement with Raso et al. (1992), Yamazaki et al.
(1995) reported that the role of the IPFs should not be ignored when trying to explain changes in εmf with temperature. They showed that variations in bed voidage
depend both on the physical properties of the fluidizing
fluid, i.e. density and viscosity, and on changes in thephysicochemical properties of the particles. In particular,
they studied the effect of water adsorbed onto the surfaceof silica particles with increasing temperature. At low
temperatures, they observed a decrease in εmf as humidity
increased, while at higher temperatures higher values ofεmf were found for the same humidity. They explainedsuch changes in terms of the amount of water adsorbed
onto the surface of the silica, and concluded that variationof the εmf with temperature is caused by a change in theadhesion forces, which at velocities as low as umf cancause the formation of clusters, making the bed settlewith a relatively looser structure.
Formisani et al. (1998) and Lettieri et al. (2000) also reported on the effect of temperature on minimum fluidization conditions, and they stated that the Ergun equationand Wen and Yu correlation are capable of predictingchanges in the minimum fluidization velocity with temperature if, in addition to changes of gas density and viscosity, the dependence of ε mf on temperature is also
accounted for.
Formisani et al. (1998) observed a linear increase of εmf
for various Group A, B and D powders and a linear increase of the voidage of the fixed bed with temperature(Fig. 1–2). Formisani et al. (1998) performed their experiments in a transparent quartz column equipped with astainless steel porous plate able to provide a homogeneousgas distribution; a video camera was used to record theexperiments. The total pressure drop across the particle
bed was measured with a pressure tap located just above
the gas distributor level; a graduated scale was used todetermine the bed height at each temperature, and the average bed voidage was subsequently determined from thevalues of the bed height. The authors reported that bed
height measurements were affected by a 0.5-mm approximation, which caused uncertainty in the bed voidage
evaluations of around 1 %.
They attributed the increase of the fixed bed voidage toan increase of the interparticle forces which was thought
to stabilize the structure of the fixed bed state, in the total
absence of a gas flow. They found a close similarity between the slope of the fixed bed voidage and εmf and sothey concluded that the increase of ε mf, and in turnchanges in umf with temperature, are not due only tochanges in the properties of the fluidizing gas but also tochanges in packing properties.

Fig. 1

Effect of temperature on the fixed bed voidage for glass
ballotini and silica sand particles of different size. Reprinted with permission from Ref. (Formisani et al.,1998). Copyright: (1998) Elsevier Limited.

Fig. 2

Effect of temperature on the bed voidage at minimum
fluidization for glass ballotini and silica sand particles
of different size. Reprinted with permission from Ref.
(Formisani et al., 1998). Copyright: (1998) Elsevier
Limited.

They attributed the capability of the bed to form alooser structure to an increase of the Van der Waals’
forces with temperature. The results of Formisani et al.
(1998) confirmed findings reported by Raso et al. (1992)
on a 2D fluidized-bed.
Lettieri et al. (2000, 2001) reported experimental observations on the effect of temperature on the fluidizationof three fresh FCC catalysts and an equilibrium (E-cat)
FCC tested at ambient pressure and at temperatures up to
650 °C. Where changes in fluidization at high temperature
were observed, the factors responsible were investigated91Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Fig. 3

umf vs temperature for a silica catalyst doped with increasing values of potassium acetate, comparison ofexperiment with prediction. Reprinted with permission
from Ref. (Lettieri et al., 2000). Copyright: (2000)
Elsevier Limited.

via thermo-mechanical analyses. The physical propertiesof a porous Group A powder were changed deliberately inorder to highlight under which conditions the fluidization
behaviour is dominated by the IPFs.
These results demonstrated how temperature can increase the effect of IPFs, causing a Group A material tobehave in a similar manner to a Group C material. Fig. 3
shows the effect of temperature on the fluidization at minimum fluidization conditions for three silica catalysts
doped with different amounts of potassium acetate(KOAc). Fig. 3 shows that the prediction and experimental values of umf match only for the catalyst sample dopedwith 1.7 %wt of KOAc. Values of umf obtained for the
doped samples with 7 % and 10 %wt of KOAc were foundto be higher than the predicted ones at ambient temperatures. Lettieri et al. (2000) explained that during the drying process, some potassium acetate might have migrated
to the mouth of the pores from within, making this responsible for the higher values of umf, hence reflecting theinfluence of the IPFs on the fluidization behaviour.
Significant changes in the fluidization quality of thesample with 10 %wt of potassium acetate occurred withincreasing temperature. The ratio between the measured
and calculated pressure drop across the bed of material isplotted against gas velocity in Fig. 4, as temperature isincreased. The fluidization behaviour of this material significantly changed when the bed temperature was about
200 °C. A rapid decrease of the pressure drop was notedand channels were seen on the X-ray images. This material exhibited a typical cohesive Group C behaviour. Good
fluidization at 200 °C could be achieved only when operating the system at a higher velocity, thus causing thechannels and rat holes to be broken down. When the bed
was cooled down to 150 °C, good fluidization was obtained again at low velocities.

92

Fig. 4

ΔPm /ΔPc vs bed velocity as a function of temperature
for a silica catalyst doped with 10 %wt of potassium
acetate. Reprinted with permission from Ref. (Lettieri
et al., 2000). Copyright: (2000) Elsevier Limited.

Lettieri et al. (2000) explained that at increasing temperature, the potassium acetate contained within the catalyst pores became mobile and migrated to the surface ofthe particles due to decreases in the surface tension andviscosity. The presence of potassium acetate on the surface of the catalyst caused the material to become sticky
and therefore to channel. On the other hand, as the bed
temperature decreased, re-absorption of the potassium acetate into the catalyst pores may have occurred, thus allowing good fluidization of the material to be re-established.
Several authors have also investigated the combined effects of temperature and particle size and particle size
distribution (PSD) on minimum fluidization velocity, Linet al. (2002), Bruni et al. (2006), Subramani et al. (2007),
Hartman et al. (2007), Goo et al. (2010), Chen et al. (2010)
and Jiliang et al. (2013).
Hartman et al. (2007) performed experimental measurements on beds of ceramsite over a temperature range
from 293 K to 1073 K, using very narrow fractions of ceramsite particles spanning a range of 0.13–2.25 mm. They
reported that umf decreases with increasing bed temperature for all the particles, except for the largest ones
(2.25 mm), which exhibited a non-monotonic dependenceof umf on temperature.
Goo et al. (2010) investigated the effects of temperatureand particle size on minimum fluidization velocity in adual fluidized bed, using silica sands over a range of temperature between 298 K and 1073 K. They reported thatumf decreases with increasing operating temperatures regardless of particle diameters or particle types, and they
proposed a correlation to predict such a velocity at different temperature. Chen et al. (2010) have also observed
similar behaviour.
Lin et al. (2002) and Jiliang et al. (2013) demonstrated
that operating temperature and PSD can influence thePaola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

minimum fluidization velocity simultaneously, making
variations of umf non-monotonic with temperature. In particular, Jiliang et al. (2013) investigated quartz sand andbottom ash in a bench-scale bubbling fluidized bed reactor at atmospheric pressure, with four PSDs between 30
and 600 °C. The PSDs evaluated included a narrow cut (asreference powder), a Gaussian-type powder, a binary mixture and a uniformly distributed powder. They concluded
that the minimum fluidization velocity of the powders
with wide PSD decreases with the increase in bed temperature and varies with the mass fraction of rough particles. In addition, they observed that the binary anduniform PSD behaved similarly to each other but generally they had higher values of umf than the Gaussian andnarrow cut distributions. To predict the correct values of
umf for wide-range-sized particles, Jiliang et al. (2013)
proposed new correlations based on their experimental
data.
Several correlations have been derived for the prediction on the minimum fluidization conditions at high temperature, these are, however, case-specific. The debate of
the phenomena causing changes in behaviour with increasing temperature remains controversial with stillmuch disagreement on the role of the hydrodynamic andinterparticle forces.

3. Effect of temperature on bed expansion andminimum bubbling conditions
Many attempts have been made to describe the fluid
dynamic properties of fluidized beds, with special attention paid to the transition between the particulate andbubbling regime of Geldart’s (1973) Group A powders.
These materials are those which exhibit a stable region ofnon-bubbling expansion between minimum fluidization
velocity, umf, and minimum bubbling velocity, umb, with
umb/umf > 1. The phenomenon of uniform expansion, ordelayed bubbling, has been the subject of much research
since it is significant to show differences between gasand liquid-fluidized beds for the formation of gas bubbles
in the former but not generally in the latter. The mechanism of bubble-free expansion in gas-fluidized beds has
been assumed by some workers to be the same as the homogeneous expansion in a liquid-fluidized bed to which
the Richardson and Zaki (1954) equation applies:u  ut  n(22)

where u is the liquid velocity of the suspension, ut is theterminal fall velocity and n is a parameter that depends onthe free-fall particle Reynolds number and normally has
values between 4.65 (viscous regime) and 2.4 (turbulent
regime). However, the validity of this comparison has notalways been accepted. Massimilla et al. (1972) and Donsi’and Massimilla (1973) made some experimental observations on the bubble-free expansion of gas-fluidized bedsof fine particles and described the expansion mechanism
as being due to the nucleation and growth of cavities
which they assumed had occurred because of a broad distribution of interparticle forces. The phenomenon of delayed bubbling is not only limited to fine Geldart’s GroupA powders, it has also been observed with large particles
fluidized under high pressure conditions, see Yates (1996),
and also with magnetised large particles, as reported by
Agbim et al. (1971) and later by Siegell (1989).
This phenomenon of delayed bubbling has been thesubject of much research since it has a strong bearing on
the difference between gas- and liquid-fluidized beds andon the reasons for the formation of bubbles in the one but
not in the other.
The limiting condition for the stability of gas-fluidized
beds is defined by the voidage at minimum bubbling, εmb,
whose determination has been at the centre of much theoretical controversy due to the ill-defined role of the HDFs
and IPFs. Mathematical models have been developed byvarious authors to predict the transition between particulate and bubbling regime. Two different approaches havebeen considered. On the one hand, criteria have been developed based on the assumption that hydrodynamic
forces (HDFs) are the controlling factor. On the other
hand, interparticle forces (IPFs) were considered to playthe dominant role over the hydrodynamic forces. The firststability criterion based solely on hydrodynamic forces
was proposed by Jackson (1963). His model failed to predict a stable region for the expansion of fluidized beds offine materials. He found that the fluidized state was intrinsically unstable towards small concentration disturbances, regardless of the fluid-particle system considered.
This conclusion is contrary to the experimental evidence
obtained for Geldart’s Group A powders.
Verloop and Heertjes (1970) re-formulated the hydrodynamic model applying the Wallis (1969) stability criterion, which compares the propagation of kinematic anddynamic wave velocities through the fluidized bed. According to this criterion, bubbles form when the propagation velocity of a voidage disturbance reaches the velocityof an elastic wave in the bed. Foscolo and Gibilaro (1984)
adopted the approach of Verloop and Heertjes and developed a model (the “particle-bed model”) to predict the onset of bubbling, ε mb, in a fluidized bed. The key toapplication of their criterion lies in the formulation of
the fluid-particle interaction model. This is discussed
in Foscolo et al. (1983). A detailed formulation of theparticle-bed model is reported in Foscolo and Gibilaro(1987) where reasonable agreement between predictions
and experimental data available at ambient and high pressure conditions was found. Furthermore, Brandani andFoscolo (1994) mapped the various f luidization be-

93Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

haviours by analysing the discontinuities arising from the
one-dimensional equations of change in the particle-bed
model.
On the other hand, interparticle forces were considered
to play the dominant role over the hydrodynamic forces(Mutsers and Rietema, 1977; Piepers et al., 1984; Cottaar
and Rietema, 1986; Rietema et al., 1993).
Rietema and co-workers formulated a stability model
which accounted for additional forces to the f luid-­
dynamic forces and gravitational weight. They assumed
that the interparticle forces between cohering particles
give rise to a powder structure with a certain mechanicalstrength even in the expanded state of homogeneous fluidization. This mechanical structure can be broken onlywith gas velocities sufficiently high so that the drag force
exerted by the fluid becomes greater than the cohesive
forces. Rietema and co-workers assumed that the mechanical strength of the powder structure controls the stabilityof a fluidized system. Similarly to Foscolo and Gibilaro(1984), Mutsers and Rietema (1977) followed the approach of Wallis to predict the stability of a fluidized system, and gave expressions for the kinematic and dynamic
wave velocities. Their formulation for the bed elasticity
and the theoretical concept behind it represents, however,the crucial difference between their model and the criteria
developed solely on the basis of the hydrodynamic forces.
According to Mutsers and Rietema, the bed elasticity is
the essential property which determines the dynamic behaviour of the bed, and whose origin is to be found in the
Van der Waals’ forces which the solid particles exert uponeach other.
However, whereas models based on HDFs such as
Foscolo and Gibilaro (1984) provide explicit formulations
for the minimum bubbling condition, the formulation proposed by Rietema and co-workers cannot be used to makea-priori predictions.
The effect of temperature on the transition between
particulate to bubbling fluidization was investigated by
Rapagna’ et al. (1994), who demonstrated the capabilityof the Foscolo and Gibilaro criterion to predict εmb for
FCC powders of mean particle sizes of 65 and 103 μm in
a 50-mm i.d. column from ambient conditions up to
900 °C. They used a fast responding pressure transducer
connected to an oscilloscope to detect the passage of bubbles in the bed. They observed a decrease in the averagebubble size and a delay of the onset of bubbling with increasing temperature for both FCC powders. This corresponded to an increase in the voidage at minimum bubblingwith temperature. Also, a larger increase in εmb was observed for the finer materials, a comparison of their experimental values for εmb with those predicted by the
Foscolo and Gibilaro fluid-bed model showed good agreement for both powders at ambient and high temperatures(Fig. 5). Xie and Geldart (1995) investigated the role ofthe IPFs on the bubbling conditions with increasing temperature by measurements of the voidage. They observed
virtually no change in εmb as the temperature increased
for any of the FCC catalysts investigated, in contrast to
the results of Rapagna’ et al. (1994). They also reported
that predictions of εmb given by the Foscolo and Gibilaro(1984) and the modified particle-bed model by Jean andFan (1992) matched experiments at ambient temperature
for FCC powders larger than 60 μm. However, both models over-predicted εmb values at ambient temperatures forthe finer powders, and they also predicted significant
changes in εmb with temperature, in contrast to the experimental results reported.
The failure of the hydrodynamic models to correctlypredict the transition between the particulate and bubbling regime was assumed to be the result of ignoring the

Fig. 5 Experimental and calculated values of bed voidage at minimum bubbling conditions as a function oftemperature for sample I (on the left) and sample II (on the right), Reprinted with permission from Ref.
(Rapagna et al., 1994). Copyright: (1994) Elsevier Limited.

94

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

interparticle forces.
The onset of bubbling was also studied by Formisani etal. (1998). They measured changes in the minimum bubbling velocity of FCC catalysts and silica sand withincreasing temperature from ambient up to 800 °C in a
55-mm i.d. quartz column. They relied on visual observation to determine the commencement of bubbling. For allmaterials tested, they observed an increase of u mb withincreasing temperature with a trend very similar to theincrease observed for umf. Unlike Rapagna’ et al. (1994),
they observed a very small bed expansion increase withincreasing temperature.
Lettieri et al. (2001) investigated the fluid-bed stabilityof three fresh FCC catalysts both experimentally and theoretically as a function of increasing temperature. Valuesof the voidage at minimum bubbling conditions were obtained from 20 °C up to 650 °C, and compared with predictions given by the Foscolo and Gibilaro particle-bed
model.
For all FCC catalysts, the experimental εmb values increased little with increasing temperature. At first, thevalues were compared with predictions obtained using the
Foscolo and Gibilaro particle-bed model. This predictedchanges in εmb much greater than found experimentally,see Fig. 6. The disagreement between predicted and experimental εmb values was related to the large discrepancy
between the values of n and ut in the Richardson and Zaki
equation, obtained from the experimental bed expansion
profiles and the calculated ones.
For all FCC catalysts, the highest values of the experimental n and ut were found at ambient conditions.
In order to correctly apply the particle-bed model tothese particle systems, Lettieri et al. (2001) generalized
the expression of the drag force given by Foscolo andGibilaro for the viscous flow regime. Consequently, theyFig. 6Comparison between measured εmb values and predictions using the original and generalized Foscolo-­
Gibilaro particle-bed model for an FCC catalyst.
Reprinted with permission from Ref. (Lettieri et al.,2001). Copyright: (2001) Elsevier Limited.

re-examined the procedure followed to obtain the Foscolo
and Gibilaro stability criterion and proposed a generalized
expression of their criterion. Predicted εmb values obtainedwith the generalized expression of the Foscolo-Gibilaro
criterion were within 5 % for all FCC catalysts. Although
the particle-bed model was originally developed on theassumption that the hydrodynamic forces govern the fluid-­
bed stability, the results presented by Lettieri et al. (2001)
suggested that the contribution of the interparticle forces
to the bed stability of the materials studied cannot be
ruled out.
As a follow-up study, Lettieri and Mazzei (2008) analysed the effect of temperature on the fluid-bed stability ofthe same three FCC catalysts used in Lettieri’s previous
work, but this time through considerations on the fluid-bed
elasticity.
They reported experimental findings on the effect oftemperature on the elasticity modulus at minimum bubbling conditions, calculated according to the theory of
Foscolo & Gibilaro (1984) and also adopting the criterionof Mutsers & Rietema (1977). In accordance with the theoretical postulation of Mutsers & Rietema, the resultsfrom bed expansion presented by Lettieri and Mazzei(2008) and previously by Lettieri at al. (2002) showed thatthe role played by the IPFs can affect the stability ofGroup A powders. However, the sensitive analysis carriedout by Lettieri and Mazzei (2008) on the parameterswhich dominate the elasticity modulus revealed that thegas viscosity was the dominant factor which defined its
variations with temperature, as shown in Figs. 7 and 8.
This highlighted the importance of both HDFs and IPFs
on the stability of Group A powders and the need for acorrect and complete description of both contributions.
In line with Lettieri et al (2001), Valverde and co-workers(2001) emphasized the importance of the interparticle
forces on the settling and particulate fluidization of fine
powders. They proposed an extension of the Richardson
and Zaki empirical correlation and the theoreticalMills-Snabre (1994) model, originally developed for thesettling of non-cohesive spheres, to predict the settling of
aggregates which may form when the interparticle forces
exceed the particle weight by several orders of magnitude.
Valverde et al. (2003) extended the previous study, investigating—from both macroscopic and local measurements—the transition between the solid-like, fluid-likeand bubbling fluidization of gas-fluidized fine powders.
They showed that the transition between the solid-likeand the fluid-like regimes takes place along an interval ofgas velocities in which transient active regions alternatewith transient solid networks. Using optical probe local
measurements, they showed the existence of meso-scale
pseudo-turbulent structures and short-lived voids in thefluid-like state, which make the prediction of the transition between the different regimes a complex task.

95

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Fig. 7

Effect of temperature on the elasticity modulus at εmb
according to the Mutsers & Rietema criterion, for all
fresh FCCs. Reprinted with permission from Ref.
(Lettieri and Mazzei, 2008). Copyright: (2008) Elsevier
Limited.

Castellanos (2005) observed that the onset of fluidization of fine and ultrafine powders was characterized by
the presence of agglomerates which give place to a highly
expanded state of uniform fluid-like fluidization. In an attempt to unify the above observations, Valverde andCastellanos (2008) proposed an extension of Geldart’s
classification of powders to predict the gas-fluidization
behaviour of cohesive particles, which reconciles the roleof the interparticle and hydrodynamic forces on the existence of a non-bubbling regime. In the new phase diagram
proposed by Valverde and Castellanos, the boundaries between the different types of fluidization are defined as afunction of fluid viscosity, particle density, the fractal dimension of the agglomerates and the powder’s compaction history, as previously reported by Valverde andCastellanos (2006).
Girimonte and Formisani (2009) reported on the influence of operating temperature on the transition to the
bubbling regime for samples of FCC, silica and corundumsands, at temperatures ranging from 30 to 500 °C. They
determined the minimum bubbling velocity using four
different methods and obtained different results for umb
with increasing temperature. The first method was basedon the direct observation of the velocity at which the firstbubble erupted on the free surface of the bed. The second
method was based on measurement of the pressure drop
across the whole bed, and umb was taken as the point
where a shallow minimum of the Δp versus u curve occurs. The last two methods were derived from analysis of
the “fluidization map”, namely examination of the expansion behaviour of the bed over a range of fluidization velocities from the fixed-bed state to the bubbling regime.
They demonstrated that the optical method and themethod based on detection of the pressure drop minimum
were unreliable for correctly determining the starting

96

Fig. 8

Influence of gas viscosity on the elasticity modulus at
εmb according to the Mutsers & Rietema criterion, forall fresh FCCs. Reprinted with permission from Ref.
(Lettieri and Mazzei, 2008). Copyright: (2008) Elsevier
Limited.

point of bubbling. They stated that only the analysis of
bed expansion as a function of the fluidization velocity allows the succession of phenomena through which a stableflow of bubbles across the solid mass ensues to be reconstructed.
More recently, Girimonte and Formisani (2014) reported on new experiments carried out on the effect oftemperature on the fluidization of FCC particles. They
used a non-invasive optical technique for acquiring images of the bubbles’ eruption at the free surface and results from bed collapse tests. Their experiments showedthat high temperature influences the quality of bubbles
and produces a smoother regime of bubbling, which they
attributed to the thermal enhancement of IPFs that in turnleads to a higher porosity and lower interstitial flow in theemulsion phase.
In summary, high temperature clearly affects the stability of fluidized beds of Group A powders; well-establishedtheories and models fail to predict correctly the voidage at
minimum bubbling with increasing temperature. Models
corrected on the basis of experimental data are capable ofreproducing correct trends; however, a-priori predictionsof the fluid-bed stability with increasing temperature are
yet to be achieved. The challenge here is still in the ability
to describe the forces that determine the transition fromparticulate to bubbling fluidization. Hence, some kind ofquantification of the effects of the IPFs on fluidization isneeded in order to advance the understanding of fluidization at high temperature.

4. Effect of fines content and fines size
distribution on fluidization
The presence of fine particles is known to improve thePaola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

fluidization quality of fluidized beds (Rowe et al., 1978;
Abrahamsen and Geldart, 1980; Newton, 1984). For thisreason many industrial fluidized bed reactors operate witha high fines fraction (dp < 45 μm) of between 10 and
50 wt% in order to improve the fluid bed performanceand to achieve high product yields (Pell and Jordan, 1987;
Lorences et al., 2003; Bruni et al., 2006). Furthermore,
the fines increase the mass transfer rate between the denseand bubble phase, the minimum bubbling velocity, the interstitial gas velocity, the bed expansion and reduce bubble size (Werther, 1983; Rowe, 1984; Sun and Grace,
1992; Lorences et al., 2003). Fresh catalysts generally
contain a higher proportion of fines and show less of atendency to segregate or cluster (Avidan and Yerushalmi,1982).
However, as pointed out by Lorences et al. (2003), there
is an associated cost in maintaining a high level of fines.
Plant investment may be higher because more cyclones
and a larger reactor volume are required due to the lower
solids suspension density. Catalyst make-up costs are also
higher because of the increased attrition rate.
Many researchers have investigated the effect of fines
on the fluidization quality in order to determine the optimal amount of fine particles to be added (Rowe et al.,
1978; Barreto et al., 1983; Newton, 1984; Avidan and
Edwards, 1986; Yates and Newton, 1986; Pell and Jordan,
1987; Sun and Grace, 1990; Lorences et al., 2003; Bruni
et al., 2006).
Rowe et al. (1978) examined the dense phase voidage εdof freely bubbling fluidized beds containing different
amounts of fines using the X-ray absorption technique.
Voidage measurements were made by comparing the
X-ray absorption of the dense phase of the freely bubbling
bed with that of a calibration wedge containing the samematerial. They carried out experiments on a commercial
silica-based catalyst of mean particle diameter 52 μm, butwith three different percentages of fines: 2.7, 20 and
27.6 %. They found that both the dense phase voidage andthe overall expansion of the bed increased with increasingfines content. Furthermore, they observed that the measured voidage of the dense phase was an order of magnitude greater than the voidage corresponding to theminimum fluidization condition. Accordingly, they deduced that the interstitial gas flow in a bubbling bed isgreater than the minimum fluidization flow, hence questioning the validity of the “two-phase” theory of Toomey
and Johnstone (1952) on beds of fine particles. This statesthat all gas in excess of that required to just fluidize theparticles passes through the bed in the form of bubbles.
Rowe et al. (1978) concluded that since the dense phase ismuch more effective in bringing about a chemical reaction than the bubble phase, the overall performance of acatalytic fluidized bed reactor can be enhanced by the addition of fines.

Later, Barreto et al. (1983) compared the Rowe et al.
(1978) measurements with those obtained with a bed collapse test technique. They found good agreement between
the dense phases properties obtained using the two techniques over the range of velocities studied.
In order to test the conclusions from the earlier study ofRowe et al. (1978), Newton (1984) and Yates and Newton(1986) also examined the influence of fines fractions on
fluidization by monitoring the conversion in catalytic fluidized bed reactors containing different amounts of fines.
They highlighted that an increase in the conversion measured for a given throughput of reactant can be obtainedby increasing the fines content and that this is due tochanges caused by fine particles in the gas distribution
between the dense phase and the bubble phase. Newton(1984) carried out experiments on a commercial oxidation
catalyst, and he used three batches containing 0, 16 and
27 % of fines with a mean particle diameter of 81, 58 and52 μm, respectively. Results at each temperature showed
an increase in conversion with increasing fines content
and for the same fines content an increase in conversion
with temperature, therefore confirming the hypothesisthat increasing fines content causes more gas to flow
through the emulsion phase and less through the bubble
phase.
Abrahamsen and Geldart (1980) had also extensively
investigated the effect of fines on the fluidization ofGeldert’s Group A powders and found that the dense
phase voidage increases as the fraction of fines increases;
they stressed, however, the need for studying the effectsof fines fractions, mean diameter and size distribution independently.
Khoe et al. (1991) investigated the effect of changing
the mean particle diameter independently of the PSD bymodifying a sample of FCC catalyst and a sample of glass
ballotini. They created three samples with a narrow size
distribution and differing in mean particle diameter (83,53 and 34 μm for the FCC and 113, 72 and 37 μm for theglass ballotini). Then they examined the effect of changing the PSD independently of the mean diameter by creating for each powder two more samples with the samemean particle diameter as the intermediate narrow sample(~53 μm for the FCC and ~72 μm for the glass), but with awide and bimodal PSD, respectively.
They performed the bed collapse test on all samples
and found that the effect of increasing the mean particle
diameter for both FCC and glass powders was to increase
the dense phase collapse rate and to decrease the voidageof the dense phase. These effects were enhanced when thesamples with the same dp and different PSD were compared. The most aeratable FCC sample was the one with awide distribution, while the most aeratable glass sample
was the one with a bimodal distribution.
Bruni et al. (2006) investigated the independent effect97

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

on fluidization of adding different fine sub-cuts to a virtually fines-free starting material. The fluidization behaviourof an alumina powder was monitored at temperatures
ranging from ambient to 400 °C and by adding two fine
sub-cuts of nominal size 0–25 and 25–45 μm to the material previously deprived of fines. Results were obtained by
performing fluidization tests such as bed collapse tests,
pressure drop profiles and bed expansion profiles. Fines
content and fines size distribution significantly affected
the fluidization behaviour of the powders tested. The fluidization tests highlighted significant differences in thecharacteristics of the settled and expanded bed of the materials investigated. The results showed that the additionof fines made the materials more capable of retaining theaeration gas hence expanding more and taking longer tocollapse when the gas was suddenly cut off than the bed
deprived of fines. The effects of the IPFs become moreevident for the powders containing the smaller fines cut
than for powders containing the same amount of bigger
fines. Bruni et al (2007a,b) subsequently developed a parallel fluidization-rheology approach (discussed in Section
7 of this paper) to establish the relative role between theinterparticle and hydrodynamic forces on the aeratabilityof the powders with increasing temperature.
The literature reported to this point reveals the complexity of the phenomena involved, and the difficulty of adirect quantification of the particle-particle interactions in
fluidized beds and of their changes at process conditions.
Within this framework, powder rheology represents anappealing tool to evaluate indirectly the effects of the
IPFs on fluidization. This is tackled in the next two sections of this paper.

5. Powder flowability measurement tests
A great deal of research has been carried out over thelast sixty years in order by means of simple tests to define
and measure parameters apt to characterize and predict
the flow properties of solid materials. To this end, various
approaches have been undertaken and are presented inthis section. These include both stationary measurements
and dynamic tests.
On one hand, stationary measurements (angle of repose, Hausner ratio, etc.) have been proposed as simple
tests to predict the flowability of bulk powders (Carr,
1965). On the other hand, dynamic tests have been used
as a means for determining the flow characteristic ofpowders. These dynamic tests can be classified into two
distinct groups: the shear-cell-type tests, first introduced
by Jenike in 1953, and the viscometer-type tests, whose
study was initiated by Schugerl in the late sixties. The
former is a test performed on the powder in a compressedstate where the shear measurements are used to obtain a98yield locus that represents the limiting shear stresses under any normal stress when failure, i.e. flow, occurs. The
shape of the yield locus is related to the material cohesiveness. The latter is based on the analogy between fluidized beds and liquids and in particular on the idea that ina fluidized bed, a resistance against flow exists just as inliquids, and that this resistance is a kind of internal friction between the particles in the suspension forming thedense phase and resembles the concept of viscosity usedin describing the rheology of liquids.
Carr (1965) developed a classification system to predict
the flow characteristics of a bulk of particulate solids. In
Carr’s method, a numerical value is assigned to the resultsof several tests and it is summated to produce a relative
flowability index for that particular bulk material. The
method is further discussed in Carr (1970). Defined here
are some of the main parameters used in Carr’s method:
the angle of repose, the angle of fall, the angle of difference, the angle of internal friction, the angle of spatulaand cohesion. In addition, the Hausner Ratio (HR, i.e. theratio between the loose and the packed bulk density) andthe compressibility are also used to define the cohesiveness and thus the flowability of a material (Geldart andWong, 1984, 1985; Bruni, 2005).
The angle of repose is defined as the constant angle tothe horizontal assumed by a cone-like pile of the material.
It is a direct indication of the potential flowability of amaterial: materials with good flowability are characterized by low angles of repose.
The angle of fall is determined by dropping a small
weight onto the platform on which an angle of repose has
been formed. The fall causes a decrease of the angle ofrepose that is called angle of fall. The more free-flowing
the material, the lower the angle of fall.
The angle of difference is the difference between theangle of repose and the angle of fall. The greater this angle, the better the flow.
The angle of internal friction is defined (Carr, 1970) as
the angle at which the dynamic equilibrium between themoving particles of a material and its bulk solid isachieved. This is of particular interest for flows in hoppers and bins.
The angle of spatula is a quick measurement of the angle of internal friction. It is the angle, measured from thehorizontal, that a material assumes on a flat spatula thathas been stuck into the dry material and then lifted up
and out of it. A free-flowing material will have formed
one angle of repose on the spatula’s blade. A cohesivematerial will have formed several angles of repose on theblade; the average of these is taken. The higher the angleof spatula of a material, the less flowable it is.
Cohesion is defined as the apparent cohesive forces existing on the surface of fine particles or powders. The cohesion test consists of passing the material through threePaola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

vibrating sieves in series. The material left on each sieve
is weighed and a cohesion index is determined from therelative amounts retained.
The Hausner Ratio (HR) is the ratio between the loose
and the packed bulk density and is used as an indicationof the cohesiveness of the materials, see Geldart andWong (1984). The loose bulk density (ρBDL) is measured
by gently pouring a sample of powder into a container
through a screen, whereas the packed or tapped bulk density (ρBDP) is determined after settling and deaeration of
the powder has occurred due to tapping of the sample. In
addition to the HR, the powder compressibility is also
used to define cohesiveness, this is expressed as 100(ρBDP-ρBDL)/ρBDP.
The main advantage of these tests is their simplicity
that makes them an attractive tool to determine powder
flowability and it explains their wide use in industry andacademia. However, these tests are scarcely reproducible
and their procedures are very difficult to standardise, as
amply discussed by Santomaso et al. (2003) with special
regard to the Hausner ratio. Furthermore, the link between the fluidization behaviour and the static properties
is not at all straightforward, due to the uncertain relationship between the IPFs and the HDFs involved when gas ispassed through a bed of particles. The use of static methods to predict the fluidization behaviour of powders, despite being widely employed, is therefore questionable,
especially when the fluidization behaviour needs to be assessed under process conditions (Bruni, 2005).

5.1 Shear testers
Shear-cell-type tests, first introduced by Jenike in 1953,
are performed on the powder in a compressed state where
the shear measurements are used to obtain a yield locus
that represents the limiting shear stresses under any normal stress when failure, or to be precise flow, occurs. The
shape of the obtained yield locus is related to the material
cohesiveness. Many conventional testers are available formeasurement of the flowability and they can be distinguished between direct (translational and rotational) andindirect (uniaxial, biaxial and triaxial) shear testers(Schwedes, 2003). Some examples of translational directtesters are Jenike’s shear cell and Casagrande’s shear tester, while some of the most important rotational directtesters are the torsional and the ring shear testers.
Shear cell measurements are widely used to assess theflowability of powders for applications that involve powder discharge. Most of the principles of such tests havetheir origin in soil mechanics and in bunker designing,but they have become increasingly useful for the generalcharacterisation of particulate materials. However, as forthe static tests, a direct link between the failure propertiesof the material measured with a shear cell and the corre-

sponding fluidization behaviour is not straightforward.
Shear testers are known to be very useful to predict thesolids flow from a tall silo in which the relatively highloading pressure of the solids prevails. However, due tothe very small or zero loads on the bulk of a fluidized
powder, standard shear testers are unlikely to provide an
accurate characterization of fluid bed behaviour, as valuesof the failure properties need to be extrapolated from data
at higher loads. Barletta et al. (2005, 2007) overcame thisproblem in their modified Peshl shear cell by introducing
a cinematic chain to counterbalance the weight of the lidof the cell and therefore allow measurements under low
normal stresses. Such a modified Peschl shear cell wasused by Bruni et al. (2007a, b), whose work is discussed
in the next section.
Moreover, existing shear cells are not designed to operate at high temperature because applications involving
powder discharge are run at ambient temperature. Therefore, shear cells are not capable of detecting possible
changes in the settled bed packing with increasing temperature.
Few studies available in the literature have addressed
the experimental evaluation of powder flow properties athigh temperature. The first attempt was carried out by
Smith et al. (1997), who preheated powder samples of
MgSO4 and CaSO4 up to 750 °C and moved them into a
Jenike shear cell and performed shear tests immediately
without any control of the temperature. Experimental results for consolidation stresses in the range 40–200 kPa
showed an increase of the unconfined yield strength withincreasing temperature for all the analysed powders. Thiswas explained with the aid of SEM observations and
X-ray diffraction measurements which showed the formation of agglomerates during preheating of the sample.
The lack of proper measurement of the temperatureduring the shear tests was to some extent overcome byplacing the shear cell inside a heated chamber with temperature control (Kanaoka et al., 2001; Tsukada et al.,2008).
Ripp and Ripperger (2010) designed a temperature-­controlled annular shear cell for the Schulze shear tester
operating from 80 °C to 220 °C. For this purpose, an electric heater was placed on the lid while the vertical walls
and the bottom of the cell were provided with a double
casing through which a heating or a cooling medium canflow.
Recently, Tomasetta et al. (2013) modified a Schulze
shear cell to perform measurements of the powder flow
properties of powders up to 500 °C. Electric heaters wereintroduced below the cell bottom and on the lid to heat thecell and the powder sample contained in it. A covering of
insulating material was placed around the trough of thecell and above the lid in order to reduce the heat flux fromthe external surface of the cell and then to minimise the99

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

temperature gradient within the sample. They evaluatedthe yield loci at room temperature and at 500 °C for
different samples: FCC powder, fly ashes, glass beads,
natural corundum and synthetic porous α-alumina. Furthermore, in order to simulate the formation of liquid
bridges derived from the melting of one of the phases andto verify the role of capillary forces on the flow propertiesof bulk solids at high temperature, a sample of glass beads
was mixed with some (1 % of the total weight) high-­
density polyethylene (HDPE) which has a relatively low
melting point. The results showed that there was no significant effect of the temperature in the range 20–500 °C on
the shear flow of the FCC powder, fly ashes, corundum
powder and alumina. On the other hand, a small but significant increase of cohesion and, therefore, of the unconfined yield strength was observed with temperature forglass beads.
More recently, Chirone et al. (2015) used the high-­temperature annular shear cell developed by Tomasetta etal (2013) to characterize the flow properties of five ceramic powder samples with different particle size distributions between ambient temperature and 500 °C.
Moreover, a model based on the multiscale approach proposed by Rumpf (1970) and Molerus (1975) was used to
predict the effect of temperature on the tensile strength ofthe powder samples. They observed a significant increaseof powder cohesion at 500 °C for different cuts of thesame powder with a particle size larger than 20 μm. This
resulted in a lower flowability of the samples. Thermal
analysis on the powder samples revealed that the temperature effect on the powder flow properties was only due tovan der Waals’ forces.

5.2 Fluid bed viscosity
Particles fluidized by a gas can be treated as a continuum at length scales much larger than the particle diameter. The rheology of such a continuum results from acumulative effect of microscopic (at length scale of theparticles) forces due to the interaction between particles,
such as Van der Waals’, capillary or electrostatic forces
and hydrodynamic drag forces. Rheological properties, inthe form of flow constitutive equations, are thus an essential component of the averaged continuum equations fordetermining the flow of fluidized particles.
Fluid bed viscosity represents an extremely attractive
parameter to characterise fluid bed behaviour, but unfortunately the results obtained in many of the works available in literature are not consistent at all with each other,as reported by Newton et al. (1999). Newton et al. reviewed the results of the available studies and reported
that different orders of magnitude for the bed viscosity
were obtained using the various methods.
It should be noted that the viscosity is influenced by the100

shape of the particles, the voidage of the bed, the fluidizing velocity and the fluidizing gas viscosity.
Moreover, according to Rietema (1991), making measurements in a freely bubbling bed is pointless because
when a bubble collides with the measuring device theshear stress will locally approach zero. Therefore, only
the measurement of the viscosity of the dense phaseseems to be meaningful.
Various measuring devices have been employed to determine the viscosity of powders. Conventional-type viscometers are those where the material is sheared by arotating element (Stormer, Brookfield and Couette) andthe resistance to the rotation is measured. Other methods
are the floating and falling ball and the torsion pendulum.
In the method developed by Grace (1970), measurementof the shape of rising bubbles is used to indirectly calculate the bed viscosity. All these methods are in some ways
“intrusive”, as an external object is immersed in the bed
in all cases. An indirect method is the quasi-solid emulsion viscosity method developed by Kono et al. (2002),
where the viscosity is measured indirectly through themeasurement of fluidization parameters.
Whether a f luidized bed has to be considered a
Newtonian or a non-Newtonian fluid is also a controversial question. In the early studies of gas-fluidized-bed
rheology, an implicit assumption was that the fluidized
beds behave as a Newtonian fluid (Matheson et al., 1949;
Kramers, 1951; Diekman and Forsythe, 1953; Furukawa
and Ohmae, 1958; Shuster and Haas, 1960; Daniels,
1965). Kai et al. (1991) also assumed Newtonian behaviour for the fluidized bed that they modelled. Others
authors, however, described f luidized beds using a
non-Newtonian visco-plastic model, in particular aBingham fluid model (Anjaneyulu and Khakhar, 1995;
Zhao and Wei, 2000).
Gibilaro et al. (2007) and Colafigli et al. (2009) also reported on the apparent viscosity of a fluidized bed andthey proposed theoretical models and experimental analysis in order to evaluate such a property on the base of theanalogy between fluidized beds and fluids.
In the next section, we discuss the relationship between
the rheological properties of powders and the corresponding fluidization behaviour in the attempt to interpret andquantify the effect of the interparticle forces on fluidized
powders.

6. Link between rheological measurements
and fluidization
As shown in the previous section, a great deal of research work has been carried out towards understanding
the powder rheology. Establishing a relationship between
the rheological properties of powders and the correspond-Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

ing fluidization behaviour is reviewed in this section.
Reiling (1992) explored the link between the apparent
viscosities of fine catalyst powders and the bubble size. In
particular, he studied the effects on viscosity of adding ultrafine silica cuts to the bed of catalyst, using a Brookfield
paddle viscometer. Reiling (1992) questioned the validityof the instability/wave perturbation theory, according towhich low-viscosity and low-density fluid beds should
promote the suppression or destruction of large bubbles
and thus contain smaller bubbles than beds of high viscosity or high density (Rice and Wilhelm, 1958; Romero
and Johanson, 1962). The experimental data reported by
the author did not support this conclusion. The addition ofultrafine silica had a large effect on the dense-phasevoidage but only a small effect on the viscosity of the fluidized bed. Therefore, Reiling (1992) concluded that thelink between bubble size and apparent viscosity is notsupported by a viscous mechanism.
Newton et al. (1999) also tried to relate the bubble sizeand bubble number to the bed viscosity. They used a ball
dropping technique in order to measure the viscosity of abed of Geldart group B polymer powder with increasing
temperature (17–96 °C). The authors reported that the effect of increasing temperature on the polymer cohesiveness and stickiness was demonstrated by the significant
changes observed on the mean bubble size and number of
bubbles. However, the viscosities measured through theball dropping technique did not show dramatic changes
with increasing temperature. Newton et al. (1999), in linewith the findings of Reiling (1992), concluded that viscosity effects do not explain bubble sizes in the case of thetested powder and that they could not assign a viscosity to
the dense phase.
Kai et al. (1991) related the apparent bed viscosity to
the bubble diameter and to pressure fluctuation measurements. The authors, in disagreement with Reiling (1992)
and Newton et al. (1999), found that the bubble diameterincreased with increasing viscosity. They also carried out
experiments with increasing temperature, but they did not
clearly state what trend they found for the apparent viscosity with such a parameter. However, they showed thatthe experimental data for the bubble diameter and the viscosity at high temperatures followed the same relationshipfound at ambient temperature. They obtained similar results by plotting experimental data obtained at high pressure by Weimer and Quarderer (1985). The authors also
proposed an empirical correlation to predict the variationof apparent bed viscosity with operating temperature.
Khoe et al. (1991) analysed the fluidization and the rheological behaviour of sets of powders differing in PSD
and fines content. According to the method described byMolerus (1975, 1978), they used shear tests measurements
and experimental values of εmf and mean particle diameter dsv to derive an expression for the average adhesive

force per contact, F. The method assumes each powder tobe a semi-continuum and derives the average tensile
strength, from which the average F value at the contact
points between monosized particles of diameter dsv is calculated. Khoe et al. (1991) found that the adhesive force
per contact, F, correlated very well with the ratio (εmbεmf )/εmf obtained from experiments. In particular, theyfound that F increases when the maximum dense-phase
expansion increases. The authors used the F values obtained from the shear cell measurements to also question
the commonly accepted assumption that at the minimum
bubbling velocity, the average adhesive forces counterbalance the overall hydrodynamic forces. They developed amodel to calculate a theoretical ΔF which would beneeded to reach the minimum bubbling without expanding the bed, and compared it with the measured values of
F. They found that ΔF << F and thus they stated that theenergy needed to reach minimum bubbling without expanding the bed is much smaller than the average interparticle bond strength. Khoe et al. (1991) also concluded
that vertically elongated microcavities are responsible fora premature weakening of the bed structure, allowing
bubbling to start at a much lower value of the hydrodynamic force than F. This result was in agreement withfindings of Donsì and Massimilla (1973), who also observed cavities and microchannels in the homogeneous
expansion of Geldart group A powders.
Kono et al. (1994) performed several fluidization tests
on powders differing in size, shape, density, chemical
properties and surface characteristics, but all fluidizableand free from any agglomeration or segregation phenomenon. They used spent FCC, glass beads, carbon blackand various starch powders with sizes ranging from 15 to
70 μm. For each powder, the fracture strength σf and theplastic deformation Y were evaluated at ambient temperature and, for the FCC experiments, were calculated attemperatures up to 500 °C as well. The authors proposeda correlation between Y and σf: f  0.11 Y 0.90

(23)which held true for all the experimental data, regardlessof the type of powder, properties or operating temperature. Kono et al. (1994) interpreted this line as a flow
characteristic line, where all powders characterized bysmooth fluidization should fall. This idea was supportedby the fact that when powders forming agglomerates werefluidized or wall effects were significant, the rheologicalparameters could not be related using Eqn. 23. Furthermore, a qualitative correspondence was found between
the measured rheological parameters and the observed
fluidization behaviour. The smaller the values of Y and/or
σf,mb, the larger the expansion of the dense phase at umb,
and the smaller the values of bubble size in freely fluidized beds observed in a 2D fluidized bed. Therefore,101Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

moving along the linear relationship given by Eqn. 23, it
was possible to predict different degrees of flowability
among fluidizable powders. In particular, the points obtained with increasing temperature systematically shiftedalong the line towards the small values of the rheologicalparameters, which was in agreement with the widely reported positive effect of temperature on the fluidization
behaviour of free-flowing materials (Lettieri, 1999).
Quintanilla et al. (2001) investigated the correlation between bulk stresses and adhesion forces in fine powders.
They presented measurements of the tensile strength as afunction of the consolidation stress for a set of fine cohesive powders (i.e. xerographic toners) of 12.7 µm particle
size and with a range of concentration of submicron
fumed silica as a flow control additive. Castellanos et al.
(2002) investigated the dynamics of fine cohesive powders
inside rotating drums with the aim of studying the transition from rigid-plastic flow to gas-fluidized regime. They
performed experimental measurements on the bed expansion as a function of the rotation velocity, using drums ofdifferent diameters and fine powders of different cohesiveness. They demonstrated that the onset of fluidizationin the rotating drum is determined by the ratio of thepowder kinetic energy per unit volume to its tensile
strength, and that once the powder is completely fluidized, the average interstitial gas velocity increases proportionally to the rotation velocity. Subsequently,
Castellanos et al. (2004) presented the so-called Sevilla
Powder Tester (SPT), a 4.45-cm diameter, 17-cm high cylinder placed on a shaker to help fluidization of cohesivematerials. The SPT showed the interdependence between
the consolidation stresses, the tensile yield stress and theparticle volume fraction for powders with different levelsof surface additives which were used to vary the powders’degree of flowability.
Bruni and co-workers (Bruni, 2005; Bruni et al., 2004,
2005, 2007a, 2007b) investigated the effects of adding
different fines cuts on the fluidization and the rheologicalbehaviour of an alumina powder. To this end, they followed a twofold approach to establish a link between the
two aspects of the work. On the one hand, the fluidization
behaviour of the alumina was studied at process temperatures ranging from ambient to 400 °C and by adding two
fine sub-cuts of nominal size 0–25 and 26–45 μm, respectively (discussed in Section 5 of this paper), to the material previously deprived of fines. Fluidization experiments
were carried out, changing the total fines content from
22 % to 30 % by weight. On the other hand, a new mechanically stirred fluid-bed rheometer (the msFBR, 15 cm
in diameter and 30 cm tall) was designed, built and commissioned at ambient temperature to determine the rheological behaviour of the powders containing different
fines sub-cuts (Bruni et al., 2005). The rheological experiments were carried out at different rates of aeration and

102

impeller depths (Figs. 9–10).
In parallel, the failure properties of the materials werealso determined using a modified Peschl shear cell (Bruni
et al., 2007a). A model was developed based on the failure
properties of the materials to determine the stress distribution corresponding to any given rate of aeration in the
msFBR (Bruni et al., 2007b). This allowed a true comparison to be made between the rheological behaviour of different powders, aiding assessment of the effect of the fines
size distribution on powder rheology. Moreover, themodel allowed prediction of the torque measured with the
msFBR and assessment of the effect of aeration on powder rheology.
The interpretation of the failure properties of the materials provided an indication of the capability of the powders to flow and underpinned the link between thefluidization and the rheological behaviour below the minimum fluidization conditions, interpreted as the capabilityof the powders to fail under a certain load and their capability to attain fluidization.
In particular, for all materials tested, measurements ofthe torque at different impeller depths and different levels
of aeration showed that the torque reaches a plateau (seeFig. 9), similar to a normal stress profile in silos (Janssen,1895).
The effect of increasing the aeration rate was to linearly decrease the torque needed to stir the materials.
These trends were explained considering that the stress
distribution in the bed changes with changing aeration, as
shown in Fig. 10. Thus, at a constant impeller depth andwhen the aeration rate is increased, the normal load on
the impeller will decrease due to the additional upwarddrag force exerted by the gas on the powder. This reduces
the apparent gravity and reduces the local state of stressthat is responsible for the measured torque. On the basisof the above results, Bruni et al. (2007b) developed amodel to estimate the state of stress by varying the impeller depth, following Janssen’s approach and Janssen’sFig. 9 Effect of impeller depth (ID) on torque—alumina containing fines sub-cut 26–45 μm. Reprinted with permission from Ref. (Bruni, 2005). Copyright: (2005)
Ph.D. Thesis, University College London.

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Fig. 10

Effect of aeration rate (Δp/Δpc) on torque—alumina
containing fines sub-cut 26–45 μm. Reprinted withpermission from Ref. (Bruni, 2005). Copyright:(2005) Ph.D. Thesis, University College London.

analysis for silo design, which was modified to take theaeration of the bed and the possible cohesiveness of thematerial into account.
The model uses properties such as the dynamic and thewall yield loci of the powders used, which were estimatedwith a Peschl shear cell modified for small loads. The
comparison between the experimental and predicted values for the torque demonstrated that the torque is definedby the plastic deformation of the powders and can be explained within a simple Mohr-Coulomb approach to powder flow.
Bruni et al. (2007a) highlighted significant differencesin the rheological behaviour of the alumina sample containing the small fines sub-cut (0–25 μm) compared to thesample containing the same amount of bigger fines (26–45 μm) or to the sample that was virtually free of fines.
Under the same normal load, the torque needed to stir
the alumina sample containing the small fines sub-cut
was found to be higher than the torque needed to stir the
sample containing the larger fines sub-cut or the sample
that was free of fines. In other words, the presence ofsmall fines increases the powder’s resistance to flow. In
Fig. 11, a comparison is also shown with glass ballotini(fines-free) requiring the least torque. These results were
found to be in agreement also with the results obtained
for the voidage of the settled bed, the voidage of the bedat the minimum fluidization velocity, and the expansion
and dearation tests. The alumina containing the smallerfines sub-cut was arranged in a more compacted packing
when settled and at the minimum fluidization comparedto the alumina sample containing the same amount of bigger fines.
It also exhibited a more pronounced change in slope
during bed expansion, which was interpreted as a weakening of the bed structure before the onset of bubbling.
More recently, Tomasetta et al. (2012) re-examined themodel developed by Bruni et al. (2007b) and performed asensitivity analysis on the wall failure properties and onsome of the original model assumptions regarding the ac-

Fig. 11

Experimental torque vs calculated normal stress σz at
impeller depth for all materials. Reprinted with permission from Ref. (Bruni et al., 2007b). Copyright:(2007) Elsevier Limited.

tive and passive state of stress in the fluid-bed rheometer.
Tomasetta et al. (2012) also proposed a novel procedure
for the inverse application of the model developed by
Bruni et al. (2007b), such that the powder flow properties
could be estimated starting from the torque measurements. The application of this procedure provided goodresults in terms of effective angle of internal friction andwas deemed promising for the ability of the system to explore powder flow at very low consolidation states.

7. Conclusions
This review has demonstrated the important role thatprocess conditions, namely temperature, fines and fines
distribution, play on the fluidization behaviour of gas-solids
fluidized beds.
Process conditions influence fundamental parametersdescribing the minimum fluidization conditions, bed expansion and contraction, and the transition from particulate to bubbling fluidization.
Analysis of the influence of elevated temperatures on
fluidization highlights the enhanced role that the interparticle forces can play on the fluidization quality over thehydrodynamic forces.
The review on the influence of adding fines to the fluid
bed and the role played by the particle size distribution
and distribution of the fines emphasized the need for aquantification of the effects of the interparticle forces.
To this end, the multidisciplinary approach based onlinking rheology and fluidization is encouraging. Further
work is, however, needed; the challenge lies in the difficulty to relate the different rheological measurements to
fluidization, mostly due to the variety of techniques employed, which makes standardisation of rheological tests
very difficult.
A more systematic assessment of the independent andthe combined effects of process conditions on fluid bed
rheology and fluidization still needs to be accomplished.

103

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Nomenclature
Ar

Archimedes number (—)

dp

Particle diameter (m)EMR

Elasticity modulus (N m –2)

gGravitational acceleration (m s –2)

HDFs

Hydrodynamic forces

IPFs

Interparticle forcesnRichardson-Zaki exponent

PSD

Particle size distribution

Re

Reynolds number (—)

Remf

Reynolds number at minimum fluidization (—)

T

Temperature (°C)

uSuperficial velocity (m s –1)

umb

Minimum bubbling velocity (m s –1)

umf

Minimum fluidization velocity (m s –1)

ut

Terminal fall velocity (m s –1)

Y

Plastic deformation coefficient (Pa)ΔpPressure drop (Pa)Δpc

Calculated pressure drop (Pa)Δpm

Measured pressure drop (Pa)εBed voidage (—)ε0

Fixed bed voidage (—)εdDense-phase voidage (—)εmb

Minimum bubbling voidage (—)εmf

Minimum fluidization voidage (—)µ

Viscosity (Pa s)µg

Gas Viscosity (Pa s)

ρBDL

Bulk density loose (kg m –3)

ρBDP

Bulk density packed (kg m –3)

ρg

Gas density (kg m –3)

ρp

Solids density (kg m –3)

σf

Fracture strength of powder structure (Pa)σzCalculated Normal stress (Pa)ϕShape factor (—)

References
Abrahamsen A., Geldart D., Behaviour of gas-fluidized beds offine powders part II. Voidage of the dense phase in bubbling
beds, Powder Technol., 26 (1980) 47–55.
Agbim J.A., Rowe P.N., Nienow A.W., Interparticle forces thatsuppress bubbling in gas fluidized beds, Chem. Eng. Sci.,
26 (1971) 1293–1294.
Anjaneyulu P., Khakhar D., Rheology of a gas-fluidized bed,
Powder Technol., 83 (1995) 29–34.
Arena U., Fluidized bed gasification, in: Scala F. (Ed.), Fluidized Bed Technologies for Near-Zero Emission Combustion
and Gasification, Woodhead Publishing Limited, Cambridge

104(UK), 2013, pp. 765–812. doi: 10.1533/9780857098801.3.765
Avidan A., Yerushalmi J., Bed expansion in high velocity fluidization, Powder Technol., 32 (1982) 223–232.
Avidan A., Edwards M., Modeling and scale-up of Mobil’s
fluid-bed MTG process, Proceeding of Fluidization V, New
York, 1986, pp. 457–464.
Babu S.P., Shah B., Talwalkar A., Fluidization correlations forcoal gasification materials-minimum fluidization velocity
and fluidized bed expansion ratio, AIChE Symp. Ser. (1978)
176–186.
Barletta D., Donsì G., Ferrari G., Poletto M., Aerated flow offine cohesive powders in a Peschl shear cell, in: Powders
and Grains 2005—Proceedings of the 5th International
Conference on Micromechanics of Granular Media, Stuttgart, 2005, pp. 1033–1037.
Barletta D., Donsì G., Ferrari G., Poletto M., A Rotational
Tester for the Characterization of Aerated Shear Flow of
Powders, Part. Part. Syst. Charact., 24 (2007) 259–270. doi:
10.1002/ppsc.200701115
Barreto G., Yates J., Rowe P., The measurement of emulsion
phase voidage in gas fluidized beds of fine powders, Chem.
Eng. Sci., 38 (1983) 345.
Basu P., Biomass Gasification, Pyrolysis and Torrefaction: Practical Design and Theory, second ed., Academic Press –Elsevier, London, 2013.
Bin A.K., Prediction of the minimum fluidization velocity. Powder Technol., 81(1993) 197–199.
Botterill J.S.M., Teoman Y., Yüregir K.R., The effect of operating temperature on the velocity of minimum fluidization,
bed voidage and general behaviour, Powder Technol., 31(1982) 101–110.
Bourgeois P., Grenier P., The ratio of terminal velocity to minimum fluidising velocity for spherical particles, Can. J. Chem.
Eng., 46 (1968) 325–328. doi: 10.1002/cjce.5450460508
Brandani S., Foscolo P.U., Analysis of discontinuities arisingfrom the one-dimensional equations of change for fluidization, Chem. Eng. Sci., 49 (1994) 611–619.
Broadhurst T.E., Becker H.A., Onset of fluidization and slugging in beds of uniform particles, AIChE J., 21 (1975) 238–
247. doi: 10.1002/aic.690210204
Bruni G., Lettieri P., Elson T., Yates J., The effect of process
conditions on the fluidization behaviour of gas fluidized
beds, AIChE Annual Meeting, 2004, pp. 2815–2820.
Bruni G., An investigation of the influence of fines size distribution and high temperature on the fluidization behaviourof gas fluidized beds linked with rheological studies, Ph.D.
Thesis, University College London, 2005.
Bruni G., Colafigli A., Lettieri P., Elson T., Torque Measurements in Aerated Powders Using a Mechanically Stirred
Fluidized Bed Rheometer (msFBR), Chem. Eng. Res. Des.,83 (2005) 1311–1318. doi: 10.1205/cherd.05092
Bruni G., Lettieri P., Newton D., Yates J.G., The influence offines size distribution on the behaviour of gas fluidized
beds at high temperature, Powder Technol., 163 (2006)
88–97. doi: 10.1016/j.powtec.2006.01.007
Bruni G., Barletta D., Poletto M., Lettieri P., A rheologicalmodel for the flowability of aerated fine powders, Chem.
Eng. Sci., 62 (2007a) 397–407. doi: 10.1016/j.ces.2006.08.060
Bruni G., Lettieri P., Newton D., Barletta D., An investigation

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

of the effect of the interparticle forces on the fluidization
behaviour of fine powders linked with rheological studies,
Chem. Eng. Sci., 62 (2007b) 387–396. doi: 10.1016/j.ces.
2006.08.059
Carman P.C., Fluid flow through granular beds, Trans. Inst.
Chem. Eng., 15 (1937) 150. doi: 10.1016/S0263-8762(97)80003-2
Carr R.L., Evaluating flow properties of solids, Chem. Eng., 72(1965) 163–168.
Carr R.L., Particle behaviour storage and flow, Bri Chem. Eng.,
15 (1970) 1541–1549.
Castellanos A., The relationship between attractive interparticle
forces and bulk behaviour in dry and uncharged fine powders, Adv. Phys., 54 (2005) 263–376.
Castellanos A., Valverde J. M., Quintanilla M., Fine cohesive
powders in rotating drums: Transition from rigid-plastic
flow to gas-fluidized regime, Phys. Rev. E-Stat. Nonlinear,
Soft Matter Phys, 65 (2002) 1–7.
Castellanos A., Valverde J., Quintanilla M., The Sevilla powder
tester: a tool for characterizing the physical properties offine cohesive powders at very small consolidations, KONA
Powder Part. J., 22 (2004) 66–81.
Chen Y.-M., Applications for Fluid Catalytic Cracking, in: Yang
W.C. (Ed.), Handbook of Fluidization and Fluid-Particle
Systems, Marcel Dekker Inc., New York, 2003, pp. 379–
396.
Chen Z.D., Chen X.P., Wu Y., Chen R.C., Study on minimum
fluidization velocity at elevated temperature, Proc. Chinese
Soc. Electr. Eng., 30 (2010) 21–25.
Chirone R., Barletta D., Lettieri P., Poletto M., Measurement ofhigh temperature powder flow properties to estimate interparticle forces in high temperature fluidization, in: The 8thInternational Conference for Conveying and Handling ofParticulate Solids, 2015.
Chitester D., Kornosky R., Characteristics of fluidization athigh pressure, Chem. Eng. Sci., 39 (1984) 253–261.
Colafigli A., Mazzei L., Lettieri P., Gibilaro L., Apparent viscosity measurements in a homogeneous gas-fluidized bed,
Chem. Eng. Sci., 64 (2009) 144–152. doi: 10.1016/j.
ces.2008.08.036
Coltters R., Rivas A.L., Minimum fluidization velocity correlations in particulate systems, Powder Technol., 147 (2004)
34–48. doi: 10.1016/j.powtec.2004.06.013
Cottaar E., Rietema K., A theoretical study on the influence ofgas adsorption on interparticle forces in powders, J. Colloid
Interface Sci., 109 (1986) 249–260.
Daniels T., Measurement of the drag on immersed bodies in fluidised beds. Rheol. Acta, 3(1965) 192–197.
Davidson J.F., Discussion following symposium on fluidization,
Trans. Instn. Chem. Eng., 39 (1961) 230–232.
Davidson J.F., Harrison D., Fluidised Particles, Cambridge University Press, Cambridge (UK), 1963.
Delebarre A., Revisiting the Wen and Yu Equations for Minimum Fluidization Velocity Prediction, Chem. Eng. Res.
Des., 82 (2004) 587–590. doi: 10.1205/026387604323142621
Diekman R., Forsythe W., Laboratory prediction of flow properties of fluidized solids, Ind. Eng. Chem., 45 (1953) 1174–
1177.
Doichev K., Akhmakov N., Fluidisation of polydisperse sys-tems, Chem. Eng. Sci., (1979) 2–4.
Donsì G., Massimilla L., Bubble-free expansion of gas-fluidized
beds of fine particles, AIChE J., 19 (1973) 1104–1110. doi:
10.1002/aic.690190604
Ergun S., Fluid flow through packed columns. Chem. Eng.
Prog., (1952) 48.
Fan L.S., Chemical looping systems for fossil energy conversions, Wiley-AIChE, New York, 2010.
Fletcher J., Deo M., Hanson F., Re-examination of minimum
fluidization velocity correlations applied to Group B sandsand coked sands, Powder Technol., 69 (1992) 147–155.
Formisani B., Girimonte R., Mancuso L., Analysis of the fluidization process of particle beds at high temperature, Chem.
Eng. Sci., 53 (1998) 951–961.
Foscolo P.U., Gibilaro L.G., Waldram S.P., A unified model forparticulate expansion of fluidized beds and flow in fixedporous media, Chem. Eng. Sci., 38 (1983) 1251–1260.
Foscolo P.U., Gibilaro L., A fully predictive criterion for thetransition between particulate and aggregate fluidization,
Chem. Eng. Sci., 39 (1984) 1667–1675.
Foscolo P.U., Gibilaro L., Fluid dynamic stability of fluidisedsuspensions: the particle bed model, Chem. Eng. Sci., 42(1987) 1489–1500.
Furukawa J., Ohmae T., Liquid like properties of fluidized systems, Ind. Eng. Chem., 50 (1958) 821–828.
Geldart D., Types of gas fluidization, Powder Technol., 7 (1973)
285–292.
Geldart D., Wong A., Fluidization of powders showing degrees
of cohesiveness—I. Bed expansion, Chem. Eng. Sci., 39(1984) 1481–1488.
Geldart D., Wong A., Fluidization of powders showing degrees
of cohesiveness—II. Experiments on rates of de-aeration,
Chem. Eng. Sci., 40 (1985) 653–661.
Geldart D., Estimation of basic particle properties for use in
fluid-particle process calculations, Powder Technol., 60(1990) 1–13. doi: 10.1016/0032-5910(90)80099-K
Gibilaro L.G., Gallucci K., Di Felice R., Pagliai P., On theapparent viscosity of a fluidized bed, Chem. Eng. Sci., 62(2007) 294–300. doi: 10.1016/j.ces.2006.08.030
Girimonte R., Formisani B., The minimum bubbling velocity of
fluidized beds operating at high temperature, Powder Technol., 189 (2009) 74–81. doi: 10.1016/j.powtec.2008.06.006
Girimonte R., Formisani B., Effects of operating temperatureon the bubble phase properties in fluidized beds of FCC
particles, Powder Technol., 262 (2014) 14–21. doi: 10.1016/
j.powtec.2014.04.041
Goo J.H., Seo M.W., Kim S.D., Song B.H., Effects of temperature and particle size on minimum fluidization and transport velocities in a dual fluidized bed, in: Proceedings ofthe 20th International Conference on Fluidized Bed Combustion, 2010, pp. 305–310.
Goroshko V.D., Rozembaum R.B., Toedes O.H., Approximate
relationships for suspended beds and hindered fall, Izv.
Vuzov, Neft. Gaz., 1 (1958) 125.
Grace J.R., The viscosity of fluidized beds, Can. J. Chem. Eng.,
48 (1970) 30–33. doi: 10.1002/cjce.5450480106
Grace J.R., Hetsroni G., Handbook of multiphase systems,
Hetsroni MG Edition, New York, 1982.
Grewal N.S., Gupta A., Total and gas convective heat transfer

105Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

from a vertical tube to a mixed particle gas-solid fluidized
bed, Powder Technol., 57 (1989) 27–38. doi: 10.1016/00325910(89)80101-80109
Hartman M., Trnka O., Pohořelý M., Minimum and Terminal
Velocities in Fluidization of Particulate Ceramsite at Ambient and Elevated Temperature, Ind. Eng. Chem. Res., 46(2007) 7260–7266. doi: 10.1021/ie0615685
Horio M., Overview of fluidization science and fluidized bed
technologies, in: Scala F. (Ed.), Fluidized Bed Technologies
for Near-Zero Emission Combustion and Gasification,
Woodhead Publishing Limited, Cambridge (UK), 2013, pp.
3–41. doi: 10.1533/9780857098801.1.3
Jackson R., The mechanics of fluidized beds, Trans. Instn.
Chem. Engnr., 41 (1963) 22–48.
Jazayeri B., Applications for Chemical Production and processing, in: Yang W.C. (Ed.), Handbook of Fluidization and
Fluid-Particle Systems, Marcel Dekker Inc., New York,
2003, pp. 421–444
Jean R., Fan L., On the model equations of Gibilaro and Foscolowith corrected buoyancy force, Powder Technol., 72 (1992)
201–205.
Jiliang M., Xiaoping C., Daoyin L., Minimum fluidization
velocity of particles with wide size distribution at high temperatures, Powder Technol., 235 (2013) 271–278. doi:
10.1016/j.powtec.2012.10.016
Kai T., Murakami M., Yamasaki K., Takahashi T., Relationshipbetween apparent bed viscosity and fluidization quality in a
fluidized bed with fine particles, J. Chem. Eng. Japan, 24(1991) 494–500.
Kanaoka C., Hata M., Makino H., Measurement of adhesive
force of coal flyash particles at high temperatures and different gas compositions, Powder Technol., 118 (1991) 107–112. doi: 10.1016/S0032-5910(01)00300-X
Khoe G., Ip T., Grace J., Rheological and fluidization behaviourof powders of different particle size distribution, Powder
Technol., 66 (1991) 127–141.
Knowlton T.M., Pressure and temperature effects in fluid-particle
systems, in: Proceeding of Fluidization VII, New York,
1992, pp. 27–46.
Kono H., Aksoy E., Itani Y., Measurement and application of
the rheological parameters of aerated fine powders—anovel characterization approach to powder flow properties,
Powder Technol., 81 (1994) 177–187.
Kono H., Narasimhan S., Richman L.M., Ohtake T., Flow properties of homogeneously aerated, expanded emulsion phaseof fine powders (quasi-solid emulsion phase viscosity),
Powder Technol., 122 (2002) 168–176.
Kramers H., On the “viscosity” of a bed of fluidized solids,
Chem. Eng. Sci., 1 (1951) 35–37.
Kunii D., Levenspiel, O., Fluidization Engineering, Butterworth-­
Heinemann, Newton (USA), 1991.
Lettieri P., A study on the influence of temperature on the flow
behaviour of solid materials in a gas fluidized bed, Ph.D.
Thesis, University College London, 1999.
Lettieri P., Yates J.G., Newton D., The influence of interparticle
forces on the fluidization behaviour of some industrial
materials at high temperature, Powder Technol., 110 (2000)
117–127. doi: 10.1016/S0032-5910(99)00274-0
Lettieri P., Newton D., Yates J.G., High temperature effects on106

the dense phase properties of gas fluidized beds, Powder
Technol., 120 (2001) 34–40. doi: 10.1016/S0032-5910(01)00344-8
Lettieri P., Newton D., Yates J.G., Homogeneous bed expansion
of FCC catalysts, influence of temperature on the parameters of the Richardson–Zaki equation, Powder Technol.,
123 (2002) 221–231. doi: 10.1016/S0032-5910(01)00463-6
Lettieri P., Mazzei L., Analysis of the fluid-bed stability of FCC
catalysts at high temperature in terms of bed elasticity, Particuology, 6 (2008) 30–37.
Leva M., Shirai T., Wen C.Y., Prediction of onset of fluidization
in beds of granular solids, Genie Chim., 75 (1956) 33.
Leva M., Fluidization, McGraw-Hill Inc., New York, Toronto,
London, 1959
Lin C.L., Wey M.Y., You S.D., The effect of particle size distribution on minimum fluidization velocity at high temperature, Powder Technol., 126 (2002) 297–301. doi: 10.1016/
S0032-5910(02)00074-8
Lorences M.J., Patience G.S., Dı́ez F. V, Coca J., Fines effects
on collapsing fluidized beds, Powder Technol., 131 (2003)
234–240. doi: 10.1016/S0032-5910(03)00004-4
Lucas A., Arnaldos J., Improved equation for the calculation ofminimum fluidization velocity, Ind. Eng. Chem. Process
Des. Dev., 25 (1986) 426–429.
Lucas A., Arnaldos J., Casal J., Puigjaner L., High temperature
incipient fluidization in mono and polydisperse systems,
Chem. Eng. Commun., 41 (1986) 121–132. doi: 10.1080/
00986448608911715
Massimilla L., Donsì G., Zucchini C., The structure of bubble-­free gas fluidized beds of fine fluid cracking catalyst particles, Chem. Eng. Sci., 27 (1972) 2005–2015.
Materazzi M., Lettieri P., Mazzei L., Taylor R., Chapman C.,
Thermodynamic modelling and evaluation of a two-stage
thermal process for waste gasification, Fuel, 108 (2013)356–369.
Materazzi M., Lettieri P., Mazzei L., Taylor R., Chapman C.,
Fate and behavior of inorganic constituents of RDF in a twostage fluid bed-plasma gasification plant, Fuel, 150 (2015)
473–485.
Matheson G., Herbst W., Holt P., Characteristics of fluid-solid
systems, Ind. Eng. Chem., 41 (1949) 1099–1104.
Mathur A., Saxena S., Particle classification scheme of saxena
and ganzha and high-temperature bed voidage data at minimum fluidization, Powder Technol., 45 (1986) 287–289.
Miller C., Logwinuk A., Fluidization Studies of Solid Particles,
Ind. Eng. Chem., (1951) 1220–1226.
Mills P., Snabre P., Settling of a Suspension of Hard Spheres,
Europhys. Lett., 25 (1994) 651–656.
Molerus O., Theory of yield of cohesive powders, Powder Technol., 12 (1975) 259–275.
Molerus O., Effect of interparticle cohesive forces on the flow
behaviour of powders, Powder Technol., 20 (1978) 161–175.
Murray J.D., On the mathematics of fluidization, J. Fluid Mech.,
22 (1965) 57–63.
Mutsers S.M.P., Rietema K., The effect of interparticle forceson the expansion of a homogeneous gas-fluidized bed, Powder Technol., 18 (1977) 239–248. doi: 10.1016/0032-5910(77)80014-4
Newby R.A., Applications for Gasifiers and Combustors, in:Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Yang W.C. (Ed.), Handbook of Fluidization and Fluid-­
Particle Systems, Marcel Dekker Inc., New York, 2003, pp.
397–420.
Newton D., PhD Dissertation, Department of Chemical Engineering, University College London, 1984.
Newton D., Smith G., Hird N., Comments on experimental dataon the suitability of assigning a viscosity to the fluidized
bed dense phase, in: Unpublished Paper Presented at theFifth International Conference on Fluid Particle Interactions, 1999.
Pattipati R.R., Wen C.Y., Minimum fluidization velocity at hightemperatures, Ind. Eng. Chem. Process Des. Dev. 20 (1981)
705–707. doi: 10.1021/i200015a022
Pell M., Jordan S.P., Effects of fines and velocity on fluid bed
reactor performance, American Institute of Chemical Engineers, New York, 1987.
Piepers H., Cottaar E., Verkooijen A., Rietema K., Effects ofpressure and type of gas on particle-particle interaction andthe consequences for gas-solid fluidization behaviour. Powder Technol., 37 (1984) 55–70.
Quintanilla M., Castellanos A., Valverde J.M., Correlation
between bulk stresses and interparticle contact forces infine powders, Phys. Rev. E. Stat. Nonlin. Soft Matter Phys.,64, 031301 (2001).
Rapagna S., Foscolo P., Gibilaro L., The influence of temperature on the quality of gas fluidization, Int. J. Multiph. Flow,
20 (1994) 305–313.
Raso G., D’Amore M., Formisani B., Lignola P., The influenceof temperature on the properties of the particulate phase atincipient fluidization, Powder Technol., 72 (1992) 71–76.
Reiling V.G., The effects of ultrafine particles on powder cohesion and fluidization, Ph.D. Thesis, Case Westner Reserve
University, 1992.
Reina J., Velo E., Puigjaner L., Predicting the minimum fluidization velocity of polydisperse mixtures of scrap-woodparticles, Powder Technol., 111 (2000) 245–251.
Riba J.P., Routie R., Couderc J.P., Conditions minimales demise en fluidisation par un liquid, Can. J. Chem. Eng., 56(1978) 26–30. doi: 10.1002/cjce.5450560104
Rice W.J., Wilhelm R.H., Surface dynamics of fluidized beds
and quality of fluidization, AIChE J., 4 (1958) 423–429. doi:
10.1002/aic.690040409
Richardson J., Zaki W.N., Sedimentation and fluidisation: Part I,
Trans. Inst. Chem. Eng., 32 (1954) 35–53
Richardson J., Jerónimo M., Velocity-voidage relations for sedimentation and fluidisation, Chem. Eng. Sci., 5 (1979) 1419–
1422.
Rietema K., The Dynamics of Fine Powders, Springer Netherlands, Dordrecht, 1991. doi: 10.1007/978-94-011-3672-3
Rietema K., Cottaar E., Piepers H., The effects of interparticle
forces on the stability of gas-fluidized beds—II. Theoreticalderivation of bed elasticity on the basis of van der Waals
forces between, Chem. Eng. Sci., 48 (1993) 1687–1697.
Ripp M., Ripperger S., Influence of temperature on the flow
properties of bulk solids, Chem. Eng. Sci., 65 (2010) 4007–
4013. doi: 10.1016/j.ces.2010.03.046
Romero J., Johanson L., Factors affecting fluidized bed quality,
Chem. Eng. Prog. Symp. Ser., 58 (1962) 28–37.
Rowe P., Santoro L., Yates J., The division of gas between bub-ble and interstitial phases in fluidised beds of fine powders,Chem. Eng. Sci., 33 (1978) 133–140.
Rowe P., The effect of pressure on minimum fluidisation velocity,
Chem. Eng. Sci., 39 (1984) 173–174.
Rumpf H., Zur theorie der zugfestigkeit von agglomeraten bei
kraftuebertragung an kontaktpunkten, Chemie Ing. Tech.,
42 (1970) 538–540.
Santomaso A., Lazzaro P., Canu P., Powder flowability and density ratios: the impact of granules packing, Chem. Eng. Sci.,
58 (2003) 2857–2874. doi: 10.1016/S0009-2509(03)00137-4
Saxena S.C., Vogel G.L., The measurement of incipient fluidisation velocities in a bed of coarse dolomite at temperatureand pressure, Chem. Eng. Res. Des., 55 (1977) 184.
Schwedes J., Review on testers for measuring flow properties ofbulk solids, Granul. Matter, 5 (2003) 1–43. doi: 10.1007/
s10035-002-0124-4
Siegell J.H., Early studies of magnetized fluidized beds, Powder
Technol., 57 (1989) 213–220.
Shuster W., Haas F., Point Viscosity Measurements in a Fluidized Bed, J. Chem. Eng. Data, 5 (1960) 525–530.
Smith D., Haddad G., Ferer M., Shear strengths of heated andunheated mixtures of MgSO4 and CaSO4 powders. Modelpressurized fluidized bed combustion filter cakes, Energy& fuels, 11 (1997) 1006–1011.
Subramani H.J., Mothivel Balaiyya M.B., Miranda L.R., Minimum fluidization velocity at elevated temperatures for
Geldart’s group-B powders, Exp. Therm. Fluid Sci., 32(2007) 166–173. doi: 10.1016/j.expthermflusci.2007.03.003
Sun G., Grace J., The effect of particle size distribution on theperformance of a catalytic fluidized bed reactor, Chem.
Eng. Sci., 45 (1990) 2187–2194.
Sun G., Grace J.R., Effect of particle size distribution in different fluidization regimes, AIChE J., 38 (1992) 716–722. doi:
10.1002/aic.690380508
Thonglimp V., Hiquily N., Laguerie C., Vitesse minimale defluidisation et expansion des couches fluidisées par un gaz,Powder Technol., 38 (1984) 233–253.
Tomasetta I., Barletta D., Lettieri P., Poletto M., The measurement of powder flow properties with a mechanically stirred
aerated bed, Chem. Eng. Sci., 69 (2012) 373–381.
Tomasetta I., Barletta D., Poletto M., The High Temperature
Annular Shear Cell: A modified ring shear tester to measure the flow properties of powders at high temperature,Adv. Powder Technol., 24 (2013) 609–617. doi: 10.1016/j.
apt.2012.11.007
Toomey R. D., Johnstone H. F., Gaseous fluidization of solid
particles. Chem. Eng. Progress, 48 (1952), 220–226.
Tsukada M., Kawashima K., Yamada H., Yao Y., Kamiya H.,
Analysis of adhesion behavior of waste combustion ash athigh temperatures and its control by the addition of coarse
particles, Powder Technol., 180 (2008) 259–264. doi:
10.1016/j.powtec.2007.04.005
Valverde J.M., Quintanilla M., Castellanos A., Mills P., The settling of fine cohesive powders, Europhys. Lett., 54 (2001)
329–334.
Valverde J.M., Castellanos A., Mills P., Quintanilla M., Effectof particle size and interparticle force on the fluidization
behavior of gas-fluidized beds, Phys. Rev. E. Stat. Nonlin.
Soft Matter Phys., 67, 051305 (2003).

107

Paola Lettieri et al. / KONA Powder and Particle Journal No. 33 (2016) 86–108

Valverde J.M., Castellanos A., Effect of compaction history on
the fluidization behavior of fine cohesive powders, Phys.
Rev. E-Stat. Nonlinear, Soft Matter Phys., 73 (2006) 1–5.
Valverde J.M., Castellanos A., Bubbling Suppression in Fluidized Beds of Fine and Ultrafine Powders, Part. Sci. Technol., 26 (2008) 197–213.
Verloop J., Heertjes P., Shock waves as a criterion for the transition from homogeneous to heterogeneous fluidization,
Chem. Eng. Sci., 25 (1970) 825–832.
Wallis G.B., One-dimensional two-phase flow, McGraw-Hill,
New York, 1969.
Weimer A.W., Quarderer G.J., On dense phase voidage and bubble size in high pressure fluidized beds of fine powders,
AIChE J., 31 (1985) 1019–1028. doi: 10.1002/aic.690310619
Wen C.Y., Yu Y.H., A generalized method for predicting theminimum fluidization velocity, AIChE J., 12 (1966) 610–
612. doi: 10.1002/aic.690120343
Werther J., Hydrodynamics and mass transfer between the bubble and emulsion phases in fluidized beds of sand andcracking catalyst, in: Proc. 4th Int. Conf. On Fluidization,
Edited by D. Kunii and R. Toei, AIChE., New York, 1983,
pp. 93–101.
Winter F., Schratzer B., Applications of fluidized bed technology in processes other than combustion and gasification, in:
in: Scala F., Fluidized Bed Technologies for Near-Zero

Emission Combustion and Gasification, Woodhead Publishing Limited, Cambridge (UK), 2013, pp. 1005–1033.
doi: 10.1533/9780857098801.5.1005
Wu S., Baeyens J., Effect of operating temperature on minimum
fluidization velocity, Powder Technol., 67 (1991) 217–220.
Xie H.-Y., Geldart D., Fluidization of FCC powders in the
bubble-free regime: effect of types of gases and temperature, Powder Technol., 82 (1995) 269–277. doi: 10.1016/
0032-5910(94)02932-E
Yamazaki R., Han N., Sun Z., Jimbo G., Effect of chemisorbed
water on bed voidage of high temperature fluidized bed,
Powder Technol., 84 (1995) 15–22.
Yang W.C., Chitester D.C., Kornosky R.M., Keairrns D.L., A
generalized methodology for estimating minimum fluidization velocity at elevated pressure and temperature, AIChE
J., 31 (1985) 1086–1092.
Yates J., Newton D., Fine particle effects in a fluidized-bed
reactor, Chem. Eng. Sci., 41 (1986) 801–806.
Yates J., Effects of temperature and pressure on gas-solid fluidization, Chem. Eng. Sci., 51 (1996) 168–205.
Zaki W.N., Richardson J.F., Sedimentation and fluidisation: Part
I, Trans. Inst. Chem. Eng., 32 (1954) 35–53.
Zhao Y., Wei L., Rheology of gas–solid fluidized bed, Fuel Process. Technol., 68 (2000) 153–160. doi: 10.1016/S0378-3820(00)00122-3

Author’s short biography
Paola Lettieri
Paola Lettieri FIChemE is a professor of chemical engineering at the University College London and head of the Fluidization Research Group. She has 20 years experiencein particle technology and fluidization with applications spanning across the chemical,
petrochemical, nuclear and energy-from-waste sectors. Her research focuses on the effect of process conditions on fluidization, encompassing experimental studies, mathematical modelling, rheology, X-ray imaging and reactor design. She has published 150
refereed articles and 6 book chapters, and has been awarded two prestigious fellowships
from the Royal Academy of Engineering for her work in fluidization.

Domenico Macrí
Domenico Macrí is currently a PhD student in chemical engineering at the University
College London. He received his Master’s degree in chemical engineering from the University of Calabria in 2014. His main research interests focus on the effect of temperatureon the defluidization of industrial reactive powders.

108KONA Powder and Particle Journal No. 33 (2016) 109–126/Doi:10.14356/kona.2016020

Review Paper

Transport Properties and Segregation Phenomena
in Vibrating Granular Beds †Chun-Chung Liao 1 and Shu-San Hsiau 2*
1
2

Department of Mold and Die Engineering, National Kauhsiung University of Applied Sciences, Taiwan
Department of Mechanical Engineering, National Central University, Taiwan

Abstract
Granular materials are common in daily life and in many industrial processes. Both fundamental research andindustrial application studies are crucial for understanding the transport properties and segregation mechanismsof vibrating granular beds. One major related research topic is granular materials subjected to external vibration;such granular materials exhibit complex movement and Brazil nut segregation. Understanding the transport
properties and the rising of an intruder immersed in granular materials is a challenge in granular flow research.
This paper presents a review of transport properties and segregation phenomena in a vibrating granular bed, anddiscusses the relationship between transport properties and granular segregation. Furthermore, how the vibration
conditions, liquid bridge force, bed height, surface roughness of granular materials, and a bumpy base of granular
beds affect the transport properties, convection, and granular segregation are reported. The results indicate thatthe transport properties and segregation behavior are significantly influenced by the addition of small amounts ofliquids and by the surface roughness and a bumpy base. Diffusive and convective motions are weakened as thebase roughness increases, leading to a weaker Brazil nut effect.
Keywords: granular flows, vibrating granular beds, transport properties, convection, granular segregation,
surface roughness

1. Introduction
Granular materials and powders are aggregates of discrete solid particles dispersed in an interstitial fluid.
Granular materials (e.g., sand, salt, sugar, metal powders,glass beads, steel balls, coffee beans, pills, wheat, andrice) are commonly encountered in daily life and widelyused in many industrial processes such as pharmaceuticalmanufacturing, gasification, pyrolysis, chemical manufacturing, mineral processing, metal powder injection
molding, additive manufacturing, and powder metallurgy.
The materials are also used for food transport and storage.
Granular flows are also found in nature (e.g., avalanches,
landslides, and debris flows) and can cause disasters.
Understanding the transport properties and physicalmechanism of granular flows is crucial; however, ourunderstanding of granular materials is poor. Random particle motions resulting from interactive collisions between
†
1
2

*

Received 9 July 2015; Accepted 4 September 2015
J-STAGE online 28 February 2016
415 Chien Kung Road, Kaohsiung 807, Taiwan
No.300, Jhongda Rd. Jhongli City, Taoyuan County 32001, Taiwan
Corresponding author: Shu-San Hsiau;
E-mail: sshsiau@cc.ncu.edu.tw
TEL: +886-3-426-7341 FAX: +886-3-425-4501©2016 Hosokawa Powder Technology Foundation

particles are the dominant mechanism influencing theflow behavior and transport properties of granular materials (Campbell, 1990). These materials do not flow homogeneously like a fluid when the external driving force issmall. By contrast, they may behave like a gas if the granular system is relatively diluted and the external force issufficiently large. Furthermore, the materials may behave
like a solid when the external energy is insufficient. These
three motion states may occur simultaneously or exist individually in granular systems (Jaeger, 1996). Although
the rheological behavior of granular matter is complex, anunderstanding of granular matter is crucial in numerous
industrial applications and for resolving various environmental problems.
Knowledge of granular segregation and transport properties has become crucial in many industrial processes(e.g., pharmaceutical manufacturing, foodstuff production, powder metallurgy, metal powder injection molding,and detergent, chemical, and plastic manufacturing) andin nature research (e.g., landslides, avalanches, and debris
flows). In particular, granular segregation is a complex andpoorly understood process. Therefore, numerous researchers have studied the transport properties and segregation
mechanisms of granular materials (Bose et al., 2007; Breuet al., 2003; Bridgwater, 1976; Brito et al., 2008; Campbell,

109

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

1997; Halsey and Levine, 1998; Herminghaus, 2005;
Hsiau and Shieh, 1999; Hsiau and Yang, 2002; Hsiau
and Yang, 2003; Hsiau et al., 2013; Khakhar et al., 1997;
Kudrolli, 2004, 2008; Liao and Hsiau, 2009, 2010; Liao etal., 2010a, 2015; Liffman et al., 2011; Lozano et al., 2015;
Lu and Hsiau, 2008; Natarajan et al., 1995; Saez et al.,2005; Shi et al., 2007; Tai and Hsiau, 2004; van der Vaartet al., 2015; Williams, 1976; Windows-Yule et al., 2015a).
The segregation phenomenon in granular materials can be
influenced by external driving conditions (Ciamarra et al.,2006; Hsiau and Yu, 1997; Hsiau et al., 2002), the interstitial fluid (Clement et al., 2010; Klein et al., 2006), the container geometry (Hsiau et al., 2002), and particle
properties such as size (Cooke et al., 1996; Knight et al.,
1993; Mobius et al., 2004; Zamankhan, 2013), density(Klein et al., 2006; Tai et al., 2010; Windows-Yule andParker, 2015; Zeilstra et al., 2008), restitution coefficient(Brito and Soto, 2009), shape (Kudrolli, 2004; Williams,
1976), and friction coefficient (Kondic et al., 2006; Liaoet al., 2012; Plantard et al., 2006). The segregation of
granular materials also occurs in vibrating granular beds.
The well-known Brazil nut problem associated with large
particles (the so-called intruders) immersed in smaller
granular materials has been studied extensively by using
experiments, computer simulations, and theoretical models. Segregation has been demonstrated as occurring
when grains vary in size, density, friction coefficient, andrestitution coefficient. Segregation has also been observedin several configurations when an external driving force,in the form of a vibrating bed, shear cell, rotating drum,or chute flow, is applied to the system.
Granular temperature is defined as the specific fluctuation kinetic energy of particles, analogous to the thermodynamic temperature of a gas, and is a key parameterfor describing the flow behavior of granular materials(Campbell, 1990; Hsiau et al., 2008; Liao et al., 2015;
Ogawa, 1978; Wildman and Huntley, 2000; Zivkovic etal., 2011). Ogawa (1978) proposed the concept of granulartemperature to quantify random motions of particles, andthis concept has been widely used in related research. In
the dense-gas kinetic theory for granular flows, granulartemperature is assumed to be isotropically distributed.
However, this key assumption is not observed in many
practical scenarios, primarily because inelastic collisions
and the frictional effect continually dissipate the energyof the granular materials. Thus, external energy must becontinually introduced into the system to maintain the
granular temperature. The granular temperature depends
on the net energy between the energy generated by external vibration and the energy dissipated by the interstitial
fluid, inelastic collisions, and frictional effect.
Understanding transport properties and segregation
mechanisms is a challenge in granular material research.
Studies have shown that granular materials may transition

110

from a solid-like state to a liquid-like state when sufficientenergy is supplied to the granular system (Hsiau and Yu,
1997; Liao et al., 2012). During the expansion of a granular bed, voids are formed between particles and they trigger particle reorganization; consequently, smaller
particles fall through the voids to the bottom of the bed,
resulting in size segregation (Hsiau and Yu, 1997; Rosato
et al., 1987). Duran et al. (1993) showed that large disksrise upward continuously in small steps through the arching effect, in which an intruder is supported by the surrounding network of smaller particles. However, smaller
particles may rise intermittently only when the excitation
amplitude is sufficiently strong. Cooke et al. (1996) foundthat the segregation rate increased with the peak acceleration and size ratio. They also observed that convection
cells play a crucial role in the segregation mechanism.
Liao et al. (2012) demonstrated that the Brazil nut effect
was reduced when the intruder surface roughness washigh. Furthermore, they found that the penetration lengthof the intruder increased with a decrease in the intruder
surface roughness and that the penetration length scaled
exponentially with the vibration frequency. Segregation
also occurs in a binary mixture system with various densities because of the buoyancy effect of denser particles
sinking to lower levels of the granular bed while lighter
particles rise. The granular temperature and convection
also influence density-induced segregation in vibrating
granular beds appreciably (Tai et al., 2010). When thenumber of filling layers is small, heavier beads with a low
granular temperature migrate to the bottom regions.
Thus, the granular temperature plays a significant role indensity-induced segregation. For the higher filling layers,bulk convective motion is the main mechanism affecting
segregation, and thus heavier beads cluster at the convection center. Tai and Hsiau (2004) determined the transport
properties in vibrating granular beds and found that theconvection strength increases when the dimensionlessvibration velocity exceeds 2. van der Vaart et al., (2015)
reported that large and small particles show an underlying
asymmetry that is dependent on the local particle concentration, with small particles segregation faster in regionsof many large particles and large particles segregation
slower in regions of many small particles.

2. Vibrating granular bed equipment andtransport property measurement techniqueVibrating granular beds are widely used in industrial
processes for drying, transporting, mixing, and segregating granular materials. Fig. 1 shows a schematic of theexperimental apparatus used in the current study. A
Techron VTS-100 electromagnetic vibration system
driven by sinusoidal signals produced by a function gen-

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

Fig. 1

Schematic drawing of the vibration granular bed apparatus.

erator (Meter Inc., DDS FG-503) and supplied through a
power amplifier (Techron Model 5530) was used to vertically shake the assembly. The vibration frequency f andvibration acceleration a were measured using a Dytran
3136A accelerometer attached to the shaker and connected to an oscilloscope (Tektronix TDS 210). Given theradian frequency (ω = 2πf ), the amplitude A of the vibration was calculated using the relation A = a/ω2. The dimensionless vibration acceleration Γ is defined as Γ = a/g,
where g is the gravitational acceleration.
The granular bed was vertically shaken. In a two-­
dimensional granular bed, rougher sidewalls induce stronger particle motions and convection cells (Hsiau et al.,2002). Therefore, a layer of glass beads with identical physical properties or a layer of emery paper was glued to thesidewalls of the container to induce a shear force sufficientfor generating motions in the granular material. A highspeed charge-coupled device camera recorded the front
view of the intruder motion. Using a particle-tracking
method and image-processing system, the position of the
intruder could be determined. All images were taken after
the system had vibrated for at least 1 minute, to ensurethat the flow field was in a steady state. The bed was divided into square bins, and an autocorrelation technique
was employed to process the recorded images and determine the shift of each tracer particle between two consecutive images. The local velocities were then calculated fromall the tracer velocities in the bin (Hsiau and Shieh, 1999;
Hsiau et al., 2002; Liao et al., 2010b; Natarajan et al., 1995).
Ni

 ui   u ki
k 1

Ni

(1)

Ni

 vki

(2)

 vi   k 1
NiHere, <ui> and <vi> denote the ensemble average velocities in the horizontal and vertical directions, respectively,in the ith bin; the average velocities are obtained using theNi tracer particles. The subscript k denotes the kth tracer
particle in the ith bin.
The fluctuation velocities in the ith bin were defined as
the root mean square value of the deviations between thelocal velocities and ensemble average velocities:Ni (u ki   ui ) 2

 ui 2 1/2  k 1

Ni

(3)

Ni

 (vki   vi ) 2

 vi 2 1/2  k 1

Ni

(4)

The granular temperature in the ith bin was then determined asTi  ui 2    vi 2 
2(5)The average granular temperature of the entire bed was
calculated as N i  Ti
T i
 Ni

(6)

i

111

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

The granular temperature quantifies the fluctuation kinetic
energy per unit mass associated with the random motionsof the particles. The granular temperature was defined as
the ensemble average of the fluctuation velocities of all
the tracer particles in the vibrating granular bed. A higher
granular temperature indicated a more energetic and fluidized vibrating bed.
Particle diffusion results from particle fluctuations. The
concept of analyzing diffusive phenomena involving suspended particles undergoing Brownian motion in a liquid
was first proposed by Einstein (1956). Campbell (1997)
and Savage and Dai (1993) used the technique to investigate the self-diffusive behavior of granular flow systems
through computer simulations. The self-diffusion coefficient Dij is defined aslim  xi (t )x j (t )  2Dijt

t 

(7)

where Δxi and Δxj are functions of t and represent the diffusive displacements in directions i and j, respectively,
relative to their initial positions. Natarajan et al. (1995)
and Liao and Hsiau (2009) employed a similar method inmeasuring self-diffusion in a vertical channel and insheared granular flows, respectively.
Convection is also a crucial mechanism that affects
particle motion and segregation in vibrating granular
beds. Hsiau and Chen (2000) suggested that within eachconvection cell, the mass flow rates in the top, right, bottom, and left portions should be conserved. In the horizontal plane including the center of the convection cells,
the horizontal velocity is zero and only a vertical velocity
component <v>c exists. Hence, at this center location, theconvection mass flow rate ṁ (at the same horizontal level
as the convection centers) can be defined asm  

x W  px 0

 v cdx
2(8)where ν is the solid fraction, ρp is the true bead density,and W is the half-width of the container measured in the
aforementioned horizontal plane including the convection
cell center. Assuming that the solid fraction is constantacross each horizontal plane, the channel can be divided
horizontally into experimental bins. Accordingly, J canbe expressed as

J

m

 p 2W gd

  v  c x
2W gd

(9)

where d is the bead diameter, Δx is the width of each bin.
Hsiau and Chen (2000) detailed the definition and formation of bins.

112

3. Results and discussion
3.1 Transport properties in wet vibrating
granular beds
Granular materials are common in many industries. In
particular, the existence of a small amount of interstitial
liquid in granular materials may complicate their handling or processing. Granular flow behavior changes considerably because of the formation and rupture of liquid
bridges in wet granular materials (Grof et al., 2008; Liaoet al., 2010c, 2013; Lim, 2014; Mason et al., 1999; Schulz
et al., 2003). Some studies have investigated the forces
generated by liquid bridges, both theoretically and experimentally (Ennis et al., 1990; Fisher, 1926; Mason and
Clark, 1965; Mehrotra and Sastry, 1980; Mikami et al.,
1998; Pitois et al., 2000). Fraysse et al. (1999) performedexperiments to study how humidity affects the stability ofsandpiles. They found that the maximal angle of stability
increased with humidity. Hsiau and Yang (2003) used thediscrete element method to study the effect of liquid content on the transport properties in a vertically vibrating
wet granular bed. They observed that the liquid bridge
force is a critical force influencing the particle motions
and transport properties of wet granular systems. Liaoand Hsiau (2010) found that the effect of the liquid bridge
force on the dynamic behavior of wet granular matter wasdependent on the kinetic energy of the granular system
and liquid viscosity.
Many researchers have studied the dynamics of dry
granular systems (Fortini and Huang, 2015; Hsiau et al.,2011; Knight et al., 1996; Tai and Hsiau, 2004). However,the flow behavior and dynamic properties of wet granularmaterials have received less attention. Most studies havefocused on how liquid addition affects the flow behavior
and dynamic properties of wet granular matter. Few experimental studies have examined how liquid surface
tension affects the dynamic properties and convective
behavior of granular matter.
The granular Bond number is somewhat reminiscent of
the Bond number in fluid mechanics. Nase et al. (2001)
defined the granular Bond number as follows:
Bo

g

Fc

Wp

2πR

3

2
4 3
πR  p g 2R  p g
3(10)

where Fc is the capillary force, Wp is the particle weight, γ
is the surface tension, g is the gravity and R is the sphereradius. The granular material becomes cohesive and theliquid bridge force becomes dominant when Bog exceeds
1. However, Nase et al. (2001) did not consider the effectof liquid content in their calculations. Some studies havereported that the number of liquid bridges and liquid
bridge volume increase with liquid content, enhancing theChun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

Fig. 2 The dimensionless convection flow rate plotted against
the dimensionless modified granular Bond number(BogM) in logarithmic scale (Hsiau S.S. et al., 2013).

Fig. 3 The average granular temperatures plotted against withthe added liquid viscosity μ with different liquid content
at specific liquid surface tension (Hsiau S.S. et al., 2013).

strength of existing liquid bridges (Herminghaus, 2005;
Kohonen et al., 2004; Liao et al., 2010c; Scheel et al.,2008). Herminghaus’ research group (Herminghaus,
2005; Scheel et al., 2008) calculated the liquid content
threshold for bridge formation to be approximately
7 × 10 –4 and that capillary bridges were predominant
when the liquid content was below 2.4 × 10 –2. Notably,
the dimensionless liquid content V* was defined as
V* = Vw/(Vp + Vw), where Vw is the total volume of liquid
added and Vp is the total volume of all beads in the bed.
The number and strength of liquid bridges increase withthe liquid content in the pendular state. Therefore, thegranular Bond number should be modified by multiplying
it with the dimensionless liquid content. The modified
granular Bond number BogM can be defined aswith the liquid bridge force. Thus, because of the stronger
liquid bridge forces, the convective flow rate decreases as
the modified granular Bond number increases.
Fig. 3 shows the average granular temperature plotted
as a function of the liquid viscosity for different V* values
at specific surface tensions. Clearly, the granular temperature decreases as the liquid viscosity increases (at a given
liquid content). Adding more viscous liquid reduces particle motion and interactive collisions, thereby increasing
the liquid bridge forces. A higher liquid viscosity leads tohigher energy dissipation because of the formation andrupture of the liquid bridges between particles and thegeneration of a larger viscous force. Hence, an increase in
the liquid viscosity reduces the strength of particle motion
and interactive collisions, lowering the granular temperature. Sheared cell studies by Liao and Hsiau (2010) andYang and Hsiau (2005) showed similar results, specifically that the presence of viscous and frictional force
within a granular system causes high dissipation of the
kinetic energy of particles. Therefore, the granular temperature decreases as the liquid viscosity and liquid content increase.

BogM  Bog  V  3
V 
2R 2  p g(11)Fig. 2 shows the dimensionless convective flow rate plotted as a function of the modified granular Bond number
on the logarithmic scale. The dimensionless convective
flow rate decreases in a power decay with an increase in
the modified granular Bond number for each specific liquid viscosity. The exponents for the different liquid viscosities (μ = 1.68, 2.33, and 3.38 cs) are –0.604, –0.651,
and –0.642, respectively. This indicates that the convective flow rate decreases as the surface tension and added
liquid content increase, resulting in stronger liquid bridge
forces. A larger modified granular Bond number implies
more and stronger liquid bridges and larger liquid bridge
forces between particles; the larger liquid bridge forces
result from the higher surface tension and liquid content.
A stronger liquid bridge force reduces particle motion andinteractive collisions. Energy dissipation caused by theformation and rupture of liquid bridges also increases

3.2 Segregation in wet vibrated granular beds
Adding liquid to granular materials creates liquid
bridges among particles. The creation and disintegrationof such bridges causes the behavior in a wet granular system to change substantially (Schulz et al., 2003). In recent
years, although dry granular systems’ segregation mechanism has been researched thoroughly, considerably few
studies have researched the effect of adding small
amounts of liquid on the segregation behavior of granularmaterials (Chou et al., 2010; Geromichalos et al., 2003; Liand McCarthy, 2003, 2006; McCarthy, 2009; Samadani

113

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

and Kudrolli, 2000, 2001). Geromichalos et al. (2003)
found segregation behavior to increase as the liquid content was increased but began to drop after reaching a critical amount of liquid content. Hsiau and Yang (2003)
researched the mixing and diffusion processes of a wet
vibrating granular bed, finding that liquid content
changed both processes. Samadani and Kudrolli (2000)
discovered that segregation behavior decreased as more
liquid was added and then reached a steady state once
reaching a specific amount of liquid. The results of Chouet al. (2010) showed that the segregation condition can be
determined from the repose angle of a granular mixture
for wet granular systems, notwithstanding the addition ofa liquid or viscosity.
The index used to calculate segregation degree is described below:nI

 (Ci  C ) 2
i 1

(12)

n 1

in which Ci represents the concentration of nylon beads inthe ith cell, C represents the average concentration of nylon beads in the entire bed ( C = 0.5), and n represents thenumber of sub-regions. The segregation index spans from
0.5 to 0; 0.5 signifies a totally segregated state while 0
signifies a consistently mixed state (Chou et al., 2010;
Khakhar, 1997; Liao et al., 2010a).
In order to understand the effect of liquid bridges on
the segregation of wet granular matter, some of the primary forces involved must first be discussed. The quasistatic force of the liquid bridge among particles can be
estimated as 2πRγ, in which γ is the surface tension of theliquid and R is the radius of the particle. When considering only capillary force, the viscosity of the added liquid
is generally not considered in the assessment of the static
properties of wet granular matter. However, the viscosityof the liquid present in the dynamic interactions among
particles influences the bonding force. With regards to lubrication theory, the Reynolds Equation provides a measurement method for the viscous force, which is derived
in this equation from two rigid spherical surfaces (Pitois
et al., 2000; Samadani and Kudrolli, 2001). The equationsuggests that the pressure P created within the liquid relies on the relative displacement of the two solid particles;
the equation is written below:d  3 dP(r ) dS
rH d (r ) 12 r
dr 
dr 
dt

Fv  

3
1 dS
π R 2
2
S dt

(14)

A liquid bridge requires a correction coefficient to be
added to Eq. (14), which creates the following equation:2

Ds  1 dDs
3Fv  π R 2 1 2
 H (b)  Ds dt(15)

in which b represents the radius of the wet area, and Ds
represents the length of the liquid bridge. The total dynamic force of the liquid bridge is found using the superposition of the capillary force and the viscous force.
Samadani and Kudrolli (2001) applied Reynolds Equationand discovered that the viscous force is proportional to
the liquid viscosity.
The segregation index was applied to measure the degree of granular segregation within a granular system.
Fig. 4 depicts variations to the final segregation index
once a steady state is reached due to adding liquids ofvarying viscosities. In most cases, the segregation index
decreased as the liquid content and viscosity increase. Increasing the quantity and viscosity of the liquid added to
the granular materials results in both more and stronger
liquid bridges created among the particles. Consequently,
the energy that dissipates as a result of the creation anddisintegration of said liquid bridges also increases, thus
decreasing particle motion within the wet system. As has
been found in prior studies, the particles’ kinetic energy
probably dissipates due to the creation of liquid bridge
forces among the particles in the wet system (Geromichalos
et al., 2003; Schulz et al., 2003). The liquid bridges generally pushed the small glass beads together to clump andform larger clusters, thereby decreasing the percolation(13)

in which Hd(r) = S + r2/R represents the distance between
the two spherical surfaces at radial distance r from thecenter of the neck, S represents the distance between particles, and η represents liquid dynamic viscosity. Assuming that the particles are totally submerged into the liquid,114

the viscous force working on the particles can be expressed through integration of Eq. (13) two times:

Fig. 4The segregation index of the final state in the shaker
with V* with different viscosities of added silicon oil(Liao C.C. et al., 2010c).

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

effect on the segregation amount (Samadani and Kudrolli,
2000, 2001). The amount of segregation did not changemuch even when increasing the liquid content with a low
liquid viscosity of 20 cs, as shown in Fig. 4, which suggests that the liquid bridge force is insignificant with nosubstantial effect on either the particles’ motion or segregation. Furthermore, Fig. 4 shows that the addition of ahighly viscous liquid (500 cs) to the granular materials
significantly decreases the final segregation index and increases the value of V*. Nevertheless, the final segregation index remained constant as V* ≥ 1.655 × 10 –3, which
is similar to that the findings of Samadani and Kudrolli(2000). Remarkably, the final segregation index showed asmall increase in the highest liquid content and viscosity(V* = 2.8137 × 10 –3 and μ = 500 cs). This is the liquid
content and viscosity that creates the strongest liquid
bridge force, which leads to the most feeble particle motion, which subsequently results in a minor increase in the
final segregation index (Fig. 4).
Figs. 5(a)–5(e) are photographs taken of the amount ofsegregation in the binary mixtures for varying liquid contents and viscosities. Fig. 5(a) depicts the initial amountof segregation, where the smaller glass beads are positioned above the larger nylon beads. Meanwhile, the lastsegregation amount of the dry system is revealed in Fig.
5(b), which shows that complete segregation was attained
once the larger particles moved to the upper bed. As presented in the aforementioned material, the liquid that wasadded to the granular materials can be assumed to restrict
the movement of particles, reducing segregation in manycases. However, the addition of extremely little liquidwith a very low viscosity did not influence the final segregation amounts (V* = 0.7428 × 10 –3, μ = 20 cs). This find-Fig. 5 Snapshots of the level of segregation of binary granular
mixtures: (a) initial segregation level of the dry system,(b) steady segregation level of the dry system, (c) steadysegregation level of the wet system, μ = 20 cs andV* = 0.7428 × 10 –3, (d) steady homogeneous and mixed
level of the wet system, μ = 500 cs and V* = 1.6550 × 10 –3,(e) steady non-homogeneous mixing level of the wet
system μ = 500 cs and V* = 2.8137 × 10 –3, with the circled part depicting the non-homogeneous mixing region (Liao C.C. et al., 2010c).

ing is depicted in Fig. 5(c) and suggests that the liquid
bridge force was too weak to lessen particle motion orsignificantly affect the segregation index. Fig. 5(d) depicts the final condition (the consistently mixed state) of
the binary mixtures for V* = 1.6550 × 10 –3 and μ = 500
cs. The adequate liquid content and viscosity reduced thedriving segregation force due to the creation of the liquid
bridge force. As a result, the amount of segregation wasconverted into the final homogeneous mixing state illustrated in Fig. 5(d). Fig. 5(e) shows the final nonhomogeneous mixing state for V* = 2.8137 × 10 –3 and μ = 500 cs.
A small portion of the upper part of the container, which
is circled in Fig. 5(e), clearly depicts the nonhomogeneous
mixing state. The particle motions were not sufficiently
adequate for obtaining the homogeneous mixing statewith the addition of the largest liquid content and viscosity to the granular materials (V* = 2.8137 × 10 –3 andμ = 500 cs). Therefore, this study has demonstrated thatthe amount of segregation can be managed by altering theliquid content and viscosity.

3.3 Influence of bed height on convection cell
formation
The convection of granular materials is vital to granular flow. Vibration acceleration, once it surpasses a specific threshold, causes convective f low patterns. In
addition, convection is important to granular segregation
behavior (Hsiau et al., 2002; Huerta and Ruiz-Suarez,
2004; Liao et al., 2014; Saez et al, 2005). The granularmaterials within a bed move in a circular pattern, fromthe top of the bed downward along the sidewalls toward thebottom and then toward the top through the center of thecontainer. The occurrence of vertical vibration causes the
granular system to become fluid, which thus transforms
the granular bed from a consolidated state to a fluid-likestate. In certain situations, the flow of granular materials
between the top and bottom occurs as convection cells. In
recent years, such convection has attracted many researchers (Chung et al., 2013; Gallas et al., 1992; Hsiau and Chen,
2000; Huerta and Ruiz-Suarez, 2004; Saez et al., 2005;
Viswanathan, 2011). Inelastic collisions cause energy dissipation in a vibrofluidized granular system, which results inlarger densities and temperature gradients. Once the temperature gradient becomes big enough, convection occurs(Wildman et al., 2001). Elperin and Golshtein (1997) applied a particle dynamics method to evaluate the influenceof convection and friction on the level of segregation withina two-dimensional vibrating bed and discovered that the
segregation rate significantly increased with convectiveroll. Furthermore, Majid and Walzel (2009) realized thatconvection cell patterns are contingent on the operating
conditions. Hsiau et al. (2002) researched the effect of container geometry, wall friction, and vibration conditions on

115

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

convection cells in a two-dimensional vibrating bed anddetermined that reverse convection cells were created when
the two sidewalls had an 8° incline. Klongboonjit andCampbell (2008) completed a soft-particle recreation, in
which convection strength was usually augmented withparticle stiffness, as well as upon reaching a maximum
value, the convective strength then decreases even withthe further increase of particle stiffness. Fortini and
Huang (2015) discovered that wall-induced convection
happens within the bouncing bed region of the parameter’s space, within which the granular bed acts similarly
to a bouncing ball.
The original bed height can be non-dimensionalized by
dividing it by bead diameter d, resulting in H = h/d. H
spans from 15 to 90 with intervals of 5, which is consistent with a range of h from 3 to 18 cm. Fig. 6 depicts thevariations of dimensionless convection strength J andoverall average granular temperature T* with the initialdimensionless bed height H for the fixed vibration conditions of f = 40 Hz and Γ = 16. The value of J has two distinct peak values, which are H = Hp1 (= 45) and Hp2 (= 70),
and a single minimum value, which is H = Hv (= 75).
Augmenting H from 15 to 45 causes the dimensionlessconvection strength to significantly increase (in an almost
linear manner). A smaller amount of material (smaller H)
in the bed fluidizes the bed materials more violently thanif they had been in a thicker bed. When height of the initial bed surpasses the first critical (maximum) value Hp1,
the convection strength starts decreasing as the bed heightincreases. Once the bed height surpasses a specific value,a “solid-like” flow region is created within the material at
the bottom of the bed due to gravity. The beads in thissolid-like region show weak relative movement and interactive collisions, which both dissipate the energy that

Fig. 6 Variations of the dimensionless convection strength J
and the overall average granular temperature T*, withchanges to the initial dimensionless bed height H, witha fixed vibration environment of f = 40 Hz and Γ = 16
(Hsiau S.S. et al., 2011).

116

comes from the shaker, thus resulting in less energy beingtransferred to the convection cells in the upper bed layers(the fluid-like region).
Increased convection strength as a result of increased
bed height stops once H surpasses the second peak Hp2, at
which point the whole solid-like region acts similar to asolid body. An increase in H signifies a thicker “solid.”
Said thicker solid can absorb more energy, thus transferring less energy to then be received by the upper convection region, ultimately resulting in weaker convection.
Fig. 6 shows the two peaks and one valley of the curve
for T*. The aforementioned variation of the initial bed
height with T* resembles the variations in the J values. A
larger J value denotes that the granular bed becomes more
active when the T* value is higher.
The granular temperature of each individual horizontal
layer can be determined by taking the average of the rootmean-square fluctuation velocity of each tracer particle thatmoves through the layer. Fig. 7 depicts variations of the
granular temperature with the dimensionless height y/dwithin a fixed vibration environment of f = 40 Hz, Γ = 16,
and H = 90. The curve can best be described categorized
into three regions. The granular temperatures of the lower
bed have small values near 0 and can vary (y/d < 60),
which suggests that the beads in this section have extremely weak motion, resulting in the lower bed acting
like a solid. HS indicates the thickness of this solid-likeregion. The beads located above the solid-like region havesubstantial convective motion (60 < y/d < 85) and significantly larger granular temperatures than those in the solid-­
like region. The so-called “liquid-like” region features
convective rolling. HC indicates the thickness of the convection rolls. The beads in certain layers (free surface,y/d > 85) have substantially higher granular temperatures.

Fig. 7

The granular temperature varying with the dimensionless height with a fixed vibration environment of
f = 40 Hz, Γ = 16, and H = 90. The inset plot depicts thetotal bed thickness in complete expansion state HE, thethicknesses of “solid-like” region HS and the thicknessof convection region HC (Hsiau S.S. et al., 2011).

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

The total bed thickness throughout the state of maximal
expansion is written as HE. The inset depicts the total bed
thickness throughout complete expansion state HE, thethickness of solid-like region HS, and the thickness of
convection region HC.
Fig. 8 depicts the various values of HE, HC, and HS, andof J with H in the fixed vibration environment of
f = 40 Hz and Γ = 16. These three thicknesses’ data were
found through the analysis of the experimental plots ofthe granular temperature versus initial bed thickness, as
shown in Fig. 7. The expansion height (open square symbols) goes up practically linearly as the initial bed heightis increased, and HE > H. The bed has two different flow
regimes: a convection region with thickness HC and asolid-like region with thickness HS (HE  HC + HS). With
H values between 25 and 45, all of the bed material moves
to create two symmetric convection cells, and the bed has
no solid-like region (HS  0, HC  H). Furthermore, convection cell height HC maintains its increasing behavior
as the initial bed height becomes larger. Once the initial
bed height surpasses 47, the mass of the bed becomes too
thick to be completely fluidized, and the solid-like region
is forced to expand. The solid-like region’s height begins
to increase with an increasing H, as shown in Fig. 8. A
thicker solid-like region shows that the beads within it
utilize more input energy, thus leaving less energy for theupper material, which results in decreased HC. This tendency reverses once H = 70. For 70 < H < 75, the solid-likeregion appears to be a rigid, solid body, in which the“solid” beads dissipate less energy, which causes moreenergy to be transferred into the upper layers. While thethicknesses of the solid-like region remain similar in bothsituations, the thickness of the convection cells increases
with a growing H. As the bed mass (H) further increases,
HC starts to decrease.

Fig. 8 The thicknesses of total expansion height HE, convection cells HC, “solid-like” region HS, and dimensionlessconvection strength J, against initial bed height H, in a
fixed vibration environment of f = 40 Hz, Γ = 16 (Hsiau
S.S. et al., 2011).

The relationship between HC and the initial bed heightclosely resembles the variation of J values with the initial
bed height, both of which feature two peaks (Hp1 and Hp2)
and a valley (Hv). Although the initial bed height influences convection strength, these two parameters were notfound to be monotonically related. The thickness of theconvection rolls substantially influences dimensionlessconvection strength, and the thicker the convection roll is,then the more fluidized the granular bed in the container
is. Therefore, J and HC ought to present similar tendencies
versus initial bed height. Fig. 8 depicts the relationshipbetween J and HC.

3.4 Influence of intruder surface roughness on
granular segregation in a vertically vibrated
bed
Much research has already investigated friction-induced
segregation, which is a serious problem in many industries (Kondic et al., 2006; Pohlman et al., 2006; Plantard
et al., 2006; Liao et al., 2014). Srebro and Levine (2003)
evaluated the effect of friction with regard to the segregation of binary granular mixtures by combining Edwards’
thermodynamic hypothesis using a simple mechanicalmodel and found that segregation took place with specificdegrees of compactness within the binary mixtures ofgrains with varying frictional properties. Furthermore,
Kondic et al. (2006) found that friction led to granularsegregation in a horizontally shaken container with amound centered at the bottom. Ulrich et al. (2007) performed experiments that resulted in a transition from thereverse Brazil nut effect to the Brazil nut effect when theparticle friction coefficient became higher due to long
periods of shaking, which was explained as the result ofsidewall-driven convection and buoyancy. Liao et al.
(2014) discovered that the internal friction did not significantly affect Brazil nut granular segregation when dealing
with a small filling bed height. However, internal friction
was vital for the rise dynamics of intruders when dealing
with a large bed height in a granular system. Unac et al.
(2014) further discovered that isolated intruders may
sometimes rise when tapped based not only on the size
ratio but also on the environment’s degree of ordering.
Recently, research regarding the rise of an intruder in a
granular fluid has garnered considerable attention. However, the influence of surface roughness on a rising intruder has not been studied as much but is worthy offurther investigation. Understanding the influence of friction on segregation in vibrating granular systems may
provide insights into unknown physical mechanisms.
Fig. 9 shows the time dependence curves of the intruder height of three degrees of surface roughness of intruders and three Γ values (3, 4.2, and 4.8) with a vibration
frequency of 35 Hz. The slow rise of the intruder from just117

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

above the bottom to the free surface occurs in all cases
because of the Brazil nut effect. The rougher the intruder
is, the longer the rise time is, and vice versa. Energy dissipation happens during the binary collision of particles
due to friction and inelastic collisions. The friction thatexists between the intruder and the beads is vital for therise dynamics of the intruder. Furthermore, the restitution
coefficient values of each intruder may affect the rise dynamics. In a considerably densely packed bed, the dragbetween the intruder and beads typically results in thedissipation of the rising intruder’s kinetic energy. Rougher
intruders experience greater degrees of dissipation due totheir larger friction coefficient and smaller restitution coefficient, thus creating weaker upward motion and alonger rise time, as is shown in the curves in Fig. 9.
Moreover, small Γ values cause the intruder dynamics tobe divided into two stages during the rise of the intruder.
The rise experienced is slower in the lower part of the
granular bed and quicker in the upper part (convection region). In such a condition, the system has less externalenergy, and bead compactness is higher, which creates
weaker motion in the bed’s lower part. Therefore, the intruder rises more slowly as a consequence of the highfriction between said intruder and the beads located in thelower part of the bed during the vibration cycle, thus
causing weaker percolation. On the other hand, the convection cell increases in size and strength as the value of Γ increases. Thus, the intruder rises more easily and quickly
and demonstrates a generally linear evolution with larger
Γ values.
Furthermore, the slopes of the intruders’ curves withthree varying degrees of surface roughness all resembled
one another in the bed’s upper part. The convection mechanism in the bed’s upper part controlled the rise dynamics,
and convection strength was found to be similar within
the studied vibration conditions. Moreover, intruders witha smooth surface had reduced friction, and the rise time

Fig. 9118

Intruder height as a function of vibrating time with theconditions of Γ = 3, 4.2, 4.8 and f = 35 Hz (Liao C.C. etal., 2012).

variation was not significant with higher Γ values.
Nahmad-Molinari et al. (2003) defined penetration
length P l and discovered that the intruder penetrates the
granular bed due to its inertia over a small distance during
each cycle. This study further found that the intruder’skinetic energy dissipates because of friction as it enters
the granular bed during each cycle. Based on the aforementioned findings, the researchers developed a model
based on a simple energy balance during each cycle:
1
miv t2   Pl
2

(16)

in which mi signifies the intruder mass, v t signifies the intruder take-off velocity upon the system reaching a negative acceleration, ai = –g, β signifies the drag between thebeads and intruder, and Pl signifies the penetration length.
The parameter vt is the value of ż(t) when ż(t) = –g, andz(t) = A sin(ωt). Therefore, the following can be derived:vt

A2 2  g 2 /  21/2(17)

The position of the intruder is graphed as a function of
the vibration cycle (tω/2π) with the condition of Γ = 3 andf = 35 Hz (Fig. 10). Each curve’s slope is the exact sameas the penetration length of the corresponding intruder(Nahmad-Molinari et al., 2003). The lower parts of thecurves have linear fits as a result of the convection’s decrease. The linear fit of the data reveals the penetration
length of each vibration cycle. The penetration length isgraphed as a function of the vibration amplitude for varying intruders (Fig. 11), which shows that the penetration
length decreases as the surface roughness of the intruder
increases under the given vibration conditions, as well asthat the penetration length increases as the vibration amplitude increases.
Eq. (16) shows that the penetration length is inversely
proportional to drag β. Rougher intruders with larger β

Fig. 10Intruder height as a function of vibrating cycle (tω/2π)(Liao C.C. et al., 2012).

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

dissipate more kinetic energy and thus have shorter penetration lengths. To test this hypothesis, we calculated β using Eq. (16). By plotting β against the vibration amplitude(Fig. 12), the β value increased with the intruder friction
coefficient under the condition of a given vibration amplitude. Higher drag causes higher intruder friction coefficients due to the increased friction between the beads andthe intruder during its rise. The curves in Fig. 12 showthat β is reduced as the vibration amplitude becomes bigger. Both convection and fluidization increase along withthe vibration amplitude within the system. The flow behavior of the granular materials further transforms from aquasistatic state into a dynamic state. The strength of boththe particle motions and the collisions is enhanced whenin the dynamic state. Consequently, friction has a less serious effect on the intruder’s rise dynamics. Even with varying intruder friction coefficient values, increasing thevibration amplitude reduces both β and its variations.

In Fig. 13, the penetration length is plotted as a function of vibration frequency on the logarithmic-normal
scale for intruders with varying degrees of surface roughness. The straight lines, which are found with the leastsquares method, reveal the exponential decay of thepenetration length with the frequency. Vanel et al. (1997)
found that the dimensionless rise time (the number of vibration cycles) is scaled exponentially when f < 15. Similarly, the current study’s dimensionless rise time wasproportional to 1/Pl, even though the vibration frequency
range does not match completely. The discrepancy is most
likely due to the use of different experimental containers,
in which would have undoubtedly affected particle motion
and flow behavior. A two-dimensional container was usedin our experiments, while a three-dimensional one was
employed by Vanel et al. (1997). Nevertheless, both studies found convection to be vital to the rise dynamics of
the intruder.

3.5 Effect of a bumpy base on granular segregationand transport properties for vertical vibration

Fig. 11 Penetration length as a function of vibration amplitude
for intruders with varying degrees of surface roughness (Liao C.C. et al., 2012).

In a real granular system, the wall conditions may also
be bumpy or uneven. The effect of a bumpy base on therise dynamics of an intruder and transport properties has
not been previously examined, and it offers insights into
the complexities of segregation. Farkas et al. (1999) performed experiments and simulations to study the horizontal transport of granular particles in a vertically vibrating
bed with a sawtooth-shaped base. Levanon and Rapaport(2000) investigated the horizontal flow of granular materials in a vertically vibrating sawtooth-shaped base. Theirsimulation results showed that the induced flow rate varies with the bed height and that counterflow could occur

Fig. 13
Fig. 12Drag β as a function of vibration amplitude for intruders of varying degrees of surface roughness (Liao C.C.
et al., 2012).

Penetration length plotted against vibration frequency
in logarithmic-normal scale for intruders with varying
degrees of surface roughness. The straight lines represent fit data obtained using the least-square method(Liao C.C. et al., 2012).

119Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

at different levels. Hsiau et al. (2002) indicated that convection and segregation rates increase with the sidewall
friction coefficient in a vertical shaker. Hsiau and Yang
(2002) reported that for sheared granular flow, rougher
wall conditions enhance the transport properties of granular materials. Hsiau et al. (2006) reported that the stressand self-diffusion coefficients of particles are inversely
proportional to the internal friction coefficient. Lu andHsiau (2008) investigated the mixing behavior in vibrating granular beds by using a discrete element method.
They demonstrated that the mixing rates increase exponentially with the convection and vertical self-diffusion
coefficient. Ma et al. (2014) found that even with the same
size and density ratio of the intruder to the backgroundparticles, the intruder exhibits a distinct behavior at thegiven vibrational conditions. Windows-Yule et al. (2015b)
demonstrated that even for a fixed input energy from thewall, energy conveyed to the granular system under excitation could vary significantly dependent on the amplitudeand frequency of the driving oscillations.
In the current study, a layer of densely packed glass
beads was glued to the base of a container to form abumpy base surface. To quantify the relative size of theparticles on the base, we defined a base factor Rf:

Rf 

Pd
db

(18)

where Pd is the diameter of the glued beads and db is thediameter of immersed glass beads (db = 2 mm in thisstudy). Four different base factors (Rf = 0, 0.5, 1, 2) wereused; Rf = 0 corresponded to a flat base surface, and
Rf = 2 represented the largest wall protrusions.
Fig. 14 shows the rise time plotted as a function of Rf
for different Γ at f = 25 Hz. The rise time increases with
Rf for each Γ because the strength of the particle motions
is reduced for a large base factor. The rise times also decrease as the Γ for each container base condition increases. Previous research has shown that for higher Γ,

Fig. 14120

Rise time as a function of Rf with different Γ for fixed
f = 25 Hz (Liao C.C. et al., 2014).

the granular bed becomes more fluidized, enhancing thepercolation effect and resulting in a faster rise time. Additionally, the influence of a bumpy base on the rise time issignificant at smaller Γ and becomes less pronounced at
higher Γ because more energy can be introduced in thesystem at higher Γ. Previous studies have shown that therise time is similar if the granular bed is fluidized. Increased fluidization could be achieved with higher Γ.
Hence, the rise time increases slightly at greater Γ inbumpy surface instances. By contrast, the particle motions are weakened and the granular bed becomes lessfluidized for smaller Γ. Under these conditions, the baseroughness has significantly influences the dynamic properties of the granular material and leads to a larger variation of the rise time for different base conditions.
The transport properties of the immersed glass beads
play a crucial role in the Brazil nut mechanism. Here, we
discuss the Brazil nut problem from the viewpoint of micromechanics by determining the transport properties ofimmersed glass beads. Fig. 15 shows the average granulartemperature and rise time as a function of Rf for f = 25 Hz
and Γ = 3. The base condition significantly influences the
granular temperature. The average granular temperature
decreases and the rise time increases with an increase in
the base factor. The total external energy is constant for
fixed vibration conditions. More energy could be introduced into the bed to increase the granular temperature
for a small base factor. The particle motions and interactive collisions become stronger and the bed shows greater
fluidization as the granular temperature increases. Hence,
the intruder experiences less resistance during the riseprocess, which leads to a shorter rise time for a larger
granular temperature.
Particle diffusion is a crucial parameter in studying themixing and segregation of granular materials. The
self-diffusion coefficients are determined from the rela-Fig. 15

Average granular temperature and rise time as a function of Rf for fixed f = 25 Hz and Γ = 3 (Liao C.C. etal., 2014).

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

tionship between mean-square diffusive displacements
and time, as shown in Eq. (7), by conducting experimental procedures similar to those of previous studies (Hsiau
and Yang, 2002; Liao and Hsiau, 2009; Tai and Hsiau,
2004). Self-diffusion coefficients and rise times are plotted as a function of Rf for f = 25 Hz and Γ = 3, as shownin Fig. 16. The self-diffusion coefficients are inversely
proportional to the rise time of the base factor. Particle
diffusion behavior results from particle fluctuations. Previous studies (Hsiau and Yang, 2002; Savage and Dai,
1993) have indicated that the self-diffusion coefficient is
proportional to the square root of granular temperature.
For a higher base factor, external energy could not be introduced into the system effectively, leading to weaker interactive collisions and a smaller granular temperature.
Therefore, a decreasing self-diffusion coefficient increases
the rise time for a large base factor, as shown in Fig. 16.
Fig. 17 shows the dimensionless convective flow rate J
and rise time plotted as a function of Rf for the fixed conditions f = 25 Hz and Γ = 3. As the base factor decreases,
J increases and the rise time decreases. The flow field ismore fluidized, which strengthens the convection for asmaller base factor. Consequently, J increases and the risetime decreases as the base factor decreases. This result
supports those of previous studies (Hsiau, 2002; Liao etal., 2012).

Granular materials are widely used in many industries
and commonly encountered in daily life. To handle andcontrol them, understanding the transport properties andsegregation mechanisms in dry and wet granular systems
is essential. Furthermore, controlling granular segregation
is difficult. This paper presents the main results obtainedby our research group; the results pertain to transport
properties and segregation in a vibrating granular bed.
The addition of a small amount of liquid to granular materials generally reduced the fluidization of the granular
bed because liquid bridges formed between particles. The
convective flow rate and granular temperature decreased
as the added liquid content, surface tension, and viscosity
increased. Increasing the added liquid content, surface
tension, and viscosity also caused higher energy dissipation in a wet vibrating granular bed. Thus, higher liquid
content, surface tension, and viscosity reduce the granulartemperature and convection strength. The convective flowrate decreases according to a power law as the modified
granular Bond number increases. The granular segregation can also be reduced by adding a small amount ofliquid to the granular materials. Both the liquid content
and viscosity considerably influence granular segregation.
We demonstrated that the initial segregation state may
change to a homogeneous mixing state when an appropriate amount of liquid with sufficient viscosity is added to
the granular matter.
This paper also discusses how the bed height influences
convection cells. The results of our study show that theconvection strength and overall average granular temperature lead to a two-peak phenomenon with the increasing bed height. We demonstrated that this two-peakpattern occurs because a solid-like region forms in the
granular bed.
The current results also indicated that intruder surface
roughness has a significantly influence on the rise dynamics of intruder. The Brazil nut segregation effect is mitigated as the intruder surface roughness increases. The
rise time for a rough intruder is longer than that for asmooth intruder because higher surface roughness resultsin more energy dissipation. The penetration length is enhanced as the intruder surface roughness is reduced, andFig. 16

Fig. 17

4. Conclusions

Self-diffusion coefficients D and rise time as a function of Rf for fixed f = 25 Hz and Γ = 3 (Liao C.C. etal., 2014).

Dimensionless convection flow rate J and rise time asa function of Rf for fixed f = 25 Hz and Γ = 3 (Liao C.C.
et al., 2014).

121

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

is scaled exponentially with the vibration frequency. Additionally, the drag increases with the intruder surface
roughness. We also observed that the intruder rise dynamics and transport properties of the immersed glass
beads are influenced significantly by the base roughness.
The penetration length is enhanced as the base factor is
reduced. Additionally, the drag force increases with thebase factor. The granular temperature, diffusion coefficients, and dimensionless convective flow rate decrease as
the base factor increases and are inversely proportional to
the rise time.

Acknowledgments
The authors acknowledge the financial support provided by the Ministry of Science and Technology, R.O.C.,
through grants MOST 103-2218-E-008-004-MY2 andMOST 103-2221-E-008-042-MY3.

NomenclatureHC

convection region

Hp1

first critical (maximum) value

Hp2

second peakHv

the valleyI

segregation index

J

dimensionless convection strengthṁconvection mass flow ratemiintruder massnnumber of subregions

Ni

number of tracer particles in the ith binPpressure generated in the liquid, N/m 2

Pd

diameter of glued beads

Pl

penetration lengthrradial distanceR

particle radius, mm

Rfbase factor

S

distance between particles, mm

t

time

T

average granular temperatureTi

granular temperature in the ith binT*overall average granular temperature<ui>

the ensemble average velocity in the horizontal direction in the ith bin<ui′2>1/2

fluctuation velocity in the horizontal direction in theith binuki

the velocity of the kth tracer particle in the horizontal
direction in the ith binV*dimensionless liquid content

Vw

total volume of silicon oil added, cm3

Vptotal volume of whole beads in the bed, cm3<v>cvertical velocity component<vi>

ensemble average velocity in the vertical direction in
the ith bin<vi′2>1/2

fluctuation velocity in the vertical direction in the ith
bin

vki

the velocity of the kth tracer particle in the verticaldirection in the ith bin

vtintruder take-off velocityWthe half-width of the container

Wpparticle weightA

vibration amplitude, mmavibration acceleration, m/s2

ai

negative gravitational acceleration

Bog

granular Bond number

BogM

modified granular Bond numberbradius of the wetted area, mmCiconcentration of nylon beads in the ith cellCaverage concentration of nylon beads in the entirebed

Ds

separation distance of liquid bridge, mm

Dij

self-diffusion coefficientdbead diameterdb

diameter of immersed glass beads

Fccapillary force, N

Fvviscous force, Nfvibration frequency, Hzggravitational acceleration, m/s2Hdimensionless initial bed heighthinitial bed height, cm

H(b)distance between the two spherical surfaces at radiusof the wetted area, mm

Hd(r)distance between the two spherical surfaces, mm

Δx

the width of each binHEtotal bed thickness in the full expansion stateΔxi

the diffusive displacement in direction iHSsolid-like region

Δxj

the diffusive displacement in direction j

122

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

y

bed heightz(t)Sine function

Greek lettersΓ

dimensionless vibration accelerationγ

surface tension of the liquid, mN/m

βdrag between the beads and the intruder

μliquid kinematic viscosity, csνsolid fractionηliquid dynamic viscosity, cPρpthe true bead density, g/cm3

ωvibration radian frequency, rad/s

References
Bose M., Rhodes M., Dynamis of an intruder in a shaken granular bed, Powder Technology, 179 (2007) 25–30. DOI:
10.1016/j.powtec.2006.11.012
Bridgwater J., Fundamental powder mixing mechanisms,
Powder Technology, 15 (1976) 215–236. DOI: 10.1016/00325910(76)80051-4
Breu A.P.J., Ensner H.M., Kruelle C.A., Rehberg I., Reversing
the Brazil-nut effect: Competition between percolation andcondensation, Physical Review Letters, 90 (2003) 014302.
DOI: 10.1103/PhysRevLett.90.014302
Brito R., Enríquez H., Godoy S., Soto R., Segregation induced
by inelasticity in a vibrofluidized granular mixture,
Physical Review E, 77 (2008) 061301. DOI: 10.1103/
PhysRevE.77.061301
Brito R., Soto R., Competition of Brazil nut effect, buoyancy,
and inelasticity induced segregation in a granular mixture,
European Physical Journal-Special Topics, 179 (2009) 207–
219. DOI: 10.1140/epjst/e2010-01204-5
Campbell C.S., Rapid granular flows, Annual Review Fluid
Mechanics, 22 (1990) 57–92. DOI: 10.1146/annurev.fl.22.
010190.000421
Campbell C.S., Self-diffusion in granular shear flows, Journalof Fluid Mechanics, 348 (1997) 85–101. DOI: 10.1103/
PhysRevE.69.031308
Chou S.H., Liao C.C., Hsiau S.S., An experimental study on theeffect of liquid content and viscosity on particle segregation
in a rotating drum, Powder Technology, 201 (2010) 266–
272. DOI: 10.1016/j.powtec.2010.04.009
Chung Y.C., Liao H.H., Hsiau S.S., Convection behavior of
non-spherical particles in a vibrating bed: Discrete element
modelling and experimental validation, Powder Technology, 237 (2013) 53–66. DOI: 10.1016/j.powtec.2012.12.052
Ciamarra M.P., Vizia M.D.D., Fierro A., Tarzia M., Coniglio A.,
Nicodemi M., Granular species segregation under verticaltapping: effects of size, density, friction, and shaking
amplitude, Physical Review Letters, 96 (2006) 058001.
DOI: 10.1103/PhysRevLett.96.058001
Clement C.P., Pacheco-Martinez H.A., Swift M.R., King P.J.,

The water-enhanced Brazil nut effect, Europhysics Letters,
91 (2010) 54001. DOI: 10.1209/0295-5075/91/54001
Cooke W., Warr S., Huntley J.M., Ball R.C., Particle size segregation in a two-dimensional bed undergoing vertical vibration, Physical Review E, 53 (1996) 2812–2822. DOI:
10.1103/PhysRevE.53.2812
Duran J., Rajchenbach J., Clément E., Arching effect model forparticle-size segregation, Physical Review Letters, 70(1993) 2431–2434. DOI: 10.1103/PhysRevLett.70.2431
Einstein A., In R. Furth, Albert Einstein, Investigations on thetheory of non-uniform gases, New York: Dover Publ. Co.
Chap. 1, (1956) 12.
Elperin T., Golshtein E., Effects of convection and on size segregation in vibration in vibrated granular beds, Physica A,
247 (1997) 67–78. DOI: 10.1016/S0378-4371(97)00400-7
Ennis B.J., Li J., Tardos G.I., Pfeffer R., The influence of viscosity on the strength of an axially strained pendular liquid
bridge, Chemical Engineering Science, 45 (1990) 3071–
3088. DOI: 10.1016/0009-2509(90)80054-I
Farkas Z., Tegzes P., Vukics A., Vicsek T., Transitions in thehorizontal transport of vertically vibrated granular layers,
Physical Review E, 60 (1999) 7022. DOI: 10.1103/
PhysRevE.60.7022
Fisher R.A., On the capillary forces in an ideal soil; correction of
formulae given by WB Haines, Journal of Agricultural Science, 16 (1926) 492–505. DOI: 10.1017/S0021859600007838
Fortini A., Huang K., Role of defects in the onset of wall-induced
granular convection, Physical Review E, 91 (2015) 032206.
DOI: 10.1103/PhysRevE.91.032206
Fraysse N., Thomé H., Petit L., Humidity effects on the stabilityof a sandpile, The European Physical Journal B, 11 (1999)
615–619. DOI: 10.1007/s100510051189
Gallas J.A.C., Herrmann H.J., Sokolowski S., Convection cells
in vibrating granular media. Physical Review Letters, 69(1992) 1371–1374. DOI: 10.1103/PhysRevLett.69.1371
Geromichalos D., Kohonen M.M., Mugele F., Herminghaus S.,
Mixing and condensation in a wet granular medium,Physical Review Letters, 90 (2003) 168702. DOI: 10.1103/
PhysRevLett.90.168702
Grof Z., Lawrence C.J., Štěpánek F., Computer simulation of
evolving capillary bridges in granular media, Granular
Matter, 10 (2008) 93–103. DOI: 10.1007/s10035-007-0071-1
Halsey T.C., Levine A.J., How sandcastles fall, Physical Review
Letters, 80 (1998) 3141–3144. DOI: 10.1103/PhysRevLett.
80.3141
Herminghaus S., Dynamics of wet granular matter, Advances inPhysics, 54 (2005) 221–261. DOI: 10.1080/00018730500167855
Hsiau S.S., Chen C. H., Granular convection cells in a verticalshaker, Powder Technology, 111 (2000) 210–217. DOI:
10.1016/S0032-5910(99)00287-9
Hsiau S.S., Liao C.C., Sheng P.Y., Tai S.C., Experimental study
the influence of bed height on convection cell formation,
Experiments in Fluids, 51 (2011) 795–800. DOI: 10.1007/
s00348-011-1099-x
Hsiau S.S., Liao C.C., Tai C.H., Wang C.Y., The dynamics of
wet granular matter under a vertical vibration bed, Granular Matter, 15 (2013) 437–446. DOI: 10.1007/s10035-0130412-1

123

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

Hsiau S.S., Lu L.S., Tai C.H., Experimental investigations of
granular temperature in vertical vibrated beds, Powder
Technology, 182 (2008) 202–210. DOI: 10.1016/j.powtec.
2007.09.015
Hsiau S.S., Shieh Y.M., Fluctuations and self-diffusion ofsheared granular material flows, Journal of Rheology, 43(1999) 1049–1066. DOI: 10.1122/1.551027
Hsiau S.S., Shin J.Y., Yang W.L., Lu L.S., Influence of internal
friction on transport properties in sheared granular flows,
AIChE Journal, 52 (2006) 3592–3599. DOI: 10.1002/
aic.10977
Hsiau S.S., Tai C.H., Chiang M.C., Effect of moisture content
on the convection motion of powders in a vibrated bed,
Advanced Powder Technology, 15 (2004) 673–686. DOI:
10.1163/1568552042456197
Hsiau S.S., Wang P.C., Tai C.H., Convection cells and segregation in a vibrated granular bed, AIChE Journal 48 (2002)
1430–1438. DOI: 10.1002/aic.690480707
Hsiau S.S., Yang S.C., Numerical simulation of self-diffusion
and mixing in a vibrated granular bed with the cohesive
effect of liquid bridges, Chemical Engineering Science, 58(2003) 339–351. DOI: 10.1016/S0009-2509(02)00519-5
Hsiau S.S., Yang W.L., Stresses and transport phenomena insheared granular flows, Physics of Fluids, 14 (2002) 612.
DOI: 10.1063/1.1428324
Hsiau S.S., Yu H.Y., Segregation phenomena in a shaker, Powder Technology, 93 (1997) 83–88. DOI: 10.1016/S00325910(97)03263-4
Huerta D.A., Ruiz-Suarez J.C., Vibration-induced granular segregation: A phenomenon driven by three mechanisms,
Physical Review Letters, 92 (2004) 114301. DOI: 10.1103/
PhysRevLett.92.114301
Jaeger H.M., Nagel S.R., Behringer R.P., Granular solids, liquids and gases, Reviews of Modern Physics, 68 (1996)
1259–1273. DOI: 10.1103/RevModPhys.68.1259
Khakhar D.V., McCarthy J.J., Ottino J.M., Radial segregation of
granular mixtures in rotating cylinders, Physics of Fluids, 9(1997) 3600–3614. DOI: 10.1063/1.869498
Klein M., Tsai L.L., Rosen M.S., Pavlin T., Candela D., Walsworth R.L., Interstitial gas and density segregation of vertically vibrated granular media, Physical Review E, 74(2006) 010301. DOI: 10.1103/PhysRevE.74.010301
Klongboonjit S., Campbell C.S, Convection in deep verticallyshaken particle beds. I. General features, Physics of Fluids,
20 (2008) 103301-1-103301-6. DOI: 10.1063/1.2996136
Knight J.B., Jaeger H.M., Nagel S.R., Vibration-induced size
separation in granular media-the convection connection,
Physical Review Letters, 70 (1993) 3728–3731. DOI:
10.1103/PhysRevLett.70.3728
Knight J.B., Ehrichs E.E., Kuperman V.Y., Flint J.K., Jaeger
H.M., Nagel S.R., An experimental study of granular convection, Physical Review E, 54 (1996) 5726-5738. DOI:
10.1103/PhysRevE.54.5726
Kohonen M.M., Geromichalos D., Scheel M., Schier C., Herminghaus S., On capillary bridges in wet granular materials, Physica A, 39 (2004) 7–15. DOI: 10.1016/j.
physa.2004.03.047
Kondic L., Hartley R.R., Tennakoon S.G.K., Painter B.,

124

Behringer R.P., Segregation by friction, Europhysics Letters, 61 (2006) 742–748. DOI: 10.1209/epl/i2003-00291-y
Kudrolli A., Size separation in vibrated granular matter,
Reports on progress in physics, 67 (2004) 209–247. DOI:
10.1.1.306.2695
Kudrolli A., Granular matter-sticky sand, Nature Materials 7(2008) 174–175. DOI: 10.1038/nmat2131
Levanon M., Rapaport D.C., Stratified horizontal flow in vertically vibrated granular layers, Physical Review Letters, 60(2001) 011304. DOI: 10.1103/PhysRevE.64.011304
Li H., McCarthy J.J., Controlling cohesive particle mixing andsegregation, Physical Review Letters, 90 (2003) 184301.
DOI: 10.1103/PhysRevLett.90.184301
Li H., McCarthy J.J., Cohesive particle mixing and segregation
under shear, Powder Technology, 164 (2006) 58–64. DOI:
10.1016/j.powtec.2005.12.018
Liao C.C., Hsiau S.S., Influence of interstitial fluid viscosity ontransport phenomenon in sheared granular materials,
Chemical Engineering Science, 64 (2009) 2562. DOI:
10.1016/j.ces.2009.02.030
Liao C.C., Hsiau S.S., Experimental analysis of dynamic properties in wet sheared granular matter, Powder Technology,
197 (2010) 222–229. DOI: 10.1016/j.powtec.2009.09.017
Liao C.C., Hsiau S.S., Chang P.S., Bottom wall friction coefficients on the dynamics properties of sheared granular
flows, Powder Technology, 270 (2015) 348–357. DOI:
10.1016/j.powtec.2014.10.043
Liao C.C., Hsiau S.S., Kiwing To, Granular dynamics of aslurry in a rotating drum, Physical Review E, 82 (2010a)
010302. DOI: 10.1103/PhysRevE.82.010302
Liao C.C., Hsiau S.S., Li J.S., Tai C.H., The influence of gravity
on dynamic properties in sheared granular flows, Chemical
Engineering Science, 65 (2010b) 2531–2540. DOI: 10.1016/
j.ces.2009.12.034
Liao C.C., Hsiau S.S., Wu C.S., Experimental study on theeffect of surface roughness on Brazil nut problem in a vertically vibrated bed, Physical Review E, 86 (2012) 061316.
DOI: 10.1103/PhysRevE.86.061316
Liao C.C., Hsiau S.S., Wu C.S., Combined effects of internal
friction and bed height on the Brazil-nut problem in a
shaker, Powder Technology, 253 (2014) 561–567. DOI:
10.1016/j.powtec.2013.12.031
Liao C.C., Hsiau S.S., Tsai T.H., Tai C.H., Segregation to mixing in wet granular matter under vibration, Chemical Engineering Science, 65 (2010c) 1109–1116. DOI: 10.1016/j.
ces.2009.09.065
Liao C.C., Hunt M.L., Hsiau S.S., Lu S.H., Investigation of theeffect of a bumpy base on granular segregation and transport properties under vertical vibration, Physics of Fluids,
26 (2014) 073302. DOI: 10.1063/1.4890363
Liffman K., Muniandy K., Rhodes M., Gutteridge D., MetcalfeG., A segregation mechanism in a vertically shaken bed,
Granular Matter, 3 (2011) 205–214. DOI: 10.1007/
s100350100093
Lim E.W.C., Pattern formation in vibrated beds of dry and wet
granular materials, Physics of Fluids, 26 (2014) 013301.
DOI: 10.1063/1.4860115
Lozano C., Zuriguel I., Garcimartin A., Mullin T., GranularChun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

segregation driven by particle interactions, Physical Review
Letters, 114 (2015) 178002. DOI: 10.1103/PhysRevLett.114.
178002
Lu L.S., Hsiau S.S., Mixing in a vibrated granular bed: Diffusive and convective effects, Powder Technology, 184 (2008)
31–43. DOI: 10.1016/j.powtec.2007.07.036
Ma H.P., Lv Y.J., Zheng N., Li L.S., Shi Q.F., Intruder motion in
two-dimensional shaken granular beds, Chinese Physics
Letters, 31 (2014) 114501. DOI: 10.1088/0256-307X/31/11/
114501
Majid M., Walzel P., Convection and segregation in verticallyvibrated granular beds, Powder Technology, 192 (2009)311–317. DOI: 10.1016/j.powtec.2009.01.012
Mason G., Clark W.C., Liquid bridges between spheres, Chemical Engineering Science, 20 (1965) 859–866. DOI:
10.1016/0009-2509(65)80082-3
Mason T.G., Levine A.J., Ertaş D., Halsey T.C., Critical angle ofwet sandpiles, Physical Review E, 60 (1999) 5044–5047.
DOI: 10.1103/PhysRevE.60.R5044
McCarthy J.J., Turning the corner in segregation, Powder Technology, 192 (2009) 137–142. DOI: 10.1016/j.powtec.2008.
12.008Mehrotra V.P., Sastry K.V.S., Pendular bond strength between
unequal sized spherical particles, Powder Technology, 25(1980) 203–214. DOI: 10.1016/0032-5910(80)87031-8
Mikami T., Kamiya H., Horio M., Numerical simulation ofcohesive powder behavior in a fluidized bed, Chemical
Engineering Science, 53 (1998) 1927–1940. DOI: 10.1016/
S0009-2509(97)00325-4
Mobius M.E., Cheng X., Karczmar G.S., Nagel S.R., Jaeger
H.M., Intruders in the dust: Air-driven granular size separation, Physical Review Letters, 93 (2004) 198001. DOI:
10.1103/PhysRevLett.93.198001
Nahmad-Molinari Y., Canul-Chay G., Ruiz-Suarez J.C., Inertia
in the Brazil nut problem, Physical Review E, 68 (2003)
041301. DOI: 10.1103/PhysRevE.68.041301
Nase S.T., Vargas W.L., Abatan A.A., McCarthy J.J., Discrete
characterization tools for cohesive granular material, Powder Technology, 116 (2001) 214–223. DOI: 10.1016/S00325910(00)00398-3
Natarajan V.V.R., Hunt M.L., Taylor E.D., Local measurementsof velocity fluctuations and diffusion coefficients for a
granular material flow, Journal of Fluid Mechanics, 304(1995) 1–25. DOI: 10.1017/S0022112095004320
Ogawa S., Multi-temperature theory of granular materials, in Proceedings of US-Japan Seminar on Continuum-Mechanicaland Statistical Approaches in the Mechanics of GranularMaterials, Tokyo 208 (1978).
Pitois O., Moucheront P., Chateau X., Liquid bridge between
two moving spheres: an experimental study of viscosity
effects, Journal of Colloid and Interface Science, 231 (2000)
26–31. DOI: 10.1006/jcis.2000.7096
Plantard G., Saadaoui H., Snabre P., Pouligny B., Surface-­roughness-driven segregation in a granular slurry undershear, Europhysics Letters, 75 (2006) 335–341. DOI: 10.
1209/epl/i2006-10088-6
Pohlman N.A., Severson B.L., Ottino J.M., Lueptow R.M., Surface roughness effects in granular matter: influence onangle of repose and the absence of segregation, PhysicalReview E, 73 (2006) 031304. DOI: 10.1103/PhysRevE.73.
031304Rosato A., Strandburg K.J., Prinz F., Swendsen R.H., Why theBrazil nuts are on top: size segregation of particulate matterby shaking, Physical Review Letters, 58 (1987) 1038–1040.
DOI: 10.1103/PhysRevLett.58.1038
Saez A., Vivanco F., Melo F., Size segregation, convection, andarching effect, Physical Review E, 72 (2005) 021307. DOI:
10.1103/PhysRevE.72.021307
Samadani A., Kudrolli A., Segregation transitions in wet granular matter, Physical Review Letters, 85 (2000) 5102–5105.
DOI: 10.1103/PhysRevLett.85.5102
Samadani A., Kudrolli A., Angle of repose and segregation incohesive granular matter, Physical Review E, 64 (2001)
051301. DOI: 10.1103/PhysRevE.64.051301
Savage S.B., Dai R., Studies of granular shear flows: wall slip
velocities, ‘layering’ and self-diffusion, Mechanics ofMaterials, 16 (1993) 225–238. DOI: 10.1016/0167-6636(93)90047-U
Scheel M., Seemann R., Brinkmann M., Michiel M.D.,
Herminghaus S., Liquid distribution and cohesion in wet
granular assembiles beyond the capillary bridge regime,
Journal of Physics-Condesend Matter, 20 (2008) 494236.
DOI: 10.1088/0953-8984/20/49/494236
Schulz M., Schulz B.M., Herminghaus S., Shear-induced
solid-fluid transition in a wet granular medium, PhysicalReview E, 67 (2003) 052301. DOI: 10.1103/PhysRevE.
67.052301
Shi D., Abatan A.A., Vargas W.L., McCarthy J.J., Eliminating
segregation in free-surface flows of particles, PhysicalReview Letters, 99 (2007) 148001. DOI: 10.1103/
PhysRevLett.99.148001
Srebro Y., Levine D., Role of friction in compaction and segregation of granular materials, Physical Review E, 68 (2003)
061301. DOI: 10.1103/PhysRevE.68.061301
Tai C.H., Hsiau S.S., Dynamic behaviors of powders in a vibrating bed, Powder Technology, 139 (2004) 221–232. DOI:
10.1016/j.powtec.2003.12.004
Tai C.H., Hsiau S.S., Kruelle C.A., Density segregation in a
vertically vibrated granular bed, Powder Technology, 204(2010) 255–262. DOI: 10.1016/j.powtec.2010.08.010
Unac R.O., Benito J.G., Vidales A.M., Pugnaloni L.A., Arching
during the segregation of two-dimensional tapped granularsystems: Mixtures versus intruders, European Physcial
Journal E, 37 (2014) 117. DOI: 10.1140/epje/i2014-14117-y
Ulrich S., Schröter M., Swinney H.L., Influence of friction on
granular segregation, Physical Review E, 76 (2007) 042301.
DOI: 10.1103/PhysRevE.76.042301
van der Vaart K., Gajjar P., Epely-Chauvin G., Andreini N.,
Gray J.M.N.T., Ancey C., Underlying asymmetry within
particle size segregation, Physical Review letters, 114(2015) 238001. DOI: 10.1103/PhysRevLett.114.238001
Vanel L., Rosato A.D., Dave R.N., Rise-time regimes of a large
sphere in vibrated bulk solids, Physical Review Letters, 78(1997) 1255. DOI: 10.1103/PhysRevLett.78.1255
Viswanathan H., Sheikh N.A., Wildman R.D., Huntley J.M.,
Convection in three-dimensional vibrofluidized granular

125

Chun-Chung Liao et al. / KONA Powder and Particle Journal No. 33 (2016) 109–126

beds, Journal of Fluid Mechanics, 682 (2011) 185–212.
DOI: 10.1017/jfm.2011.209
Wildman R.D., Huntley J.M., Novel method for measurement of
granular temperature distributions in two-dimensional
vibro-fluidised beds, Powder Technology, 113 (2000) 14–22.
DOI: 10.1016/S0032-5910(99)00286-7
Wildman R.D., Huntley J.M., Parker D.J., Convection in highly
fluidized three-dimensional granular beds, Physical Review
Letters, 86 (2001) 3304–3307. DOI: 10.1103/PhysRevLett.
86.3304
Williams J.C., Segregation of particulate materials—a review,
Powder Technology, 15 (1976) 245–251. DOI: 10.1016/00325910(76)80053-8
Windows-Yule C.R.K., Douglas G.J.M., Parker D.J., Competition between geometrically induced and density-driven
segregation mechanisms in vibrofluidized granular systems, Physical Review E, 91 (2015a) 032205. DOI: 10.1103/
PhysRevE.91.032205
Windows-Yule C.R.K., Rosato A.D., Parker D.J., Thornton A.R.,
Maximizing energy transfer in vibrofluidized granular sys-tems, Physical Review E, 91 (2015b) 052203. DOI: 10.1103/
PhysRevE.91.052203
Windows-Yule K., Parker D., Density-driven segregation inbinary and ternary granular systems, Kona Powder andParticle Journal, 32 (2015) 163–175. DOI: 10.14356/
kona.2015004
Yang W.L., Hsiau S.S., Wet granular materials in sheared flows,
Chemical Engineering Science, 60 (2005) 4265–4274. DOI:
10.1016/j.ces.2005.03.001
Zamankhan P., Sinking and recirculation of large intruders invertically vibrated granular beds, Advanced Powder Technology, 24 (2013) 1070–1085. DOI: 10.1016/j.apt.2013.03.
010
Zeilstra C., van der Hoef M.A., Kuipers J.A.M., Simulation ofdensity segregation in vibrated beds, Physical Review E, 77(2008) 031309. DOI: 10.1103/PhysRevE.77.031309
Zivkovic V., Biggs M.J., Glass D.H., Scaling of granular temperature in a vibrated granular bed, Physical Review E, 83(2011) 031308. DOI: 10.1103/PhysRevE.83.031308

Author’s short biography
Chun-Chung Liao
Dr. Chun-Chung Liao received his PhD degree from National Central University(Taiwan) in 2010, majored in Mechanical Engineering. After receiving the PhD degree,
he started his academic career as a postdoctoral fellow at National Central University in
Jhongli, Taiwan. He was a visiting scholar in Mechanical Engineering in Caltech from
Oct. 2013–Jul. 2014. Currently, he is the assistant professor at Department of Mold and
Die engineering in National Kaohsiung University of Applied Sciences from Feb. 2015.
His research interests include powder technology, granular flows, fast pyrolysis of biomass.

Shu-San Hsiau
Dr. Shu-San Hsiau received his MS and PhD degrees from California Institute ofTechnology, majored in Mechanical Engineering in 1993, and started his professorshiplife in National Central University (NCU). Currently he is a Distinguished Professor of
Mechanical Engineering, and also Institute of Energy Engineering of NCU. He received
the Humboldt award and went to Darmstadt University of Technology (Germany) in
2003. His research area includes Powder Technology, Clean Coal Technology, Biomass(Pyrolysis, Gasification), Modeling and Design of MOCVD, Energy Technology, HotGas Cleanup, Debris Flow and Avalanche, Powder Technology, Hydrogen Energy, Energy Education, Creative Engineering Education, Thermo-Fluids, PharmaceuticalEngineering. He served as the chairman of Mechanical Engineering (2010–2013) andalso as the director of Institute of Energy Engineering (2008–2010). Currently he is thedeputy dean of R&D office (NCU), and also the director of Clean Coal Research Center.

126

KONA Powder and Particle Journal No. 33 (2016) 127–149/Doi:10.14356/kona.2016021

Review Paper

Mixing and Fluid Dynamics Effects
in Particle Precipitation Processes †Jerzy Bałdyga
1

Faculty of Chemical and Process Engineering, Warsaw University of Technology, Poland

Abstract
Precipitation defined as the rapid formation of moderately soluble crystalline or amorphous solid particles from aliquid solution under high supersaturation conditions is considered. It involves the simultaneous and fastoccurrence of primary nucleation and growth of particles together with the secondary processes such as
aggregation and breakage. It is shown how the effects of fluid flow and mixing affect the subprocesses forming
the overall precipitation process. Examples are presented for reactive precipitation and antisolvent precipitation
with supercritical fluids applied as the antisolvents.
The elementary subprocesses forming the overall precipitation process (i.e. macro-, meso-, micromixing, chemical
reaction, nucleation and growth of particles, aggregation and breakage) are characterized by the related time
constants and the application of time constants in the modeling and scale-up of precipitation processes is presented.
The effects of turbulence on particle formation are simulated using mechanistic models, CFD and population
balances (including the method of moments and the quadrature method of moments). The results of modeling arecompared with experimental data.
Finally, it is shown in the context of practical applications to what extent the approaches discussed in this paper
can be applied to “design” particles.
Keywords: aggregation, breakage, fluid dynamics, mixing, population balance, precipitation

1. Introduction
Precipitation refers to the rapid formation of moderately soluble crystalline or amorphous solid particles froma liquid solution, whereas particle formation occurs underhigh supersaturation conditions. Precipitation involves thesimultaneous and fast occurrence of primary nucleationand growth together with the secondary processes such as
Ostwald ripening, aggregation and breakage.
Precipitation processes are of importance in technology
as they are employed in the production of bulk and finechemicals including fertilizers, pigments, catalysts, magnetic materials, drugs, etc. The solid product usually has
a wide crystal size distribution (CSD) which determines
the filtration, washing and settling abilities of suspensions, and thus the quality of the product (crystal size homogeneity, surface area, etc.). Other factors which canstrongly influence the product quality are the morphology
and purity of the precipitating particles. It is well known
that both the CSD and the morphology can strongly de†1

Received 25 June 2015; Accepted 17 August 2015
J-STAGE online 28 February 2016
ul. Waryńskiego 1, Warsaw, Poland
E-mail: j.baldyga@ichip.pw.edu.pl
TEL: +48-22-234-6376 FAX: +48-22-825-6037©2016 Hosokawa Powder Technology Foundation

pend on mixing conditions during the process.
The high supersaturation is usually created either byperforming chemical reactions or by mixing the solution
with an antisolvent which decreases the solute solubility.
Chemical reactions applied in so-called reactive crystallization processes are usually very fast, which means that
their course depends on mixing as well. This also meansthat the role of mixing, including the effects of macro-,meso- and micromixing, can be very important for the creation and dilution of supersaturation. Very important arealso effects of the fluid flow as they affect mixing and may
affect phenomena of particle agglomeration and coagulation; agglomeration dominates the precipitation processes
at high supersaturation, in the case of coagulation only
physical forces are involved to hold particles together.
Another aspect of high supersaturation is its effect on
the nucleation mechanism. Namely at high supersaturation, there is negligible secondary nucleation resulting
from the presence of solute crystalline material but rather
primary homogeneous or heterogeneous nucleation dominates the process. Due to the high sensitivity of primary
nucleation to supersaturation, the number of primary particles is controlled by nucleation and affected by mixing,
whereas crystal growth is less important, because after
complete unloading of supersaturation, it is more the127Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

number of crystals than the rate of their growth that determines their final size. The growth rate can, however,affect their shape.
In this paper, the effects of mixing and fluid flow observed in reactive precipitation, and antisolvent precipitation with supercritical fluids applied as the antisolventsare considered. The material is organized as follows.
First, the elementary subprocesses forming the overall
precipitation process (i.e. macro-, meso-, micromixing,
chemical reaction, nucleation, and growth of particles) arecharacterized by the related time constants and lengthscales. The application of time constants for the modelingand scale-up of precipitation processes is then presented.
Second, because in most of the precipitation devices theflow and mixing are turbulent, it is shown how to predict
interactions of turbulence with particle formation phenomena. In this context, the phenomenological models, mechanistic and based on CFD (including solution of the closure
problem for chemical reaction and precipitation) and population balances (including method of moments and quadrature method of moments) are presented and the results ofmodeling are compared with experimental data.
Third, an attempt to classify aggregation, i.e. agglomeration and coagulation processes, is presented from thepoint of view of the characteristic length and time scales.
Fourth, suitable kernels for aggregation processes (fromorthokinetic to perikinetic, including effects of fluid dynamics and colloidal forces) and breakage processes arepresented, and the results of application of these kernels,
implemented in some cases in a CFD environment, arepresented and compared with experimental data.
This paper finishes with discussion on practical applications where the properties of precipitated particles areof key importance and thus great attention is paid to “particle design”. It is also discussed to what extent the approaches presented in this paper can be applied to“design” particles and scale-up processes.
This is a review paper and the intention of the author isto review important problems, ideas and challenges related in the author’s opinion to the most important aspects
of precipitation. The approaches and examples of resultspresented are based on the author’s own work, and supported by the ideas and results of other researchers who
investigated this fascinating problem of precipitation.
Despite its practical importance and a long tradition of
investigating precipitation, it still requires better understanding and development of theoretical approaches forprocess interpretation. This is, for example, depicted by asmall number of books devoted to precipitation. There aresome fundamental books on precipitation including Kinetics of Precipitation by Nielsen A.E. (1964), The Formation
and Properties of Precipitates by Walton A.G. (1964) andPrecipitation by Söhnel O. and Garside J. (1992); there arealso chapters devoted to precipitation in the Crystallization

128

Technology Handbook, edited by Mersmann A. (2001),
Handbook of Industrial Crystallization edited by Myerson
A.S. (2002), Turbulent Mixing and Chemical Reactions by
Bałdyga J. and Bourne J.R. (1999), Supercritical Fluid
Technology for Drug Product Development edited byYork, P. et al. (2004), Handbook of Industrial Mixing, Science and Practice edited by Paul E.L. et al. (2003) andFormulating Poorly Water Soluble Drugs by Rowe, J.M.
and Johnston, K.P. (2012); discussion on computational
methods that can possibly be used to model precipitation
can be found in Computational Models for Polydisperse
Particulate and Multiphase Systems by Marchisio D.L. and
Fox R.O. (2013).

1.1 Driving force for precipitation
Precipitation is a kinetic process and its rate depends
on the appropriately defined driving force, called supersaturation in the case considered here. The supersaturation, σ, is defined as the dimensionless difference between
the molar chemical potential (the molar Gibbs free energy) μ in supersaturated solution and μeq in the crystalline state (equal to the potential in saturated solution) thatcan be expressed using solute activities, a, (Davey R. andGarside J., 2000).

 - eq

a
 ln
RT
aeq

(1)

where μ and a are the actual molar chemical potential andthe actual activity of the solute in solution, respectively,
whereas T is the absolute temperature.
The value μ – μeq = 0 defines equilibrium: for μ – μeq > 0
spontaneous crystallization may occur, and μ – μeq < 0means that an opposite transformation, i.e. dissolution,
becomes spontaneous. The sign of Δμ determines the direction of the possible phase transformation, whereas theabsolute value of Δμ signifies the distance from the equilibrium. Δμ/RT represents the thermodynamic driving force
for crystallization, and both the rate of nucleation and therate of crystal growth depend on Δμ/RT. The driving force
is usually expressed in a simpler form than Eqn. (1): a a  aeq ln 
Sa 1

 
a
aeq
 eq x  xeq S 1 xeq(2)

In chemical engineering, concentration is often expressed in the units appropriate for the specific application, e.g., molar concentration, c

S
a

ac
c
S

ac,eq
ceq

(3)

where ac represents the activity of dissolved componentJerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

of concentration c, ceq is the equilibrium solubility andSa ≈ S is the supersaturation ratio that is also known as
relative supersaturation or saturation ratio. Supersaturationis also sometimes expressed as a concentration differencec  c  ceq(4)

The solution is supersaturated when S a > 1, S > 1,
Δc > 0 or σ > 0. For electrolytes, which dissociate giving
v+ cations and v– anions, v = v+ + v–, and1 a +a- -    eqln   ++ ln S a  RT a + a -  eq (5)which defines both the ionic activity product a++a- - andthe thermodynamic solubility product a++a- - eq . For dilute solutions, activities can be replaced by concentrations
yielding c+ +c- - and c+ +c- - eq for both products. In practice, supersaturation is often expressed as σ = Sa – 1 or asa “concentration” difference11c c+ +c- -  c+ +c- - eq (6)where v represents the number of moles of ions formed
from one mole of electrolyte.
Notice that Eqns. (5) and (6) should be combined withthe relations describing equilibrium in the solution to account for the effect of solution composition on both solubility and driving force.

1.2 Kinetics of precipitation
The formation rate of a new solid phase, i.e. nucleation,and the rate of crystal growth from solution as well as thesecondary changes such as agglomeration and aging all
depend on supersaturation. Nucleation proceeds by two
mechanisms: primary nucleation, occurring without influencing the crystallizing material, and secondary nucleation that is induced by the presence of the solid phasebeing formed. There are two types of the primary nucleation: homogeneous nucleation, in which the solid phaseformation is not caused by the presence of any solid
phase, and heterogeneous nucleation, which is induced by
the presence of the foreign solid phase.
An expression for the rate of homogeneous nucleation
was developed by Nielsen A.E. (1964), B 
 G  J
 exp 
 exp 
 k BT  (ln S ) 2 (7)16π 3v 2
is the energy barrier for howhere G  
2
3k B 2T 2 ln S mogeneous nucleation and the pre-exponential term1

D  4G   2reads  
, B is a constant defined as
d 5 N   3πk BT 

16π 3v 2
where D is the diffusion coefficient of the
3k B 3T 3
solute, k B is Boltzmann’s constant, d is the molecular diameter, N* is the number of molecules or ions forming
the critical embryo-nucleus, σ is the surface energy and v
is the molecular volume. Nielsen A.E. (1964), Nývlt J.
(1971), and Söhnel O. and Garside J. (1992) have shownthat over limited ranges of supersaturation, Eqn. (7) may
be simplified toBJ  k NS n(8)where n, the kinetic ‘order’ of nucleation, varies between
1 and 10.
Heterogeneous nucleation results from presence of foreign substances that are always present in a solution andmay act as ‘catalysts’ of nucleation. This catalytic effectresults mainly from decrease the energy barrier to nucle  G  thanks to smaller σ for nucleus formation, Ghet
ing on the surface of the foreign phase. Hence, rate ofheterogeneous nucleation (Turnbull D. and Vonnegut B.,
1952) can be presented in similar way as homogeneous
nucleation, Eqn. (7).
* Ghet
J het   het exp  k BT (9)where Ωhet < Ω. Heterogeneous nucleation is observedmuch more frequently than homogeneous nucleation.
In true secondary nucleation, nuclei are either formedon the surface of the solid phase as dendrites that are subsequently broken off the crystal or in the surface proximity (adsorption layer, liquid adjacent to the crystal); in
contact secondary nucleation, nuclei result from the effects of impact energy of the crystal-crystal and crystal-­equipment collisions. In the case of precipitation ofmoderately soluble materials, the secondary nucleation is
not an important mechanism of nuclei formation because
the particles are too small for the high-impact collisions
that would be able to produce secondary nuclei, and theeffects of crystal-solution interactions are negligible(Söhnel O. and Garside J., 1992). It is only in the case of
rapid agglomeration that the resultant particles can belarge enough to induce a sufficiently high impact energy
for the generation of nuclei.
Once nuclei are formed in a supersaturated solution,
they start to grow into crystals. The rate of crystal growth
can be presented as the rate of growth of each crystal
face. In the case of precipitation, particles are small andthus a concept of the overall rate of growth, G = dL/dt isused with the crystal volume expressed by vc = k VL3, k V
being the volume shape factor. The rate of growth is affected by two processes: the mass transfer of growth units
to the crystal-solution interface by bulk diffusion and thesurface integration of growth units into the crystal lattice.
This can be represented by129

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

G k D (c  ci ) k R (ci  ceq ) r(10)

cluding its effect on complex formation, is presented inFig. 1, and the rate of growth expressed by Eqn. (14).

where k D = 2kd /ρc, kd is the mass transfer coefficient, k R is2
 cBa 2 ,i cSO 2 ,i  0.5

the surface integration constant that is independent of4
G k R   ±,int  1
crystal size and r represents the “order” of
the surface inK SP



(14)
tegration process. In Eqn. (10), c is the bulk solute con k D cBa 2  cBa 2 ,i  k D cSO 2  cSO 2 ,icentration, the subscript i describes the concentration at
44
the crystal-solution interface and the subscript eq indicates the concentration at equilibrium. When the resiswith kr = 9.1 × 10 –12 ms –1 (Vicum L. et al., 2003) andtance of mass transfer to the crystal k D1 is much lower
k D = 4.0 × 10 –5 m4s–1kmol–1 (Bałdyga J. et al., 1995).
than that of the subsequent surface integration step1r
k R1 c  ceq  , the surface integration process controls theoverall rate of growth, G = k R(c – ceq)r. In the opposite
2. Time constants for precipitation andcase, the overall rate of growth is determined by the rate
mixingof mass transport from the solution to the crystal
G = k D(c – ceq). When the growth is controlled by comIt has been well known since years (Bałdyga J. andbined mechanisms, then the concentration at the crystal-­
Bourne J.R., 1999) that the history of supersaturation andsolution interface ci in Eqn. (10) has to be eliminated, inthe history of supersaturation structure determine thethis way relating the rate of growth to the bulk and equiPSD and the crystal morphology in precipitation.
librium solute concentrations.
Before starting scale-up or modeling the precipitation
A similar approach can be used in the case of more
processes, it is good policy to assess the importance ofcomplex systems when there is, for example, a reaction
mixing in these processes. One should consider the time
between the constituent ions on the crystal surface before
constants for all mixing and precipitation phenomena that
integration into the lattice. As an example, consider preaffect the distribution of supersaturation and compare thecipitation of the barium sulfate BaSO4. Vicum L. et al.
characteristic time constants for these processes (Bałdyga
2+and Bourne, 1999).
(2003) applied thermodynamic models for aqueous Ba ,SO 24 , Na+, Cl– solutions differing in composition.
The dependence of solubility on composition is then
2.1 Time scales for precipitation
expressed using the thermodynamic solubility productA comparison of the characteristic time constants for
K SP  aBa 2 (aq) aSO 4 2 (aq)
precipitation and mixing should yield the rate of the con(11)trolling mechanism. In the following section, the timecccBa 2 cSO 4 2-  2
Ba 2 SO 4 2 Ba 2+ SO 4 2 constants that can be used to characterize precipitation
where ci represents the concentration of ions i with γi being the activity coefficient of the ionic species i, and γ±
represents the mean ionic activity coefficient that depends
on the ionic strength.
The mean ionic activity coefficient γ± can be calculatedusing semi-empirical forms of the Debye-Hückel law, either that proposed by Bromley L.A., (1973), applicable forthe ionic strength up to 6M, or the Pitzer K. (1991) model,
valid for higher values of the ionic strength. The equilibrium between ions and undissociated ion pairs is expressed by the dissociation equilibrium, with Ka being thedissociation constant



Ka 

cBa 2 cSO 4 2  2

cBaSO 4 (aq) BaSO 4 (aq)



(12)

The driving force for precipitation, S, is then expressedby:0.5

 c 2 c 2  
S   Ba SO 4   ±
K SP



(13)

The impact of the composition on the driving force, in-130

Fig. 1 Precipitation diagram for BaCl2 and Na2SO4 in aqueous
solution at 25 °C (constructed based on Vicum L. et al.,2003)

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

are presented. The first one is the time constant for thenth-order chemical kinetics of homogeneous reaction, R  k n cAn01

1

(15)

where kn is the rate constant of the chemical reaction oforder n and cA0 is the initial or feed concentration of reactant A. This time constant characterizes the rate of consumption of reactant A and thus the rate of creation ofsupersaturation provided that the chemical reaction controls this process. For a more general definition and moreexact identification of τ R, the method given by Bałdyga J.
and Bourne J.R. (1999) is recommended; for example, fork2
reaction of the type A  BB  C , this method yields

R 1 Bk 2cA  k 2cB(16)

For the primary nucleation, the time constant can beapproximated by the induction period, i.e. the time thatelapsed between mixing on the molecular scale two reacting solutions and the appearance of the first crystals of
precipitate; the induction period is considered to be inversely proportional to the rate of nucleation (Dirksen J.A.
and Ring T.A., 1991).

N 

6dN *
K

D ln SJ(17)

where K is a constant. The time constant τ N represents acharacteristic time that is necessary to form a characteristic, significant number of nuclei per unit volume.
The characteristic time for crystal growth can be expressed in relation to the rate of product concentration decrease caused by the crystal growth G 
 Gcr  c Ag 
 Mc


1

cc  cc*(18)

where cc is the concentration of precipitating substance,
and Ag is the specific surface area per unit volume of suspension.
Note the different role of processes characterized by
the time constants presented above; the time constants τ Rand τGcr characterize processes that in a similar way tomixing directly affect the distribution of supersaturationin the system, but only τGcr depends on supersaturation itself. On the other hand, the nucleation process that ischaracterized by τ N significantly depends on the supersaturation distribution but does not directly affect this distribution.

2.2 Time scales for mixing
The time constants for precipitation should be correlatedwith the process time and time constants for mixing.
The process time represents the batch time in the caseof a batch precipitator and can be identified with the mean

residence time τ in the case of the continuous flow systems.
Chemical reactions, nucleation and crystal growth areessentially the molecular-molecular-level process, whichmeans that mixing on the molecular scale can directly influence the course of precipitation. However, the larger-­scale mixing mechanisms often indirectly affect the
precipitation process by changing the environment for local
mixing. Hence the complete sequence of mixing processes,
namely macromixing, mesomixing and micromixing,needs to be considered.
Macromixing can be identified as fluid blending on thescale of the precipitator. The characteristic length scale
for this process is thus the size of the precipitator. Macromixing in the tank of volume Vt can be identified with themacroscopic flow pattern; e.g. the blending time τ b or circulation time τCb C 
const.

Vt
NDS3

(19)

can be used as simple characteristics of macromixing in a
stirred tank. In Eqn. (19), N represents the stirrer frequency and DS is the stirrer diameter.
There are two mechanisms of mixing on a meso-scale.
The first mechanism of mixing on the meso-scale refers to
the coarse-scale turbulent exchange between the fresh feed
and its surroundings. The time constant τ D for meso-scale
mixing by turbulent diffusion can be expressed by

D 

Q
u Dt

(20)

where Q is the feeding rate, Dt represents turbulent diffusivity, and ū is the local, average fluid velocity. The related length scale can be estimated either as (Q/ū)1/2 forhigh feeding rates or can be represented by the feed pipe
diameter df for low feeding rates, when (Q/ū)1/2 < df.
The second mechanism of mesomixing refers to disintegration of large eddies of a size larger than the Kolmogorov microscale, λK = v3/4/ε1/4, but smaller than the integral
scale of concentration fluctuations, ΛC. Inertial-convective
disintegration proceeds without being influenced bymolecular-scale mixing, however, it itself affects the
micromixing by changing the local environment. The
time scale τS for meso-scale mixing resulting from theinertial-­convective disintegration of the large eddies readsS  A

2C3

1 3

(21)

where A is the proportionality constant equal roughly to
1.2.
Micromixing processes are driven by the mechanism of
the viscous-convective deformation of fluid elements, followed by molecular diffusion. A characteristic time constant for viscous convective mixing, i.e. for eddies smaller131

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

than the Kolmogorov microscale scale, λK is known as theengulfment time constant (Bałdyga J. and Bourne J.R.,
1989):

 1/2
 E  17  
 

(22)

that for a high Schmidt number can be replaced by thetime constant for the viscous-convective and viscous-­
diffusive mixing

 12
 VCVD  0.5   ln Sc
 

(23)

Based on these characteristic times, Bałdyga J. andBourne J.R. (1999) proposed a classification of precipitation by comparing the characteristic times for mixing andreactive precipitation. Using τ M to denote the characteristic mixing time (this can be any mixing time listed
above), one can classify the precipitation subprocesses
from the point of view of competition between precipitation and mixing. Therefore the reaction can be instantaneous (τ M >> τ R), fast (τ M ≈ τ R) or slow (τ M << τ R). The
effects of mixing should be observed in the first twocases.
It should be noted that the chemical reaction can beclassified as slow compared to micromixing, but fast or
instantaneous when compared to mesomixing or macromixing. For an instantaneous, mixing-controlled reaction(i.e. for τ R much smaller than any mixing time scale) but
with τN > τVCVD, the influence of molecular diffusion on
nucleation can be neglected. However, when τ N ≈ τ E, theviscous-convective micromixing (engulfment) affects
precipitation. Usually, τ N << τGcr —thus mixing affects
precipitation mainly through the nucleation step.
Note also that for a sufficiently slow feeding τ >> τC,
one can assume a uniform concentration of the bulk andjust model the reaction zone using meso- and/or micromixing modeling. If precipitation is slow compared tomeso- and micromixing, the assumption of ideal mixing
can be used. Considerations similar to the ones presented
above can be helpful in choosing the model of mixing thatis most suitable for the modeling and scaling up of precipitation processes.

2.3 Scale-up principles for precipitation
Scaling up industrial apparatus represents a fundamental step in the realization of industrial plants. The scale-up
procedures are, however, not obvious in the case of complex processes, including precipitation. To scale up theprocess, one can use an empirical approach, i.e. carry out
the process at a number of scales and gain enough information to make an empirical prediction of the system performance on a larger scale, i.e. extrapolate the processusing principles of similarity. Dimensionless similarity

132

criteria can be derived using the available governing
equations for mass, momentum, species, energy, andpopulation balances; when they are not available, theBuckingham’s Pi Theorem can be used instead (ZlokarnikM., 2002).
In the geometrically similar systems, a complete similarity occurs if all necessary dimensionless criteria derived either from the differential equations or obtainedusing the Pi Theorem are equal. For example, to have asimilar velocity distribution in a single-phase system, it isenough to apply the same Reynolds number in both systems. In complex precipitation processes, such complete
similarity is impossible and in fact not necessary. We
usually want to reproduce the product quality, for example, the particle size and morphology at the larger scaleand obtain identical not similar products from the systems
differing in scale. Thus, one should apply a partial similarity and in this way obtain a reduced number of themost important similarity criteria for the process.
A more fundamental approach to scale-up is based onmodeling with the goal of predicting the effect of scale-up
and of understanding the process in sufficient detail.
The strategy recommended here is to start by performing an analysis of time constants to identify which
sub-processes are less important on the large scale, andgain information on which requirements related to suchprocesses can be neglected. Afterwards, a physical model
employing CFD should be applied to predict the scale-up
effects more correctly and finally to optimize the process.
As an example of scaling up with a combined applicationof time scale analysis and CFD, let us consider an antisolvents precipitation method known as the solution-enhanced
dispersion by supercritical fluids process (SEDSTM) thathas been proposed by York P. and Hanna M. (1996). In
the SEDSTM process, the supercritical fluid (SCF) acts as
an antisolvent and the substrate solution is co-introduced
into a nozzle and mixed intensively with the antisolvent ina nozzle chamber, from where they both flow into the precipitation vessel at high velocity as a turbulent jet, as
schematically depicted in Fig. 2. The process is carriedout above the mixture critical pressure, so the antisolvent(CO2) is completely miscible with the solvent (ethanol).
As mentioned earlier, the distribution of supersaturationand the supersaturation history of fluid elements determine the PSD and the crystal morphology in precipitation, which means that macro-, meso-, and micromixing
processes can affect the particle size distribution.
To scale a process up or down, one needs invariabilityof all characteristic times: τ = idem, and τi = idem from
Eqn. (15) to (23), which is of course impossible.
Details on the scale-up of the SEDS process can befound in Bałdyga J. et al. (2010). Interpreting the precipitation vessel shown in Fig. 2 as a pipe-in-pipe system,with the outer tube fluid velocity equal to zero, one canJerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

characterize a similarity of the flow pattern using theCurtet number (Becker H.A., et al., 1963; Craya A. andCurtet R., 1955), which characterizes turbulent self-­entrainment.

Ct 

d 0 Dv
1  0.5 d 0 Dv

2

(24)

where d 0 is the nozzle orifice diameter and D v is thediameter of the precipitation vessel. This means that under process conditions, there is a recirculation eddy induced as shown in Fig. 3, and the flow similarity requires
d 0/D v = const. The recirculation eddy causes the exhausted, residual fluids to dilute the fresh supersaturated
solution, which significantly reduces the nucleation rate.
The supersaturation is then unloaded mainly due to theparticle growth.
Using the jet nozzle diameter d0 for the length scale,and the nozzle velocity u0 for the velocity scale, we express the rate of energy dissipation by

d
(26)
u0
and the fluid elements should have the same dimensionless positions x d 0 after the same dimensionless time period t/τC.
The time constants for meso-scale mixing then read   C 0QuDt

 D 

u 0d 02d0

13 43
u 0 0 d 0
u0

(27)

where Dt  u C   d 01 3  d 0, andS  C2 313



d 02 3

u 03

13

d 0



d0
u0

(28)

The time constants for the viscous-convective andviscous-diffusive micromixing processes scale as ε–1/2 see
12
Eqns. (22) and (23), which means that 
u03 2
E 1 E  d0
1232
and  VCVD  d 0 u0 when the fluid properties are thesame in both scales considered. Hence, the ratio of any
mesomixing time constant and micromixing time con
u 03



 x d 0 
f  x d 0  0 f  x d 0stant scales as Re10 2 , where Re 0 = u 0 d0/v, for example(25)d0
 S  E  Re10 2 . This shows that at large Reynolds numbervalues, τS >>  τ E, with mesomixing controlling the process
where function f  x d 0 represents the dimensionless distributions of the rate of energy dissipation.
and negligible micromixing effects. Based on this,This means that both the mean residence time τ and the
Bałdyga J., et al. (2010) proposed the following:
characteristic convection time τC can be expressed by(1) for developed turbulence, characterized by thehigh Re 0 values, micromixing effects are negligible,
the scale-up criterion is then (d0/u0)1 = (d0/u0)2 and thus(u0)2/(u0)1 = X1/3, (d0)2/(d0)1 = X1/3 for the throughput ratio
2
2

X Q
2 Q1 u 0d 0  2 u 0d 0 1,(2) for weakly turbulent flows micromixing dominates,
and the scale-up cr iter ion is then τ E1 =  τ E2 oru03 d 01  u03 d 0 2 and thus (u0)2/(u0)1 = X1/7 and (d0)2/(d0)1 = X3/7. As a safe scale-up method, the criterion basedon high Reynolds number flows has been recommended.

Fig. 2

Solution-Enhanced Dispersion by Supercritical Fluid
Process (SEDS Process).

Fig. 4
Fig. 3 The structure of the flow in the SEDS particle formation vessel, represented by the stream lines (Bałdyga J.
et al., 2010, copyright 2010 Elsevier Ltd., reprinted
with permission).

Particle size distribution of the product obtained in vessels of different diameter Dv with a nozzle diameterd0 = 9 × 10 –4 m; Ct = 0.036 for Dv = 0.025 m, Ct = 0.018
for Dv = 0.050 m, Ct = 0.0082 for Dv = 0.11 m (p = 120
bar, T = 323 K). (Bałdyga J. et al., 2010, copyright 2010
Elsevier Ltd., reprinted with permission).

133

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

To validate this criterion, CFD simulations and experimental investigations for acetaminophen precipitation werecarried out for three equipment scales: a laboratory scalewith d0 = 2.0·10 –4 m, a pilot scale with d0 = 4.0·10 –4 m,and a small manufacturing plant (SMP) scale withd0 = 9.0·10 –4 m. Fig. 4 shows the importance of the geometric similarity; the particle size distributions obtained
for the same process conditions, but different vessel diameters and thus different Ct values, are shown in Fig. 4.
Fig. 5 shows the results of application of the scale-up
criterion (d0/u0)1 = (d0/u0)2. The micromixing effects decrease with increasing system scale, following the relation
Re02/Re01 = (d02/d01)2, and are negligible in the case of boththe pilot plant and the SMP results presented in Fig. 5.
Fig. 6 shows an example of the CFD prediction of thesupersaturation distribution in the small manufacturing
plant; a similar distribution was observed in the case of
the pilot plant, whereas in the case of the laboratory scale,the supersaturation was about 7 % higher due to a toosmall Reynolds number.
The importance of the time constants, either for the di-rect modeling-based scaling up or for the formulation ofscale-up rules, has been emphasized in several publications. For example, Zauner R. and Jones A.G. (2000) haveshown that the calcium oxalate precipitation can be scaled
up by using the time constants for micro- and mesomixing in combination with a simple segregated feed model.
An interesting discussion on using time constants is presented by Gavi E. et al. (2007), in the context of thescale-up of Confined Impinging Jet Reactors (CIJR) basedon the results of Marchisio D.L. et al. (2006).

3. Modeling of precipitation
3.1 Concept of population balance
The balancing of populations is related to classic problems of statistical mechanics that were defined in the 19th
century, whereas the fundamentals of the methods oftreating populations of aggregating particles were introduced by Smoluchowski M. (1917). The present status of
the population balance equation is a result of applicationof the Liouville equation to particulate processes by
Hulburt H.M. and Katz S. (1964). Hulburt H.M. and Katz
S. (1964) transformed the Liouville equation by replacing
the momentum coordinates by other coordinates, equivalent to particle properties.

n 3  u p i  x, t n m  wp j  x, t nt i 1 
xi
x j

j 1


 B  x, t   D  x, t 

Fig. 5

Scale-up from pilot plant to small production scale: (log
PSD, p = 120 bar, T = 323 K, Ct ≈ 0.015). (Bałdyga J. etal., 2010, copyright 2010 Elsevier Ltd., reprinted withpermission).

where xi are the external coordinates representing positions in the physical space and xj are the internal coordinates representing properties of particles, n is the
population number density, upi refers to the component of
the rate of change of particle position in the physicalspace, m is the number of particle properties, and wpj refers to the component of the rate of change of the particle
property, and B and D are the birth and death functions at
the point in the phase space.
Very often, the property of the particle is identified
with the particle size, L, and then Eqn. (29) takes the form
n 3  u p i  x, t n  G  x, t nt i1xiL B  x , t   D  x, t 

Fig. 6

134

Supersaturation ratio distribution in the nozzle and thejet of SMP. (Bałdyga J. et al., 2010, copyright 2010 Elsevier Ltd., reprinted with permission).

(29)

(30)

Eqn. (30) can be used under assumption that the shapeof the particles remains unchanged during particle
growth, and the concept of constant shape factors can beused, with the particle volume vp = kvL3, the particle surface area ap = ka L2, and the characteristic size L chosen sothat the ratio of the shape factors ka/kv = 6. However, thisJerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

is not always the case. When, for example, the particle
shape is determined by the growth of two faces, Eqn. (29)should be used asn 3  u p i  x, t n  G1n  G2n
t i 1
xi
L1
L2


 B  x, t   D  x , t m j  x, t   n x, t, L L j dL0(31)

Eqn. (29) should be solved together with the mass, momentum, energy, and species balances to determine theparticle velocity in the physical space as well as the rates
of change of the particle properties. This creates numerical difficulties because the population balance equation
should be solved in (m + 3)-dimensional space, whereas
all other equations are defined in the 3-dimensional physical space.
To reduce the dimensionality, the volume-averaged
population balance can be used by applying either amechanistic model of micromixing, (Bałdyga J. et al.,1995) or another macroscopic domain including the entire
equipment, e.g. the crystallizer (Randolph A.D. and Larson
M.A., 1971). This reduces the number of coordinates tothe number of particle properties, m.
In the literature, for m > 1, one can find mostly publications dealing with such volume-averaged populations.
Ramkrishna D. and Mahoney A.W. (2002) considered theapplication of the multi-dimensional population balances
to represent the dynamics of particle morphology. They
discussed the possibility of finding precipitation kinetics
from the dynamics of the particle size distribution by the
inversion of the multidimensional population balances (i.e.
by solving the inverse problem). Efficient numerical techniques to treat the problem numerically were consideredby Qamar S. and Seidel-Morgenstein A. (2009). Briesen
H. (2006) tried to simulate the crystal size and shape bymeans of the population balance model based on the crystal volume and the shape factor as internal coordinates.
Ochsenbein, D.R. et al. (2015ab) considered the growth
and agglomeration of particles including effects on theirshape.
To reduce the problem to the 3-dimensional physicalspace, one can use the moment transformation of the population balance. For example, for particles described by
one internal coordinate L, Eqn. (30) can be used, so form = 1, one gets the set of equations represented by Eqn.
(32)m j  u p i m j 

t
xi
j
 0 J  jG m j -1  Bf j  Df j

and the moments of order j are defined as(32)

for i = 1, 2, 3 and j = 0, 1, 2, ...


where u p  u p1, u p 2, u p3 is the vector of particle velocity,(33)

and n  x, t, L represents the crystal size distribution at thetime t and position x , and Bfj and Dfj are the birth anddeath terms for the moments of order j.
The averaged birth and death terms for the moments of
the order j are not closed as the form of the CSD is notknown in advance. This creates the first “closure problem” that can be solved using an idea of the presumed
distribution. A good example here is the quadrature
method of moments (McGraw R., 1997).

3.2 Application of CFD
Consider the process of precipitation of a solid productfrom two liquid ionic solutions A and B A n+ + Bn– → C↓
involving an instantaneous chemical reaction that produces a supersaturated solution of the product C, and its
subsequent crystallization-precipitation, in the systems
with turbulent flow.
A quantitative description of this process requires acomplete knowledge of the spatial distribution of the instantaneous local concentration of ions A n+ and Bn–, product C, and other ions present in the system to properly
express the driving force. Identification of these concentrations requires the solving of a system of partial differential equations describing the momentum, mass, species,
and population balances in the system.
The Reynolds averaging procedure, typical for treating
turbulent flows, produces more unknowns than the number of the balance equations, and the set of the partial differential equations becomes unclosed. We thus have thesecond “closure problem”. This “closure problem” is usually solved by forming a “closure hypothesis”. The “closure hypothesis” introduces additional information basedon the experimental data, investigator’s experience, or intuition. They are included in CFD models such as the k-ε
model, Reynolds Stress Model or Large Eddy Simulation
model. To simulate the course of precipitation, CFD
should be combined with the averaged population balance
equation. In the simplest case it can be the standard k-ε
turbulence model (Launder B.E. and Spalding D.E., 1972)
which employs the hypothesis of gradient diffusion for
momentum and species transport.
Eqn. (32) employs the local, instantaneous values of thevelocity and concentration. Application of Eqn. (32) to thecase of turbulent flow by utilizing the Reynolds decomposition and the Reynolds averaging (denoted with overbar)
was obtained by Bałdyga J. and Orciuch W. (2001).

m j u p i m j 0 j J  jG m j 1  Bf  Df
t
xi

(34)

135

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

After introducing a concept of gradient diffusion forparticles and employing a concept of mixture fraction fthat can be interpreted as a volume fraction of the non-­reacting fluid originating in the A stream when A and B
solutions are mixed, one gets Eqn. (34) in the closed formm j  m ju p i m j 
 


DpT
0jJ
t
xi
xi 
xi 
1

 j  G  f  m j 1   f  df  Bf  Df

(35)

0

where DpT is the local value of the particle turbulent diffusivity coefficient.
The statistics of fluid elements is expressed using theprobability density functions, PDF, Φ( f ), for example thepresumed beta distribution (Bałdyga J. and Bourne J.R.,
1999). The distributions of the average concentrations f
2
and the inert tracer concentration variance, σSf  f 2

are necessary to define the presumed PDF and can be calculated using CFD and one of the mixing models: themultiple-time-scale model of the turbulent mixer(Bałdyga J., 1989) or the spectral relaxation model (Fox
R.O., 1995). Eqn. (35) should be solved together with thepartial differential equations describing the species balances.
c α
c
 
c 
 ui α Dα  Dt  α 

t
xi
xi 
xi 
1
k ρ
 a p  G  f  m 2  f    f  df
2 MC 0(36)

The results of application to the SEDS process of themodel presented above were already shown in Fig. 6.
More results are presented in Fig. 7. The method of SEDS
modeling is described in detail by (Bałdyga J. et al., 2004,
2008a) and (Henczka M. et al., 2005).
Fig. 7 explains how one can control the particle size byvarying the flow rate (expressed here by Re); this effectresulting from the competition between the creation ofsupersaturation by mixing fresh fluids and its decrease by
dilution with residual fluids was considered in Section 2.3
for scaling up precipitation processes. Fig. 7 also illustrates the competition between meso- and micromixing.
The comparison of CFD models, the first model neglecting and the second including micromixing effects showsthat micromixing dominates the process at small Reynolds number turbulent flows, whereas mesomixing dominates at high Re. It is interesting that predictions of themechanistic E-model (Shekunov B.Y. et al., 2001) are very
good and illustrate very well the effects of creation anddilution of supersaturation due to mixing.
Other methods applying the presumed PDF are basedon the application of groups of the Dirac delta functions

136

Fig. 7Comparison of the model predictions with the experimental results for mean particle size of acetaminophen
crystals obtained in the SEDS process—E-model from
Shekunov B.Y. et al. (2001), CFD from Henczka M. etal. (2005)

representing different environments (Piton D. et al., 2000,
Marchisio D.L. et al., 2001), or combining DQMOM
with the IEM model of micromixing (Gavi E. et al.,2007). A full PDF method was applied by Di Veroli G.
and Rigopoulos S. (2010). The comparison of these three
approaches, based on the presumed beta-PDF, multienvironment, and the full PDF, was carried out by Fissore D.
et al. (2002). The following examples present the effectsof mixing on the precipitation of BaSO4 in the single-feed
and double-feed stirred tank reactors operating in semibatch mode.
Fig. 8 presents a visualization of the mixing zone forthe single-feed simulations, which is regarded as the zone

I S  f of the intensity of segregation,2

 f 1  f larger than 0.01. The intensity of segregation represents
the normalized variance of concentration of the inert
tracer. Clearly, the micromixing is not instantaneous andnot localized at the feed point.
Fig. 9 compares the measured and predicted effects of
the initial concentration on the particle size for the single-­feed mode. The effect of concentration is predicted well,especially at the higher concentration values.
In the case of the double-feed precipitation, the effectsof mixing are more significant. Segregation of the fresh
feeds A and B prevents the creation of supersaturation,and thus micromixing decreases segregation, increases
supersaturation and the nucleation rate close to the feedpipe.
At a further distance from the feed point, micromixing
with the residual solution from the bulk decreases supersaturation and limits nucleation. The local intensity of thesegregation of fresh fluids A and B is presented in Fig. 10,
showing the zone where mixing affects the precipitation

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

Fig. 8 Visualization of the mixing zone (Bałdyga J. et al.
2006) for the single-feed mode for N = 5s –1 and
tf = 1230 s. (Bałdyga et al. 2006, reprinted with permission).

Fig. 9Comparison of the model predictions with the experimental results for single-feed mode with the feed introduced above the impeller (Bałdyga J. et al., 2006,
reprinted with permission). Model II accounts for the
micromixing effect.

process.
Fig. 11 presents a comparison of two models: model I
neglecting fluctuations of species concentrations, thus neglecting the effect of the inertial-convective and viscous-­
convective concentration fluctuations, and based on thelocal average concentrations, with complete model II including the effects of micromixing. There is a satisfactory
agreement of the experimental data with the model predictions only in the case of model II.
Neglecting the micromixing (model I) results in a verylarge amount of very small particles, due to the fact thatvery high supersaturation values are created in the region

Fig. 10 Mixing in the feed zone of a double-feed precipitator
operating at N = 3s –1 and qf = 0.12 × 10 –6m3s –1 3D
representation of the feed zone for the intensity ofsegregation IS ≥ 0.01 (Bałdyga et al. 2006, reprinted
with permission).

Fig. 11 The effect of the stirrer speed on the mean crystal sizefor qf = 0.12 × 10 –6m3s –1, c0  0.0045 kmolm 3 , feed
concentrations cA0 = cB0 = 0.234 kmol m–3 and αV = 50.
Solid line—predicted by Model II accounting for the
micromixing effects; dashed line—predicted by ModelI neglecting the micromixing effects (Bałdyga et al.
2006, reprinted with permission).

of the impeller which—due to the strong nonlinearity of
Eqn. (7)—results in a very large amount of small particles. The time constant for nucleation (about 10 –5 s), ismuch smaller than the time constant for micromixing
that—close to the impeller—is of the order of milliseconds. This clearly shows that at high supersaturation, themicromixing effects need to be included to match thesimulation results with the experimental data.

137

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

4. Time and length scales for aggregation

lence: the Kolmogorov time microscale, τ K,

According to Söhnel O. and Garside J. (1992), aggregation denotes all processes of clustering of separate particles to form larger particles, whereas in the agglomeration
process, strong secondary particles are formed that areheld together by crystalline bridges or physical forces. In
the case of coagulation, only very small particles are involved and only physical forces hold them together. Let us
now discuss what we mean by small particles, and let us
try to classify them based on their size. In the case of solid-­
liquid particulate systems, particles of a size smaller than
1 μm are called colloidal, whereas the larger ones—suspended (Elimelech M. et al., 1995).
In the case of a dispersion subjected to turbulent flow,
the classification should be based on the comparison of
the particle size expressed by the particle radius a withthe characteristic length scales for turbulence.
We apply two characteristic scales of turbulence: theKolmogorov microscale λK = v3/4/ε1/4, the smallest scale ofturbulent flow, and Le = (u′)3/ε, the scale of the large, energy-­
containing eddies; here u′ is the root-mean-square velocity fluctuation.
Particles of the size a, smaller than the Kolmogorov
length microscale, λK, are classified as small. The verysmall particles are groups of the small particles for which12a  DB E 
or Pe Ea 2 DB  1, so their movements
are dominated by the Brownian motion, which leads toperikinetic collisions and perikinetic aggregation. The
Péclet number is defined as the ratio of the diffusion timea 2 DB to the convection time 1/E. E represents the rate of
deformation here and DB is the coefficient of Brownian
diffusion for a particle separated from other particles.
In the group of large particles (a > λK), there are inertial-­
convective effects; aggregation of very large particles,a > Le, is hardly possible in practice.
The time-scale analysis is also important to make thedescription more complete as it considers more mechanisms which affect aggregation. The time scales characterizing particles are: the diffusion time τ B, and therelaxation time τp, respectively:B a1  a2 2

D12

 1   2
16 p 1 
a
 2 p p 
3 C D Rep

(37)

(38)

 is the mutual Brownian diffusion coefficientwhere D12for large particle separation, CD is a friction factor (alsoknown as a drag coefficient), ρp is the particle density, theReynolds number, Rep, is defined using the relative velocity fluid-particle and the equal volume sphere diameter.
They can be compared with the time scales for turbu-

138

 12
 K   
 

(39)

and the Lagrangian integral time scale, TL that can be estimated using Eqn. (40) (Hinze J.O., 1975).
f
TL 

u

 u 2
u 2
 0.2

2

(40)

where Λf is the longitudinal integral scale of turbulence,u′ is the r.m.s. (root-mean-square) velocity fluctuation,and β ≈ 0.4.
For τp < τ K, there is no correlation between particle motions if τ B < τ K and the motions are correlated for τ B > τ K.
For τ K < τp < TL and τ B > TL there is a partial correlation ofparticle motions; for τp > TL and τ B > TL there is no correlation between motions of the particles.
One can see that it is not enough that particles are smalla < λK, to understand the mechanism of aggregation because either perikinetic or orthokinetic aggregation canbe observed depending on the relation between timescales or related values of the Péclet number. Similarly inthe case of large particles, a > λK, with partial or no correlation of particle motions, the appropriate kernels including inertial effects should be applied.

5. Modeling of aggregation and breakage of
agglomerates
5.1 Aggregation
The aggregation rates can be considered either inthe Lagrangian or the Eulerian frame of reference. The
Lagrangian approach is based on the trajectory analysisof the particles and the Eulerian approach is based on theconcept of the pair distribution function that mimics in acontinuous way the particle concentration distribution. It
enables identification of the probability of finding anotherparticle (and its orientation if not spherical) in the vicinityof the considered particle.
In the Lagrangian approach, instantaneous values of
the relative particle velocity are calculated from Newton’s
second law, including or not including Brownian motions.
If they are included then the model takes the form of astochastic Langevin-type differential equation with solutions representing stochastic trajectories. In practice, theconcept of limiting trajectories is most often applied,which means that the random movements are neglected andthe limiting trajectories define the collision cross-section.
To include random movements, the methods of dynamic
simulations can be used; a detailed description of thesemethods can be found in the book by Elimelech M. et al.
(1995).

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

In the Eulerian approach, the Fokker-Planck equations
are applied. Consider small particles with τp << τ K. The
particles are conveyed by the fluid, diffused by the
Brownian motion, and are subjected to colloidal DLVO-typeforces. They can be in the perikinetic τ B < τ K or orthokinetic τ B > τ K regime, or in the regime in between the twoτ B ≈ τ K.
The collision rate constant, βc, can be calculated fromthe pair probability function, cpair, distribution:


βc



 D B cpair  uf cpair  uintcpair   n dS C






SC

(41)


where uf is the relative particle velocity resulting from
the fluid flow, uint is the relative particle velocity resulting
from the colloidal interactions, SC is the entire collision
surface and n is the normal to this surface (Melis S. et al.,1999). Eqn. (41) can be simplified (Bałdyga J. and OrciuchW., 2001) by taking an ensemble average of Eqn. (41) andusing a concept of the gradient turbulent diffusion, expressed in terms of a generalized form of the stability ratio W.
 c 4πD12 a1  a2 W

Fig. 12The effect of the process conditions on the stability
ratio, W, for 10 nm particles (Bałdyga J. et al., 2012,
copyright 2012 Elsevier Ltd., reprinted with permission).

Fig. 13

The effect of the pH and ionic strength on the surface
potential according to the DLM and GLM models.

(42)
r

W
 a1  a 2

a1 a2

1
 dV  ds
RkT ds 
 D B

exp 

RD G r,   r 2

dr

(43)

where V is the interaction potential, G(r,λ) is the hydrodynamic function describing the squeezing of the liquid outof the space between particles, r is the center-to-center
distance, λ is the particle size ratio, and RD represents theoverall (turbulent and Brownian) diffusivity ratio.
Fig. 12 shows the stability ratio values calculated using
the DLM model to predict the surface potential.
The predicted values of the surface potential are shownin Fig. 13. They have been calculated using either the
Double Layer Model, DLM, Löbbus M. et al. (1998), or
the Gel Layer Model, GLM, (Löbbus M. et al., 1998;
Adler J.J. et al., 2001). One can see strong repulsion effects for pH > 6, with strong effects on the stability ratio,
and almost no repulsion below pH = 6.
Fig. 14 shows that at Pe → 0 (because ε → 0), we observe perikinetic aggregation with no effect of the rate ofenergy dissipation, whereas for large ε, the orthokinetic
aggregation controlled by convection (Pe >>> 1) is observed. The effects of colloidal forces are observed at the
perikinetic limit only and are negligible when the orthokinetic mechanism dominates.
When the relaxation time is large enough, τp ≥ τ K, theinertial effects cannot be neglected and then the rate ofcollision depends on fluctuations of the relative velocityof particles w, which depends on the fluid velocity fluctu-

ations and relaxation time (Kruis F.E. and Kusters K.A.,
1996)

β

8π
ai  a j  2 w 2
3(44)

Because not every collision leads to agglomeration, theconcept of the probability or efficiency of agglomeration,PS, can be used (Bałdyga J. et al., 2003).


PS exp  t c ti 

(45)

where tc represents the average time necessary to build astrong enough bridge between the particles and ti is theaverage interaction time.
The time period tc can be interpreted as the time nec-

139

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

Fig. 15

Fig. 14Collision rate constant, β, as a function of the energy
dissipation rate and particle size. A) no repulsion, B)
weak repulsion. Ha is Hamaker constant, ϕs is surface
potential and χ represents the dielectric permittivity.

essary to reach the bridge diameter D b with the local rateof crystal growth G,
tc 

Dbf ( )G

(46)

where f(λ) represents the shape function as proposed byDavid R. et al. (1991). The results of the simulations showthat at constant supersaturation, both an increase of the140

Crystals of benzoic acid produced with the feeding tube
tip positioned close to the impeller. Q = 50 cm3/mina) cA0 = 0.5 mol/dm3, cB0 = 0.5 mol/dm3, N = 200 rpm;b) cA0 = 2.0 mol/dm3, cB0 = 2.0 mol/dm3, N = 300 rpm.
(based on Bałdyga J. and Krasiński A., 2005).

energy input and an increase of the particle size lower the
probability of agglomeration during collisions. The agglomeration kernel β(L1,L2) can then be expressed as the
product of the collision kernel, denoted here by βc, and theagglomeration probability, PS:  c  PS

(47)

The results of Bałdyga J. and Krasiński A. (2005) illustrate this phenomenon for the reactive, agglomerative precipitation of benzoic acid in a semibatch, stirred tankreactor. Supersaturation was created in this case by thehomogeneous chemical reaction between sodium benzoate and hydrochloric acid. Figs. 15 and 16 illustrate main
results.
The model correctly predicts the trends of the effect of
the concentration and the stirrer revolutions observed in
the experiments.
The microscopic pictures of the primary particles andJerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

Fig. 17

Fig. 16

Comparison of the experimental data with the model
predictions for agglomerates (agg) L30, and primaryparticles (pp) Lp,30. Feeding point close to the stirrer(based on Bałdyga J. and Krasiński A., 2005).

the aggregates confirm that the model correctly predicts
both the size of the primary particles forming agglomerates and the size of non-agglomerated particles. Noticethe effects of supersaturation on both:size of primary particles due to particle growth andsize of agglomerates due to building strong bridges between primary particles forming agglomerates.

5.2 Breakage
Disintegration processes play a significant role in thechemicals industry. There are many types of devices (e.g.
rotor-stators, high-pressure nozzles, stirred tanks) thatcan be applied to generate stresses. There are of course
different mechanisms involved in stress generation andthus different mechanisms of deagglomeration. For example in the case of high-pressure systems, disintegration

Structure of large agglomerates (clusters).

results from hydrodynamic and cavitation stresses,
whereas in the case of the rotor-stator system, only hydrodynamic stresses are active. To select the most suitableequipment for the desired parameters of the final product,
one can use relatively simple mechanistic modeling in
combination with CFD. The modeling presented in thispaper includes the effects of agglomerate structure andsuspension rheology on the disintegration processes.
The strength of the agglomerate depends on its structure, which in turn determines the mechanism of its
breakage. A typical agglomerate structure of Aerosil
fumed silica particles is represented by the cluster-fractal
model as shown on Fig. 17. Large clusters of size Li consist of smaller primary aggregates of a size La, which arecomposed of primary particles of size L0. They are characterized by the fractal dimension Df. The primary particles forming primary aggregates are connected by strong
chemical bonds. Such aggregates are strong, stable andresistant against mechanical forces. They are characterized by an average fractal dimension Df0. The clusters ofsize Li, connected by adhesion forces, are relatively unstable, and they can be disintegrated by mechanical forces.
Fractal geometry is used to estimate the tensile strengthof agglomerates.
There is experimental evidence (Baldyga J. et al.,2008bc) that the break-up of Aerosil 200 V agglomerates
occurs through erosion in a rotor-stator mixer, and throughshattering and rupture in both a high-pressure nozzle disintegrator and the ultrasonic device. Break-up processes
control the agglomerate size distribution; details of
break-up depend on the agglomerate structure strengthand the stresses generated in the fluid. On the other hand,
the rheological properties of the suspension depend on theconcentration of particles, their size distribution and morphology.
To describe all these phenomena and simulate the process, a model based on the population balance was developed. In the model, the population balance equations aresupplemented with adequate breakage kinetics and solved141

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

using the quadrature method of moments (McGraw, R.,
1997) in the CFD environment. The commercial CFD
code Fluent (the k-ε model of turbulence) is supplemented
with UDFs for suspension rheology. Suspension rheology
is described with the Buyevich Yu. A. and Kabpsov S.K.
(1999) model. Models considered in this work were compared with experimental data in publications (Baldyga J.
et al., 2008bc). In the simulations, two mechanisms ofstress generation were considered: hydrodynamic stresses
generated by the flow with frequency Γ h(L), and stresses
generated by cavitation events with cavitation itself generated either by the flow or by the ultrasonic waves, at
frequency Γc(L) for agglomerates of size L. In the simulations, a superposition of breakage mechanisms is assumed.

 L   h L   c L

48  a L2a H 2
1.11   a  HaL0

(49)

where τ is the stress, Ha is the effective Hamaker constantand H represents the surface-to-surface distance.
Fractal geometry is used to estimate the tensile strengthof agglomerates applied in Eqn. (49). The number ofbonds connecting aggregates in an agglomerate can be related to the agglomerate porosity by means of the follow-

142

Rotor-stator computational grid showing device geometry.

Fig. 19

Hydrodynamic stresses τh and mean particle size L30
distributions in the rotor-stator system for Q = 0.6 dm3/s,
N = 9000 rpm (150 s –1) and 5 wt.% of Aerosil, for conditions applied by Bałdyga et al. (2008c).

(48)

The related time constants can be defined as reciprocalsof these frequencies: τ hydr = 1/Γh(L), τcav = 1/Γc(L).
Comparison of these time constants with residence
time values in the region of stresses higher than the tensile strength of agglomerates characterizes the extent of
disintegration. Typical results for the rotor-stator,
high-pressure nozzle and ultrasonic device are presented
below.
The geometry of the rotor-stator device is shown in
Fig. 18. Fig. 19 shows the distribution of hydrodynamic
stresses and particle size in the rotor-stator mixer.
The stresses are not strong enough to disperse agglomerates in one passage through the system, and thus todisperse agglomerates, the effective residence time should
be increased and this is possible by multiplying the numberof passages through the rotor-stator as shown in Fig. 20.
Much higher stresses (hydrodynamic and induced bycavitation) are generated in the high-pressure nozzle device (Fig. 21), and the system is so effective that one passage through the system results in effective dispersion of
agglomerates (Fig. 22). Comparing the model with theexperimental results, one can exclude the rupture mechanism; because the stresses are much higher than those in
the rotor-stator mixer, one can expect that the breakage
mechanism is shattering. More precisely this is represented by the fragmentation number defined in Rwei S.P.
et al. (1990, 1991); for the stress τ, it can be expressed in amore general form (Bałdyga et al., 2008b) by:Fa Fig. 18

ing equation (Rumpf H., 1962; Tang S. et al., 2001):

 T  1.1

1 a F
 a La 2(50)

where F represents the bonding force between aggregatesJerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

Fig. 20 Progress of deagglomeration in the rotor-stator system. Experimental results are marked by solid lines,
and results of simulations by broken lines (Bałdyga etal., 2008c, copyright 2008 American Chemical Society, reprinted with permission)

Fig. 22
Fig. 21 Schematic of the high-pressure nozzle.

in the agglomerate that is calculated using the classic
DLVO theory. In the present case, F is expressed by thevan der Waals’ attractive forces FA that are calculated assuming that all primary particles have equal diameters,

Comparison of experimental data with model predictions based on: erosion, shattering and rupture mechanisms of deagglomeration for the nozzles of threedifferent diameters dn. Experimental results aremarked by points, solid lines represent the results ofsimulations and dashed lines show the mean size ofparticles in pre-dispersion. (Bałdyga et al., 2009,
copyright 2009 The Institution of Chemical Engineers, reprinted with permission)143Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

L0:
FA  

Ha L0
48 H 2(51)

To complete the description, one should define thestresses τ for each mechanism of generation.
In the case of turbulent flow for aggregates smaller thanthe Kolmogorov microscale, λK = (v3/ε)1/4, Eqn. (52) works
for breakage kernel Γ with deformation rate defined by     0 5.

 Cb  if     T 0if     T(52)

Fig. 23

Geometry of the ultrasonic device. (Bałdyga et al.,2008b, copyright 2008 The Institution of Chemical
Engineers, reprinted with permission.)Fig. 24Comparison of model predictions with experimental
data for one passage through the ultrasonic cell. Effectof Aerosil concentration for energy density
Ev = 300 GJm –3. (Bałdyga et al., 2008b, copyright2008 The Institution of Chemical Engineers, reprinted
with permission.)

For turbulent flows, and for aggregates larger than the
Kolmogorov microscale, L > λK, one has

1 3


 h C b 2 3L  h 0if   2 3L2 3   Tif

  2 3L2 3  

(53)

T

where Cb is a proportionality constant.
When cavitation is induced by the flow then the breakage kernel is recalculated from the condensation rate RC
using the model by (Singhal A.K. et al., 2002).
The rate of collapse of the bubbles serving as the breakage kernel is defined as a ratio of the mass condensation
rate to the mass of a bubble:

RCπ
R03  v
3

Cbc 4
C 

(54)

with Cbc being an empirical constant and ρv the vapor density. The bubble radius value, R0, is expressed using theBlake equation for the critical bubble diameter (Brennen,
C.E., 2005).
The stresses generated by bubble implosion (generatedby shock waves and high-velocity microjets) can be estimated using the semi-empirical equation (Crum, L., 1988) c   cV j

(55)

where c is the velocity of compressional waves in the liquid, Vj is the microjet velocity and α is a constant that varies from 0.41 to 3.0.
The breakage kernel for ultrasonic-wave-induced cavitation and for ultrasonic frequency f, reads: c C bc ffor
 c   cV j   T

 c 0 otherwise(56)

for the ultrasonic wave propagation, we use the wave
equation (Saez V. et al., 2005)
1 2 p1  p  0  c 2 t 2

144

(57)

Fig. 23 shows the experimental system, whereas comparisons of model predictions with experimental data isgiven in Fig. 24. The model predicts well the small effectof Aerosil concentration on the average agglomerate size.
Fig. 25 shows the distribution of cavitation stresses generated in the ultrasonic cell. They are many orders ofmagnitude larger than the stresses generated in the rotor-­
stator (Fig. 19a), and the deagglomeration process has either a rupture or shattering mechanism.
Experimental results and model predictions for thedeagglomeration of silica nanoparticle agglomerates(Aerosil 200 V) confirmed that the fragmentation number
can be used to predict the mechanism of deagglomeration.
Deagglomeration is possible for Fa > 1; for Fa < 100 erosion is observed, rupture is for Fa > 100 and shattering
dominates for Fa > 1000.

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

Fig. 25 Distribution of stresses generated by cavitation in theultrasonic cell for 20 wt.% of Aerosil. (Bałdyga et al.,2008b, copyright 2008 The Institution of Chemical
Engineers, reprinted with permission.)

6. Final discussion
There are many practical applications where the properties of precipitated particles are of key importance. The
most important reason for this is that there is an increasing emphasis on high added value specialty chemicals
such as pharmaceuticals, dyes, pigments, paints, andprinting inks regarding the crystal size distribution, particle morphology and product purity. As presented by
Karpinski (2006), many precipitated pharmaceutical organic products can form a range of polymorphs or solvatomorphs. There is also the effect of the precipitate
properties on the downstream processes such as filtration
or drying. This explains why attention is paid to “particle
design”.
The results presented in this paper show that particle
size distribution (Figs. 4, 5, 7, 9 and 11), particle morphology and many other properties depend on the spatial
distribution of the supersaturation (Fig. 6) and the timingof its evolution. Important is also the evolution of fluid elements in the supersaturated zone which depends on theflow pattern (Fig. 3) and the rate of mixing (Fig. 10).
Mixing controls both creation and dilution of supersaturation. However, not only the level but also the chemical
structure of supersaturation is important, which can beshown using precipitation diagrams (Söhnel O., 1991); a
simple example of such a diagram is shown in Fig. 1. One
can plot on the precipitation diagram such important information on the type of precipitate that results from thesolution composition represented by points on the diagram. The composition of the solution affects the chemical reaction and resulting properties of the solid phase.
One can thus show those regions where a solid phase of
uniform composition, morphology, color or crystal habit

exists.
Another effect resulting from the solution composition
is related to electrostatic interactions (Fig. 13) between
colloidal particles that may control the stability of colloidal suspensions (Fig. 12) and particle aggregation (Figs.
14, 15 and 16).
Another important distribution is the distribution ofhydrodynamic properties such as the velocity gradients,
deformation and rotation tensors, and resulting hydrodynamic stresses. The relation between fluid deformation
and rotation affects the efficiency of micromixing, thus
also affecting the creation rate of supersaturation. Very
important is the distribution of stresses generated in thesystem, including hydrodynamic stresses (Fig. 19) andthe stresses resulting from cavitation (Fig. 25). Direct interaction between the distribution of hydrodynamic properties, the distribution of supersaturation and the
distribution of the particle populations results in the size
distribution and morphology of agglomerates as shown in
Fig. 15 and Fig. 16. There is of course the direct effect ofstress distribution on deagglomeration which is presented
in Fig. 20, Fig. 22 and Fig. 24.
Precipitation is a very complex process, which meansthat when designing and scaling up precipitation processes, complete similarity is impossible. In fact when
scaling up, complete similarity is not necessary because
as explained earlier, we want to obtain identical products
from the systems differing in scale. As a first attempt todesign the process, the time and length scale analysis is
recommended in this paper, which is illustrated with examples. The comparison of time and length scales shows
which of them are important for the process. This information can be used for scaling up and for formulating adequate models. The precipitation models have a multiscale character and are very complex. It is thus very
important to choose an adequate model of mixing and to
decide at which scale modeling is not necessary.

Nomenclature
CFD

Computational Fluid Dynamics

PDF

Probability Density Function

SEDS
A, A

n+

Solution-Enhanced Dispersion by Supercritical Fluid
reactantaactivity of solute (–)

aparticle radius (m)aeqequilibrium activity of solute (–)

ap

particle surface area (m 2)B, Bn+ reactant
B

constant in Eqn. (7)145Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

B  x, t 

birth functionkasurface shape factor (–)

Bfjbirth term for the moments of order j (mj – 3s –1)

kB

Boltzmann’s constant (JK–1)

C

product

kd

mass transfer coefficient (ms –1)

CD

friction factor (–)

kR

surface integration constant (mdm3rmol–rs –1)

Ct

Craya-Curtet number (–)

kv

volume shape factor (–)

cvelocity of compressional waves (ms –1)

L

particle size (m)cmolar concentration (mol dm –3)

Le

scale of large, energy-containing eddies (m)ceqsolubility (mol dm )MCmolecular mass of C (kg mol–1)

ci0

initial or feed concentration of i (mol dm –3)

mnumber of particle properties (–)

cpair

pair probability function (–)

mj

moment of order j (mj – 3)D

molecular diffusion coefficient (m 2s –1)

N

stirrer speed (s –1)D12

mutual Brownian diffusivity (m 2s –1)

N*

number of molecules or ions forming the criticalembryo-nucleus (–)

nnpopulation number density
normal to collision surface (–)

PS

efficiency of agglomeration (–)

Pe

Péclet number (–)

ppressure (Pa)Q

feeding rate (m3s –1)

R

gas constant (J mol–1K–1)

Re

Reynolds number (–)

RD

turbulent to Brownian diffusivity ratio (–)

Repparticle Reynolds number (–)

rorder of surface integration (–)

Ssupersaturation ratio based on x (–)

Sasupersaturation ratio based on a (–)

SC

entire collision surface (m 2)Sc

Schmidt number (–)

ttime (s)tctime to build strong crystalline bridge (s)tiaverage interaction time (s)

–3

2 –1

DB

coefficient of Brownian diffusion (m s )Dbcrystalline bridge diameter (m)Dfjdeath term for the moments of order j (mj – 3s –1)

DpT

turbulent diffusivity of particles (m 2s –1)

DS

stirrer diameter (m)Dtturbulent diffusion coefficient (m 2s –1)

Dv

diameter of the precipitation vessel (s)Dαmolecular diffusivity of α (m 2s –1)dmolecular diameter (m)d43mean crystal size (μm)

dffeed pipe diameter (m)d0nozzle orifice diameter (m)Eengulfment parameter (s –1)

Fa

fragmentation number (–)

Fbonding force (N)fmixture fraction (–)

D x, t death functionf x d 0  dimensionless distribution of the rate of energy dissipation (–)–1

Grate of crystal growth (ms )Ttemperature (K)G(r,λ)hydrodynamic function (–)

TL

Lagrangian integral time scale (s)G *energy barrier for homogeneous nucleation (J)uvelocity (ms –1)

*
Ghet

energy barrier for heterogeneous nucleation (J)r.m.s. velocity fluctuation (ms –1)

H

surface-to-surface distance (m)

u′

uf

Ha

Hamaker constant (J)IS

intensity of segregation (–)

J

nucleation rate (m –3s –1)

Ka

dissociation constant (mol dm –3)

KSP

146


uint

2

–6

thermodynamic solubility product (mol dm )relative particle velocity resulting from the fluid flow
(ms –1) relative particle velocity resulting from colloidal
interactions (ms –1)
relative particle velocity resulting from colloidal interactions (ms –1)

upi

component of particle velocity (m)Vinteraction potential (J)Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

tank volume (m3)τccavitation stress (Pa)Vj

–1microjet velocity (ms )τDtime constant for turbulent diffusion (s)vmolecular volume (m3)τE

engulfment time constant (s)τGcrtime constant for crystal growth (s)τhhydrodynamic stress (Pa)

Vt

3

vp

particle volume (m )Wstability ratio (–)–1w

relative velocity of particles (ms )τ hydrtime constant for hydrodynamic stresses (s)wpj

rate of change of particle property j (property j s –1)

τcavtime constant for cavitation stresses (s)xmol fraction of solute (–)τK

Kolmogorov time microscale (s)

xeq

x

equilibrium mol fraction of solute (–)τMtime constant for mixing (s)position vector (m)τNtime constant for nucleation (s)xiexternal coordinates (m)τprelaxation time (s)τRtime constant for chemical reaction (s)τStime constant for inertial-convective mixing (s)τ VCVD

time constant for viscous-convective and viscous-­
diffusive mixing (s)Ωpre-exponential term in Eqn. (7) (m –3s –1)

Φ( f )

PDF of mixture fraction (–)

βc
β

3 –1

collision rate constant (m s )3 –1

aggregation kernel (m s )
–1

Γh(L)

hydrodynamic frequency (s )Γc(L)cavitation frequency (s –1)

rate of deformation (s –1)

γiactivity coefficient of the ionic species i (–)ε

rate of energy dissipation (m 2s –3)

εaporosity of agglomerate (–)ΛCintegral scale of concentration fluctuations (m)λparticle size ratio (–)Λflongitudinal integral scale of turbulence (m)λKKolmogorov length microscale (m)μmolar chemical potential (J mol–1)

μfluid viscosity (Pas)μeq

molar chemical potential in saturated solution (J mol–1)

νkinematic viscosity (m 2s –1)

ν+, ν–

stoichiometric coefficients for ions (–)ρ

liquid density (kg m –3)

ρvvapour density (kg m –3)

ρcmolar density of solute (mol m –3)

ρpdensity of solute (kg m –3)

σsupersaturation (–)σ

surface energy (J m–2)

σT

tensile strength (Pa) S2variance of mixture fraction (–)τ

mean residence time (s)τin eqn. (49) stress τc or τh (Pa)τB

Brownian diffusion time (s)τbblending time (s)τCcirculation time (s)References
Adler J.J., Rabinovich Y.I., Moudgil B.M., Origins of the nonDLVO force between glass surfaces in aqueous solution,
Journal of Colloid and Interface Science, 237 (2001) 249–
258.
Bałdyga J., Turbulent mixer model with application to homogeneous, instantaneous chemical reactions, Chemical Engineering Science, 44 (1989) 1175–1182.
Bałdyga J., Bourne J.R., Turbulent Mixing and Chemical Reactions, Wiley, Chichester, 1999.
Bałdyga J., Czarnocki R., Shekunov B.J., Smith K.B., Particle
formation in supercritical fluids—Scale-up problem’,Chemical Engineering Research & Design, 88 (2010) 331–
341.
Bałdyga J., Henczka M., Shekunov B.Y., Fluid dynamics, masstransfer, and particle formation in supercritical fluids, in:York P., Kompella U.B., Shekunov B.Y. (Eds.), Supercritical
Fluid Technology and Drug Product Development, Marcel
Dekker, New York, 2004, pp. 91–157.
Bałdyga J., Jasińska M., Orciuch W., Barium Sulphate Agglomeration in a Pipe—An Experimental Study and CFD Modeling, Chemical Engineering & Technology, 26 (2003) 334–
340.
Bałdyga J., Jasińska M., Jodko K., Petelski P., Precipitation of
amorphous colloidal silica from aqueous solutions—Aggregation problem, Chem. Eng. Sci., 77 (2012) 207–216.
Bałdyga J., Krasiński A., Precipitation of benzoic acid in continuous stirred tank—effects of agglomeration, Proceedings of 16th International Symposium on Industrial Crystallization, in: Ulrich J. (Ed.), VDI-Verlag, Düsseldorf, 2005,
pp. 411–416.

147

Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

Bałdyga J., Kubicki D., Shekunov B.Y., Smith K.B., Mixing
effects on particle formation in supercritical fluids, Proceedings of 17th International Symposium on Industrial
Crystallization, in: Jansen J.P., Ulrich J. (Eds.), Maastricht,
2008a, pp. 141–148.
Bałdyga J., Makowski Ł., Orciuch W., Sauter C., Schuchmann
H.P., Deagglomeration processes in high-shear devices,
Chemical Engineering Research and Design, 86 (2008b)
1369–1381.
Bałdyga J., Makowski Ł., Orciuch W., Sauter C., Schuchmann
H.P., Agglomerate dispersion in cavitating flows, Chemical
Engineering Research and Design, 87 (2009) 474–484.
Bałdyga J., Orciuch W., Some hydrodynamic aspects of precipitation, Powder Technology, 121 (2001) 9–19.
Bałdyga J., Orciuch W., Makowski Ł., Malik K., Ozcan-TaskinG., Eagels W., Padron G., Dispersion on nanoparticle clusters in a rotor-stator mixer, Ind. Eng. Chem. Res., 47(2008c) 3652–3663.
Bałdyga J., Podgórska W., Pohorecki R., Mixing-precipitation
model with application to double feed semibatch precipitation, Chem. Eng. Sci., 50 (1995) 1281–1300.
Becker H.A., Hottel H.C., Williams G.C., Mixing and flow inducted turbulent jets, Proceedings of 9th Int. Symp. on
Combustion, Combustion Institute, New York, 1963, pp.
7–20.
Brennen C.E., Fundamentals of Multiphase Flow, Cambridge
University Press, New York, USA, 2005.
Briesen H., Simulation of crystal size and shape by means of areduced two-dimensional population balance model, Chem.
Eng. Sci., 61 (2006) 104–122.
Bromley L.A., Thermodynamic properties of strong electrolytes
in aqueous solutions, AIChE J., 19 (1973) 313–320.
Buyevich Yu. A. Kapbsov S.K., Segregation of a fine suspension in channel flow, Non-Newt. Fluid. Mech., 86 (1999)
157–184.
Craya A., Curtet R., On the spreading of a confined jet’,
Comptes Rendus. Acad. Des Sciences, 241 (1955) 611–622.
Crum L., Cavitation microjets as a contributory mechanism forrenal disintegration in ESWL., J. Urol., 148 (1988) 1587–
1590.
Davey R., Garside J., From molecules to Crystallizers. An
Introduction to Crystallization, Oxford University Press,
New York, 2000.
David R., Marchal P., Klein J.P., Villermaux J., Crystallizationand precipitation engineering III. A discrete formulation of
the agglomeration rate of crystals in a crystallization process, Chem. Eng. Sci., 46 (1991) 205–213.
Dirksen J.A., Ring T.A., Fundamentals of crystallization: kinetics effects on particle size distribution and morphology,
Chem. Eng. Sci., 46 (1991) 2389–2427.
Di Veroli G., Rigopoulos S., Modeling of turbulent precipitation: A transported population balance-PDF method, AIChEJ, 56 (2010) 878–892.
Elimelech M., Gregory J., Jia X., Williams R.A., Particle Deposition and Aggregation: Measurement, Modelling and Simulation, Butterworth-Heinemann, Oxford, 1995.
Fissore D., Marchisio D.L., Barresi A.A., On the comparison
between probability density function models for CFD

148

applications, Can. J. Chem. Eng., 80 (2002) 1–11.
Fox R.O., The spectral relaxation model of the scalar dissipation
rate in homogeneous turbulence, Phys. Fluids, 7 (1955)
1082–1094.
Gavi E., Marchisio D.L., Barresi A.A., CFD modeling andscale-up of confined impinging jet reactors, Chem. Eng.
Sci., 62 (2007) 2228–2241.
Henczka M., Bałdyga J., Shekunov B.Y., Particle formation by
turbulent mixing with supercritical antisolvent, Chem. Eng.
Sci., 60 (2005) 2193–2201.
Hinze J.O., Turbulence, McGraw-Hill, New York, 1975.
Hulburt H.M., Katz S., Some problems in particle technology—
A statistical mechanical formulation, Chem. Eng. Sci., 19(1964) 555–574.
Karpinski P.H., Polymorphism of Active Pharmaceutical Ingredients, Chemical Engineering & Technology, 29 (2006)
233–237.
Kruis F.E., Kusters K.A., The Collision Rate of Particles in Turbulent Media, J. Aerosol Sci., 27 (1996) S263–S264
Launder B.E., Spalding D.B., Mathematical Models of Turbulence, Academic Press, London/New York, 1972.
Löbbus M., Vogelsberger W., Sonnefeld J., Seidel A., Current
considerations for the dissolution kinetics of solid oxides
with silica, Langmuir, 14 (1998) 4386–4396.
Marchisio D.L., Barresi A.A., Fox R.O., Simulation of turbulent
precipitation in a semi-batch Taylor-Couette reactor using
CFD, AIChE J., 47 (2001) 664–676.
Marchisio D.L., Rivautella L., Barresi A.A., Design andscale-up of chemical reactors for nanoparticle precipitation,
AIChE J, 52 (2006) 1877–1887.
Marchisio D.L., Fox R.O., Computational Models for
Polydisperse Particulate and Multiphase Systems, Cambridge University Press, Cambridge, 2013.
McGraw R., Description of aerosol dynamics by the quadrature
method of moments, Aerosol Science and Technology, 27(1997) 255–265.
Melis S., Verduyn M., Storti G., Morbidelli M., Bałdyga J.,
Effect of fluid motion on the aggregation of small particles
subject to interaction forces, AIChE J., 45 (1999) 1383–
1393.
Mersmann A. (Ed.), Crystallization Technology Handbook, 2nded., Marcel Dekker, New York, 2001.
Myerson A.S. (Ed.), Handbook of Industrial Crystallization,
Elsevier, Amsterdam, 2002.
Nielsen A.E., Kinetics of Precipitation, Pergamon Press,
Oxford: 1964.
Nývlt J., Industrial Crystallisation from Solutions, Butterworths, London, 1971.
Ochsenbein D.R., Schorsch S., Salvatori F., Vetter T., Morari M.,
Mazzotti M., Modeling the Facet Growth Rate Dispersionof β LGlutamic Acid—Combining Single Crystal Experiments with nD Particle Size Distribution Data., Chem. Eng.
Sci., (2015a), DOI: 10.1016/j.ces.2015.02.026.
Ochsenbein D.R., Vetter T., Schorsch S., Morari M., Mazzotti
M., Agglomeration of Needle-like Crystals in Suspension: I.
Measurements., Cryst. Growth Des., 15 (2015b) 1923–1933.
Paul E.L., Atiemo-Obeng V., Kresta S.M., (Eds.), Handbook of
Industrial Mixing: Science and Practice., Wiley-Interscience,Jerzy Bałdyga / KONA Powder and Particle Journal No. 33 (2016) 127–149

Hoboken, 2003.
Piton D., Fox R.O., Marcant B., Simulation of fine particle formation by precipitation using computational fluid dynamics, Can. J. Chem. Eng., 78 (2000) 983–993.
Pitzer K., Activity coefficients in electrolyte solutions, CRC
Press, Boca Raton, 1991.
Qamar S., Seidel-Morgenstern A., An efficient numerical technique for solving multi-dimensional batch crystallization
models with size independent growth rates, Computers andChemical Engineering, 33 (2009) 1221–1226.
Randolph A.D., Larson M.A., Theory of Particulate Processes,
2nd ed., Academic Press, New York, 1988.
Ramkrishna D., Mahoney A.W., Population balance modeling.
Promise for the future, Chemical Engineering Science, 57(2002) 595–606.
Rowe J.M., Johnston K.P., Precipitation Technologies for
Nanoparticle Production in Formulating Poorly Water Soluble Drugs, in: Williams III, R.O.O., Watts A.B., Miller D.A.
(Eds.), AAPS Advances in the Pharmaceutical Sciences
Series, 3 (2012) 501–568.
Rumpf H., The strength of granules and agglomerates, inAgglomeration, in: Knepper W.A. (ed.), Wiley, New York,USA, 1962, pp. 379–418.
Rwei S.P., Manas-Zloczower I., Feke D.L., Observation of carbon black agglomerate dispersion in simple shear flows,
Polym. Eng. Sci., 30 (1990) 701–706.
Rwei S.P., Manas-Zloczower I., Feke D.L., Characterization of
agglomerate dispersion by erosion in simple shear flows,
Polym. Eng. Sci., 31 (1991) 558–562.
Saez V., Frias-Ferrer A., Iniesta J., Gonzalez-Garcia J., Aldoz
A., Riera E., Characterization of a 20 kHz sonoreactor. Part
I: analysis of mechanical effects by classical and numericalmethods, Ultrasonics Sonochemistry, 12 (2005) 59–65.
Singhal A.K., Athavale M.M., Li H., Jiang Y., Mathematical

Basis and Validation of the Full Cavitation Model, Journal
Fluids Eng., 124 (2002) 617–624.
Shekunov B.Y., Bałdyga J., York P., Particle formation by mixing with supercritical antisolvent at high Reynolds numbers, Chem. Eng. Sci., 56 (2001) 2421–2433.
Smoluchowski M., Versuch einer matamatischen Theorie derKoagulationskinetik kolloider Lösungen, Z. Phys. Chem.,
92 (1917) 129–168.
Söhnel O., Chemical design of precipitation processes inAdvances in Industrial Crystallization. in: Garside J.,
Davey R.J., Jones A.G. (Eds.), Butterworth-Heinemann,
Oxford (1991) 63–74.
Söhnel O., Garside J., Precipitation. Butterworth-Heinemann,
Oxford:, 1992.
Tang S., Ma Y., Shiu C., Modeling of mechanical strength offractal aggregates, Colloids Surf. A: Physical and Engineering Aspects, 180 (2001) 7–16.
Turnbull D., Vonnegut B., Nucleation catalysis, Ind. Eng.
Chem., 44 (1992) 1292–1298.
Vicum L., Mazzotti M., Bałdyga J., Applying a thermodynamic
model to the non-stoichiometric precipitation of barium
sulphate, Chem. Eng. Technol., 26 (2003) 325–333.
Walton A.G., The Formation and Properties of Precipitates,
Interscience Publishers, New York, 1967.
York P., Hanna M., Particle engineering by supercritical fluid
technologies for powder inhalation drug delivery in Respiratory Drug Delivery V. Proceedings of the Conference on
Respiratory Drug Delivery, Phoenix, Arizona, 1996, pp.
231–239.
Zauner R., Jones A.G., Scale-up of continuous and semi-batch
precipitation processes, Industrial Engineering Chemistry,
39 (2000) 2392–2403.
Zlokarnik M., Scale-up in chemical engineering, Wiley-VCH,
Weinheim, 2002.

Author’s short biography
Jerzy Bałdyga
Jerzy Bałdyga, PhD; DSc, has been a professor of chemical engineering at the Warsaw
University of Technology, Warsaw, Poland since 1997. He received his MSc in 1974, PhD
in 1981, and DSc in 1989, all from the Warsaw University of Technology. In 1997, he wasawarded the chair of mechanical engineering and process dynamics at the Chemical Engineering Faculty, Warsaw University of Technology. He chairs the working party on
mixing of the European Federation of Chemical Engineering. The research of Professor
Bałdyga can be characterized as the application of fluid mechanics to problems of chemical engineering and chemical reaction engineering.

149

KONA Powder and Particle Journal No. 33 (2016) 150–168/Doi:10.14356/kona.2016022

Review Paper

Granular Temperature and Segregationin Dense Sheared Particulate Mixtures †Kimberly M. Hill 1* and Yi Fan 2
1
2

St. Anthony Falls Laboratory and the Department of Civil, Environmental, and Geo-Engineering, University of Minnesota, USA
The Dow Chemical Company, USA

Abstract
In gravity-driven flows of different-sized (same density) particles, it is well known that larger particles tend tosegregate upward (toward the free surface), and the smaller particles downward in the direction of gravity.
Alternatively, when the particles are of the same size but different density, lighter particles tend to segregate
upward and heavier particles, downward. When particles differ in both size and density, true of most mixtures ofinterest in industry and nature, the details are complicated and no rule based on gravity alone has captured thesegregation behaviours. Gradients of granular temperature and kinetic stress (i.e., energy and stress associatedwith velocity fluctuations) offer alternative segregation driving forces, but have, until recently, been discounted asthese dynamics are relatively small in dense flows. Recently, gradients in kinetic stress have been shown to play asignificant role in segregating densely sheared particle mixtures, even where the kinetic stress is a relatively small
percentage of the total stress. We review recent modelling advances accounting for this effect and validation incomputational experiments. We show how this framework may be useful in capturing the complicated segregation
phenomenology that emerges for dense sheared flows of particles different in both size and density.
Keywords: segregation, mixing, DEM simulations, mixture model

1. Introduction
Sheared particulate mixtures of different sizes and/or
densities segregate into a wide range of segregation patterns, important for a wide range of industrial processes(e.g., Mosby et al., 1996; Hogg, 2009), and complex evenunder simple boundary conditions. Examples include relatively simple experimental flows in a shear cell (Duffy
and Puri, 2002; May et al., 2010a, 2010b), in an inclined
plane flow (Savage and Lun, 1988; Gray and Thornton,
2005; Gray and Chugunov, 2006), and in a rotated drum
(Hill et al., 1997; Khakhar et al., 1997; Hill et al., 1999b;
Taberlet et al., 2004; Shinbrot and Muzzio, 2000), to relatively complex geophysical flows (e.g., Middleton, 1970),
such as in a riverbed (Paola and Seal, 1995; Dietrich et al.,1989) and in a debris flow (Stock and Dietrich, 2006; Hsu
et al., 2008; Yohannes et al., 2010).
The details of the segregation dynamics depend on several factors. Gravity has long been known to drive sorting
†
1
2

*

Received 8 June 2015; Accepted 30 July 2015
J-STAGE online 28 February 2016
Minneapolis, MN 55455, USA
Midland, MI 48667, USA
Corresponding author: Kimberly M. Hill;
E-mail: kmhill@umn.edu
TEL: +1-612-626-0311

150

of different types of particles (depending on relative size,
density, etc) (Donald and Roseman, 1962; Bridgwater,
1976; Williams, 1976). For flow of very fine particles inair, the effect of air drag on particles likely is a cause ofsegregation (e.g., Williams, 1976; Schulze, 2008) just as
in rivers, where drag from the moving water can lead toselective transport of smaller particles downstream, contributing to “downstream fining”. Under many conditions,particularly in collisional flows, interparticle interactions
play a dominant role. Through these interactions, gradients in granular temperature (i.e., the random kinetic energy of the particles, as in Jenkins and Mancini, 1987; Xu
et al., 2003; Galvin et al., 2005), similar to gradients inkinetic stress (Fan and Hill, 2011b), and solid fraction gradients (Hill and Fan, 2008) drive segregation. We focus
here on the dynamics associated with the unmixing of particles in high-concentration (high solids volume fraction)
sheared flows, applicable to a wide range of systems ofvariable boundary conditions and advective flow patterns.
The focus of modeling efforts of segregation in highconcentration gravity-driven flows has been largely associated with gravity as the driving force. In most cases, in
mixtures of particles differing only in size, small particles
sink in the direction of gravity, away from the free surface in gravity-driven flows, while large particles rise. In
mixtures of particles differing only in density, the less©2016 Hosokawa Powder Technology Foundation

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

dense particles rise, and the denser particles sink. While
granular temperature has been shown to play a role insegregating sparse mixtures (e.g., Jenkins and Mancini,
1987; Xu et al., 2003; Galvin et al., 2005), stresses related
to velocity fluctuations in dense flows are significantly lessthan stresses associated with gravity, so they have largely
been neglected in modeling segregation in dense flows.
The gravity-driven flow models have taken a few different forms (e.g., Duffy and Puri, 2003). Savage, S. andLun, C. (1988) developed a model for size-dependent
segregation based on two simultaneous mechanisms:
geometrically-associated, gravity-driven sorting they described as a ‘random fluctuating sieve’, and a mass-­balancing ‘squeeze expulsion’ mechanism. Essentiallygravity pushes all particles in one direction, but the structureassociated with the high packing fraction prevents mostparticles from responding. Statistically, in a mixture ofdifferent-sized particles it is more likely that small particles will find holes sufficiently large for them to enter. As
the mixture is excited and the particles shift, holes of different sizes open and close, giving the small particles opportunities to drop downward via what they refer to as a‘sieving’ mechanism. A higher shear rate results in a more
frequent availability of holes and thus a higher segregation rate. Mass balance is achieved when the downward
migrating smaller particles squeeze the larger particles
upward via the ‘squeeze expulsion’ mechanism. Savage, S.
and Lun, C. (1988) developed a detailed statistical model
to predict segregation trends based on this process. However, gravity is not explicitly included in the model so thedirection of segregation has to be determined implicitly.
Another model framework that is particularly usefulfor predicting evolution of a mixture is based on mixture
theory. Specifically, there is a family of these models thatcan predict local segregation flux and constituent concentration evolution based on other details of the flow such as
local and instantaneous concentrations of each constituent. We review a few such models for systems in simple
shear flows, where the system is uniform in all directions
except one, and the average flow is normal to that direction. In all cases here, we will adjust the coordinate system so that the direction of non-uniformity and the
direction of segregation is the y-direction, though theother details of the coordinate system vary a bit withboundary conditions.
Khakhar, D. et al. (1997) proposed a ‘buoyancy’ mechanism for gravity-driven segregation according to particle
density that took a form similar to pressure on a submerged object by a surrounding fluid. For example, forflow of such a mixture down a plane inclined by ζ relative
to the horizontal, where y is increasing away from thefree surface (in the direction of gravity), the concentration
flux of the less dense particles normal to the flow may be
expressed according to:ld v ld  v  ld 
 ld
C K 1  md  ld 1   ld  gcos  D 
m 
y


(1.1)

In the original form of this theoretical framework, CK wasan empirically determined parameter with dimensions oftime that is inversely related to the resistance to local motion, what might be called an inverse drag function; D isthe diffusivity. vi is the local average velocity of species iin the segregation direction (normal to the average flow), mi is the material density of species i, and i is the local
volu me concent ration of par ticles of species i.
i  f i / Σf i , where f  i is the local solids fraction of constituent i. The subscripts i = d, ld denote denser and lessdense particles, respectively, and for the variables of the
mixture no subscript is used (e.g., Σf  i = f  ld + f  d, and v =Σf ivi/f ). ɡ refers to gravitational acceleration.
Gray, N. and Thornton, A. (2005) and Gray, N. andChugunov, V. (2006) developed a continuum frameworkfor segregation according to particle size for equal density
particles based in part on mixture theory. Similar to thesegregation model of Khakhar, D. et al. (1997), the focusof the segregation mechanism is on the gradient in ‘lithostatic pressure’ induced by gravity, p. That is, for flow ofa mixture down a plane inclined by ζ relative to the horizontal, and, again, where y is increasing away from thefree surface (in the direction of gravity), ∂p/∂y = ρɡcosζ.
The segregation mechanism is represented by the partitioning of that pressure among the different species. Specifically, each constituent i bears a fraction of the totalpressure pi = ψp,ip, where ψp,i is a partial pressure coefficient, not necessarily equal to the local concentration of iin the mixture, i . The original argument posed by Gray,
N. and Thornton, A. (2005) indicated that whichever constituent bears more of the stress relative to its local concentration moves to lower lithostatic pressures and theother constituent moves to higher pressures. For example,
again, for y increasing away from the free surface (in the
direction of gravity):i v i 
v

1 i
 i
  p,i  gcos  D 
y
cD

(1.2)

In this Equation, cD is a drag coefficient. They made someassumptions about the pressure partitioning coefficients,
i.e., for the particles greater in size i = b, ψp,i = ϕb + BGTϕb(1– ϕb), where BGT is the magnitude of a non-dimensional
perturbation of the partial pressure coefficients awayfrom the solid volume fractions for each species. Then,for a flowing bidisperse mixture, Eqn. (1.2) takes a form
not dissimilar from that in Eqn. (1.1):
B b b 1  b  gcos  D cDyb v b  v 

(1.3)

151

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

The function B/cD in Eqn. (1.3) corresponds to the function CK   mld   md  in Eqn. (1.1).
For the purpose of the mathematical development inthese original papers (Khakhar et al., 1997; Gray andThornton, 2005; Gray and Chugunov, 2006), coefficients
related to drag and diffusion (e.g., CK, cD, and D) were
initially approximated as constant. These frameworks
have been shown to be effective in reproducing segregation trends in a range of granular flows, e.g., in the case of
the original model by Khakhar et al. (1997), segregationin cylindrical and spherical drums (e.g., Hill et al., 1999a,
1999b; Gilchrist and Ottino, 2003) and in the case of theGray-Thornton-Chugunov framework in other simple andcomplex granular flows by, for example, Wiederseiner etal. (2011); Thornton et al. (2012); Weinhart et al. (2013);
Fan et al. (2014). However, these studies have also provided evidence that some details are not fully captured by
the model.
Subsequent work has suggested specific modifications
to this general framework to better capture the discrepancies between model and experimental and computational
data. For example, more recent work by Khakhar, andcolleagues (Sarkar and Khakhar, 2008; Tripathi and
Khakhar, 2011, 2013) derived a more physical form of theinverse drag function CK by considering movement ofparticles differing in density through an effective medium
and showed the drag increased with an effective temperature. May, L. et al. (2010a, 2010b) was the first who considered the effects of a non-constant shear rate on the
Gray-Thornton-Chugunov model in the form of a shearrate dependent maximal segregation rate, though a quantitative comparison between theory and experiments has
not yet been performed. Weinhart, T. et al. (2013) demonstrated that the linear drag law contained within the original Gray-Thornton-Chugunov framework may not be
sufficient for capturing the drag in the system, which appeared to vary with time in their system. Gray, J. andAncey, C. (2011) expanded the framework into a theory
incorporating multiple discrete particle sizes. Marks, B.
et al. (2012) partitioned the stresses explicitly according
to particle size ratio; they derived a shear-rate-dependent
segregation velocity, and they extended the model forcontinuously varying particle size distributions. They
demonstrated that their new model captured segregation
dynamics in a simulated polydisperse mixture. Most recently, Fan, et al. (2014) considered both shear-rate-­
dependent coefficients of segregation, velocity, anddiffusion in the framework and showed a more quantitative agreement with experiments and DEM simulations.
Tunuguntla et al. (2014) extended this framework to mixtures of particles varying in density and size and, with ashear rate dependent drag coefficient, achieved good
agreement with data from computational simulations of abroad range of mixtures.

152

As flexible as these frameworks are, they do not havethe capacity to represent how shear rate gradients and associated effects such as granular temperature gradientsmight affect the direction and degree of segregation. Recent work has shown that dynamics associated with shear
rate gradients not only may segregate particles in cases
where gravity does not play a role in segregating the particles (Hill and Fan, 2008; Fan and Hill, 2010, 2011a,
2011b), as illustrated in Fig. 1 but they also appear to playa significant role comparable to that of gravity (Hill andTan, 2014).
A model developed by Fan, Y. and Hill, K. (2011b)
showed how the basic development used for the original
Gray-Thornton-Chugunov model (Gray and Thornton,
2005; Gray and Chugunov, 2006) for different sized particles could be expanded to include consideration of shear
rate gradients, also in the context of a gravitational field(Hill and Tan, 2014) and for particles differing in density(Fan and Hill, 2015). As explained in more detail in thenext section, the model considered the local stress to be
the sum of contact and kinetic stresses, σc and σ k, respectively, so that σ = σc + σ k (similar to others, such as
Chikkadi, V. and Alam, M. 2009). The contact and kinetic
stresses are separately partitioned between the mixture
components according to partial stress coefficients ψc,ic,i   c,i c , and  k,i   k,i k . For theand ψ k,i, e.g.,  yyyyyyyy
flow of a mixture down a plane inclined by ζ, and y normal to the average flow direction as for Eqns. (1.1–1.3):
Σ j c,k ( yxj / x   yyj / y   yzj / z)  gcos , for particles differing in size and density: c,i  k,i   yyk
 i v i  v 
cD



y

mi  c,i   gcos  D  i
cD

y

(1.4)

All variables are as previously defined, and ρi is the local bulk density of constituent i, related to the material
density of constituent i and its local solids fraction f i according to  i  f i  mi . While Equation (1.4) is of similar
form to Eqns. (1.1–1.3), there are some key difference,
most notably the new term dependent on the gradient in
k / y .
kinetic stress  yy
In the following sections, we show how Eqn. (1.4) canbe derived and the similarities to Eqns. (1.1–1.3) underanalogous conditions where the gradient in kinetic stressis negligible. Then, we provide a few examples validating
this framework for dense flows. We conclude with somediscussion of both the potential generalizability and also
limitations of this model in its current form and some next
steps that are needed for further improving the model.

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

Fig. 1Illustration of segregation driven by dynamics associated with shear rate gradients in a vertical chute, from Fan, Y.
and Hill, K. (2011b). (a) Sketch of a vertical chute. (b)–(d) Time-averaged profiles of kinematic quantities for four
mixtures at steady state, here t = 5–6 s for f = 0.2 (red solid curve), t = 20–30 s for f = 0.4 (blue dotted curve),t = 30–40 s for f = 0.5 (red dotted curve), and t = 300–310 s for f = 0.6 (blue solid curve): (b) streamwise velocity w of the mixture, (c) kinematic granular temperature T = (u′u′ + v′v′ + w′w′)/3 of the mixture, (d) local solid fraction of the mixture f.

2. Mixture theory with effects of velocity
fluctuations

2.1 Conservation equations for gravity-driven
mixtures

Generalizing the work by Fan, Y. and Hill, K., (2011b,
2015) and Hill, K. and Tan, D. (2014) we consider a mixture of particles differing in size and/or density in highsolids fraction sheared flows. For simplicity, we present
the model in terms of binary mixtures of two constituentsi = 1, 2 which may differ in material density  mi and diameter d i, though the model is generalizable to multiple
constituents. We denote bulk Eulerian properties of eachspecies with superscripts and those of the mixture of both
species together as variables without superscript.
As is true of standard mixture theory, the local bulk
mixture density ρ is the sum of the densities of each of thei ΣΣ i f i  mi .
constituents ρi, i.e.,  i  f i  mi , soi
We recall the volume concentration of species i isi  f i / f and define the mass concentration of species i
as mi   i /  , both of which satisfy Σiϕi = 1. We approximate the solid fraction f = Σi f  i as constant, but, if the particles of the two constituents differ in material density,
 mi , the average local density ρ = Σi ρ i varies with the local concentrations of the constituents. The velocity of theconstituents is allowed to differ from the average mixture
velocity, and we define it in terms of the volume concentrations of the species: u  Σ iu ii .

We first consider conservation of mass and momentum
for the mixture when subjected to gravity:    u  0
t(2.1a)

  u  u  u      F
t(2.1b)

Here, σ represents the stress tensor, and F represents bodyforces per volume. Since our mixture is subjected to gravity, F = ρɡ, where ɡ is the gravitational acceleration vector.
The conservation equations are similar for the individual constituents:

 i    i u i   0
t(2.2a)

  i u i     i u i  u i     i  F i   i
t(2.2b)

Here, all of the terms used for constituent i are identicalin form to those used for the mixture in Equation (2.1b),with the exception of the new term β i. β i represents theinteraction force exerted on constituent i by the other species. σi is the total local stress borne by species i, where
the total stress σ = Σiσi.

153

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

We consider these equations in the context of flows ofrelatively high concentration where the mixture kinematics reach steady state long before segregation reaches
steady state, as demonstrated for simulations of flows off ≈ 0.6 in a vertical chute (Fan and Hill, 2011b, 2015) andin a rotated drum (Hill and Tan, 2014). Then, we can set∂ρ/∂t, ∂(ρu)/∂t = 0 in Eqns. (2.1a) and (2.1b). We then follow operations similar to Reynolds decomposition(Schlichting, 1979) and set each variable q at position r
equal to a sum of the local temporal average q (r ) and thedifference between its instantaneous and average valuesqr , t q r , t  q r  . This mathematical manipulation
allows for the explicit representation of the effect of fluctuating velocities on flow dynamics. In particular, thesecond term on the left side of Eqn. (2.1b)    u u may
be written as        u  u  u  u. Taking the
average of this expression, assuming temporal correlations between velocity fluctuations and densities are negligible (detailed in Fan and Hill, 2011b), yields terms like  vv   v ' v ' / y. Assuming the simple flows that are
non-uniform in only the y-direction and the average flow
direction is in a direction normal to that direction, Eqn.
(2.1b), the conservation of momentum equation normal to
the flow for the mixture may be simply expressed according to:
c
k
 yy
 yy
 gcos


y
y

(2.3)

This indicates that the sum of the gradients in contact
stresses and kinetic stresses balance the lithostatic pressure generated by the weight of the particles. Here,k   v ' v ' is a normal component of the kinetic stress yy
c represents atensor (Chikkadi and Alam, 2009) and  yy
component of the normal contact stresses generated byrepulsive contacts between particles. We note that granular temperature scales as the trace of the kinetic stresstensor T ~  vxvx  vyvy  vzvz  , and has a similar form tok (e.g., Fan and Hill, 2011).
 yy
By performing Reynolds decomposition and then averaging the conservation of momentum equation for theconstituents, Eqn. (2.2b), we may write:
c,i
k,i
 yy
 yy

  i gcos   yi
y
y

(2.4)

Since all terms in Eqns. (2.3) and (2.4) are averaged, fromthis point on, we drop the overbar, so that unless noted foreach variable q alone refers to the average q .

2.2 Stress partitioning and interaction forces
To account for stress partitioning, this theory follows
the suggestion of Gray, N. and Thornton, A. (2005) and
Gray, N. and Chugunov, V. (2006) to allow for the partitioning of stresses between the constituents, but differs

154

somewhat in the separate partitioning of the kinetic andcontact. Specifically:c,i   c,i c
k,i
k,i k
 yy
yy , and  yy    yy

(2.5)

where we explicitly define the normal contact and kinetic
stress partition coefficients for these stress components as
ψ c,i and ψ k,i, respectively. Neither stress partition coefficient is required to follow the concentrations of the constituents, for example,  c,i , k,i  i . To assure thatc,i   ck,ik
Σ i yy
yy , and Σ i yy   yy two constraints must be satc,ik,iisfied: (1) that Σiψ , Σiψ = 1, and (2) wherever only one
species is present (i.e., when i  1) ψc,i, ψ k,i = δij, whereδij is the Kronecker Delta function. Otherwise, no functional form is specified for either.
For the interaction term  yi , we can use a similar form
to that proposed by Gray, N. and Chugunov, V. (2006) for
granular mixtures and for flow through porous media provided by Morland, L. (1992):c yi   yy c,iyk
 yy

 k,i
 i
  ic D v i  v  d
y
y

(2.6)

The first two terms on the right side of the equation ensure that the segregation processes are driven by intrinsic
rather than partial stress gradients. The third term is effectively a linear drag law, where cD is the linear drag coefficient. The fourth term acts as a ‘remixing force’ along
gradients in the concentration of each species, where thecoefficient d is similar to a linear diffusion coefficient.

2.3 General segregation flux expression
Combining Eqn. (2.4)–(2.6) with Eqn. (2.3), we can express the theoretical net flux of species i, Φ iT   i v i  vT,
as:Φ iT   i v i  vT  Φ iσk  Φ ig  Φ id

(2.7)

where:

 c,i  k,i   yyk

Φ iσk Φ ig 

cD

y mi   c,i   gcos

Φ id  (2.8a)

cD(2.8b)d  icD y(2.8c)

This expression is identical to that in Eqn. 1.4, with thesubstitution D = d/cD, and suggests that there are three‘forces’ dictating the segregation behavior. The first, Φ iσk ,Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

is controlled by the gradient in the kinetic stress of the
mixture and the difference between the two stress partition coefficients. If a constituent bears a higher contact
stress than kinetic stress then it moves in the direction of
an increasing kinetic stress. The other two terms are similar to other models where the effects of kinetic stresses
are not included. The second term in Eqn. (2.7), Φ ig , is thesegregation associated with gravity, specifically, the pressure gradient induced by gravity. If a species supports ahigher fraction of the contact stress than its local concentration, the species moves opposite the direction of gravity, toward the free surface. The third term in Eqn. (2.7),
Φ id , is akin to a diffusion force. Like normal Fickian diffusion with diffusion coefficient D = d/cD, this term results in a flux in the direction opposite to that of theconcentration gradient of a particular species, and thus
serves to limit the degree of local segregation.
In the next section, we show how the general flux expression in Eqn. (2.7) can be simplified to one of two scenarios: (1) where there is no kinetic stress or temperature
gradient (as one would expect where fields such as the velocity and velocity fluctuations are uniform), and (2) tosituations where there is a gradient in velocity fluctuations
but no gravity. In these cases, the models are similar to
those previously published. We demonstrate this in thenext section.

3. Segregation flux in special cases
As noted above, Eqn. 2.7 is meant to be a general expression for high concentration sheared flows. For moreinsight about this expression, in this section we first investigate the implications of this expression for systems
where the kinetic stress gradient is negligible and gravity
dominates. Then we investigate this for systems where
the effect of gravity on segregation is negligible, and kinetic stress gradients dominate.

3.1 Segregation flux for a negligible kinetic stressgradient
We mentioned in Section 1 that the general flux expression in Eqn. (2.7) is similar to those derived for mixturesof particles differing only in size or density (Eqn. (1.1)
and (1.2), respectively) for cases where these is a negligible kinetic stress gradient. To demonstrate that, we firsteliminate the kinetic stress gradient in Eqn. (1.4):v
 i v i cD

mi  c,i  gcos  D

 i
y

(3.1)

In the case of particles of the same material density, if f isuniform, ρ is uniform and constant as well. Additionally,
the mass and volumetric concentration of constituent i are

the same, so one can substitute  i  mi , i . Then one may
divide both sides of Eqn. (3.1) by ρ to get a segregation
prediction similar to the one from Gray, N. and Chugunov,
V. (2006) expressed by Eqn. (1.2):

i v i 
v

1 i
 i
  c,i  gcos  D 
cD
y

(3.2)

For the expressions in Eqns. (1.2) and (3.2) to be identical,
the contact stress must be partitioned between the constituents identically to the pressure and the kinetic stress.
Then, for the large particles to rise to the top, the large
particles must have more of the contact stress partitioned
relative to their concentration (ϕl) than the small particles.
In particular, for Eqns. (1.2) and (3.2) to be identical, we,ll  Bsl . We explore this in thewould require  cnext section using computational simulations of shearedflows.
In the case of particles of the same size, different material density, we cannot simply divide by ρ. We recall thati i i f
f i  mi , and divide Eqn. (3.1) by f  mi :
m


 i
mi  c,i  gcos  D iycD f  m

i v i  v 

(3.3)

The model summarized by Eqn. (1.1) does not seem
equivalent, at first glance. However, with a bit of algebra,
one can show that if  c,i  i , Eqns. (1.1) and (3.3) are actually equivalent. We will discuss this in more detail in
Section 5 in the context of computational measurementsof ψc,i for special cases in Section 4.

3.2 Segregation flux for a negligible gravitational
effectThere have also been situations where a gradient in velocity fluctuations have been shown capable of segregating particles without help from gravity. Examples include:
microgravity experiments (Xu et al., 2003); experiments
using thin layers (Conway et al., 2006) or internal shear
bands (Hill and Fan, 2008), and simulations of verticalchutes (Fan and Hill, 2011a). These cases may be modeledusing the general flux expression in Eqn. (2.7) by eliminating the term associated with gravity: i v i  v c,i  k,i   yyk  D  i
cD

y

y

(3.4)

For a qualitative picture of the segregation process dictated by this term, we consider that the kinetic stressscales similarly to the granular temperature (e.g., Fan and
Hill, 2011b). A constituent that bears more kinetic stressmight be considered more mobile and more likely to findspaces or vacancies to move away from the high temperature region, somewhat like ‘kinetic sieving’ of the particles
to the ‘cooler’ region. On the other hand, a constituent
that bears more contact stress is physically pushed by in-

155

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

terparticle contacts to a region of lower contact stress andhigher temperature, somewhat like ‘squeeze expulsion’ inthe qualitative balance of movement induced by kinetic
sieving suggested by Savage, S. and Lun, C. (1988). For
mixtures of particles differing only in size, the mixture
density ρ is nearly constant, so we can rewrite Eqn. (3.4)
for a form identical to that originally proposed by Fan, Y.
and Hill, K. (2011b):

i v i  v c,i  k,i  1  yyk  D i
cD



y

y

(3.5)

While it is rare to have a situation where granular temperature works alone to segregate a particulate mixture,understanding its effect is important as in most cases
gravity and granular temperature gradients coexist.
In the next section we review two examples of segregation associated with shear rate gradients and associatedkinetic stress gradients in dense flows, one where gravitational effects do not contribute in the direction of segregation, and one where both kinetic stress gradients andgravity are both important.

4. Examples of shear-induced segregation
Examples supporting the framework described above
have mainly been obtained from Discrete Element
Method (DEM) Simulations first introduced by Cundall,
P. and Strack, O. (1979). This simulation framework is
powerful as it allows the user to input mechanistically
representative interparticle forces and then essentiallyperform a computational experiment. The user may track
all particles and associated forces throughout the simulations so all quantities in the model such as contact stresses
and kinetic stresses can be calculated and used to test thetheory. On the other hand, there can be significant limitations to the model. Even though contact forces can be represented reasonably well, they still have limitations in theirrepresentations for real dissipative materials. Additionally, variability of the surface such as roughness elements
and asperities cannot be represented well. Ultimately, experiments are the best test of models. To this point, since
we do not have access to experimental contact stresses,
we limit our review to simulation results.
The simulations we review here use a nonlinear soft
sphere contact force model. As is common, the particle
deformations resulting from particle-particle contacts are
represented by small overlaps between particles and related to interparticle forces. The model representing therelationship between deformations (overlaps) and interparticle forces follows Hertz-Mindlin contact theory with
damping components related to the coefficient of restitution as developed by Tsuji et al. (1992) and Coulomb sliding friction:

156

Fn 
k n n 3/2   n n1/4n(4.1a)

Ft 
min{k t n 1/2 t   t n1/4t ,  Fn}(4.1b)

where Fn and Ft are the contact forces in the directions
normal and tangential to the contact plane between two
contacting particles, δn and δt are the corresponding deformations, and n 
 t / t are the local
 n / t and t 
time derivatives of these fluctuations. The coefficients inthe force model (kn, kt, ηn, and ηn) are related to material
properties of the two contacting particles such as elastic
moduli, mass, and diameter as suggested in the original
papers (e.g., Hertz, 1882; Mindlin and Deresiewicz, 1953;
Johnson, 1985; Tsuji et al., 1992) and detailed in the relevant recent papers cited below (i.e., Fan and Hill, 2011;
Hill and Tan, 2014).

4.1 Vertical chute flows
To isolate the effect of shear rate gradients from the effect of gravity, Fan, Y. and Hill, K. (2011a, 2011b; 2015)
used DEM simulations of mixtures of particles differing
only in size (Fan and Hill, 2011a) and only in density (Fanand Hill, 2015) sheared in a vertical chute [Fig. 1(a)]. The
vertical chute is ideal for studying the effect of shear rate
gradients and associated granular temperature gradientson segregation because of its simple geometry but inhomogeneous flow structure [Fig. 1].
Fig. 1 presents some kinematics and segregation snapshots for a mixture of 2 mm and 3 mm particles of different average system solids fractions, f = 0.2 to 0.6. Here,
we use angular brackets to indicate that these values represent the solid fraction of the whole mixture. For thehigh concentration system, f = 0.6 Fan, Y. and Hill, K.
(2011b) calculated partial stress coefficients throughout
the simulations to test the applicability of the theory presented in Eqn. (3.4) for cases where the flow was dense
and kinetic stress gradients dominated in the direction ofsegregation.
The results for the normalized coefficients R c,i c, i / i , and R k,i   k,i / i from the simulations for
f = 0.6 are plotted in Fig. 2. As shown, R c,i ≈ 1 everywhere except adjacent to solid boundaries, so that ψc,i ≈ ϕifor these systems. In other words, the contact stress ispartitioned between species exactly according to the volumetric concentration in the mixture. In contrast, Rk,s > 1
and Rk,l < 1 everywhere, so that  k,s  s , and  k,b  b ,except where either ϕs = 1 or ϕb = 1. In other words, except in locations completely segregated where there are
only large or small particles, the smaller particles carry agreater share of the kinetic stress than their concentration,
while the larger particles carry less of the kinetic stressthan their concentration. Considering this and the observation that the kinetic stress is greatest near the walls andKimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168
k / y  0 for y < 0 anddecreases inward, that is,  yyk yy / y  0 for y > 0 (similar to temperature in Fig.
1(a)), Eqn. (3.5) predicts that small particles segregate to
the center of the chute, consistent with observation (Fig.
1).
For a more quantitative prediction, Fan, Y. and Hill, K.,(2011b) adapted a simplifying form of the kinetic stress partition coefficient from the partial pressure coefficient from
Gray, N. and Thornton, A. (2005). Specifically, they approximated  k,s   s  BFH,1sb , and  k,b  b  BFH,1sb ,where BFH,1 is the magnitude of a non-dimensional perturbation of the kinetic stress partition coefficients awayfrom the solid volume fractions for each species. This
form satisfies some basic restrictions on the partial pressure coefficients, namely, that they must sum to 1 so thatthat stresses of the constituents sum to the stress of the
mixture, regardless of the concentration of the individual
constituents. Using this form for the partial kinetic stresscoefficients and the previously mentioned finding that
BFH,1sb . Then, we c,i  i , we can write  c,b  k,b 
can write Eqn. (3.5) for large particles as:b v b  vk
BFH,1sb 1  yy
 b
D 
y
 y
cD

(4.2)

Upon substituting Eqn. (4.2) into Eqn. (2.2a), we find thetime dependence of the local concentration of larger particles:0k
BFH,1   b
1  yy    b  1  b 
D

(4.3)


 y  y  y 
c D y 

The tempo-spatial profiles of concentration of large particles can then be obtained by solving Eqn. (4.3) nuk / y, , D = d/c D, and americally provided that ρ,  yy
segregation coefficient defined according to qs = B/cD canbe obtained. Fan, Y. and Hill, K. (2011b) used empiricallydetermined values: qs = 2.5 × 10 –3 s, and D = 0.05 mm2/s.
The comparison between simulation and theoretical prediction is shown in Fig. 3.
Figs. 3(a) and (b) show tempo-spatial profiles of concentration of large particles from simulation data and theoretical predictions, respectively. In both the simulation
results and theoretical predictions, the large particles segregate to the side walls, and small particles segregate toward the center of the chute. For physical intuition about
how this process is related to kinetic stresses, we can consider that particles tend to be pushed away from regionsof high granular temperature associated with high collisional interactions. This is especially true for systems oflower solids fraction when all particles can move awayfrom regions of high granular temperature (e.g., as in Fig.
1(e)–(f) and in other sheared systems, as those reported

Fig. 3Fig. 2

Profiles of partial stress coefficients R c,i   c,i / i , andR k,i   k,i / i from the first 50 s of chute flow simulations for 2 mm and 3 mm particles (e.g., Fig. 1g–1h)from Fan, Y. and Hill, K. (2011b). Data are typical of
those throughout the simulation.

b

t

Spatiotemporal profiles of the large-particle concentration b from the first 1000 s of chute flow simulationsfor 2 mm and 3 mm particles (e.g., Fig. 1g–1h). (a)Simulation results. (b) Theoretical predictions. The
legend indicates the shade of gray that corresponds toparticular fraction of large particles. For example,b  1 for white pixels and b  0 for black pixels. FromFan, Y. and Hill, K. (2011b).

157

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

by Conway, S. et al., 2004). In high solid fraction shearedflows, where particles cannot “escape” en masse from thehigher temperature regions, the smaller particles, with
their higher kinetic stress or higher granular temperature
but the same material density have more variability intheir displacements (direction and magnitude). Thus havea higher likelihood of finding a way around other particles
away from the region of higher granular temperature.
In the center of the chute, where the gradient of normal
kinetic stress is very small, the segregation process ismuch slower than other regions. All of these indicate agood qualitative agreement between theoretical predictions and simulation results, though they do not reach aperfect quantitative agreement.
Hill, K. and Fan, Y. (2015) performed analogous chuteflow simulations of mixtures of particles differing only indensity (2mm particles of densities 2520 kg/m 3 and7800 kg/m3). In contrast with the mixtures of particles ofdifferent size, in this case, the heavier (denser) particles
segregated to regions of low kinetic stress, low granulartemperatures in the center of the chute.
The results for the normalized coefficients R c,i   c,i / i ,and R k,i   k,i / i from the simulations for f = 0.6 areplotted in Fig. 4. As shown, Rc,i ≈ 1 everywhere except
adjacent to solid boundaries, so that  c,i  i for thesesystems. In other words, the contact stress is partitioned
between species exactly according to the volumetric concentration in the mixture. In contrast, R k,d > 1, andRk,ld < 1 everywhere, so that  k,d  d , and  k,ld  ld ,except where either ld  1 or d  1. In other words, except in locations completely segregated where there are
only denser or less dense particles, the denser particles
carry a greater share of the kinetic stress than their concentration, while the less dense particles carry less of the
kinetic stress than their concentration. Considering thisand the observation that the kinetic stress is greatest neark / y  0 for
the walls and decreases inward, that is,  yyky < 0 and  yy / y  0 for y > 0, Eqn. 3.4 predicts that
denser particles segregate to the center of the chute, consistent with observation.
For a more quantitative prediction, similar to the case fordifferent sized particles, Fan, Y. and Hill, K., (2015) approximated the variation of the kinetic stress partition functions
as:  k,d  d  BFH,2ldd , and  k ,ld  ld  BFH,2ldd ,where BFH,2 is the magnitude of a non-dimensional perturbation of the kinetic stress partition coefficients awayfrom the solid volume fractions for each species. This
form satisfies some basic restrictions on the partial pressure coefficients, namely, that they must sum to 1 so thatthat stresses of the constituents sum to the stress of the
mixture, regardless of the concentration of the individual
constituents. Using this form for the partial kinetic stresscoefficients and the previously mentioned finding that
BFH,2ldd . Thenψc,i ≈ ϕi , we can write  c,ld  k,ld 158

Fig. 4

Profiles of partial stress coefficients R c,i   c,i / i , andR k,i   k,i / i at t = 50–100 s of chute flow simulationsof 2 mm glass and steel particles from Fan & Hill
(2015).

Eqn. 3.4 may be rewritten for less dense particles as: ld v ld  vk
BFH,2ldd  yy
 ld
D
y
y
cD

(4.4)

Upon substituting Eqn. (4.4) into Eqn. (2.2a), we find thetime dependence of the local concentration of less dense
particles:k1  yy ld BFH,2   ld0
 1  ld  y 
c D y t  ld 
D
y 
y 

(4.5)

i i
 i f
f i  mi , we divide by
Again, noting that,m
f  mld for the following expression for the evolution of theconcentration:kB
1  yy ld  ld FH,2ld 1  ld c D f  m y t
 y   ld 
D
y 
y 

(4.6)

The tempo-spatial profiles of concentration of less dense
particles can be obtained by solving Eqn. (4.6) numeri-

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168
k / y,cally provided that ρ,  yy, D = d/cD, and a segregation coefficient defined according to qs = B/cD can beobtained. For this case, Fan, Y. and Hill, K. (2015) foundthe best empirically determined values: qs = 1 × 10 –3 s,and D = 0.2 mm2/s. qs is approximately half that found forthe mixture differing only in size, and D for this mixture
is approximately four times that found for the mixture
differing only in size. This is consistent with the observation that segregation is slower for these systems. However,
it is not clear whether or not this difference is significant,particularly given that it is likely that both cD and D varyspatially with shear rate or other quantity so the difference in these variables is only a qualitative indicator. The
comparison between simulation and theoretical prediction
for the mixture differing in density is in Fig. 5.
Fig. 5(a) and (b) show tempo-spatial profiles of concentration of denser particles from simulation data andtheoretical predictions, respectively. In both the simulation results and theoretical predictions, the less dense particles segregate to the side walls, and denser particles
segregate toward the center of the chute. For physical intuition about this process, we note first that, unlike thecase of the different sized same density particles, for thisFig. 5

Spatiotemporal profiles from the first 1000 s of chuteflow simulations for binary mixtures of particles of two
different densities. The gray scale indicates the solid
fraction of higher material density particles d from: (a)Simulation results. (b) Theoretical predictions. The
legend indicates the shade of gray that corresponds toparticular fraction of denser particles. For example,d  1 for white pixels and ld  0 for black pixels.
From Fan & Hill (2015).

case of the particle differing only in density, the particles
have the same magnitude of velocity fluctuations. Thus
the greater magnitude of the kinetic stresses in the dense
particle constituent arises from the higher material density of particles alone. In this context, we can still consider that particles tend to be pushed away from regionsof high granular temperature associated with high collisional interactions. In high solid fraction sheared flows,
we posit that the denser particles are able to push througha relatively tight mass of particles toward the “cooler” regions, whereas the lighter particles cannot “escape” aseasily. As is the case for the mixture of particles differing
only in size, in the middle slow creeping region, where
the gradient of normal kinetic stress is very small, thesegregation process is much slower than other regions.
There is evidence of the discrete nature of the particles inthe segregation trends in Fig. 5(a) not realizable in thecontinuum model results shown in Fig. 5(b). Nevertheless, the average trends are similar in both.

4.2 Rotating drum flows
In contrast with the simulations described in Section
4.1, in many sheared flows (such as inclined chute flows
and rotated drums, e.g., Fig. 6), gravity, velocity gradients, and corresponding gradients in kinetic stress andtemperature coexist in the direction of segregation. Hill,
K. and Tan, D. (2014) investigated the effectiveness of thesegregation expression in Eqn. (2.7) for the segregation in
the thin flowing layer in a rotated drum (Fig. 6). In particular, they investigated whether the segregating effect of
the kinetic stress gradient shown in vertical chute flows
where the gravity had negligible segregating effect wassignificant in the presence of gravity.
Their simulated circular drum had periodic boundaries
in the axial direction (e.g., no front and back walls in Fig.
6(a)) to eliminate side wall segregation effects (e.g. Hill
and Zhang, 2008). The drum diameter is 72 mm; thethickness (periodic length in axial direction) is 30 mm.
They ‘filled’ the drum partway with binary mixtures ofparticles of the same density and two different diameters,
2 mm and 3 mm, with a 10 % variability in particle sizeof each constituent. The particles were initially wellmixed when drum rotation was commenced at a speed ofω ≈ 16 rotations per minute (rpm). They studied the segregation dynamics for five different concentrations oflarge particles (by volume) in the mixture: b = 10 %,25 %, 50 %, 75 %, and 90 %. In all cases, the flow in theradial center of the drum (i.e., the center of the flowing
layer) was nearly uniform in the streamwise (x-) andtransverse (y-) directions, so the basic assumptions underwhich Eqn. (2.7) was developed hold, even though the entirety of the flow is relatively non-uniform.
The segregation dynamics vary somewhat from one

159

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

Fig. 6

The first row shows details of a 50/50 mixture of 2 mm and 3 mm diameter particles of the same material
density in a drum near the beginning of the simulation when the constituents are well-mixed. (a) a snapshot (b) solid fraction f i profile of each constituent i and (c) the vertical flux profile f  iΔvi = f  i(vi – v) of
each constituent. The second row shows (d)–(f) snapshots from the central part of the flowing layer inthe simulations, denoted by the white box in (a). (The axes indicate the direction; all images were takenfrom the same location on the drum, indicated by the white box in (a).) (d) initial state (e) 5 s into thesimulation (f) the steady segregated state of the system. (g)–(h) Sketches illustrating how the segregationin the top flowing layer is recorded into a radial segregation pattern in the solid-like granular materials
in the majority of the drum (from Hill et al. (2004)). The plotted data were calculated from the middle
1/7 of the drum (as illustrated by the red box in (a)), and obtained by averaging over a 500 ms period andfor three different equivalent runs differing only in the initial random configuration of the particles.
(From Hill and Tan, 2014). The axes in (a) and (d)–(f) indicate the x- and y-directions. The z-direction isnormal to the page.

mixture to the next, as shown in Fig. 7. Specifically, foreach, Fig. 7 row 1 shows snapshots of the near steadystate segregation patterns in the first row, the corresponding plot for the solid fractions f  i in the second row, and theearly-time segregation fluxes f  iΔvi in the third row. In allcases, the large particles segregated upward in the flowing
layer and toward the outside of the drum. However, thesegregation flux magnitudes and steady state relative concentrations of the two constituents varied from one mixture to the next.
While the segregation dynamics varied from one system to the next, the stress partition coefficients did not.
Similar to the vertical chute flows, R c,i ≈ 1 everywhere except adjacent to solid boundaries, so that  c,i  i for all
mixtures. In other words, the contact stress is partitioned
between species exactly according to the volumetric concentration in the mixture. That is, Eqn. (2.7) reduces toEqn. (3.5) in this circumstance, as with the additional
substitution that  c,i  i , this yields:  gcos 0Φ ig 
i   c,i(4.7a)cD

i v i  v160

i  k,i  1  yyk  D i
cD



y

y

(4.7b)

In contrast, but, again, similar to the chute flows,
Rk,s > 1 and Rk,b < 1 everywhere, so that  k,s  s , and k,b  b , except where either s  1 or ϕl = 1. In otherwords, except in locations completely segregated where
there are only large or small particles, the smaller particles carry a greater share of the kinetic stress than theirconcentration, while the larger particles carry less of the
kinetic stress than their concentration. In fact, the relationship between ψ k,b and b was essentially the same forall mixtures at all times (Fig. 8):2

 k,b  0.39  b   0.61 b(4.8)

These results are similar in form to the pressure coefficient suggested by Gray and colleagues note in Section 1,
though here we would write:,b  b  B c b s
 c

 

(4.9a)

 k,b
 b  B kbs(4.9b)

where based on the simulations results, Bc = 0, and
Bk = –0.39. Unlike the positive value of B for the analogous total pressure partition coefficient suggested by Grayand colleagues, these two values, Bc and Bk are distinct,

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

Fig. 7Segregation of mixtures of different sized particles (2 mm and 3 mm) of equal material densities, where the system-­averaged large (3 mm) particle concentration, b , varies from one column to the next. Specifically, b increases
from left to right from system-averaged large-particle concentrations b from 0.1 to 0.9 as noted above each column. Row 1: Snapshots of the segregated mixtures from the time at which mixtures were under statistically steadystate conditions (after ≈ 30 s of rotation). Rows 2 and 3: Solid fraction and segregation flux data from the region of
the drum indicated by the box in the first snapshot in row 1. In both rows, the y-direction is positive downward andnormal to the free surface as indicated in the first snapshot in row 1. Row 2: Solid fraction profile for each species
and for the mixture in the region corresponding to the simulation represented by each snapshot in the first row. The
data are taken after ≈ 30 s of rotation once each system has reached a statistically steady segregated state. Row 3:
The segregation flux at the beginning of the simulation for each mixture. In each case the mixture started relatively
well-mixed, and the data shown in Row 3 was taken from ≈ 0.5 s after beginning rotation while the system was still
relatively well-mixed. Negative values of the segregation flux indicate segregation upward toward the free surface,and positive values of the flux indicate segregation downward. The data shown in Rows 2 and 3 were obtained byaveraging over a 500 ms period. From Hill, K. and Tan, D. (2014).

and both less than or equal to zero because the large particles actually carry less than (or equal to) the portion of
the stress compared to their concentration in the mixture.
These results are not inconsistent with the results fromthe chute flows described in Section 4.1, for which Fan, Y.
and Hill, K. (2011b, 2015) assumed,  c,b  k,b Bbs .
In these drum flows,  c,b  k,b (B c  B k )bs , so that
B = Bc – Bk = 0.39.
Finally, a predictive form for these gravity-driven
flows, including effects of kinetic stress gradients associated with the shear rate gradients, can be written:k
B kb 1  b   yy
 b
D 
cD 
y
y
k
s
s
k
B  1    yy bs v s  v 

D 
y
y
cD 

b v b  vFig. 8

Kinetic stress partition coefficient as a function of concentration in the flowing layer of the drum simulations:(a) For the mixture with 50 % large particles, at different times during segregation. (b) For mixtures of different global concentration, at steady state (t ≈ 29.5 to30 s). The fitted lines in both (a) and (b) are Eqns. 4.5a,
4.5b, Bk = –0.39.

(4.10a)(4.10b)for the large or big (b) and small (s) particle constituents,
respectively.
For any particular mixture, a reasonable functional
form for the drag and diffusion coefficients cD and D may
be found empirically using simulations of the mixtures.
At early times, cD may be empirically derived by noting161

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

the gradient in concentration i / y  0 , so the total
flux in Eqn. (4.8) reduces to that determined by the kinetic stress gradient alone:(4.12a)Concentrations and calculations described in text to determine cD and D, both sets shown for 50 % large particles. Row 1: early times where the mixture is relatively
uniform. Row 2: late times when the segregation isnearly under steady state conditions. Column 1: Local
concentration of each species. Column 2, top row: simulation (points) and model (lines) data from Eqns 4.11–
4.12. Results shown for both large particles (purple)
and small particles (orange). From Hill, K. and Tan, D.
(2014).

Fig. 10 Theoretical fluxes (Eqns 4.10(a)–(b)) using empirically-­
determined values for Bk, cD, and D compared withsimulated fluxes. Results for two different time steps
for two different global concentrations of the bigger
particles as noted at the top of each column. From
Hill, K. and Tan, D. (2014).

b v b  v s v s  v 



cD 

  yykyk
B ks 1  s   yyycD (4.11a)(4.11b)Once the functional form for Bk is determined as described above (e.g., Eqn. 4.8), all other variables in Eqns.
(4.9a–4.9b) can be measured directly from the simulations,to determine the functional form for cD. While it is likelythat cD is not constant, for a first order model, Hill and Tan(2014) first approximated cD as constant for each mixture.
Then they used the calculated values for each of the variables at early times to separately determine b v b  v andk / y) throughout the flowing layer.
B k (b 1  b  /  )( yy
By using the method of least squares fitting, they determined the best value for cD for each mixture. An exampleof these data for a 50/50 mixture is shown in Fig. 9, row 1.
The diffusion coefficient D can be similarly empiricallydetermined. Hill and Tan (2014) used conditions at latertimes under steady segregated conditions, where the segregation flux i (v i  v)  0 . In this case, Eqns. (4.10a–4.10b)
may be rewritten:k
B kb 1  b   yy b cD D 
y
y


162

(4.12b)Once Bk and cD are determined as described above, allother variables can be determined for the simulations, except D. Again, Hill and Tan (2014) approximated D asconstant for each mixture. Then, they used the values foreach value computed from the simulations under latertimes at steady state conditions to independently deterk / y) and c ∂ϕb /∂y throughmine B k (b 1  b  /  )( yyDout the flowing layer. Then they used the method of leastsquares fitting to determine the best value for D for each
mixture. An example for a 50/50 mixture is shown in Fig.
9, row 2.
They found that there was some variability from one
mixture to the next, but not a systematic variation of either cD or D with b . While this is likely an artifact ofperforming these calculations assuming values for cD andD this still provides a manner for validating the model
more quantitatively and a first order estimate for these parameters. For a predictive form for all mixtures in theirsimulations they used the average value they obtained foreach mixture: cD = 6.3 s –1 and D = 0.20 mm 2/s. Whenthey used the results for different mixtures at different
time steps (e.g., Fig. 10), they found reasonable agreement. While not a rigorous test, these data indicate that,indeed, the gradient kinetic stress is a dominant segregation driver, even in the presence of a gravitational field.

B kb 1  b

Fig. 9k
B ks 1  s   yy b cD D yyKimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

f i f j  mi   mj 

5. Outlook for generalizability

 i v i  vT,g 

For this model to be easily applicable to other systems,
a mechanistic understanding and general expressions areneeded for parameters such as the partition coefficientsand the drag and diffusion coefficients. As mentioned in
the introduction, there has been much work recently on
developing these expressions for various types of particle
mixtures. Additionally, some expressions are derivablefrom kinetic theory, e.g., Jenkins, J. and Mancini, F.
(1987) and Larcher, M. and Jenkins, J. (2013). On theother hand, relatively little work has been done investigating how contact and kinetic stress are partitioned between
the constituents. To investigate the generalizability of thesimulation results for the stress partition coefficients described for certain mixtures in Section 4, we borrow insight from previous related work.

5.1 Contact stress partition coefficients—insightfrom density segregation model
The systems in Section 4 are relatively special in thatthe particles differ only in size or density. However, there
are some considerations we can use to test the generalizability for other systems where the particles differ in bothsize and density.
First, we recall that the simulation data obtained by
Fan, Y. and Hill, K. (2011b, 2015) and Hill, K. and Tan, D.
(2014) indicate that for mixtures of particles differingmodestly in density or size, the constituents bear a portionof the contact stress equal to their volumetric concentration in the mixture, i.e.,  c,i  i . For a working definition of “modest” for here and for the rest of the paper, weconsider those where a different mechanism is not obviously in play, so we limit our immediate consideration tosize differences in bimodal mixtures that do not exceedthat for which the smaller particles can passively sieve
through the larger particles and to density differences that
range up to a factor of 7, approximately that of a mixtureof steel and plastic particles. We revisit the general expression in Eqns. (2.7–2.8) and note this impacts the gravity flux. That is, based on the results that  c,i  i we canrewrite theoretical expression for the segregation flux associated with gravity in Eqn. (2.8b) as:mi  i   gcos i v i  vT,g 
cD

(5.1)

If we substitute expressions for the definitions ofi  f i / f and mi   i /  in terms of the solid fractions
and material densities of the constituents, e.g., i  f i / fand mi   i /    mi f i / Σ i ( mi f i ) , with a little algebra
we can rewrite this as:

fcD

gcos

(5.2)

This is similar to what one might predict qualitatively
from simple expectations that denser particles should sink
relative to lighter particles, and the magnitude of the segregation flux associated with this should depend on theirrelative concentration in the mixture.
Moreover, we can compare by the model proposed by
Khakhar, D. et al. (1997) by setting i = ld and recalling
i
i
that

 i 
 mi i f . Then with a little rearranging ofmf
Eqn. (5.2), we can write:d m cD ldm ldd 1 ld v ld  vT,g  mld gcos
 md 

(5.3)

This is nearly identical to the model originally proposed by Khakhar, D. et al. (1997) in Eqn. (1.1), with
CK   md / ( mldcD ) . This consistency between the generalmodel predictions of Eqns. (2.7–2.8), with the more mechanistic derivation of the model in Eqn. (1.1) (e.g., Sarkar,
S. and Khakhar, D., 2008; Tripathi A. and Khakhar, D.,2013) , lends credence to the results from the general theoretical framework and the further generalizability that c,i  i for a wider range of mixtures. These results havenot been tested extensively, but one might hypothesizethat this is likely limited to cases where size and density
are only modestly different.

5.2 Kinetic stress partition coefficients—insightfrom granular temperature
While similarly little work has been done investigating
how kinetic stress is partitioned among constituents in a
mixture, there has been some related work on granulartemperatures, that is, kinetic energy associated with velocity fluctuations. In particular, Hill, K. and Zhang, J.
(2008) investigated how granular temperature varies forparticles in a mixture. We now consider the generalizability of the kinetic stress coefficient derived here based on
their work. Then, we consider similar issues regarding thesegregation flux associated with kinetic stress gradients.
Hill, K. and Zhang, J. (2008) investigated the variationof granular temperature of particle mixtures of different
sizes and densities in the dense flows using experiments
and simulations of drum flows similar to that in Section
4.2. In particular, they noted how the granular temperature and velocity fluctuations varied for different constituents in a mixture. While not exactly equal to the kinetic
stress, they are related. In particular, Zhang, K. and Hill,
J. (2008) reported on results for what they called a “kinematic temperature” (in 2d):
b
b
(5.4)
T k,i  uu  vv .
iHere, u corresponds to the streamwise velocity, and uu163

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

is the variance of the velocity fluctuations of constituent iiin the streamwise direction, analogous to vv in the direction normal to flow. Hill, K. and Zhang, J. (2008) notedthat the fluctuations in the different directions scale simii
i
larly, that is, uu is roughly proportional to vv for mixtures of 2 mm and 3 mm particles differing in densities
up to a factor of 3. While there are likely limits to this approximate relationship, we do not explore these limits
here. Rather we use this as a method to explore the implication for how this theoretical framework may be further
extended for similar systems. Based on this simplifying
assumption, we can approximate:b

T k,b / T k,s  vv / vvsFig. 11(5.5)

We can compare this to the results for the mixtures described in Section 4 by noting

Sketch illustrating how in dense sheared flows of similar shear rates ( ~ u rel /  y in the image above) geometric constraints could cause the smaller particles tohave higher velocity fluctuations than the larger particles. From Hill, K. and Zhang, J. (2008).

b k,b  b vv
 b T k,b.
 k,s  s vv s  s T k,s(5.6)

For a 50/50 mixture of equal density particles, ρb = ρs, andso for this special case ψ k,b/ψ k,s ≈ T k,b/T k,s. We return tothis analogy in a moment, but first explore these previous
results for how relative kinematic temperature varies according to relative particle size and density in a mixture.
Hill, K. and Zhang, J. (2008) reported that in the dense
region of the flowing layer in physical experiments, thevelocity fluctuations scale primarily with relative size ofparticles in the mixture (increasing as relative particle
size decreases) and have very little dependence on density. In other words, in mixtures of particles differing
only in density the less dense particles have velocity fluctuations of similar magnitude to the dense particles, i.e.,
T k,ld ≈ T k,d. In mixtures of particles differing only in size,
small particles have higher average velocity fluctuations
than large particles T k,b < T k,s. Moreover, they used simulations to investigate the effect of systematic variations of
the relative particle density of particles of two different(fixed) sizes and found that T k,b/T k,s did not change. In
other words, they found that T k,b/T k,s for particles differingin both size and density only depended on the relative sizesof particles in the mixture, not their relative densities.
To explain their results, Hill and Zhang reasoned that,in contrast with sparser regions of the flow, this scaling is
associated with geometric considerations. They developed
a simple model, the essence of which is as follows. Particle movement in sheared dense flow is primarily laminarin nature, where particles move in enduring contact overneighboring layers of particles. Velocity fluctuations are
generated when beads must slide around neighbors below
or when the neighbors below push them as they themselves slide around their neighbors. One can extend thisqualitatively to particles of different properties by notingthat smaller particles sliding over larger neighbors will bejostled more than larger particles sliding over smaller

164

neighbors (e.g., Fig. 11). On the other hand, density
should have relatively little effect on this primarily geometric problem. The model, presented in more detail inthe paper, fit the data well and the results are consistent
with the kinetic stress partition coefficients described in
Section 4.
The overlap between the measured temperature datafrom Hill, K. and Zhang, J. (2008) and the mixture results
described in section 4 are in the 50/50 mixtures of particles of 2 mm and 3 mm. Hill, K. and Zhang, J. (2008)
found the relative kinematic temperatures for a 50/50
mixture of 3 mm and 2 mm particles was approximately
b

b

s

s

(uu  vv ) / (uu  vv )  0.7. Again, using the simpli-

fying assumption that uui ~ vvi we use this to approxibsmate vv / vv  0.7 . As noted above, for a 50/50
mixture of equal density particles, ρb = ρs. Thus, the results from Hill, K. and Zhang, J. (2008) indicate that for
a 50/50 m i xt u re of 2 m m a nd 3 m m pa r t icle sbsk,b /  k, svv / vv  0.7 . Using this with the requirement ψ k,b + ψ k,s = 1, these results imply ψ k,b ≈ 0.41, andψ k,s ≈ 0.59. The results from Hill, K. and Tan, D. (2014) inbs
Eqn. (4.9b) with Bk ≈ –0.39 and    0.5 indicate thatk,bk,s
ψ ≈ 0.40, and ψ ≈ 0.60, very similar.
Based on these results, Eqn. (5.6) and the results expressed by Eqn. (4.9b), we would suggest that for particles
of modest size and density differences, the kinetic stresspartition coefficients can be found using material densities of the two constituent particles, local volumetric concentrations, and one parameter Bk, according to: k,b  mb b b  B kbs k,s  ms s s  B kbs(5.7)

Based on the results by Hill, K. and Zhang, J. (2008), theparameter Bk may likely be a function only of relative
particle size in a mixture, but their theoretical expression
for the relative temperatures is not easily expressed in thisKimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

form. While it is beyond the scope of this review, it provides some indication that the framework is generalizable
to a range of mixtures modestly differing in both sizeand density.

6. Summary and outlook
In this paper, we reviewed recent developments indicating the importance of velocity fluctuations, such as thatmanifested in a “granular temperature” and “kinetic
stress gradients” in segregating mixtures of particles ofdifferent sizes and densities. Simulations support recent
model development. The model captures the segregationby considering two different types of stresses, the kinetic
and contact stress, and allowing for independent partitioning of stresses between constituents that is different
than their concentration in the mixture. In this way, themodel illustrates that segregation can be driven solely by
dynamics associated with gradients in kinetic stresses, or
temperatures, such as those produced by a shear rate gradient.
Comparing predictions with computational experiments based on DEM simulations indicates that the model
framework is reasonable for capturing shear-induced segregation in dense flows. Further, comparing with othermodel efforts and recent complimentary simulations,
there is indication that the model is generalizable for awide range of mixtures.
However, for a predictive model a deeper understanding of the rheology and other kinetics of dense sheared
mixtures is needed. First, we need a predictive relationship between ψk,i and flow properties such as particle concentrations, relative particle sizes and flow velocities toclose the governing equations. Similarly, it is not clearthat the drag force on the particles should be linear withrelative velocity, or, even if it is linear, what the coefficient of drag should be. Further, it is unlikely that diffusivity D and the drag coefficient cD are constant for any
particular mixture. As mentioned by Wiederseiner, S. etal. (2011) and Natarajan, V. et al. (1995) and demonstrated
by Hill et al. (2003) and others, D may depend on the local shear rate as well as other factors. A more mechanistic
way to obtain relationships for D and cD as they dependon kinematics on the flow is necessary for a predictivemodel for shear-induced segregation.
We conclude by briefly noting the potential importanceof interstitial fluids in some segregating mixtures, not discussed in this review. Indeed, in many industrial and natural segregating systems, interstitial fluid (e.g., air inindustrial powder or grains or water and mud in geophysical flows) is unavoidable and may alter or even drive (e.g.,water over a riverbed) the system dynamics. For example,
Burtally et al. (2002) demonstrated that the presence of aninterstitial fluid can have a strong influence on a segregation pattern in a granular system, particularly for smaller
particles. They showed that for mixtures composed of
micron-sized glass and brass spheres, in the presence of air
at atmospheric pressures, the segregation was markedly
different compared to that where the air pressure was significantly reduced. On the other hand, in sediment transport in riverbeds, the particle transport itself is not only
influenced by, but also, driven by fluid stresses; there isevidence that particle-particle dynamics can still play asignificant role in driving segregation (e.g., Recking et al.,2009; Frey and Church, 2011). Thus, while the work reviewed in this paper takes a significant step toward amore complete representation of the importance of granular temperature in influencing segregation dynamics, fora more complete picture of segregation in a wide range ofsystems, the influence of fluid-particle interactions needs
to be incorporated into this framework.

Acknowledgements
We gratefully acknowledge helpful discussions with
Prof. Jenkins and funding that supported the original research described here from NSF Grant No. CBET0932735 and Proctor & Gamble Co.

NomenclatureB

Bc – B k

Bcmagnitude of a non-dimensional perturbation of the contact stress partition coefficients away from the solid volume fractions for each speciesBkmagnitude of a non-dimensional perturbation of the
kinetic stress partition coefficients away from the solid
volume fractions for each species

CK

a parameter in the model originally proposed by Khakhar,
D. et al. (1997) related to an inverse drag coefficient (s)cDa drag coefficient (1/s)

dparticle diameter (m)Dthe diffusivity (m 2/s)

BGT the magnitude of a non-dimensional perturbation of thepartial pressure coefficients away from the solid volume
fractions for each species in the model proposed by Gray,
N. and Thornton, A. (2005). Specifically, for constituent i(consisting of larger or smaller particles) in a bimodal
mixture of large and small particles of equal material
density, BGT ≡ |ψp,i – ϕi|/[ϕi(1 – ϕi)]f  ilocal solids fraction of constituent iflocal solids fraction of the mixturefsystem-averaged solid fraction of the mixture

Fninterparticle normal force (N)165

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

Ft

interparticle tangential force (N)ηndamping coefficient associated with compressive deformation of a particle (Ns/m5/4)

μinterparticle friction coefficent

ρ

local bulk density of the mixture:   Σ mi (kg/m3)

ρilocal bulk density of constituent i, related to the material
density of constituent i and its local solids fraction f  iaccording to  i  f i  mi (kg/m3)

2

g

gravitational acceleration (m/s )iindicative of the particles or constituent in the mixture;i = b, s, d, or ld if the constituent is comprised of bigger,
smaller, denser, or less dense particles, respectivelykncoefficient of stiffness associated with compressive deformation of a particle (N/m3/2)

kt

coefficient of stiffness associated with shear deformationof a particle (N/m3/2)

 mi

the material density of the particles of which constituent i
is comprised (kg/m3)plithostatic pressure, that is, the normal stress associatedwith the weight of a material (Pa).

σc

R c,i

local contact stress tensor of the mixture σc = Σψc,i σc,i(N/m 2)

contact stressR c,i   c,i / ipartitioncoefficientfor

species

i.

σk

Rk,i

local contact stress tensor of the mixture σ k = Σψ k,i σ k,i(N/m 2)kinetic stressR k,i   k,i / ipartitioncoefficientfor

speciesi.

σc,ilocal contact stress tensor of species i (N/m 2)

ttime (s)Tkinematic granular temperature T = (u′u′ + v′v′ + w′w′)/3;(m 2/s2)uiuu′

the local average velocity of species i in the direction normal to the average flow, and in the direction of segregation (m/s)

v

the local average velocity of the mixture in the direction
normal to the average flow and in the direction of segregation (v = Σf  ivi/f ) (m/s)

v′

the local root mean square velocity of the mixture in the
direction of segregation (m/s)

w

i

w

the local average velocity of species i in one of the two
directions normal to the direction of segregation (m/s)
the local average velocity of the mixture in one of the two
directions normal to the direction of segregation(w = Σf  iwi/f ) (m/s)

w′

the local root mean square velocity of the mixture in oneof the two directions normal to the direction of segregation (m/s)

xcoordinate normal to the direction of segregation (m)ycoordinate normal to the average flow direction and in the
direction of segregation (m)zcoordinate normal to the average direction of segregation(m)

shear rate (s –1)

δninterparticle overlap in normal direction (m)δtinterparticle overlap in tangential direction (m)δythickness of the flowing layer (m)ζthe angle of inclination of a plane, above the horizontal
down which a mixture flows

166

local kinetic stress tensor of species i, where the components
are stresses associated with velocity fluctuation correlations,iiii uu ; k,ii uv ; k,ik,ie.g.,  xx
 i vv (N/m2)

xy
yy

 mi
the local mass concentration of particles of species i:i  i / Σf i  ithe local average velocity of species i in one of the two mi f
i / 
mm
directions normal to the direction of segregation (m/s)
ithe local volume concentration of particles of species i:the local average velocity of the mixture in one of the twoi  f i / Σf i
directions normal to the direction of segregation
Φithe segregation flux of species i: Φi = ρi(vi – v)
(u = Σf  iui/f ) (m/s)
iΦ T the theoretical net segregation flux of species i derived in
the local root mean square velocity of the mixture in one
Section 2, according to Eqn. (2.7): Φ iT  Φ iσk  Φ ig  Φ idof the two directions normal to the direction of segregation (m/s)

vi

σ k,i

Φ iσk the kinetic stress segregation flux of species i; accordingk / y   c,i   k,i / c D 
to Eqn. 2.8a: Φ iσk  yy
Φ ig

the gravity-driven segregation flux of species i; accordingito Eqn. 2.8b: Φ gcosζ  mi  c,i  / cDg

Φ id the diffusion flux of species i; according to Eqn. 2.8c:Φ id  d / cD    i / yψc,i

the partial contact stress coefficient of species iψk,ithe partial kinetic stress coefficient of species iψp,ithe partial pressure coefficient of species i

References
Bridgwater J., Fundamental powder mixing mechanisms, Powder Technol., 15 (1976) 215–231.
Burtally N., King P.J., Swift M.R., Spontaneous air-driven separation in vertically vibrated fine granular mixtures, Science, 295 (2002) 1877–1879.
Chikkadi V., Alam M., Slip velocity and stresses in granularpoiseuille flow via event driven simulation, Phys. Rev. E,
80 (2009) 021303.
Conway S.L., Liu X., Glasser B.J., Instability-induced clustering and segregation in high-shear couette flows of model
granular materials, Chemical Engineering Science, 61(2006) 6404.
Dietrich W.E., Kirchner J.W., Ikeda H., Iseya F., Sediment supply and the development of the coarse surface layer in gravel-­bedded rivers, Nature, 340 (1989) 215.
Donald M.B., Roseman B., Mixing and de-mixing of solid par-

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

ticles: Part I. mechanisms in a horizontal drum mixer, Br.
Chem. Eng., 7 (1962) 749–752.
Duffy S.P., Puri V.M., Primary Segregation Shear Cell for
Size-Segregation Analysis of Binary Mixtures, KONA
Powder and Particle Journal, 20 (2002) 196–207.
Duffy S.P., Puri V.M., Development and Validation of a Constitutive Model for Size-Segregation during Percolation,
KONA Powder and Particle Journal, 21 (2003) 196–207.
Fan Y., Hill K.M., Shear-driven segregation of dense granular
mixtures in a split-bottom cell, Phys. Rev. E, 81 (2010)
041303.
Fan Y., Hill K.M., Phase transitions in shear-induced segregation of granular materials, Phys. Rev. Lett., 106 (2011a)
218301.
Fan Y., Hill K.M., Theory for shear-induced segregation ofdense granular mixtures, New Journal of Physics, 13(2011b) 095009.
Fan Y., Hill K.M., Shear-induced segregation of particles bymaterial density, Phys. Rev. E (2015) 022211.
Fan Y., Schlick C.P., Umbanhowar P.B., Ottino J.M., Lueptow
R.M., Modelling size segregation of granular materials: theroles of segregation, advection and diffusion, J. Fluid
Mech., 741 (2014) 252–279.
Frey P., Church M., Bedload: A granular phenomenon, Earth
Surf. Process. Landforms, 36 (2011) 58–69.
Galvin J.E., Dahl S.R., Hrenya C.M., On the role of nonequipartition in the dynamics of rapidly flowing, granular mixtures, J. Fluid Mech., 528 (2005) 207.
Gilchrist J.F., Ottino, Competition between chaos and order:
Mixing and segregation in a spherical tumbler, Phys. Rev.
E, 68 (2003) 061303 .
Gray J.M.N.T., Ancey C., Multi-component particle-size segregation in shallow granular avalanches, J. Fluid Mech., 678(2011) 535.
Gray J.M.N.T., Chugunov V.A., Particle-size segregation anddiffusive remixing in shallow granular avalanches, J. Fluid
Mech., 569 (2006) 365.
Gray J.M.N.T., Thornton A.R., A theory for particle size segregation in shallow granular free-surface flows, Proc. R. Soc.
A, 461 (2005) 1447.
Hertz H.R., Ueber die Beruehrung elastischer Körper (On contact between elastic bodies), J. Reine Angew. Math., 92(1882) 156–171.
Hill K.M., Gilchrist J.F., Ottino J.M., Khakhar D.V., McCarthy
J.J., Mixing of granular materials: a test-bed dynamicalsystem for pattern formation, International Journal of
Bifurcation and Chaos, 9 (1999a) 1467–1484.
Hill K.M., Gioia G., Tota T., Structure and kinematics in dense
free-surface granular flow, Phys. Rev. Lett., 91 (2003)
064302.
Hill K.M., Caprihan A., Kakalios J., Bulk segregation in rotatedgranular material measured by magnetic resonance imaging, Phys. Rev. Lett., 78 (1997) 50.
Hill K.M., Fan Y., Isolating segregation mechanisms in a
split-bottom cell, Phys. Rev. Lett., 101 (2008) 088001.
Hill K.M., Khakhar D.V., Gilchrist J.F., McCarthy J.J., Ottino
J.M., Segregation-driven organization in chaotic granular
flows, Proc. Nat. Acad. Sci., 96 (1999b) 11701.

Hill K.M., Tan D.S., Segregation in dense sheared flows: gravity, temperature gradients, and stress partitioning, J. Fluid
Mech., 756 (2014) 54–88.
Hogg R., Mixing and Segregation in Powders: Evaluation,
Mechanisms and Processes, KONA Powder and Particle
Journal, 27 (2009) 3–17.
Hsu L., Dietrich W.E., Sklar L.S., Experimental study of bedrock erosion by granular flows, J. Geophysical Research,
113 (2008) F02001.
Jenkins J.T., Mancini F., Balance laws and constitutive relations
for plane flows of a dense, binary mixture of smooth, nearly
elastic, circular disks, J. Appl. Mech., 54 (1987) 27.
Johnson K.L., Contact Mechanics, Cambridge Univ. Press, New
York, 1985.
Khakhar D.V., McCarthy J.J., Ottino J.M., Radial segregation of
granular mixtures in rotating cylinders, Phys. Fluids, 9(1997) 3600.
Marks B., Rognon P., Einav I,. Grainsize dynamics ofpolydisperse granular segregation down inclined planes, J.
Fluid Mech., 690 (2012) 499–511.
May L.B.H., Golick L.A., Phillips K.C., Shearer M., Daniels
K.E., Shear-driven size segregation of granular materials:
Modeling and experiment, Phys. Rev. E, 81 (2010a) 051301.
May L.B.H., Shearer M., Daniels K.E., Scalar Conservation
Laws with Nonconstant Coefficients with Application toParticle Size Segregation in Granular Flows, J. Nonlinear
Sci., 20 (2010b) 689–707.
Middleton G.V., Experimental studies related to problems offlysch sedimentation, in: Lajoie J. (Ed.), Flysch Sedimentology in North America, Business and Economics Science
Ltd., Toronto, 1970, pp. 253–272.
Mindlin R.D., Deresiewicz H., Elastic spheres in contact undervarying oblique forces, J. Appl. Mech., 20 (1953) 327–344.
Morland L.W., Flow of viscous fluids through a porous deformable matrix, Surv. Geophys., 13 (1992) 209.
Mosby J., de Silva S.R., Enstad G.G., Segregation of Particulate
Materials—Mechanisms and Testers, KONA Powder andParticle Journal, 14 (1996) 31–43.
Natarajan V.V.R., Hunt M.L., Taylor E.D., Local measurementsof velocity fluctuations and diffusion coefficients for a
granular material flow, J. Fluid Mech., 304 (1995) 1.
Paola C., Seal R., Grain size patchiness as a cause of selective
deposition and downstream fining, Water Resources
Research, 31 (1995) 1395.
Recking A., Frey P., Paquier A., Belleudy P., An experimental
investigation of mechanisms responsible for bedload sheet
production and migration, J. Geophys. Res. Earth Surface,
114 (2009) F03010
Sarkar S., Khakhar D.V., Experimental evidence for a description of granular segregation in terms of the effective temperature. Europhys. Lett., 83 (2008) 54004.
Savage S.B., Lun C.K.K., Particle size segregation in inclined
chute flow of dry cohesionless granular solids, J. Fluid
Mech., 189 (1988) 311.
Schlichting H., Boundary-Layer Theory, McGraw-Hill, New
York, 1979.
Schulze D., Powders and Bulk Solids: Behavior, Characterization, Storage, and Flow, Springer, Berlin, Germany, 2008.

167

Kimberly M. Hill et al. / KONA Powder and Particle Journal No. 33 (2016) 150–168

Shinbrot T., Muzzio F., Nonequilibrium patterns in granularmixing and segregation, Physics Today, 53 (2000) 25.
Stock J.D., Dietrich W.E., Erosion of steepland valleys by debris
flows, Geol. Soc. Am. Bull., 118 (2006) 1125.
Taberlet N., Losert W., Richard P., Understanding the dynamics
of segregation bands of simulated granular material in arotating drum, Europhys. Lett., 68 (2004) 552.
Thornton A., Weinhart T., Luding S., Bokhove O., Modeling ofparticle size segregation: calibration using the discrete particle method, International Journal of Modern Physics C, 23(2012), 8.
Tripathi A., Khakhar D.V., Numerical simulation of the sedimentation of a sphere in a sheared granular fluid: a granular
Stokes experiment, Phys. Rev. Lett., 107 (2011) 108001.
Tripathi A., Khakhar D.V., Density difference-driven segregation in a dense granular flow, J. Fluid Mech., 717 (2013)
643–669.
Tsuji Y., Tanaka T., Ishida T., Lagrangian numerical simulationof plug flow of cohesionless particles in a horizontal pipe,
Powder Technol., 71 (1992) 239.

Tunuguntla D.R., Bokhove O., Thornton A.R., A mixture theory
for size and density segregation in shallow granularfree-surface flows, Journal of Fluid Mechanics, 749 (2014)99-112. doi:10.1017/jfm.2014.223
Weinhart T., Luding S., Thornton A.R., From discrete particles
to continuum fields in mixtures, Powders and Grains 2011,
1542 (2013) 1202–1205. American Institute of Physics.
Wiederseiner S.N., Andreini, Epely-Chauvin G., Moser G.,
Monnereau M., Gray J.M.N.T., Ancey C., Experimental
investigation into segregating granular flows down chutes,
Phys. Fluids, 23 (2011) 013301.
Williams J.C., The segregation of particulate materials, Powder
Technol., 15 (1976) 245–256.
Xu H., Louge M., Reeves A., Solutions of the kinetic theory forbounded collisional granular flows, Continuum Mech.
Thermodyn., 15 (2003) 321.
Yohannes B., Hsu L., Dietrich W.E., Hill K.M., Boundarystresses due to impacts from dry granular flows, J. Geophys.
Res., 117 (2010) F02027.

Author’s short biography
Kimberly Hill
Kimberly Hill is an Associate Professor in the Department of Civil, Environmental,
and Geo- Engineering at the University of Minnesota, Twin Cities. Prior to this she held
faculty positions in the Department of Theoretical and Applied Mechanics, University of
Illinois, Champaign-Urbana and in the Department of Physics, University of Wisconsin,
Whitewater and Postdoctoral Research positions in the Department of Chemical Engineering at Northwestern University and in Sandia National Laboratory, Albuquerque.
Her research interests and experience are related to fundamental and applied particulate
flows. They include segregation and rheology of granular mixtures including applications in geophysical and industrial flows.

Yi FanYi Fan is currently a senior R&D engineer at the Solids Processing group in the Corporate R&D of The Dow Chemical Company. Prior to this, he was a Postdoctoral Fellow
in the Department of Mechanical Engineering at Northwestern University. He received
his Bachelor (2003) and Mater (2006) degrees in Thermal and Power Engineering from
Tsinghua University of China and received his PhD (2011) in Civil engineering from the
University of Minnesota – Twin Cities. His research interests and experience focus onunderstanding the fundamental physics of particulate flows including granular flows andgranular-fluid flows, as well as their applications in chemical industry.

168KONA Powder and Particle Journal No. 33 (2016) 169–178/Doi:10.14356/kona.2016023

Review Paper

How Should the Discrete Element Method Be Appliedin Industrial Systems?: A Review †Mikio Sakai
1

Resilience Engineering Research Center, School of Engineering, The University of Tokyo, Japan

Abstract
In this paper, we describe an industrial application of the discrete element method (DEM). The DEM has been
applied to various powder systems thus far and therefore appears to be an established approach. However, itcannot be applied to many industrial systems because of several critical problems such as modeling of large-scale
simulations, complexly shaped wall boundaries and free surface fluid flow. To solve these problems, novel models
were developed by our group. A coarse-grain DEM model was developed for large-scale simulations, a signed
distance function-based wall boundary model was developed for complexly shaped walls and a DEM-moving
particle semi-implicit method was developed for solid-liquid flow involving a free surface. The adequacy of thesemodels was demonstrated through verification and validation tests. Our approach shows promise in industrial
applications.
Keywords: large-scale discrete element modeling, signed distance function-based wall boundary, multi-phaseflow modeling, discrete element method, Lagrangian computational fluid dynamics, multi-physics
modeling

1. Introduction
Granular flows usually differ from fluid flows. They are
regarded as complex because constitutive equations are
influenced by many parameters such as coefficient of restitution, coefficient of friction, particle size distribution,
humidity, etc. Modeling of granular flow is extremely difficult. It may not be possible to model even simple phenomena in industries. Many requirements for numerical
technologies should be applied in industrial design. The
question arises as to how granular flow should be simulated in industrial systems. Granular flow could be simulated when individual particle behavior could be modeled.
The discrete element method (DEM) (Cundall and Strack,1979) was developed based on this idea and it became asuccessful model in the simulation of granular flow. The
DEM is a Lagrangian approach and solid particle behavior
is computed based on the Newton’s second law of motion.
The contact force acting on a solid particle is calculatedusing the Voigt model, namely, a spring-dashpot system.
Recent improvements in computer hardware have allowed
for an increase in number of solid particles and for com-†
1

Received 27 July 2015; Accepted 15 September 2015
J-STAGE online 28 February 2016
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
E-mail: mikio_sakai@n.t.u-tokyo.ac.jp
TEL: +81-3-5841-6977 FAX: +81-3-5841-6977©2016 Hosokawa Powder Technology Foundation

plex phenomena such as solid-fluid coupling problems tobe simulated. When solid-fluid coupling problems arecomputed, the DEM is coupled with computational fluid
dynamics (CFD), where the local volume average technique (Anderson and Jackson, 1967) is used in the gover ning equations and semi-empirical momentum
exchange coefficients (Wen and Yu, 1966; Ergun, 1952)
are often used. This approach is referred to as the DEMCFD method (Tsuji et al., 1993). Tsuji’s contribution was
significant to simulate solid-fluid coupling problems(Tsuji, 2007) and the DEM-CFD method became a standard global approach. Tsuji also investigated the application of a linear spring model, devised an efficient
calculation setup (Tsuji et al., 1993) and proposed how
physical properties should be set in a non-linear spring
system (Tsuji et al., 1992). His research was exceptionaland his proposal has become the standard approach.
At present, the DEM appears to be an established approach. It has been applied in various engineering fields
and many DEM applications exist. With respect to single
flow such as granular flow, the DEM has been applied tosolid mixing (Radeke et al., 2010; Chandratilleke et al.,2012; Halidan et al., 2014; Eitzlmayr et al., 2014; Huang
and Kuo, 2014), die filling (Guo et al., 2009; Wu, 2008;
Mateo-Ortiz et al., 2014; Yue et al., 2014), powder transportation (Tsuji et al., 1992; Mio et al., 2009; Kuang et al.,2013), and granulation (Washino et al., 2013), among others. Since the DEM-CFD method was developed, the DEM

169

Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

has been applied to new fields along with fluidized beds
and cyclones. The DEM-CFD method was applied to simulations of coarse particles that belong to the Geldart B
and D group (Kawaguchi et al., 1998; Wang et al., 2013;
Hernández-Jiménez et al., 2014; Olaofe et al., 2014; Patilet al., 2014) and fine particles such as Geldart A (Pandit etal., 2006; Kobayashi et al., 2013; Hong et al., 2015). The
DEM-CFD method has been validated in f luidized(Alobaid et al., 2013; Wu et al., 2013) or spouted (Jajcevic
et al., 2013; Salikov et al., 2014) beds. The DEM-CFD
method can be used in simulations of geometrically complex fluidized beds such as circulating fluidized beds (Xuet al., 2012; Lu et al., 2014). Hence, DEM technologies are
regarded as a reliable approach.
Unfortunately, some critical problems exist for industrial application of the DEM. Although it has been applied
to various engineering fields, the number of calculatedparticles is extremely small compared with the actual
number in industrial processes. A maximum of one million calculated particles can be used on a single personal
computer, whereas over one billion particles are required.
Hence, the number of calculated particles becomes extremely small in industrial applications. In addition, modeling of solid-liquid flow involving a free surface is also asignificant problems. Limited simulations of a wet ballmill and slurry transportation have been performed thus
far because accurate simulation of free surface fluid flow
is difficult, and this problem is common in a single flow.
Recently, mesh-free Lagrangian CFD (Koshizuka et al.,
1996; Monaghan, 1988) such as smoothed-particle hydrodynamics or the moving particle semi-implicit, volumeof-fluid (Yokoi, 2007; Xiao et al., 2011; Ohta et al., 2011)
and front tracking methods (Vu et al., 2015) have beenused extensively in simulations of free surface fluid flow.
Indeed, solid-liquid flow could not be simulated in industrial systems. Perhaps coupling the DEM with these models for free surface fluid flow is a promising approach tosimulate solid-liquid flow involving a free surface. Currently, models for solid-liquid flow that include a free surface are required. Finally, modeling of an arbitrary shaped
wall is also important in industrial systems. In previous
studies, a combination of simple surface equations (Mioet al., 2002; Liu et al., 2011; Sakai et al., 2005), the location of DEM particles and meshes (Shigeto and Sakai,
2011; Mateo-Ortiz et al., 2014) was used to create a wallboundary in the DEM. The combination of simple surface
equations was difficult to create an arbitrarily shaped domain. In addition, the wall boundary model composed of
the DEM particles exhibited difficulties in terms of accuracy for application to industrial systems. Although amesh-based wall was often used to model a geometricallycomplex wall, collision detection may be difficult in programming the algorithm.
Hence, the DEM exhibits several problems when it was

170

applied to industrial systems. To solve these problems, theauthors have developed original models, namely, thecoarse-grain DEM (Sakai and Koshizuka, 2009; Sakai etal., 2010; Sakai et al., 2012b; Sakai et al., 2014) for largescale powder systems, the DEM-MPS method (Sakai etal., 2012a; Yamada and Sakai, 2013; Sun et al., 2014) for
solid-liquid flow involving a free surface, and the signed
distance function (SDF)-based wall boundary model(Shigeto and Sakai, 2013; Sakai et al., 2015) for granular
flow in a geometrically complex device. These models aredescribed in this paper.

2. Large-scale discrete element simulation
2.1 Existing problems
Although the DEM seems to be well established, it isproblematic in that the number of calculated particles isrestricted when the simulation needs to be completed in areasonable time on a single personal computer. This is especially critical because a number of DEM simulations
are performed without using a supercomputer and the calculation time increases significantly on a single personal
computer when an excessive number of particles are used.
This problem cannot be solved even with the latest multicore processors (Liu et al., 2014; Yue et al., 2014). Many
industries require application of the DEM to large-scalepowder systems on a single personal computer.

2.2 Coarse-grain DEM model
In order to solve this problem, the authors have developed a physics model, which is termed the coarse-grain
DEM model (Sakai and Koshizuka, 2009; Sakai et al.,2010; Sakai et al., 2012b; Sakai et al., 2014). The coarsegrain model was developed to model contact, drag, gravitational and cohesive forces as the scaling law model. As
illustrated in Fig. 1, l3 original particles exist in thecoarse-grain particle whose size is l times larger than theoriginal particle. Here, the coarse-grain ratio l was 2.0. In
the coarse-grain model, the translational motion of theFig. 1 Image of coarse-grain model (coarse-grain ratio l = 2.0).
Reprinted with permission from Ref. (Sakai et al.,Chem. Eng. J., 2014). Copyright: (2014) Elsevier B.V.

Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

coarse-grain particle is assumed to be equivalent to be theaverage of that of the original particles. Hence, the velocity and displacement of the coarse-grain particle are assumed to be averages of the original particles, namely,v O  v CGM and  O   CGM, respectively. Besides, the original particles in the coarse-grain particle are assumed torotate around each mass center with equal angular velocity, where the angular velocity is averaged as illustrated
in Fig. 1(b). The contact force acting on the coarse-grain
particle was estimated assuming that the total energy of
the coarse-grain particle is consistent with that of theoriginal particles. When a binary collision of the coarsegrain particles occurs, binary collisions between each of
the original particles (i.e., l3 binary collisions due to theoriginal particles) are assumed to occur simultaneously.
The contact force acting on the coarse-grain particle issimulated using springs, dash-pots and a friction slider, as
for the existing DEM. From the assumption of the translational motion of the coarse-grain model, the drag force
and gravitational force acting on a coarse-grain particle
become l3 times larger than that of the original particle.
Consequently, the following relationship is obtained between the coarse-grain and original particles:

mCGMv CGM
 Ff CGM  VCGMp   FCCGM  Fg CGMl 3Ff O  l 3VOp  l 3

FCO  l 3Fg O

l 3 k t CGM   v t CGM 
FC t CGM  
3l  FC n CGM v t CGM v t CGM
l 3 k t O   v t O 3l  FC n O v t O v t O(1)

FC n CGM l 3 k n CGM   v n CGM (2)where k, δ and η are the stiffness, the overlap and thedamping coefficient, respectively. As described above, thetranslational motion of the coarse-grain particle is assumed to agree with that of the original particles. Hence,
the CGM subscript can be replaced by O in Eq. (2). In thecoarse-grain model, the normal component of the displacement is modeled by overlap as is the case with theexisting DEM. Hence, the potential energy, i.e., elastic(3)

where µ is the coefficient of friction. When the solid particle does not slip on the surface of solid particle or wall,
upper equation is applied. The translational motion of thecoarse-grain particle is assumed to agree with that of theoriginal particles.
From the assumption of the translational motion of thecoarse-grain model, namely, agreement of kinetic energy
between the coarse-grain and original particles, the dragforce acting on a coarse-grain particle becomes l3 times
larger than that of the original particle. The drag force Ff
acting on the coarse-grain particle is given by:
Ff CGM


1 

 l3

where m, v, l, Ff, V, p, FC and Fg are the solid mass, solid
velocity, coarse-grain ratio, drag force, particle volume,
pressure, contact force and gravitational force, respectively. Subscripts CGM and O refer to the coarse-grainmodel and original particle, respectively. An important
point of the coarse-grain model is that original physicalproperties such as density, coefficient of friction and coefficient of restitution can be applied directly.
Now, modeling of the contact force is described in thecoarse-grain model. The contact force can be divided into
a normal and tangential component. Both components arefunctions of relative particle position and velocity. The
normal component of the contact force is given by:
l 3 k n O   v n O energy, is agreed between the coarse-grain particle andgroup of original particles by the modeling.
In the same manner as for the normal component, thetangential component of the contact force is given by:u  v CGM VCGM
1 u  v O VO(4)where ε, u and β are the void fraction, the fluid velocity
and an interphase momentum transfer coefficient, respectively.
The following relationship is obtained for the rotationalmotion, namely, angular velocity and torque, in the original and coarse-grain particles:

 CGM



rCGM  FCCGM t
TCGM

I CGM
I CGM



lrO  l 3FCO t
l 4TO 1
 
 O
5
l IO
l 5I Ol(5)

where ω, I and T are the angular velocity, the inertial momentum of the particle and the torque, respectively. Eq. (5)also ensures that the rotational energy between the coarsegrain particle and group of original particles agrees.
The governing equations of the gas phase, namely, thecontinuity and the Navier-Stokes equations are the sameas those of the original DEM-CFD method even for thecoarse-grain model.
The solid particle motion, namely, the velocity, position, angular velocity and angle, could be updated using
time integration schemes (Fraige and Langston, 2004;
Danby et al., 2013; Kruggel-Emden et al., 2008). A
semi-implicit finite volume method that uses a staggered
grid was used to discretize the incompressible fluid flow.
A hybrid scheme (Patankar, 1980), which is a combina-

171

Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

tion of a first-order upwind scheme and a second-order
central scheme was used in the convection term. The
pressure-velocity coupling was based on the fractional
step method for incompressible fluid flows.

2.3 Numerical example of coarse-grain model
Adequacy of the coarse-grain model with high coarsegrain ratio is shown through the validation tests (Sakai etal., 2014). The validation results were described in thispaper.
The calculation domain was 50 mm wide, 20 mm longand 200 mm high. The domain was discretized using
2.5 mm structural grids in the x, y and z directions. The
grid size was chosen to be large relative to the solid particle diameter, where the fluid flow characteristics could be
simulated accurately. The number of grids was 20, 80 and
8, in the x, y and z directions, respectively. The number ofcalculated particles was 230,400, which corresponded tothe actual mass of powder used in the experiment. Initially, the spherical particles were packed randomly. Air
was injected from the base, where the superficial velocity
was 0.070 m/s. The density, stiffness, coefficient of restitution and coefficient of friction were 2,500 kg/m3, 10 N/m,
0.9 and 0.3, respectively. The gas was air, where the density and the viscosity were 1.0 kg/m3 and 1.8 × 10 –5 Pa s,
respectively.
Fig. 2 illustrates typical snapshots obtained from simulation and experimental results of the fluidized state at
quasi-steady state. In the simulation and experiment,
splashes and bubbles were observed in the quasi-steadystate. From the appearance, macroscopic flow properties
agreed qualitatively between the simulation and experiment.
The pressure drop and bed height were measured in theexperiment and simulation for quantitative comparison.
Fig. 3 compares the pressure drop between the simulationand experiment, which agreed quantitatively. The bedheight was measured by visual observation to compare
the calculated and experimental results fairly. The simulated bed height had an error of 7.8 % against the experimental height, which is an allowable margin of error froman engineering perspective.
The coarse-grain model can therefore simulate the bulk
bed state by setting a higher coarse-grain ratio. This implies that the particle-particle and particle-fluid interactions were modeled precisely in the coarse-grain model.
We can easily predict that the coarse-grain model may
simulate other phenomena related to bulk states, such as
bubbling, channeling and slugging. The coarse-grainmodel has a sufficient potential to be used with much highcoarse grain ratio. Hereafter, the possibility will be investigated by various validation tests.

172

Fig. 2

Particle spatial distribution at quasi-steady state in thevalidation test.
Reprinted with permission from Ref. (Sakai et al.,Chem. Eng. J., 2014). Copyright: (2014) Elsevier B.V.

Fig. 3Comparison of pressure drop between calculated andexperimental results in a fluidized bed.
Reprinted with permission from Ref. (Sakai et al.,Chem. Eng. J., 2014). Copyright: (2014) Elsevier B.V.

3. Modeling of solid-liquid flow involving afree surface
3.1 Existing problems
Solid-liquid flows involving a free surface occur inchemical engineering applications such as wet ball milling and slurry transport. Whereas numerical simulationof the process should be used for the design and optimization of operational conditions, such simulations have notyet been implemented extensively. This situation has
arisen because of difficulties in modeling and computationally expensive calculations.
Currently, there is no broadly used numerical approachfor modeling such solid-liquid flows, although some simulations have been reported previously. These approaches
exhibit some problems, namely, some difficulties in mod-

Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

eling large deformations of the fluid free surface, difficulties in modeling hydrodynamic forces and difficulties inmodeling contact force. Therefore, the existing methodology cannot be used for many solid-liquid flow systems inchemical engineering applications. The development of a
new method is desired to solve the above difficulties orproblems. Many industrial solid-liquid flows involve large
deformations and/or a high volume fraction of solid phase,where the contact force acting on a solid particle becomes
important. Lagrangian method such as the SPH or MPS
method makes it possible to simulate free surface fluid
flow precisely. These methods have been applied to various engineering fields thus far. Lagrangian-Lagrangian
approaches are applicable to these important industrial
flows. Hence, the MPS method and the DEM are combined to simulate solid-liquid flow involving free surfaces(Sakai et al., 2012a; Yamada and Sakai, 2013; Sun et al.,2014). The DEM-MPS method whose algorithm was fully
explicit is described in this paper.

3.2 DEM-MPS method
For a locally averaged description of continuous fluid
motion, the mass and momentum conservation equations
can be written as:
D     u  0
Dt

Du
1
1
 p  v 2u 
f g


Dt

(6)

dv
 FC  Ff  V p  mg
dt

(7)

(8)

In a Lagrangian particle method such as the MPS
method, the continuum phase is modeled as an assemblyof fluid particles. In the MPS method, fluid particles are
not connected and therefore so-called weight functions
are used to define discretization schemes on the particle
positions.
Weakly compressible and incompressible methods weredeveloped in the MPS method. In the weakly compressible approach, fluid pressure is calculated by the modified
Tait equation of state, which agrees well with the real behavior of water under typical circumstances.
  0c 2  N  
 1 n   n0p     n0 n   n00where c is the speed of sound in a fluid at reference density and γ is a constant often set to 7.0 for water. For numerical purposes within a weakly compressible method,
the speed of sound c can be tuned artificially to relax therestriction on time increments because of stability conditions. The c should be 10 times higher than the maximum
main stream velocity to constrain density fluctuations up to
1 % and guarantee a nearly divergence-free velocity field.
The pressure gradient and viscous terms are discretizedby the original MPS method.

3.3 Numerical example of DEM-MPS method

where ρ, p, ν, f and g are fluid density, pressure, kinematic
viscosity, drag force and gravitational acceleration, respectively.
The governing equation for the solid particles is given
by Newton’s second law of motion:mFig. 4 Initial configuration of fluid particles.
Reprinted with permission from Ref. (Sun et al., Chem.
Eng. J., 2014). Copyright: (2014) Elsevier B.V.

(9)

In order to illustrate the adequacy of the DEM-MPS
method, a validation test was performed in our previous
study (Sun et al., 2014). A cylindrical tank was used with
inner diameter of 120 mm and depth of 100 mm. Waterand glass beads were placed in the tank. The average diameter of the glass beads was ~2.7 mm, and their density
was 2500 kg/m3. In the laboratory experiment, the tank
was revolved at 63 rpm and 1.25 kg of glass beads wereinserted. Water was injected afterwards and adjusted tofill approximately half of the tank.
2D and 3D simulations were performed in this study.
The diameter of the DEM particles was 2.7 mm and theirdensity was 2,500 kg/m3. The stiffness was set to be
10,000 N/m, coefficient of restitution was 0.9 and coefficient of friction was 0.3. For the liquid phase, the physicalproperties are equal to those of water, namely, the density
was 1000 kg/m 3 and the kinematic viscosity was
0.89 mm2/s. The fluid particle size, which was defined as
the initial interval between two fluid particles, was chosen
to be 2.7 mm.
Fig. 5(a) and (b) shows a snapshot of the 2D and 3D
calculation results at quasi steady state. In this figure, liquid phase was visualized by using meshes. These observations have been confirmed by our experiment. The bed

173Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

4. Arbitrary shape boundary wall model
4.1 Existing problems
In the previous DEM simulations, the calculation domain, i.e., the wall boundary, was modeled using surface
equations (Sakai et al., 2005) or meshes (Shigeto andSakai, 2011; Mateo-Ortiz et al., 2014). The use of surface
equations is an easy approach to create simple regimes.
Modeling of geometrically complex walls is often required in industrial systems. These domain shapes aresubstantially more difficult to express with combinationsof the equations. Hence, arbitrary domain shapes weremodeled using meshes. The meshes are more adaptable
for arbitrary shapes than the equations, but there is a difficulty in introducing the algorithm of collision detection
between particles and walls. To solve these problems, a
simple boundary model was proposed, where the wallboundary is expressed by a scalar field, which is referred
to as the SDF. The SDF model was based on the level set
method (Osher and Fedkiw, 2002), which was originallyproposed in the CFD. The uniqueness of the model is thatcollisions can be detected without complicated procedures. We developed the SDF model, which includes conservation of energy in non-dissipative systems. The DEM/
SDF can also be applied to cohesive particle and rotating
vessel systems (Shigeto and Sakai, 2013).

4.2 DEM/SDF model
As mentioned above, we developed the SDF model toretain energy conservation in a non-dissipative system.
This idea is important to simulate cohesive and contact
forces precisely in the DEM simulations. The contact
force model based on the DEM/SDF is addressed now.
The contact force is modeled based on elastic potential
energy when the solid particles interact with walls. Theenergy is related to displacement by:
Fig. 5

Quasi-steady solution of circulating flow by the DEMMPS method.
Reprinted with permission from Ref. (Sun et al., Chem.
Eng. J., 2014). Copyright: (2014) Elsevier B.V.

width and height were measured in the validation tests asshown in Fig. 5(c), where 2D and simulation results are
given by blue and red line respectively. The bed width andheight obtained from the 2D and 3D simulation results
agreed quantitatively with the experimental data, though
bed shape obtained from the 3D simulation was more precise than the 2D simulation. Hence, adequacy of the
DEM-MPS method was demonstrated through the validation tests.

174

SDF 
Pelastic

1
1
2
SDF 2
k 
k r   
n
2
2

(10)

where ϕ and r are the SDF and minimal distance between
the wall and particle, respectively. The gradient of the energy P gives the elastic force between the particle and the
SDF based wall boundary:
SDF  P SDF
Felasticelastic

 k r     k SDF  |  |(11)

Hence, the normal component of the contact force is given
by:
FCSDF

k nSDF |  |  v nn(12)

Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

Fig. 6

Cross-sectional view of SDF in a screw conveyer.
Reprinted with permission from Ref. (Shigeto andSakai, Chem. Eng. J., 2013). Copyright: (2013) Elsevier
B.V.

which conserves the energy in non-dissipative systems
because of the correction term |  |.
As a matter of course, our SDF model is applicable tocohesive particle systems that are affected by van der
Waals force. In the same manner as for the elastic force,the van der Waals force is modeled based on the potential
energy, namely:
SDF  Pvdw

HA  r
6hSDF(13)where hSDF is the intersurface distance.
h SDF   r(14)

The gradient of Eq. (13) yields the van der Waals force
between the particle and the SDF based wall boundary:
SDF  P SDF  
Fvdw
vdw

HA r

6 h SDF 

2  .

(15)

The conservation of energy is guaranteed theoretically in
our SDF model. Hence, this scheme may stabilize the calculation. In addition, the algorithm is simpler the existing
wall boundary composed of meshes.
We expected that the SDF will become a standard wallboundary model in the DEM simulations. Hereafter the
DEM/SDF is going to be applied to investigate lots ofphenomena in a geometrically complex boundary.

4.3 Numerical examples of DEM/SDF model
4.3.1 Screw conveying

In order to illustrate the adequacy of the DEM/SDF
method, a verification test was performed in a screw conveyor (Shigeto and Sakai, 2013). In this test, simulations
were carried out with the mesh- and new SDF-based models. Initially, 62,500 particles was located randomly at thebottom of the calculation domain. The particles have auniform diameter of 200 μm and density of 2,500 kg/m3.
Fig. 6 designates the scalar value distribution of the SDF
model of the inner screw blade. The sign becomes posi-

Fig. 7 Typical snapshots of granular flow in a screw conveying system.
Reprinted with permission from Ref. (Shigeto andSakai, Chem. Eng. J., 2013). Copyright: (2013) Elsevier
B.V.

tive inside the calculation domain and vice versa. The
screw was rotated at 30 rpm. In the verification tests, cohesive particles were used, where the Hamaker constantwas 5.0 × 10 –20 J.
Fig. 7 shows typical snapshots at 0.5, 1.0 and 1.5 s in
the verification tests. Similar particle distributions, agreement of velocity distribution of the solid particles andagreement of transported mass were obtained in the SDF
and mesh systems. Besides, the load torque on the screw
agreed well between the SDF and mesh models. Consequently, these results illustrate that the accuracy of the
SDF model is equivalent to the existing mesh model inthe reproduction of granular flows in the screw conveying
system.

4.3.2 Twin-screw kneader

Application of the DEM/SDF method to a twin-screw
kneader is described according to past study (Sakai et al.,2015). In order to show the adequacy of the DEM/SDF
method, a validation test was performed in a twin-screw
kneader where the focus was on dry powder mixing. Simulation and experimental results were compared for theapparent solid particle location and particle bed height.
For the validation system, the height, width and length
were 106 mm, 186 mm and 46 mm, respectively. Four
paddles were used in the depth direction. The paddles
were rotated clockwise, and the paddle rotational velocity
was 60 rpm in the validation tests. Glass beads (~1.0 mm)
were used in the tests. The total amount of powder was
0.524 kg. Calculation conditions were set to compare thesimulation and experimental results fairly. Mono-sized
solid particles with 1.0 mm diameter were used in the calculations. A simple linear contact model was used in thisstudy. The spring constant, coefficient of friction and coefficient of restitution were set at 1,000 N/m, 0.3 and 0.9,

175Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

Fig. 8Cross-sectional view of SDF in a twin-screw kneader
in a twin-screw kneader.
Reprinted with permission from Ref. (Sakai et al.,Chem. Eng. J., 2015). Copyright: (2015) Elsevier B.V.

more particles were located on the left side. The quantitative bed height was compared between the simulations
and the experiments. The bed heights were estimated tobe 101.81 and 101.24 mm, respectively, in the simulationand experiment. Thus, differences between the simulationand experiment were quite small. The validation test result indicates that the DEM/SDF simulates the particle location precisely.

5. Conclusions
New modeling of the granular flow simulations were
described for industrial applications. At first, problems of
the existing DEM was designated from a view point of
the industrial application such as modeling of large-scale
simulations, complexly shaped wall boundaries and freesurface fluid flow. In order to solve these problems, novel
models were developed by our group. A coarse-grain
DEM model was developed for large-scale simulations, a
SDF-based wall boundary model was developed for complexly shaped walls and a DEM-MPS method was developed for solid-liquid flow involving a free surface. The
adequacy of these models was demonstrated through verification and validation tests. Combination of these models becomes a key technology in applying the DEM toactual powder process.

Acknowledgments
My heartfelt gratitude and appreciation goes to Prof
Seiichi Koshizuka whose enthusiastic tutelage was of inestimable value for my life. I’m sincerely grateful to Prof.
Jusuke Hidaka and to Prof. Yutaka Tsuji for their encouragement and instructions. I would like to thank Dr.
Yoshinori Yamada, Dr. Yusuke Shigeto and Dr. Xiaosong
Sun for their insightful discussion on developing thenumerical models.
Fig. 9 Validation test result in a twin-screw kneader.
Reprinted with permission from Ref. (Sakai et al.,Chem. Eng. J., 2015). Copyright: (2015) Elsevier B.V.

respectively. The number of solid particles in the calculation was 400,000 and their mass corresponded to that in
the experiment. The time step was 1.0 × 10 –5 s and thenumber of iterations was 1.6 × 106. The wall boundary
was modeled by the SDF. Fig. 8 shows the SDF for fourcross-sectional views of the paddle. Once again, in the
figure, positive and negative regions exist inside and outside the paddle, respectively. Distance and sign were
given for the entire computational domain, i.e., the two
paddles and the vessel. Fig. 9 shows the validation test results. The simulation and experiment agreed qualitatively
in terms of the spatial distribution of particles, namely,

176

Nomenclature
ω
CGM
l
μ
FC
η
δ
Ff
f
ρ
u
g
Fg
HA

angular velocity (rad/s)coarse-grain modelcoarse-grain ratio (–)
coefficient of friction (–)
contact force (N)damping coefficient (–)
displacement (m)drag force (N)drag force (N/m3)
fluid density (kg/m3)
fluid velocity (m/s)
gravitational acceleration (m/s2)
gravity (N)Hamaker constant (J)Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

I
β
h
ν
O
m
PW
v
V
p
SDF
ϕ
c
k
T
ε

inertia momentum of particle (kg m 2)
inter-phase momentum transfer coefficient (kg/m 2s)
intersurface distance (m)kinematic viscosity (m 2/s)
original
particle mass (kg)particle-to-wallparticle velocity (m/s)
particle volume (m3)pressure (N/m 2)
signed distance function (–)
signed distance function (–)
speed of sound (m/s)
stiffness (N/m)
torque (N m)void fraction (–)

References
Alobaid F., Ströhle J., Epple B., Extended CFD/DEM model forthe simulation of circulating fluidized bed, Adv. Powder
Technol., 24 (2013) 403–415.
Anderson T.B., Jackson R., Fluid Mechanical Description of
Fluidized Beds. Equations of Motion, luid Mech. Descr.
Fluid. Beds. Equations Motion, 6 (1967) 527–539.
Chandratilleke G.R., Yu a.B., Bridgwater J., A DEM study ofthe mixing of particles induced by a flat blade, Chem. Eng.
Sci., 79 (2012) 54–74.
Cundall P.A., Strack O.D.L., A discrete numerical model for
granular assemblies, Geotechnique, 29 (1979) 47–65.
Danby M., Shrimpton J., Palmer M., On the optimal numericaltime integration for DEM using Hertzian force models,Comput. Chem. Eng., 58 (2013) 211–222.
Eitzlmayr A., Koscher G., Reynolds G., Huang Z., Booth J.,
Shering P., Khinast J., Mechanistic Modeling of Modular
Co-Rotating Twin-Screw Extruders, Int. J. Pharm., 474(2014) 157–176.
Ergun S., Fluid flow through packed columns, Chem. Eng.
Progr., 48 (1952) 89–94.
Fraige F.Y., Langston P.a, Integration schemes and damping
algorithms in distinct element models, Adv. Powder Technol., 15 (2004) 227–245.
Guo Y., Kafui K.D., Wu C.Y., Thornton C., Seville J.P.K., A
coupled DEM/CFD analysis of the effect of air on powder
flow during die filling, AIChE J., 55 (2009) 49–62.
Halidan M., Chandratilleke G.R., Chan S.L.I., Yu A.B.,
Bridgwater J., Prediction of the mixing behaviour of binary
mixtures of particles in a bladed mixer, Chem. Eng. Sci.,
120 (2014) 37–48.
Hernández-Jiménez F., Li T., Cano-Pleite E., Rogers W.,
Acosta-­I borra A., Characterization of the particle-wall frictional forces in pseudo-2D fluidized beds using DEM,
Chem. Eng. Sci., 116 (2014) 136–143.
Hong K., Chen S., Wang W., Li J., Fine-grid two-fluid modelingof fluidization of Geldart A particles, Powder Technol.,2015.
Huang A.N., Kuo H.P., Developments in the tools for the investigation of mixing in particulate systems—A review, Adv.
Powder Technol., 25 (2014) 163–173.

Jajcevic D., Siegmann E., Radeke C., Khinast J.G., Large-scale
CFD-DEM simulations of fluidized granular systems,
Chem. Eng. Sci., 98 (2013) 298–310.
Kawaguchi T., Tanaka T., Tsuji Y., Numerical simulation of
two-dimensional fluidized beds using the discrete element
method (comparison between the two- and three-dimensionalmodels), Powder Technol., 96 (1998) 129–138.
Kobayashi T., Tanaka T., Shimada N., Kawaguchi T., DEMCFD analysis of fluidization behavior of Geldart Group A
particles using a dynamic adhesion force model, Powder
Technol., 248 (2013) 143–152.
Koshizuka S., Nobe A., Oka Y., Moving-particle semi-implicit
method for fragmentation of incompressible fluid, Nucl.
Sci. Eng., 123 (1996) 421–434.
Kruggel-Emden H., Sturm M., Wirtz S., Scherer V., Selectionof an appropriate time integration scheme for the discreteelement method (DEM), Comput. Chem. Eng., 32 (2008)
2263–2279.
Kuang S.B., Li K., Zou R.P., Pan R.H., Yu A.B., Application of
periodic boundary conditions to CFD-DEM simulation ofgas-solid flow in pneumatic conveying, Chem. Eng. Sci., 93(2013) 214–228.
Liu H., Tafti D.K., Li T., Hybrid parallelism in MFIX CFDDEM using OpenMP, Powder Technol., 259 (2014) 22–29.
Liu P.Y., Yang R.Y., Yu A.B., Dynamics of wet particles inrotating drums: Effect of liquid surface tension, Phys. Fluids, 23 (2011).
Lu L., Xu J., Ge W., Yue Y., Liu X., Li J., EMMS-based discrete
particle method (EMMS-DPM) for simulation of gas-solid
flows, Chem. Eng. Sci., 120 (2014) 67–87.
Mateo-Ortiz D., Muzzio F.J., Méndez R., Particle size segregation promoted by powder flow in confined space: The die
filling process case, Powder Technol., 262 (2014) 215–222.
Mio H., Higuchi R., Ishimaru W., Shimosaka A., Shirakawa Y.,
Hidaka J., Effect of paddle rotational speed on particle
mixing behavior in electrophotographic system by using
parallel discrete element method, Adv. Powder Technol., 20(2009) 406–415.
Mio H., Kano J., Saito F., Kaneko K., Effects of rotational direction and rotation-to-revolution speed ratio in planetary ball
milling, Mater. Sci. Eng. A, 332 (2002) 75–80.
Monaghan J.J., An introduction to SPH, Comput. Phys. Comm.,
48 (1988) 89–96.
Ohta M., Kikuchi D., Yoshida Y., Sussman M., Robust numerical analysis of the dynamic bubble formation process in a
viscous liquid, Int. J. Multiph. Flow, 37 (2011) 1059–1071.
Olaofe O.O., Patil A.V., Deen N.G., van der Hoef M.A., Kuipers
J.A.M., Simulation of particle mixing and segregation inbidisperse gas fluidized beds, Chem. Eng. Sci., 108 (2014)
258–269.
Osher S., Fedkiw R., Level Set Methods and Dynamic Implicit
Surfaces, Springer, 2002.
Pandit J.K., Wang X.S., Rhodes M.J., On Geldart Group A
behaviour in fluidized beds with and without cohesiveinterparticle forces: A DEM study, Powder Technol., 164(2006) 130–138.
Patankar S.V., Numerical heat transfer and fluid flow, Routledge,
1980.
Patil A.V., Peters E.aJ.F., Kolkman T., Kuipers J.aM., Modelingbubble heat transfer in gas-solid fluidized beds using DEM,
Chem. Eng. Sci., 105 (2014) 121–131.
Radeke C.A., Glasser B.J., Khinast J.G., Large-scale powder
mixer simulations using massively parallel GPUarchitec-

177

Mikio Sakai / KONA Powder and Particle Journal No. 33 (2016) 169–178

tures, Chem. Eng. Sci., 65 (2010) 6435–6442.
Sakai M., Abe M., Shigeto Y., Mizutani S., Takahashi H., ViréA., Percival J.R., Xiang J., Pain C.C., Verification and validation of a coarse grain model of the DEM in a bubbling
fluidized bed, Chem. Eng. J., 244 (2014) 33–43.
Sakai M., Koshizuka S., Large-scale discrete element modelingin pneumatic conveying, Chem. Eng. Sci., 64 (2009) 533–
539.
Sakai M., Shibata K., Koshizuka S., Effect of Nuclear Fuel Particle Movement on Nuclear Criticality in a Rotating Cylindrical Vessel, J. Nucl. Sci. Technol., 42 (2005) 267–274.
Sakai M., Shigeto Y., Basinskas G., Hosokawa A., Fuji M., Discrete element simulation for the evaluation of solid mixing
in an industrial blender, Chem. Eng. J., 279 (2015) 821–839.
Sakai M., Shigeto Y., Sun X., Aoki T., Saito T., Xiong J.,
Koshizuka S., Lagrangian-Lagrangian modeling for a solid-liquid flow in a cylindrical tank, Chem. Eng. J., 200-202(2012a) 663–672.
Sakai M., Takahashi H., Pain C.C., Latham J.-P., Xiang J., Studyon a large-scale discrete element model for fine particles ina fluidized bed, Adv. Powder Technol., 23 (2012b) 673–681.
Sakai M., Yamada Y., Shigeto Y., Shibata K., Kawasaki V.M.,
Koshizuka S., Large-scale discrete element modeling in a
fluidized bed, Int. J. Numer. Meth. Fluids, 64 (2010) 1319–
1335.
Salikov V., Antonyuk S., Heinrich S., Sutkar V.S., Deen N.G.,
Kuipers J.aM., Characterization and CFD-DEM modellingof a prismatic spouted bed, Powder Technol., 270 (2014)
622–636.
Shigeto Y., Sakai M., Arbitrary-shaped wall boundary modelingbased on signed distance functions for granular flow simulations, Chem. Eng. J., 231 (2013) 464–476.
Shigeto Y., Sakai M., Parallel computing of discrete element
method on multi-core processors, Particuology, 9 (2011)
398–405.
Sun X., Sakai M., Sakai M.-T., Yamada Y., A Lagrangian-­
Lagrangian coupled method for three-dimensional solid-liquid flows involving free surfaces in a rotating cylindrical tank, Chem. Eng. J., 246 (2014) 122–141.
Tsuji Y., Multi-scale modeling of dense phase gas-particle flow,
Chem. Eng. Sci., 62 (2007) 3410–3418.

Tsuji Y., Kawaguchi T., Tanaka T., Discrete particle simulationof two dimensional fluidized bed, Powder Technol., 77(1993) 79–87.
Tsuji Y., Tanaka T., Ishida T., Lagrangian numerical simulationof plug flow of cohesionless particles in a horizontal pipe,
Powder Technol., 71 (1992) 239–250.
Vu T.V., Tryggason G., Homma S., Wells J., Takakura H., Front
Tracking Computation of Trijunction Solidification with
Volume Change, Procedia IUTAM, 15 (2015) 14–17.
Wang J., van der Hoef M.a., Kuipers J.aM., Particle granulartemperature of Geldart A, A/B and B particles in dense
gas-fluidized beds, Chem. Eng. Sci., 97 (2013) 264–271.
Washino K., Tan H.S., Hounslow M.J., Salman a.D., A new capillary force model implemented in micro-scale CFD-DEM
coupling for wet granulation, Chem. Eng. Sci., 93 (2013)
197–205.
Wen C.Y., Yu Y.H., Mechanics of fluidization, Chem. Eng.
Progr. Symp. Ser., 62 (1966) 100–111.
Wu C.Y., DEM simulations of die filling during pharmaceutical
tabletting, Particuology, 6 (2008) 412–418.
Wu G., Ouyang J., Yang B., Li Q., Use of compromise-based
local porosities for coarse grid DEM simulation of bubbling
fluidized bed with large particles, Adv. Powder Technol., 24(2013) 68–78.
Xiao F., Ii S., Chen C., Revisit to the THINC scheme: A simple
algebraic VOF algorithm, J. Comput. Phys., 230 (2011)7086–7092.
Xu M., Chen F., Liu X., Ge W., Li J., Discrete particle simulation of gas-solid two-phase flows with multi-scale CPUGPU hybrid computation, Chem. Eng. J., 207-208 (2012)
746–757.
Yamada Y., Sakai M., Lagrangian-Lagrangian simulations ofsolid-liquid flows in a bead mill, Powder Technol, 239(2013) 105–114.
Yokoi K., Efficient implementation of THINC scheme: A simple
and practical smoothed VOF algorithm, J. Comput. Phys.,
226 (2007) 1985–2002.
Yue X., Hao Z., Chunhai K., Congshu L., Shi S., Yuanqiang T.,
Chunsheng F., A GPU-based Discrete Element Modelling
Code and its Application in Die Filling, Comput. Fluids,
110 (2014) 235–244.

Author’s short biography
Mikio Sakai
Dr Mikio Sakai is an Associate Professor at The University of Tokyo. He earned his
Ph.D. degree from The University of Tokyo in 2006 and became an Assistant Professor
in 2007. He then became an Associate Professor in 2008. His interests include modelingof multi-phase flow such as gas-solid flows, solid-liquid flows and gas-liquid-solid flows,
the arbitrary shaped wall boundary model for granular flows and parallel computing
techniques. He is now an associate editor of Chemical Engineering Science.

178

KONA Powder and Particle Journal No. 33 (2016) 179–202/Doi:10.14356/kona.2016024

Review Paper

Developments of Blocking Filtration Modelin Membrane Filtration †Eiji Iritani* and Nobuyuki Katagiri
1

Department of Chemical Engineering, Nagoya University, Japan

Abstract
Blocking filtration laws consist of four different filtration mechanisms: complete blocking, standard blocking,intermediate blocking, and cake filtration. Blocking filtration laws for describing both the pore blocking and cakeformation have been extensively employed over the past several decades to evaluate the increase in filtration
resistance with the progress of filtration in the field of classical particulate filtration. In recent years, blocking
filtration laws become widely used also in membrane filtration such as microfiltration and ultrafiltration ofcolloids. This paper gives an overview of the developments of blocking filtration laws and equations underconstant pressure and constant rate conditions reported for the filtrate flow of Newtonian and non-Newtonian
fluids. The fouling index evaluating the degree of membrane fouling was examined on the basis of the blocking
filtration equations. The blocking filtration laws were reexamined to extend the range of their application.
Moreover, various combined models developed based on the blocking filtration laws were introduced fordescribing more rigorously the complicated filtration behaviors controlled by more than one mechanism which
occurs successively or simultaneously.
Keywords: membrane fouling, pore blocking, blocking filtration law, cake filtration, membrane filtration,
filtration rate

1. Introduction
Membrane filtration processes such as microfiltration
and ultrafiltration of dilute colloids play an increasingly
important and indispensable role in widely diversified
fields ranging from industry to drinking water production, treatment of domestic and industrial effluents, andproduction of water suitable for reuse. While membrane
filtration is a key process which has the widespread application, it is generally recognized that one of the major
drawbacks to more widespread use of membrane filtration
is a significant increase in the filtration resistance known
as membrane fouling, resulting in a dramatic flux decline
over time under constant pressure conditions or a remarkable pressure rise over time under constant rate conditions.
The membrane fouling is affected by several factors, e.g.,pore blocking and/or pore constriction (Hermans andBredée, 1935, 1936; Grace, 1956; Shirato et al., 1979;
Hermia, 1982; Iritani et al, 1992, 2009, 2013), cake formation (Reihanian et al., 1983; Chudacek and Fane, 1984;†
1*

Received 27 August 2015; Accepted 25 September 2015
J-STAGE online 28 February 2016
Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
Corresponding author: Eiji Iritani;
E-mail: iritani@nuce.nagoya-u.ac.jp
TEL: +81-52-789-3374 FAX: +81-52-789-4531©2016 Hosokawa Powder Technology Foundation

Iritani et al., 1991a, 2014a, b; Nakakura et al., 1997;
Mohammadi et al., 2005; Thekkedath et al., 2007; Sarkar,
2013; Salinas-Rodriguez et al., 2015), solute adsorption(Fane et al., 1983; Iritani et al., 1994), and concentration
polarization (Kimura and Sourirajan, 1967; Vilker et al.,1981). Initially, foulants smaller than the pore size ofmembrane deposit or adsorb onto the pore walls, thereby
leading to the pore constriction. This induces a significant
reduction in the cross-sectional area available to the filtrate flow. In contrast, larger foulants deposit or adsorb
onto the pore entrances, resulting in a marked increase in
the filtrate flow resistance. In either case, the pore constriction and pore plugging are followed by the formationof filter cake accumulating on the membrane surface, thus
severely increasing the filtration resistance. Therefore, itis essential to elucidate the underlying mechanism controlling the membrane fouling such as the pore constriction, pore plugging, and cake formation during the courseof membrane filtration.
So far, a number of models have been proposed to describe the fouling of filter medium during the classicalliquid filtration. The theory of cake filtration in which thefilter cake forms on the surface of filter medium was initially established by Ruth (1935, 1946) and then extended
to deal with the case of the compressible filter cake, as
referred to as the modern filtration theory (Grace, 1953;

179

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

Tiller, 1953; Okamura and Shirato, 1955; Tiller andShirato, 1964; Shirato et al., 1969).
The classical blocking filtration laws describe three
types of physical mechanisms controlling the blockage ofmembrane pores, in addition to the cake filtration model.
The blocking filtration laws were originally presented byHermans and Bredée (1935, 1936) and later systematizedby Grace (1956), Shirato et al. (1979), and Hermia (1982).
The model consists of four different filtration mechanisms: complete blocking, standard blocking, intermediate blocking, and cake formation. Among them, bothcomplete and intermediate blocking laws describe thepore plugging due to foulants reaching the top surfaces ofpores. In contrast, the standard blocking law deals withthe pore constriction caused by the deposition of foulants
onto the pore wall. Interestingly enough, these four filtration mechanisms reduce to a common differential equation with different values of power index. While blocking
filtration laws are summarized in quite simple mechanisms, still present today they provide a powerful tool toreasonably evaluate the increasing behavior of filtration
resistance in liquid filtration of relatively dilute suspension. Nowadays, blocking filtration laws have become
widely used in the analysis of the flux decline behaviors
observed not only in classical liquid filtration but also inmembrane filtration such as microfiltration and ultrafiltration. Therefore, it is considered that there is a significant
value to have an overview of the developments of blocking filtration laws and the related mechanisms.
This review paper initially describes the classicalblocking filtration laws derived under constant pressure
conditions. Then, the blocking filtration laws are extended
to be applied to constant rate (flux) filtration and are generalized through the inclusion of filtrate (permeate) flowof non-Newtonian fluids. A common characteristic filtration form derived from the blocking filtration laws is revisited by considering the membrane pore fouling
represented by Kozeny-Carman equation describing theflow through the granular bed. The paper explains that theblocking filtration laws are made available for evaluating
the degree of membrane fouling, e.g., the maximum filtrate volume and the fouling index such as SDI and MFI.
Finally, the combined models stemming from the blocking filtration laws are described to reasonably evaluate
two fouling mechanisms occurring sequentially or simultaneously, which are frequently observed in the actual
processes of membrane filtration of colloids. Specifically,much emphasis is placed on the combination of membrane pore blockage and cake formation on the membranesurface. These combined model can approximate highly
complicated membrane fouling behaviors often encountered in membrane filtration.

180

2. Blocking filtration law
2.1 Mechanism of blocking filtration
Blocking filtration laws are applied to four different
fouling patterns for describing the deposit of particles onfilter media and membranes, as schematically illustrated
in Fig. 1. The complete, intermediate, and standard blocking laws describe the blocking of membrane pores, while
the cake filtration law is applied to the description of thegrowth of filter cake comprised of particles accumulatingon the membrane. For simplicity, it is postulated that themembrane consists of parallel pores with constant diameter and length. Both the complete and intermediate blocking laws are applicable in case that the particle diameteris larger than the pore size. Thus, each particle reaching
the membrane due to convection is inevitably trapped on
the membrane surface in either case. However, pore
blocking behaviors are substantially different from eachother. In the complete blocking law, it is assumed thateach particle blocks an open pore completely, as shown in
Fig. 1(a). When more appropriate, it is assumed that theprobability that a particle blocks an open pore is constantduring the course of filtration, considering the possibility
that a particle deposits on the membrane surface otherthan pores. Therefore, the number of blocked pores is directly proportional to the filtrate volume v per unit effective membrane area. The variation of the number of open
pores during the course of filtration is given by  N 0  xv ,where N 0 is the total number of open pores per unit effective membrane area at start of filtration, x is the numberof particles blocking pores per unit filtrate volume. Since

Fig. 1

Schematic view of four fouling patterns in blocking filtration laws: (a) complete blocking law, (b) standard
blocking law, (c) intermediate blocking law, and (d)cake filtration law.

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

the filtration rate J is directly proportional to the numberof open pores, it can be written asJdv k c p  N 0  xv
dt(1)

where t is the filtration time, kc is the proportional constant, and p is the applied filtration pressure. Differentiating Eq. (1) under the constant pressure condition (p = p0 =const.), one obtains
22d 2t
 dt  K  dt 


k
p
x




c
0
b
dv 2
 dv 
 dv 

(2)where Kb (= kcp0x) is the blocking constant for complete
blocking law.
However, in practice, the probability that a particle blocks
an open pore varies with v during the course of filtration.
As the number of open pores decreases due to the progress of filtration, particles newly reaching the membrane
may deposit onto the particles that have already blocked
the open pores, as shown in Fig. 1(c). In the intermediate
blocking law, it is assumed that the rate of pore blocking
is proportional to the number of open pores, and thus dN′/
dv may be written asdN  K i N 
dv(3)

where N′ is the number of open pores per unit effective
membrane area at the filtrate volume v per unit effective
membrane area, and Ki is the blocking constant for intermediate blocking law. Integrating Eq. (3), the number ofopen pores becomes N 0 exp  K iv. Consequently, the filtration rate is given byJdv k c pN 0 exp  K iv
dt(4)

Differentiating Eq. (4) under the constant pressure condition, one obtains (Hermia, 1982)

d 2t
dt
 K i  dv 2
 dv 

(5)

Although the intermediate blocking law had been considered to be empirical over the years (Grace, 1956),
Hermia (1982) originally verified the theoretical background of the intermediate blocking law by considering
the decrease in the probability blocking membrane poreswith the progress of filtration. Around the same time, Hsu
and Fan (1984) proposed the intermediate blocking equations applicable to constant rate filtration on the basis of
the stochastic model (pure birth model) in order to usethem in the analysis of sand filtration behaviors. Iritani etal. (1991b) derived the intermediate blocking equations
for constant pressure filtration based on the stochastic
model by considering the probabilistic event with theprogress of the filtrate volume v per unit membrane area

instead of the filtration time t because the number of particles reaching the membrane is proportional to v in thecase of constant pressure filtration. Fan et al. (1985a, b)
modified the intermediate blocking equations based on
the birth-death model. In the model, scouring of particles
blocking pores was also taken into consideration as thedeath process.
In the standard blocking law shown in Fig. 1(b), theparticle diameter is considerably smaller than the poresize. Therefore, solid-liquid separation proceeds by thedeposition of particles on the pore wall and the pore gradually constricts with the progress of filtration. For simplicity, it is postulated that the pore volume decreases
proportionally to the filtrate volume v per unit membranearea. Consequently, the filtration rate under the constantpressure condition gradually decreases with decreasing
pore size. On the assumption that the pore radius decreases by dr by obtaining an infinitesimal amount of filtrate volume dv per unit membrane area, the mass balance
produces (Grace, 1956)c
2LN 0rdr 
dv
1 p

(6)

where L is the membrane thickness, c is the volume ofparticles trapped per unit filtrate volume v per unit membrane area, and εp is the packing porosity of the particle
layer formed on the pore wall. Integrating Eq. (6) fromr = r0 at v = 0 to r = r at v = v, one obtainsr  K sv  1 

r0 
2 

0.5

(7)

where Ks is the blocking constant for standard blocking
law and defined byKs 

2cN L 1   p  r0 2

(8)

In general, the relation between the average flow rate uand pore radius r can be given by the Hagen-Poiseuillelaw applicable to the laminar flow in a capillary, and it is
written as
u

r2p
8 L

(9)

where μ is the viscosity of the filtrate. Thus, the filtration
rate through the membrane comprised of capillaries canbe described byr 2p
J 0  N πr0 2 0 0
8 L

(10)

at the start of filtration;
J  N πr 2

r2p
8 L

(11)

at any time of filtration, where the subscript “0” indicates181

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

the initial value. Substituting Eqs. (10) and (11) into Eq.
(7) under the constant pressure condition (p = p0 = const.),one obtains2
K
J J 0 1  s v
2 


(12)

Differentiation of Eq. (12) yields
1.5
d 2t
0.5  dt 

K
J


s
0
dv 2
 dv 

(13)

The filtration data of most dilute suspensions is describedby the standard blocking law.
In contrast to the pore blocking, in cake filtration
shown in Fig. 1(d), the filter cake consisted of the particles deposited on the membrane surface gradually grows
as filtration proceeds. The filter cake is viewed as a kindof granular bed which produces the increase in the thickness with the progress of filtration, thereby resulting in
the increase in the additional resistance to flow. According to the Ruth theory, the filtration rate J is given by(Ruth, 1935, 1946)
1
dt
 
J
dv

 av  sp 1  ms

v  vm (14)where αav is the average specific cake resistance, ρ is thedensity of filtrate, s is the mass fraction of solids in colloids,
m is the ratio of the mass of wet to the mass of dry cake,and vm is the fictitious filtrate volume per unit membrane
area required to obtain the cake with the flow resistanceequivalent to that of the membrane. Since both αav and m
are considered to be constant throughout the period of
filtration under the constant pressure condition, Eq. (14)becomes

1
dt
 
J
dv

2
v  vm 
Kv(15)

where Kv (= 2p(1 – ms)/(μαavρs)) is the Ruth coefficient inconstant pressure cake filtration and constant (Ruth, 1935,
1946). Although the filter cake generally exhibits compressible behavior in which αav varies with the applied filtration pressure, it should be noted that Eq. (15) isapplicable to not only the incompressible cake but also
compressible cake (Tiller and Cooper, 1960; Tiller andShirato, 1964). Differentiating Eq. (15) with respect to thetime t, one gets
d 2t

dv 2

2
 Kc
Kv

(16)

where Kc (= 2/Kv) is the blocking constant for cake filtration law.
Interestingly enough, Eqs. (2), (5), (13), and (16) derived for four different filtration mechanisms reduce tothe following common differential equation (Hermans
and Bredée, 1935, 1936; Grace, 1956).

182

n
d 2t
 dt k dv 2
 dv 

(17)

where k and n are constants. The value of the blocking index n depends on the mode of filtration mechanisms, andindicates 2.0 for complete blocking, 1.5 for standard
blocking, 1.0 for intermediate blocking, and 0 for cakefiltration. The constant k is the resistance coefficient depending on the properties of suspension, membrane, andthe operating conditions in filtration such as the applied
filtration pressure. It is found from Eq. (17) that thechanging rate in the filtration resistance is proportional to
the filtration resistance raised to a power n. When theblocking filtration laws are represented by using the filtration rate J (= dv/dt), Eq. (17) reduces to (Sun et al., 2003)dJ
 kJ 3ndt

(18)

A double logarithmic plot of d2t/dv2 vs. dt/dv is depictedbased on the flux decline behaviors in order to understandthe membrane fouling mechanism of constant pressure
filtration with the aid of Eq. (17). The fouling mode canbe easily determined from the slope of a linear regression
fitting to the plot. However, since d 2t/dv2 is the second order differential of the filtration time t with respect to thefiltrate volume v per unit membrane area, the value of ndetermined from the direct use of Eq. (17) is likely to be
influenced by the noise in the experimental data measured
as the cumulative filtrate volume v per unit membrane
area vs. the time t. Nevertheless, this plot provides important information on the fouling mechanism of membranes, as mentioned later (Bowen et al., 1995; Iritani etal., 1995, 2014c).
Table 1 systematically edits mathematical equations
derived for each blocking filtration law under the constantpressure condition and represents v as functions of t andthe filtration rate J as functions of t or v (Hermans andBredée, 1935, 1936; Grace, 1956). These expressions canbe used as a means for identifying the membrane foulingmechanism, just like the double logarithmic plot of d 2t/dv2
vs. dt/dv. The fouling pattern of membranes can be judged
from the plots shown in Fig. 2 based on the linear expressions. For example, if the membrane fouling is controlled
by the complete blocking filtration mechanism, the plot ofJ vs. v and the semi-logarithmic plot of J vs. t should
show straight lines. Thus, the predominant blocking filtration law describing membrane fouling pattern can be
determined from the graphical expression best fitted
based on a linear regression analysis.
Integration of Eq. (18) with respect to the filtration time
t produces the relation between the filtration rate J vs. thetime t (Herrero et al., 1997; Ho and Zydney, 1999). It
should be noted that the standard blocking (n = 1.5), intermediate blocking (n = 1.0), and cake filtration (n = 0)

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

Table 1

Fig. 2Blocking filtration equations for constant pressure filtration

Graphical identification of blocking filtration laws for constant pressure filtration: (a) complete blocking law, (b)standard blocking law, (c) intermediate blocking law, and (d) cake filtration law.

modes are represented as a common equation given byJJ01  k jt n j(31)

where k j (= k(2 – n)J0(2–n)) and nj (= 1/(2 – n)) are constants. In contrast, in the case of the complete blocking
law (n = 2), integrating Eq. (18), one obtains
J J 0 exp kt

(32)

Conventionally, the experimental data of the flux decline during the blocking filtration period have been analyzed by only one of above mentioned blocking filtration
laws (Granger et al., 1985; Hodgson et al., 1993; Blanpain
et al., 1993; Ruohomäki and Nyström, 2000; Gironès etal., 2006; Lee et al., 2008; de Lara and Benavente, 2009;

Nandi et al., 2010; Li et al., 2012; Lim and Mohammad,
2012; Pan et al., 2012; Masoudnia et al., 2013, Palencia etal., 2014). Tettamanti (1982) proposed five blocking filtration laws by adding the adhesive filtration law in which
the value of k in Eq. (17) is represented as zero. When astepwise procedure is employed, the blocking filtration
equation is derived in the form (Heertjes, 1957)N   Sc
J  J 0 
 N   axv (33)

where a and Sc are constants. In the case of complete
blocking, a = 1.0 and Sc = 1.0. For partial blocking, a < 1.0
and Sc > 1.0. As the slurry concentration increases, a decreases and Sc increases, and both a and Sc depend on theform of pore and of the particle. In recent years, several
problems are pointed out in applying the blocking filtra-

183

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

tion laws to the analysis of the experimental data of theflux decline during blocking filtration (Tien and Ramarao,
2011; Tien et al., 2014).

2.2 Blocking filtration equations for constant rate
filtration
Although the membrane blocking research has been focused mostly on constant pressure filtration, which is easily tested on the laboratory scale, the membrane blocking
in constant rate filtration is also crucially important fromthe viewpoint of the industrial level. While the flux decline behaviors are examined in constant pressure filtration, the pressure rising behaviors resulting from theincrease in the filtration resistance are investigated inconstant rate filtration in which the filtration rate is kept
constant (Blankert et al., 2006; Liu and Kim, 2008; Sun etal., 2008; Mahdi and Holdich, 2013; Raspati et al., 2013).
The blocking filtration equations for constant rate filtration can be obtained in accordance with a proceduresimilar to that used in constant pressure filtration. For example, in complete blocking law, Eq. (1) is rewritten aspJk c  N 0  xv

(34)

Differentiating Eq. (34) with respect to v under constantrate condition (J = J0 = const.), one gets

Table 2

Fig. 3184

dp k c x 2
 
p
dv
J0

Kb 2

p
K br p 2
J 0 p0(35)

where Kbr (= kc x/J0) is the blocking constant for complete
blocking law in constant rate filtration. Eventually, also inthe case of constant rate filtration, four blocking filtration
laws are represented by a common differential equationin the form (Grace, 1956)dp
 k p n
dv(36)

where k′ and n′ are constants. A power index n′ is theblocking index which defines the filtration mechanism,
and indicates the same value as constant pressure filtration for each blocking filtration law. Table 2 lists theequations derived for constant rate filtration (Grace,1956). Graphical indications are illustrated in Fig. 3 andare employed in order to judge the blocking filtration law.
The filtrate volume v per unit membrane area is directly
proportional to the filtration time t in constant rate filtration, i.e., v = J0t. Consequently, it is also possible to identify the dominant blocking filtration law by plotting thedata against t in place of v shown in Fig. 3.
The value of n′ is 0 for cake filtration in which an incompressible filter cake forms during filtration. However,when the filter cake exhibits a compressible behavior, theuse of Eq. (36) requires a considerable attention. Equation(14) is rewritten as

Blocking filtration equations for constant rate filtration

Graphical identification of blocking filtration laws for constant rate filtration: (a) complete blocking law, (b) standard
blocking law, (c) intermediate blocking law, and (d) cake filtration law.

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202p
 av  sJ

v  vm (41)

1  ms
The average specific cake resistance αav in Eq. (41) is related to the applied filtration pressure p by (Sperry, 1921;
Murase et al., 1987; Iritani et al., 2002; Zhou et al., 2015)

 av  1 p n1(42)where α1 and n1 are constants, and n1 is especially termed
the compressibility coefficient of the filter cake. The
higher the n1-value, the more compressibility the cake is.
Substituting Eq. (42) into Eq. (41), one obtains (Tiller,
1955; Shirato et al., 1968)p1n1


1 sJv  vm

1  ms(43)

Differentiation of Eq. (43) with respect to v under the constant rate conditions leads todp 1  n1 1 sJ n1 p
k1 p n1dv
1  ms(44)

where k1 (= (1 – n1)μα1ρsJ/(1 – ms)) is a constant. Therefore, the value of n′ in Eq. (36) is not 0 for constant rate
cake filtration in which a compressible filter cake forms
during filtration and increases as the compressible behaviors become more pronounced. If the compressibility coefficient n1 is 1.0, as seen in highly compressible filter
cake, the power index n′ in the differential equation forcake filtration shows the same value as that derived forthe intermediate blocking law, thereby leading to serious
confusion.
Since v is directly proportional to the time t in constantrate filtration, Eq. (36) is rewritten asdp
 k 2 p n
dt

(45)

where k 2 is a constant. It should be noted that the characteristic differential equation can be represented also in theform (Hlavacek and Bouchet, 1993)
d 2t
dp 2

 dt 
 k1  
 dp 

n1

(46)

where k1 and n1 are constants. Equation (46) is similar toEq. (17) in the form, by considering that p in Eq. (46) corresponds to v in Eq. (17). Alternatively, the characteristic
form is represented by (Chellam and Xu, 2006; Chellam
and Cogan, 2011)
d2 p
dp n 22  k
dv 2
 dv 

the analysis of constant rate filtration behaviors, as described by av 0 2 p

where α0 and α2 are constants, and α0 is the average specific cake resistance at null stress. Eq. (48) has been applied to microbial suspensions, which exhibit highly
compressible behaviors under relatively high pressure
conditions. In this case, the value of exponent n2 in Eq.
(47) becomes 1.5, and thus is the same as the complete
blocking law.
Blocking filtration equations applicable to variable
pressure and variable rate filtration were proposed by
Suarez and Veza (2000). For instance, in the complete
blocking law, the relation among the filtration rate J, applied filtration pressure p, and filtrate volume v per unit
membrane area can be obtained on the basis of Eq. (1) andthus is represented by

J

p

J0 Kb

v
p0
p0

(49)

The model adequately described the blocking filtration
behaviors of effluent water from a municipal wastewater
treatment plant.

2.3 Blocking filtration equations for filtrate of
non-Newtonian fluids
When the filtrate flow presents with non-Newtonian
behaviors, the analysis for blocking filtration law becomeseven more complex than the filtrate flow of Newtonian
fluids. In power-law non-Newtonian fluids, which are thesimplest case, the rheological equation representing therelation between the shear stress τ and shear rate  iswritten as  K  N

(50)

where K is the fluid consistency index, and N is the fluid
behavior index representing the intensity of non-Newtonian
behaviors. When the fluid is characterized by Newtonian
fluid, the value of N is equal to 1.0 and departs from 1.0
as non-Newtonian behaviors are more pronounced. The
values of N less than 1.0 characterize pseudo-plastic orshear thinning fluids and melts. On the basis of Eq. (50),the relation between the average flow rate u and the tube
radius r for power-law non-Newtonian fluids is given by(Kozicki et al., 1966)(47)

where k2 and n2 are constants. Also in this case, the useof Eq. (47) requires a considerable attention in applying it
to filtration forming compressible filter cakes. Chellam
and Xu (2006) employed the expression of the average
specific cake resistance αav increasing with pressure p in

(48)

u

N  p 1/ N N 1/ N r
3N  1  2KL (51)

When one puts N as 1.0 and K as the Newtonian viscosity
μ, Eq. (51) reduces to the Hagen-Poiseuille equation applicable to Newtonian flow represented as Eq. (9).
While the number of open pores varies with the prog-

185

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

ress of filtration in both complete blocking and intermediate blocking laws, the radius of open pores remains
constant since the start of filtration. Therefore, the formsof the blocking filtration equations for complete blocking
and intermediate blocking laws are independent of theflow behaviors in pores, irrespective of the difference between Newtonian and non-Newtonian fluids. However,since the pore radius gradually decreases with the progress of filtration in the standard blocking law, the difference between Newtonian and non-Newtonian fluid isquite obvious, as inferred by Eq. (51). The filtration rates
at the start and any time of filtration are, respectively, represented as

J 0  N π
J  N π

1/ N

N  p0 
3N  1  2KL 

1/ N

N  p 

3N  1  2KL r0 3N 1 / N(52)

r 3N 1 / N(53)

Substituting Eqs. (52) and (53) into Eq. (7) under the constant pressure condition (p = p0 = const.), one gets
3N 1 /2 NK

J J 0 1  s v
2 


(54)

Differentiating Eq. (54), one obtains (Shirato et al., 1979)
5 N 1/ 3N 1
d 2t
3N  12 N / 3N 1  dt 

K
J


s
0
dv 2
4N
 dv 

(55)

The filtration rate in cake filtration is described by(Kozicki et al., 1968, 1972; Shirato et al., 1977, 1980;
Murase et al., 1989)
Table 3186

1  N  dt  N

 


J
 dv 

K  av  sv  vm p 1  ms

(56)

where γav is the average specific cake resistance for power-­
law non-Newtonian flow. Differentiating Eq. (56) with respect to v under the constant pressure condition(p = p0 = cont.) where γav and m are treated as constantthroughout the course of filtration, one obtains
d 2t
K  av  s  dt 1 N

 
dv 2
Np0 1  ms  dv (57)

Consequently, four blocking filtration laws for power-­
law non-Newtonian filtration under the constant pressure
condition can be represented by a common differential
equation with two constants k N and nN, as described by(Shirato et al., 1980; Hermia, 1982; Rushton, 1986)nN
d 2t
 dt 

k


N
dv 2
 dv 

(58)

In the case of constant rate filtration, the characteristic
form is represented asdp
 k N p nN
dv

(59)

where kN and nN are constants. The blocking filtration
equations are listed in Table 3 for constant pressure andconstant rate filtration processes for power-law non-­
Newtonian flow. In the case of constant pressure filtration,
the complete and intermediate blocking filtration equations for the filtrate flow of non-Newtonian fluids are thesame as those for the flow of Newtonian fluids and thus

Blocking filtration equations for power-law non-Newtonian fluids-solids mixtures: (a) constant pressure filtration and (b)constant rate filtration

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

they are omitted from the table. It should be stressed thatthe equations for Newtonian fluids is the special case of
those for power-law non-Newtonian fluids.
Fig. 4 shows the experimental results of clarification
filtration of dilute suspensions prepared by suspending diatomaceous earth in aqueous sodium polyacrylate exhibiting the behavior of power-law non-Newtonian fluid(Iritani et al., 1991b). The plots of J2N/(3N+1) vs. v yield linear relationships in accordance with Eq. (54) for the standard blocking law since the diameter dp of the suspended
solids is much smaller than the pore size dm. As the solids
mass fraction s in suspension decreases, the slope ofstraight line decreases and thus the filtrate volume obtained during clarification filtration increases.

2.4 Significance of blocking index n in blocking
filtration laws
The unified characteristic form of blocking filtration
laws is derived from four different filtration mechanisms.
The blocking index n in Eq. (17) results in values of 2.0,
1.5, 1.0, and 0 for complete blocking, standard blocking,intermediate blocking, and cake filtration laws, respectively. However, in practice, the experimental data frequently exhibit the value other than these. Moreover, eventhough the pore size is much larger than the particle size,
the blocking index n occasionally exhibits the value of 2.0.
Therefore, from this point of view it is of significance tothrow a new look at the underlying model of the characteristic form of blocking filtration laws.
In the blocking filtration model, for simplicity, it is assumed that the membrane consists of parallel cylindricalpores with constant diameter and length. However, inpractice, the porous structure of most membranes is of acomplex geometry with irregular pore morphology. The
Kozeny-Carman equation can describe the flow throughsuch a porous medium and is written as


J

dv
3
p


2
2
dt

L
k 0S 1   

where ε is the porosity, S is the specific surface area, and
k0 is the Kozeny constant. Iritani et al. (2007a) derived thecharacteristic form of blocking filtration laws on the basisof the Kozeny-Carman equation (70), by considering thevariations of the porosity and the specific surface area ofthe membrane caused by the particle deposition within
the porous membrane during filtration, as schematicallyillustrated in Fig. 5. The porosity ε in Eq. (70) decreases
by particle deposition on the pore walls with the progressof filtration and is represented by 0  K pv 
1 Flux decline behaviors in clarification filtration of dilute suspensions prepared by suspending diatomaceous
earth in aqueous sodium polyacrylate exhibiting behavior of power-law non-Newtonian fluid.

K pv 
0 0 

(71)

where ε 0 is the initial porosity of clean membrane, and Kp
is a constant. The specific surface area S of the membranein Eq. (70) also varies with the progress of filtration and it
is assumed that S is described as K v
2
2
S 2 1    1  p  S 0 2 1   0
0 


(72)

where S0 is the initial specific surface area of the clean
membrane, and β is a constant which depends on themode of the morphology of the deposit assemblages anddefined asDs  D 
Ds0  D0 

Fig. 4(70)

Fig. 5

(73)

Schematic view for illustrating the mechanism of membrane fouling in the model presented by Iritani et al.
(2007a).

187

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

where Ds is the representative diameter of pores on a wetted perimeter basis, D is the representative diameter ofpores on a flow cross-sectional area basis, and the subscript 0 indicates the clean membrane. The characteristic
form represented by Eq. (17) can be obtained by substituting Eqs. (71) and (72) into Eq. (70) and then by differentiating the reciprocal filtration rate (dt/dv) with respect to v
under constant pressure condition (p = p 0 = const.). It
should be stressed that Eq. (17) derived in this way is nolonger limited by the specific values of n. In this sense, it
is concluded that the derivation of Eq. (17) based on the
Kozeny-Carman equation is more universal than the classical blocking filtration laws. In a similar way, Eq. (36)applicable to constant rate filtration can be derived basedon the Kozeny-Carman equation (Iritani et al., 2011). Some
researches were conducted on pore fouling behaviors by
employing the Kozeny-Carman equation (Broeckmann etal., 2006; Zhong et al., 2011; Wu et al., 2012). Cheng et al.
(2011) derived the characteristic form described by Eq.
(17) based on not the Kozeny-Carman equation but theHagen-Poiseuille equation.
In the derivation of Eq. (17), it is implicitly assumed
that the particles are in complete retention, or that theamount of particles deposited within the pores of themembrane increases linearly with v. However, in practice,there often exists some solid leakage through the membrane (Iritani et al., 1994; Rodgers et al., 1995; Hwang etal., 2006; Hwang and Sz, 2010; Polyakov, 2008; Polyakov
and Zydney, 2013). As a result, the sieving coefficient ofsolids varies during the course of filtration. Therefore, thecharacteristic form represented by Eq. (17) can be generalized by accounting for the variation with time of theamount of the particle deposition within the pores of themembrane. By employing the mass σ of particles deposited on the pore wall per unit membrane area, referred toas the specific deposit (Maroudas and Eisenklam, 1965;
Ives and Pienvichitr, 1965; Tien and Payatakes, 1979;
Choo and Tien, 1995), the variations of ε and S with theprogress of filtration can be, respectively, represented by



 
 0 K m 
1 

K m 
0 0 

(74) K 
S 2 1    2 1  m  S 0 2 1   0  2
0 


(75)

where Km is a constant. In a similar way to the derivationof Eq. (17), on the basis of Eq. (70), (74), and (75), themodified form of Eq. (17) can be derived as (Iritani et al.,2010)
d dt / dvdt n k  d
 dv 

(76)

Therefore, when the relation between σ and v is experimentally obtained, the left-hand side value of Eq. (76)188

Fig. 6

Characteristic filtration curves for blocking filtration ofdilute suspensions of PSL under constant pressure condition using diatomaceous ceramic membranes which
are semi-permeable to PSL: (a) logarithmic plots ofd 2t/dv2 vs. dt/dv and (b) logarithmic plots of d(dt/dv)/dσ
vs. dt/dv.

may be calculated from the stepwise difference quotient
using the experimental data of dt/dv vs. v with the aid of
the relation between σ and v. Consequently, the values ofn and k in Eq. (76) can be easily obtained from the double
logarithmic plots of d(dt/dv)/dσ vs. dt/dv.
Fig. 6 compares the logarithmic plots of d2t/dv2 vs. dt/dv
with those of d(dt/dv)/dσ vs. dt/dv for the experimental
results in membrane filtration of dilute suspensions ofmonodisperse polystyrene latex (PSL) under the constantpressure condition using diatomaceous ceramic membranes which are semi-permeable to the PSL (Iritani etal., 2010). The plots for each run show a convex curvewith the exception of the case when the initial porosity ε 0

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

vmax 

2
Ks(78)

Combining Eq. (78) with Eq. (24) yieldsv

 J 
 1  vmax
 J0 
Fig. 7

Schematic view for illustrating the mechanism of membrane fouling in the fiber-coating model presented by
Bolton et al. (2005): (a) clean fibers and (b) fouled fibers.

of the membrane is 0.7. The slope of curve gradually decreases with the increase in dt/dv in accordance with theprogress of filtration. In contrast, the plots of d(dt/dv)/dσ
vs. dt/dv show a linear relation with the same slope of 2.35
for each initial porosity except for the last part of filtration. Thus, Eq. (76) provides a much better description ofblocking filtration behaviors obtained for semi-permeable
membranes than Eq. (17).
On the basis of the Kozeny-Carman equation, Bolton etal. (2005) proposed a fiber-coating model in which the filter medium becomes plugged as solids coat the surface of
cylindrical fibers that constitutes the filter medium. In themodel, the fibers become thicker with the progress of filtration, as shown in Fig. 7. As a result, the effective radius of fibers increases with time, and correspondingly
the porosity decreases, reducing the filter permeability.
Therefore, with the aid of the Kozeny-Carman equation,
the relation between the filtration rate J and v becomes3

dv
1  K f v

J 
J0
0

dt 
K
v
1

f 
 1 

0(77)

where Kf is the fiber coating constant and equal to the inverse of the solution volume filtered until the filter void
volume is completely filled with solids.

2.5 Evaluation of degree of membrane fouling
It is essential to evaluate the degree of membrane fouling during filtration on the basis of the theoretical background in the design of new filter equipment andoptimization of commercial filtration operations. The
maximum filtrate volume vmax per unit membrane area isdefined as the value of v obtained by the time when thefiltration rate drops to zero. If the flux decline behavior iscontrolled by the standard blocking law, on the basis of
Eq. (24), vmax can be given as (Badmington et al., 1995;
van Reis and Zydney, 2007)

0.5

(79)

Consequently, one can evaluate what percentage of vmax
has been already obtained when the filtration rate ratio(J/J0) is known. On the basis of Eqs. (10) and (11), thethickness Δr of the layer deposited on the pore wall is givenby (Zeman, 1983; Bowen and Gan, 1991; Blanpain-Avet etal., 1999; Persson et al., 2003)r
 J 
 1  r0
 J0 

0.25(80)

Similarly to the derivation of Eq. (78), vmax for complete
blocking law is written as

vmax 

J0
Kb

(81)

Although infinite time is required until the filtration
rate becomes zero for intermediate blocking and cake filtration laws, it is possible to calculate the filtrate volume
per unit membrane area at an arbitrary ratio (J/J0) of theflux decline. For instance, when the filtration rate J decreases to y percent of the initial filtration rate J0, influenced by the intermediate blocking law, the filtrate
volume v y per unit membrane area can be obtained basedon Eq. (27) and is represented as
y 
ln 

100


vy 
Ki

(82)

For the cake filtration law, on the basis of Eq. (30), v y canbe written asvy 

100  yyK c J 0(83)

Fouling index (FI) such as the silt density index (SDI)
has been employed to predict and evaluate the foulingpotential of the feed water in membrane filtration. The
SDI is the most widely applied method for many decades(Nagel, 1987; Yiantsios and Karabelas, 2003; Alhadidi etal., 2011a, 2011b; Koo et al., 2012). According to the
ASTM standard (2007), the filtration test is performed
under the constant pressure condition of 207 kPa (30 psi)
using a microfiltration membrane with the pore size of
0.45 μm. Both the time t1 required to collect the first
500 ml and the time t2 required to collect the second
500 ml after 15 min are measured to obtain SDI. Basedon the time ratio (t1/t2), SDI is calculated as

189

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

SDI

100  t1 
1
15  t 2 

(84)

SDI values less than 1.0 and 4–5 are preferable for hollow
fiber and spiral wound reverse osmosis membranes, respectively. In spite of the widespread application of SDI,
it has been long thought that SDI lacks a theoretical basis
and that it makes no distinction between different filtration mechanisms. In response to this criticism, White(1996) first clarified the theoretical background based on
the cake filtration model. In this case, SDI is written by

SDI 

50K v
vm 2

(85)

More recently, the relation between SDI and blocking filtration laws has been examined in detail for four types ofblocking filtration laws consisted of complete blocking,intermediate blocking, standard blocking, and cake filtration (Matsumoto et al., 2009; Alhadidi et al., 2011a; Weiet al., 2012).
The modified fouling index (MFI) proposed by Schippers et al. (1981) was also developed to measure the fouling potential of feed water in membrane filtration. While
the feed water is filtered under the constant pressure condition through a 0.45 μm microfiltration membrane indead-end mode, as is the case in SDI, the filtrate volume
is recorded every 30 seconds over the filtration period in
the MFI measurement. On the basis of cake filtration
model, integrating Eq. (15) under the initial condition thatv = 0 at t = 0, one obtainst2 MFI  v 
vm
v
Kv

(86)

Consequently, MFI is the reciprocal of the Ruth coefficient Kv in constant pressure filtration appeared in Eq.
(15). Thus, MFI is calculated from the slope obtained by
the linear approximation to the plot of the reciprocal average filtration rate (t/v) vs. the filtrate volume v per unit
membrane area (Keskinler et al., 2004; Srisukphun et al.,2009). This means that MFI is defined on the assumptionthat the separation mechanism is controlled by cake filtration. Since MFI includes the solid concentration s in feedwater (Park et al., 2006), it is more convenient than the
average specific filtration resistance αav to evaluate thefouling potential of feed water in which the solid concentration is unknown. In order to evaluate the fouling potential of smaller colloidal particles or macromolecules, MFI
was developed by using ultrafiltration membranes andnanofiltration membranes (referred to as MFI-UF (Boerlage
et al., 2002, 2003, 2004) and MFI-NF (Khirani et al.,2006), respectively). Jin et al. (2015) proposed the cakefouling index (CFI) in which the true fouling cake layer
resistance can be accurately evaluated by eliminating theeffect of pore blocking.

190

3. Combined model based on blocking
filtration law
3.1 Developments of consecutive combined model
Classical blocking filtration laws comprise of threepore blocking mechanisms and a cake formation mechanism. In the generality of cases, the membrane foulingproceeds in two steps: the initial membrane foulingcaused by pore blockage and/or pore constriction followedby the long-term fouling arising from the filter cake gradually accumulating on the membrane surface (Kim et al.,
1993; Tracey and Davis, 1994; Madaeni and Fane, 1996;
Huang and Morrissey, 1998; Blanpain-Avet et al., 1999;
Altman et al., 1999; Lim and Bai, 2003; Purkait et al.,2004, 2005; Wang and Tarabara, 2008; Juang et al., 2010;
Mohd Amin et al., 2010; Ozdemir et al., 2012). The initial
pore blocking frequently causes the irreversible fouling of
membranes, resulting in the decrease in the efficiency ofmembrane cleaning. Once a sufficient fraction of thepores becomes clogged depending on the retentiveness ofthe membrane, an external cake begins to form on thefouled membrane.
In Fig. 8, the logarithmic plots of d 2t/dv2 as a functionof dt/dv are shown as the characteristic filtration curves
for filtration of pond water in which the turbidity and concentration of suspended solids are 19.4 NTU and 18.0 mg/l,
respectively (Iritani et al., 2007a). In the first stage of filtration (i.e., small dt/dv), the plots show a unique linear
relationship, irrespective of the applied filtration pressurep. As filtration proceeds, the reciprocal filtration rate
(dt/dv) increases and thus its derivative (d 2t/dv2) increases.
Once the value of d 2t/dv 2 reaches the limiting value,
which depends on the filtration pressure, the second stage

Fig. 8Characteristic filtration curves for constant pressure filtration of pond water.

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

begins and the value of d 2t/dv2 remains constant, as given
by Eq. (16). Therefore, the transition point from the initial
pore blocking to the following cake filtration during a filtration run can be determined from the change of theslope of the straight line in the double logarithmic plot ofd2t/dv2 vs. dt/dv according to Eq. (17). It should be notedthat the pore fouling is frequently represented by the pore
constriction described by the standard blocking law followed by the pore plugging described by the complete orintermediate blocking law (Herrero et al., 1997; Griffiths
et al., 2014).
The pore blockage and cake formation may be treatedas two resistances in series. According to the resistance-­
in-series model based on Darcy’s law, the filtration rate
J is related to the filtration resistances in series as (Iritani
et al., 2007b)
J


dv
p
p
 
 R  Rm  Rc
dt

(87)

where R is the overall filtration resistance, and Rc is thefilter cake resistance. It should be noted that Rm indicates
not the resistance of clean membrane but the resistance offouled membrane.
Bowen et al. (1995) and Iritani et al. (1995) found thatthe value of n in Eq. (17) gradually varied with the courseof filtration in constant pressure dead-end microfiltrationof bovine serum albumin (BSA) solution. Later, Hwang etal. (2007) reported a similar result in constant pressure
dead-end microfiltration of particulate suspension. In
their study, complete blocking (n = 2) initially occurred,then gradually changed to standard blocking (n = 1.5),
and finally cake filtration (n = 0) started (Hwang andChiu, 2008). Therefore, the values of n successively decreased with the progress of filtration.
It should be stressed that several researchers (Bowen etal., 1995; Iritani et al., 1995; Costa et al., 2006; Kim et al.,2007, Yukseler et al., 2007) reported the negative valuesof n in the later stages of filtration. Fig. 9 shows the logarithmic plots of d 2t/dv2 vs. dt/dv as the characteristic formof blocking filtration described by Eq. (17) for constantpressure microfiltration of BSA solution (Iritani et al.,1995). The curve shows a convex shape. The slope of thecurve decreases with the increase in dt/dv due to the progress of filtration. Eventually, the slope of the curve has
negative values after the slope reaches zero.
Strictly speaking, the blocking filtration laws can beapplied only to unstirred dead-end filtration. It is impossible to apply the blocking filtration laws to crossflow filtration where the filter cake growth is restricted by externalcrossflow of the feed suspension. However, at the earlier
stage of crossflow filtration where the solids deposited inside the pore structure, the blocking filtration laws are
frequently employed to describe the progressive poreclogging. For instance, Murase and Ohn (1996) adopted

Fig. 9Characteristic filtration curve for constant pressure microfiltration of BSA solution.

the intermediate blocking law to describe the membrane
fouling behavior in the initial stage of crossflow microfiltration of polymethyl methacrylate (PMMA) suspension.
The blocking filtration law has been frequently employed in the analysis of the flux decline in crossflow filtration (Jonsson et al., 1996; Prádanos et al., 1996;
Keskinler et al., 2004). In this case, the flux decline behaviors should be analyzed by introducing the term of asteady-state flux controlled by crossflow (Field et al.,
1995; de Bruijn et al., 2005). Field et al. (1995) modified
the blocking filtration equation (18) by accounting for the
back-transport effect arising from crossflow asdJk  J  J lim  J 2n
dt

(88)

where Jlim is the critical filtration rate under steady-state
condition. Equation (88) has been widely employed in theanalysis of the flux decline behaviors in crossflow membrane filtration of a variety of colloids (Todisco et al.,
1996; Blanpain and Lalande, 1997; de Barros et al., 2003;
Rai et al., 2006; Cassano et al., 2007; Mondal and De,
2009; Vera et al., 2009; Ma et al., 2010; Chang et al., 2011;
Daniel et al., 2011; Field and Wu, 2011; Huang et al.,2014). Electric field-assisted membrane filtration in which
the particle deposition was restricted due to an external
electric field was also investigated on the basis of blocking filtration laws as well as crossflow membrane filtration (Sarkar and De, 2012).

3.2 Developments of concurrent combined model
While membrane fouling generally proceeds in two

191

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

steps consisted of pore blocking followed by cake formation, as mentioned above, pore blocking and cake formation may be frequently occurring simultaneously during
the filtration process (Takahashi et al., 1991; Matsumoto
et al., 1992; Katsoufidou et al., 2005; Fernández et al.,2011; Li et al., 2011; Nakamura et al., 2012). Bolton et al.
(2006a) combined two blocking filtration laws occurringconcurrently among four blocking filtration laws. On thebasis of the Darcy’s law, the volumetric flow rate Q
through the membrane is related to the overall filtration
resistance R and the effective filtration area A in the form


Q

A p

R 

(89)

Since the apparent filtration area remains constant during
the course of filtration, the filtration rate J (= dv/dt) is directly proportional to the volumetric flow rate Q in Eq.
(89) asJQ
J 0 Q0(90)

where the subscript “0” indicates the value at the start offiltration. Substituting Eq. (89) into Eq. (90), one obtains

J
R A
 0
J0
RA0

(91)

Both complete and intermediate blocking mechanisms
contribute the decrease in the effective filtration area A.
In contrast, both standard blocking and cake filtration
mechanisms increase the filtration resistance R. On thebasis of Eqs. (21) and (27), the variations of the effective
filtration area A in Eq. (91) for complete and intermediate
blocking mechanisms are, respectively, represented asAK 1 b V
A0
J0

(93)

where K b and K i are the blocking constants for complete
and intermediate blocking laws, respectively, and V is thefiltrate volume. On the basis of Eq. (24) and (30), the variations of the filtration resistance R in Eq. (91) for standard
blocking and cake filtration models are, respectively,
given in2


R R0 1  K c J 0V 

(94)

(95)

where K s and K c are the blocking constants for standard
blocking and cake filtration laws, respectively. Consequently, for instance, the governing equation for the com-

192

J

exp K iV  1  K c J 0V  1
J0

(96)

Just around the same time, Duclos-Orsello et al. (2006)
also proposed a very similar combined model for describing the membrane fouling. Rezaei et al. (2011) employed
the combined model developed by Bolton et al. (2006a) toanalyze the fouling mechanism in crossflow microfiltration of whey. Affandy et al. (2013) well described fouling
behaviors in sterile microfiltration of large plasmids DNA
with the use of the standard—intermediate model.
Bolton et al. (2006b) combined the adsorption modelwith the classical blocking filtration model by a method
similar to that mentioned above. In the adsorption model,
it is assumed that foulant adsorption occurs at the porewalls with zeroth-order kinetics, thereby reducing thepore size and thus increasing the filtration resistance. As aresult, the increase in the resistance R with filtration time
t is written asR0 1  K at 4
R(97)

where Ka is the adsorption blocking constant. Consequently, for instant, in a combined intermediate blocking—adsorption model, substituting Eqs. (93) and (97)
into Eq. (91), one getsJ4
exp  K iV 1  K at

J0

(98)

Integration of Eq. (98) lead to the relation between V and t
in the form:

(92)
V


A
 exp  K iV 
A0

K V

R R0 1  s 2 bined process in which both intermediate blocking andcake formation occur simultaneously is derived using
Eqs. (91), (93), and (95) and is given by

1  KiJ 05ln 
1  1  K at  1
K i  5K a






(99)

Giglia and Straeffer (2012) applied the combined intermediate blocking—adsorption model to the evaluation of
filtration performance of microfiltration membranes operated in series. The combined cake—adsorption model canbe described by adding the filtration resistances due to theadsorption and cake formation as1J1  K at 4  K c J 0V
J0





(100)

In the initial stage of cake filtration, pore blocking of amembrane often occurs. As a result, the membrane resistance gradually increases with time and approaches to thesaturated value, as indicated by Notebaert et al. (1975).
On the assumption that the membrane resistance approaches a finite value due to the progress of filtration, thefollowing equation was presented for describing the variation of the clogged membrane resistance Rm during theEiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

course of filtration for cake filtration of liquefied coal
(Tiller et al., 1981; Leu and Tiller, 1983).

Rm  Rm0
1  exp  wRm  Rm0(101)

where Rm0 and Rm∞ are the initial and infinite membrane
resistances, respectively, η is the blocking rate constant,
and w is the net solid mass in the filter cake per unit membrane area. Thereafter, Lee (1997) developed a more rigorous model by introducing the intermediate blocking law
and derived the equation with two fitting parameters as
Rm  Rm0
1  exp  wRm  Rm0 1   exp  w

(102)

where λ is a constant. Interestingly, Eq. (102) reduces to
Eq. (101) found experimentally by Tiller et al. (1981) whenthe membrane clogging is quite-serious, i.e., at the limit λ→ 0.
By imposing the condition that some of pores of themembrane still remain open finally in the intermediate
blocking law, Iritani et al. (2005) described the clogged
membrane resistance Rm as
Rm0
Rm
1  exp  w

Rm0
1
Rm

Fig. 10 Schematic view for illustrating the mechanism ofmembrane fouling comprised of both pore blocking
and cake formation occurring simultaneously in themodel presented by Iritani et al. (2005).

1

(103)

It should be noted that the variation of Rm with w can be
represented by only one fitting parameter η in Eq. (103)
while two fitting parameters η and λ are required in theanalysis of pore clogging using Eq. (102). The cake resistance Rc is related to w as
Rc   av w

(104)

Fig. 11 Effect of solid mass fraction in suspension on characteristic filtration curves in constant pressure microfiltration.

Thus, the increasing behaviors of filtration resistance canbe described by adding the cake resistance Rc represented
by Eq. (104) to clogged membrane resistance Rm represented by Eq. (103) on the basis of Eq. (87) describing thethe initial period of filtration since the filtration behaviors
resistance-in-series model. In this case, all the particles
are influenced both by the pore blocking of membraneretained by the membrane contribute as the cake resisand by cake formation, as reported by several researchers
tance Rc and a part of them also serve as the clogged(Bowen et al., 1995; Iritani et al., 1995; Ho and Zydney,
membrane resistance Rm by clogging the membrane pores
2000; Hwang et al., 2007; Yukseler et al., 2007). However,in accordance with the intermediate blocking law considas filtration proceeds, cake filtration has a dominant influering the limiting value of the clogging resistance on theence on the filtration behaviors and thus the slope of thebasis of the concurrent combined model, as schematicallyplot becomes equal to zero. Substituting Eqs. (103) andshown in Fig. 10.
(104) into Eq. (87) and using the relation that w = ρsv on
Fig. 11 shows the characteristic curves of blocking filthe assumption that suspension is very dilute, one obtains
tration laws, which is plotted in the form of d2t/dv2 vs. dt/dv,
dt  Rmfor different feed concentrations in constant pressure mi-   av  sv
(105)

d
1

v
p
F


crofiltration of monodisperse PSL with a particle diameter
dp of 0.522 μm filtered under the applied pressure p ofwhere F is a function defined by
196 kPa using the track-etched polycarbonate membraneRm  Rm0with a nominal pore size dm of 0.2 μm (Iritani etal.,F
exp  sv
(106)
Rm0
2015). Each plot results in the distinct negative slope in193

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

Subsequently, differentiating Eq. (105) with respect to v
under constant pressure condition, one gets
d 2t
 s   F  av 22
p  1  F 
dv


(107)

Therefore, the curve of d 2t/dv2 vs. dt/dv can be evaluatedfrom Eq. (107) with the aid of Eq. (105). The solid curves
in Fig. 11 are the calculations and it is of significance tonote that the calculations roughly reflect the trends of experimental data, indicating the negative slope in the initialperiod followed by the straight line with the slope of zero.
Hwang et al. (2006) evaluated the protein capture into
the interstices of filter cake in crossflow microfiltration ofparticle/protein binary mixtures on the basis of the deepbed filtration mechanism. The apparent protein rejection
Robs is evaluated from
Robs 
1Cp
1  exp  Lc
Cb

(108)

where Cp and Cb are protein concentrations in the filtrate
and in the bulk feed suspension, respectively, γ is ascreening parameter which represents the protein fraction
rejected by the filter cake per unit cake thickness, and Lc
is the thickness of the filter cake.
Ho and Zydney (2000) presented a unique model describing pore blocking and cake formation occurring simultaneously in microfiltration processes. As schematicallyshown in Fig. 12, the filter cake only forms over the regions of the membrane which have already been blocked
by the initial deposit in the membrane pores. This meansthat the pore blockage did not lead to complete loss offlow through the pore. As a result, according to Darcy’slaw, the flux Jblocked through the already blocked pores canbe written as
J blocked p Rm0  Rc

(109)

The cake resistance Rc increases with time during thecourse of filtration asdRc
 f  av J blockedCb
dt

(110)

where f  ′ is the fraction of solutes which contribute to thegrowth of filter cake. Integrating Eq. (110) with the aid of
Eq. (109), one obtains
Rc Rm0  Rc0 1 

2 f  av pC bt
 Rm0
 Rm0  Rc0 2(111)

where Rc0 is the flow resistance of the first cake layer. The
filtration rate J can be given as the sum of the flow rate
through open and blocked pores. Thus, with the use of Eq.
(111), the variation of J with t can be approximately written asJRm0   pC b   pCb  exp  t t 
1  exp  J0  Rm0  Rm0  Rc   Rm0 (112)where α is the pore blocking constant in the complete
blocking law. The first term in Eq. (112) represents a simple exponential decline in the flux through the open porescontrolled by the classical complete blocking law. As filtration proceeds, the deposit continued to grow due tosome fluid flow through pores partially blocked by thecomplete blocking mechanism, as illustrated by Fig. 12,
and thus the filtration rate becomes governed by the classical cake filtration law represented by the second term in
Eq. (112). The model has been extensively used to analyze
the fouling behaviors in membrane filtration of a numberof colloids under various operating conditions (Yuan etal., 2002; Ho and Zydney, 2002; Palacio et al., 2002, 2003;
Taniguchi et al., 2003; Ye et al., 2005; Chandler andZydney, 2006; Cogan and Chellam, 2009; Byun et al.,2011).

4. Conclusions and prospective viewFig. 12

194

Schematic view for illustrating the mechanism ofmembrane fouling comprised of both pore blocking
and cake formation occurring simultaneously in themodel presented by Ho and Zydney (2000).

The present article overviewed the blocking filtration
laws comprised of the complete blocking, standard blocking, intermediate blocking, and cake filtration, which
could describe the increase in the filtration resistanceduring the course of filtration in membrane filtration ofcolloids. The equations derived based on the blocking filtration laws were reported to describe the filtrate flow of
Newtonian and non-Newtonian fluids through membranes
for both constant pressure and constant rate filtration processes. The blocking filtration laws are quite useful due tothe simplicity in use of the model to identify the prevailing fouling mechanism from the experimental data of theflux decline in constant pressure filtration or pressure risein constant rate filtration. In order to evaluate more com-

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

plicate fouling behaviors in membrane filtration, several
combined models have been developed based on theblocking filtration laws and well described the foulingphenomena in which more than one filtration mechanism
occurred successively or simultaneously.
While the blocking filtration laws and their combinations largely contributed to the optimal choice of themembrane and membrane-cleaning strategy in industrialuse, it is essential to develop more sophisticated models
which can describe more accurately the complicated behaviors of membrane fouling actually encountered in industrial membrane filtration. In particular, there is apressing need for developing the models which are applicable not only the simple model colloids but also to theactual colloids containing a wide variety of ingredients,
as frequently encountered in water treatment.
In any case, the elucidation of mechanism predominating the membrane fouling in membrane filtration is everlasting problems crying out for solutions. We believe that
this article provides a valuable insight into the further developments of models which can reasonably describe thefouling behaviors in membrane filtration.

Acknowledgements
This work has been partially supported by Grant-inAid for Scientific Research from The Ministry of Education, Culture, Sports, Science and Technology, Japan. The
authors wish to acknowledge with sincere gratitude thefinancial support leading to the publication of this article.

NomenclatureJ

filtration rate (m/s)

J0

initial filtration rate (m/s)

Jblocked flux through already blocked pores (m/s)
Jlim

critical filtration rate under steady-state condition incrossflow filtration (m/s)

K

fluid consistency index for power-law non-Newtonian
fluids (kg m –1 sN–2)

Ka

adsorption blocking constant in Eq. (97) (s –1)

Kb

blocking constant in Eq. (2) for complete blocking law(s –1)

K b

blocking constant in Eq. (92) for complete blocking law(m–2 s –1)

Kbrblocking constant in Eq. (35) for complete blocking law
in constant rate filtation (kg–1 s2)

Kc

blocking constant in Eq. (16) for cake filtration law (s/
m 2)

K c

blocking constant in Eq. (95) for cake filtration law (s/m4)

Kffiber coating constant in Eq. (77)Kiblocking constant in Eq. (3) for intermediate blocking
law (m–1)

K i

blocking constant in Eq. (93) for intermediate blocking
law (m–3)

Km

constant in Eq. (74) (m 2/kg)

Kp

constant in Eq. (71) (m–1)

Ksblocking constant in Eq. (7) for standard blocking law
(m–1)

K s

blocking constant in Eq. (94) for standard blocking law
(m–3)

Kv

Ruth coefficient in constant pressure cake filtration (m 2/s)kresistant coefficient in Eq.(17) (m n–2 s1–n)k′resistant coefficient in Eq. (36) for constant rate filtration (kg1–n′ m n′–2 s2n′–2)k0

Kozeny constant

k1

constant in Eq. (44) ( kg1-n1 m n1-2 s 2n1-2)

k1

constant in Eq. (46) ( kg n12 m 2-2n1 s 5-5n1)

k2

constant in Eq. (45) (kg1–n′ m n′–1 s2n′–3)k 2

constant in Eq. (47) ( kg1-n2 m 2n2 3 s 2n2 -2)Aeffective filtration area (m 2)A0initial effective filtration area (m 2)aconstant in Eq. (33)Cb

protein concentration in bulk feed suspension (kg/m3)

Cp

protein concentration in filtrate (kg/m3)

cvolume of particles trapped per unit filtrate volume v per
unit membrane area (m)Drepresentative diameter of pores on flow cross-sectional
area basis (m)kcproportional constant in Eq. (1) (kg–1 m4 s)

kj

constant in Eq. (31) (s –1)

Ds

representative diameter of pores on wetted perimeterbasis (m)kNconstant in Eq. (58) ( m nN 2 s1-nN)

dmpore size (m)k Nconstant in Eq. (59) ( kg1-nN m nN 2 s 2nN -2 )dp

diameter of suspended solids (m)Lmembrane thickness (m)Ffunction defined by Eq. (106)Lc

thickness of filter cake (m)f  ′fraction of solutes which contribute to growth of filter
cakemratio of mass of wet to mass of dry cake

MFI

modified fouling index defined by Eq. (86) (s/m 2)

195

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

N

fluid behavior index for power-law non-Newtonian fluids

wN′

number of open pores per unit effective membrane area
at filtrate volume v per unit effective membrane area
(m–2)

net solid mass in filter cake per unit membrane area (kg/m 2)

xnumber of particles blocking pores per unit filtrate volume (m –3)

N 0

total number of open pores per unit effective membrane
area at start of filtration (m –2)

ypercentage of initial value of filtration raten

blocking index in Eq. (17)αpore blocking constant in Eq. (112) for complete blocking law (m 2/kg)

n′blocking index in Eq. (36) for constant rate filtration

α0n1compressibility coefficient in Eq. (42)average specific cake resistance at null stress in Eq. (48)(m/kg)

n1

constant in Eq. (46)α1constant in Eq. (42) ( kg -1-n1 m1 n1 s 2n1 )n2

constant in Eq. (47)α2constant in Eq. (48) (kg–2 m 2 s2)

nj

constant in Eq. (31)αav

average specific cake resistance (m/kg)

nN

constant in Eq. (58)βnNconstant in Eq. (59)constant which depends on mode of morphology ofdeposit assemblagespapplied filtration pressure (Pa)γscreening parameter in Eq. (108) (m –1)

p0

initial applied filtration pressure (Pa)

shear rate (s –1)

Q

volumetric flow rate (m3/s)

γav

Q0

initial volumetric flow rate (m3/s)

average specific cake resistance for power-law non-­
Newtonian flow (m 2–N/kg)

R

overall filtration resistance (m –1)

Δr

thickness of layer deposited on pore wall (m)R0initial overall filtration resistance (m –1)

εporosity of membraneRc

filter cake resistance (m –1)

ε0initial porosity of clean membrane

Rc0

flow resistance of the first cake layer (m –1)

εppacking porosity of particle layer formed on pore wallRm

clogged membrane resistance (m –1)

ηblocking rate constant (m 2/kg)

Rm0

initial membrane resistance (m–1)

λ

constant in Eq. (102)Rm∞

infinite membrane resistance (m–1)μ

viscosity of filtrate (Pa s)rpore radius (m)ρdensity of filtrate (kg/m3)

r0

initial pore radius (m)σSspecific surface area of membrane (m–1)

specific deposit, i.e., mass of particles deposited on porewall per unit membrane area (kg/m 2)

S0

initial specific surface area of clean membrane (m–1)τ

shear stress (Pa)Sc

constant in Eq. (33)smass fraction of solids in colloids

SDI

silt density index defined by Eq. (84)tfiltration time (s)t1time required to collect the first 500 ml of filtrate volume (s)t2time required to collect the second 500 ml of filtrate
volume after 15 min (s)uaverage flow rate (m/s)

Vfiltrate volume (m3)v

filtrate volume per unit effective membrane area (m)vmfictitious filtrate volume per unit membrane area
required to obtain cake with flow resistance equivalent
to that of membrane (m)vmaxmaximum filtrate volume per unit membrane area (m)vyfiltrate volume per unit membrane area obtained until
filtration rate decreases to y percent of initial filtration
rate (m)196

References
Affandy A., Keshavarz-Moore E., Versteeg H.K., Applicationof filtration blocking models to describe fouling and transmission of large plasmids DNA in sterile filtration, Journalof Membrane Science, 437 (2013) 150–159.
Alhadidi A., Blankert B., Kemperman A.J.B., Schippers J.C.,
Wessling M., van der Meer W.G.J., Effect of testing conditions and filtration mechanisms on SDI, Journal of Membrane Science, 381 (2011a) 142–151.
Alhadidi A., Kemperman A.J.B., Schippers J.C., Wessling M.,
van der Meer W.G.J., The influence of membrane properties
on the Silt Density Index, Journal of Membrane Science,
384 (2011b) 205–218.
Altman M., Semiat R., Hasson D., Removal of organic foulantsfrom feed waters by dynamic membranes, Desalination,
125 (1999) 65–75.
ASTM Standard (D4189-07), Standard test method for silt density index (SDI) of water, D19.08 on membranes and ion
exchange materials, 2007.

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

Badmington F., Wilkins R., Payne M., Honig E.S., Vmax testing for practical microfiltration train scale-up in biopharmaceutical processing, Pharmaceutical Technology, 19(Sep.)(1995) 64–76.
Blankert B., Betlem B.H.L., Roffel B., Dynamic optimization ofa dead-end filtration trajectory: Blocking filtration laws,
Journal of Membrane Science, 285 (2006) 90–95.
Blanpain P., Hermia J., Lenoël M., Mechanisms governing permeate flux and protein rejection in the microfiltration ofbeer with a cyclopore membrane, Journal of Membrane
Science, 84 (1993) 37–51.
Blanpain P., Lalande M., Investigation of fouling mechanisms
governing permeate flux in the crossflow microfiltration ofbeer, Filtration & Separation, 21 (1997) 1065–1069.
Blanpain-Avet P., Fillaudeau L., Lalande M., Investigation of
mechanisms governing membrane fouling and protein
rejection in the sterile microfiltration of beer with anorganic membrane, Transactions of The Institution ofChemical Engineers, Part C, Food and Bioproducts Processing, 77 (1999) 75–89.
Boerlage S.F.E., Kennedy M.D., Dickson M.R., El-Hodali
D.E.Y., Schippers J.C., The modified fouling index using
ultrafiltration membranes (MFI-UF): Characterisation, filtration mechanism and proposed reference membrane,
Journal of Membrane Science, 197 (2002) 1–21.
Boerlarge S.F.E., Kennedy M., Aniye M.P., Schippers J.C.,
Application of the MFI-UF to measure and predict particulate fouling in RO systems, Journal of Membrane Science,
220 (2003) 97–116.
Boerlage S.F.E., Kennedy M., Tarawneh Z., de Feber R., Shippers
J.C., Development of the MFI-UF in constant flux filtration,
Desalination, 161 (2004) 103–113.
Bolton G.R., LaCasse D., Lazzara M.J., Kuriyel R., The
fiber-coating model of biopharmaceutical depth filtration,
AIChE Journal, 51 (2005) 2978–2987.
Bolton G.R., Lacasse D., Kuriyel R., Combined models of
membrane fouling: Development and application to microfiltration and ultrafiltration of biological fluids, Journal of
Membrane Science, 277 (2006a) 75–84.
Bolton G.R., Boesch A.W., Lazzara M.J., The effects of flowrate on membrane capacity: Development and applicationof adsorptive membrane fouling models, Journal of Membrane Science, 279 (2006b) 625–634.
Bowen W.R., Gan Q., Properties of microfiltration membranes:
Flux loss during constant pressure permeation of bovine
serum albumin, Biotechnology and Bioengineering, 38(1991) 687–696.
Bowen W.R., Calvo J.I., Hernández A., Steps of membrane
blocking in flux decline during protein microfiltration,
Journal of Membrane Science, 101 (1995) 153–165.
Broekmann A., Busch J., Wintgens T., Marquardt W., Modelingof pore blocking and cake layer formation in membrane filtration for wastewater treatment, Desalination, 189 (2006)
97–109.
Byun S., Taurozzi J.S., Alpatova A.L., Wang F., Tarabara V.V.,
Performance of polymeric membranes treating ozonatedsurface water: Effect of ozone dosage, Separation and Purification Technology, 81 (2011) 270–278.

Cassano A., Marchio M., Driori E., Clarification of bloodorange juice by ultrafiltration: Analyses of operating
parameters, membrane fouling and juice quality, Desalination, 212 (2007) 15–27.
Chandler M., Zydney A., Effects of membrane pore geometry
on fouling behavior during yeast cell microfiltration, Journal of Membrane Science, 285 (2006) 334–342.
Chang E.E., Yang S.Y., Huang C.P., Liang C.H., Chiang P.C.,
Assessing the fouling mechanisms of high-pressure nanofiltration membrane using the modified Hermia model andthe resistance-in-series model, Separation and Purification
Technology, 79 (2011) 329–336.
Chellam S., Xu W., Blocking laws analysis of dead-end constantflux microfiltration of compressible cakes, Journal of Colloid and Interface Science, 301 (2006) 248–257.
Chellam S., Cogan N.G., Colloidal and bacterial fouling during
constant flux microfiltration: Comparison of classicalblocking laws with a unified model combining pore blocking and EPS secretion, Journal of Membrane Science, 382(2011) 148–157.
Cheng Y.L., Lee D.J., Lai J.Y., Filtration blocking laws: Revisited, Journal of the Taiwan Institute of Chemical Engineers,
42 (2011) 506–508.
Choo C.U., Tien C., Simulation of hydrosol deposition in granular media, AIChE Journal, 41 (1995) 1426–1442.
Chudacek M.W., Fane A.G., The dynamics of polarization in
unstirred and stirred ultrafiltration, Journal of Membrane
Science, 21 (1984) 145–160.
Cogan N.G., Chellam S., Incorporating pore blocking, cake filtration, and EPS production in a model for constant pressure bacterial fouling during dead-end microfiltration,
Journal of Membrane Science, 345 (2009) 81–89.
Costa A.R., Pinho M.N., Elimelech M., Mechanism of colloidal
natural organic matter fouling in ultrafiltration, Journal of
Membrane Science, 281 (2006) 716–725.
Daniel R.C., Billing J.M., Russell R.L., Shimskey R.W., Smith
H.D., Peterson R.A., Integrated pore blockage – cake filtration model for crossflow filtration, Chemical Engineering
Research and Design, 89 (2011) 1094–1103.
de Barros S.T.D., Andrade C.M.G., Mendes E.S., Peres L.,
Study of fouling mechanism in pineapple juice clarification
by ultrafiltration, Journal of Membrane Science, 215 (2003)
213–224.
de Bruijn J.P.F., Salazar F.N., Bórquez R., Membrane blocking
in ultrafiltration: A new approach to fouling, Transaction of
The Institution of Chemical Engineers, Part C, Food andBioproducts Processing, 83 (2005) 211–219.
de Lara R., Benavente J., Use of hydrodynamic and electricalmeasurements to determine protein fouling mechanisms for
microfiltration membranes with different structures andmaterials, Separation and Purification Technology, 66(2009) 517–524.
Duclos-Orsello C., Li W., Ho C.C., A three mechanism model todescribe fouling of microfiltration membranes, Journal of
Membrane Science, 280 (2006) 856–866.
Fan L.T., Hwang S.H., Chou S.T., Nassar R., Birth-death modeling of deep bed filtration: Sectional analysis, Chemical
Engineering Communications, 35 (1985a) 101–121.

197

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

Fan L.T., Nassar R., Hwang S.H., Chou S.T., Analysis of deep
bed filtration data: Modeling as a birth-death process,
AIChE Journal, 31 (1985b) 1781–1790.
Fane A.G., Fell C.J.D., Suki A., The effect of pH and ionic environment on the ultrafiltration of protein solutions withretentive membranes, Journal of Membrane Science, 16(1983) 195–210.
Fernández X.R., Rosenthal I., Anlauf H., Nirschl H., Experimental and analytical modeling of the filtration mechanisms of a paper stack candle filter, Chemical Engineering
Research and Design, 89 (2011) 2776–2784.
Field R.W., Wu D., Howell J.A., Gupta B.B., Critical flux concept for microfiltration fouling, Journal of Membrane Science, 100 (1995) 259–272.
Field R.W., Wu J.J., Modeling of permeability loss in membrane
filtration: Re-examination of fundamental fouling equations and their link to critical flux, Desalination, 283 (2011)
68–74.
Giglia S., Straeffer G., Combined mechanism fouling model andmethod for optimization of series microfiltration performance, Journal of Membrane Science, 417–418 (2012) 144–
153.
Gironès M., Lammertink R.G.L., Wessling M., Protein aggregate deposition and fouling strategies with high-flux silicon
nitride microsieves, Journal of Membrane Science, 273(2006) 68–76.
Grace H.P., Resistance and compressibility of filter cake, Part I,
Chemical Engineering Progress, 49 (1953) 303–318.
Grace H.P., Structure and performance of filter media. II. Performance of filter media in liquid service, AIChE Journal,
2 (1956) 316–336.
Granger J., Dodds J., Leclerc D., Filtration of low concentrations of latex particles on membrane filters, Filtration &Separation, 9 (1985) 58–60.
Griffiths I.M., Kumar A., Stewart P.S., A combined networkmodel for membrane fouling, Journal of Colloid and Interface Science, 432 (2014) 10–18.
Heertjes P.M., Studies in filtration, Blocking filtration, Chemical Engineering Science, 6 (1957) 190–203.
Hermans P.H., Bredée H.L., Zur kenntnis der filtrationsgesetze,
Recueil des Travaux Chimiques des Pays-Bas, 54 (1935)
680–700.
Hermans P.H., Bredée H.L., Principles of the mathematic treatment of constant-pressure filtration, Journal of The Societyof Chemical Industry, 55T (1936) 1–4.
Hermia J., Constant pressure blocking filtration laws—Application to power-law non-Newtonian fluids, Transaction of
The Institution of Chemical Engineers, 60 (1982) 183–187.
Herrero C., Prádanos P., Calvo J.I., Tejerina F., Hernández A.,
Flux decline in protein microfiltration: Influence of operative parameters, Journal of Colloid and Interface Science,
187 (1997) 344–351.
Hlavacek M., Bouchet F., Constant flowrate blocking laws andan example of their application to dead-end microfiltrationof protein solutions, Journal of Membrane Science, 82(1993) 285–295.
Ho C.C., Zydney A.L., Effect of membrane morphology on theinitial rate of protein fouling during microfiltration, Journal

198

of Membrane Science, 155 (1999) 261–275.
Ho C.C., Zydney A.L., A combined pore blockage and cake formation model for protein fouling during microfiltration,
Journal of Colloid and Interface Science, 232 (2000) 389–
399.
Ho C.C., Zydney A.L., Transmembrane pressure profiles during
constant flux microfiltration of bovine serum albumin,
Journal of Membrane Science, 209 (2002) 363–377.
Hodgson P.H., Leslie G.L., Schneider R.P., Fane A.G., Fell
C.J.D., Marshall K.C., Cake resistance and solute rejection
in bacterial microfiltration: The role of the extracellularmatrix, Journal of Membrane Science, 79 (1993) 35–53.
Hsu E.H., Fan L.T., Experimental study of deep bed filtration: A
stochastic treatment, AIChE Journal, 30 (1984) 267–273.
Huang L., Morrissey M.T., Fouling of membranes during microfiltration of surimi wash water: Roles of pore blocking andsurface cake filtration, Journal of Membrane Science, 144(1998) 113–123.
Huang Q., Gomaa H.G., Hashem N., Flux characteristics of oil
separation from O/W emulsions using highly hydrophilic
UF membrane in narrow channel, Separation Science andTechnology, 49 (2014) 12–21.
Hwang K.J., Chou F.Y., Tung K.L., Effects of operating conditions on the performance of cross-flow microfiltration offine particle/protein binary suspension, Journal of Membrane Science, 274 (2006) 183–191.
Hwang K.J., Liao C.Y., Tung K.L., Analysis of particle fouling
during microfiltration by use of blocking models, Journal of
Membrane Science, 287 (2007) 287–293.
Hwang K.J., Chiu H.S., Influence of membrane type on filtration characteristics and protein rejection in crossflow
microfiltration, Filtration, 8 (2008) 317–322.
Hwang K.J., Sz P.Y., Filtration characteristics and membrane
fouling in cross-flow microfiltration of BSA/dextran binary
suspension, Journal of Membrane Science, 347 (2010)75–82.
Iritani E., Nakatsuka S., Aoki H., Murase T., Effect of solution
environment on unstirred dead-end ultrafiltration characteristics of proteinaceous solutions, Journal of Chemical
Engineering of Japan, 24 (1991a) 177–183.
Iritani E., Sumi H., Murase T., Analysis of filtration rate in clarification filtration of power-law non-Newtonian fluids-solids
mixtures under constant pressure by stochastic model,
Journal of Chemical Engineering of Japan, 24 (1991b) 581–
586.
Iritani E., Itano Y., Murase T., Ultrafiltration of proteinaceous
solutions by use of dynamic membranes formed by bodyfeed method, Membrane, 17 (1992) 203–206.
Iritani E., Tachi S., Murase T., Influence of protein adsorption
on flow resistance of microfiltration membrane, Colloids
and Surfaces A: Physicochemical and Engineering Aspects,
89 (1994) 15–22.
Iritani E., Mukai Y., Tanaka Y., Murase T., Flux decline behavior in dead-end microfiltration of protein solutions, Journalof Membrane Science, 103 (1995) 181–191.
Iritani E., Mukai Y., Hagihara E., Measurements and evaluationof concentration distributions in filter cake formed in deadend ultrafiltration of protein solutions, Chemical Engineer-Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

ing Science, 57 (2002) 53–62.
Iritani E., Mukai Y., Furuta M., Kawakami T., Katagiri N.,
Blocking resistance of membrane during cake filtration ofdilute suspensions, AIChE Journal, 51 (2005) 2609–2614.
Iritani E., Katagiri N., Sugiyama Y., Yagishita K., Analysis offlux decline behaviors in filtration of very dilute suspensions, AIChE Journal, 53 (2007a) 2275–2283.
Iritani E., Katagiri N., Sengoku T., Yoo K.M., Kawasaki K.,
Matsuda A., Flux decline behaviors in dead-end microfiltration of activated sludge and its supernatant, Journal of
Membrane Science, 300 (2007b) 36–44.
Iritani E., Modeling and evaluation of pore clogging of membrane in membrane filtration, Kagaku Kogaku Ronbunshu,
35 (2009) 1–11.
Iritani E., Katagiri N., Tadama T., Sumi H., Analysis of clogging behaviors of diatomaceous ceramic membranes during
membrane filtration based upon specific deposit, AIChE
Journal, 56 (2010) 1748–1758.
Iritani E., Katagiri N., Sugiyama Y., Clogging properties ofmembrane pores in constant-rate and constant-pressure
microfiltration of dilute colloids, Kagaku Kogaku Ronbunshu, 37 (2011) 323–326.
Iritani E., A review on modeling of pore-blocking behaviors ofmembranes during pressurized membrane filtration, Drying
Technology, 31 (2013) 146–162.
Iritani E., Katagiri N., Tsukamoto M., Hwang K.J., Determination of cake properties in ultrafiltration of nano-colloids
based on single step-up pressure filtration test, AIChE
Journal, 60 (2014a) 289–299.
Iritani E., Katagiri N., Nakajima R., Hwang K.J., Cheng T.W.,
Cake properties of nanocolloid evaluated by variable pressure filtration associated with reduction in cake surface
area, AIChE Journal, 60 (2014b) 3869–3877.
Iritani E., Katagiri N., Ishikawa Y., Cao D.Q., Cake formationand particle rejection in microfiltration of binary mixturesof particles with two different sizes, Separation and Purification Technology, 123 (2014c) 214–220.
Iritani E., Katagiri N, Takenaka T., Yamashita Y., Membranepore blocking during cake formation in constant pressure
and constant flux dead-end microfiltration of very dilute
colloids, Chemical Engineering Science, 122 (2015) 465–
473.
Ives K.J., Pienvichitr V., Kinetics of the filtration of dilute suspension, Chemical Engineering Science, 20 (1965) 965–
973.
Jin Y., Ju Y., Lee H., Hong S., Fouling potential evaluation bycake fouling index: Theoretical development, measurements, and its implications for fouling mechanisms, Journalof Membrane Science, 490 (2015) 57–64.
Jonsson G., Prádanos P., Hernández A., Fouling phenomena in
microporous membranes: Flux decline kinetics and structural modification, Journal of Membrane Science, 112(1996) 171–183.
Juang R.S., Lin S.H., Peng L.C., Flux decline analysis in micellar-­enhanced ultrafiltration of synthetic waste solutions formetal removal, Chemical Engineering Journal, 161 (2010)
19–26.
Katsoufidou K., Yiantsios S.G., Karabelas A.J., A study ofultrafiltration membrane fouling by humic acids and flux
recovery by backwashing: Experiments and modeling,
Journal of Membrane Science, 266 (2005) 40–50.
Keskinler B., Yildiz E., Erhan E., Dogru M., Bayhan Y.K.,
Akay G., Crossflow microfiltration of low concentration-­
nonliving yeast suspensions, Journal of Membrane Science,
233 (2004) 59–69.
Khirani S., Ben Aim R., Manero M.H., Improving the measurement of the modified fouling index using nanofiltration
membranes (NF-MFI), Desalination, 161 (2006) 1–7.
Kim K.J., Chen V., Fane A.G., Ultrafiltration of colloidal silver
particles: Flux, rejection, and fouling, Journal of Colloid
and Interface Science, 155 (1993) 347–359.
Kim J., Shi W., Yuan Y., Benjamin M.N., A serial filtration
investigation of membrane fouling by natural organic matter, Journal of Membrane Science, 294 (2007) 115–126.
Kimura S., Sourirajan S., Analysis of data in reverse osmosiswith porous cellulose acetate membranes used, AIChE
Journal, 13 (1967) 497–503.
Koo C.H., Mohammad A.W., Suja F., Meor Talib M.Z., Reviewof the effect of selected physicochemical factors on membrane fouling propensity based on fouling indices, Desalination, 287 (2012) 167–177.
Kozicki W., Chou C.H., Tiu C., Non-Newtonian flow in ducts ofarbitrary cross-sectional shape, Chemical Engineering Science, 21 (1966) 665–679.
Kozicki W., Tiu C., Rao A.R.K., Filtration of non-Newtonian
fluids, The Canadian Journal of Chemical Engineering, 46(1968) 313–321.
Kozicki W., Rao A.R.K., Tiu C., Filtration of polymer solutions,Chemical Engineering Science, 27 (1972) 615–626.
Lee D.J., Filter medium clogging during cake filtration, AIChE
Journal, 43 (1997) 273–276.
Lee S., Park P.K., Kim J.H., Yeon K.M., Lee C.H., Analysis offiltration characteristics in submerged microfiltration for
drinking water treatment, Water Research, 42 (2008) 3109–
3121.
Leu W.A., Tiller F.M., Experimental study of mechanism ofconstant pressure cake filtration: Clogging of filter media,
Separation Science and Technology, 18 (1983) 1351–1369.
Li W.W., Sheng G.P., Wang Y.K., Lie X.W., Xu J., Yu H.Q.,
Filtration behaviors and biocake formation mechanism ofmesh filters used in membrane bioreactors, Separation and
Purification Technology, 81 (2011) 472–479.
Li F., Tian Q., Yang B., Wu L., Deng C., Effect of polyvinyl
alcohol to model Extracellular Polymeric Substances (EPS)
on membrane filtration performance, Desalination, 286(2012) 34–40.
Lim A.L., Bai R., Membrane fouling and cleaning in microfiltration of activated sludge wastewater, Journal of Membrane Science, 216 (2003) 279–290.
Lim Y.P., Mohammad A.W., Influence of pH and ionic strength
during food protein ultrafiltration: Elucidation of permeate
flux behaviors, fouling resistance, and mechanism, Separation Science and Technology, 47 (2012) 446–454.
Liu Q.F., Kim S.H., Evaluation of membrane fouling models
based on bench-scale experiments: A comparison between
constant flowrate blocking laws and artificial neural net-

199

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

work (ANNs) model, Journal of Membrane Science, 310(2008) 393–401.
Ma N., Zhang Y., Quan X., Fan X., Zhao H., Performing a
microfiltration integrated with photocatalysis using an
Ag-TiO2/HAP/Al2O3 composite membrane for water treatment: Evaluating effectiveness for humic acid removal andanti-fouling properties, Water Research, 44 (2010) 6104–
6114.
Madaeni S.S., Fane A.G., Microfiltration of very dilute colloidal
mixtures, Journal of Membrane Science, 113 (1996) 301–
312.
Mahdi F.M., Holdich R.G., Laboratory cake filtration testing
using constant rate, Chemical Engineering Research and
Design, 91 (2013) 1145–1154.
Maroudas A., Eisenklam P., Clarification of suspensions: A
study of particle deposition in granular media, Part II—A
theory of clarification, Chemical Engineering Science, 20(1965) 875–888.
Masoudnia K., Raisi A., Aroujalinan A., Fathizadeh M., Treatment of oily wastewaters using the microfiltration process:Effect of operating parameters and membrane foulingstudy, Separation Science and Technology, 48 (2013) 1544–
1555.
Matsumoto K., Hirata S., Ohya H., Microfiltration model considering incomplete pore blocking and incomplete captureof particles by cake, Kagaku Kogaku Ronbunshu, 18 (1992)
455–462.
Matsumoto K., Furuichi M., Nakamura K., Nittami T., Evaluation of water quality by the modified SDI in the membrane
filtration process, Membrane, 34 (2009) 94–103.
Mohammadi T., Kohpeyma A., Sadrzadeh M., Mathematicalmodeling of flux decline in ultrafiltration, Desalination, 184(2005) 367–375.
Mohd Amin I.N.H., Mohammad A.W., Marcom M., Peng L.C.,
Hilal N., Analysis of deposition mechanism during ultrafiltration of glycerin-rich solutions, Desalination, 261 (2010)
313–320.
Mondal S., De S., Generalized criteria for identification of fouling mechanism under steady state membrane filtration,
Journal of Membrane Science, 344 (2009) 6–13.
Murase T., Iritani E., Cho J.H., Nakanomori S., Shirato M.,
Determination of filtration characteristics due to sudden
reduction in filtration area of filter cake surface, Journal ofChemical Engineering of Japan, 20 (1987) 246–251.
Murase T., Iritani E., Cho J.H., Shirato M., Determination of
filtration characteristics of power-law non-Newtonian fluids-­
solids mixtures under constant-pressure conditions, Journalof Chemical Engineering of Japan, 22 (1989) 65–71.
Murase T., Ohn, T., New decline pattern of filtrate flux in crossflow microfiltration of dilute suspension, AIChE Journal,
42 (1996) 1938–1944.
Nagel R., Seawater desalination with polyamide hollow fibre
modules at DROP, Desalination, 63 (1987) 225–246.
Nakakura H., Yamashita A., Sambuichi M., Osasa K., Electricalconductivity measurement of filter cake in dead-end ultrafiltration of protein solution, Journal of Chemical Engineering of Japan, 30 (1997) 1020–1025.
Nakamura K., Orie T., Matsumoto K., Response of zeta poten-

200

tial to cake formation and pore blocking during the microfiltration of latex particles, Journal of Membrane Science,
401–402 (2012) 274–281.
Nandi B.K., Moparthi A., Uppaluri R., Purkait M.K., Treatmentof oily wastewater using low cost ceramic membrane:
Comparative assessment of pore blocking and artificial
neural network models, Chemical Engineering Researchand Design, 88 (2010) 881–892.
Notebaert F.F., Wilms D.A., van Haute A.A., A new deduction
with a larger application of the specific resistance to filtration of sludges, Water Research, 9 (1975) 667–673.
Okamura S., Shirato M., Liquid pressure distribution withincakes in the constant pressure filtration, Kagaku Kogaku,
19 (1955) 104–110.
Ozdemir B., Saatci A., Yenigun O., Evaluation of cake filtration
biological reactors (CFBR) vs. membrane biological reactor(MBR) in a pilot scale plant, Desalination, 288 (2012) 135–
144.
Palacio L., Ho C.C., Zydney A.L., Application of a pore-­blockage—cake-formation model to protein fouling during
microfiltration, Biotechnology and Bioengineering, 79(2002) 260–270.
Palacio L., Ho C.C., Prádanos P., Hernández A., Zydney A.L.,
Fouling with protein mixtures in microfiltration: BSA-­
lysozyme and BSA-pepsin, Journal of Membrane Science,
222 (2003) 41–51.
Palencia M., Rivas B.L., Valle H., Size separation of silver
nanoparticles by dead-end ultrafiltration: Description offouling mechanism by pore blocking model, Journal of
Membrane Science, 455 (2014) 7–14.
Park C., Kim H., Hong S., Choi S.I., Variation and prediction of
membrane fouling index under various feed water characteristics, Journal of Membrane Science, 284 (2006) 248–
254.
Pan Y., Wang T., Sun H., Wang W., Preparation and applicationof titanium dioxide dynamic membranes in microfiltrationof oil-in-water emulsions, Separation and Purification
Technology, 89 (2012) 78–83.
Persson A., Jönsson A.S., Zacchi G., Transmission of BSA
during cross-flow microfiltration: Influence of pH and salt
concentration, Journal of Membrane Science, 223 (2003)
11–21.
Polyakov Y.S., Depth filtration approach to the theory of standard blocking: Prediction of membrane permeation rate andselectivity, Journal of Membrane Science, 322 (2008)
81–90.
Polyakov Y.S., Zydney A.L., Ultrafiltration membrane performance: Effects of pore blockage/constriction, Journal of
Membrane Science, 434 (2013) 106–120.
Prádanos P., Hernández A., Calvo J.I., Tejerina F., Mechanismsof protein fouling in cross-flow UF through an asymmetric
inorganic membrane, Journal of Membrane Science, 114(1996) 115–126.
Purkait M.K., DasGupta S., De S., Resistance in series model
for micellar enhanced ultrafiltration of eosin dye, Journal of
Colloid and Interface Science, 270 (2004) 496–506.
Purkait M.K., Bhattacharya P.K., De S., Membrane filtration ofleather plant effluent: Flux decline mechanism, Journal ofEiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202

membrane Science, 258 (2005) 85–96.
Rai P., Majumdar G.C., Sharma G., DasGupta S., De S., Effectof various cutoff membranes on permeate flux and quality
during filtration of Mosambi (Citrus Sinensis (L.) Osbeck)juice, Transactions of The Institution of Chemical Engineers, Part C, Food and Bioproducts Processing, 84 (2006)
213–219.
Raspati G.S., Meyn T., Leiknes T., Analysis of membrane andcake layer resistances in coagulation: Constant flux deadend microfiltration of NOM, Separation Science and Technology, 48 (2013) 2252–2262.
Reihanian H., Robertson C.R., Michaels A.S., Mechanism ofpolarization and fouling of ultrafiltration membranes byproteins, Journal of Membrane Science, 16 (1983) 237–258.
Rezaei H., Ashtiani F.Z., Fouladitajar A., Effects of operating
parameters on fouling mechanism and membrane flux incross-flow microfiltration of whey, Desalination, 274 (2011)
262–271.
Rodgers V.G.J., Oppenheim S.F., Datta R., Correlation of permeability and solute uptake in membranes of arbitrary poremorphology, AIChE Journal, 41 (1995) 1826–1829.
Ruohomäki K., Nyström M., Fouling of ceramic capillary filtersin vacuum filtration of humic acid, Filtration + Separation,
37(1) (2000) 51–57.
Rushton A., The flow and filtration of non-Newtonian fluids(Part A), Filtration & Separation, 10 (1986) 41–43.
Ruth B.F., Studies in filtration, III. Derivation of general filtration equations, Industrial & Engineering Chemistry, 27(1935) 708–723.
Ruth B.F., Correlating filtration theory with industrial practice,
Industrial & Engineering Chemistry, 38 (1946) 564–571.
Salinas-Rodriguez S.G., Amy G.L., Schippers J.C., Kennedy
M.D., The Modified Fouling Index Ultrafiltration constantflux for assessing particulate/colloidal fouling of RO systems, Desalination, 365 (2015) 79–91.
Sarkar B., De S., A combined complete pore blocking and cakefiltration model for steady-state electric field-assisted ultrafiltration, AIChE Journal, 58 (2012) 1435–1446.
Sarkar B., A combined complete pore blocking and cake filtration model during ultrafiltration of polysaccharide in abatch cell, Journal of Food Engineering, 116 (2013) 333–
343.
Schippers J.C., Hanemaayer J.H., Smolders C.A., Kostense A.,
Predicting flux decline of reverse osmosis membranes,
Desalination, 38 (1981) 339–348.
Shirato M., Aragaki T., Mori R., Sawamoto K., Predictions ofconstant pressure and constant rate filtrations based upon
an approximate correction for side wall friction in compression permeability cell data, Journal of Chemical Engineering of Japan, 1 (1968) 86–90.
Shirato M., Sambuichi M., Kato H., Aragaki T., Internal flowmechanism in filter cakes, AIChE Journal, 15 (1969) 405–
409.
Shirato M., Aragaki T., Iritani E., Wakimoto M., Fujiyoshi S.,
Nanda S., Constant pressure filtration of power-law
non-Newtonian Fluids, Journal of Chemical Engineering ofJapan, 10 (1977) 54–60.
Shirato M., Aragaki T., Iritani E., Blocking filtration laws forfiltration of power-law non-Newtonian fluids, Journal ofChemical Engineering of Japan, 12 (1979) 162–164.
Shirato M., Aragaki T., Iritani E., Analysis of constant pressure
filtration of power-law non-Newtonian fluids, Journal ofChemical Engineering of Japan, 13 (1980) 61–66.
Sperry D.R., Note and correspondence: A study of the fundamental laws of filtration using plant-scale equipment,
Industrial & Engineering Chemistry, 13 (1921) 1163–1164.
Srisukphun T., Chiemchaisri C., Yamamoto K., Modeling of RO
flux decline in textile wastewater reclamation plants using
variable fouling index, Separation Science and Technology,
44 (2009) 1704–1721.
Suarez J.A., Veza J.M., Dead-end microfiltration as advanced
treatment for wastewater, Desalination, 127 (2000) 47–58.
Sun S., Yue Y., Huang X., Meng D., Protein adsorption on blood
contact membranes, Journal of Membrane Science, 222(2003) 3–18.
Sun X., Kanani D.M., Ghosh R., Characterization and theoretical analysis of protein fouling of cellulose acetate membrane during constant flux dead-end microfiltration, Journalof Membrane Science, 320 (2008) 372–380.
Takahashi K., Ohmori N., Ishi K., Yokota T., Cake formationand spatial partitioning in batch microfiltration of yeast,
Journal of Chemical Engineering of Japan, 24 (1991) 372–
377.
Taniguchi M., Kilduff J.E., Belfort J., Modes of natural organic
matter fouling during ultrafiltration, Environmental Science & Technology, 37 (2003) 1676–1683.
Tettamanti B., Modeling and scaling of filtration in industry,
International Chemical Engineering, 22 (1982) 561–571.
Thekkedath A., Naceur W.M., Kecili K., Sbai M., Elane A.,
Auret L., Suty H., Machinal C., Pontié M., Macroscopic
and microscopic characterization of a cellulosic ultrafiltration (UF) membrane fouled by a humic acid cake deposit:
First step for intensification of reverse osmosis (RO)pre-treatments, Comptes Rendus Chimie, 10 (2007) 803–
812.
Tien C., Payatakes A.C., Advances in deep bed filtration,
AIChE Jornal, 25 (1979) 737–759.
Tien C., Ramarao B.V., Revisiting the laws of filtration: An
assessment of their use in identifying particle retention
mechanisms in filtration, Journal of Membrane Science,
383 (2011) 17–25.
Tien C., Ramarao B.V., Yasarla R., A blocking model of membrane filtration, Chemical Engineering Science, 111 (2014)421–431.
Tiller F.M., The role of porosity in filtration, I. Numerical methods for constant rate and constant pressure filtration basedon Kozeny’s law, Chemical Engineering Progress, 49 (1953)
467–479.
Tiller F.M., The role of porosity in filtration, Part 2, Analyticalequations for constant rate filtration, Chemical Engineering
Progress, 51 (1955) 282–290.
Tiller F.M., Cooper H.R., The role of porosity in filtration: IV.
Constant pressure filtration, AIChE Journal, 6 (1960) 595–
601.
Tiller F.M., Shirato M., The role of porosity in filtration: VI.
New definition of filtration resistance, AIChE Journal, 10201

Eiji Iritani et al. / KONA Powder and Particle Journal No. 33 (2016) 179–202(1964) 61–67.
Tiller F.M., Weber W., Davies O., Clogging phenomena in thefiltration of liquefied coal, Chemical Engineering Progress,
77(Dec.) (1981) 61–68.
Todisco S., Peña L., Drioli, E., Tallarico P., Analysis of the fouling mechanism in microfiltration of orange juice, Journal of
Food Processing and Preservation, 20 (1996) 453–466.
Tracey E.M., Davis R.H., Protein fouling of track-etched polycarbonate microfiltration membranes, Journal of Colloid
and Interface Science, 167 (1994) 104–116.
van Reis R., Zydney A., Bioprocess membrane technology,
Journal of Membrane Science, 297 (2007) 16–50.
Vera M.C.V., Blanco S.Á., García J.L., Rodríguez E.B., Analysisof membrane pore blocking models adapted to crossflow
ultrafiltration in the ultrafiltration of PEG, Chemical Engineering Journal, 149 (2009) 232–241.
Vilker V.L., Colton C.K., Smith K.A., Concentration polarization in protein ultrafiltration, AIChE Journal, 27 (1981)
632–645.
Wang F., Tarabara V.V., Pore blocking mechanisms during earlystages of membrane fouling by colloids, Journal of Colloid
and Interface Science, 328 (2008) 464–469.
Wei C.H., Laborie S., Ben Aim R., Amy G., Full utilization ofsilt density index (SDI) measurements for seawater pre-­treatment, Journal of Membrane Science, 405–406 (2012)
212–218.
White D.A., The interpretation of the SDI for water solids content using the filtration equation, Transactions of the Institution of Chemical Engineers, Part B, Process Safety andEnvironmental Protection, 74 (1996) 137–140.
Wu J., He C., Zhang Y., Modeling membrane fouling in a submerged membrane bioreactor by considering the role ofsolid, colloidal and soluble components, Journal of Membrane Science, 397–398 (2012) 102–111.
Ye Y., Clech P.L., Chen V., Fane A.G., Jefferson B., Foulingmechanisms of alginate solutions as model extracellularpolymeric substances, Desalination, 175 (2005) 7–20.
Yiantsios S.G., Karabelas A.J., An assessment of the Silt Density Index based on RO membrane colloidal fouling experiments with iron oxide particles, Desalination, 151 (2003)
229–238.
Yukseler H., Tosun I., Yetis U., A new approach in assessing
slurry filterability, Journal of Membrane Science, 303(2007) 72–79.
Yuan W., Kocic A., Zydney A.L., Analysis of humic acid fouling during microfiltration using a pore blockage – cake filtration model, Journal of Membrane Science, 198 (2002)
51–62.
Zeman L.J., Adsorption effects in rejection of macromolecules
by ultrafiltration membranes, Journal of Membrane Science, 15 (1983) 213–230.
Zhong Z., Li W., Zing W., Xu N., Crossflow filtration of nanosized catalysis suspension using ceramic membranes. Separation and Purification Technology, 76 (2011) 223–230.
Zhou J., Wandera D., Husson S.M., Mechanisms and control offouling during ultrafiltration of high strength wastewater
without pretreatment, Journal of Membrane Science, 488(2015) 103–110.

Author’s short biography
Eiji Iritani
Eiji Iritani is currently Professor of Department of Chemical Engineering at Nagoya
University. He received his Docter degree of Engineering from Nagoya University in
1981. His research interests are in the area of cake filtration, membrane filtration, deliquoring due to expression, sedimentation, centrifugation, and water treatments based onsolid-liquid separation technology.

Nobuyuki Katagiri
Nobuyuki Katagiri is currently Assistant Professor of Department of Chemical Engineering at Nagoya University. He received his Doctor degree of Agriculture from Gifu
University in 1998. His research interests are in the area of filtration and expression of
biologically-derived materials, and waste water treatments.

202

KONA Powder and Particle Journal No. 33 (2016) 203–218/Doi:10.14356/kona.2016001

Original Research Paper

Preparation of LaB6 Powders via Calciothermic Reduction
using Mechanochemistry and Acid Leaching †Duygu Ağaoğulları *, Özge Balcı, M. Lütfi Öveçoğlu and İsmail Duman
1

Istanbul Technical University, Faculty of Chemical and Metallurgical Engineering, Department ofMetallurgical and Materials Engineering, Particulate Materials Laboratories (PML), Turkey

Abstract
This study reports a room temperature mechanochemical route for the synthesis of LaB6 powders originated from
related metal oxide powders as La2O3 and B2O3. Ca granules and B2O3 powders were respectively used as
reducing agent and boron source in the experiments. This study is actually meaningful to create added value by
using the native boron source of Turkey for the production of high-technology boron materials. Milling duration
as the most important parameter of mechanochemistry was examined to reveal the ideal production conditions.
Mechanochemically synthesized powders were subjected to selective HCl leaching in order to eliminate unwanted
phases. Thermochemical software of HSC ChemistryTM program was utilized to determine the reaction
probability and to estimate the predicted products. Characterization investigations were carried out by X-ray
diffractometer (XRD), differential scanning calorimeter (DSC), particle size analyzer (PSA), stereomicroscope(SM), scanning electron microscope (SEM), transmission electron microscope/energy dispersive spectrometer(TEM/EDS) and atomic absorption spectrometer (AAS). Nanosized LaB6 powders were achieved with/without aninsignificant amount of Ca3(BO3)2 phase via mechanochemistry in a high-energy ball mill for 3 h and via leachingwith 4 M and 6 M HCl. Lastly, the experimental outputs obtained by a calciothermic reduction were comparedwith those of magnesiothermic reduction.
Keywords: lanthanum hexaboride powders, calciothermic reduction, mechanochemistry, room temperatureprocess, lanthanum oxide, boron oxide

1. Introduction
Materials based on boron compounds have been explored since 1950s because of their superior properties inrespect to chemical bonding, crystal structure and phononand electron conduction. Especially, rare-earth metal hexaborides have numerous useful physical and chemical characteristics which make them important to investigate (Postet al., 1956; Futamoto et al., 1980; Otani and Ishizawa,
1996; Balakrishnan et al., 2004; Selvan et al., 2008; Wanget al., 2010; Ji et al., 2011; Brewer et al., 2011; Aprea et al.,2013).
Lanthanum hexaboride (LaB6) has attracted significant
interest in the last 60 years amongst transition metal
borides and rare-earth metal borides (Samsonov et al.,
1963; Paderno et al., 1979; Xu et al., 2012; Dou et al.,2012). LaB6 is a refractory ceramic material characterizedby high melting point, high strength, high chemical and†
1*

Received 17 December 2014; Accepted 27 January 2015
J-STAGE Advance published online 10 April 2015
Ayazağa Campus, 34469 Maslak, Istanbul, Turkey
Corresponding author: Duygu Ağaoğulları;E-mail: bozkurtdu@itu.edu.tr
TEL: +90-212-285-6893 FAX: +90-212-285-3427©2016 Hosokawa Powder Technology Foundation

thermal stability, low vapor pressure, low electronic work
function, low resistivity, low thermal expansion coefficient,high transmission stability, high current and voltage capability and high neutron absorbability (Gao et al., 2005; Xu
et al., 2006; Zhang et al., 2008; Selvan et al., 2008). LaB6
is built from B6 type crystal structure (octahedrons in
cubic hexaboride) in which boron sublattice is electron-­
deficient and requires electron transfer from the metal
atoms in order to be stabilized (Swanson and McNeely,
1979; Mitterer et al., 1996). Furthermore, these boron octahedrons are united by very strong covalent bonds giving
the crystals their characteristic hardness (Ji et al., 2011).
LaB6 is defined as a dense Kondo material which show
several interesting phases such as antiferro-quadrupolarordered phase and antiferromagnetic ordered phase in theeffect of magnetic field and temperature (Tanaka et al.,
2004; Carlsson et al., 2005; Jha et al., 2012). According to
the phase diagram of the La-B system, there is one congruently melting LaB6 compound at 2715 °C, a peritectic
LaB4 phase at 1800 °C and an unstable LaB9 phase at~2007 °C (Massalski et al., 1990; Schlesinger et al., 1999).
All these superior properties make LaB6 suitable to use
as components of high-energy optical systems, sensors
for high resolution detectors, electrical coatings for resis-203

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

tors, coatings for high-current hot cathodes, thermionicmaterials and to use in nuclear technology (Balakrishnan
et al., 2004; Bao et al., 2011). It can also be used in wear
and corrosion resistant hard coatings for decoration of
consumer products such as eye-glass frames and wristwatch casings (Mitterer et al., 1996; Selvan et al., 2008).
Single crystalline LaB6 nanowires are one of the mostwidely used electron emitter in electron gun, which canoffer about 10 to 15 times higher brightness, higher emission stability, lower energy spread and longer service life
than the tungsten cathodes utilized in a large variety ofdevices such as high-resolution electron microscopes,
electron beam writing units, vacuum electron beam welding machines, microwave tubes, free electron lasers,
X-ray tubes, electron beam surface reforming and electron beam lithography devices (Ahmed et al., 1975; Perkins
et al., 1999; Chen et al., 2004; Wen et al., 2004; Zhang etal., 2006, 2007, 2008; Wang et al., 2009; Qin et al., 2010;
Bao et al., 2011). Moreover, LaB6 nanomaterials are candidates to motivate the new designs of nanoscale electronic devices operated at high temperatures with the needof enhanced electron collection and injection properties(Brewer et al., 2011). Also, LaB6 has exceptional thermoelectric properties at low temperatures providing their usage in solid-state cryocooling, thermoelectric refrigerators,
generators and single-photon detectors operating at temperatures near to the boiling point of liquid helium which
serve in technical applications including quantum computing, quantum cryptography, homeland security, defectcontrol in microchips, astronomy, chemical analysis andparticle physics (Carlsson et al., 2005; Petrosyan et al.,2012; Jha et al., 2012). Recently, LaB6 nanoparticles havebeen proved as effective materials in near-infrared absorption enabling application in reduction of solar heat gain
(Yuan et al., 2011). Additionally, LaB6 is considered as apotential grain refining agent in Al alloys due to its role asan effective, stable and reliable nucleation substrate for Al
during the solidification process (Li et al., 2012).
Many production techniques have been applied in the
preparation of lanthanum hexaboride (Baranovskiy et al.,2007; Loboda et al., 2009). Traditionally, LaB6 has beensynthesized by high temperature reaction processes suchas direct solid-phase reaction of the corresponding elements or compounds, carbothermal reaction of the lanthanum oxide and B or boron carbide (B4C) method (Post etal., 1956). In industry, carbothermal reduction and boron
carbide method have been widely used due to their low
cost and simple equipment (Dou et al., 2011; Hasan et al.,2013). However, disadvantages in these methods such as
high content of carbon or high processing temperature
limit the possibility of preparing LaB6 powders with highpurity and small particle sizes (Dou et al., 2012). Moreover, powders prepared by these conventional methods
have generally poor sinterability (Dou et al., 2012). Float-

204

ing zone method and electrosynthesis have been also developed conventionally in order to obtain LaB6 crystals(Verhoeven et al., 1976). Essentially, floating zone method
has been carried out to produce single crystals by using
laser or RF heating (Otani and Ishizawa, 1996; Otani etal., 2000). Electrosynthesis of LaB6 crystals has been applied by molten salt technique from an electrolyte consisting of La chlorides/oxides, boron sources (B2O3,
Li2B4O7, etc.) and chloride/fluoride agents (LiCl, LiF, etc.)(Amalajyothi et al., 2008, 2011). For a number of years,
researchers have tried to obtain LaB6 in different forms
such as particles, whiskers, rods or cubes using various
fabrication techniques. Recently, nanosized (nanocubes andnanorods) or submicron LaB6 powders have been obtainedby using low temperature synthesis in an autoclave andself-propagating high-temperature synthesis (SHS) methods (Ji et al., 2011; Dou et al., 2012). Autoclave systems
provide an alternative route for synthesizing LaB6 atlower temperatures than used in traditional methods(Wang et al., 2010). SHS methods have high reaction
speed, simple equipment and low energy consumption but
after ignition, the reaction process becomes uncontrolledwhich results in the occurrence of intermediate phases in
the reaction products (Dou et al., 2011). LaB6 whiskers,
wires, tubes and obelisks in nano- or micro-scales havebeen produced by chemical vapor deposition technique(CVD) (Motojima et al., 1978; Zhang et al., 2006). CVD
methods include self-catalyst or metal-catalyst (Au, Pt,etc.) growth of LaB6 on a substrate based on the catalytic
reaction of La element/compound containing powders andboron (BCl3, B10H14, etc.), hydrogen and argon containing
gas mixtures (Ji et al., 2011).
It is significant to develop an efficient method of preparing technologically important LaB6 powders in high purity,
small particle size and various morphologies. It has been
attracting more attentions to find new synthesis strategies
with low reaction temperature, easy to handle precursors,
simple process control, less equipment requirements andlow cost. Compared with the above-mentioned production
techniques, mechanochemistry is a novel, simple androom temperature process which enables a great degree ofcontrolling the product microstructure (content, shape, size,etc.) and obtaining highly pure materials (Suryanarayana,2001). To the best of our knowledge, there is recently one
report on the fabrication of LaB6 powders via magnesiothermic reduction forced by ball milling process(Ağaoğulları et al., 2012b; Ağaoğulları, 2014).
In this study, LaB6 powders originated from related
metal oxide powders (as La2O3 and B2O3) were synthesized at room temperature, via calciothermic reduction
using mechanochemistry. Consequently, this study willcontribute the first results of the mechanochemically synthesized LaB6 powders from La2O3-B2O3-Ca blends to thearchival literature.

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

2. Experimental procedure
The experimental procedure of this study comprises theidentification of raw materials, the step-by-step synthesisof LaB6 powders, the purification treatments conducted on
the synthesized powders and the characterization investigations carried out at each stage of the overall process.

2.1 Raw materials
The synthesis of LaB6 was originated from its oxide
powders such as La2O3 (ABCRTM, in purity of 99.99 %).
B2O3 (ETI Mine, in purity of 98 %) powders were used asa native boron source for the formation of boride phase.
Ca (Alfa AesarTM, in purity of 99.5 %) granules were utilized as a reducing agent in order to examine their influence on the production of LaB6. The images of all raw
materials were captured by using a ZeissTM Discovery.V12
stereomicroscope (SM) coupled with a ZeissTM Axiocam
ERc5s high resolution digital camera. SM images of the
raw materials are represented in Fig. 1(a) through Fig.
1(c). Figs. 1(a) and (b) more or less give an idea about the
average particle sizes of La2O3 and B2O3 since their SM
images respectively have the magnification scale (25X) of
200 and 500 µm. However, Fig. 1(c) whose magnification
scale (8X) is 1000 µm obviously shows the average size of
the Ca granules as 3 mm.
The crystalline phases of all raw materials were identified using a BrukerTM D8 Advanced Series X-ray diffractometer (XRD) with CuKα (1.54060 Å) radiation in the2θ range of 10–90° incremented at a step size of 0.02° at a
rate of 2°/min. International Center for Diffraction Data®
(ICDD) powder diffraction files were utilized for the crystalline phase identification of the raw materials. The XRD
patterns of La2O3, B2O3 and Ca granules are illustrated inFig. 2(a) through Fig. 2(c), respectively. Each figure
shows diffraction peaks belonging only to the pure material with no trace of impurities. Although Fig. 2(a) exhibits intense crystalline peaks belonging to its content, Fig.
2(b) shows the characteristic peak structure of amorphous
B2O3. The XRD peaks of Ca given in Fig. 2(c) have low
intensities arising from its granule form which was very
difficult to grind in an agate mortar applied for the proper
analysis in the powder diffractometer.
Particle size measurements were conducted in a MalvernTM Mastersizer 2000 particle analyzer using distilled water as the aqueous media in order to determine
the accurate particle sizes of the raw materials having
powder forms. Figs. 3(a) and (b) represent the respective
particle size distributions of the La2O3 [d(0.1) = 7.5 μm,
d (0. 5 ) = 2 8 . 8 μ m , d (0.9) = 67.9 μ m] a n d B 2 O 3[d(0.1) = 268.5 μm, d(0.5) = 438.8 μm, d(0.9) = 708.2 μm]
powders. As seen in Figs. 3(a) and (b), the average particle sizes of the La2O3 and B2O3 starting powders were
measured as 33.6 µm and 466.9 µm, respectively.
Powder blends containing stoichiometric amounts ofreactants for synthesizing LaB6 powders were preparedaccording to the ideal reduction reaction given in Eq. (1).
La2O3 + 6B2O3 + 21Ca → 2LaB6 + 21CaO

Fig. 1

SM images of the raw materials: a, La2O3, b, B2O3 and c, Ca.

Fig. 2

XRD patterns of the raw materials: a, La2O3, b, B2O3 and c, Ca.

(1)

205

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

vent surface oxidation and to prevent contamination of
powder particles with oxide and nitride phases arisingfrom the presence of air. After sealing the vials, milling
was continually conducted at different durations up to
3 h. Preliminary experiments carried out intermittently
and continuously up to a certain milling duration resulted
in different products because the complete reaction took
place by means of the internal heat generated from the repeated collisions of the reactant particles. The milled
powders were unloaded again under Ar atmosphere in the
glove-box. They are hereafter referred to as mechanochemically synthesized La2O3-B2O3-Ca powders.

2.3 Selective acid leaching

Fig. 3

Particle size distributions of the raw materials having
powder forms: a, La2O3 and b, B2O3.

For each run, powder batches of 6 g were weighed in a
PrecisaTM XB320M sensitive balance (precision: 0.001 g).
Powder batches contained 1.233 g La2O3, 1.581 g B2O3
and 3.186 g Ca in regard of the 1.543 g LaB6 formation reaction in Eq. (1). The prepared powder batches are hereafter referred to as La2O3-B2O3-Ca powder blends.

2.2 Mechanochemical synthesis
The prepared La2O3-B2O3-Ca powder blends were homogenized in a WABTM T2C Turbula blender for 1 h prior
to mechanochemical synthesis experiments. The homogenized powder blends were mechanochemically synthesized by using a multi-axial and vibratory high-energy
ball mill, SpexTM 8000D Mixer/Mill, with a rotation
speed of 1200 rpm. Non-milled powders, homogenized
but not subjected to mechanochemical synthesis, were
hereafter referred to as-blended (ab) powders and wereused as references for the comparison with the milled ones.
In the mechanochemical synthesis experiments, ball-topowder weight ratio (BPR) was chosen as 10:1. The milling container was a hardened steel vial with a capacity of
50 ml and the milling media was 50 pieces hardened steel
balls with a diameter of 6 mm. Ar gas (LindeTM, in purityof 99.999 %) was preferred as the milling atmosphere.
The milling vials were evacuated to about 10 –2 Pa andback filled with Ar gas in a PlaslabsTM glove box to pre-206

Mechanochemically synthesized La2O3-B2O3-Ca powders in which the reduction reaction completed contained
LaB6, CaO, etc. phases together as the intermediate products. Selective HCl (MerckTM, in concentration of 37 %)leaching was applied on these products under the effect ofultrasonic stirring and heating using a Bandelin SonorexTM RK-100 H ultrasonic bath due to the fact that LaB6
is insoluble in HCl. Leaching treatment enabled the removal of the unwanted phases and also Fe impurity whichwere worn off from the milling vial and milling balls.
4 M and 6 M HCl were used as the leaching solutions.
This means that the intermediate products were leachedusing excess HCl molarity with the intention of obtaining
pure LaB6 powders. After a series of pre-experiments,
leaching parameters such as solid-to-liquid ratio of theleaching solution, leaching duration and leaching temperature were fixed respectively to 1 g/10 cm3, 30 min
and 80 °C. During the first few seconds of the leachingtreatments, the intermediate products violently reactedwith the leaching solutions, arising from the high solubility of CaO in HCl solution as in the reactions given in
Eqs. (2)–(4).
CaO + H2O → Ca(OH)2
CaO + 2HCl → CaCl2 + H2O
Ca(OH)2 + 2HCl → CaCl2 + 2H2O

(2)
(3)
(4)

At the end of leaching treatment, the solutions containing insoluble solids were subjected to repeated centrifuging in a HettichTM Rotofix 32A centrifuge with a rotation
speed of 3500 rpm for 30 min, repeated decanting and repeated washing with distilled water and lastly remainedsolids were dried under air in a FN 500 stove at 120 °C
for 24 h. Supernatant liquids decanted from the solids after each centrifuging-decanting-washing treatment werecollected into a volumetric flask and were equated to
150 ml with distilled water for the subsequent instrumental analysis. After each centrifuging-decanting-washingtreatment, a few drop of leaching solution was added into

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

a 1 M AgNO3 solution (FlukaTM, in purity of 99.5 %) due
to the detection of chlorine by precipitating AgCl andthese treatments were repeated until any AgCl precipitation was not detected. Since the leaching process was carried out by using excess HCl molarity, the HCl adsorption
on the boride powders can be also prevented by this detection method. Moreover, pH measurements were carriedout on the supernatant liquids decanted from the solids
after the last washing procedure, by using Thermo ScientificTM Orion Star A211 pH meter. The measured pH values varied between 5.5 and 6.5, indicating that there was
not residual HCl in the media at the end of the repeatedtreatments. These findings conformed well to the resultsof AgCl precipitation method. So, the final products originated from the mechanochemically synthesized andleached La2O3-B2O3-Ca powders.

2.4 Characterization
XRD investigations of the as-blended, mechanochemically synthesized and leached powders were performed ina BrukerTM D8 Advanced Series powder diffractometerand in a PANalyticalTM X'Pert³ powder diffractometer using the same conditions for the raw materials. As-blended
and mechanochemically synthesized powders were imaged by the same stereomicroscope utilized for the raw
materials. Particle size measurement was conducted on
the leached powders using a MicrotracTM Nano-flex particle size analyzer (PSA) equipped with a Bandelin SonopulsTM ultrasonic homogenizer. Thermal properties of theas-blended and mechanochemically synthesized powders
were examined in a TATM Instruments SDT Q600 differential scanning calorimeter (DSC): For each run, 15 mgof powders was placed in an alumina crucible and heated
up to 1100 °C at a heating rate of 10 °C/min under Ar atmosphere. After DSC analyses, mechanochemically synthesized and heated powders were subjected to an
additional XRD analysis for detecting the obtainedphases. Microstructural characterizations and energy dispersive spectroscopy (EDS) analyses of the mechanochemically synthesized and leached powders were carriedout using a HitachiTM TM-1000 scanning electron microscope (SEM) operated at 15 kV and also using a
JeolTM-JEM-2000EX transmission electron microscope(TEM) operated at 160 kV. For SEM analysis, the specimens were prepared by following the procedure of dissolving the powders in C2H5OH (MerckTM, in purity of
99.9 %), syringing them onto a base plate, drying them in
air and coating their surfaces with a thin layer of gold using a PolaronTM SC7620 Sputter Coater to enhance theirconductivities. For TEM analysis, the particles were dispersed in the C2H5OH and a drop of it was taken on a porous carbon film supported on a copper grid and dried ina JingkeTM Scientific Instrument vacuum oven. EDS re-sults were reported as the arithmetic mean of three different measurements taken from the same region in thesample. Furthermore, the amounts of elements (B, Ca andFe) in the supernatant liquid after the purification byleaching process were analyzed in a Perkin ElmerTM
1100B atomic absorption spectrometer (AAS).

3. Results and discussion
HSC ChemistryTM Ver. 4.1 program was utilized for thereaction interpretations of the La 2O3-B2O3-Ca system.
The Gibbs free energy change and the enthalpy change
versus temperature curves of the La2O3-B2O3-Ca system
up to 2000 °C were graphed by using thermochemical
software and they were illustrated in Fig. 4. The reaction
has a large negative free energy change between −4100
and –2700 kJ in the temperature range of 0–2000 °C. It
means that the reaction in Eq. (1) takes place spontaneously and therefore it is thermodynamically feasible atroom temperature and above. Furthermore, a large negative enthalpy change between –4150 and –4510 kJ in thetemperature range of 0–2000 °C implies that a large
amount of heat is released during the occurrence of thereaction. It can be predicted that the vial temperature will
significantly increase during milling due to these exothermic reactions. As seen in Fig. 4, the slope of the enthalpy
change-temperature curve varies at some intervals. Thisis likely due to the melting of B2O3 at 450 °C, the formation of calcium borate phases in different compositions(CaB2O 4, CaB 4 O7, CaB6 O10, Ca 2B2O5, Ca 2B6 O11 andCa3B2O6) at 600–1000 °C, the melting of Ca at 840 °C
and the boiling of Ca at 1485 °C (Chen et al., 2008; Erfani
et al., 2012).
It is already known from our previous study conductedon the La2O3-B2O3-Mg system that its related reaction has
a large negative free energy change between –3300 and–1700 kJ and a large enthalpy change between –3440 and–3930 kJ, in the temperature range of 0 –2000 °C(Ağaoğulları et al., 2012b). Besides, the slope of the enthalpy change-temperature curve for the La2O3-B2O3-Mg
system arises from the melting of B2O3 at 450 °C, themelting of Mg at 650 °C, the formation of magnesium borate phases in different compositions (MgB4O7, Mg2B2O5
and Mg3B2O6) at 700–1000 °C and the boiling of Mg at
1095 °C (Ağaoğulları et al., 2012a; 2012b). On the basis of
the enthalpy changes calculated by thermochemical software in case of using Ca or Mg as a reducing agent, it canbe stated that calcium borate phases emerged at about
100 °C lower temperatures than those of magnesium borate phases. Furthermore, it is clearly understood that thecalciothermic reduction of related oxides is more favorable than their magnesiothermic reduction since their freeenergy difference at the same temperature is about 800–207

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

Fig. 4The Gibbs free energy change (■) and enthalpy change(▲) versus temperature curves of the reaction given in
Eq. (1).

1000 kJ. Thus, the use of Ca instead of Mg as a reductant
seems to be more suitable in the synthesis of LaB6 powders according to the thermodynamic calculations. However, the calcium borates regarded as unwanted phases inthe boride synthesis form in several compositions at relatively lower temperatures than those of magnesium borates and hence their negative influence on the production
step could be considered as a probability. It should be also
noted that the results of the thermodynamic calculations
show only the equilibrium phase composition at room andelevated temperatures without considering synthesis history and reaction kinetics. Actually, mechanochemical
synthesis process is related to the repeated fracturing andrepeated welding mechanism. Besides, the contact points
between the particles provide favorable conditions for theformation of the products. Due to the fact that mechanochemical synthesis process is far from equilibrium, synthesized phases can not be expected to have complete
consistence with the thermodynamically calculated phases.
Figs. 5(a) through (g) represents the XRD patterns of
the as-blended and mechanochemically synthesized
La2O3-B2O3-Ca powders using a 10:1 BPR in a SpexTM
8000D Mixer/Mill up to a milling duration of 3 h.
Since Ca granules had an average size of 3 mm, they
were milled for 30 min prior to the homogenization treatment, in the same conditions (BPR, media, atmosphereand container of milling, type and speed of mill, etc.) withthose of mechanochemical synthesis experiments in orderto obtain reduction in their sizes. According to Fig. 5(a),as-blended powders contain La2O3 (ICDD Card No: 7154 08, Br ava is lat t ice: pr i m it ive hexagon a l,
a = b = 0.393 nm, c = 0.614 nm), B2O3 (ICDD Card No:
76 -1655, Br ava is lat t ice: pr i m it ive hexagon al,a = b = 0.433 nm, c = 0.839 nm) and Ca(OH)2 (ICDD Card
No: 72-0156, Bravais lattice: primitive hexagonal,
a = b = 0.358 nm, c = 0.489 nm) phases. Although B2O3
has a large characteristic amorphous peak in the 2θ rangeof 15–40° (Fig. 2(b)), it can be seen very slightly at the208

Fig. 5 XRD patterns of the as-blended La2O3-B2O3-Ca powders
and those mechanochemically synthesized for different
durations: (a) as-blended, (b) 30 min, (c) 45 min, (d)1 h, (e) 1.5 h, (f) 2 h and (g) 3 h.

same range in the XRD pattern of the as-blended powders(Fig. 5(a)) with the peaks of other phases. It is well known
that Ca powders easily absorb humidity and form calcium
hydroxide at room temperature. Milled Ca powders reacted with humidity during handling in the laboratory atmosphere and hence Ca(OH)2 phase was observed insteadof Ca in the XRD pattern of the as-blended powders. As
seen in Figs. 5(b), (c) and (d), mechanochemically synthesized La2O3-B2O3-Ca powders for 30 min, 45 min and 1 h
durations have La2O3 and Ca(OH)2 phases, indicating that
there is still no reaction between the contacting particles.
However, the intensities of these phases decrease as milling duration increases from 30 min to 1 h. This designatesa gradual decrease in their crystallite sizes and an increase in their lattice deformations, which means that thereaction is getting closer step by step. Unlike Fig. 5(a), noslight peaks belonging to the B2O3 phase can be detectedin the XRD patterns of the 30 min, 45 min and 1 h milled
powders (Figs. 5(a)–(c)), whose absence is attributed toits further amorphization during mechanochemical synthesis. Besides, there are very small incubations of LaB6(ICDD Card No: 34-0427, Bravais lattice: primitive cubic,
a = b = c = 0.416 nm) and CaO (ICDD Card No: 70-5490,
Bravais lattice: face-centered cubic, a = b = c = 0.482 nm)
phases at about 37.5° at the end of 1 h milling (Fig. 5(d)).
Further 30 min milling (Fig. 5(e)) results in LaB6, CaO
and Ca3(BO3)2 (ICDD Card No: 70-0868, Bravais lattice:primitive rhombohedral, a = b = 0.864 nm, c = 1.185 nm)

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

phases in addition to very small amounts of unreacted
La2O3 and Ca(OH)2. X-ray reflections for the LaB6 phase
comprise eight peaks at values of 21.354°, 30.387°,
37.445°, 43.517°, 48.969°, 53.995°, 67.564° and 71.757°
which are respectively indexed as (100), (110), (111), (200),(210), (211), (300) and (310) family of planes. After milling for 2 h, La2O3 and Ca(OH)2 phases disappear and thecomposition of the mechanochemically synthesized powders includes only LaB6, CaO and Ca3(BO3)2 phases (Fig.
5(f)). The formation mechanism of Ca3(BO3)2 phase canbe explained with the reaction of B2O3 and CaO particles
in regard of Eq. (5). However, the emergence of Ca3(BO3)2phase is unexpected considering the theoretical productsof the ideal reduction reaction given in Eq. (1).
3CaO + B2O3 → Ca3(BO3)2(5)

As compared with the mechanochemical synthesis of
La2O3-B2O3-Mg powder blends using the same milling
conditions, it is obvious that calciothermic reduction provides a complete reaction 45 min earlier than that of magnesiothermic one in which LaB 6 and MgO phases
occurred after milling for 2 h 45 min (Ağaoğulları et al.,2012b). Dissimilar to the magnesiothermic reduction of
La2O3 and B2O3 powder blends, milled products of thecalciothermic reduction contain a borate compound of the
reducing agent since Ca is more favorable to form borate
phase than Mg. Fig. 5(g) does not show any difference inthe phase types of the 3 h milled powders in comparison
with Fig. 5(f) because LaB6, CaO and Ca3(BO3)2 are stillin the microstructure. However, there is a remarkable increase in the intensities of LaB6 and CaO phases. On thebasis of this intensity increment, extended milling durations up to 5 h were conducted on the La2O3-B2O3-Ca
powder blends: Broadening of the LaB6 and CaO peaks
were observed instead of further intensity increment. Itmeans that prolonged milling time does not contribute to
the chemical reaction anymore after 3 h, but it contributes
to the size reduction of the particles. Additionally, it
should be stated that the actual mechanochemical reaction
is different than the ideal reduction reaction in Eq. (1). So,
the actual mechanochemical reaction in equilibrium
amounts can be indicated as in Eq. (6) on the basis of theexperimental results in Fig. 5.
La2O3 + 7B2O3 + 21Ca → 2LaB6 + 18CaO + Ca3(BO3)2(6)
The ideal reduction reaction in Eq. (1) includes 6 molesof B2O3 whereas 7 moles of B2O3 is present in the actual
reaction to represent the formation of Ca3(BO3)2 phase.
Due to the fact that the initial powder blends were prepared in regard of ideal reaction in Eq. (1) without considering the occurrence of borate phase, it should be alsomentioned that the obtained LaB6 phase can include anamount of sub-boride phases such as La0.983B6 (ICDD
Card No: 70-8265, Bravais lattice: primitive cubic, a = b =c = 0.41561 nm), LaB5.83 (ICDD Card No: 78-2381, Bravais lattice: primitive cubic, a = b = c = 0.41566 nm),
LaB5.832 (ICDD Card No: 75-1414, Bravais lattice: primitive cubic, a = b = c = 0.41566 nm), LaB5.784 (ICDD Card
No: 75-1402, Bravais lattice: primitive cubic, a = b = c =0.41569 nm) and LaB5.892 (ICDD Card No: 75-1415, Bravais lattice: primitive cubic, a = b = c = 0.41571 nm). The
possibility of containing a sub-boride phase such as
LaB6-x is higher than that of La1-xB6. Because an amountof Ca3(BO3)2 emergence and 1 mole lack of B2O3 according to the reaction in Eq. (5) could decrease the number ofboron atoms for the boride formation. Although the use of
Ca as a reductant provided shorter reaction duration thanthat of Mg, it could change the composition and hence thequality of the boride products, considering the formation
probability of sub-borides.
Figs. 6(a) through (g) shows the SM images of the asblended and mechanochemically synthesized La 2O3 B2O3-Ca powders, for different durations up to 3 h. SM
images can be helpful to control the reaction proceedingfrom the general appearance of the powders. The difference between as-blended and milled powders is clearlyseen from Figs. 6(a)–(g). As-blended powders containwhite La2O3, B2O3 and Ca(OH)2 clusters in the microstructure (Fig. 6(a)). After 30 min milling, white powders
turned to gray with an indication of cluster disappearance
and size reduction (Fig. 6(b)). The microstructures of the
30 min, 45 min and 1 h milled powders are very similar(Figs. 6(b)–(d)) in accordance with the XRD patterns in
Figs. 5(b)–(d) exhibiting the same phases. The blurred
SM images in Figs. 6(b)–(d) seem as Ca(OH)2 having thehighest weight ratio in the overall powder blend (~53 %)covered the La2O3 and B2O3 particles during milling process. As compatible with the XRD pattern in Fig. 5(e),the microstructure changed after 1.5 h milling (Fig. 6(e))
in which the reaction took place but unreacted La2O3 andCa(OH)2 are still present. As the reaction completely took
place at the 2 h milling duration without leaving unreacted La2O3 and Ca(OH)2 behind (Fig. 5(f)), the color of
the powders turned to dark and its morphology changes toa more stable agglomerated form (Fig. 6(f)) in whichsmall LaB 6 par ticles are embedded in CaO andCa3(BO3)2.
The SM images in Figs. 6(f) and (g) are almost thesame with each other since they only have LaB6, CaO andCa3(BO3)2 phases after the complete reaction obtained at
2 h and after extended milling up to 3 h. It should be
noted that the kinetics or the overall rate of the mechanochemical synthesis process depends on the kinetics of themilling in which newly fractured surfaces become active
for the reaction initiated by the reducing agent. Mean-

209

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

Fig. 6

SM images of the as-blended La2O3-B2O3-Ca powders and those mechanochemically synthesized for different
durations: a, as-blended, b, 30 min, c, 45 min, d, 1 h, e, 1.5 h, f, 2 h and g, 3 h.

while, fresh surfaces of the particles have tendencies toagglomerate with each other. This phenomenon results ina morphological difference between Figs. 6(b)–(d) and
Figs. 6(e)–(g).
The DSC thermograms of the as-blended La 2O3 B2O3-Ca powders and those mechanochemically synthesized for different durations up to 3 h are illustrated in
Figs. 7(a)–(g). DSC analyses conducted on the as-blended
and milled powders suggest about the thermal behaviorsof the samples and the reaction completion. As seen in
Fig. 7(a), the DSC curve of the as-blended powders havea small endotherm peaking at about 330 °C and a broad
endotherm peaking at about 525 °C, which respectively
correspond to the dehydration of small amount of H3BO3
in the structure of B2O3 and the decomposition of
Ca(OH)2 into CaO by releasing water. The broad endotherm emerged between the temperature range of 420 and
595 °C could include the partial melting of B2O3 (Tmelting:
450 °C) and residual Ca(OH)2 (Tmelting: 580 °C). Furthermore, as-blended powders have three respective small
exotherms with a maximum point at about 690, 820 and
1010 °C (Fig. 7(a)), indicating the formations of calcium
borate phases in different compositions (Erfani et al.,2012) and the emergence of LaB6 phase since there are
La2O3, B2O3 and CaO phases in the structure of the sample after heating up to the end point of the broad endotherm. On the basis of Figs. 7(b), (c) and (d), 30 min,
45 min and 1 h milling cause shifts in the temperatures ofthe endothermic and exothermic peaks. In other words,
milled powders up to 1 h have the same endothermic andexothermic peaks with those of as-blended one, corresponding to the dehydration of H3BO3 and Ca(OH)2 andformation of calcium borate and lanthanum hexaboride
phases, with a significant difference in their peaking temperatures. As compared with Fig. 7(a), the temperature
difference in the peaking points of the broad endotherm
and three exotherms is respectively about 55, 110, 115 and

210

155 °C for the 1 h milled powders (Fig. 7(b)). However,the peaking points of the endotherm and consecutive exotherms change with a maximum value of 20 °C, as milling duration increases from 30 min to 1 h (Figs. 7(b)–(d)).
It can be said that milling process incredibly changes thethermal behavior of the La2O3-B2O3-Ca powders, if DSC
curve of the as-blended powders (Fig. 7(a)) are comparedwith those in Figs. 7(b)–(d). Milling provides an homogeneous distribution of particles throughout the microstructure and hence the reactivity of the touching particles
increases to form calcium borate and lanthanum hexaboride phases. Since as-blended, 30 min, 45 min and 1 hmilled powders comprise La 2O3, Ca(OH)2 and B2O3
phases (Figs. 5(a)–(d)), general appearance of their DSC
thermograms resemble each other, excluding the peaking
temperatures. Figs. 7(e)–(g) only shows the broad endothermic peaks respectively at about 445, 440 and 410 °C,
pertinent to the dehydration of Ca(OH)2. Since 1.5 hmilled powders contain LaB6 , CaO, Ca 3(BO3)2 andCa(OH)2 phases, its DSC curve only exhibits the dehydration endotherm, as compatible with the XRD pattern in
Fig. 5(e). The heat flow of the broad endotherm decreases
as milling duration increases from 1.5 to 3 h, as seen in
Figs. 7(e)–(g). After the reaction took place at the end of
1.5 h, the DSC curves do not include any exotherms belonging to the formation of calcium borates and lanthanum hexaboride. Although 2 and 3 h milled powders had
LaB6, CaO and Ca3(BO3)2 phases according to the XRD
patterns in Figs. 5(f) and (g), the presence of adsorbed
Ca(OH)2 was proved by its dehydration endotherms in
Figs. 7(f) and (g).
The contradiction between the outcomes of the XRD
and DSC analyses arises from the amount of Ca(OH)2
which is below the detection limit of XRD (< 2 wt.%). Similarly in the La2O3-B2O3-Mg system, DSC thermogram ofthe powders milled for 2 h 45 min, in which reaction was
assumed to take place completely according to the XRD

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

Fig. 7

DSC thermograms of the as-blended La2O3-B2O3-Ca
powders and those mechanochemically synthesized for
different durations: (a) as-blended, (b) 30 min, (c)45 min, (d) 1 h, (e) 1.5 h, (f) 2 h and (g) 3 h.

analysis, shows a very small and narrow exotherm peaking at 651 °C corresponding to the oxidation of residual
Mg in the powder blend (Ağaoğulları et al., 2012b).
The XRD patterns of the mechanochemically synthesized La2O3-B2O3-Ca powders after DSC analyses up to1100 °C are given in Figs. 8(a)–(e). The powders milled
for 45 min, 1 h, 1.5 h, 2 h and 3 h were subjected to thesubsequent XRD analyses in order to detect the existing
phases after heating since as-blended powders and thosemilled for 30 min adhered into the alumina crucible
during DSC experiments and hence they could not be discharged from the crucible. As seen in Figs. 8(a) and (b),the powders contain CaO, Ca3(BO3)2 and CaB2O4 (ICDD
Card No: 72-1859, Bravais lattice: primitive orthorhombic,
a = 0.838 nm, b = 1.382 nm, c = 0.501 nm) phases with avery small amount of LaB6 phase after 45 min or 1 h milling and heating processes. Heating up to 1100 °C resultsin the formation of a new borate phase, CaB2O4, in addition to the Ca3(BO3)2 which is already obtained after
mechanochemical synthesis. Erfani et al. (2012) also obtained CaB2O4 phase after heating the reactants between
750 and 900 °C, which corresponds to the same temperature interval of borate formations in the DSC curves in
Figs. 7(a)–(d). After 1.5 h milling and heating, the intensities of the phases, especially that of LaB6, get higher(Fig. 8(c)) and this increasing tendency continues as milling duration increases up to 3 h (Figs. 8(d) and (e)). Thesefindings conform well to the results of the XRD analyses
in Figs. 5(c)–(g) and DSC analyses in Figs. 7(c)–(g).

Fig. 8XRD patterns of the La2O3-B2O3-Ca powders mechanochemically synthesized for different durations, after
DSC analyses carried out up to 1100 °C: (a) 45 min, (b)1 h, (c) 1.5 h, (d) 2 h and (e) 3 h.

Since La2O3-B2O3-Ca powders milled for 45 min and 1 hcontain La2O3, B2O3 and Ca(OH)2 phases (Figs. 5(c) and(d)) after mechanochemical synthesis, they react to form
LaB6, CaO, Ca3(BO3)2 and CaB2O4 phases by the effect ofapplied external heat. However, the desired LaB6 phase is
too few in the composition. Only heating up to 1100 °C
without a holding time does not result in a complete conversion of the reactants even if short milling duration was
conducted on the powders providing a homogeneous microstructure. Moreover, the powders mechanochemicallysynthesized for 1.5 h and for longer times as 2 and 3 hhave already LaB6, CaO and Ca3(BO3)2 phases before
heating (Figs. 5(e)–(g)) and it is expected to get increasein their intensities by the effect of heating which ensures
grain growth. Although any exotherm indicating Ca borate formation were not detected in the DSC thermogramsof the powders mechanochemically synthesized for 1.5, 2
and 3 h in Figs. 7(e)–(g), an amount of CaB2O4 phase occurred in the powders after heating (Figs. 8(c)–(e)). The
emergence of CaB2O4 phase can be attributed to the decomposition of Ca3(BO3)2 with the applied heat, in regardof the reaction in Eq. (7).
Ca3(BO3)2 → CaB2O4 + 2CaO

(7)

Although the heating temperature is a little lower thanthe melting temperature of Ca3(BO3)2 phase obtained after mechanochemical reaction for 1.5 h, an amount of it
can decompose during heating by the effect of mechano-

211

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

chemistry which results in active surfaces in the reaction
products. Furthermore, LaB6 phase formed during milling(≥ 1.5 h) are thermally very stable because they have not
any tendency to transform into an amount of lanthanum
borate phases when exposed to high temperature. The results indicate that if milling are not carried out on the
La2O3-B2O3-Ca powder blends, the heating process has
only chance of LaB6 incubation with additional borate
compounds. The way of obtaining complete conversion of
the reactants is to carry out milling process for longer durations than 2 h or to apply heating on these milled samples with a holding time. Therefore, the significance ofthe mechanochemical synthesis and its effect on the reduction mechanism of La2O3-B2O3-Ca powder system canbe emphasized once more by means of the XRD patterns
and DSC thermograms given in Fig. 5 through Fig. 8.
Similarly, an amount of LaBO3, Mg3B2O6 and MgOphases occurred in the as-blended La2O3-B2O3-Mg powders in addition to LaB6, after heating up to 1200 °C(Ağaoğulları et al., 2012b). In magnesiothermic reduction,
the formation of borate phases arose from both reducing
agent and lanthanum source. However, the emergence of
LaBO3 phase were not detected in any step of the analyses. This can be evaluated as an advantage of the calciothermic reduction since its reaction does not consume thelanthanum oxide source to form borate, instead of that reducing agent participates in the generation of borates. The
higher tendency of yielding Ca borates rather than that of
La provides the generation of LaB6 from all La2O3 reactant, after heating.
After the detailed characterizations of the as-blended
and mechanochemically synthesized La 2O3 -B2O3 -Ca
powders, it can be stated that LaB6 and CaO products arecompatible with the reaction in Eq.(1). However, the presence of Ca3(BO3)2 or CaB2O4 (after heating) conflicts withthe theoretical reaction. Following the mechanochemical
synthesis, reaction products should be purified to achievefine-grained powders without any or with tolerable contaminations. Thus, leaching treatment was conducted on
the powders mechanochemically synthesized for 3 hwhich contain LaB6, CaO, Ca3(BO3)2 and a very small
amount of adsorbed Ca(OH)2 according to the XRD (Fig.
5(g)) and DSC (Fig. 7(g)) analyses.
Fig. 9(a) is the XRD pattern of the La2O3-B2O3-Ca
powders after mechanochemical synthesis for 3 h andleaching with 4 M HCl. As seen from the pattern,leached powders contain LaB6 with a very slight and insignificant amount of Ca3(BO3)2. Leaching with 4 M HCl
solution provides the complete removal of CaO and
Ca(OH)2 in regard of reactions in Eqs. (2)–(4) and also
results in the partial removal of Ca3(BO3)2 phase. Due tothe fact that some parameters of the leaching process suchas solid-to-liquid ratio, leaching temperature and duration,
centrifuging duration and speed, etc. were well-chosen,

212

Fig. 9XRD pattern of the (a) La2O3-B2O3-Ca powders after
mechanochemical synthesis for 3 h and leaching with
4 M HCl, and (b) commercial LaB6 powders, for comparison.

any CaCl2, CaCl2.xH2O and CaOHCl phases do not remain in the structure of the final powders as a leachingproduct. Furthermore, the AAS analysis of the supernatant liquid decanted from the leached powders gives theelemental results of 5710 ppm Ca, 309.3 ppm B and3.76 ppm Fe. The Ca element in the supernatant liquid
arises from the complete dissolution of CaO and the partial dissolution of Ca3(BO3)2. So, the amount of Ca islower than the expected amount since there is still remaining Ca 3(BO3)2 phase in the leached powders. It isbelieved that the presence of B element in the supernatantliquid can only arise from the slight dissolution of LaB6phase in the concentrated HCl solution since there is no unreacted B2O3 phase in the 3 h mechanochemically synthesized powders as verified by the DSC analysis in Fig. 7(g).
Fig. 9(b) shows the XRD pattern of the commercial
LaB6 powders (Alfa AesarTM, in purity of 99.5 %, with anaverage particle size of 4 µm). It can be obviously seenthat there is not any observable contamination in the commercial LaB6 powders and their intensities are higher thanthose of laboratory-synthesized one, comparing the XRD
patterns in Figs. 9(a) and (b). Besides, the production
method of commercial LaB6 powders is not known. Dueto obtain uncontaminated LaB6 powders having an XRD
pattern similar to that of commercial one, additional
treatments should be carried out or concentration of HCl
should be increased.
Fig. 10(a) is the SEM image of the La2O3-B2O3-Ca
powders after mechanochemical synthesis for 3 h, inwhich rounded-shaped particles having sizes below 3 µm
can be easily seen. Although the XRD pattern of the 3 hDuygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

Fig. 10 SEM images of the La2O3-B2O3-Ca powders after: a, mechanochemical synthesis for 3 h, b, leachingwith 4 M HCl, 2.000X, c, leaching with 4 M HCl, 10.000X, and d, its corresponding PSA analysis.

milled powders revealed the presence of LaB6, CaO andCa3(BO3)2 phases (Fig. 5(g)), there are also white dendritic
Ca(OH)2 phases adsorbed on the small rounded-shaped
particles, which agrees very well with the DSC analysis
in Fig. 7(g). The dendritic Ca(OH)2 phase was also detected in a previous study related with the synthesis of
CaB6 powders via mechanochemical reaction of Ca-B2O3
blends (Balcı et al., 2012). Figs. 10(b) and (c) show the
SEM images of the La2O3-B2O3-Ca powders after mechanochemical synthesis for 3 h and leaching with 4 M HCl,in different magnifications. SEM images illustrate therounded-shaped LaB6 particles together with the embedded
Ca3(BO3)2, ranging in sizes between 250 nm and 1 µm.
Since Figs. 10(a) and (b) have the same magnification scale,
it is evident to comprehend the effect of leaching on the 3 hmilled powders. Before leaching treatment, Ca-based particles (CaO, Ca3(BO3)2 and Ca(OH)2) hold the LaB6 particles
together and it results in an agglomerated microstructure inwhich the presence of smaller particles can not be observed(Fig. 10(a)). Besides, leached powders do not consist of
perfect spheroidal-shaped particles throughout the structure (Figs. 10(b) and (c)) because the repeated fracturing
and repeated welding mechanism changed their morphologies during milling (Suryanarayana, 2001). After leaching, there is still agglomeration between the particles dueto the existence of Ca3(BO3)2 phase (Figs. 10(b) and (c))
and the measured size range of the particles monitored by
SEM can result in misleading results. So, the average particle size of the leached powders was accurately deter-

mined under the effect of ultrasonic homogenizer. PSA
graph in Fig. 10(d) displays the average size of theleached particles as about 80 nm.
As a result of analyses carried out after leaching treatment, it can be interpreted that Ca has an excessive tendency to form borate phases which can be completely
eliminated by leaching with excess HCl in concentration
above 4 M. Also, it is comprehensible that higher concentrations of HCl can cause the higher dissolution of synthesized LaB6 phase at the same leaching conditions and so
this can result in more decrease in the LaB6 intensities.
However, 6 M HCl was also used in order to remove undesirable Ca3(BO3)2 phase for obtaining pure LaB6 powders.
Fig. 11 represents the XRD pattern of the La 2O3B2O3-Ca powders after mechanochemical synthesis for
3 h and leaching with 6 M HCl. As seen from Fig. 11,
6 M leached powders have only LaB6 phase without any
detectable contamination. AAS analysis of the supernatant liquid decanted from these leached powders gives theamounts of the elements as 5820 ppm Ca, 356.4 ppm B
and 3.78 ppm Fe. In comparison with the elemental resultsof 4 M leached powders, the amounts of Ca and B increase significantly, indicating the disappearance of
Ca3(BO3)2 phase in the powder composition and the dissolution of LaB6 phase. Thus, the XRD pattern of the 6 M
leached powders (Fig. 11) and also AAS analysis of thedecanted supernatant liquid demonstrate that final LaB6
powders are sufficiently pure and they do not contain any
remnants of residual elements during HCl leaching. Dis-213

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

Fig. 11 XRD pattern of the La2O3-B2O3-Ca powders after
mechanochemical synthesis for 3 h and leaching with
6 M HCl.

similarly, 3.6 M HCl is enough to remove all undesirable
MgO in the milled La2O3-B2O3-Mg powders due to theabsence of Mg borate phase in the microstructure(Ağaoğulları et al., 2012a).
Figs. 12(a) and (b) are the bright-field (BF) micrographs
taken from the La2O3-B2O3-Ca powders after mechanochemical synthesis for 3 h and leaching with 6 M HCl.
Fig. 12(a) shows both polygonal/spheroidal-shaped andequiaxed-shaped particles in sizes varying between 50
and 350 nm. Moreover, different morphologies of the particles arising from the milling mechanism can be distinctly observed from the BF image of the powders inhigher magnification given in Fig. 12(b). In comparison
with Fig. 12(a), the presence of smaller particles with anaverage size of 20 nm is proved in Fig. 12(b). Fig. 12(c)displays the corresponding general EDS measurement
taken from the particles in BF image (Fig. 12(b)).
EDS measurement reveals that there is 69.15 ± 1.60
wt.% La and 30.85 ± 0.75 wt.% B in the composition of
the particles indicating the existence of LaB6 phase,
which is consistent with the amounts of elements in the
stoichiometric phase. The detected Cu element arises
from the copper grid on which the powders were supported. EDS results also prove that any contamination
originating from the unwanted Ca3(BO3)2 phase does notremain in the structure after 6 M leaching treatment,
which is in good agreement with XRD analysis in Fig. 11.
Consequently, pure LaB6 powders were obtained from
La2O3-B2O3-Ca powders after mechanochemical synthesis for 3 h and leaching with 6 M HCl, having particles insize between 20 and 350 nm.
Calciothermic reduction of La2O3 and B2O3 containing
powder blends by mechanochemistry at room temperature
can be an alternative method for the preparation of nanosized LaB6 powders in high purity, rather than magnesiothermic reduction methods and other conventional
production techniques.

214

Fig. 12

TEM micrographs of the La2O3-B2O3-Ca powders after
mechanochemical synthesis for 3 h and leaching with
6 M HCl: a, BF image (in 0.2 µm scale), b, BF image(in 100 nm scale), and c, its corresponding general
EDS analysis.

4. Conclusions
In this study, nanosized LaB6 powders were successfully synthesized from the related oxides of the elements,
via calciothermic reduction by mechanochemistry performed at room temperature. Based on the results reported in the present study, the following conclusions canbe drawn:• After mechanochemical synthesis of the La 2O3 B2O3-Ca powder blends in SpexTM 8000D Mixer/Mill
using a 10:1 BPR for 1.5 h, LaB6, CaO and Ca3(BO3)2phases were obtained in addition to very small

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

•

•

•

•

•

•

•

amounts of unreacted La2O3 and Ca(OH)2.
XRD analyses showed that extending the milling duration to 2 h resulted in a complete reaction in which
unreacted La2O3 and Ca(OH)2 phases disappeared andthe composition only contained LaB6 , CaO andCa3(BO3)2 phases. Besides, calciothermic reduction
by mechanochemistry was achieved 45 min earlier
than that of magnesiothermic one.
All mechanochemically synthesized powders had thesame LaB6, CaO, Ca3(BO3)2 and CaB2O4 phases after
heating up to 1100 °C and there was an increasing
tendency in the occurrence of the phases as the milling duration increased.
LaB6 phase synthesized during mechanochemical reactions (≥ 1.5 h) are thermally very stable since they
have not any tendency to transform into an amount oflanthanum borate phases when exposed to high temperature.
DSC results indicated that if longer milling durations
were not applied on the La2O3-B2O3-Ca powder blends,
the heating process of powders only yielded the incubation of LaB6 with additional Ca borate compounds.
DSC and SEM analyses exhibited that a dendritic
Ca(OH)2 phase adsorbed on the LaB6, CaO andCa3(BO3)2 particles in the microstructure of the 2 and3 h milled powders, which can be easily eliminated byHCl leaching.
LaB6 powders were obtained with an average particle
size of 80 nm and in the presence of very small
amount of Ca 3(BO3)2 phase from La 2O3 -B2O3 -Ca
powder blends after mechanochemical synthesis for
3 h and leaching with 4 M HCl.
Pure LaB6 powders were obtained from La 2O3 B2O3-Ca powders after mechanochemical synthesisfor 3 h and leaching with 6 M HCl, having particles insize between 20 and 350 nm.

Nomenclature
AAS

Atomic Absorption Spectrometer

BF

Bright-Field

BPR

Ball-to-Powder Weight Ratio

CVD

Chemical Vapor Deposition

DSC

Differential Scanning Calorimeter

EDS

Energy Dispersive Spectrometer

ICDD

International Center for Diffraction Data

PSA

Particle Size Analyzer

RFRadio Frequency

SEM

Scanning Electron Microscope

SHS

Self-propagating High-temperature Synthesis

SM

Stereomicroscope

XRD

X-ray Diffraction

TEM

Transmission Electron MicroscopeTM

Trademarka, b, clattice parameters°C

degree Celsiusabas-blended

cm

centimeter

ΔG

Gibbs free energy change

ΔH

enthalpy change

g

gram

h

hour

kJ

kilojoulekVkilovoltMmolaritymg

miligramminminutemlmililitermm

milimeterµm

micronnm

nanometerPa

Pascal

Acknowledgements

rpm

rotation per minuteT

temperatureThis study was supported by “The Scientific and Technological Research Council of Turkey (TÜBİTAK)” withthe project title of “Synthesis of Lanthanum, Cerium andSamarium Borides by Solid-State Reaction at Room Temperature” and with the project number of 109M364. It wasalso funded by “Istanbul Technical University Scientific
Research Projects” with the project title of “Mechanochemical Synthesis and Characterization of Some Rare-Earth
Metal Borides” and with the project number of 33549.

θdiffraction angle

wt.%

weight percentage

ReferencesAğaoğulları D., Synthesis, Development and Characterizationof Some Rare-Earth Hexaboride Powders and Their Sintered Products, Ph.D. Thesis, Advisor: Prof. Dr. İsmail
Duman, Graduate School of Science, Engineering andTechnology, Istanbul Technical University, 2014.
Ağaoğulları D., Balcı Ö., Gökçe H., Duman İ., Öveçoğlu M.L.,
Synthesis of magnesium borates by mechanically activatedannealing, Metallurgical and Materials Transactions A,
43A (2012a) 2520–2533.

215

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

Ağaoğulları D., Duman İ., Öveçoğlu M.L., Synthesis of LaB6
powders from La2O3, B2O3 and Mg blends via a mechanochemical route, Ceramics International, 38 (2012b) 6203–
6214.
Ahmed H., Kanitkar P.L., Dharmadhikari C.V., Joag D.S., A
new method for melting and recrystallization of lanthanum
hexaboride (LaB6) for preparing field emitters, Journal of
Physics E: Scientific Instruments, 9 (1975) 4–5.
Amalajyothi K., Berchmans L.J., Angappan S., Visuvasam A.,
Electrosynthesis of cerium hexaboride by the molten salt
technique, Journal of Crystal Growth, 310 (2008) 3376–
3379.
Amalajyothi K., Berchmans L.J., Visuvasam A., Angappan S.,
Electrosynthesis of cerium hexaboride using lithium tetra
borate melt, Materials and Manufacturing Processes, 26(2011) 792–795.
Aprea A., Maspero A., Masciocchi N., Guagliardi A., Albisetti
A.F., Giunchi G., Nanosized rare-earth hexaborides:
low-temperature preparation and microstructural analysis,
Solid State Sciences, 21 (2013) 32–36.
Balakrishnan G., Lees M.R., Paul D.McK., Rare earth hexaborides: large single crystals, Journal of Magnetism andMagnetic Materials, 272–276 (2004) 601–602.
Balcı Ö., Ağaoğulları D., Duman İ., Öveçoğlu M.L, Synthesis
of CaB6 powders via mechanochemical reaction of Ca/B2O3
blends, Powder Technology, 225 (2012) 136–142.
Bao L., Zhang J., Zhou S., Zhang N., Xu H., Floating zone
growth and thermionic emission property of single crystal
CeB6, Chinese Physics Letters, 28 (2011) 088101-1-0881014.
Baranovskiy A.E., Grechnev G.E., Fil V.D., Ignatova T.V.,
Logosha A.V., Panfilov A.S., Svechkarev I.V., Shitsevalova
N.Y., Filippov V.B., Eriksson O., Electronic structure, bulk
and magnetic properties of MB6 and MB12 borides, Journalof Alloys and Compounds, 442 (2007) 228–230.
Brewer J.R., Jacobberger R.M., Diercks D.R., Cheung C.L.,
Rare earth hexaboride nanowires: general synthetic design
and analysis using atom probe tomography, Chemistry ofMaterials, 23 (2011) 2606–2610.
Carlsson M., Garcia-Garcia F.J., Johnsson M., Gulian A., Synthesis of LaxCe1-xB6 whiskers, Journal of Materials Science,
40 (2005) 2991–2994.
Chen C., Aizawa T., Iyi N., Sato A., Otani S., Structural refinement and thermal expansion of hexaborides, Journal of
Alloys and Compounds, 366 (2004) L6–L8.
Chen X., Li M., Chang X., Zhang H., Xiao W., Synthesis andcrystal structure of a new calcium borate, CaB6O10, Journalof Alloys and Compounds, 464 (2008) 332–336.
Dou Z., Zhang T., Guo Y., Jicheng H., Research on preparation
optimization of nano CeB6 powder and its high temperaturestability, Journal of Rare Earths, 30 (2012) 1129–1133.
Dou Z., Zhang T., Zhang Z., Zhang H., He, J., Preparation andcharacterization of LaB6 ultra fine powder by combustion
synthesis, Transactions of Nanoferrous Metals Society of
China, 21 (2011) 1790–1794.
Erfani M., Saion E., Soltani N., Hashim M., Abdullah W.S.B.W.,
Navasery M., Facile synthesis of calcium borate nanoparticles and the annealing effect on their structure and size,

216

International Journal of Molecular Sciences, 13 (2012)
14434–14445.
Futamoto M., Nakazawa M., Kawabe U., Thermionic emission
properties of hexaborides, Surface Science, 100 (1980)
470–480.
Gao R., Min G., Yu H., Zheng S., Lu Q., Han J., Wang J., Fabrication and oxidation behavior of LaB6 -ZrB2 composites,
Ceramics International, 31 (2005) 15–19.
Hasan M., Sugo H., Kisi E., Low temperature carbothermal andboron carbide reduction synthesis of LaB6, Journal of
Alloys and Compounds, 578 (2013) 176–182.
Jha M., Patra R., Ghosh S., Ganguli A.K., Novel borothermalroute for the synthesis of lanthanum cerium hexaborides
and their field emission properties, Journal of Solid State
Chemistry, 194 (2012) 173–178.
Ji X.H., Zhang Q.Y., Xu J.Q., Zhao Y.M., Rare-earth hexaborides nanostructures: recent advances in materials,
characterization and investigations of physical properties,
Progress in Solid State Chemistry, 39 (2011) 52–69.
Li P., Tian W., Wang D., Liu X., Grain refining potency of LaB6
on aluminum alloy, Journal of Rare Earths, 30 (2012) 1172–
1176.
Loboda P.I., Kysla H.P., Dub S.M., Karasevs’ka O.P., Mechanical properties of the monocrystals of lanthanum hexaboride, Materials Science, 45 (2009) 108–113.
Massalski T.B., Okamoto M., Subramanian P.R., Kacprzak L.,
Binary Alloy Phase Diagrams, ASM International, Materials Park, Ohio, 1990.
Mitterer C., Waldhauser W., Beck U., Reiners G., Structure andproperties of decorative rare-earth hexaboride coatings,
Surface and Coatings Technology, 86–87 (1996) 715–721.
Motojima S., Takahashi Y., Sugiyama K., Chemical vapor
growth of LaB6 whiskers and crystals having a sharp tip,
Journal of Crystal Growth, 44 (1978) 106–109.
Otani S., Ishizawa Y., Thermionic emission properties of boronrich LaB6 and CeB6 crystal cathodes, Journal of Alloys andCompounds, 245 (1996) L18–L20.
Otani S., Nakagawa H., Nishi Y., Kieda N., Floating zone
growth and high temperature hardness of rare-earth hexaboride crystals: LaB6, CeB6, PrB6, NdB6, and SmB6, Journal of Solid State Chemistry, 154 (2000) 238–241.
Paderno V.N., Paderno Yu.B., Pilyankevich A.N., Lazorenko
V.I., Bulychev S.I., The micromechanical properties ofmelted borides of rare earth metals, Journal of theLess-Common Metals, 67 (1979) 431–436.
Perkins C.L., Trenary M., Tanaka T., Otani S., X-ray photoelectron spectroscopy investigation of the initial oxygen
adsorption sites on the LaB6 (100) surface, Surface Science
Letters, 423 (1999) L222–L228.
Petrosyan V., Vardanyan V., Kuzanyan V., Konovalov M., Gurin
V., Kuzanyan A., Thermoelectric properties and chemicalcomposition of CeB6 crystals obtained by various methods,
Solid State Sciences, 14 (2012) 1653–1655.
Post B., Moskowitz D., Glaser F.W., Borides of rare earth metals, American Chemical Society, 78 (1956) 1800–1802.
Qin L., Zhang H., Zhang Q., Tang J., Single-crystalline, rareearth metal hexaboride nanowires used as electron emitterfor electron emission cathode used for electron gun, con-

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

tains rare-earth metals such as yttrium, lanthanum, cerium,samarium and gadolinium, US Patent, (2010) 2010028235A1.
Samsonov G.V., Paderno Y.B., Fomenko V.S., Hexaborides of
the rare-earth metals, Soviet Powder Metallurgy and Metal
Ceramics, 2 (1963) 449–454.
Schlesinger M.E., Liao P.K., Spear K.E., The B-La (boron-­lanthanum) system, Journal of Phase Equilibria, 20 (1999)
73–78.
Selvan R.K., Genish I., Perelshtein I., Moreno M.C., Gedanken
A., Single step, low-temperature synthesis of submicron-­sized rare earth hexaborides, Journal of Physical ChemistryC, 112 (2008) 1795–1802.
Suryanarayana C., Mechanical alloying and milling, Progressin Materials Science, 46 (2001) 1–184.
Swanson L.W., McNeely D.R., Work functions of the (001) faceof the hexaborides of Ba, La, Ce and Sm, Surface Science,
83 (1979) 11–28.
Tanaka Y., Staub U., Narumi Y., Katsumata K., Scagnoli V.,
Shimomura S., Tabata Y., Onuki Y., Non-resonant X-ray
Bragg diffraction by CeB6, Physica B: Condensed Matter,
345 (2004) 78–81.
Verhoeven J.D., Gibson E.D, Noack M.A., Conzemius R.J., An
arc floating zone technique for preparing single crystal lanthanum hexaboride, Journal of Crystal Growth, 36 (1976)
115–120.
Wang L., Xu L., Ju Z., Qian Y., A versatile route for the convenient synthesis of rare-earth and alkaline-earth hexaborides
at mild temperatures, CrystEngComm, 12 (2010) 3923–
3928.

Wang X., Jiang Y., Lin Z., Qi K., Wang B., Field emission characteristics of single crystal LaB6 field emitters fabricatedby electrochemical etching method, Journal of Physics D:
Applied Physics, 42 (2009) 055409.
Wen C.H., Wu T.M., Wei W.J., Oxidation kinetics of LaB6 inoxygen rich conditions, Journal of the European CeramicSociety, 24 (2004) 3235–3243.
Xu J.Q., Mori T., Bando Y., Golberg D., Berthebaud D., Prytuliak A., Synthesis of CeB6 thin films by physical vapor
deposition and their field emission investigations, Material
Science and Engineering B, 177 (2012) 177–120.
Xu J.Q., Zhao Y.M., Zou C., Self-catalyst growth of LaB6
nanowires and nanotubes, Chemical Physics Letters, 423(2006) 138–142.
Yuan Y., Zhang L., Liang L., He K., Liu R., Min G., A solid­state reaction route to prepare LaB6 nanocrystals in vacuum, Ceramics International, 37 (2011) 2891–2896.
Zhang H., Tang B., Zhu P.W., Zhang Q., Zhou O., Qin L.C., Fabrication of lanthanum hexaboride single nanowire fieldemitter and their field emission properties, Transactions of
the Materials Research Society of Japan, 32 (2007) 747–
750.
Zhang H., Tang J., Zhang L., An B., Qin L.C., Atomic force
microscopy measurement of the Young’s modulus andhardness of single LaB6 nanowires, Applied Physics Letters, 92 (2008) 173121.
Zhang H., Tang J., Zhang Q., Zhao G., Yang G., Zhang J., Zhou
O., Qin L.C., Field emission of electrons from single LaB6
nanowires, Advanced Materials, 18 (2006) 87–91.

217

Duygu Ağaoğulları et al. / KONA Powder and Particle Journal No. 33 (2016) 203–218

Author’s short biography
Duygu Ağaoğulları
Duygu Ağaoğulları received her M.Sc. degree in 2007 and Ph.D. degree in 2014 inMaterials Science and Engineering Department from Istanbul Technical University. She
worked as a research assistant between 2005 and 2013, and has been affiliated since 2013
in I.T.U. She has been assigned as researcher/scholar in 10 scientific projects. Now, she isworking as a postdoctoral scholar in Materials Science and Engineering Department in
Stanford University. Her main research activities are powder metallurgy, high-tech boron
products and composite materials. She is author/co-author of 21 papers, 1 Patent cited in
Web of Science and 75 international conference proceedings.

Özge BalcıÖzge Balcı received her M.Sc. degree in Metallurgical and Materials Engineeringfrom Istanbul Technical University in 2010 and continues her Ph.D. in the same department. She has been assigned as researcher in 4 scientific projects and gained professional
fellowships in 3 research projects. She has been as a visiting researcher at IFW Dresdenfor 6 months during her Ph.D studies. Her main research activities are powder metallurgy, high-tech boron products and composite materials. She is author/co-author of 11
papers and 1 Patent cited in Web of Science and 42 proceedings presented in international conferences.

M. Lütfi Öveçoğlu
Prof. Dr. M. Lütfi Öveçoğlu received his M.Sc. and Ph.D. degrees in Materials Scienceand Engineering from Stanford University in 1984 and 1987. He has been affiliated with
the Department of Metallurgical and Materials Engineering at Istanbul Technical University since 1990. He is the founder and technical director of Particulate Materials Laboratories (PML), a cluster of 9 laboratories. His main research activities are mechanicalalloying and mechanochemical synthesis of W-based, Al-based and boride-based materials. He is author/co-author of 145 papers and 1 patent cited in Web of Science, 2 bookchapters and 3 edited conference proceedings having about 905 citations.

İsmail Duman
Prof.Dr. İsmail Duman received his M.Sc. degree from Istanbul Technical University
in 1978 and Ph.D. degree from Berlin Technical University in 1985 in Metallurgical Engineering. He has been affiliated with Department of Metallurgical and Materials Engineering at Istanbul Technical University since 1980. His main research activities are
extractive metallurgy, powder metallurgy, development of high-tech boron products and
CVD technologies. He has been the supervisor of 17 M.Sc. and 8 Ph.D. dissertations andhe has been assigned as project director or researcher in 23 industrial/scientific research
projects. He is author/co-author of 49 papers and 7 Patents cited in Web of Science.

218

KONA Powder and Particle Journal No. 33 (2016) 219–227/Doi:10.14356/kona.2016002

Original Research Paper

Precipitation Process of Calcium Phosphatefrom Calcium Carbonate Suspension †Nayane Macedo Portela da Silva, Fabienne Espitalier * and Ange Nzihou
1

Université de Toulouse, Centre RAPSODEE UMR CNRS 2392, France

Abstract
The Ca-HA is synthesized by reacting calcium carbonate (CaCO3) and ammonium dihydrogen orthophosphate(NH4H2PO4) in stoichiometric proportions. In this precipitation process, the ratio of Ca/P by moles is 1.67. The
experiments were performed in a batch reactor at 25 °C. From control parameters, the pH and the temperature
were measured in line for all experiments. The tests were performed with two liquid-solid mass ratios (H 2O/
CaCO3) of 3 and 5. For each mass ratio, three stirring rates of 260, 400, and 600 rpm were tested. Samples of thesynthesis were collected at different intervals and analysed by laser granulometry and by environmental scanningelectron microscopy.
For the synthesis conducted at 260 rpm, synthesis monitoring was made using a contact probe in solution coupled
to a Raman spectrometer in order to follow the formation of solid phase. This technique is valuable to follow thesynthesis of Ca-HA in a concentrated solids suspension (around 20–30 wt%).
The results make it possible to propose a mechanism of the precipitation process of Ca-HA. It can be divided into
four main stages: (i) dissolution of calcium carbonate (CaCO3), (ii) precipitation of brushite (CaHPO4.2H2O), (iii)transformation of brushite into Ca-HA (Ca10(PO4)6(OH2)) and (iv) nucleation, growth and agglomeration of Ca-HA.
Keywords: precipitation, hydroxyapatite, brushite, Raman spectroscopy

1. Introduction
The exponential population growth has led to a similar
increase in requirements for resources, energy, food,housing and land as well as an enormous increase in industrialization and urbanization. This rapid industrialization and urbanization have led to great demand for natural
resources and created an increase in waste production. A
continuing issue facing society today is the disposal ofmunicipal and industrial wastes (Wey et al., 2006). Waste
calcium carbonate (CaCO3) from the paper, cosmetics,
pharmaceuticals, catalyst, ceramics, rubber and paint industries often ends up in landfill (Wen et al., 2007). These
CaCO3 wastes can be used as the calcium compound in
the synthesis of hydroxyapatite (Ca-HA). Ca-HA,
Ca10(PO4)6(OH2), is an insoluble calcium phosphate mineral. It is a main constituent of bones and teeth. Due to its
excellent biocompatibility, slow biodegradation, good mechanical stability, great sorption properties and heteroge†

Received 13 May 2014; Accepted 10 December 2014
J-STAGE Advance published online 28 April 2015
1
Ecole des Mines dʼAlbi-Carmaux, Campus Jarlard F-81013 Albi
Cedex 09, France* Corresponding author: Fabienne Espitalier;E-mail: fabienne.espitalier@mines-albi.fr
TEL: + 33-5-63493151 FAX: + 33-5-63493025©2016 Hosokawa Powder Technology Foundation

neous photocatalytic degradation under UV irradiation(Wang et al., 2003), it has been used extensively for bone
repairs, bone implants, bioactive materials and for purification and separation of biological molecules and organic
contaminants. It can accept a series of cationic and anionic substitution within its structure (Elliott, 1994). Similarly, it has been reported to be an efficient adsorbent totreat heavy metals (Chen et al., 1997) and to have a very
high capacity for removing divalent heavy metal ions (Maet al., 1993). Consequently, hydroxyapatite is used for theremoval of heavy metals from contaminated soils, wastewater and fly ashes (Elliott, 1994). Several methods havebeen developed to prepare Ca-HA, including a dry process, precipitation, hydrolyzation of calcium phosphateand hydrothermal synthesis. Spray pyrolysis, freeze drying, gel diffusion, a sol-gel technique and electrochemical
deposition have all also been used. Much work has been
done on the synthesis of Ca-HA using different methods
such as solid-solid reaction (Nakamura et al., 2001; Silva
et al., 2003), ultrasonic spray freeze-drying (Itatani et al.,2000) or spray-drying techniques generating a Ca-HA
powder of controlled morphology (Luo and Nieh, 1996),
sol-gel technique (Anee et al., 2003; Bezzi et al., 2003;
Salimi et al., 2012), precipitation in aqueous solution(Jarcho, and Bolen, 1976; Rodriguez-Lorenzo et al., 2001;
Bailliez and Nzihou, 2004; Swain et al., 2012), emulsion

219

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

route (Saha et al., 2009; Okada et al., 2012), microwave-­assisted synthesis (Ipekoglu and Altintas, 2010) and synthesis under hydrothermal conditions (Vasile et al., 2012;
Murakami et al., 2012). Among all these methods, precipitation is characterized by the simplicity of the process, its
low cost and its easy application in industrial production(Liu et al., 2001). The main objective of this work is to investigate this synthesis of calcium phosphate powders to
be used as adsorbents for pollutants removal. To achieve
this goal, the hydroxyapatite is synthesized by a crystallization reaction (precipitation) from solid calcium carbonate in a batch reactor in order to investigate systematicallythe effects of operating conditions such as the stirring
speed and the liquid-solid mass ratios. These parameters
determine the crystallization performance. Moreover inthis study we show that the solid formations can be followed using Raman spectroscopy.

2. Experimental section
2.1 Materials and methods
In order to synthesize Ca-HA, laboratory-grade calcium carbonate (CaCO3) powder (98 % pure) and ammonium dihydrogen orthophosphate powder (NH4H 2PO4)(94.4 % pure) were used. Both were obtained from Fisher
Scientific U.K. and were used as received. The suspension
of CaCO3 was introduced into a 0.5 L glass vessel (diameter 10 cm) with double jacket equipped with a helicoidal
stirrer (diameter 4.5 cm). Agitation was powered by aEurostar IKA labortechnick 2000 motor. The reactor was
equipped with four baffles (Fig. 1). The NH4H2PO4 powder was dissolved for a minimum time of 30 min at ambient temperature. Then, this solution was gradually
pumped into the reactor with a peristaltic pump (MasterFig. 1220

Experimental set-up for precipitation.

flex pump) at a flow rate of 11.11 cm3/min in order tomaintain the pH value higher than 7. The temperature wascontrolled by a water bath (Julabo VC F30). The pH value
was monitored throughout the duration of the experimentusing a pH meter (Meterlab PHM210). The reaction continued for 72 h at atmospheric pressure and temperature(25 ± 2 °C). The ratio of Ca/P by moles was 1.67. Two different liquid-solid mass ratios (water/CaCO3 solid) of 3
and 5 and three stirring rates (260, 400 and 600 rpm)
were tested. Addition times were equal to 18.6 min and
18.0 min, respectively, for a mass ratio of 3 and 5.

2.2 Analytical methods
The reactor suspension, composed of calcium carbonate and calcium phosphates, was sampled at different intervals in order to characterize the solid and the solution.
Particle-size distributions: The particle size distributions were measured by laser diffraction with a Malvern
Master Sizer 2000 instrument covering a size range of
0.1–2000 μm. Suspension samples were diluted and dispersed in water by ultrasonication for 4 min before measurement to disaggregate the solid. Direct sampling of thereactor suspension was used for these measurements.
For the other analysis, the collected samples were filtered. Solids were washed and dried in a vacuum oven
(Heraeus Vacutherm—VT-6025) at 60 °C for 24 hours toyield a fine white powder. The dry solid was not very cohesive and it was simply broken up by manual grinding.
Ion chromatography was used to analyse the liquid, and
TG-DSC and ESEM for the solids.
Ion phosphate concentration: The phosphate ion concentration was analysed using ion chromatographic separation and it is performed using a Dionex ICS-300 ion
chromatograph system consisting of: a single pump(Dionex ICS-3000 SP), an eluent generator (Dionex ICS3000 EG), a conductivity detector (Dionex Conductivity
Detector P/N 061830), a precolumn (Dionex IonPac AG
19, 4 × 50 mm) and a column (Dionex IonPac AS 19(4 × 250 mm). The eluent was the ultra-pure water withthe pH value kept constant by using the KOH eluent generator. The eluent flow was 1 ml/min corresponding to astable pressure in the column of about 120 bar. The results
were analysed by using the software Chromelon.
Habitus: A scanning electron microscope (Philips
XL30 ESEM FEG) equipped with EDAX fluorescenceanalysis following platinum sputtering of the sample, wasused to study the morphology of the gold-coated intermediate and final products.
Weight losses: Thermal gravimetric analysis was performed using a Thermogravimetrical Analyser (TGADSC 111 Setaram). The experiment was performed with aheating rate of 2 °C/min using nitrogen flow (25 ml/min).
The percentage weight loss of the samples (approximately

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

12 mg) was monitored from 20 to 800 °C.
Raman spectra: For the synthesis conducted at
260 rpm, the synthesis monitoring was conducted using acontact probe in solution coupled to a Raman spectrometer RXN2C785, Mettler-Toledo/Kaiser analysers (Kaiser
Optical Systems, Ann Arbor, Michigan, USA). A 400 mW
external cavity stabilized by an Invictus NIR diode laser
at 785 nm was used for sample illumination. All the spectra were recorded at a resolution of 4 cm–1.
XR diffraction: For the synthesis conducted at 260 rpm,
the phase identification of some samples was carried out
using XR diffractometry (Phillips PanAlytical X’Pert Pro
MPD diffractometer), which had a Cu Ka radiation(l = 1.54 A) at 45 kV and 40 mA. The reflections werecollected in the 2θ ranges from 10 to 75° with a step sizeof 0.17° and a time of 13.02 s per step. The phase identification was carried out with the JCPDS database.

3. Results
During the process for the formation of hydroxyapatite, it was possible to form other calcium phosphates.
Their solubilities decrease with the pH value (Ferreira
et al., 2003). The comparisons of the solubility of the
CaHPO4.2H2O named brushite and the solubility of hydroxyapatite Ca-HA for a pH value lower than 4, showthat brushite is a stable compound and for a pH value
higher than 4, the Ca-HA is prominent. Fig. 2 shows theevolution of the pH value during the synthesis for the two
liquid-solid mass ratios (3 and 5) at 400 rpm. It can beobserved that the pH value readily decreases from 10.2 to
6.9 during the first 15 minutes of reaction. Then it gradually increases to 8.5 and after 40 hours, the pH value stabilizes at about 9. The liquid-solid mass ratio does not
influence the pH value.
At 25 °C in the pH range [8-9] (Fig. 2), it was possible toform three solids: the monetite (CaHPO4), the brushite(CaHPO4.2H2O) and the hydroxyapaptite (Ca10(PO4)6(OH2)).

Fig. 2Evolution of the pH value during synthesis at 400 rpm
for two mass ratios (3 □ and 5 ■).

Their formation follows these three reactions respectively(Chkir, 2011; Verwilghen, 2009):(1) Monetite (metastable phase):
CaCO3 + NH4H2PO4 → CaHPO4 + CO2 + H2O + NH3(2) Brushite:
CaCO3 + NH4H2PO4 + H2O →CaHPO4.2H2O + CO2 + NH4OH(3) Hydroxyapatite:
10CaCO3 + 6NH4H2PO4 →Ca10(PO4)6(OH2) + 10CO2 + 2H2O + 6NH4OH(4) Transformation of brushite into hydroxyapatite
10CaHPO4.2H2O + 4NH4OH →Ca10(PO4)6(OH2) + 4NH4H2PO4 + 18H2O
The analysis did not detect monetite.
When any amount of ammonium dihydrogen phosphate
is introduced into the reactor containing the calcium carbonate suspension, gas bubbles with an ammonium odor
are observed. This confirms the formation of carbon dioxide and ammonium during the reaction.
As the pH showed low values at the beginning of thesynthesis, this may have favored the formation of brushite.
The end of synthesis, which sees the stabilization of the pH
value at around 9, favors the formation of hydroxyapatite.

3.1 Influence of mass ratio
Two syntheses with two liquid-solid mass ratios of 3
and 5, respectively, carried out at a low stirring rate of
260 rpm in the presence of a RAMAN probe to study the
influence of this ratio on the synthesis of Ca-HA.
Tsuda and Arends (1993) give the Raman bands observed for three pure minerals (brushite, octacalcium
phosphate OCP and Ca-HA). The spectral features of solids can be characterized as Raman bands due to phosphate (PO34–) vibrational modes of ν4, ν2, ν1 and ν3. The
intensity of the ν1, band is the strongest, and can be used
for the identification of minerals.
Figs. 3, 4 and 5 show the Raman spectra during thesynthesis made with a mass liquid-solid ratio of 5 and astirring rate of 260 rpm. In Fig. 3, we observe the appearance of a peak at 983 cm–1 after 20 min of synthesis. This
peak is relatively wide. Its intensity increases with time.
This peak noted on Fig. 4 corresponds to the brushite, acompound observed in previous studies (Chkir, 2011).
The presence of brushite is confirmed by XR diffraction analysis (Fig. 6).
After 19 h, a second peak close to the brushite peak appears at 962 cm–1; this corresponds to the Ca-HA. While
the peak intensity of brushite decreases, the peak inten-

221

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

Fig. 3Fig. 4

Fig. 5

Raman spectra measured every 19 min between 0 and
3 h, (liquid-solid mass ratio = 5 and N = 260 rpm).
Times from the bottom to the top.

Raman spectra measured every 4 h between 3 h 10 min
and 24 h 19 min, (liquid-solid mass ratio = 5 andN = 260 rpm). Times from the bottom to the top.

Fig. 7

XR diffraction spectra at 44, 50 and 68 hours (liquid-­
solid mass ratio = 5 and N = 260 rpm). Times from thebottom to the top.

Raman spectra measured every 8 h between 19 h
20 min and 91 h 20 min, (liquid-solid mass ratio = 5
and N = 260 rpm). Times from the bottom to the top.

sity of Ca-HA increases (Figs. 4 and 5). After 30 h, the intensities of all peaks are stable. This assumption has beenverified qualitatively by XR diffraction analysis (Fig. 7).
At 44 h, the brushite is no longer visible by this technique. The large peak observed at 30–33° corresponds to
Ca-HA.
Fig. 8 shows the weight losses of the different samples
measured between 20 and 800 °C by TG analysis for dry
solids obtained during the synthesis made with a mass ratio of 5 and a stirring rate of 260 rpm. For the pure cal-

222

Fig. 6 XR diffraction spectra at 0.75, 2 and 4 hours (liquid-­
solid mass ratio = 5 and N = 260 rpm). Times from thebottom to the top.

Fig. 8 Weight losses between 20 and 800 °C at 0, 0.75, 4, 20,
44, 50, 68 and 72 h (liquid-solid mass ratio = 5 andN = 260 rpm).

cium carbonate, two weight losses are observed: one
between 350 and 400 °C and another after 600 °C. The
first loss is due to the degradation of impurities containedin CaCO3. The decomposition of calcium carbonate(CaCO3) in calcium oxide (CaO) and carbon dioxide (CO2)(decarbonation) induces the second loss.
For the other samples, three weight losses are observed.

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

Fig. 9Mass percentages of brushite, calcium carbonate and
Ca-HA during the synthesis.

The first is between 180 and 200 °C, the second between
350 and 400 °C and the third after 600 °C. This first is
attributed to loss of the hydration water of brushite(detected by RAMAN spectra analysis), the second to
impurities and the third to the decarbonation of unreacted CaCO3. The weight loss observed between 50 and
120 °C is due to insufficient drying of these samples. Fromthese mass losses, by assuming that only three solids arepresent in the reactor (CaCO3, CaHPO 4.2H 2 O andCa10(PO4)6(OH 2)), the mass percentage of these threecomponents in the solid can be calculated (Fig. 9).
Four steps can be observed. The first step (I) corresponds to the dissolution of calcium carbonate and formation of apatite: a sharp decrease in calcium carbonate(around 40 %) during the first hour, and a sharp increasein brushite (around 40 %) and Ca-HA (around 20 %).
During the second step (II) between 4 and 20 h, theweight percentage of solids seems to be constant. After
20 h, we can observe an increase in the weight percentage
of Ca-HA up to 80 wt% and a decrease in the CaCO3 andbrushite weight percentages. This step (III) corresponds
to the transformation of brushite into Ca-HA by dissolution-­crystallization or by solid-solid transformation. However,
the reaction is not complete and 20 wt% of the solid remaining is composed of calcium carbonate. After 68 h,
two solids are in suspension: CaCO3 and Ca-HA. The
mass balance on calcium leads to the total weight as afunction of time from mass fractions and initial weight of
CaCO3:
mT (t) =

i
mCaCO
3

1

M CaCO3  wbrushite
w
wHAP
+ 10
+ CaCO3 MMM
brushite
HAP
CaCO

3 

(1)

i
where mCaCO
is the initial weight of CaCO3, Mj the molar
3weight and wj the mass fraction of the component j in thesolids mixture.
Fig. 10 presents the evolution of weight of each solid
during a synthesis for both liquid-solid mass ratios, 3 and 5.
Furthermore, by coupling the results of the loss of mass

Fig. 10Masses during the synthesis (N = 260 rpm) for liquid-­
solid mass ratio = 5, symbols: hollow square and - -;and liquid-solid mass ratio = 3, symbols: black squareand - .

and the RAMAN spectra, it is possible to calibrate thearea of RAMAN peaks observed in 983 and 962 cm –1,
respectively, for the brushite and the Ca-HA according
to the mass fractions obtained by TGA. This calibration
leads to the continuous curves given in Fig. 10. Bothweight ratios lead to final masses that are close to CaCO3
initial masses.
The conversion does not seem to depend on the initial
weight ratio. This conversion is not total, but is of the order of 73–74 wt%. We also note that the maximum massof brushite formed for a liquid-solid mass ratio equal to 3
is 13 times weaker than that formed for a liquid-solid
mass ratio of 5 (Fig. 10 (c)).
Beyond 20 h for a liquid-solid ratio of 3 and 40 h for a223

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

Fig. 11Morphology of brushite at 0.75, 4 and 20 h (liquid-­
solid mass ratio = 5 and N = 260 rpm).

Fig. 13Morphology of Ca-HA 50 and 72 h (liquid-solid mass
ratio = 5 and N = 260 rpm).

dmCa-HA= K S j ∆C g
dt
t =0

Fig. 12Morphology of Ca-HA at 44 h (liquid-solid mass ratio= 5 and N = 260 rpm).

liquid-solid ratio of 5, the brushite seems to have totallydisappeared. In parallel, the formation of Ca-HA is much
faster for an L/S ratio of 3 within the first hours (77 wt%of the final mass is reached (Fig. 10 (a)). After 10 h ofsynthesis, a slowing down is observed.
The particles of brushite are formed preferably in thesolution (Fig. 11), while the particles of Ca-HA are created on the surface of the brushite particles (Fig. 12) or(Fig. 11). The particles of CaCO3 are attacked by orthophosphate ions. Therefore, CaCO3 is used to form monetite. This compound is transformed into brushite which
evolves to the hydroxyapatite (calcium phosphate thermodynamically more stable). When the liquid-solid mass ratio decreases, the available surface of CaCO3 is larger,
allowing the reaction (3) to occur on the surface of
CaCO3. This behavior limits the brushite formation by reaction (2). The mass growth rate of Ca-HA at the initialtime can be written as:

224

(2)

where K is a kinetic constant, ΔC is the absolute supersaturation of Ca-HA, and S is the CaCO3 disposable surface
at the time at which Ca-HA is detectable.
As the initial size distribution of calcium carbonate is
identical, the surface (S) is assumed proportional to mass.
The ratio of these initial rates for liquid-solid mass ratios of 3 and 5 leads to a coefficient j of the order of 12.5,
thereby showing a strong influence of the available surface of calcium carbonate on the Ca-HA formation. As
the Ca-HA layer forms on the surface of CaCO3, the diffusion of orthophosphate and calcium ions is limited thus
reducing the mass growth formation of Ca-HA observed
beyond 10 h for a liquid-solid mass ratio of 3. In the caseof a higher ratio, it can be assumed that reactions (2) and(4), which represent the CaCO3 and brushite formations,
respectively, occur in parallel.
Fig. 14 shows the evolution of the mass mean diameter as a function of the time based on both mass ratios(liquid-solid mass ratios of 3 and 5) for a stirring speed of
260 rpm. For a liquid-solid mass ratio of 5, the increase inaverage diameter (7–22 microns) between 0 and 3 is dueto the brushite formation (Fig. 14 (a)). After 3 h, the mean
diameter decreases assumed by brushite dissolution.
Beyond 20 h, the mean diameter increases following thesame trend as Ca-HA mass (Fig. 14 (b)).
Brushite dissolution allows Ca-HA formation. This
compound is thermodynamically more stable than brushite (Ferreira et al. 2003) and it forms on the surface of
CaCO3 causing the increase in diameter. For a liquid-solid
mass ratio of 3 (Figs. 15 (a) and (b)), the same behavior isobserved, meaning that the mass mean diameter increases, then decreases and finally increases again. However, beyond 20 h, the mean diameter does not change(about 20 μm) because the Ca-HA mass stabilizes.

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

Fig. 14

Fig. 15CaHPO4.2H2O and Ca-HA masses calculated from
RAMAN spectra and mean volume diameter during
synthesis (N = 260 rpm and liquid-solid mass ratio= 3), symbols: black circle (d43) and -- (compound
masses).

Fig. 16

Mean volume diameter during the synthesis, symbols:
black circle (liquid-solid mass ratio = 3) and hollow
circle (liquid-solid mass ratio = 5).

CaHPO4.2H2O and Ca-HA masses calculated from
RAMAN spectra and mean volume diameter during
synthesis (N = 260 rpm and liquid-solid mass ratio= 5), symbols: hollow circle (d43) and -- (compound
masses).

Fig. 16 presents the mean diameters of the particles
during synthesis for different stirring speeds (400 and
600 rpm) for both mass ratios (L/S = 3 and 5). The results
obtained at 400 and 600 rpm show the same behavior obtained at 260 rpm (increase, decrease, re-increase andstabilization of mean diameter). After 40 h of synthesis,
the measured diameters are identical (23 ± 1 μm) for astirring speed equal to 400 rpm (Fig. 16 (a)). Larger agglomerates are observed at a low stirring speed after 72 hof synthesis. The stabilization of the mean diameter isobserved after 40 h for all the syntheses except for thesynthesis performed at 600 rpm. In this case after 20 h ofsynthesis, the mass mean diameter is stable and equals
16 ± 1 μm (Fig. 16 (b)).

4. Conclusions
In this study, we show that Raman spectroscopy is avaluable technique to follow the synthesis of Ca-HA in aconcentrated solids suspension (around 20–30 wt%). This
technique makes it possible to follow the formation of
Ca-HA and brushite and to deduce from complementarymeasurements a formation mechanism described as fol-225

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

lows:•	The dissolution of calcium carbonate is observed in
the first minutes of synthesis.
•	The precipitation of brushite and Ca-HA particles isobserved in the first four hours of synthesis.
•	The transformation of brushite into hydroxyapatite
begins after one day. It seems to be the limiting step.
In terms of morphology, agglomerates of hollow ellipsoidal particles were obtained corresponding to the calcium carbonate, while platelets agglomerated like stars
corresponded to the brushite and agglomerated spongy
spheres or round stick-like compounds of spongy spheres
corresponded to the hydroxyapatite.
Through the mass balance obtained from thermogravimetric analysis and from the phosphate ions, it was also
verified that at the end of the synthesis, the calcium carbonate was not completely consumed.
Therefore, the reaction is not complete at the end of thesynthesis (72 h).
From the analysis performed by laser granulometry
during the synthesis with a mass equal to five and three at
260, 400 and 600 rpm, there was a strong influence of thestirring speed on the process. The two effects (positive
and negative) that explain the agglomeration process havebeen presented. For the synthesis with a mass ratio equal
to five, the first effect called the size-enlargement phenomenon predominated. In contrast, for the synthesis witha mass ratio equal to three, a predominance of the second
effect called the breakage phenomenon was observed.
These phenomena were confirmed by the observations
made using the environmental electron microscopy ofthe synthesized solids.

5. Acknowledgements
The authors would like to thank M. H. Lucas for support in the data analysis of RAMAN spectra. They also
thank Ms S. Del Confetto, Ms C. Rolland, Ms N. Lyczko
and M.P. Accart, respectively, for TG measurements, microscopy analysis and X-Ray, and particle size analysis.

References
Anee T.K., Ashok M., Palanichamy M., Kalkura S.N., A novel
technique to synthesize hydroxyapatite at low temperature,
Materials Chemistry and Physics, 80 (2003) 725–730.
Bailliez S., Nzihou A., The kinetics of surface area reduction
during isothermal sintering of hydroxyapatite adsorbent.
Chemical Engineering Journal, 98 (2004) 141–152.
Bezzi G., Celotti G., Landi E., La Torretta T.M.G., Sopyan I.,
Tampieri A., A novel sol−gel technique for hydroxyapatite
preparation. Materials Chemistry and Physics, 78 (2003)

226

816–824.
Chen X., Wright J.V., Conca, L.M., Peurrung J.L., Effects of phon heavy metal sorption on mineral apatite. Environmental
Science and Technology, 31 (1997) 624–631.
Chkir M., Synthèse de gels phosphocalciques issus de déchets
industriels carbonates. Caractérisation physico-chimique,thermique et rhéologique. PhD thesis, Thèse de l’Universitéde Toulouse Institut National Polytechnique de Toulouse,
2011.
Elliott J.C., Structure and Chemistry of the Apatites and Other
Calcium Orthophosphates, Elsevier, Amsterdam, 1994.
Ferreira A., Oliveira C., Rocha F., The different phases in the
precipitation of dicalcium phosphate dihydrate, Journal of
Crystal Growth, 253 (2003) 599–611.
Itatani K., Iwafune K., Howell F.S., Aizawa M., Preparation ofvarious calcium-phosphate powders by ultrasonic sprayfreeze-drying technique. Materials Research Bulletin, 35(2000) 575–585.
Ipekoglu M., Altintas S., Silver substituted nanosized calcium
deficient hydroxyapatite. Materials Technology: Advanced
Performance Materials, 25 (2010) 295–301.
Jarcho M., Bolen C.H., Hydroxylapatite synthesis and characterization in dense polycrystalline form, Journal of Materials Science, 11 (1976) 2027–2035.
Liu C., Huang Y., Shen W., Cui J., Kinetics of hydroxyapatie
precipitation at pH 10 to 11, Biomaterials, 22 (2001) 301–
306.
Luo P., Nieh T.G., Preparing hydroxyapatite powders withcontrolled morphology, Biomaterials, 17 (1996) 1959–1964.
Ma Q.Y., Traina S.J., Logan T.J., Ryan J.A., In situ lead immobilisation by apatite, Environnental Science and Technology, 27 (1993) 1803–1810.
Murakami S., Kato K., Enari Y., Kamitakahara M., WatanabeN., Ioku K., Hydrothermal synthesis of porous hydroxyapatite ceramics composed of rod-shaped particles and evaluation of their fracture behaviour. Ceramics International, 38(2012) 1649–1654.
Nakamura S., Isobe T., Senna M., Hydroxyapatite nano sol prepared via a mechanochemical route. Journal of Nanoparticle Research, 3 (2001) 57–61.
Okada M., Fujii S., Nishimura T., Nakamura Y., Takeda S.,
Furuzono T., Solvent-free formation of hydroxyapatitecoated biodegradable particles via nanoparticle-stabilized
emulsion route, Applied Surface Science, 262 (2012) 39–44.
Rodriguez-Lorenzo L.M., Vallet-Regi M., Ferreira J.M.F., Fabrication of hydroxyapatite bodies by uniaxial pressing froma precipitated powder, Biomaterials, 22 (2001) 583–588.
Saha S.K., Banerjee A., Banerjee S., Bose S., Synthesis of
nanocrystalline hydroxyapatite using surfactant templatesystems: Role of templates in controlling morphology.
Materials Science and Engineering: C, 29 (2009) 2294–
2301.
Silva C.C., Pinheiro A.G., Miranda M.A.R., Góes J.C., Sombra,
A.S.B., Structural properties of hydroxyapatite obtained bymechanosynthesis, Solid State Science, 5 (2003) 553–558.
Swain S.K., Dorozhkin S.V., Sarkar D., Synthesis and dispersion of hydroxyapatite nanopowders, Materials Science andEngineering: C, 32 (2012) 1237–1240.

Nayane Macedo Portela da Silva et al./ KONA Powder and Particle Journal No. 33 (2016) 219–227

Tsuda H., Arends J., Raman spectra of human dental calculus,
Journal of Dental Research, 72 (1993) 1609–1613.
Vasile E., Popescu L.M., Piticescu R.M., Burlacu A., Buruiana
T., Physico-chemical and biocompatible properties ofhydroxyapatite based composites prepared by an innovativesynthesis route, Materials Letters, 79 (2012) 85–88.
Verwilghen C., Chkir M., Rio S., Nzihou A., Sharrock P.,
Depelsenaire G., Convenient conversion of calcium carbonate to hydroxyapatite at ambient temperature, Materials
Science and Engineering: C, 29(3) (2009) 771–773.

Wang C.X., Wang M., Zhou X., Nucleation and growth of apatite on chemically treated titanium alloy: an electrochemical impedance spectroscopy study, Biomaterials, 24 (2003)
3069–3077.
Wen C.F., Zhao Y.L., Liang R.Z., Recycle of low chemical
potencial substance resources, Conservation and Recycling,51 (2007) 475–486.
Wey M.Y., Liu K.Y., Tsai T.H, Chou J.T., Thermal treatment of
the y ash from municipal solid waste incinerator with rotarykiln, Journal of Hazardous Material, 137 (2006) 981–989.

Author’s short biography
Nayane Macedo Portela da SilvaDr Nayane Macedo Portela da Silva was born in 1986. She completed her graduate
studies at UFRN university of Natal, Brazil. Before her diploma, she joined the EMAC
school at Albi, France, to do an internship concentrating on hydroxyapatite synthesis byreaction crystallization (precipitation). She prepared a PhD thesis on microfluidic systems under high pressure in the Laboratory Rapsodee of EMAC school and received her
PhD from Toulouse University in 2014.

Fabienne EspitalierProfessor Fabienne ESpitalier is the head of the research group “Powder and Processes” at the Research Centre in Albi for Particulate Solids, Energy and Environment(Rapsodee research centre)-CNRS UMR 5302 at Ecole des Mines Albi in France.
Her major research interests concern crystallization assisted or not by ultrasound, non-­standard solvents such as ionic liquids, population balance simulation, characterizationand particle technology.

Ange Nzihou
Professor Ange Nzihou is the Director of the Research Centre in Albi for Particulate
Solids, Energy and Environment (Rapsodee research centre)-CNRS UMR 5302 at Ecoledes Mines Albi in France. He is also editor in chief of the international peer-reviewed
journal Waste and Biomass Valorization (Springer), and chair of the WasteEng conference series. He has developed outstanding research in the field of the engineering ofphosphate-based materials for sorbents and catalysts, and heavy metals contaminants remediation. He has published about 87 papers in peer-reviewed journals.

227

KONA Powder and Particle Journal No. 33 (2016) 228–238/Doi:10.14356/kona.2016003

Original Research Paper

A Novel Fluorescent Chemosensor Based on
β-(2-Pyridyl)acrolein-Rhodamine B Derivative:
Polymer Particle Interaction with an
Enhanced Sensing Performance †Kyo-Sun Ku, Ji-Yong Hwang, Palanisamy Muthukumar and Young-A Son *1

Department of Advanced Organic Materials Engineering, Chungnam National University, South Korea

Abstract
A novel, fluorescent chemosensor based on β-(2-pyridyl)acrolein-rhodamine B (RB-AC) derivative was synthesized
and its sensing performance with poly(ethylene glycol) dimethacrylate (PEGDMA) polymer particle wasinvestigated. The prepared β-(2-pyridyl)acrolein-rhodamine B/poly(ethylene glycol)dimethacrylate (PEGDMA/RB-AC) particle was used for sensing of Al3+. The PEGDMA/RB-AC particles showed immediate “off–on”
fluorescent responses toward Al3+. The fluorescent response was attributed to the spirolactam ring opening of RBAC. This sensor particle showed high selectivity towards Al3+ in the presence of other competing metal ions. The
sensitivity of PEGDMA/RB-AC particle was demonstrated by confocal laser scanning microscopy (CLSM) andscanning electron microscopy (SEM). The binding stoichiometry and binding mode of the metal complex wasestablished by Job’s plot and FT-IR spectroscopy.
Keywords: β-(2-pyridyl)acrolein-rhodamine B derivative, rhodamine/polymer particles, aluminium sensing,fluorescent sensor

1. Introduction
The development of selective and sensitive probe forthe detection of biologically and environmentally important species has emerged as a significant goal in the fieldof chemical sensors (Zhou et al., 2014; Xu et al., 2010;
Wang et al., 2014). So far, a number of fluorescent probeswith different excitation and emission wave lengths havebeen used as signal receptors of chemosensors such as
coumarin (Chen et al., 2014), pyrene (Wang et al., 2013),
1,8-naphthalimide (Dai and Xu, 2011), xanthenes (Chen etal., 2012), squaraine (Akkaya, 1997), cyanine (Zheng etal., 2012) and boron dipyrromethene difluoride (BODIPY)(Li et al., 2012). Among these, xanthene derivatives are ofgreat interest due to their excellent photophysical properties such as long absorption and emission wavelengths,
large absorption co-efficient and high fluorescence quantum yield (Beaumont et al., 1993). Rhodamine is a wellknown dye of xanthenes derivative having spirolactam
ring which is color less and non-fluorescent, whereas its†
1*

Received 14 April 2015; Accepted 18 May 2015
J-STAGE Advance published online 30 June 2015
99 Daehak-ro, Yuseong-gu, Daejeon 305-764, South Korea
Corresponding author: Young-A Son;
E-mail: yason@cnu.ac.kr
TEL: +82-42-821-6620 FAX: +82-42-821-8870

228

ring opened amide form gives strong color and fluorescentemission. Based on this, many rhodamine derivatives
have been used as a fluorescent probe for the detection ofvarious metal ions (Kim et al., 2008). The sensing behavior of most of the rhodamine based fluorescent probes wasstudied in the solution (Zhang et al., 2012; Lee et al., 2015;
Lee et al., 2010; Weerasinghe et al., 2009; Ju et al., 2011).
In order to use these probes in the fields, it should be materialized. In recent years, the preparation of rhodaminefunctionalized fluorescent particles for the removal of
metal ions received much interest (Jung et al., 2011).
Molecular imprinting polymerization (MIP) is also
known as template polymerization and widely usedmethod for synthesizing host polymer which has beenrecognizing target species. Due to their chemical and mechanical stability, simple preparation, low cost and highselectivity, MIP has been used in wide range of fields
such as chromatographic separation, solid-phase extraction,
catalysts and sensors (Yang et al., 2009; Andersson, 2000;
Li and Husson, 2006). The imprinting mechanism is explained as the trapping of template molecule into thepolymer matrix during polymerization as a result themolecular information of the template is retained in thepolymeric material in the cross linked powders after complete extraction of the template from the matrix membranes. Two types of interactions between the template©2016 Hosokawa Powder Technology Foundation

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

molecule and functional monomers have been explainedsuch as covalent and non-covalent interactions. The covalent interaction (Wulff and Sarhan, 1972) creates bonds
between the template and polymer whereas non-covalentinteraction (Haupt and Mosbach, 2000) creates electrostatic interactions such as π-π interaction and hydrogenbonds. The non-covalent technique has been preferentially used for different reasons such as easily conducted,
the removal of the template is achieved through continuous extraction and a substantial number of functionalities
can be introduced into the binding sites (Yan and Row,
2006). Immobilization of the indicators in matrices has
substantial effects on the performance of the optical sensors in terms of selectivity, sensitivity, and stability.
Keeping these factors in mind, in this study, we haveretained the template in polymer matrices as a recognizing unit instead of extraction of template from polymer
matrices.
Recently, our research group involves the developmentof colorimetric/fluorogenic chemosensors for selective
detection of different types of analytes (So et al., 2014;
Jeong et al., 2015). It is well documented that the spirolactam ring of the rhodamine derivative is open when it interacts with the metal ions and in turn it shows thefluorescence. Thus, in this article we have made an attempt to synthesis a simple new rhodamine derivative andused this as a template for the preparation of PEGDMA/
rhodamine solid sensory particles. The prepared PEGDMA-­
rhodamine particles have been used for the sensing of
Al3+. The particle shows fluorescent turn on when it interact with Al3+. The prepared rhodamine template was characterized using 1H NMR and mass spectroscopy and the
PEGDMA/rhodamine solid sensory particles were characterized confocal image and scanning electron microscopy.

2. Experimental section
2.1. Materials
The chemicals for the synthesis of β-(2-pyridyl)acrolein-­rhodamine B derivative (RB-AC) were purchased from
Aldrich and Kanto Chemicals Japan and were used without further purification. All reagents and solvents are ofanalytical grade and used without further purification.
The metal ions such as Li+, Na+, K+, Cs+, Mg2+, Ca 2+,
Fe2+, Co2+, Ni 2+, Cu 2+, Zn 2+, Ag+, Cd 2+, Hg2+, Pb2+, Al3+
as perchlorate salts were purchased from commercial
suppliers and used without further purification. Ethylene
glycol dimethacrylate (EGDMA) was obtained from the
Sigma Chemical Company, USA, and was used as received without purification. Azobisisobutyronitrile(AIBN) and poly(vinylpyrrolidone) (PVP) were obtained
from Acros Organics, New Jersey, USA. Ethylene gly-

col dimethacrylate (EGDMA) was used as the monomer.
2,2-Azobisisobutyronitrile (AIBN) and poly(vinyl pyrrolidone) (PVPK-85-95, Mw = 1,300,000) were used as theinitiator and stabilizer, respectively.

2.2. Instrumentation
Nuclear magnetic resonance (NMR) spectra wererecorded in CDCl3 unless otherwise stated, with tetra­
methylsilane (TMS) as internal reference at ambient
temperature, mainly on a BRUKER AVANCE III 300
Magnet: Ascend TM series, 14.1 Tesla, 1H resonance frequency 300 MHz, Top Spin 3.1 (software) spectrometer
Germany spectrometers. HR-MS spectrum was recordedwith Bruker autoflex III mass spectrometer. FT-IR spectra
were recorded with an FTS-175C spectrometer. UV-Visible
absorption spectra were recorded on an Agilent 8453
spectrophotometer. Fluorescence emission spectra wererecorded on a Shimadzu RF-5301PC spectroflurophotometer. The slit width was 1.5 nm for both excitation andemission. Samples were contained in 10.0 nm path length
quartz cuvettes (3.5 mL volume). Fluorescence imaging
was performed by confocal fluorescence microscopy on
LSM5Live, (Germany) with Zeiss LSM5 Live Release
version 4.2.SP1 Image Browser software were used. The
scanning electron microscope (SEM) images were obtained
by JEOL, JSM-7000F.

2.3. Synthesis of RB-AC
Rhodamine B hydrazide and β-(2-pyridyl)acrolein wereprepared following the literature method (Zhang et al.,2011; Krasnaya et al., 1997). The synthesis of RB-AC isshown in Scheme 1. In a 100 mL three necked round bottom flask, rhodamine B hydrazide (1.23 g, 2.6 mmol) was
dissolved in hot ethanol (30 mL). Then, a solution of 0.5 g(3 mmol) of β-(2-pyridyl)acrolein in 20 ml of ethanol wasadded drop wise to the flask in 1 h. After the addition ofβ-(2-Pyridyl)acrolein, 1 ml of acetic acid was added to
the reaction mixture. Under stirring, the reaction mixture
was refluxed for 5 h. After 5 h, the reaction mixture wascool to room temperature and the formed precipitate
was filtered and washed with cold ethanol. The crude
product was purified by column chromatography using
hexane-ethyl acetate (60:40) as eluent to get pure product (1.01 g, yield = 69.1 %). The Product was confirmed by
1
H NMR and mass spectroscopy (Fig. S1 and S2).

Scheme 1

Synthesis of RB-AC.

229

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

2.4. Synthesis of PEGDMA/RB-AC particles
The dispersion medium (stabilizer) was prepared bydissolving 1.5 g of poly(vinyl pyrrolidone) (PVP) in deionized water (150 mL). The synthesis was performed in athree-necked flask equipped with a stirrer, water condenser and thermometer. A mixture containing 17 g ofethylene glycol dimethacrylate (EGDMA), initiator,
0.255 g of azobisisobutyronitrile (AIBN) and 0.0017 g of
RB-AC was diluted with a mixed solvent (toluene (8 mL)/butanol (8 mL)).The reaction mixture transferred to the
PVP dispersion medium and this reaction mixture waspurged with N2 gas for 10 min after that the reaction mixture was sealed under this nitrogen atmosphere andheated at 80 °C for overnight. The produced polymer was
filtered using a Whatman filter number 1 and washed withacetone and methanol to get the RB-AC encapsulated
PEGDMA (PEGDMA/RB-AC) as white powder.

Fig. 1

Absorbance spectra of 10 μM of RB-AC in ethanol/
DMF (9:1) in the presence of 10 μM of different metal
ions.

Fig. 2

Fluorescence spectra of 10 μM of RB-AC in ethanol/
DMF (9:1) in the presence of 10 μM of different metalions.

3. Results and discussion
3.1. Preliminary studies
In order to evaluate the sensing behavior of PEGDMA/RB-AC, we have examined the absorbance, fluorescenceand visible color change of the RB-AC in solution in thepresence of various metal ions including alkali, alkaline-­earth and transition-metal ions such as Li+, Na+, K+, Cs+,
Mg2+, Ca2+, Fe2+, Co2+, Ni2+, Cu2+, Zn2+, Ag+, Cd 2+, Hg2+,
Pb2+ and Al3+, in ethanol/DMF (v/v = 9/1). The absorbance
spectrum of RB-AC doesn’t show any peaks longer than
500 nm which was attributed to the ring closed spirolactam
form of RB-AC (Fig. 1) (Chen et al., 2012). As shown in
Fig. 1, the absorbance intensity of RB-AC was significantly
increased at 560 nm upon the addition of Al3+ whereas nochanges in absorbance intensity of RB-AC were observed
upon the addition of other competitive metal ions. Fig. 2
shows the fluorescence changes of RB-AC in the presenceof different metal ions. The RB-AC alone doesn’t show
any fluorescence peak around 580 nm. The addition of
Al3+ into RB-AC showed the considerable enhancementin fluorescence intensity at 580 nm. Further the additionof other competitive metal ions doesn’t lead the fluorescence enhancement of the RB-AC.
Furthermore, the sensing behavior of RB-AC with different significant metal ions was checked by ‘naked eye’detection methods. As shown in Fig. 3, the synthesized
RB-AC displays a ‘naked-eye’ color change from color
less to pink (Fig. 3, top) after the addition of 10 μM of
Al3+ in ethanol/DMF (9:1) mixture. While in the case ofother cations such as Li+, Na+, K+, Cs+, Mg2+, Ca2+, Fe2+,
Co2+, Ni2+, Cu2+, Zn2+, Ag+, Cd2+, Hg2+ and Pb2+ does notproduce any significant color changes. Further, the addi-

230tion of Al3+ exhibits intense yellow fluorescence underillumination of UV-light (Fig. 3, bottom). Thus, thesensing of Al3+ by RB-AC can be detected by the naked
eye without the assistance of any instrument.

3.2. Spectroscopic recognition of RB-AC towards
Al3+
UV-Vis and fluorescence spectral methods were used
to study the recognition of RB-AC towards Al3+. UV-vis
spectra of RB-AC in ethanol/DMF (9:1) mixture in thepresence of different concentrations of Al3+ are shown in
Fig. S3. In the absence of Al3+, the absorption spectrumof RB-AC doesn’t exhibit any characteristic absorption of
rhodamine moiety whereas upon the addition of Al3+, a
new absorption band centered at 560 nm was observed.
Further addition of Al3+ into RB-AC leads to the increasein absorbance at 560 nm. This indicated a clear and gradually color change from colorless to pink due to the opening of the closed rhodamine spirolactam ring.

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

Fig. 3

Photographs of color changes of RB-AC (10 μM) in ethanol/DMF (9:1) in the presence of various metal
ions detected by naked eye (top) and by UV-lamp (bottom).

Due to its high sensitivity, fluorescence spectroscopy
has been widely used in the study of molecular interactions between rhodamine and metal ions (Chen et al.,2009). Fig. S4 shows the fluorescence spectra of RB-ACin ethanol/DMF (9:1) mixture was recorded against thedifferent concentrations of Al3+. As shown in Fig. S4,
while increasing the concentration of Al3+ the fluorescence intensity at 580 nm was gradually increased. The
binding constant of RG-HN with Al3+ was determined using Benesi-Hildebrand equation (Fig. S5). It was found to
be 4.70 × 104 which was within the range of reported Al3+
chemosensors (Kim et al., 2014).
The detection limit of the receptor RB-AC towards
Al3+ was calculated based on 3δ/k (Lohani et al., 2010).
Where, δ is the standard deviation of the blank solution
and k is the slope of the calibration plot. A linear response
was observed when the concentration of Al3+ is plottedagainst fluorescence intensity with a correlation coefficient of 0.982 (Fig. S6). The detection limit was calculated from slope and it was found to be 5.4 × 10 –8 M.

3.3. Sensing property of PEGDMA/RB-AC
particles towards Al3+
The results discussed in previous sections indicatedthat the synthesized RB-AC has good recognition towards
Al3+ and it can be used for the preparation of sensing
probe for Al3+. In order to improve the sensing performance of RB-AC, we have addressed the task of designing of RB-AC encapsulated PEGDMA particles. To
demonstrate the encapsulation of RB-AC by PEGDMA,
we have studied the color change of PEGDMA/RB-AC
particles in ethanol/DMF (9:1) mixture with and without
Al3+. In the absence of Al3+, PEGDMA/RB-AC particles
in ethanol/DMF (9:1) mixture doesn’t show any color.
After the addition of Al3+ to the PEGDMA/RB-AC particles in ethanol/DMF mixture (9:1), the color of the particles was changed colorless to pink and the particle

Fig. 4

Photographs of PEGDMA/RB-AC in absence (A), presence (B) of 10 μM of Al3+ and reversibility with 10 μM
of EDTA (C) detected by naked eye (top) and UV-lamp(bottom).

showed yellow fluorescence under the illumination of
UV-light as shown in the Fig. S7. Further, the sensing
performance was examined by the addition of 1 mL of
10 μM of Al3+ in ethanol/DMF (9:1) mixture into the 1.0 gof synthesized PEGDMA/RB-AC particles. Besides thatthe mixture was stirred for 5 min up to attain the pinksolid and filtered through the filter paper. As shown in
Fig. 4, after the addition of Al 3+, the color of the
PEGDMA/RB-AC was turned from colorless to pink andit showed yellow emission under illumination of UV light,
which is sensible via naked eye. The reversibility of
PEGDMA/RB-AC was checked by the addition of 1 mLof 10 μM of EDTA extraction. Upon addition of EDTA in
to Al3+ bounded PEGDMA/RB-AC, the color of the particle was turned from pink to color less and the yellow fluorescent emission was also turned off (Fig. 4). Further,
the selectivity of PEGDMA/RB-AC particles towards
Al3+ in the presence of 10 μM of other competitive metal
ions was checked. As depicted in Fig. 5, Al 3+ onlychanges the color of PEGDMA/RB-AC particles fromcolor less to pink in visible light and yellow fluorescence
under UV light whereas other competitive metal ions did
not showed any changes. This indicates that the231

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

Fig. 5 Photographs of PEGDMA/RB-AC in the presence of 10 μM of various metal ions detected by naked eye(top) and by UV-lamp (bottom).

PEGDMA/RB-AC particles show the fluorescence turn on
by sensing of Al3+ with good selectivity and reversibility.

3.4. Depicting the sensing property of PEGDMA/RB-AC by confocal laser scanning microscopy(CLSM)
Consequently, it was of great interest to investigate thesensory property by CLSM (Tao et al., 2011). The 1 mL of
1 × 10 –6 M Al3+ (ethanol/DMF (9:1)) was dropped on thesurface of a dry PEGDMA/RB-AC particles and allowed
to stand for 1 min, resulting in the immediate adsorptionof the Al3+ by particles, after drying the particles in anoven at 150 °C, the CLSM was performed. As shown in
Fig. 6A, PEGDMA/RB-AC particles did not show any
fluorescence under the selective excitation whereas after
the dropping of Al3+ (1 mL) in to the PEGDMA/RB-AC
particles, a significant increase in the fluorescence wasobserved from the selective excitation area (Fig. 6B).
3+

3.5. Al binding on PEGDMA/RB-AC by SEM
analysis
The CLSM results confirmed that the sensing propertyof PEGDMA/RB-AC towards Al3+, to get further insight,
SEM analysis was carried out. Fig. 7 showed the SEM
images of prepared PEGDMA polymer encapsulated
RB-AC in the absence and presence of Al3+. The morphology of prepared PEGDMA/RB-AC particles isspherical in shape, and was arranged in an orderly232

three-dimensional shape and the particle size distribution
is given in Fig. S8. There is only a little bonding between
the particles (Fig. 7A). While in the presence of1 × 10 –5 M of Al3+, the PEGDMA/RB-AC particles were
aggregated together (Fig. 7B). The difference in the surface morphology of these samples may be due to the formation of complex between RB-AC and Al3+ followed byring opening. Energy-dispersive analysis of X-rays(EDAX) was used to analyze the elemental constituentsof prepared PEGDMA/RB-AC particles in the presenceof Al3+. Fig. 8 illustrates the EDAX spectra of PEGDMA/RB-AC particles after the sensing of Al3+. The presenceof aluminum in elemental analysis confirmed that theAl3+ has been entrapped into PEGDMA/RB-AC by formation of complex with RB-AC.

3.6. Mechanism for the sensing of Al3+ by
PEGDMA/RB-AC
Since the sensing part of PEGDMA/RB-AC is RB-AC,
to investigate the recognition of PEGDMA/RB-AC
towards Al3+, the stoichiometry and complex formationof RB-AC to Al3+ were studied. The stoichiometry of
RB-AC to Al3+ was calculated by Job’s plot analysis andthe complex formation was explained by FT-IR binding
study. Total concentration of RB-AC and Al3+ was kept
constant at 10 μM according to the continuous variations
changing the mole fraction of RB-AC from 0.1 to 0.9 (Fig.
S9). The stoichiometry ratio of RB-AC with Al3+ wasanalyzed by absorbance method. The maximum of curveKyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

Fig. 6

CLSM images of PEGDMA/RB-AC in absence (A) and presence (B) of Al3+.

lactam rings amide carbonyl at 1698 cm–1 was completely
disappeared and the imine (C = N) and pyridine (C = N)stretching vibrations are shifted to 1643 and 1580 cm–1,
respectively. Which confirms the Al3+ was opened the
spirolactam ring, formed the complex with oxygen andimine and pyridine nitrogen. It has been well establishedthat Al3+ forms complex with oxygen and nitrogen richligands (Dhara et al., 2014; Ghosh et al., 2014). Based on
this, we have proposed the complex formation of Al3+
with RB-AC (Scheme 2).

4. Conclusion

Fig. 7SEM images of PEGDMA/RB-AC in absence (A) andpresence (B) of Al3+.

was showed at 0.5 mole fraction, indicating that the formation of 1:1 complex between RB-AC and Al3+. Fig. 9shows the FT-IR spectra of RB-AC before and after
binding with Al3+. The FT-IR spectrum of RB-AC alone
shows the peaks at 1678, 1610 and 1550 cm–1 corresponding to spirolactam amide carbonyl, imine (C = N) andpyridine (C = N) stretching vibrations, respectively.
While the addition of 1 eq of Al3+, the stretching frequency of these peaks was changed. Upon the addition of
1 eq Al3+ the characteristic stretching frequency of spiro-

In conclusion, we have developed a novel fluorescentprobe β-(2-pyridyl)acrolein-rhodamine B/poly(ethylene
glycol) dimethacr ylate (PEGDM A / R B-AC). The
PEGDMA/RB-AC particles showed good selectivity andsensitivity towards Al3+. Fluorescence enhance mechanism is presumably due to the chelation of Al3+ with theoxygen atoms of the amide groups of RB-AC results inthe formation of the open-ring form, which leads to theenhancement of fluorescence of PEGDMA/RB-AC particles. The selectivity for Al3+ can be attributed to the rigid
hydrazone binding site as well as the affinity of the pyridine group toward Al3+.

Acknowledgements
This work was supported by research fund of Chungnam National University.

233Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

Fig. 8EDAX spectra of PEGDMA/RB-AC in the presence of Al3+.

Scheme 2

Plausible binding mode of RB-AC with Al3+.

Fig. 9 FT-IR spectra of RB-AC before (a) and after (b) binding
with Al3+.

References
Akkaya E.U., Squaraine-based long wavelength fluorescentchemosensors for ions, Chemosensors of Ion and Molecule
Recognition, 492 (1997) 177–188.
Andersson L.I., Molecular imprinting for drug bioanalysis:
A review on the application of imprinted polymers tosolid-phase extraction and binding assay, J. Chromatogr. B.,
739 (2000) 163–173.
Beaumont P.C., Johnson D.G., Parsons B.J., Photophysicalproperties of laser dyes: picosecond laser flash photolysis
studies of rhodamine 6G, rhodamine B and rhodamine 101,
J. Chem. Soc., Faraday Trans., 89 (1993) 4185–4191.
Chen F., Liu .G., Shi Y., Xi P., Cheng J., Hong J., Shen R., Yao
X., Bai D., Zeng Z., A coumarin-derived fluorescent chemosensor for selectively detecting Cu 2+: Synthesis, DFT
calculations and cell imaging applications, Talanta, 124(2014) 139–145.
Chen X., Jou M.J., Lee H., Kou S., Lima J., Nam S-W., Park S.,
Kim K.W., Yoon J., New fluorescent and colorimetric chemosensors bearing rhodamine and binaphthyl groups forthe detection of Cu 2+, Sen. Actuators B, 137 (2009) 597–
602.
Chen X., Pradhan T., Wang F., Kim J.S., Yoon J., Fluorescent234

chemosensors based on spiroring-opening of xanthenes andrelated derivatives, Chem. Rev., 112 (2012) 1910–1956.
Dai H., Xu H., A water-soluble 1,8-naphthalimide-based ‘turnon’ fluorescent chemosensor for selective and sensitive recognition of mercury ion in water, Bioorg. Med. Chem.
Lett., 21 (2011) 5141–5144.
Dhara A., Jana A., Guchhait N., Ghosh P., Kar S.K.,
Rhodamine-based molecular clips for highly selective recognition of Al3+ ions: synthesis, crystal structure and spectroscopic properties, New. J. Chem., 38 (2014) 1627–1634.
Ghosh K., Majumdar A., Sarkar T., Selective sensing of Al3+
by naphthyridine coupled rhodamine chemosensors, RSC
Adv., 4 (2014) 23428–23432.
Haupt K., Mosbach K., Molecularly imprinted polymers andtheir use in biomimetic sensors, Chem. Rev., 100 (2000)
2495–2504.
Jeong J.W., Rao B.A., Son Y-A., Rhodamine-chloronicotinaldehyde-based “OFF–ON” chemosensor for the colorimetricand fluorescent determination of Al3+ ions, Sens. Actuator
B, 208 (2015) 75–84.
Ju H., Lee M.H., Kim J., Kim J.S., Kim J., Rhodamine-based
chemosensing monolayers on glass as a facile fluorescent“turn-on” sensing film for selective detection of Pb2+,
Talanta, 83 (2011) 1359–1363.
Jung J.H., Lee J.H., Shinkai S., Functionalized magnetic

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

nanoparticles as chemosensors and adsorbents for toxic
metal ions in environmental and biological fields, Chem.
Soc. Rev., 40 (2011) 4464–4474.
Kim H.N., Lee M.H., Kim H.J., Kim J.S., Yoon J., A new trend
in rhodamine-based chemosensors: application of spirolactam ring-opening to sensing ions, Chem. Soc. Rev., 37(2008) 1465–1472.
Kim K.B., You D.M., Jeon J.H., Yeon Y.H., Kim J.H., Kim C.,
A fluorescent and colorimetric chemosensor for selective
detection of aluminum in aqueous solution., Tetrahedron
Lett., 55 (2014) 1347–1352.
Krasnaya Zh.A., Burova S.A., Bogdanov., Smirnova Yu.V.,
Synthesis of δ-Hetraryl-α,α-dicarbonylalkadines and astudy of their isomerization, Chem. Hetrocycl. Compd., 33(1997) 410–422.
Lee Y.H., Lee M.H., Zhang J.F., Kim J.S., Pyrene excimer-based
calix[4]arene FRET chemosensor for mercury(II), J. Org.
Chem., 75 (2010) 7159–7165.
Lee S., Rao B.A., Y-A Son, A highly selective fluorescent chemosensor for Hg2+ based on asquaraine–bis(rhodamine-B)
derivative: Part II, Sens. Actuators B., 210 (2015) 519–532.
Li Q., Guo Y., Shao S., A BODIPY based fluorescent chemosensor for Cu(II) ions and homocysteine/cysteine, Sens.
Actuators B., 171–172 (2012) 872–877.
Li X., Husson S.M., Two-dimensional molecular imprinting
approach to produce optical biosensor recognition elements,
Langmuir, 22 (2006) 9658–9663.
Lohani C.R., Kim J.-M., Chung S.-Y., Yoon J., Lee K.-H.,
Colorimetric and fluorescent sensing of pyrophosphate in
100 % aqueous solution by a system comprised of rhodamine
B compound and Al3+ complex, Analyst, 135 (2010) 2079–
2084.
So H-S., Rao B.A., Hwang J., Yesudas K., Son Y-A., Synthesis ofnovel squaraine-bis(rhodamine-6G): A fluorescent chemosensor for the selective detection of Hg2+, Sens. Actuators
B, 202 (2014) 779–787.
Tao X., Fernandez B., Azucena O., Fu M., Garcia D., Zuo Y.,
Chen D.C., Kubby J., Adaptive optics confocal microscopyusing direct wavefront sensing, Optics Letters, 36 (2011)
1062–1064.
Wang F., Wang L., Chen X., Yoon J., Recent progress in thedevelopment of fluorometric and colorimetric chemosensors for detection of cyanide ions, Chem. Soc. Rev., 43(2014) 4312–4324.
Wang M., Xu J., Liu X., Wang H., A highly selective pyrene
based “off–on” fluorescent chemosensor for cyanide, New.
J. Chem., 37 (2013) 3869–3872.
Weerasinghe A.J., Schmiesing C., Sinn E., Highly sensitiveand selective reversible sensor for the detection of Cr3+,
Tetrahedron Lett., 50 (2009) 6407–6410.
Wulff G., Sarhan A., Über die Anwendung von enzymanalog
gebauten Polymeren zur Racemattrennung, Angew. Chem.
84 (1972) 364–365.
Xu Z., Yoon J., Spring D.R., Fluorescent chemosensors for Zn 2+,
Chem. Soc. Rev., 39 (2010) 1996–2006.
Yan H., Row K.H., Characteristic and synthetic approach ofmolecularly imprinted polymer, Int. J. Mol. Sci. 7 (2006)
155–178.
Yang Q.Y., Sun Q., Zhou T.S., Shi G.Y., Jin L.T., Determinationof parathion in vegetables by electrochemical sensor basedon molecularly imprinted polyethyleneimine/silica gel films,
J. Agric. Food Chem., 57 (2009) 6558–6563.
Zhang J., Yu C., Qian S., Lu G., Chen J., A selective fluorescentchemosensor with 1, 2, 4-triazole as subunit for Cu (II)and
its application in imaging Cu (II) in living cells, Dyes
Pigm., 92 (2012) 1370–1375.
Zhang Z., Zheng Y., Hang W., Yan X., Zhao Y., Sensitive andselective off–on rhodamine hydrazide fluorescent chemosensor for hypochlorous acid detection and bioimaging,
Talanta, 85 (2011) 779–786.
Zheng H., Yan M., Fan X-X., Sun D., Yang S-Y., Yang L-J., Li
J-D., Jiang Y-B., A heptamethine cyanine-based colorimetricand ratiometric fluorescent chemosensor for the selective
detection of Ag+ in an aqueous medium, Chem. Commun.,48 (2012) 2243–2245.
Zhou Y., Zhang J.F., Juyoung Yoon J., Fluorescence and colorimetric chemosensors for fluoride-ion detection, Chem.
Rev., 114 (2014) 5511–5571.

Appendix: Supporting information

235

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

Fig. S3

236

Fig. S1

1

Fig. S2

HR-MS spectrum of RB-AC.

H NMR spectrum of RB-AC.

UV-Vis spectra of 10 μM of RB-AC in ethanol/DMF(9:1) in the presence of different concentrations of
Al3+. Inset: Absorbance at 560 nm as a function of theconcentration of Al3+.

Fig. S4

Fluorescence spectra of 10 μM of RB-AC in ethanol/
DMF (9:1) in the presence of different concentrationsof Al3+. Inset: Fluorescence at 580 nm as a function of
the concentration of Al3+.

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

Fig. S8Size histogram of PEGDMA/RB-AC particles.

Fig. S5 Benesi-Hildebrand plot and equation for binding of
Al3+.

Fig. S6 Calibration plot for fluorescence intensity against concentration of Al3+.

Fig. S9

Job’s plot for RB-AC with Al3+ ion.

Fig. S7 Photographs of PEGDMA/RB-AC particles in ethanol/
DMF mixture (9:1) (A) absence and (B) presence of
Al3+.

237

Kyo-Sun Ku et al. / KONA Powder and Particle Journal No. 33 (2016) 228–238

Author’s short biography
Kyo-Sun Ku
Kyo-Sun Ku received his B.Sc. (Advanced Organic Materials Engineering) in 2015 at
Chungnam National University, Daejeon, South Korea, under the supervision of Prof.
Dr. Young-A Son. He is currently a master candidate in the Department of Advanced
Organic Materials and Textile System Engineering, Chungnam National University. His
research interests lie in the synthesis of rhodamine, pyridine derivatives and their sensing applications towards cation and anion.

Ji-Yong Hwang
Ji-Yong Hwang is currently a master candidate in the Department of Advanced Organic
Materials and Textile System Engineering, Chungnam National University, South Korea.
His main research fields are rhodamine based fluorescent chemosensors.

Palanisamy Muthukumar
Palanisamy Muthukumar received his Ph.D. degree in chemistry under the supervision of Prof. Dr. S. Abraham John, from Gandhigram Rural Institute (Deemed University), Gandhigram, India. Currently, he is working as a Post-Doctoral Fellow in Prof.
Young-A. Son’s group, Chungnam National University, Daejeon, South Korea. His current interests are rhodamine based fluorescent probes design and chemical sensors forcations and anions.

Young-A Son
Young-A Son is a professor in the Department of Advanced Organic Materials
Engineering, Chungnam National University. He received his doctoral degree in color
chemistry from the Department of Color Chemistry, University of Leeds, United Kingdom
in 2001. His current research interests include luminescent organic materials, chemosensors, thermochromic dyes, color filter, functional dye materials and biosensors.

238

KONA Powder and Particle Journal No. 33 (2016) 239–248/Doi:10.14356/kona.2016004

Original Research Paper

Controlling of Segregation in Rotating Drums
by Independent End Wall Rotations †Hsiu-Po Kuo *, Wei-Ting Tseng and An-Ni Huang
1

Department of Chemical and Materials Engineering, Chang Gung University, Taiwan

Abstract
We present in this study that particle segregation in rotating drums can be controlled by end wall rotations. Whilethe end wall rotational speed dominates the time required for reaching the steady state, the rotational direction of
the end walls determines the segregation patterns and the shearing zone size. New segregation patterns with twowell-mixed regions close to the end walls are observed in the drums with the end wall rotates in the direction
opposite to the cylindrical wall. The end wall rotation causes the formation of the local valley and hill next to thewall. Particles flow into the valley and down the hill causing the formation of the convective flow cell at bed
surface. It is the difference of the axial velocities between the large particles and small particles close to the end
walls separating the particles of difference sizes in the axial direction. The controlling of the end wall roughness
and rotating directions effectively enlarge the size of the end wall shearing zone; resulting segregation patterns
which are different from the previous simple segregation band patterns.
Keywords: rotating drum, segregation, mixing, end wall rotation, spatio-tempral diagram

1. Introduction
Particulate systems in most processes usually consist ofcomponents of different physical properties. These properties include sizes, densities, shapes, surface roughness,elasticity and etc. During the particle processing, differences in the particle physical properties may cause segregation, the phenomenon by which particles of similar
properties separate into their individual components(Huang and Kuo, 2014). Although size-induced segregation (Klisiewicz et al., 2015), density-induced segregation(Oshitani et al., 2013), elasticity-induced segregation (Kuoet al., 2006a) and surface stickiness-induced segregation(Troiano et al., 2014) have been studied, the underlying
physics of such a unique granular segregation phenomenon
remains incompletely understood still now (Windows-Yuleand Parker, 2015).
Rotating drums, showing rich granular behavior,widely used in industry for mixing, drying, agglomeration, calcinations, cooling and roasting are commonlyused to study granular flows and/or particle segregation(Abouzeid and Fuerstenau, 2010; Grajales et al., 2012;†
1*

Received 29 May 2015; Accepted 8 June 2015
J-STAGE Advance published online 30 June 2015
No. 259, Wenhua 1st Road, Guishan District, Taoyuan City 33302,
TaiwanCorresponding author: Hsiu-Po Kuo;
E-mail: hpkuo@mail.cgu.edu.tw
TEL: +886-3-2118800 ext 5488 FAX: +886-3-2118668©2016 Hosokawa Powder Technology Foundation

Dhawan et al., 2014). Studies of particles segregation inrotating drums have long history since 1939 (Oyama,1939). Researchers used particles of different sizes,
masses, densities, shapes and elasticities to experimentally investigate segregation in dry rotating drums (Kuoet al., 2006a; Arntz et al., 2014) or in drums with different
interstitial fluids (Chou et al., 2010). Numerically studiesof particle flows/segregation in rotating drums are also
conducted by a number of groups using DEM and CFD(Marigo et al., 2012; Huang et al., 2013a; Alizadeh et al.,2014; Marigo and Stitt, 2015). Segregation structures havebeen reported three-dimensionally using MRI and thefreeze-slicing method (Kawaguchi et al., 2006; Sederman
et al., 2007; Huang and Kuo, 2012). Although other complex segregation patterns have been reported in high filllevel drums (Kuo et al., 2006b), particle segregation inrotating drums generally consist of a radial segregationcore and segregation bands in the axial direction. The intensity of segregation is affected by the physical properties of the materials and by the drum operating parameters(Huang and Kuo, 2011).
A reasonable level of understanding of the segregation
processes within a bidisperse rotating drum system has
been achieved. Small particles are wrapped by large
particles in the radial direction after several revolutions
by the particle percolation mechanism and balancing between the centrifugal and the gravitational forces. Alternative small-particle-rich and large-particle-rich bands
in the axial direction are then observed later at the bed

239

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

surface (Turner and Nakagawa, 2000; Maneval et al.,2005; Alizadeh et al., 2013). Comparing to the level ofcomprehension of the radial segregation cores, our knowledge to the axial segregation bands is poor. Controversially formation mechanisms of the axial segregation
bands in the rotating drums have been reported, including
the Stochastic diffusion model (Fan and Shin, 1979), thedifference of the static/dynamic angle of repose induced
diffusional model (Hill and Kakalios, 1994, 1995), thecore–thickening model (Khan et al., 2004), and the end
wall shearing induced local percolation theory (Kuo et al.,2005). Although most of these models reasonably explain
the evolution of the axial segregation bands, the fact thatthe segregation bands originating close to the two end
walls is not satisfactory explained.
Nakagawa is probably the first person who pointed out
the importance of the end walls in axial segregation bandformation (Nakagawa, 1994). The angle of repose of theparticles next to the end wall is different to that in thebulk and particle motion in the axial direction accelerates,
causing the formation of the segregation bands. Related
reports focused on the influences of the end wall roughness on particle f low/segregation in rotating drums(Maneval et al., 2005; Arntz et al., 2013; Chand et al.,2012). End wall roughening increases the rate of axial
segregation and reduces the particle near-wall transverse
speed. The change of the frictional force between the end
walls and the particles by end wall roughness alters thenumbers of the axial segregation bands. A specially designed rotating drum with two independent rotating end
walls has been used to improve the comprehension of therole of end wall on the segregation dynamics (Huang etal., 2013b). The central segregation bands were initiated
where the end wall shearing was higher.
Understanding and controlling of particle segregationby the end walls has not yet been successful. In this work,
the rotating drum with two end walls rotating in the same
direction or in the opposite direction to that of the cylindrical wall is used to improve our understanding and tocontrol segregation in rotating drums. The design of thedrum and the operating conditions are shown in section 2.
The influence of the end wall rotation on particle segregation and the controlling of particle segregation by end
wall rotation are in section 3. Finally, we summarize ourmajor findings and conclusions arising from this study insection 4.

roughness are investigated: PSS, in which end wall surface is polish stainless steel, and #220, in which #220sand paper (i.e., 220 mm × 220 mm, 68 μm diameter sandparticles per square inch) is glued on the surface of theend walls. The frictional angles of the end walls with different roughness are measured. The frictional coefficients
between the particles used in this study and PSS end wall,and the #220 end wall are 0.47 and 0.57, respectively.
While the cylindrical column rotational speed is set as
10 rpm, which corresponds to a tip speed of 0.05 m/s, therotational speed of the end wall is 5 rpm, 10 rpm, 15 rpm,
30 rpm, 50 rpm, 70 rpm, or 90 rpm. In our experiments,
two end wall rotational directions are studied: S, in which
the rotational direction of the end wall is the same as thatof the cylindrical wall, and O, in which the rotational
direction of the end wall is opposite to that of the cylindrical wall. The schematic drawing of the experimental
set-up is shown in Fig. 1.
Particle segregation is investigated in a bidisperse
system using two species of glass particles (density =2510 kg m–3), each of equal physical properties but differing in their sizes and colors. The large particles are black
and within the sieving size range of 1.19 mm–1.41 mm.
The small particles are white and within the sieving size
range of 0.81 mm–1.00 mm. The large and small particles
are only different in colors and sizes. The dynamic angles
of repose of small and large particles in the 10 rpm PSS
rotating drum are 24° and 27°, respectively. The relatively
large size of the particles and the materials used in thisstudy allow us to neglect the triboelectric effects within
the system.
In a typical segregation experiment, 20 % of the drum
volume is firstly filled by large particles. Small particles
are then placed on top of the large particles and the total

2. Experimental details
The experimental set-up is a rotating drum of 98 mm
inner diameter and 138 mm length. The drum consists ofa Perspex cylindrical wall and the two end walls whichrotate independently to the cylindrical wall. Two end wall

240

Fig. 1

Schematic drawings of the (a) S drum and (b) O drum.

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

level is set as 40 %. Two motors independently control
the rotational speeds/directions of the cylindrical wall andthe end walls. Two motors start at the same time and the
bed surface is simultaneously recorded by a video camera(1 fps, HDR XR-520, Sony, Japan) for 60 min and by ahigh speed camera (300 fps, MotionPro-Y3, Nikon, Japan)
for 5 s. The lightings are adjusted to show the best color
contrast between the two species.
The trajectories of the tracer particles are recorded by
the MotionPro-Y3 high speed camera and the Particle Imaging Velocimetry (PIV) technique is used to visualize theflow patterns at the bed surface (Chung et al., 2010). The
tracer particles are identical to the particles used in the
segregation experiments but differing in their colors. The
cell size for flow pattern visualization is 2.4 mm × 2.4 mm.
The development and long lasting instabilities of the
bed surface segregation patterns are studied by the spatio-tempral diagrams (Caps et al., 2003). Here, the spatio-tempral diagrams are prepared by stacking 3600 300
pixel (W) × 1 pixel (H) video camera images. The colorsof the spatio-tempral diagrams are in gray-scale. The
color calibration confirms that the large black particle
show a gray-scale color less than 80. The large black
particle concentrations in the near-wall region (i.e., within
2 cm next to the end walls) is approximated by the fractions of the black pixels in the left-most 43 pixel(W) × 3600 pixel (H) area and in the right-most 43 pixel(W) × 3600 pixel (H) area in the spatio-tempral diagrams.

3. Results and discussion
The bed surface spatio-tempral diagrams of the drums
operating at different conditions are shown in Fig. 2. The
end walls of the drums in Fig. 2(a) and Fig. 2(b) are PSS
and #220, respectively.
Symmetric segregation patterns are observed after arelatively long operation time since the two end walls
rotate in the same direction and at the same speed. The
spatio-tempral diagrams represent the development of thesegregation patterns. While the end wall rotational speed
dominates the time required for reaching the steady state,
the rotational direction of the end walls determines thesegregation patterns. In S drums, the segregation pattern ischaracterized by two separating small-particle-rich whitebands at two sides of the drum. In O drums, the segregation pattern is characterized by one small-particle-richwhite band at the central of the drum. At the same rotational time, the color of the small-particle-rich white bandturns grayer when the rotational speed of the end wall
increases, indicating the increasing of the amount of theblack large particles onto the small-particle-rich whitebands.
In O drums, the local mixing at steady state is goodFig. 2The spatio-tempral diagrams of the segregation patterns in the drums rotating at different end wall speeds:(a) PSS and (b) #220 end walls.

and the comparisons between different rotational speeds
are neglected. The local mixing in the near end wall region (say 2 cm next to the end wall) as a function of theend wall rotational speed is analyzed in S drums. The
fractions of the large black particle pixels at steady state(i.e., the last 200 s in Fig. 2) are calculated and the results
are shown in Fig. 3. As the rotational speed of the end

241Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

Fig. 3

The fractions of the black pixels in the next to wall region at steady state as a function of the end wall rotational speed in S drums.

Fig. 4242

walls increases, the number of the small white particles in
the near end wall large-black-particle-rich band increases,
causing the decreasing of the fraction of the black pixels
in the next to wall region. The fraction of the black pixels
in the next to the end wall region at steady state is lower
when the end wall surface is rougher #220. The end wallroughness enhances local mixing in the region next to theend walls in S drums.
The small white particles migrate from the center ofthe drum to two end walls in S drums in Fig. 2, causing thedisappearance of the (central) small-white-particle-richsegregation band and an increasing of the width of thewhite-particle-rich segregation bands at two sides. While
the widths of the steady state white-particle-rich segregation bands at two sides are similar in all cases in S drums,
the width of the steady state central white-particle-rich(a) Large particle PIV flow patterns in the left-hand side of the drum fitted with PSS end walls. (b) The
enlargement of 90 rpm cases.

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

segregation band in O drums is a function of the end wallrotational speed. It is interesting to note that the largest
central band width at steady state occurs at 70 rpm in O
drums with PSS end walls and at 30 rpm in O drums with#220 end walls. Although the existence of the maximum
steady state central band width has been reported (Huang
et al., 2013b), its dependence on the end wall rotational
speed and roughness is initially reported.
The large particle PIV velocity profiles at the bed surface in the left-hand side of the drum with PSS end walls
are shown in Fig. 4(a) and the enlargements of 90 rpm
cases are shown in Fig. 4(b). The motion of the particles
close to the end walls is affected by the end wall rotation.
Depending on their positions, the particles may be pushedinside the bed by the end wall rotation, forming a local
valley next to the wall, or they may be pushed outside the
bed by the end wall rotation, forming a local hill next to
the wall.
The direction of the end wall rotation determines positions of the local valley and the local hill and hence thedirection of the convective flow cell. The flows of the particles into the valley and down the hill cause the axial
motion of the particles and the formation of the convective flow cell observed in Fig. 4. In O drums, while theparticles at the upper half of the bed surface fall into thevalley and move towards the end walls, the particles at thelower half of the bed surface flow down the hill and movetowards to the center of the drum.
Axial velocities of the large and small particles in S
drums with PSS end walls and in O drum with PSS end
walls are shown in Fig. 5 and Fig. 6, respectively. The
values of the particle axial velocity just next to the end
walls are not shown here due to the limitations of the resolution of the camera. The influence of the end wall rotational speed on the particle axial velocity is more
pronounced in O drums. The large and small particles located to the region next to the end walls are both dragged
up to the local hill by the rotation of the end walls toabout the same height. The local hills are in the upper 1/3of the bed surface close to the end walls in S drums andin the lower 1/3 of the bed surface close to the end walls
in O drums. Since the large particles experienced greater
inertia than the small particles, when the particles fall
down from the local hills, the large particles show greater
downslope axial velocities than the small particles inFig. 5(a) and Fig. 5(b) and in Fig. 6(c) and Fig. 6(d).
The differences between the axial velocities of thesmall and large particles in rotating drums with PSS end
walls are shown in Fig. 7. The differences between theaxial velocities of the small and large particles are greaterin O drums than those in S drums. We show direct evidence that it is the difference of the axial velocities between the large particles and small particles close to theend walls causing the separation of the particles in theFig. 5

Axial velocities of (a) large particles in the upper 1/3 ofbed surface, (b) small particles in the upper 1/3 of bed
surface, (c) large particles in the lower 1/3 of bed surface, and (d) small particles in the lower 1/3 of bed surface in S drums with PSS end walls.

243

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

Fig. 6 Axial velocities of (a) large particles in the upper 1/3 ofbed surface, (b) small particles in the upper 1/3 of bed
surface, (c) large particles in the lower 1/3 of bed surface, and (d) small particles in the lower 1/3 of bed surface in O drums with PSS end walls.

244

Fig. 7The difference of the particle axial velocities between
small particles and large particles in the (a) upper 1/3of the bed surface in S drums, (b) upper 1/3 of the bed
surface in O drums, (c) lower 1/3 of the bed surface in
S drums, and (d) lower 1/3 of the bed surface in O
drums with PSS end walls.

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

Fig. 8 The comparison of the sizes of the shearing zones atdifferent operation conditions.

axial direction. The region far away from the end walls is
less affected by the end wall rotation and the particle axial
velocity shows a near zero value. The shearing zone affected by the end wall rotation is determined and the sizesof the shearing zone are compared at different operating
conditions.
To avoid the errors in the measurement, the shearingzone is defined as the large particles therein a cell (2.4 mm
× 2.4 mm) have an absolute average cell axial velocity
greater than 0.005 m/s for at least 2 successive cells in theaxial direction. The sizes of the shearing zone determinedin the upper 1/3 of the bed surface are shown in Fig. 8.
While the surface roughness increases the size of theshearing zone in S drums, it has little effects on the shearing zone size in O drums.
The size of the shearing zone is about 60 %–80 % of
the half drum length in O drums and about 15 %–35 % of
the half drum length in S drums. In previous reports, theblade shearing zone is only about 5–10 particle diameter(Nedderman and Laohakul, 1980; Kuo et al., 2003), whichcorresponds to about 10 %–20 % of the half drum length
in this work. The shearing zone in O drums is apparently
much larger than the previous findings.
Although the rougher surface contributes to end wall
shearing and slightly increases the shearing zone sizes, itis the direction of the end wall rotation dominating themagnitude of end wall shearing and hence the size of theshearing zone. It is probably the relatively large shearingzone causing the fast evolution of the segregation bands in
O drums in Fig. 2. The controlling of the end wall roughness in S drums and the end wall rotational directions effectively enlarge the size of the end wall shearing zone, andthe resulting segregation patterns are different from theprevious simple alternative small-white-particle-very-richand large-black-particle-very -rich bands.
The comparisons of the axial velocities of the large

Fig. 9The differences of the large particle axial velocities inthe drums with PSS end walls and those in the drums
with #220 end walls: (a) in the upper 1/3 of bed surface
in O drums, (b) in the lower 1/3 of bed surface in O
drums, (c) in the upper 1/3 of bed surface in S drums,and (d) in the lower 1/3 of bed surface in S drums.

245

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

particles in drums with different end wall roughnesses
are shown in Fig. 9. Only three rotational speeds are presented here to ease the comparisons.
The differences are observed in the shearing zones
only and are greater in O drums than in S drums. Since
the influence of the end wall surface roughness on particle axial motion is not significant in S rums, previous researchers who studied the influence of the end wallroughness on segregation patterns in rotating drums did
not observed new segregation patterns as those shown in
Fig. 2. The dual effects of the end wall roughness androtational speeds in O drums noticeably change the particle axial velocities. New segregation patterns with twoquite well mixed regions close to the end walls shown in
O drums are thus observed in Fig. 2.

4. Conclusions
An original rotating drum is used to study the effectsof the end wall rotation/shearing on particle segregationin rotating drums. The rotational direction of the end
walls determines the segregation patterns and new segregation patterns with two well-mixed regions close to theend walls are initially observed in O drums. The end wallroughness enhances local mixing in the region next to theend walls in S drums. There exists a maximum central
band width at steady state in O drums: at 70 rpm while
using polished stainless steel end walls and at 30 rpm
while using #220 sand paper end walls. The end wall rotation causes the formation of the local valley and hillnext to the wall. Particles flow into the valley and down
the hill cause the formation of the convective flow cell at
bed surface. The influence of the end wall rotational
speed on the particle axial velocity is more pronounced in
O drums. The difference of the axial velocities between
the large particles and small particles close to the end
walls causing the separation of the particles in the axial
direction. The dual effects of the end wall roughness androtational speeds in O drums noticeably change the particle axial velocities. The controlling of the end wall rotational speed and direction effectively enlarge the size ofthe end wall shearing zone.

Acknowledgements
The authors are grateful for the financial support from
Ministry of Science and Technology, Taiwan (MOST 1032221-E-182-068) and Chang Gung Memorial Hospital(BMRP 634). We would also like to gratefully acknowledge Professor S.S. Hsiau at National Central University,
Taiwan for his help in the PIV measurements.

246

References
Abouzeid A.-Z.M., Fuerstenau D.W., Flow of non-homogeneousparticulates in rotating drums, KONA Powder and Particle
Journal, 28 (2010) 155–166.
Alizadeh E., Bertrand F., Chaouki J., Comparison of DEM
results and lagrangian experimental data for the flow andmixing of granules in a rotating drum, AIChE Journal, 60(2014) 60–75.
Alizadeh E., Dubé O., Bertrand F., Chaouki J., Characterizationof mixing and size segregation in a rotating drum by a particle tracking method, AIChE Journal, 59 (2013) 1894–
1905.
Arntz M.M.H.D., Beeftink H.H., den Otter W.K., Briels W.J.,
Boom R.M., Segregation of granular particles by mass,
radius, and density in a horizontal rotating drum, AIChE
Journal, 60 (2014) 50–59.
Arntz M.M.H.D., Den Otter W.K., Beeftink H.H., Boom R.M.,
Briels W.J., The influence of end walls on the segregation
pattern in a horizontal rotating drum, Granular Matter, 15(2013) 25–38.
Caps H., Michel R., Lecocq N., Vandewalle N., Long lasting
instabilities in granular mixtures, Physica A, 326 (2003)
313–321.
Chand R., Khaskheli M.A., Qadir A., Ge B., Shi Q., Discrete
particle simulation of radial segregation in horizontally
rotating drum: Effects of drum-length and non-rotating
end-plates, Physica A: Statistical Mechanics and its Applications, 391 (2012) 4590–4596.
Chou S.H., Liao C.C., Hsiau S.S., An experimental study on theeffect of liquid content and viscosity on particle segregation
in a rotating drum, Powder Technology, 201 (2010) 266–
272.
Chung Y.C., Hsiau S.S., Liao H.H., Ooi J.Y., An improved PTV
technique to evaluate the velocity field of non-sphericalparticles, Powder Technology, 202 (2010) 151–161.
Dhawan N., Rashidi S., Rajamani R.K., Population balance
model for crushed ore agglomeration for heap leach operations, KONA Powder and Particle Journal, 31 (2014) 200–
213.
Fan L.T., Shin S.H., Stochastic diffusion model of non-ideal
mixing in a horizontal drum mixer, Chemical Engineering
Science, 34 (1979) 811–820.
Grajales L.M., Xavier N.M., Henrique J.P., Thoméo J.C., Mixing and motion of rice particles in a rotating drum, Powder
Technology, 222 (2012) 167–175.
Hill K.M., Kakalios J., Reversible axial segregation of binary
mixtures of granular materials, Physical Review E, 49(1994), R3610–R3613.
Hill K.M., Kakalios J., Reversible axial segregation of rotating
granular media, Physical Review E, 52 (1995) 4393–4400.
Huang A.N., Kao W.C., Kuo H.P., Numerical studies of particle
segregation in a rotating drum based on Eulerian continuum approach, Advanced Powder Technology, 24 (2013a)
364–372.
Huang A.N., Kuo H.P., A study on the transition between neighbouring drum segregated bands and its application to functionally graded material production, Powder Technology,

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

212 (2011) 348–353.
Huang A.N., Kuo H.P., A study of the three-dimensional particle size segregation structure in a rotating drum, AIChE
Journal, 58 (2012) 1076–1083.
Huang A.N., Kuo H.P., Developments in the tools for the investigation of mixing in particulate systems—A review,
Advanced Powder Technology, 25 (2014) 163–173.
Huang A.N., Liu L.C., Kuo H.P., The role of end wall shearing
in the drum segregation band formation, Powder Technology, 239 (2013b) 98–104.
Kawaguchi T., Tsutsumi K., Tsuji Y., MRI measurement of
granular motion in a rotating drum, Particle and Particle
Systems Characterization, 23 (2006) 266–271.
Khan Z.S., Tokaruk W.A., Morris S.W., Oscillatory granularsegregation in a long drum mixer, Europhysics Letters, 66(2004) 212–218.
Klisiewicz P., Roberts J.A., Pohlman N.A., Segregation of titanium powder with polydisperse size distribution: Spectral
and correlation analyses, Powder Technology, 272 (2015)
204–210.
Kuo H.P., Burbidge A.S., Knight P.C., Parker D.J., Adams M.J.,
Seville J.P.K., Non-equilibrium particle motion in thevicinity of a single blade, Powder Technology, 132 (2003)
1–9.
Kuo H.P., Hsiao Y.C., Shih P.Y., A study of the axial segregation
in a rotating drum using deformable particles, Powder
Technology, 166 (2006a) 161–166.
Kuo H.P., Hsu R.C., Hsiao Y.C., Investigation of axial segregation in a rotating drum, Powder Technology, 153 (2005)
196–203.
Kuo H.P., Shih P.Y., Hsu R.C., Coupled axial-radial segregationin rotating drums with high fill levels, AIChE Journal, 52(2006b) 2422–2427.
Maneval J.E., Hill K.M., Smith B.E, Caprihan A., FukushimaE., Effects of end wall friction in rotating cylinder granular
flow experiments, Granular Matter, 7 (2005) 199–202.

Marigo M., Cairns D.L., Davies M., Ingram A., Stitt E.H., A
numerical comparison of mixing efficiencies of solids in acylindrical vessel subject to a range of motions, Powder
Technology, 217 (2012) 540–547.
Marigo M., Stitt E.H., Discrete element method (DEM) for
industrial applications: Comments on calibration and validation for the modelling of cylindrical pellets, KONA Powder and Particle Journal, 32 (2015) 236–252.
Nakagawa M., Axial segregation of granular flows in a horizontal rotating cylinder, Chemical Engineering Science, 49(1994) 2540–2544.
Nedderman R.M., Laohakul C., The thickness of the shear zoneof flowing granular materials, Powder Technology 25 (1980)
91–100.
Oshitani J., Ohnishi M., Yoshida M., Franks G.V., Kubo Y.,
Nakatsukasa S., Dry separation of particulate iron ore using
density-segregation in a gas-solid fluidized bed, Advanced
Powder Technology, 24 (2013) 554–559.
Oyama Y., Bull. Inst. Phys. Chem Res. (Tokyo), Rep. 5.
1939;18:600. (Oyama’s work was reported by Weidenbaum
SS in Advances in Chemical Engineering, Vol. 2, p.211,
edited by Drew TB and Hoopes JW. New York: Academic,
1958.)
Sederman A.J., Gladden L.F., Mantle M.D., Application ofmagnetic resonance imaging techniques to particulate systems, Advanced Powder Technology, 18 (2007) 23–38.
Troiano M., Salatino P., Solimene R., Montagnaro F., Walleffects in entrained particle-laden flows: The role of particle stickiness on solid segregation and build-up of wall
deposits, Powder Technology, 266 (2014) 282–291.
Turner J.L., Nakagawa M., Particle mixing in a nearly filled
horizontal cylinder through phase inversion, Powder Technology, 113 (2000) 119–123.
Windows-Yule K., Parker D., Density-driven segregation inbinary and ternary granular systems, KONA Powder andParticle Journal, 32 (2015) 163–175.

247

Hsiu-Po Kuo et al. / KONA Powder and Particle Journal No. 33 (2016) 238–248

Author’s short biography
Hsiu-Po Kuo
Hsiu-Po Kuo received his B.Sc. degree and Ph.D. degree in Chemical Engineeringfrom National Taiwan University, Taiwan in 1995 and from the University of Birmingham,
UK in 2001, respectively. He joined Chang Gung University in 2002 and promoted asfull Professor in 2012. He won the most-contributed editor title of Advanced Powder
Technology journal from 2010 to 2013. He works closely with the Formosa Plastic
Groups in Taiwan. He is interested in all particle technology related studies.

Wei-Ting Tseng
Mr. Wei-Ting Tseng received his bachelor in Department of Chemical and Materials
Engineering, Chang Gung University, Taiwan in 2013. He is currently in his master program in Department of Chemical and Materials Engineering, Chang Gung University,
Taiwan.

An-Ni Huang
Dr. An-Ni Huang received her bachelor degree from Yuan-Ze University, Taiwan
in 2006 and doctor degree in Chemical and Materials Engineering from Chang Gung
University, Taiwan in 2012. She is currently a post-doctoral researcher at Chang Gung
University, Taiwan. Her research interests include CFD-DEM modeling of multiphaseflows, mixing and ash management.

248

KONA Powder and Particle Journal No. 33 (2016) 249–263/Doi:10.14356/kona.2016006

Original Research Paper

Consolidation of Non-Colloidal Spherical Particles
at Low Particle Reynolds Numbers †Kevin P. Galvin, Marveh Forghani, Elham Doroodchi and Simon M. Iveson *1

Centre for Advanced Particle Processing and Transport, Newcastle Institute for Energy and Resources,
The University of Newcastle, Australia

Abstract
When a system of identical spheres settles under conditions of negligible surface and inertial forces an idealised
form of sediment consolidation unfolds amenable to a universal description. We have described this complex
process using a simple constitutive model expressed as an elementary scaling law in time, t, applied at the local
particle level. The free-volume surrounding a particle consists of two volume contributions occupied by fluid, one
portion fixed and the other portion variable, the latter of which declines with t–2. A comprehensive system ofanalytical equations was derived using this one idea, and associated boundary conditions, to describe all aspectsof the batch settling process. An experimental system exhibiting negligible surface and inertial forces was used tovalidate the model and hence assess the merits of the scaling law. Excellent agreement was achieved. The precise
physics responsible for this scaling law, and the applicable boundary conditions, remain unclear at this stage.
Hence this work is likely to motivate further work in this area, concerned with the dynamics of random
consolidation of settling spheres.
Keywords: sedimentation, consolidation, scaling laws, non-colloidal spheres, low Reynolds number

1. Introduction
Concha F. and Bürger R. (2002) have provided a usefulhistorical review of batch settling, evolving from a mere“art” into one of the most studied problems in solid-liquid
separation. Batch settling has been used to provide thedata needed to design industrial thickeners (Fitch B.,
1979), and to examine the effectiveness of settling aids
such as flocculants. Coe H. and Clevenger G. (1916) are
credited with providing the first insights into the flux
limits of continuous thickening, basing their approach on
multiple batch settling tests conducted at different initialconcentrations.
A batch settling test is generally used to develop anunderstanding of the sedimentation of a given feed, andits expected response to the thickening process. This test
is illustrated in Fig. 1 and the batch settling test curve ispresented in Fig. 2. Given most industrial feeds are subjected to flocculation or coagulation, the ultrafine particles form larger entities called flocs. These can beconsidered to be effectively of the same size and density,†
1*

Received 2 June 2015; Accepted 25 June 2015
J-STAGE Advance published online 11 July 2015
University Drive, Callaghan NSW 2308, Australia
Corresponding author: Simon M. Iveson;
E-mail: simon.iveson@newcastle.edu.au
TEL: +61-2-4033-9079 FAX: +61-2-4033-9095©2016 Hosokawa Powder Technology Foundationand to therefore act as distinct particles during the sedimentation. Thus, in general, batch settling is often examined in terms of the settling of particles of one size anddensity.
Consider an initially uniform suspension of mono-sizedparticles of one density (Fig. 1). The subsequent processof sedimentation can be divided into two stages. The first,
termed “free-settling”, involves minimal inter-particle
contact, hence the net particle gravitational force in thefluid (taking into account gravity and buoyancy) is balanced entirely by the fluid drag force. The initial suspension height is Hi and volume fraction of the particles ϕi.
The bulk of the particles will start to settle with a constantsettling velocity, forming a supernatant or clear waterlayer as the suspension interface moves downwards.
Meanwhile particles start to form sediment at the base of
the container, producing a settled bed that rises upwards(Fig. 1). Ultimately the rising bed meets the falling suspension interface at time to and height Ho. The process of
bed consolidation continues, causing the surface of thesettled bed to decline gradually until the bed reaches its
final bed height, Hf.
Kynch G. (1952) developed the first complete description of sedimentation linking the free-settling and consolidation zones to achieve a unified theory. His fundamental
assumption was that the settling velocity of the particles
is governed by the local volume fraction of solids only.

249

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Fig. 1

Fig. 2

Diagram of the zones formed during batch settling illustrating some of the key variables. B = free settling zone of initial uniform suspension of concentration ϕi and settling velocity Vsi. A = clear supernatant. S = sediment bed which propagates upwards at a velocity Vpo with a constant volume fraction ofsolids ϕo at its upper surface until time to, the critical settling point, when the rising bed meets the fallinginterface between the free-settling and supernatant. The maximum volume fraction of the solids is ϕm.

A typical batch settling curve. Hi is the height of theupper free-settling interface at time t = 0. Ho and to are
the height and time when the upper interface meets therising sediment bed interface. Hf is the final sedimentheight. These quantities are also explained in the text.

His model, which was based on the continuity equation,
provided a basis for calculating the concentration of theparticles at the upper interface of the sediment, allowing athickener to be sized for the first time on the basis of asingle batch settling test.
The analysis of Kynch neglected the compressive stateof the sediment. A given particle within the sedimentrests, at least in part, on the particles below, which rest atleast in part on those even further below, which in turnrest on the bottom of the container. Thus, a particle within
the sediment experiences a state of compression, and is
not entirely free to settle. Rather, its movement is constrained by the presence of those particles below, and thesubsidence of those particles. Therefore, the perceived
settling velocity of the particles in the sediment at a given
volume fraction of solids cannot be the same as the settling velocity observed when a uniform suspension is prepared at the same volume fraction of solids.
Since then many more complex models of the processof sedimentation and consolidation have been proposed.
Buscall R. and White L. (1987) and later Howells I. et al.
(1990) assumed that a complex, irreversible, network
structure evolved within the consolidation zone, exhibit-

250

ing a compressive yield stress condition. Bürger R. andConcha F. (1998) developed a one-dimensional transientmodel of an operating thickener incorporating the effectsof compression while Usher S. et al. (2006) focused on theeffects of compression over long time-scales.
Dimitrova T. et al. (2000) applied the ideas of Buscall
R. and White L. (1987) to describe the process of creaming, during which the particles settle in the upwards direction to form the cream, leaving clear serum below.
Again, inter-particle forces result in a complex network
structure that yields under the influence of a compressive
load. They presented the following governing equation
for describing the process of sedimentation and consolidation. That is,∂φ v ∂ 
dp ∂φ  ∆ρ gv ∂0−
(φ K (φ )) =
 K (φ )−∂t ζ ∂z dφ ∂z ζ ∂z(1)where K(ϕ) is the hindered settling factor, ζ the friction
coefficient of an isolated particle in an infinite medium,Δρ the particle-liquid density difference, g the acceleration due to gravity and v the volume of one particle. The
dimensionless particle pressure developed by the network
structure is usually assumed to be a function of the volume fraction only,
φ 
p = po   φo 

4(2)

with po ~ 0.002. We have solved their equation, setting
po = 0.0003, the smallest possible value for a stable solution, with the aim being to minimize the critical yieldstress. That solution is compared later with the solution
obtained using our new theoretical model.

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

1.1 Outline of the problem
In this paper we examine an idealised system consisting of smooth, incompressible, mono-sized, sphericalparticles settling within a Newtonian fluid. Both inertial
forces and surface forces are assumed to be negligible,
while viscous forces dominate. All particles exhibit somescale of surface roughness that places limits on the magnitude of the viscous lubrication force, hence even for“smooth” spheres some level of inter-particle friction will
also play a role (Galvin K. et al., 2001). The system undergoes a complex, well defined, process of gradual particle
packing, driven towards a state of minimum potential energy. The movement of a given particle is invariably constrained by the extent of the cumulative movement of theparticles below, while occasionally promoted by the ad
hoc presentation of local vacancies.
This paper seeks to reduce this problem of sedimentconsolidation to the most elementary description that is
possible, recognising the existence of two volume contributions to the space surrounding a given spherical particle, one portion fixed and the other variable. The variable
portion declines with time according to a simple, constitutive, scaling law due to the consolidation. This idea was
first presented in a conference forum by Forghani M. etal. (2013), and has been built upon here into a more complete theory, sufficient for experimental validation. Theirsimple scaling law, inferred from the experimental findings of Galvin K. (1996), is used here to derive a comprehensive system of equations to describe the fall of theupper interface with time, the consolidation velocity ofthe particles versus time at any fixed elevation, and thechanging concentration profile of the sediment. This description should be universal, within the constraints of theassumptions, and in the absence of surface asperities. An
experimental system, reasonably consistent with theabove assumptions, was used to validate the model.
Galvin K. (1996) undertook an experimental study investigating the sediment consolidation of a model particle
system. The particles, known as Sephadex, swelled inwater to form spheres with a mean diameter of 220 μm
and low density of 1018 kg m–3. The relatively large particle size and hence gravitational force minimized the significance of surface forces, while the very low particle
density minimized the particle Reynolds number. According to Evans I. and Lipps A. (1990), these particles settle
to form a sediment with a volume fraction of 0.64. Within
the limits of the experimental conditions, the particles canbe considered incompressible hence the final volume fraction of the particles is not governed per se by the compressive load that arises from the gravitational force
minus that of the buoyancy force of the sediment bed.
Galvin measured the velocity of individual particles at
a fixed elevation during the process of consolidation, andrepeated the approach at other fixed elevations. Within
the field of view the particles appeared to move, largely
en masse, in a downwards direction, while sometimes individual particles would momentarily free themselvesfrom others and settle before re-contacting other particles.
At each elevation the velocity of the particles decreased
gradually, until there was virtually no movement at thatelevation. He found that the particle velocities, Vs, at eachlevel scaled with time, t, as Vs ∝ t–3. This scaling law
forms the basis of a simple constitutive relationship usedin this study. Integration suggests that changes in height
due to consolidation should scale with t–2.
Our work is motivated by the curious proposition that asimple constitutive scaling law may underpin much ofwhat is observed in this idealised form of batch settling.
Thus our work is not motivated by the need to develop yet
another method for sizing thickeners. Rather the findings
should motivate new lines of enquiry, in particular the
dynamics of random particle packing and consolidation
using discrete element modelling, which should help explain the physical underpinnings of the assumed scaling
law. Further, the transition to systems influenced by inertial and surface forces, and to systems with different particle sizes, should then follow.

2. Consolidation modelThis study is concerned with the batch settling of initially uniform suspensions of particles of the same sizeand density. The settling commences via a process of freesettling at the initial concentration, ϕi, resulting in an upper interface H which falls at constant velocity Vsi (Fig. 1).
The gravitational force on the particles during thisfree-settling stage is balanced entirely by the buoyancy
and drag force. When the particles deposit onto the baseof the container, evolving a bed of particles, much of theparticle gravitational force minus that of the buoyancy
force, become supported by the reaction force from off
the base of the container. The system is then in a state ofcompression.
It is assumed that when material deposits onto the surface of the rising sediment there are three distinct components. These components may be described as the solids
portion, ΔS, the fixed free-volume portion corresponding
to the retained fluid, ΔR, and the variable free-volume
portion corresponding to the expressible fluid, ΔW, alltaken as a volume per unit bed cross-sectional area. Thisdistinction between these three components is made in
the knowledge that ultimately the quantum of solids, ΔS,
will be associated in the end with a quantum of fluid, ΔR,
corresponding to the maximum volume fraction of solids,
ϕm = ΔS/(ΔS + ΔR) at the completion of the consolidation.
Thus the remaining portion, ΔW, decreases with time due251

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

to the consolidation. Clearly, during the consolidation of
the bed, the fluid associated with the solids below is expressed through the void spaces surrounding the particles
and hence in physical terms the fluid does not have an intimate association with the particles. Thus the portion of
the so-called retained fluid, ΔR, reflects a portion of thevoid space around the particles that will always retain some
fluid. The remainder of the void space, referred to as theexpressible portion, ΔW, gradually decreases with time.
Galvin K. (1996) examined in detail the consolidationof a model sediment at a fixed elevation, showing that theobserved consolidation velocity, Vs, scaled with the time, t,
as Vs ∝ t–3. Thus, we hypothesize that the volume of expressible fluid, ΔW, decreases with time t according to thesimple scaling law,(3)

∆W ∝ t −2

Equation 3 is effectively a guess, which is hereby used toderive a system of equations to describe the consolidation.
It is shown later by Equation 38 that the resulting systemof equations is in fact consistent with the scaling law
identified by Galvin K. (1996).
A key objective of this paper is to show how this simple
scaling law can be used to derive other properties of theconsolidating system, such as the particle velocity andvolume fraction as a function of time and height, and totest these predictions against experimental data.

When the particles first join the bed surface, the initial
sediment volume fraction of solids is,∆S∆S + ∆R + ∆Wo(4)where ΔWo is the initial value of the variable fluid volume
observed at the top of the sediment. In general, the volume fraction of solids at any other bed location k and time
t is,

φk =

∆S
∆S + ∆R + ∆Wk(5)

where ΔW k is the value of the variable fluid volume at atime t = nΔt, directly associated with the particles that arrived at the surface of the rising sediment at time tk = kΔt.
These time values are defined in Fig. 3. Introducing ourfundamental constitutive scaling relationship, it follows
that,
 t 
∆Wk =
∆Wo   tk 

−2k=∆Wo  
n

2

(6)

The maximum volume fraction of the solids, ϕm, isreached once all of the expressible fluid has left the portion of solids. Thus,

252∆S∆S + ∆R(7)

We have used ϕm ≈ 0.64, the accepted value for the
non-interacting random packing of mono-sized spheres,as obtained by Shannon P. et al. (1964). It should be notedthat for other systems the final volume fraction of the bed
can be considerably lower due to specific attraction between the particles comprising the particle network(Franks G. et al., 1999).
A useful identity derived from (4) and (7) is,1  ∆Wo 1
φ − φ  =
∆S
 o
m 

(8)

Substituting Eqn. 6 into Eqn. 5 gives,

φk =

∆S
t
∆S + ∆R + ∆Wo  k 
 t 

2

(9)

As already noted, tk, is the time of arrival of the particles at the surface of the rising sediment, and t is the timesince the commencement of the batch settling test. Again
we refer the reader to Fig. 3. Rearranging Eqn. 9 while
incorporating Eqn. 8 gives a relationship for the volume
fraction of the particles as a function of the time spent in
the sediment, defined by tk and t,
1/2

tk
1 
 1
=
φ − φ t km 

2.1 Variation of concentration with time

φo =

φm =

1/2

1 
 1
φ − φ 
 o
m 

(10)

Eqn. 10 is combined later with the integrated sum of
the sediment height, relevant to the time values defined by
tk and t, to produce a description of the concentration profile at time t. It is noted that the time, tk, is linked intimately with the particles that arrive at the sediment at thattime. For instance, the time tk can be replaced with thetime, to, that the rising sediment meets the falling interface of the suspension. Eqn. 10 then provides a measureof the volume fraction of the particles, ϕk, at the surfaceof the suspension as a function of time.

2.2 Variation of sediment height with time
Fig. 3 shows the deposition of particles onto the risingsediment. The sediment is discretised into the elements ofmaterial that are added to the sediment as a function oftime, providing the framework for describing the accounting system. These elements arrive at a time t1 = Δt, thent2 = 2Δt, t3 = 3Δt, and so on. Thus in general the kth layerof material joins the surface of the bed at a time, tk = kΔt.
Each element carries the portions, ΔS solids, ΔR retained
fluid, and ΔWo expressible fluid. Eqn. 6 describes the variation in the expressible fluid with time. For example, attime interval n = 4 the expressible fluid in elements k = 1,

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Fig. 3

Schematic of the proposed discretised model for the bed formation and consolidation process up to timet = 4Δt when four layers identified k = 1 to 4 have settled to form a consolidating bed. Each layer has avarying volume of expressible fluid ΔW (given by Eqn. 6) which results in each layer having a variable
thickness Δh.

Thus the effective solids feed flux, GF, of the batch settling test is,2, 3 and 4 are,
1 2
2 2
∆W1 =
∆Wo   ; ∆W2 =∆Wo   ;
4
4
3 2
4 2
∆W3 =
∆Wo   ; ∆W4 =∆Wo  
4
4

GF
=

(11)

Hence the height hk to the top of sediment layer k at atime t = nΔt (where n ≥ k) is,
hk=

k

∆W

k

∑ ∆hi= k (∆S + ∆R) + n 2 o ∑ i 2
i =1

i =1

(12)

Incorporating the series summation, the height becomes,

hk= k (∆S + ∆R) +∆Wok (k + 1)(2k + 1)
6n 2

(13)

Noting that for large k the term k(k + 1)(2k + 1) → 2k 3,
Eqn. 13 becomes,
hk= k (∆S + ∆R) +∆Wok 33n 2(14)Introducing the times tk = kΔt and t = nΔt, and incorporating Eqns. 7 and 8 gives,hk∆S  3  1
1   t k  2
=
+
−
  

tk
3∆t  φm  φo φm   t  (15)

Let Ho be the total height of the sediment layer at the
moment to when the rising sediment meets the interfaceof the falling suspension. Invoking the boundary condition, t = tk = to, gives,

Ho∆S  1
2 
= V=
+
po

to
3∆t  φo φm 

(16)

3Vpo
∆S
=
2 
 1
∆t
φ + φ 
 o
m 

(17)

At relatively high initial concentrations, ϕi, there should
be no discontinuity with the concentration just below thesurface of the rising bed and hence ϕo = ϕi. In this casethe feed flux is given by GF = (–Vsi + Vpo)ϕi. Thus, in thiscase we can calculate Vpo and in turn locate the boundaryof the rising sediment. This boundary condition is usedlater in the analysis of the batch settling tests.
Incorporating Eqn. 17 into Eqn. 15 gives the height hkof particles that arrived at the sediment surface at time tk,
at any given later time, t. That is, 3  1Vpohk
1   t k  2
=
+
−
 
2   φm  φo φm   t  
 1
tk
+
φ

 o φm (18)

In batch settling, the last particles arrive at the sediment surface at the time, tk = to. Hence the observed consolidation, defined in terms of the falling upper interface,proceeds according to,H= 3  1t oVpo1   t o  2+− 
2   φm  φo φm   t  
 1
φ + φ 
 o
m 

(19)

where H is the total height of the sediment bed at timet ≥ to.
It is evident from Eqn. 19 that at time t = to,=H H=t oVpo
o

(20)

253

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

and that at time t = ∞ the final sediment height is,
Hf =That is,

3H o

1/2

φm 

2 + φ 

o 

(21)

Substituting these conditions into Eqn. 19 gives a simple dimensionless description for the height of the fallinginterface, applicable for t ≥ to and H ≤ Ho. That is,
H − Hft=  o 
Ho − Hf  t 

2

(22)

This equation is used formally to validate this model
against a series of batch settling test results.

2.3 Description of the concentration profile
Sections 2.1 and 2.2 provided expressions for the concentration of the particles ϕk and height of the particles hk
as a function of time t, in terms of the time, tk, that theseparticles first reached the rising sediment. Combining
Eqns. 10 and 18 gives,
1 

 1
−
 3  1tVpot
1   φk φm  
hk
=
+
−


2   φm  φo φm   1
1 
tk  1
φ + φ  φ − φ 
 o
 o
m  
m 
(23)
Simplifying Eqn. 23 leads to,
tVpo
t
1 
 3  1hk=+−
2   φm  φk φm 
tk  1
φ + φ 
 o
m 

(24)

Incorporating Eqn. 10 again eliminates tk and gives theconcentration profile of the sediment as a function oftime. Here we drop the subscript k, noting that h = h(ϕ, t).

Fig. 4254

2  11 
1
 φ + φ  φ − φ m m 
h = tVpo 
1/2
2  1
1 
 1
+
−
φ


 o φm  φo φm 

(25)

Eqn. 25 shows that the concentrations propagate from theorigin as a fan (Lester D. et al., 2005) over the full range
from ϕo to ϕm, reflecting the absence of a traditional network structure.
Fig. 4a shows the concentration profile of the system as
a function of time, obtained using the model of Dimitrova
T. et al. (2000) with the parameter defining the strength ofthe particle pressure, po = 0.0003, set at the lowest possible value for a stable numerical solution. The objective
was to obtain the solution for a system with a very low
critical yield stress. The profiles are reported for different
dimensionless time values. It is evident that the bed concentration evolves towards the maximum possible volume
fraction of particles, set equal to 0.64. Fig. 4b shows thesolution given by Eqn. 25, and Eqn. 22, obtained using
the new theoretical model developed in this Section. Goodagreement is evident.
Defining the propagation velocity of a wave of concentration ϕ as,

Vpφ =

dh
dt φ

(26)

it follows that the propagation velocity is a function of thesolids concentration, ϕ, and is influenced strongly by theboundary conditions, Vpo, ϕo, and ϕm.
1/2

2  11 
1
 φ + φ  φ − φ 


m
m 
Vpφ = Vpo
1/2
2  1
1 
 1
+
−
φ


 o φm  φo φm Comparison of predicted concentration profiles as a function of time from (a) the model of Dimitrova T.
et al. (2000) with po = 0.0003, and (b) the model developed in this work. The profile curves are given by
dimensionless time values, based on common initial values of the suspension height, concentration, settling velocity, and maximum concentration.

(27)

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

2.4 Consolidation velocity of bed particles
The consolidation velocity of the particles within thesediment, relative to the vessel, is governed by the reorganisation of the sediment, and hence the gradual releaseof expressible fluid from below. This particle velocity, defined as negative in the downwards direction, is obtainedby differentiating the height, hk, in Eqn. 18 with respect totime, t, with the value of tk held constant. Therefore,

Vsk =

dhk
dt

(28)

−2Vpo  1
1   tk 3− 
2   φo φm   t 
 1
φ + φ 
 o
m 

(29)

and
Vsk =

Incorporating Eqn. 10 which describes the concentration as a function of time, and again dropping the subscript k and noting that Vs = Vs(ϕ), gives,
3/2

1 
1
−2Vpo  −
 φ φm 
Vs =1/2
2  1
1 
 1
+
−
φ


 o φm  φo φm 

(30)

While Eqn. 30 is a function of the local volume fractionof the particles, it is emphasized that this dependence isfundamentally different to that in the free settling zone,
as it reflects the constrained nature of the consolidation.
Indeed the consolidation velocity is always less than thefree settling velocity. Eqns. 27 and 30 conform to the requirements of continuity. That is,
Vp = −d (Vsφ )dφ(31)

2.5 Settling velocity at constant elevation
Galvin K. (1996) examined the settling of the individual particles within the sediment at a fixed elevation. Particles passed downwards into and out of the field of view,and hence the data collected involved different particles.
Firstly we set
t 3
 t 

β k =  k 

(32)Then, combining Eqns. 17, 29 and 32 gives,Vs =−2GFβ k  1
1 
−


3
 φo φ m (33)Returning to Eqn. 18, and incorporating Eqns. 17, and32 gives,

hk GFβ k1/3 GFβ k  1
1 
=
+
−
t
φm
3  φo φm (34)

Setting the height, hk, to be equal to the constant elevation, hc, provides the corresponding time, t.
t=

GFβ k1/3

φmhc
GF β k  1
1 
+
−

3  φo φm (35)

A series of values of βk provide a common independent
variable for calculating the consolidation velocity, Vs,from Eqn. 33, and the value of the time, t, from Eqn. 35,
revealing the scaling law, Vs ∝ t–3. Alternatively, Eqn. 33
can be inserted into Eqn. 35, giving
13




−3Vs
hc GF 


=
1 
 1
t
φm 
 2GF  φ − φ  

 o
m 

−

Vs
2

(36)

Note that the consolidation velocity, Vs, is numericallynegative. During consolidation the value of (–Vs)1/3 eventually dominates over the value of –Vs, and hence the second term on the right hand-side of Eqn. 36 can beneglected. It then follows that at fixed elevation,
2

−Vs ≈

2φm3  1
2   11   hc 
+
−
 



2
27Vpo  φo φm   φo φm   t 

3

(37)

Eqn. 37 provides the asymptotic behaviour of the consolidation process within the sediment at fixed elevation,
as observed by Galvin (1996). That is,h−Vs ∝  c 
 t 

3

(38)

Galvin K. (1996) obtained data on the settling velocity
at various fixed elevations. The data sets collapsed to produce a single curve in accordance with Eqn. 38.

3. Experimental method
Sephadex G-200 grade granules, made by cross-linking
dextran with epichlorohydrin under alkaline conditions,were sourced from Pharmacia. The dry particles, withdensity of 1538 kg m –3, were sieved to obtain the –75+63 μm size fraction. These particles were added to a
beaker of distilled water and allowed to swell. Evans I.
and Lips A. (1990) studied a number of different gradesof Sephadex particles and found that the settled bed packing fraction was always close to 0.64, as expected for random tight packing of spheres (Shannon P. et al., 1964).
Galvin K. (1996), who used the same system of particles,

255

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

reported the particles swelled over a 24 h period to a diameter of 220 μm, forming a settled bed of 30 cm3 per 1 gof dry Sephadex. Assuming the settled bed volume fraction was 0.64 and that the volumes of Sephadex and water
were additive, this implies that the swollen particles had adensity of 1018 kg m–3.
In the present work, the same batch of Sephadex particles was re-used multiple times over a prolonged period.
Fig. 5 shows that whilst much of the swelling was complete within the first 24 h, there was clear evidence of additional swelling over the following months as the final
settled bed height was observed to increase from around
2.0 cm/g (volume of 39 cm3/g) to over 2.4 cm/g (47 cm3/g).
Again, assuming a constant final settled bed volume fraction of 0.64, this suggests the density of the swollen particles decreased from 1014 kg m–3 down to 1012 kg m–3.
Thus, for internal consistency, it was necessary to limit
comparisons between experiments to insure that the degrees of swelling were similar.
Eventually, the observed diameter of the spheres wasaround 250 μm, with a density of about 1013 kg m –3.
Hence these particles settled in water according to Stokes’
law (Stokes, 1901) with a very low particle Reynolds
number of ~ 0.1. Although the final particles were in principle deformable, within the limits of the experimental
conditions, the particles did not compress or deform.
Evans I. and Lips A. (1990) formed the same conclusions
while also noting that surface forces were also negligible.
Thus this experimental system is a very close approximation to the ideal system of non-deformable, mono-sized
spherical particles settling at low Reynolds numbers withnegligible surface forces.
A set of batch settling tests was conducted, focused initially on the fall of the upper interface versus time. A
given series of experiments involved the same mass of theSephadex granules. In the first series the mass was 12 g.

Fig. 5256

Extent of swelling of the Sephadex particles over 24months, accounting for different bed volumes in experiments conducted at different times. Legend shows themass of dry Sephadex in the system. Line is drawn to
guide the eye.

Then this batch of material was halved to perform a seriesof tests using 6 g of Sephadex, and then halved again totest 3 g of Sephadex. Then all this material was recombined and an additional 12 g of Sephadex added in orderto perform a series of tests using 24 g. Within each series,
different initial suspension concentrations were obtainedby adding or removing different quantities of distilledwater.
At the start of an experiment the Sephadex spheres
were suspended within a 1.25 m high Perspex cylinder ofinternal diameter 50 mm using a perforated plunger, andthe initial height, Hi, noted. A quiescent state was formed
within seconds. A ruler located down the wall of the cylinder provided elevations accurate to within 1 mm.
During the experiment the height of the upper interface,
H, was recorded versus the time, t. The final height of thesediment, Hf, was recorded after the material had beenleft to consolidate for at least 24 h. Based on the findingsof Evans and Lips (1990), it was assumed the final volume
fraction of the spheres was ϕm = 0.64. Hence the initial
volume fraction was calculated to be ϕi = (Hf/Hi)ϕm. Experiments were conducted using initial volume fractionsin the range 0.28 to 0.55.
Following the initial set of experiments, approximately
12 months later additional experiments were conductedusing the same batch of Sephadex in order to study the
consolidation velocity of spherical particles at fixed elevations. A CCD video camera was positioned at a specificelevation during the experiment. The movement of different particles within a height range of about 1 mm was recorded over an extended time period. Experiments wererepeated in order to observe the internal consolidation at
different elevations. In addition, the height of the upperinterface versus time was recorded during each of theseexperiments in order to properly characterise the settlingprocess.
Finally, about 24 months after the first set of experiments, additional experiments were conducted in order toexamine the evolution of the concentration profile versus
time. Suspension concentration was measured using thelight extinction method (Hinds W., 1982). The experimental system shown in Fig. 6 was located in a dark room,
free of spurious light sources. A 5 mW 532 nm laser beam
was directed horizontally into the suspension at a given
elevation and the light exiting the opposite side recorded
using a CCD camera. The laser and camera were thenmoved in unison to a new elevation in order to record thetransmitted light at that point. A set of these measurements at different elevations was recorded within a relatively brief time period, allowing a single time value to be
assigned to a given concentration profile data set. This
process was repeated at multiple nominal time values
during the batch settling.
Each image was later analysed to measure the average

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Fig. 6

Photograph of the experimental set up which consistedof a vertical 1.25 m high Perspex column with internal
diameter of 50 mm. A 5 mW 532 nm laser was mounted
on one side and a camera mounted on the opposite side
on a frame with adjustable height.

intensity of the transmitted light. The signal obtained at
the start of the batch settling test corresponded to the initial volume fraction, ϕi, while the signal obtained after
one week near the base of the sediment, corresponded to
ϕm. Tests showed that the transmission for intermediateconcentrations could be obtained by linear interpolation
between these two extremes. Thus it was possible to obtain the volume fraction value corresponding to a given
signal.

Fig. 7GF =4. Results and discussion

The initial suspension exists in a state of uniform concentration hence the particle gravitational force, less thatof the buoyancy force is fully supported by the hydrodynamic drag force. Thus, the initial settling velocity of theparticles is a well-defined function of the volume fractionof the particles, referred to as the hindered settling function. Fig. 7 shows a typical batch settling test curve ranging in height from the initial value, Hi through to the final
height, Hf. The suspension settles at the initial velocity,
Vsi, while the sediment rises at the velocity Vpo, producing
an initial sediment height, Ho, in a time period to.
The analysis of the batch settling test commences with
the identification of the boundary condition (to, Ho), something that can be difficult to achieve objectively. The approach adopted in this study was to locate this point using
the experimental data in conjunction with the consolidation model. The feed flux, GF, given by Eqn. 17, can beequated with the magnitude of the initial settling flux, relative to the velocity of the rising sediment. That is,

3Vpo= φi (−Vsi + Vpo )
1
2 

+
φ

 o φm (39)

where the initial settling velocity, Vsi, has a negative value
indicating its downwards direction. Rearranging and setting Vpo = Ho/to gives,=Vpo

4.1 Boundary condition between free-settling andconsolidating bed

Representation of the batch settling test showing theinitial constant settling rate followed by the consolidation phase. The rise velocity of the sediment is
Vpo = Ho/to.

Ho
=
to

φ 
φ
−Vsi  i + 2 i 
φm 
 φo
φi 2φi 3 − φ − φ 

o
m 

(40)

With reference to Fig. 7, it follows thatH − Ho−Vsi =ito(41)

It is tacitly assumed that the concentration at the surface of the rising sediment is equal to the initial concentration, ϕo = ϕi. This assumption appears to be valid when
the initial volume fraction is sufficiently high, and inertial
forces are negligible. Thus the condition appears to apply
in this study. Eqn. 40 then becomes,=Vpo

H o −Vsi (φm + 2φi )=to
2 (φm − φi )(42)

It is noted that this equation potentially approaches asingularity when ϕi → ϕm. This singularity can only be
avoided if Vsi approaches 0 as ϕ i → ϕ m . Hence theRichardson-Zaki equation (given later by Eqn. 45) cannot
be used near ϕi = 0.64 because it incorrectly predicts thatVsi still has finite values up to the limit ϕi = 1.0. However,as the highest ϕi value used in this work was 0.55, thislimitation was not an issue here.
Substituting Eqn. 41 into Eqn. 42 to eliminate Vsi gives,

257

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Fig. 8Ho=

Batch settling test results obtained using 3 g of Sephadex, with different initial concentrations. The curves
through the data are based on the model developed inthis paper.

H i  2φi 
1+
3  φm (43)

and hence the value of to is,to=

2H i 
φ 
1− i−3Vsi  φm (44)

The boundary condition defining the point at which therising sediment meets the falling interface of the suspension can be obtained through trial and error. The first few
data points can be used to estimate the initial settling velocity, Vsi, which in turn can be used to obtain to via Eqn.
44. Then the initial settling velocity can be based on alinear best-fit to all of the data covering the range of timevalues up to to. The resultant value, Vsi, can then be used
to update the value of to, leading to confirmation of theboundary condition, (to, Ho), and the value of Vpo. Thisobjective approach insures the boundary condition is consistent with the consolidation model.

4.2 Free-settling velocity—The hindered settlingfunction
In order to properly describe the velocity of propagation of the rising sediment, given by Eqn. 42, we need to
firstly describe the initial free-settling velocity, Vsi. The
empirical hindered settling function of Richardson J. andZaki W. (1954),=
Vsi Vt (1 − φi )

n

(45)

provides an accurate description. The exponent, n, varies
with the particle Reynolds number while the terminal velocity, Vt, of an isolated spherical particle settling according to Stokes’ law is:258

Fig. 9Initial free-settling velocity of the particles versus theinitial volume fraction of the particles. The curve denotes the best fit of Eqn. 45 to the experimental datawith Vt = –399 μm/s and n = 5.23.

Vt = −d 2 (ρ s − ρ )
18µ

(46)

Here, d is the particle diameter, ρs the particle density, ρ
the fluid density, g the acceleration due to gravity, and μ
the fluid viscosity.
Fig. 8 shows batch settling test results for experiments
conducted using 3 g of Sephadex particles, using different
initial concentrations. Additional results are shown inForghani M. (2015). Fig. 9 shows the initial settling velocities of the particles used in the batch settling tests versus the initial volume fractions of the particles. These
data can be described very well using Eqn. 45 with
Vt = –399 μm/s and n = 5.23. As mentioned above, Eqn.
45 incorrectly predicts finite values of Vsi for ϕ > 0.64.
However, within the range of the experimental conditions
up to ϕi = 0.55, the prediction is sufficiently accurate.
Fig. 10 shows the data for velocity of propagation, Vpo,
versus the initial volume fraction of the particles, sourced
directly from the batch settling tests. The solid curve
passing through the data was formed by substituting theexpression for Vsi (Eqn. 45) into Eqn. 42. The singularity
as ϕi approaches 0.64 is clearly evident. However, within
the range of experimental conditions the data are reasonably consistent with the curve, given the inherent scatter.
By comparison, Kynch theory predicts a propagation velocity:
Vp = −∂ (Vsφ )∂φ(47)

which combined with Eqn. 45 gives,V=
p Vs − Vsnφ(1 − φ )(48)

The result obtained using Eqn. 48 is also shown in Fig.

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Fig. 10 Propagation velocity of the rising sediment versus thesuspension initial volume fraction. The solid curve isbased on the theoretical consolidation model (Eqn. 42)
and the dashed curve is based on the theory of Kynch(Eqn. 48).

10. It is evident the actual propagation velocities werehigher than those predicted using Kynch theory, reflecting
the constrained nature of the consolidation process compared to free-settling.

4.3 Consolidation
According to the theoretical model, the consolidation
process within the bed is characterised by the normalisedfunction given by Eqn. 22. That is,
H − Hft 2=  o 
Ho − Hf  t (22)

where Hf is the final sediment height after infinite time. It
is noted that the pair of starting values required in Eqn.
22, Ho and to, were set equal to the values at the end of thefree-settling period as described in Section 4.1. Fig. 11,
which is based on the experimental data shown in Fig. 8,shows the full collapse of the data sets along the diagonal
from the upper coordinate (1, 1) to the lower coordinate (0,0). These data provide strong evidence to support the proposed scaling law in this study. Additional data sets areprovided in Forghani M (2015) for different initial quantities of Sephadex.

4.4 Analysis of the internal consolidation
The analysis in the previous section was focused on thefalling height of the upper interface versus time. While
this work provided support for the consolidation model,
the strength of the validation is limited given the relatively small changes in the measured height versus time,
especially towards the end of the consolidation.
Thus, in this section a much more thorough validation

Fig. 11

Normalized plots of the batch settling data shown in
Fig. 8 based on 3 g of Sephadex and different initial
volume fractions. The linearity between (0,0) and (1,1)
provides strong support for the consolidation model,
and hence the underlying scaling law.

of the model is presented. Two approaches are taken. The
first is based on the original approach of Galvin K. (1996).
In this work the vertical motion of the actual Sephadex
particles during the consolidation was examined versus
time at a fixed elevation. Initially the particles consolidated at a relatively high velocity. But over time, the consolidation velocity decreased by a significant factor. Eqns.
33 and 35 describe the consolidation velocity versus time,
at fixed elevation hc.
This second phase of the study occurred some 12
months after the previous work using the same particles
of Sephadex. It is noted that the particles underwent additional swelling during this period resulting in larger particles of marginally lower density and hence a final
sediment height greater than obtained previously. The increase in the final sediment height was consistent with theincrease in the observed size of the particles. Thus we aresatisfied the final sediment volume fraction of the particles remained at the standard random packing value of
0.64.
Fig. 12 shows the batch settling test curve obtained using 24 g of Sephadex, with an initial volume fraction of
0.40. The smooth curve through the data was based on thetheoretical consolidation model, with Vsi = 20.7 μm/s and
Vpo = 59.5 μm/s.
Fig. 13 shows log-log plots of the observed particle velocity within the system at fixed elevation. The velocities
were obtained by observing the movement of specificparticles present at that moment in the field of view. The
quality of the images was not high, but was sufficient for
identifying the boundaries of the particles, and hence sufficient for measuring the consolidation velocity. In general, movement occurred en masse and therefore thevelocity of a given particle was representative of the rest.

259

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Fig. 12

Batch settling test with 24 g of Sephadex used to validate the model for describing the internal consolidation within the sediment.

Fig. 13

Consolidation velocity versus time based on observations at elevations of hc = 15 cm, 30 cm, and 45 cm forexperiments with 24 g of Sephadex and ϕi = 0.40. Lineshows prediction of Eqns. 33 and 35.

Fig. 14260

The data sets correspond to observations at elevations of
15, 30, and 45 cm above the base of the vessel, well below
the final sediment height of approximately 58 cm.
The results in Fig. 13 are very significant. Effectively
they describe the actual consolidation velocity as a function of both time and position. Moreover, it is evident that
initially the velocity is constant, approximately 20 μm/s,
reflecting the corresponding free-settling of the particles.
The velocity then decreases to lower and lower values as
the consolidation proceeds. The exact predictions of thetheoretical consolidation model are given by the threesolid curves. These curves show the formal scaling according to Vs ∝ t–3, consistent with the original finding ofGalvin (1996). While the agreement between the modeland the experimental data is weakest at 15 cm, the agreement appears to strengthen at the higher elevation of
30 cm, and become very strong at 45 cm. The improvement in the scaling with distance above the base is a reasonable expectation, given that the scaling should fail at
the fixed base of the system. These results are remarkable,
predicting the consolidation velocity to within 1 μm/s
after a period of 20000 s at a significant elevation in the
bed of 45 cm.
The knowledge of the consolidation velocity as a function of time and position provides arguably the most definitive description that is possible. Correct prediction of
the consolidation velocities should lead to the correct prediction of the liquid expression and thus the volume fractions of the particles as a function of time and position.
Fig. 14 shows the concentration profiles of the consolidation zone versus time, measured using the experimental
method outlined in Section 3. The theoretical predictions
based on Eqn. 25 show excellent overall agreement,
though there is some discrepancy again near the base.
These findings provide comprehensive validation of theConcentration profiles at different times, showing the height versus the volume fraction of the particles.
The experimental data is for 24 g of Sephadex at an initial concentration ϕi = 0.40. Lines show theoretical predictions of Eqn. 25 which uses the Vpo value obtained from Fig. 12 and the assumption thatϕ o = ϕ i.

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

model describing sediment consolidation of an ideal system consisting of mono-sized, non-deformable spheres at
low Reynolds number and with negligible inter-particle
forces.
In the above analysis we have assumed that there is fullcontinuity between the free settling and consolidation
zones. Thus the volume fraction of solids just below thesurface of the rising sediment, ϕo, is equated with the initial volume fraction, ϕi, used in the batch settling. In turnthe model requires that the particles at the surface of thesediment consolidate at the free settling velocity, eventhough the particles at the surface of the rising sediment
have achieved formal contact with the structure below. It
should be noted, however, that the consolidation velocityof the particles within the sediment varies with the volume fraction according to the consolidation model, andnot according to the free settling function. Thus, theagreement between the consolidation and free settling velocities only exists at the interface.
This paper requires further discussion concerning theboundary condition at the surface of the rising sediment.
In most studies, the minimum sediment concentration isdefined by the so-called gel concentration, an input into
the model. The theory presented in this paper does not offer a general solution for determining the relevant volume
fraction just below the surface of the sediment. In thecontext of the present problem there is scope for developing a Discrete Element Model (DEM) to improve our understanding of the boundary condition. We have recently
established, using a Monte Carlo approach, that a network
formed through a simple random deposition model, with
fixed attachment between the particles, produces a bed
with a volume fraction of approximately 0.194. Clearly ahigher concentration is expected in the absence of particle-­particle attachment.
Although Kynch theory has its limitations, the approach offers some insight here into the effects of an increase in the particle Reynolds number. According todrift flux theory (Wallis G., 1962) there is an inflection in
the flux curve at the volume fraction, ϕI = 2/(n + 1), where
n is the exponent defined by the Richardson and Zaki
equation. When the initial volume fraction of the particles, ϕi, is higher than the inflection value, ϕI, there is, inprinciple, no discontinuity in the concentration between
the free settling particles and the surface of the risingsediment. This study has focused on such a concentration
regime, ϕi > ϕI. The nature of the problem becomes astrong function of the particle Reynolds number, Ret, withn ≈ 2.4 applicable for large Ret and n = 5.2 applicable forthe very low Ret used in this study. The corresponding
concentrations at the flux curve inflexions are ϕI = 2/
(1 + 2.4) = 0.59 for large Ret with n = 2.4, and ϕI = 2/
(1 + 5.2) = 0.32 for small Ret with n = 5.2. Thus large
spherical glass marbles settling in water at a high Ret

should form densely packed sediment, with the volume
fraction of the particles ranging from at least ϕI = 0.59
just below the surface of the sediment. Thus, for large
glass spheres, the sediment forms with a bed concentration ranging from at least 0.59 through to 0.64. Here there
is little prospect for particle re-ordering and hence consolidation, as the high inertial forces drive the particles
rapidly towards the lowest potential energy state, and ahigh volume fraction. For highly viscous systems, however, the volume fraction of the particles just below thesurface of the sediment should range from at least 0.32.
Thus viscous systems present the most interesting problem of sediment consolidation, with the potential for significant particle re-ordering towards the lowest potential
energy state, slowed by significant viscous forces.
Further work is needed to account for (i) the boundary
condition at the interface between the free settling andconsolidating sediment bed and (ii) the scaling law that
underpins the model developed in this study. Why does
the volume of expressible fluid associated with the particles decay proportional to t-2? A discrete element model
could be used to investigate both of these issues.

5. Conclusions
This work commenced with a simple idea, which inturn led to the development of a comprehensive theoretical model of the consolidation that occurs during batchsettling. This model provided a description of the fall in
the height of the upper interface with time, a descriptionof the internal consolidation velocity as a function of timeand position, and a description of the evolving concentration profile. An experimental study then followed, providing validation of the new model.
In this model a given portion of solids is associatedwith a void space that is occupied by a fixed portion of retained fluid and a further portion of expressible fluid. The
space occupied by the expressible fluid decays according
to a scaling law proportional to t-2. As noted, further work
is recommended based on a discrete element modelling
approach to further understand the physical processes that
result in this scaling law.

Nomenclaturedparticle diameter (m)GF

solids feed flux (m3 m–2 s –1)

gacceleration due to gravity (m s –2)

Hheight of suspension upper interface (m)Hiinitial suspension height (m)Hffinal height of sediment (m)261

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Hoheight where upper interface of settling suspension meetsrising sediment bed (m)hca constant elevation within the bed (m)hkheight of layer k within the sediment bed (m)kbed layer increment number (-)

ntime increment number (-)

ncoefficient in Richardson-Zaki equation (-)

ttime (s)tktime material reached sediment surface (s)totime when falling suspension interface meets rising sediment bed (s)Δttime step size (s)ΔSvolume of solids (m3 m–2)

ΔR

volume of retained fluid (m3 m–2)

Ret

particle Reynolds number at its terminal settling velocity
(-)

Vpo

upwards propagation velocity of initial sediment surface
concentration (m s –1)

Vp ϕ

upwards propagation velocity of layer of concentration ϕ(m s –1)

Vs

consolidation velocity of particles (m s –1)

Vsi

initial settling velocity of particles (m s –1)

Vt

terminal free settling velocity (m s –1)

ΔW

volume of expressible fluid (m3 m–2)

ΔWo

initial volume of expressible fluid (m3 m–2)

βkdefined by Eqn. 32 as (tk /t)3 (-)ϕ

solids volume fraction (-)ϕIsolids volume fraction at inflection point (-)ϕiinitial suspension solids volume fraction (-)ϕmmaximum solids volume fraction (-)ϕovolume fraction of solids at upper surface of rising sediment bed for t < to (-)μ

fluid viscosity (Pa s)ρfluid density (kg m –3)

ρssolid density (kg m –3)

References
Bürger R., Concha F., Mathematical model and numerical simulation of the settling of flocculated suspensions, Int. J. of
Multiphase Flow, 24 (1998) 1005–1023.
Buscall R., White, L.R., The Consolidation of Concentrated
Suspensions, Part I. The Theory of Sedimentation, J. Chem.
Soc. Faraday Trans. 1, 83 (1987) 873–891.
Coe H.S., Clevenger G.H., Methods for Determining the Capacities of Slime-Settling Tanks, Trans. Am. Inst. Min. Eng.,
60 (1916) 356–384.
Concha F., Bürger R., A century of research in sedimentation

262

and thickening, KONA Powder and Particle, 20 (2002)
38–70.
Dimitrova T.D., Gurkov T.D., Vassileva N., Campbell B.,
Borwankar R.P., Kinetics of Cream Formation by the
Mechanism of Consolidation in Flocculating Emulsions,
Journal of Colloid and Interface Science, 230 (2000) 254–
267.
Evans I.D., Lips A., Concentration dependence of the linear
elastic behaviour of model microgel dispersions, Journal of
the Chemical Society, Faraday Transactions, 86 (1990)
3413–3417.
Fitch B., Sedimentation of Flocculent Suspensions: State of theArt, AIChE Journal, 25 (1979) 913–930.
Forghani M., Doroodchi E., Galvin K.P. Universal Scaling ofConsolidation in Batch Settling, in: Chemeca 2013: Challenging Tomorrow. Barton, ACT: Engineers Australia, (2013)
376–382.
Forghani M. (2015). Consolidation of Large Spherical Particles
at Low Reynolds Numbers, MPhil Thesis, University of
Newcastle, Australia (submitted).
Franks G.V., Johnson S.B., Scales P.J., Boger D.V., Healy T.W.,
Ion Specific Strength of Attractive Particle Networks,
Langmuir, 15 (1999) 4411–4420.
Galvin K.P., Measurement of particle velocity during sedimentconsolidation, Chemical Engineering Science, 51 (1996)
3241–3246.
Galvin K.P., Zhao Y., Davis R.H., Time-averaged hydrodynamic roughness of a non-colloidal sphere in low Reynolds
number Motion Down an Inclined Plane, Physics of Fluids,
13 (2001) 3108–3119, DOI: 10.1063/1.1409368
Hinds W.C., Aerosol Technology, Wiley-Interscience, New York(1982).
Howells I., Landman K.A., Panjkov A., Sirakoff C., White L.R.,
Time-dependent batch settling of flocculated suspensions,Appl. Math. Modelling, 14 (1990) 77–86.
Richardson J.F., Zaki W.N., Sedimentation and Fluidisation:
Part I., Trans. Instn. Chem. Engrs. 32 (1954) 82–100.
Kynch G.J., A Theory of Sedimentation, Trans. Farady Soc., 48(1952) 166–176.
Lester D.R., Usher S.P. Scales P.J., Estimation of the Hindered
Settling Function R(ϕ) from Batch-Settling Tests, AIChE J.,
51 (2005) 1158-1168. DOI: 10.1002/aic.10333
Shannon P.T., DeHaas R.D., Stroupe E.P., Tory E.M., Batch andContinuous Thickening. Prediction of Batch Settling
Behavior from Initial Rate Data with Results for Rigid
Spheres, Ind. Eng. Chem. Fundamen., 3 (1964) 250–260.
Stokes G.G., Mathematical and Physical Papers, Cambridge
University Press, 3 (1901).
Usher S.P., Scales P.J., White L.R., Prediction of Transient Bed
Height in Batch Sedimentation at Large Times, AIChE J.,
52 (2006) 986–993, DOI: 10.1002/aic.10709
Wallis G.B., A simplified one-dimensional representation of
two-component vertical flow and its application to batch
sedimentation, in: Proc. of the Symposium on the Interaction between Fluids and Particles, London, Instn. Chem.
Engrs. (1962) 9–16.

Kevin P. Galvin et al. / KONA Powder and Particle Journal No. 33 (2016) 249–263

Author’s short biography
Kevin P. Galvin
Kevin Galvin obtained his BE (Chemical Engineering) with 1st Class Honours and the
University Medal from the University of Newcastle in 1987 while working full-time for
BHP Central Research Laboratory. He then obtained his PhD (1990) from Imperial College, London, returning to BHP for 3 years, prior to joining the University of Newcastle.
His research interests are concerned with bubbles, drops, and particles in process systems. He is the inventor of the Reflux Classifier, a novel fluidized bed device, deployedaround the world in coal and mineral processing. He is the Director of the ARC Research
Hub for Advanced Technologies for Australian Iron Ore and a Fellow of the AustralianAcademy of Technological Sciences and Engineering.

Marveh Forghani
Marveh Forghani completed her undergraduate degree in Civil Engineering-Building
at the Shomal University of Amol, Iran in 2007. She graduated with a Master of Engineering Science (Specialisation in Energy) at the University of Newcastle, Australia, in2011. She is in the process of completing her MPhil in Chemical Engineering at the University of Newcastle, Australia.

Elham Doroodchi
Elham Doroodchi obtained her PhD from The University of Newcastle in 2005 after
completing her undergraduate studies in Chemical Engineering at the same university
with 1st Class Honours and The University Medal. Her research interests are in the areaof particle technology and fluidization with a focus on developing energy efficient processes. She is the inventor of GRANEX, an emission-free engine that turns heat fromlow-grade sources such as geothermal and industrial waste heat into electricity.

Simon M. Iveson
Simon Iveson completed his BE (Chemical Engineering) with university medal (1992)
and PhD (1997) at the University of Queensland, Australia. His research was on granuleconsolidation, deformation and growth. In 1997 he moved to the Centre for Multiphase
Processes at the University of Newcastle, Australia, where he continued his research onparticle agglomeration until 2002. From 2003 to 2006 he lectured at the Universitas
Pembangunan Nasional, Yogyakarta, Indonesia. In 2007 he returned to the University of
Newcastle where he has since been lecturing and researching in the field of coal andmineral processing.

263

KONA Powder and Particle Journal No. 33 (2016) 264–277/Doi:10.14356/kona.2016008

Original Research Paper

Effect of Drag Models on Residence Time Distributions ofParticles in a Wurster Fluidized Bed: a DEM-CFD Study †Liang Li 1,2, Johan Remmelgas 2, Berend G.M. van Wachem 3,
Christian von Corswant 2, Staffan Folestad 2, Mats Johansson 2
and Anders Rasmuson 1*
1

Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Sweden
Pharmaceutical Development, AstraZeneca R&D, Sweden
3
Division of Thermofluids, Department of Mechanical Engineering, Imperial College London, United Kingdom
2

Abstract
Fluidized bed coating has been used to coat pellets or tablets with functional substances for a number of purposes.
In this coating process, particle wetting, drying and film formation are coupled to particle motion. It is thereforeof interest to study particle motion in such fluidized beds and to use the results to develop a model for predicting
the quality of the final product. In this paper, we present results from DEM-CFD simulations, i.e. discrete element
method and computational fluid dynamics simulations of particle motion in a laboratory-scale Wurster fluidized
bed that was also employed in positron emission particle tracking (PEPT) experiments. As the drag force is thedominant interaction between the gas flow and the particle motion in this type of fluidized bed, the effect of dragmodels on the particle motion is investigated. More specifically, the particle velocity and residence time
distributions of particles in different regions calculated from five different drag models are presented. It is foundthat the Gidaspow and Tang drag models predict both particle cycle and residence times well. The HKL and
Beetstra drag models somewhat overestimate the particle velocity in the Wurster tube and therefore predict a
reduced number of recirculations and a significantly shorter cycle time.
Keywords: fluidized bed, particles, residence time distributions, drag, DEM, CFD

1. Introduction
The Wurster coating process (Wurster D.E., 1959),
which takes place in a fluidized bed, has been utilized forpellet coating for several decades. In this process, particles are circulated in the fluidized bed and are coated in aspray zone for different functional needs (Teunou E. andPoncelet D., 2002; Ström D. et al., 2005). At the bottom of
the fluidized bed, a fluidization air flow is supplied
through a distributor plate. The distributor plate usuallyconsists of a number of orifices that have been distributed
so as to provide a specific flow distribution. An atomization air flow and a liquid solution in the form of droplets
are introduced by one or more two-fluid nozzles at thebottom of the fluidized bed.
†
1
2
3

*

Received 14 May 2015; Accepted 24 June 2015
J-STAGE Advance published online 31 July 2015
SE-412 96 Gothenburg, Sweden
SE-414 83 Mölndal, Sweden
Exhibition Road, London SW7 2AZ, United Kingdom
Corresponding author: Anders Rasmuson;
E-mail: rasmuson@chalmers.se
TEL: +46 31 772 2940 FAX: +46 31 81 46 20

264

This type of fluidized bed can be divided into different
regions (Christensen F.N. and Bertelsen P., 1997; Karlsson
S. et al., 2009), as illustrated in Fig. 1. In an ideal coating
cycle, a particle begins to receive coating in the sprayzone, is then dried in the Wurster tube and the fountainregion, and moves back towards the horizontal transport
region through the downbed region. After travellingthrough these different regions, the particle is ready to
begin the next coating cycle when it moves into the sprayzone again. The sequence of coating and drying that occurs as the particles travel through these different regions
is usually regarded as a particle coating cycle (Li L. et al.,2015a). It has been pointed out that the cycle time distribution (CTD) and the residence time distribution (RTD)
in different regions are critical factors in determining thequality of the final product such as the film thickness andthe film thickness variability (Cheng X.X. and Turton R.,
2000; Shelukar S. et al., 2000; Li L. et al., 2015a). For
example, the particles must spend sufficient time in theregions for drying before returning to the horizontaltransport region; otherwise, the risk of agglomeration increases due to collisions between wet particles.
The CTD and RTD of particles in similar fluidized©2016 Hosokawa Powder Technology Foundation

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Fig. 1A schematic of the Wurster process: (1) spray zone, (2)Wurster tube, (3) fountain region, (4) downbed region,and (5) horizontal transport region.

beds have been studied using both experimental (Mann V.
and Crosby E., 1975; Shelukar S. et al., 2000; San José
M.J. et al., 2013; Li L. et al., 2015a) and modeling (Fries L.
et al., 2011; Yang S. et al., 2014; Li L. et al., 2015b) methods. It was found that particles spend most of the cycle
time in the horizontal transport region and that different
residence times in this region can cause a large variability
in the cycle time, and therefore a large variability in thefilm thickness. Due to limitations of experimental techniques, it is difficult to measure how particles behave indetail in the spray zone. For example, in the positronemission particle tracking (PEPT) measurements (Parker
D.J. et al., 1993), the γ-rays tend to be absorbed by themetal in the lower region of the fluidized bed, which results in poor data quality in the spray region (Li L. et al.,2015a). However, the particle behavior in the spray zone isimportant for coating. Thus advanced modeling methods,
such as the discrete element method (DEM), in combination with computational fluid dynamics (CFD), offer an
attractive way of addressing this kind of issue that cannot
be handled experimentally.
The discrete element method was initially proposed for
granular assemblies by Cundall P.A. and Strack O.D.
(1979), and was introduced into particle flow in fluidized
beds by Tsuji Y. et al. (1993). In this approach, every
particle is modeled as an individual element and the gas
phase is treated as a continuum. Particle-particle andparticle-wall interactions can be accounted for in astraightforward fashion and it is also simple to include
particles of different sizes. Owing to this benefit, a number of reviews and applications of this method have beenpresented in the literature (Kafui K.D. et al., 2002; Deen
N.G. et al., 2007; Van der Hoef M.A. et al., 2008; Link
J.M. et al., 2008; Zhu H.P. et al., 2008; Ketterhagen W.R.
et al., 2009; Turton R., 2010; Darabi P. et al., 2011; Fries L.
et al., 2013).
On the other hand, since every particle is modeled, thediscrete element method is not practical for systems involving a considerable number of particles. For many particle coating processes in pharmaceutical development,
the systems often have a batch size ranging from a few
grams to a few hundred grams, which corresponds tofrom tens of thousands to a few million particles. Nowadays, simulating tens of thousands particles can be done
at a reasonable computational cost, while modeling systems with a few million particles is challenging, thoughstill feasible (Jajcevic D. et al., 2013). Hence, DEM-CFD
modeling offers a great opportunity to assist process andproduct development.
In DEM-CFD simulations, particles and gas flow arecoupled through an interphase momentum transfer term
via a suitable drag model. It is well known that in fluidized beds, particles interact strongly with the gas flow,
and consequently the drag model implemented affects thepredicted particle motion, as well as the CTD and RTD.
In the assessment of drag models by Du W. et al. (2006)
based on the two-fluid model (TFM), the Gidaspow dragmodel (Gidaspow D., 1994) gave the best agreement withexperimental observations in modeling of spouted beds.
With the recent increase in computational power, it has
become feasible to derive the drag model by fully solving
the flow between particles (Kriebitzsch S.H.L. et al.,2013). New drag models have been proposed and developed in recent years based upon simulations using theLattice-Boltzmann method (LBM) or immersed boundary
method (IBM) (Hill R.J. et al., 2001a; 2001b; Van der
Hoef M.A. et al., 2005; Benyahia S. et al., 2006; Beetstra
R. et al., 2007; Tang Y. et al., 2015). In a combined experimental and simulation study, Link J.M. et al. (2005) evaluated the Gidaspow and Hill drag models and found thatthey could reproduce several important regimes in spoutfluid beds. At this moment, however, it is still unclear how
the drag model affects the residence time distributions ofparticles in different regions of fluidized beds.
In the present study, the objective is to investigate theeffect of drag models on the residence time distributionsof particles in different regions of a Wurster fluidized bed.
The CTD and RTD are obtained using DEM-CFD simulations with different drag models and are compared torecent PEPT experimental data (Li L. et al., 2015a). The
performance of the selected drag models for fluidized bed
coating applications is discussed.

265

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

2. Mathematical model
2.1 Equations of particle motion
For every particle, the particle motion is described using Newton’s second law


dvi
V  

mi 
β i u g - vi   mi g -ViP + Fc,i
(1)
dt
εs


where mi is the particle mass, vi is the particle velocity, u gis the gas velocity, g is the gravitational acceleration, Vi is
the particle volume, P is the gas pressure gradient, εs isthe particle volume fraction, Fc,i is the contact force
during particle-particle and particle-wall collisions and β
is the interphase momentum transfer coefficient. It isnoted that other forces such as a cohesion force (Thornton
C. and Yin K.K., 1991) can be incorporated into Eqn. (1)in a straightforward manner.
The angular momentum of each particle is described
using


dωi
Ii
  Ti(2)dtwhere Ii is the moment of inertia of the particle, ωi is therotational velocity and Ti is the total torque acting on theparticle.
∆

2.2 Equations of gas flow
As discussed by Li L. et al. (2015b), turbulence can beneglected in this study. Hence, the continuity and momentum equations are written as follows
ε g ρg     ε g ρgug  0
t

(3)



 
ε g ρgug     ε g ρgugu g   ε gP
t
n

 T
 
  ε g ρg μg u g  u g    β u g - vi 







(4)

i 1

where εg = 1 – εs is the gas volume fraction, ρg is the density of air and μg is the dynamic viscosity of air, respectively.

2.3 Soft sphere modelIn accounting for interparticle forces, i.e. Fc,i (see Eqn.
(1)), there are two common models available (Crowe C.T.
et al., 2012), the hard sphere model and the soft spheremodel. In the current study, the latter is selected since it ismore appropriate for describing the behavior in a dense
fluidized bed where particles can remain in contact for along time. In this model, particle-particle collisions aredescribed via a spring-dashpot system (Tsuji Y., 1993), as

266

Fig. 2A schematic of the soft sphere model.

sketched in Fig. 2. In the present work, the contact forcesin the normal and tangential directions are calculated using the Hertz-Mindlin theory (Hertz H., 1882).

2.4 Drag models
In Eqn. (1), the interphase momentum transfer coefficient can be specified using different drag models. In thisstudy, five drag models are examined: the Gidaspow, Min,
HKL, Beetstra and Tang drag models. Their respective
expressions are presented in Table 1 and a brief summaryis given below.
As mentioned in the introduction, Ergun S. (1952) obtained an expression for the average fluid particle force
based on a classic study of pressure drop through packed
beds. Wen C. and Yu Y. (1966) also conducted a series of
fluidization experiments and presented their correlation.
Later, Gidaspow D. (1994) suggested a model based on
the Ergun equation for the dense regime and the Wen-Yu
correlation for the dilute regime. The modification madeby Gidaspow D. was found to be the best in modeling of
spouted beds (Du W. et al., 2006) and has been widelyused in engineering practice (Deen N.G. et al., 2007).
In other cases, the minimum value given by the Ergun
equation and the Wen-Yu correlation is adopted (the Min
drag model), for example, in the work of Link J.M. et al.
(2005).
Using accurate numerical data from LBM simulations,
Hill R.J. et al. (2001b) derived a new drag model for moderate Reynolds-number flows in ordered and random arrays of spheres. In this model, a dimensionless drag force,
F(εs,Rep), is defined and solved via a force balance. That is,
the total force exerted on a particle
by the gas flow, Fgs , is
the sum of the drag force, Fd , and the buoyancy-type force
due to the gas pressure gradient, Fb . Van der Hoef M.A.
et al. (2005) pointed out that the drag force and the totalforce differ by a factor of εg, i.e. Fd  ε g Fgs , and that thedimensionless drag force can be converted to the interphase momentum transfer coefficient in Eqn. (1) via thefollowing expression:β

18 μgε sε gF ε s ,Rep 
dp2

(5)

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Table 1Drag models

Drag
model

Expression



 μ
εs2
 1.75ε s Rep  g2
150
βErgun 
εg


 dp
βGidaspow  με3βRe C g s ε 2.65
 Wen Yu 4 p D d p 2 g
Gidaspow

ε s  0.2
ε s  0.2

  d
Rep  ε g ρg u g - vi pμg

  1  0.15Rep 0.687  Rep  1000
24 
CD   
Rep

0.44
Rep  1000
Min

min  βErgun , β Wen Yu 
βHKL 

18 μgε gε g 2F ε s ,Rep dp2

F εs ,Rep F2  0.5F3Rep

HKL
1  3 ε  135 ε ln ε   17.89ε ssss  εs 642
ω 10 ε 3   1  ω  1  0.681ε  11.03ε 2  15.41ε 3  ε s  0.4

g 
s
s
s


F2   



ε
10 s
ε s  0.4
 ε g 3
ε s  0.0953
0.9351 ε s  0.03667
F3  
5 ε  0.0953
0.06730.212
ε0.0232
ε


s
g
s

 10 0.4  ε s  
w  exp εsβ Beetstra 

18 μgε sε gF ε s ,Rep 
dp2

BeetstraF εs ,Rep  βTang 0.413Rep  ε g 1  3 ε sε g  8.4Rep 0.343 
10 ε s ε g 2 1  1.5 ε s 2εg
24ε g 2  1  103 ε s Rep 1 4ε s 2



18 μgε sε gF ε s ,Rep 
dp2

Tang
F εs ,Rep
10 ε s
0.00456 
0.0644  ε g 2 1  1.5 ε s  0.11 ε s 1  ε s   0.169 ε g  Rep 0.343 Rep
2
4
4
εg
εg
εg 








267

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Later on, this drag model was extended by Benyahia S.
et al. (2006) to cover a wider range of Reynolds numbers
and particle volume fraction. The model proposed is complicated and in Table 1, a simplification suggested by
Benyahia S. et al. (2006) is provided1.
Beetstra R. et al. (2007) used the same method to derive a drag model where a more complex functional form
was proposed instead of a linear scaling with Re to describe the inertial effects. Moreover, their extensive LBM
simulations covered a wider range of parameters and were
performed for Reynolds numbers up to 1000.
Quite recently, Tang Y. et al. (2015) proposed a new
drag model based on simulation results obtained using
fully resolved IBM simulations. In order to obtain highly
accurate (essentially grid-independent) results, they resolved the flow past fixed assemblies of monodisperse
spheres. It was stated that the grid resolution effects are
consistently better resolved compared to the previous
LBM simulations (Hill R.J. et al., 2001b; Beetstra R. etal., 2007).
For these different drag models, the interphase momentum transfer coefficient, β, is plotted in Fig. 3 as afunction of the particle volume fraction, εs. Since thecharacteristic slip velocity and the particle volume fraction are to a large extent different in different regions of
the present fluidized bed, β is calculated for a slip velocityof 5 m/s and a particle volume fraction up to 0.2 for the
dilute region, e.g. the Wurster tube. For the dense region,e.g. the horizontal transport region, β is calculated for aslip velocity of 1 m/s and a particle volume fraction ranging from 0.2 to 0.6. It can be seen that in the dilute region,
β decreases in the following order: the HKL, Beetstra,Tang, Gidaspow and Min drag models. More specifically,
β for εs < 0.2 due to the HKL and Beetstra drag models isnearly twice that for the other three. For the moderately
dense region with 0.2 ≤ εs < 0.4, there is only a slight difference in the interphase momentum transfer coefficient
between the different drag models. For the very dense
region with εs ≥ 0.4, the Beetstra, Tang and HKL dragmodels give a significantly larger interphase momentum
transfer coefficient than the Gidaspow and Min dragmodels.

3. Method
3.1 Experimental

Fig. 3Comparison of drag models, the relative velocity between gas flow and particles is (a) 5 m/s and (b) 1 m/s,
respectively.

1
It must be noted that this extension was developed following theoriginal derivation by Hill R. J. et al, where the total force exerted on theparticle was referred to as the drag force. Thus another factor of the gas
volume fraction is required when converting the dimensionless dragforce to the interphase momentum transfer coefficient using Eqn. (5).

268

Recent PEPT experimental data presented by Li L. et
al. (2015a) are used in this study to evaluate different dragmodels by comparing the calculated particle velocity, cycle and residence time distributions with the measured
values.
In the PEPT experiments, a tracer particle labeledwith Fluorine-18 is incorporated into the fluidized bed.
As the tracer particle moves around in the vessel, the radioisotope decays and releases back-to-back γ-rays. Theseγ-rays are detected via two large position-sensitive detectors located on both sides of the fluidized bed. Each pairof γ-rays can be used to calculate the location of the tracer
particle by means of three-dimensional triangulation.
More details about the technique and the algorithms fordata processing can be found in the literature (Parker D.J.
et al., 1993; 1997; 2002; Forster R. et al., 2000).
The fluidized bed used in the PEPT experiments was
based on the STREA-1TM laboratory fluidized bed from
Aeromatic-Fielder. The fluidized bed is 380 mm high,with top and bottom diameters of 250 mm and 114 mm,
respectively; detailed dimensions are provided elsewhere(Li L. et al., 2015a). A nozzle with a diameter of 5 mm
was employed in order to avoid the numerical difficulties
in simulating sonic or even supersonic flow while retaining the feature of high jet velocity due to the atomization
air flow. The nozzle was only a big circular orifice and noliquid solution was introduced. The fluidization air flow
was supplied through a bowl-shaped distributor, consist-Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

ing of a central base and an outer annular region. The
central base of the distributor is fully open, while theouter annular region of the distributor has a number oforifices that correspond to approximately 4 % of the areaof the outer annulus region. A wire mesh screen was put
over the distributor to prevent particles from falling down
below the distributor.
Microcrystalline cellulose (MCC) pellets, a common
material in the pharmaceutical industry, were employedin the PEPT experiments. The particle size was measured
using QICPIC Particle Size Analysis (Sympatec GmbH),
and the particle density was determined using mercury
intrusion porosimetry (Micromeritics AutoPore III 9410).
The material parameters are given in Table 2; they wereselected to match those of MCC. A photograph of a sample of the MCC pellets is provided in Fig. 4.
Table 2The material parameters used in the numerical simulations (Roberts R. et al., 1994; Bharadwaj R. et al.,2010; Darelius A. et al., 2007; Li L. et al., 2015b)

VariableUnit

Air density

kg/m

Air viscosity

Pa·sParticle diameter

μm

Value
3

1.204
1.837 × 10 –5
1749

3

1420

Particle density

kg/mParticle sphericity—

0.85–0.95

Poisson’s ratio of particles

—

0.30

Young’s modulusPa

1.0 × 106

Particle-particle friction coefficient

—

0.53

Particle-particle restitution coefficient —0.83

Poisson’s ratio of walls

—

0.33

Particle-wall friction coefficient

—

0.20

Particle-wall restitution coefficient

—

0.80

Fluid time steps

5.0 × 10 –5

Particle time steps

5.0 × 10 –6

Fig. 4A photograph of a sample of the MCC pellets.

The shape of particles may affect the drag force acting
on the particles. In this case, the so-called sphericity factor can be used to account for the effect of the shape ofparticles (see, e.g. (Zastawny M. et al., 2012)). In order toevaluate the drag force more accurately, correlations are
required according to, e.g. Crowe C.T. (2006), using thesphericity of particles provided in Table 2. In addition,
the shape of particles may affect the porosity in the dense
downbed region and therefore the air flow passing throughthis region. However, this is beyond the scope of the current study.

3.2 Numerical3.2.1. General

A fully coupled multi-phase flow solver, MultiFlow(www.multiflow.org), was employed in this study to solve
the equations of particle motion and gas flow. The fluid
mesh, which is refined near the nozzle and in the Wurster
tube, contains approximately 65000 hexahedral cells.
Simulations of single-phase gas flow were performed toensure grid independence.
In order to specify the boundary conditions in the
DEM-CFD simulations, the flow rates of the atomizationand fluidization air flow, as well as the flow distribution at
the distributor are required. The global flow rates were
measured using a rotameter in the PEPT experiments (LiL. et al., 2015a). However, it was not possible to measure
the flow distribution in the experiments. Instead, a single-­phase CFD model including the air supplying chamber,the distributor, the wire mesh screen, and the fluidized
bed was developed in Ansys Fluent to determine the flow
distribution at the distributor. The results (not shown)obtained using this single-phase CFD model show thatthe air flow passing through the central base of the distributor is between 53 % and 64 % of the total fluidization
air flow. Since the particle velocity profile is an indicationof the flow distribution, DEM-CFD simulations with different flow distributions at the distributor were performed
to compare the particle velocity at different heights above
the distributor with the corresponding experimental data.
These additional simulations were used to verify that aflow distribution for which approximately 55 % of thefluidization flow enters normal to the boundary in thecentral base of the distributor and 45 % enters normal tothe boundary in the outer annular region (Li L. et al.,2015b) gave adequate agreement with the experimental
data.
The atomization nozzle is modeled as a flow normal tothe inlet boundary, while the outlet of the domain is considered to be a pressure outlet. The walls of the fluidized
bed and the Wurster tube are set to be no-slip for the gas
flow.
In order to obtain a reasonable compromise between

269

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

data quality and computational cost, the CTD and RTD in
different regions were evaluated for different simulation
times. It was found that the CTD becomes fairly steadyafter a simulation time of 25 s. It was also found that asimulation time of 10 s was appropriate to determine the
RTD in different regions, except in the horizontal transport region.

3.2.2. Simulation parameters

The numerical parameters and the operating conditions
are presented in Tables 2 and 3, respectively. The partition gap is set to 15 mm and the Wurster tube length is
150 mm. The fluidization and atomization air flow rates
are 73.3 m3/h and 3.50 m3/h, respectively. A base casewith a batch size of 200 g was selected in this study to investigate the drag models. In addition to the base case,which uses the Gidaspow drag model (Run #1), DEMCFD simulations with the same operating conditions are
performed for the other drag models in Runs #2–5 and forlarger batch sizes in Runs #6–9. A typical simulation was
run in parallel using 32–40 CPU cores and took about 12
hours wall clock time per simulated second.

3.2.3. Post-processing

From the point of view of a typical coating cycle, we
define the start of a particle coating cycle as the point
when a particle enters the spray zone from the horizontaltransport region or the lower region in the Wurster tube.
The particle coating cycle then ends when the particle
reappears in the spray zone after having subsequently
traveled through the Wurster tube, the fountain, downbed,
and horizontal transport regions. When one particle coating cycle ends, the next one begins. The particle cycle
time is thus defined to be the time it takes for a particle tocomplete one cycle.
In the PEPT experiments, it was difficult to detect thetracer particle in the lower region of the fluidized bed. In

Table 3270

Set-up of simulations

Simulation #Drag modelBatch size (g)1Gidaspow

200

2

Min

200

3

HKL

200

4

Beetstra

200

5

Tang

200

6

HKL

400

7

Tang

400

8

HKL

600

9

Tang

600

the experiments, it was therefore not possible to identify aspray region and the particle cycle was defined so that it
begins and ends in the Wurster tube. A spray zone istherefore not defined in the simulations either, and theparticle cycle is defined in the same way as in the experiments.
It is noted that in the PEPT experiments, only one
tracer particle was incorporated into each run and continuously followed for 1.5 hours. In the simulations, on theother hand, different particle trajectories are followed for
25 s. As a result, the cycle and residence time distributions were calculated from different cycles for the same
tracer particle in the experiments, while they are obtained
from many different particles in the DEM-CFD simulations. For the pseudo-steady particle dynamics in this fluidized bed, it is thus assumed that the information
obtained (such as the particle velocity or the cycle time
distribution) by following one particle for a long time isequivalent to the information obtained by following all
particles for a short period of time (Li L. et al., 2015b).

4. Results and discussion
In Table 4, the mean cycle time and mean residence
times of particles in different regions are summarized forthe batch size of 200 g; the corresponding experimental
data is also provided. Figs. 5 and 6 show the cycle time
distributions and mean residence times of particles in different regions, respectively, including values both measured in the PEPT experiment and calculated from the
DEM-CFD simulations for different drag models. For all
selected drag models, the general feature of the particle
residence times in different regions is captured. As can beseen in Table 4, the fraction of the cycle time that particles spend in different regions corresponds closely to
PEPT measured values for the different drag models.
However, the cycle time and the residence time in the
different regions, particularly in the Wurster tube and in
the horizontal transport region, exhibit distinct differences for the different drag models. It can be seen thatboth the mean cycle time in Table 4 and the CTD in
Fig. 5 calculated with the Gidaspow and Tang drag models agree quantitatively with the experimental data. For
the Min drag model, there is a greater fraction of particles
with a longer cycle time, which can be attributed to asomewhat slower acceleration in the Wurster tube and alonger residence time in the Wurster tube (see in Table 4).
Interestingly, the CTDs predicted using the HKL and
Beetstra drag models peak at a shorter cycle time and arenarrower than the others. These two effects result in amuch shorter mean cycle time for these two drag models.
In order to understand the cause of these differences,the vertical particle velocity at selected heights above theLiang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Table 4

Summary of particle cycle and residence time distributions for the batch size of 200 g
Residence time in

Fig. 5Fraction of cycle time inthehorizontaltransport
region (s)theWurstertube (–)

thefountainregion(–)

thedownbedregion(–)

thehorizontaltransport
region (–)

0.30

6.61

0.19

0.05

0.04

0.72

0.27

0.25

5.61

0.19

0.05

0.04

0.73

1.50

0.27

0.30

5.68

0.21

0.04

0.04

0.71

3.6

0.46

0.30

0.14

2.72

0.14

0.09

0.04

0.72

Beetstra

4.9

0.73

0.28

0.25

3.67

0.16

0.07

0.05

0.71

Tang

7.1

1.27

0.27

0.22

5.36

0.19

0.05

0.04

0.73

The cycle time distribution of particle cycles shorter
than 25 s for the base case.

Fig. 6

Cycle
time (s)theWurstertube (s)thethefountain downbed
region (s) region (s)

PEPT

8.5

1.33

0.27

Gidaspow

7.4

1.27

Min

7.7

HKL

bottom of the fluidized bed, i.e. 45 mm, 75 mm and
105 mm, is plotted in Fig. 7. At 45 mm, which is the lowest location where reliable PEPT data could be obtained,
the particle velocity is close to the PEPT measured velocity in the Wurster tube for all drag models studied, while
the HKL drag model shows better agreement with the
PEPT data in the downbed region. At 75 mm, all the dragmodels give very similar results. At 105 mm, only theparticle velocity in the Wurster tube is shown, and it canbe seen that all the drag models overpredict the particle
velocity to a great extent in a central region corresponding to a radial location smaller than approximately 5 mm.
This latter difference is probably due to the fact that in
the experiments, there is turbulence in this central region
induced by the atomization air flow, while the simulations
employ a laminar model to predict the air flow.
Nevertheless, since this central region contains only afew particles, it is not expected to affect the general particle motion in the fluidized bed and the predicted CTD and
RTD. It can also be seen that the HKL and Beetstra dragmodels give a distinctly higher velocity than the otherdrag models in this central region. Outside of this central
region, the particle velocity for all of these drag models

The residence times of particles in different regions forthe base case (the secondary y-axis is used for the residence time in the horizontal transport region due to thehigher values).

shows better agreement with the PEPT measured velocity.
But a larger overprediction of the particle velocity compared to the PEPT measurement—though smaller thanthat in the central region—can be found for the HKL dragmodel than for the other drag models. This is similar to
the results presented by Link J.M. et al. (2005) for apseudo-two-dimensional spout-fluid bed, where it was
concluded that the HKL drag model has the best predictive capabilities.
Fig. 8 shows the particle volume fraction examined at
two heights above the bottom of the fluidized bed, corresponding to the inlet (15 mm) and outlet (165 mm) of the
Wurster tube, respectively. It can be seen that the particle
volume fraction is lower for the HKL and Beetstra dragmodels at the inlet of the tube and vice versa at the outletof the tube. Since the Beetstra, Tang and HKL dragmodels give a larger interphase momentum exchange coefficient in the dense downbed region (cf. Fig. 3), there isa larger pressure drop in the downbed region for thesemodels. As a result of the larger pressure drop in the271

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Fig. 7

The time-averaged particle volume fraction at 15 mm(the tube inlet) and 165 mm (the tube outlet) for thebase case.

Fig. 9The time-averaged air velocity in the downbed region
at 15 mm (the tube inlet) for the base case.

The time-averaged vertical particle velocity at 45 mm,
75 mm and 105 mm, for the base case.

downbed region, a certain amount of fluidization air flow
is diverted from this region into the Wurster tube. This is
supported by a lower air velocity at 15 mm in the
downbed region for these models, as shown in Fig. 9.
Since the particles are accelerated by the air flow throughthe Wurster tube, the particle volume fraction at the outletof the tube decreases compared to that at the inlet for eachdrag model. For the HKL and Beetstra drag models, thelarger interphase momentum exchange coefficient in the
dilute Wurster tube (cf. Fig. 3) results in a higher particle
velocity in the tube compared to other drag models. Correspondingly, a larger mass flow rate through the Wurster
tube is expected and a higher particle volume fraction at
the outlet of the tube is obtained for the HKL and Beetstra
models than for the other drag models.
The higher particle volume fraction at the inlet of the
Wurster tube for the Tang, Gidaspow and Min drag models can be attributed to a lower air flow rate in the Wurster

272

Fig. 8tube and a lower interphase momentum transfer coefficient. The resulting slower acceleration of particles in the
Wurster tube leads to particles having a greater risk of recirculating in the Wurster tube and moving back towards
the horizontal transport region from the lower edge of the
Wurster tube. This type of behavior was also observed in
the PEPT experiments (Li L. et al., 2015a) and is sketchedin Fig. 10. Thus, it is of interest to examine the number ofparticle recirculations in the Wurster tube, as shown in
Fig. 11. It is significant in Fig. 11 that for the HKL and
Beetstra drag models, the fraction of ideal cycles, i.e. thefraction of particle cycles without any recirculation, is approximately 63 % and 43 %, which is much higher than

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Fig. 10 An illustration showing particle recirculation. The
particle moves back towards the horizontal transport
region from the lower edge of the Wurster tube.

Fig. 12 The mean particle cycle times for different batch sizes.

5. Conclusions

Fig. 11 The fraction distribution of particle recirculations in
the Wurster tube for the base case.

the PEPT measured value of 12 %. The reduced numberof particle recirculations for the HKL and Beetstra dragmodels can thus decrease the residence time in the
Wurster tube and prevent a very long residence time in
the horizontal transport region. This again emphasizes
how important the detailed particle motion in the Wurster
tube is to the operation of this type of fluidized bed.
In order to explore a scenario where the dense region is
likely to have a greater effect, simulations of larger batch
sizes of 400 g and 600 g were performed for the HKL and
Tang drag models. The results obtained for the mean particle cycle times are presented in Fig. 12 for different
batch sizes. In agreement with the recent PEPT experiments, all selected drag models predict a shorter cycle
time for a larger batch size. Notably, the Tang drag modelshows the closest agreement for the increased batch sizeof 400 g. The results also indicate that, as the batch size
increases, the deviation from the experiments is reduced
for the HKL drag model, since the effect due to particle
recirculations in the Wurster tube is reduced.

In this study, DEM-CFD simulations of particle motion
in a Wurster fluidized bed have been performed in orderto study the effect of drag models on particle cycle andresidence times. A comparison between the calculatedparticle cycle and residence time distributions from thesimulations and recent PEPT experimental data was
made. It was found that the Gidaspow, Min and Tang dragmodels predict the particle cycle time, the CTD and the
RTDs qualitatively and quantitatively. The Tang dragmodel also demonstrates better agreement with experimental data for a larger batch size. The HKL and Beetstra
drag models slightly overestimate the particle velocity in
the Wurster tube. This overestimation of the particle velocity in the Wurster tube results in a much smaller number of recirculations, and therefore a much shorter
residence time in the horizontal transport region and amuch shorter cycle time. For the larger batch sizes, where
the effect of particle recirculations is less pronounced andthe dense region becomes more dominant, the agreement
between the experimental data and the predictions of the
HKL drag model improves. The present results thus suggest that the drag model must be selected carefully for the
purpose of studying residence time distributions and,hence, coating processes in fluidized beds.

Acknowledgements
This work was undertaken within the EU seventh
framework program, the PowTech Marie Curie Initial
Training Network (Project no. EU FP7-PEOPLE-2010ITN-264722). Dr. Andy Ingram at the University ofBirmingham is gratefully acknowledged for his contribution to the PEPT experiments. The authors would also
like to thank the high-performance computing (HPC)
team at AstraZeneca R&D for their kind support duringcomputations.

273

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Nomenclature
DEM

Discrete Element Method

CFD

Computational Fluid Dynamics

TFM

Two Fluid Model

PEPT

Positron Emission Particle Tracking

CTD

Cycle Time Distribution

RTD

Residence Time Distribution

LBM

Lattice Boltzmann Method

IBM

Immersed Boundary MethodCDdrag coefficient (–)

dp

Fc,i

g

particle diameter (m)miparticle mass (kg)Iimoment of inertia (kg·m 2)

Ppressure (N/m 2)

Rep

Ti

particle Reynolds number (–)

t

ug

vi

time (s)Viparticle volume (m3)irotational velocity of the particle (rad/s)

εggas volume fraction (–)εsparticle volume fraction (–)β

interphase momentum transfer coefficient (kg/(m3·s))

μg

dynamic viscosity (Pa·s)ρg

air density (kg/m3)

contact force (N)gravity vector (m/s2)

torque (N·m)
gas velocity (m/s)
particle velocity (m/s)

References
Beetstra R., van der Hoef M.A., Kuipers J.A.M., Drag force
of intermediate Reynolds number flow past mono- andbidisperse arrays of spheres, AIChE Journal, 53 (2007)
489–501.
Benyahia S., Syamlal M., O’brien T.J., Extension of Hill–Koch–
Ladd drag correlation over all ranges of Reynolds number
and solids volume fraction, Powder Technology, 162 (2006)
166–174.
Bharadwaj R., Smith C., Hancock B.C., The coefficient of restitution of some pharmaceutical tablets/compacts, International journal of pharmaceutics, 402 (2010) 50–56.
Cheng X.X., Turton R., The prediction of variability occurringin fluidized bed coating equipment. I. The measurement ofparticle circulation rates in a bottom-spray fluidized bed
coater, Pharmaceutical Development and Technology, 5(2000) 311–322.
Christensen F.N., Bertelsen P., Qualitative description of the274

Wurster-based fluid-bed coating process, Drug Development and Industrial Pharmacy, 23 (1997) 451–463.
Crowe C.T., Multiphase flow handbook, CRC, Boca Raton, FL,
2006.
Crowe C.T., Schwarzkopf J.D., Sommerfeld M., Tsuji Y., Multiphase Flows with Droplets and Particles, Second Edition,
CRC Press Inc, GB, 2012.
Cundall P.A., Strack O.D., A discrete numerical model for granular assemblies, Geotechnique, 29 (1979) 47–65.
Darabi P., Pougatch K., Salcudean M., Grecov D., DEM investigations of fluidized beds in the presence of liquid coating,Powder Technology, 214 (2011) 365–374.
Darelius A., Lennartsson E., Rasmuson A., Niklasson Björn I.,
Folestad S., Measurement of the velocity field and frictional
properties of wet masses in a high shear mixer, Chemical
Engineering Science, 62 (2007) 2366–2374.
Deen N.G., Annaland M.V., van der Hoef M.A., Kuipers J.A.M.,
Review of discrete particle modeling of fluidized beds,
Chemical Engineering Science, 62 (2007) 28–44.
Du W., Bao X., Xu J., Wei W., Computational fluid dynamics(CFD) modeling of spouted bed: Assessment of drag coefficient correlations, Chemical Engineering Science, 61 (2006)
1401–1420.
Ergun S., Fluid flow through packed columns, Chemical Engineering Progress, 48 (1952) 89–94.
Forster R., Seville J., Parker D., Ding Y., Tracking Single
Particles in Process Equipment or Probing Processes Using
Positrons, KONA Powder and Particle Journal, 18 (2000)
139–148.
Fries L., Antonyuk S., Heinrich S., Dopfer D., Palzer S., Collision dynamics in fluidised bed granulators: A DEM-CFD
study, Chemical Engineering Science, 86 (2013) 108–123.
Fries L., Antonyuk S., Heinrich S., Palzer S., DEM–CFD modeling of a fluidized bed spray granulator, Chemical Engineering Science, 66 (2011) 2340–2355.
Gidaspow D., Multiphase flow and fluidization: continuum andkinetic theory description, Academic, Boston, 1994.
Hertz H., Ueber die Berührung fester elastischer Körper, Journal für die reine und angewandte Mathematik, 92 (1882)
156–171.
Hill R.J., Koch D.L., Ladd A.J.C., The first effects of fluid inertia on flows in ordered and random arrays of spheres, Journal of Fluid Mechanics, 448 (2001a) 213–241.
Hill R.J., Koch D.L., Ladd A.J.C., Moderate-Reynolds-number
flows in ordered and random arrays of spheres, Journal of
Fluid Mechanics, 448 (2001b) 243–278.
Jajcevic D., Siegmann E., Radeke C., Khinast J.G., Large-scale
CFD–DEM simulations of fluidized granular systems,
Chemical Engineering Science, 98 (2013) 298–310.
Kafui K.D., Thornton C., Adams M.J., Discrete particle-continuum
fluid modelling of gas–solid fluidised beds, Chemical Engineering Science, 57 (2002) 2395–2410.
Karlsson S., Rasmuson A., van Wachem B., Björn I.N., CFD
modeling of the Wurster bed coater, AIChE Journal, 55(2009) 2578–2590.
Ketterhagen W.R., Am Ende M.T., Hancock B.C., Processmodeling in the pharmaceutical industry using the discreteelement method, Journal of Pharmaceutical Sciences, 98

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277(2009) 442–470.
Kriebitzsch S.H.L., van der Hoef M.A., Kuipers J.A.M., Dragforce in discrete particle models—Continuum scale or single particle scale?, AIChE Journal, 59 (2013) 316–324.
Li L., Rasmuson A., Ingram A., Johansson M., Remmelgas J.,
Von Corswant C., Folestad S., PEPT study of particle cycle
and residence time distributions in a Wurster fluid bed,
AIChE Journal, 61 (2015a) 756–768.
Li, L., Remmelgas, J., van Wachem, B.G.M., von Corswant, C.,
Johansson, M., Folestad, S., Rasmuson, A., Residence time
distributions of different size particles in the spray zone ofa Wurster fluid bed studied using DEM-CFD, Powder Technology, 280 (2015b) 124–134.
Link J.M., Cuypers L.A., Deen N.G., Kuipers J.A.M., Flowregimes in a spout–fluid bed: A combined experimental andsimulation study, Chemical Engineering Science, 60 (2005)
3425–3442.
Link J.M., Deen N.G., Kuipers J.A.M., Fan X., Ingram A.,
Parker D.J., Wood J., Seville J.P.K., PEPT and discrete particle simulation study of spout-fluid bed regimes, AIChE
Journal, 54 (2008) 1189–1202.
Mann V., Crosby E., Cycle time distribution measurements in
spouted beds, Canadian Journal of Chemical Engineering,53 (1975) 579–581.
Parker D.J., Allen D.A., Benton D.M., Fowles P., Mcneil P.A.,
Tan M., Beynon T.D., Developments in particle tracking
using the Birmingham Positron Camera, Nuclear Instruments and Methods in Physics Research Section A:
Accelerators, Spectrometers, Detectors and AssociatedEquipment, 392 (1997) 421–426.
Parker D.J., Broadbent C.J., Fowles P., Hawkesworth M.R.,
Mcneil P., Positron emission particle tracking—a technique
for studying flow within engineering equipment, Nuclear
Instruments and Methods in Physics Research Section A:
Accelerators, Spectrometers, Detectors and AssociatedEquipment, 326 (1993) 592–607.
Parker D.J., Forster R.N., Fowles P., Takhar P.S., Positron emission particle tracking using the new Birmingham positroncamera, Nuclear Instruments and Methods in Physics
Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 477 (2002) 540–545.
Roberts R., Rowe R., York P., The Poisson’s ratio of microcrystalline cellulose, International journal of pharmaceutics,
105 (1994) 177–180.
San José M.J., Alvarez S., Peñas F.J., García I., Cycle time indraft tube conical spouted bed dryer for sludge from paperindustry, Chemical Engineering Science, 100 (2013) 413–
420.
Shelukar S., Ho J., Zega J., Roland E., Yeh N., Quiram D., Nole
A., Katdare A., Reynolds S., Identification and characteri-zation of factors controlling tablet coating uniformity in a
Wurster coating process, Powder Technology, 110 (2000)
29–36.
Ström D., Karlsson S., Folestad S., Niklasson Björn I., LaurellT., Nilsson J., Rasmuson A., A new device for coating single particles under controlled conditions, Chemical Engineering Science, 60 (2005) 4647–4653.
Tang Y., Peters E.A.J.F., Kuipers J.A.M., Kriebitzsch S.H.L.,van der Hoef M.A., A new drag correlation from fully
resolved simulations of flow past monodisperse static arraysof spheres, AIChE Journal, 61 (2015) 688–698.
Teunou E., Poncelet D., Batch and continuous fluid bed coating—review and state of the art, Journal of Food Engineering, 53 (2002) 325–340.
Thornton C., Yin K.K., Impact of elastic spheres with and without adhesion, Powder Technology, 65 (1991) 153–166.
Tsuji Y., Discrete particle simulation of gas-solid flows (Fromdilute to dense flows), KONA Powder and Particle Journal,
11 (1993) 57–68.
Tsuji Y., Kawaguchi T., Tanaka T., Discrete particle simulationof two-dimensional fluidized bed, Powder Technology, 77(1993) 79–87.
Turton R., The application of modeling techniques to film-coatingprocesses, Drug Development and Industrial Pharmacy, 36(2010) 143–151.
Van der Hoef M.A., Beetstra R., Kuipers J.A.M., Lattice-­
Boltzmann simulations of low-Reynolds-number flow pastmono- and bidisperse arrays of spheres: results for the permeability and drag force, Journal of Fluid Mechanics, 528(2005) 233–254.
Van der Hoef M.A., van Sint Annaland M., Deen N.G., Kuipers
J.A.M., Numerical simulation of dense gas-solid fluidized
beds: A multiscale modeling strategy, 40 (2008) 47–70.
Wen C., Yu Y., Mechanics of Fluidization, Fluidization, Chemical Engineering Progress Symposium Series 62, (1966)
100–111.
Wurster D.E., Air-suspension technique of coating drug particles. A preliminary report, Journal of the American Pharmaceutical Association, 48 (1959) 451–454.
Yang S., Luo K., Fan J., Cen K., Particle-scale investigation of
the solid dispersion and residence properties in a 3-D spoutfluid bed, AIChE Journal, 60 (2014) 2788–2804.
Zastawny M., Mallouppas G., Zhao F., van Wachem B., Derivation of drag and lift force and torque coefficients fornon-spherical particles in flows, International Journal ofMultiphase Flow, 39 (2012) 227–239.
Zhu H.P., Zhou Z.Y., Yang R.Y., Yu A.B., Discrete particle simulation of particulate systems: A review of major applications and findings, Chemical Engineering Science, 63(2008) 5728–5770.

275

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Author’s short biography
Liang Li
Liang Li obtained a B.Eng. from Sichuan University and a M.Sc. in fluid mechanics
from Chongqing University, China. Since Sept. 2011, he has received a Marie Curie fellowship within EU 7th framework program and has worked as a Ph.D. candidate, under
the supervision of Professor Anders Rasmuson, in chemical engineering at the Chalmers
University of Technology, Sweden. His current research is focused on developing an advanced modeling tool for fluidized bed coating. In order to understand the link between
process conditions and film formation at the level of a single pellet, the discrete element
method (DEM), together with computational fluid dynamics (CFD), is selected.

Johan Remmelgas
Johan Remmelgas is an associate principal scientist in pharmaceutical development at
AstraZeneca R&D, Mölndal, Sweden. He is currently working on developing and applying mechanistic models to predict product and process performance. He holds degrees inchemical engineering from Stanford University and the University of California, Santa
Barbara, where he worked on developing and evaluating constitutive equations forpolymer solutions. Before joining AstraZeneca, Johan worked on the development ofheavy-duty diesel engines at Volvo Powertrain, Gothenburg, Sweden. He is the author or
co-author of patents for applications ranging from diesel engines to inhalation devices.

Berend van Wachem
Berend van Wachem is a professor of fluid mechanics at the Department of Mechanical Engineering of the Imperial College London. He has been active in developing, employing and validating advanced computational and modelling methods to predict the
behaviour of complex flows. The ultimate aim of his research is understanding the underlying physics at the various length and time scales involved. He has presented comprehensive reviews on computational techniques for the modelling of multiphase flows,
including droplets, interfaces and particles in fluids. He is co-editor and co-author of two
handbooks on multiphase flow computations. He has also written highly cited papers on
gas-liquid interface modelling. He is also the author of computational codes, such as
www.multiflow.org.

Christian von Corswant
Dr. Christian von Corswant studied to be a chemical engineer at the Chalmers University of Technology and wrote his thesis at the Lund University of Technology. The title ofthe thesis is “Lecithin-based microemulsions for pharmaceutical use”. He has worked at
AstraZeneca R&D for more than 20 years in different positions within the pharmaceuticaldevelopment unit and since 2008, has a specialist role as principal scientist for advanced
drug delivery systems with a special focus on orally controlled release formulations.
Christian has authored/co-authored about 20 publications to date.

276

Liang Li et al. / KONA Powder and Particle Journal No. 33 (2016) 264–277

Author’s short biography
Staffan Folestad
Dr Staffan Folestad is a senior principal scientist in AstraZeneca with global responsibilities in manufacturing sciences and pharmaceutical innovation. On a part-time basis,
he was adjunct prof at Uppsala University (from 1999), and is currently affiliated to the
Chalmers University of Technology. His research focus is in advanced in-situ process
sensors and in-silico process modeling for powder processes. He is the author of > 85
scientific papers, 5 book chapters, and > 20 patents and is a member of the Royal Swedish Academy of Engineering Sciences, on the board of the Chalmers Center for Chemical
Process Engineering, and initiator of the pan-European science conference series EuPAT.
He received the “New Safe Medicines Faster Award 2007” from the European Federation of Pharmaceutical Sciences.

Mats O. Johansson
Mats Johansson is a senior scientist in the pharmaceutical development unit at AstraZeneca R&D, Mölndal, Sweden. Mats has a background in chemical engineering. Mats
has played a major role in establishing fluidized bed technology for orally controlled release products at AstraZeneca over the past decades. He is currently leading a networkon developing and further optimizing fluidized bed equipment and processes for modified release coatings for high-performance pharmaceutical products. He is the author or
co-author of several patents for pharmaceutical applications.

Anders Rasmuson
Anders Rasmuson has been a professor in chemical engineering at the Chalmers University of Technology in Gothenburg since 1990. He has published approximately 150
papers in international scientific journals with nearly 2000 citations in the areas of particulate processes, multi-phase flows and transport phenomena, mixing and separation
processes. He is a member of the Royal Swedish Academy of Engineering Sciences, amember of the EFCE working party of mixing, and a member of the board of the Chalmers
Centre of Chemical Process Engineering. He has recently participated in the EU Marie
Curie project POWTECH as an examiner/supervisor for three PhD candidates.

277

KONA Powder and Particle Journal No. 33 (2016) 278–286/Doi:10.14356/kona.2016009

Original Research Paper

Reduction Kinetics of Zinc Powder from Brass Converter Slagby Pyrometallurgical Method Using Hydrogen Gas †Tae-Hyuk Lee 1, Sin-Hyeong Joo 1, Hayk H. Nersisyan 1,2, Man-Sik Kong 3,
Jae-Woo Lee 4, Ki-Won Park 4 and Jong-Hyeon Lee 1,2*
1

Department of Materials Science & Engineering, Chungnam National University, Republic of Korea
Rapidly solidified Materials Research Center, Republic of Korea
3
Advanced Materials & Processing Center, Institute for Advanced Engineering, Republic of Korea
4
Seowon Co., LTD, Republic of Korea
2

Abstract
A pyrometallurgical reduction process for the recovery of copper and zinc from brass secondary slag (BSS) was
studied. Specifically, the effect of reduction temperature and time on the conversion to metallic phases wasinvestigated. The brass secondary slag was characterized by X-ray diffraction, inductively coupled plasma-atomic
emission spectrometry, automatic elemental analysis, thermogravimetric analysis, and field emission scanningelectron microscopy. A two-step reduction of BSS was identified. The step 1 comprised the reduction of ZnO,
while the step 2 featured the reduction of ZnO·Al2O3. Furthermore, the application of a first-order reaction modelwith Arrhenius analysis, indicated a conversion of ZnO to Zn(g) that had a rate constant increasing from
1.4 ± 0.13 × 10 –3 s –1 at 900 °C to 2.18 ± 0.15 × 10 –2 s –1 at 1050 °C. This reaction had an activation energy of
233.2 ± 26.1 kJ/mol. Secondly, the conversion of ZnO·Al2O3 to Zn(g) and Al2O3 increased from 1.54 ± 0.21 × 10 –5 s–1
at 900 °C to 1.09 ± 0.19 × 10 –3 s –1 at 1050 °C, and the activation energy was 376.7 ± 22.4 kJ/mol. This reaction
mechanism and its associated kinetic data can be applied to optimize the operation conditions of recycling processes
for Cu-containing wastes.
Keywords: pyrometallurgy, copper converter slag, kinetic analysis, zinc powder

1. Introduction
Copper and zinc are important base metals required forvarious applications in metallurgical and chemical industries. The annual global output of copper and alloy products represents around 16.1 million tons, of which about
15.3 million tons (75–80 %) originate from refined copper, while about 4.2 million tons (20–25 %) are derived
from direct melt scrap (Moskalyk P.R. and Alfantazi
A.M., 2003).
However, the increasing worldwide demand for zincand copper has necessitated intensive studies into the recovery and extraction of these metals from different
sources (Rudnik E. et al., 2009). Some of these metals are†

Received 27 May 2015 Accepted 14 July 2015
J-STAGE Advance published online 5 August 2015
1,2
99 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
3
51 Goan-ro, Baekam-myeon, Cheoan-gu, Yongin, Republic of
Korea
4
6B-15 Banwol Ind. #402-1 Moknae-dong, Danwon-gu, Ansan,
Kyunggi-do, Republic of Korea* Corresponding author: Jong-Hyeon Lee;
E-mail: jonglee@cnu.ac.kr
TEL: +82-42-821-6596 FAX: +82-42-822-5850

278

recovered from different secondary slags, e.g., brassdross, brass ash, and flue dusts in electric furnaces. These
brass secondary slags (BSS) are by-products obtained
during brass scrap smelting and crushing of brass slag. It
has been estimated that for every tons of copper production about 2.2 tons of slag are generated, so that 24.6 million tons of slag result from world copper production
every year (Gorai B. et al., 2003).
BSS usually contains Cu, Zn, and other minor elements
such as Pb, Fe, Ni, Si, and Sb (Jha M.K. et al., 2001).
However, these slags contain different level of impurities
depending on source. Recovery and extraction of these
metals is necessary and important from both economicand environmental viewpoints.
In industries, various methods are used to extract metals from BSS. One of these, the hydrometallurgical process, is used to recover and recycle metals from BSS
using different leaching agents, including sulfuric acid
(Anand S. et al., 1983), hydrochloric acid (Sharma K.D.
and Row B.R.L., 1985), cyanide (Anand S. et al., 1980)
acetic acid (Altundogan H.S. and Tumen F., 1997), andammonia (Sukla L.B. et al., 1986). However, the use ofleaching agents is harmful to the human body and cancause environmental pollution.

©2016 Hosokawa Powder Technology Foundation

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

These drawbacks can be overcome using pyro-­
metallurgical smelting to recover Cu and Zn from BSS.
The top submerged lance (TSL) process is a typicalpyro-metallurgy method that was commercialized and researched to improve recovery efficiency. The TSL processprovides highly efficient mixing and a heat source for reaction, due to combustion of the fuel and oxygen-mixed
gas with the BSS. Moreover, TSL process can easily remove the impurities that are generated through the formation of slag in the upper furnace (Bakker M.L. et al., 2011).
However, it is difficult to directly feed certain raw
materials that are fine dusts to a TSL furnace because ofdust-scattering during the injection step. To solve thisproblem, the density of the BSS can be increased during
the reduction process using a fluidized bed reactor. This isdone in a stage prior to the TSL furnace, and can be effective for increasing process throughput. The reduction
rate and reaction mechanism of BSS are very complex todetermine because such slags can contain mixtures ofcopper, zinc, and various elements. Therefore, further
research into the thermodynamics and reaction kinetics of
BSS recovery processes is necessary to optimize process
conditions in fluidized bed reactors.
The aim of this work is to determine the activation energy and reaction mechanism of BSS reduction by applying a pyro-metallurgical method using different reductiongas. Firstly, different parameters affecting the reduction
ratio, such as reduction temperature and reduction time,
were investigated. Then, based on the results of the reduction, the activation energy and reaction mechanism of
BSS were determined using the Arrhenius equation (Igor
I. K., 2008).

2. Experimental
The BSS that was used in this experimental was takenfrom the brass smelter of SEOWON CO. LTD in South
Korea. The BSS sample was analyzed by inductively coupled plasma-atomic emission spectrometry (ICP-AES,
ARL 3410, Switzerland) and X-ray diffraction (XRD,
Siemens D-5000, Germany). The carbon concentration of
the BSS sample was determined by an automatic elemental analyzer (AEA, Flash EA 1112 series, Thermo Fisher
Scientific, U.S.A.). The extent of reduction of the BSS
sample was estimated by a simultaneous thermogravimetric analyzer (TGA, TA SDT model 2060, TA Instruments,
U.S.A.), under a flow of 5 % H2 + 95 % N2, at a flow rate
100 cm3/min. The sample was heated to 850 °C at a heating rate of 5 °C/min.
The reduction of the BSS sample was carried out undera hydrogen atmosphere in a horizontal alumina tube furnace. The hydrogen flow rate was fixed at 100 cm3/min in
all experiments. The respective effects of reduction tem-

perature and time on the reaction rates of the brass secondary slags were investigated. Specifically, the reduction
temperature was varied from 900 to 1050 °C, and the reduction time was varied from 10 to 480 min. Each sampleof BSS, weighing 15 g, was placed in an alumina crucible
and then put in the tube furnace. Then, the samples were
heated to the desired reduction temperature at a heating
rate of 5 °C/min. Finally, the samples were cooled in the
furnace.
After reduction, phase analysis of the reduction products was performed by X-ray diffraction, using Cu Kα
radiation (40 kV, 30 mA) at step size increments of 0.1°.
The microstructures of the reduction samples were observed using a field emission scanning electron microscopy (FE-SEM, JSM 6330F, Jeol, Japan). The respective
chemical compositions of copper and zinc were analyzed
using energy-dispersive X-ray spectroscopy (EDS, INCA,
Oxford, U.K.).

3. Results and discussion
3.1 Characteristic of BSS
The chemical composition of the BSS sample used inthis study is presented in Table 1, as determined by ICPAES and AEA. ZnO and CuO were identified by X-ray diffraction analysis as major components in the BSS, whereas
Zn-spinel (ZnO·Al2O3) and Al2SiO4 were detected as aminor component, as shown in Fig. 1. Fig. 2a shows all themorphological features of the initial BSS sample. The latter
consisted mainly of well-distinguishable, submicron-sized
ZnO particles (Fig. 2b). The BSS samples were alsoleached with HNO3 + HCl (3M: 1M) to measure the otherimpurities. Bright particles indicate aluminum and siliconcompounds, whereas other particles indicate silicon dioxide (Figs. 2c, d).
The thermogravimetric curve of the reduction process,which was recorded during a linear program at a constantheating rate, is shown in Fig. 3. As shown in Fig. 3, theTGA curve of the initial sample reveals two major weightlosses: one has a monotonous weight decrease of 1.3 % in
the range of 50–760 °C, and the other is very rapid andoccurs at 760 °C with approximately 1.4 % weight loss.
The first weight loss is possibly related to the combustionof impurities, evaporation of water, and a chemical reaction between carbon in the BSS sample (2.2 mass% in

Table 1

Chemical composition of the initial brass secondary
slags (mass%)

Cu

Zn

Si

Al

Pb

Fe

Mn

C

Other

14.74

70.54 3.92

5.7

1.01

0.74 0.32

2.2

0.83

279

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

Table 1) and hydrogen gas. The other weight loss (1.4 %)is due to the reduction of zinc oxide in the BSS sample tozinc gas phase, by hydrogen gas (Gioia F. et al., 1977).

3.2 Reduction mechanism of BSS
Fig. 4 shows the XRD patterns of BSS samples prior toand after reduction at various temperatures (900–1050 °C)
for varying reaction times (10–480 min). Generally, XRD
patterns for reductions at the four temperatures are similar, with the exception of time-scale, indicating that thesame reduction mechanism prevails in the range of 900–
1050 °C. These results suggest a stepwise reduction

Fig. 1

Fig. 2280

X-ray diffraction pattern of initial BSS sample.

mechanism of BSS with hydrogen gas. Thus, based onthese XRD results, a two-step reaction was identified.
The main reactions at each step are as follows: Step 1 isthe ZnO reduction (Eqn. 1), and step 2 is the ZnO·Al2O3
reduction (Eqn.2).
ZnO(s) + H2(g) → Zn(g) + H2O(g)(1)

ZnO·Al2O3(s) + H2(g) → Zn(g)+Al2O3(s) + H2O(g) (2)The morphology changes in the BSS samples versus
the reduction times at 1050 °C are shown in Fig. 5. It isseen that the initial fine ZnO powder is aggregated at theFig. 3

TGA analysis of the BSS sample in mixed gas (5 %H2 + 95 % N2).

SEM morphologies of BSS samples: a) whole morphological features of the initial samples, b) thesubmicron-size ZnO, c) after ZnO leaching with HCl + HNO3, and d) EDS analysis results.

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

initial stage of the reaction, after which the [ZnO·Al2O3/
Cu] mixture forms a more dense structure as shown in
Figs. 5a, b, respectively. The amount of ZnO powder decreased with increasing reduction time because Zn evaporated after reduction by hydrogen gas according to Eqn. 1.
Also, it should be noted that Cu powder, having sphericalparticles of ~1 μm, appeared after removal of the ZnO at
120 min of the reaction time. Furthermore, there was no
significant change in the shape or particle size of the Cu
particles, even after 480 min.

Fig. 4

XRD patterns of BSS, prior to, and after, hydrogen reduction at 1000 °C for various times.

Fig. 5The distilled Zn was recovered as metallic Zn in thecold zone of the alumina tube, as shown in the inset of
Fig. 6a. In the cold zone, it was condensed and formed Zn
powder immediately. The latter had spherical particles
with a size range of approximately 1–6 μm. The XRD
pattern confirms that the recovered powder is composedof pure Zn, without any other components. This is due tomuch difference in evaporation temperature between Zn(907 °C) and Cu (2562 °C), SiO 2 (2950 °C), Al 2 O3(2977 °C) and CuO (2000 °C), respectively.
From the XRD and SEM data, the process of Cu reduction from BSS comprises several stages. The first step isthe conversion of brass to Cu with distilled Zn. According
to previous research, the brass is generated by the reaction of CuO and ZnO with hydrogen gas at above 600 °C(Kappenstein C. et al., 1996). The concentration of Zn inbrass decreased with increasing reduction temperatureand times. This was due to loss of Zn by distillation fromthe brass during the first step. Fig. 7 shows the respective
concentrations of Cu and Zn in brass as a function of
reduction temperature and reaction time. Here, Cu concentration increases with reduction time; moreover, therate of increase of Cu concentration is reduced with decreasing reduction temperature as shown in Fig. 7a.
The Cu concentration increase is attributed to not only
reduction of CuO but also evaporation of reduced Zn. The
contribution of Zn is presented in Fig. 7b, where Zn concentration linearly decreases in the early of the reaction
time at most of the reduction temperature. In the case of

The morphology change in BSS versus hydrogen reduction times at 1050 °C: a) 10, b) 60, c) 120, andd) 480 min.

281

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

Fig. 6a) Metal zinc powders recovered from ZnO in BSS at the cold zone of furnace, b) morphology of metal
Zn powder, and c) XRD pattern of metal Zn powder.

reactions performed at 1000 °C and 1050 °C, all Zn iseliminated by 480 min and 360 min, respectively. Lastly,a significant amount of Zn remained in the Cu phase at
900 °C for all reduction times.

3.3 The kinetics analysis of BSS
In order to understand the reduction mechanism for
BSS, a series of kinetic analyses were performed. For
this, the extent of BSS reduction was integrated with respect to time and plotted cumulatively as a reaction ratio(α). The reaction ratio was defined as 0 before the reaction
and 1 when it was completed. The rate of each reaction
was expressed as a function of Arrhenius parameters withreaction model in Eqn. 3:
dα/dt = k(T) f(α)(3)

where dα/dt is the reaction rate, α the reaction ratio, k(T)is the temperature-dependent rate constant, t is the time,
T is temperature, and f(α) is the differential form of thereaction model (Santosh K.U., 2006). We used the first-order reaction model f(α) = 1 – α for this reaction. Thismodel is applicable to a wide range of solid-state reactions: Reactant(s) → Product(s) + Gas (g) (James E.H.,
2007). For the purpose of this kinetic analysis, the residual mass at various reduction times was expressed as thereaction ratio (α), by means of Eqn. 4:
α = (M0 – Mt)/M0(4)

where Mt represents the mass of the sample at a reduction
time t and M0 is the initial mass of the sample. Therefore,a more general form of the first-order reaction equation
can be written as follows:–ln(1 – α) = k(T) t

282

(5)

Fig. 7

Concentration change of Cu and Zn in brass by Zn
vaporization according to reduction times and temperature: a) Cu, and b) Zn.

Thus, by plotting –ln(1 – α) versus t, the rate constant (k)can be determined from the slope and intercept of the fitted
straight line. Values for the rate constant (k) calculated for α,
in the range 0.2–1, are given in Table 2. The transformation step 2 is not as rapid as step 1, although the amountof ZnO·Al2O3 is relative small. Specifically, the rate constant of step 1 increases from 1.4 ± 0.13 × 10 –3 s–1 at 900 °C
to 2.18 ± 0.15 × 10 –2 s–1 at 1050 °C. Meanwhile, the rate
constant of step 2 increases from 1.54 ± 0.21 × 10 –5 s–1 at

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

Table 2

Summary of rate constants k of BSS sample
reduction at different temperature and step
STEP 1 (s –1)

STEP 2 (s –1)

900 °C1.4 ± 0.13 × 10 –3

1.54 ± 0.21 × 10 –5

950 °C

5.52 ± 0.72 × 10 –3

5.61 ± 0.68 × 10 –5

1000 °C

1.16 ± 0.24 × 10 –2

3.45 ± 0.52 × 10 –4

1050 °C

2.18 ± 0.15 × 10 –2

1.09 ± 0.19 × 10 –3

900 °C to 1.09 ± 0.19 × 10 –3 s–1 at 1050 °C. The temperature-­dependence of the rate constant is traditionally describedby the Arrhenius law:k(T) = A exp – (Ea /RT)(6)

In Eqn. 6, A is the pre-exponential factor, E is the activation energy, and R is the universal gas constant. Rewriting Eqn. 6 (Jelic D. et al. 2011),
ln k = ln k – (Ea/RT)(7)

The activation energies (Ea) calculated for steps 1 and 2
are 233.2 ± 26.1 and 376.7 ± 22.4 kJ/mol, respectively, as
shown in Fig. 8. The significant coefficients of determination (R2) of the activation energy indicated 0.963 and0.989 as confidence level, respectively. The reaction
mechanism, deduced from the kinetic data, is depicted
graphically as shown in Fig. 9. In step 1, the reductions of
CuO and ZnO begin at 500 °C and 600 °C, respectively.
ZnO starts to evaporate as soon as it is reduced to themetallic phase, due to its high vapor pressure, and thisevaporation is the main cause of the weight loss in this reaction system. In the temperature up to 600 °C, Cu formsa brass phase with Zn, whereas the brass phase decomposes into solid Cu and Zn vapor at 900 °C, which is step
2. It should be noted that ZnO in the ZnO·Al2O3 spinel
phase is also reduced in this step; hence, Cu, SiO2, andAl2O3 are the final phase formed in this BSS reduction
system. It is supposed that the ceramic impurities are removed by the succeeding smelting process.

4. Conclusion
The reduction kinetics of BSS in a hydrogen atmosphere has been studied at 900 °C to 1050 °C, and for the
reduction times between 10 min and 480 min. The characterization of the samples was done by means of SEM
and XRD analysis. The results indicated that the reduction of BSS samples was found to occur in a two-stepprocess, with recovery of Zn powder (spherical particles,
size: approximately 1~6 μm). The first step involved a
rapid transformation of ZnO to Zn (g), whereas the sec-

Fig. 8Arrhenius plot for the dominant reaction in the range of
900–1050 °C with hydrogen gas: a) step 1 (ZnO reduction); and b) step 2 (ZnO·Al2O3 reduction).

ond step involved the slow conversion from ZnO·Al2O3 toZn (g).
A first-order function and Arrhenius analysis were utilized to calculate the rate constants and activation energies for the BSS reduction. In particular, the rate constantfor the transformation from ZnO to Zn (g) increased from
1.4 ± 0.13 × 10 –3 s–1 at 900 °C to 2.18 ± 0.15 × 10 –2 s–1 at1050 °C with an activation energy of 233.2 ± 26.1 kJ/mol
in step 1. In step 2, the corresponding rate constant for thetransformation from ZnO·Al2O3 to Zn (g) increased from1.54 ± 0.21 × 10 –5 s–1 at 900 °C to 1.09 ± 0.19 × 10 –3 s–1 at1050 °C with an activation energy of 376.7 ± 22.4 kJ/mol.
It is believed that further understanding of the reaction
mechanism observed in this study can contribute to thefurther optimization of process conditions in fluidized
bed reactors.

Acknowledgements
This research was supported by a grant from the Fundamental R & D Program for Energy (No. 2012T100100092,(“Development of pyrometallurgical technology for low
quality urban mining by-products”) funded by the Ministryof Knowledge Economy, Republic of Korea.

283

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

Fig. 9

Schematic diagram of BSS samples reduction mechanism between room temperature and 1050 °C withhydrogen gas.

References
Altundogan H.S., Tumen F., Metal recovery from copper converter slag by roasting with ferric sulphate, Hydrometallurgy, 44 (1997) 261–267.
Anand S., Kanta R.P., Jena P.K., Recovery of metal values fromcopper converter and smelter slags by ferric chloride leaching, Hydrometallurgy, 5 (1980) 355–365.
Anand S., Sarveswara R.K., Jena P.K., Pressure leaching ofcopper converter slags using dilute sulphuric acid for the
extraction of cobalt, nickel and copper values, Hydrometallurgy, 67 (1983) 1–7.
Bakker M.L., Nikolic S., Mackey P.J., ISASMELTTM-Application
for nickel, Minerals Engineering, 24 (2011) 610–619.
Gioia F., Mura G., Viola A., Experimental study of the direct
reduction of sinterized zinc oxide by hydrogen, Chemical
Engineering Science, 33 (1977) 1401–1409.
Gorai B., Jana R.K. Premchand, Characteristics and utilisationof copper slag—a review, Resources conservation andrecycling, 39 (2003) 299–313.
Igor I. Katkov, Challenge from the simple: Some caveats in linearization of the Boyle-van’t Hoff and Arrhenius plots,
Cryobiology, 57 (2008) 142–149.
James E.H., Principles of chemical kinetics 2nd eds., Academic
Press, New York, 2007.
Jelic D., Tomic-Tucakovic B., Mentus S., A kinetic study of284

copper oxide powder reduction with hydrogen, based onthermogravimetry, Thermochimica Acta, 521 (2011) 211–
217.
Jha M.K., Kumar V., Singh R.J., Review of hydro-metallurgicalrecovery of zinc from industrial wastes, Resources conservation and recycling, 33 (2001) 1–22.
Kappenstein C., Cernak H., Brahmi R., Duprez D., Chomic J.,
Copper-zinc oxide catalysts. Part IV. Thermal treatment in
air, argon and hydrogen and XRD study of new bimetallic
precursors-direct formation of alloys, Thermochimica
Acta, 279 (1996) 65–76.
Moskalyk P.R., Alfantazi A.M., Review of copper pyrometallurgical practice: today and tomorrow, Minerals Engineering,
16 (2003) 893–919.
Rudnik E., Burzynska L., Gumoska W., Hydrometallurgicalrecovery of copper and cobalt from reduction-roasted copper converter slag, Minerals Engineering, 22 (2009) 88–95.
Santosh K.U., Chemical kinetics and reaction dynamics,
Springer, New Delhi, 2006.
Sharma K.D. and Row B.R.K., An electrolytic process forrecovery of zinc dust from melting furnace slag, Hydrometallurgy, 13 (1985) 377–383.
Sukla L.B., Panda S.C., Jena P.K., Recovery of cobalt, nickel
and copper from converter slag through roasting withammonium sulphate and sulphuric acid, Hydrometallurgy,
16 (1986) 153–165.

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

Author’s short biography
Tae-Hyuk Lee
Tae-Hyuk Lee is a doctor candidate for Materials Engineering, Chungnam National
University, South Korea. He received his M.D. from Chungnam National University,
South Korea in 2012. He is now pursuing his Ph.D. in Chungnam National University
focusing on stress corrosion cracking of Inconel 690 for steam generator tubes. His research interests include stress corrosion cracking, recycling of metals, and metal matrix
composites.

Sin-Hyeong Joo
Sin-Hyeong Lee is a master’s course for Materials Engineering, Chungnam National
University, South Korea. He received his B.S. from Chungnam National University,
South Korea. His research interests include powder metallurgy materials and processing,in particular non-ferrous materials, such as boron, boron composite, silver and silicon.

Hayk H Nersisyan
Hayk Nersisyan is research professor of Materials Science & Engineering at Chungnam National University in South Korea and Rapidly Solidified Materials ResearchCenter. He received his B.S., M.S. and Ph D degrees from Yerevan State University inArmenia. His major research field is powder metallurgy materials and processing, inparticular non-ferrous materials and their composites such as magnesium, titanium, aluminium and copper.

Man-Sik Kong
Man-Sik Kong is senior researcher of Plant Engineering Center, Institute for Advanced
Engineering, South Korea. He received his B.S., and M.S degree from Gyeongsang
National University and Ph D. degree from Ajou University in South Korea. Dr. Kong’s
research interests include powder metallurgy materials, processing and recycling of rare
metals.

285

Tae-Hyuk Lee et al. / KONA Powder and Particle Journal No. 33 (2016) 278–286

Author’s short biography
Jae-Woo Lee
Jae-Woo Lee is director of SEWON CO., LTD, South Korea. He received his B.S.
degree from Hanyang University in South Korea. His research interests include recycling
process of copper and copper alloy, recovery of metals from urban mine.

Ki-Won Park
Ki-Won Park is head of research center of SEWON CO., LTD, South Korea. He received
his B.S. degree from Inha University in South Korea. His research interests include recycling process of copper and copper alloy, recovery of metals from urban mine.

Jong-Hyeon Lee
Jong-Hyeon Lee is Professor in Department of Materials Science & Engineering at
Chungnam National University in South Korea and head of Rapidly Solidified Materials
Research Center. He received his B.S., M.S, and Ph D degrees from Chungnam National
University. His major research field is electro refining, winning, and coating of rare
metals. He also studies on powder synthesis by self-propagating high temperature synthesis. He published more than 100 articles and 30 patents.

286

KONA Powder and Particle Journal No. 33 (2016) 287–295/Doi:10.14356/kona.2016011

Original Research Paper

Mechanical Properties of Agglomerates Producedby the Mechanical Vibration of Cohesive Powders †Hamid Salehi Kahrizsangi, Diego Barletta and Massimo Poletto *1

Department of Industrial Engineering, University of Salerno, Italy

Abstract
The process of granule formation from aggregative cohesive powders under the action of mechanical vibration is
studied. Vibration frequency and acceleration levels were set independently in the experiments. The process of
agglomerate formation is examined by measuring the size distributions of the agglomerates and their resistanceunder uniaxial compression. The results indicate the formation of wide particle size distributions and hard andcompact agglomerates. The experimental results and the interpretation of data suggest that, in order to produce
agglomerates by mechanical vibration, powders should have flow functions with a flow factor value smaller than
3. In agreement with the theoretical framework proposed, agglomerate consolidation pressures and deformation atbreakage seem to be almost independent of the agglomerate diameter and the vibration conditions.
Keywords: cohesive powders, mechanical vibration agglomerates, compression test

1. Introduction
Powder granulation is used in many different industrial
applications such as the production of pesticides, fertilizers and ceramics. The general purpose is to improve thehandling and the storage behavior of these solid materials.
On the one hand, with fine and cohesive powders, mechanical vibrations can be used to produce stable agglomerates (Kua N. et al., 2014; Mawatari Y. et al., 2007). On
the other hand, the self-agglomeration of cohesive powders may happen in processes involving mechanical vibrations, leading to undesired problems. Among these, we
mention solids segregation in processes involving powder
flow and also gas-solids segregation in industrial applications which require an intimate gas-solids contact. The
aggregative behavior of fine cohesive powders under theaction of vibrations was described by Barletta D. andPoletto M. (2012) following the analysis introduced previously (Barletta D. et al., 2007). Nano-powders also produce self-aggregation phenomena in vibrated beds (Van
Ommen J.R. et al., 2012). Mechanical vibrations have also
been applied together with powder drying processes tocontrol and promote powder agglomeration (Cruz M.A.A.
et al., 2005). The objective of this paper, however, will be
the agglomeration of cohesive powders due to compaction†
1*

Received 16 May 2015; Accepted 6 July 2015
J-STAGE Advance published online 21 August 2015
Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
Corresponding author: Massimo Poletto;
E-mail: mpoletto@unisa.it
TEL: +39-089-96-4132 FAX: +39-089-96-8781©2016 Hosokawa Powder Technology Foundationin vibrated beds without the use of a fluidizing gas or atleast under application of a minimum gas rate with the
purpose of system conditioning, but not the ability to sustain the bed.
One of the most significant characteristics of a granulation process is the granules’ mechanical resistance. In
fact, the produced granules should be able to resist the external forces during storage and transportation in order toavoid dust formation and changes to the granules’ size
distribution. A recent study on the resistance of agglomerates under the action of compression and their mechanical behavior was given by Antonyuk S. et al. (2005). They
provided a significant insight into the rupture mechanism
and into the mechanical response of agglomerates forelastic, elastic-plastic and plastic agglomerate deformation regimes. The resistance of agglomerates in dynamic
conditions was studied by Antonyuk S. et al. (2006).
In this paper, the process of granule formation from aggregative cohesive powders under the action of mechanicalvibration is studied. The equipment used allows the vibration
frequency and acceleration levels to be set independently.
The process of agglomerate formation is examined bymeasuring the agglomerate’s size distributions and also by
the agglomerate’s resistance under uniaxial compression.

2. Experimental
2.1 Apparatus
The apparatus used is described by Barletta D. et al.

287

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295(2013). A sketch of the apparatus, a vibrated fluidization
column, is shown in Fig. 1. The fluidization column wasmade of Perspex with an 85-mm ID and a height of
400 mm (1). At the bottom, the air was distributed by a
10-mm thick porous plate of sintered brass particles. The
porous plate was clamped in the flange connecting thewind box and the fluidization column. In the column
flange, a pressure port was connected to a U-tube manometer (6) filled with water. Desiccated air from the laboratory line was fed to the wind box by a thermal mass
flow controller (Tylan FC2900V) with a maximum flowrate of 5 10 –5 Nm3 s–1 (4). The column was fixed to the vibrating plane of the actuator by means of a rigid steel andaluminum structure (3). The actuator (2) was an electric
inductance vibrator (V100 Gearing and Watson, USA)
that was able to produce a sinusoidal vertical movementin the range between 2 and 6500 Hz with displacement
amplitudes of up to 12.7 mm, exerting a maximum forceof 26.7 kN. The vibrator amplifier was connected to a vibration controller Sc-121 (Labworks inc., USA) (7). The
controller measured the effective vibrations by means of apiezoelectric accelerometer (8636B60M05 Kistler, USA)(5) fixed on the metal structure supporting the fluidization
column. The system was able to control vibrations by fixing, within the operating range, any of the two possible
pairs of vibration parameters, i.e. acceleration, amplitudeFig. 1

Schematic of a mechanically vibrated bed. Legend: 1.
fluidization column; 2. electro-dynamic vibrator; 3.
metallic frame; 4. mass flow controller and air feed
unit; 5. accelerometer; 6. water manometer; 7. vibration
control block.
Table 1288

or frequency. In fact, the maximum acceleration, a, is related to the oscillation amplitude, A, and the oscillation
frequency, f, or the pulsation value, ω by means of the following relationship:a A
 2 A 2πf (1)In our experiments, we chose to define the vibration acceleration and the frequency.

2.2 Materials
Two different powders were used in the experiments, acalcium carbonate (CaCO3) powder and a titanium dioxide (TiO2) powder. The properties of these two powders
are reported in Table 1. The rheological properties were
measured with a Schulze ring shear tester (Schulze D.,1994). The flow functions (unconfined yield strength as afunction of the major principal stress) at high consolidation values, namely between 10 kPa and 170 kPa, were
measured with a uniaxial testing procedure carried out on
an Instron tester (Series 5860) equipped with a 1-kN loadcell. The mold was a steel cylinder made of two detachable halves as described by Parrella L. et al. (2008). It allowed the production of cylindrical samples of compacted
powder of 50 mm in diameter and about the same height.
In order to compensate for wall friction in the consolidation phase, a Janssen (Janssen H.A., 1895) correction wasused to determine the vertical consolidation stress during
the uniaxial compression tests

Fig. 2 Flow functions of calcium carbonate (○) and of titanium
dioxide (□) measured with a uniaxial testing procedure.

Material properties

Materialdpμm

ρp
kg m –3

σ1
Pa

fc
Pa

ρb
kg m–3

ϕ
deg

ϕe
deg

ϕw
deg

Sc*/daEqn. (15)

CaCO3

4.1

2540

471

410

350

35

50

30

0.039

TiO2

0.2

4500

345

300

785

40

50

39

0.019

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295

4 Kz
  0 exp   w 
D 


(2)

where D is the mold diameter, z is the final sample heightafter consolidation, σ0 is the applied stress on the sample
surface, μw = tan ϕw is the mold wall friction, ϕw is thewall friction angle, K is the vertical to horizontal stressratio that, for lack of a better evaluation (Barletta D. andPoletto M., 2013), it is calculated as suggested by Kwade
A. et al. (1994):(3)


K 1.2 1  sin e 

where ϕe is the effective angle of internal friction. The
wall friction tests of the powders were measured by a
Brookfield Powder Flow tester by using a stainless steel
coupon.

2.3 Procedure
Beds of cohesive calcium carbonate and titanium dioxide powders were vibrated vertically at frequencies of
25 or 50 Hz and at acceleration levels, a/g, of 6 or 9. A
desiccated air flow was used to keep the powder humidity
constant during the experiments. The experimental conditions for both materials are reported in Table 2. For
calcium carbonate, if we assume test C3 as a reference
case, then the other test conditions change for one or maximum two variables. Namely, in Test C1, half of the process time is considered; in Test C2, the frequency isdoubled and the processing time is tripled; in test C4, theacceleration is reduced by 1/3. Similarly, for titanium dioxide, we can assume test T2 as a reference case, then in

Test T1, half of the bed mass is considered; in Test T3, theprocessing time is tripled; in tests T4 to T6, the frequency
is doubled; double process time is used in T5 and 4 times
the process time in T6.
The resulting granules were classified by coarse sieving
using a stack of three sieves of 400 μm, 1 mm and 2 mm
aperture size, respectively. For some of the test conditions, the portion of granules bigger than 2 mm was collected to measure the granules’ mechanical resistance.
For this purpose, a uniaxial compression test was adopted
by using an Instron machine (Series 5860) equipped with
a 1-N load cell.

3. Results
3.1 Agglomerates’ size distribution
Sieve analysis was applied to the processed powders
including agglomerates. Significant losses of fines wererecorded due to adhesion to the column wall and to elutriation of dust. Fig. 3a shows the 2-mm oversize fraction
for calcium carbonate. Besides the weight distribution in
the different classes identified by the sieving process inTable 3, Fig. 4 also reports an estimate of the median-­mass-weighted agglomerate size and the error bars are
the diameter range covering the 80 %-by-weight of the
distribution of agglomerate size. This latter size range
was estimated by fitting the weight distribution by meansof a power law. By comparing results, it is possible to observe a limited effect of the operating parameters. In general, the median agglomerate size is between 0.5 and1.6 mm and the largest agglomerates are above 3–4 mm.

Table 2

Operating conditions in the vibration experiments

Test
code

Material

Frequency AccelerationHz
a/g

Displacement Air ratemm
NL h-1

Bed massg

Test time
Description
min

C1

CaCO3

25

9

6.8

1200

100

10

half time

C2

CaCO3

50

9

1.5

1200

100

30

double freq.,
long time

C3

CaCO3

25

9

6.8

1200

100

20

reference

C4

CaCO3

25

6

4.6

1200

100

20

reduced accel.

T1

TiO2

25

9

6.8

1200

100

5

half mass

T2

TiO2

25

9

6.8

1200

200

5

reference

T3

TiO2

25

9

6.8

1200

200

10

double time

T4

TiO2

50

9

1.5

1200

200

5

double freq.

T5

TiO2

50

9

1.5

1200

200

10

double freq.,
double time

T6

TiO2

50

9

1.5

1200

200

20

double freq.,
long time289

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295

Fig. 3

Examples of agglomerates formed: a) 2-mm oversize
fraction for CaCO3; b) CaCO3; c) TiO2.

Table 3Fig. 4 Agglomerate size distributions for different test conditions obtained from a fitting regression of a power law
distribution on the experimental data of Table 3. Dotsare the median values and bars cover the 80 % range ofdistributions between the 10th and 90th percentiles.

Experimental agglomerate size distributions

Test code

sampled weight fractions in da ranges, %< 0.4 mm 0.4–1 mm

1–2 mm> 2 mm

C1

11.2

22.6

27.6

38.6

C2

43.4

24.4

9.8

22.4

C3

13.6

27.4

21.1

37.9

T1

35.8

19.4

10.5

34.3

T2

26.7

24.8

19.0

29.6

T3

21.2

25.1

18.5

35.2

T4

31.7

23.5

15.3

29.4

T5

29.4

37.5

25.5

35.6

T6

26.5

22.3

22.2

29.0

deformation. The force vs. displacement curve is always
well represented by a line segment indicating, as it will be
discussed later, a plastic deformation regime of the agglomerate.
The agglomerate rupture is well identified by the peakforce. Within the agglomerate deformation regime, thecontact deformation, sc, will be assumed to be half of theagglomerate deformation, sa, that is the distance travelledby the compression piston starting from the first contact
position. Given the contact deformation at breakage, scb,
and the agglomerate deformation at breakage, sab, it is
scb = 1/2sab, and assuming a spherical agglomerate shape,
it is possible to estimate the contact area Acb at breakage
Acb = πda scb(4)

Increasing the frequency from 25 to 50 Hz has negligible
effects on the size of the titanium dioxide agglomerates,
while it seems to produce smaller agglomerates of calcium carbonate. Increasing the vibration times results inlarger titanium dioxide agglomerates.

Consequently, the average pressure at the contact at
breakage can be easily calculated from the force measured at breakage, F b:

3.2 Mechanical resistance of agglomerates

The force Fb, the relative deformation of the contact,
scb/da, the contact area, Ac, and the pressure pfb measured
at breakage in the agglomerate breakage tests are reported
as a function of the agglomerate diameter in Fig. 7 andFig. 8 for both calcium carbonate and titanium dioxide,
respectively. Data are separated for each of the examined
conditions and, correspondingly, a best-fit linear approximation is drawn to highlight possible trends. These figures indicate a clear dependence of both the breakage
force (for calcium carbonate only) and also of the contact

Fig. 5a shows a schematic of the uniaxial compression
test carried out on the largest agglomerates with the main
geometrical variables of the deformed agglomerate during
the test as defined in the following text. An example ofthe trace of the mechanical response of the agglomerates
is reported in Fig. 6. The size of the agglomerates, da canbe determined from the first contact position at which theforces ramp, corresponding to the start of agglomerate

290

pb 

Fb
πd a scb(5)

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295

Fig. 5

Uniaxial compression test for agglomerates and contact deformation parameters: a) plastic deformation;b) breakage conditions.

area at breakage on the agglomerate diameter. If any, onlya slight dependence on the agglomerate diameter is evident for the relative deformation and the contact pressure
at breakage. Furthermore, it is not possible to identify aclear trend of the vibrations and other aggregation processparameters on the observed results. Some differences canbe observed comparing two different materials, in fact,with respect to calcium carbonate, the population of agglomerate tested for titanium dioxide is a bit smaller and,in any case, tends to show a slightly higher breakage force(averages around 0.2 N for CaCO3 and about 0.3 N for
TiO2), a slightly smaller relative deformation at breakage(averages around 0.025 for CaCO3 and less than 0.020 for
TiO2), correspondingly a slightly lower contact area at
breakage (averages around 0.4 mm 2 for CaCO3 and0.2 mm2 for TiO2) and significantly higher pressures atbreakage (averages less than 40 kPa for CaCO3 andaround 100 kPa for TiO2).
According to Antonyuk S. et al. (2006), a spherical agglomerate compressed against a flat surface will deform
and a force will set in between the agglomerate and thesurface. This force expression will depend upon the regime of deformation which might be elastic, elastic-plastic
or completely plastic. In the presence of an elastic component, the agglomerate deformation will follow a Hertzianlaw of deformation that reveals an upward concavity in theforce-displacement curve. Instead, a purely plastic deformation can be hypothesized in the case of a perfectly linear plastic relationship between the contact deformationand the force. In fact, this is the case in almost all our
tests, as is shown in the force vs. displacement curve ofFig. 6. The simple Eqn. 6 is used in this case:Fpl  πpf d a sc

(6)

where da is the agglomerate diameter, sc is the contact deformation and pf is the material strength at deformationon the contact point. Eqn. 6 is very simple since it is theFig. 6Force vs displacement curve for agglomerate breakage
experiments.

combination of the area of the flattened surface contact
point:
Ac = πda sc(7)

and of the pressure exerted by the material pf.
According to the theoretical works of Hencky H. (1923)
and Ishlinsky A.J. (1944) as reported by Molerus O.
(1975), pf can be approximated to three times the material
unconfined yield strength. In the present case of agglomerates we have:pf  3 f c(8)

We assumed that the agglomerate grows by hittingsome powder laying on other agglomerates or on the vibrating column surface. Accordingly, the material willconsolidate on the agglomerate with subsequent hits inwhich the consolidation stress σ1 is equal or smaller thanthe material strength pf. The resulting consequence of thisassumption and Eqn. 8 is that the flow factor at which the291

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295

292

Fig. 7Force (a), relative deformation of the contact (b), contact area (c) and pressure (d) measured at breakage
test for the aggregates as a function of the aggregate diameter for Calcium Carbonate. Test conditions fordata points: ●, C1; ■, C2; ▲, C3; ◆ , C4. Test conditions for regression lines: ——, C1; —   —, C2; – · –,C3; – ·· –, C4.

Fig. 8Force (a), relative deformation of the contact (b), contact area (c) and pressure (d) measured at breakage
test for the aggregates as a function of the aggregate diameter for Titanium dioxide. Test conditions fordata points: ●, T1; ■, T2; ▲, T3; ◆ , T4; ○, T5; □, T6. Test conditions for regression lines: ——, T1;—   —, T2; – · –, T3; – ·· –, T4; ······, T5; –  –  –, T6.

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295

material consolidates is:
FF


1


fc

1
pf

33

fc 
(9)

2C cos 
1  sin 

(12)

and
The flow factor line FF = 3 is reported in Fig. 2. Accord2C cost 
ing to the reasoning above, flow functions with flow fac(13)1  sin 
tors values close to 3, suggest an equilibrium in the
agglomerate growth process. Materials with flow funcCombining Eqns. 7 and 9 to 12, it istions showing flow factors values smaller than 3 should
6C cos 
not be able to produce agglomerated with vibrations. In2K d a sc d a
1  sin 
stead, the agglomeration process by vibration should belargely favored in materials with flow functions charac2C cos  πd a2 2d a d a sc 
terized by flow factors values larger than 3. This
is the(14)1  sin   4
case of the tested powders, for which it is verified thatboth materials have a flow factor smaller than 3. In genThat iseral, it should be said that according to the mechanism2
sc *  π  1  sin  hypothesized to derive Eqn. 9, the agglomerate strength


K
3
1
(15)


 1  sin  
d a  8  should be largely independent of the vibration parameters
and of the agglomerate diameter. This seems to be conIt appears that the agglomerate deformation at breakage
firmed by findings in Fig. 7d. Fig. 8d, instead, may sugexpressed by Eqn. 14 should be a function only of the material properties. The values predicted for the deformation
gest some dependence of the material strength on theat breakage for the two materials are reported in Table 2.
agglomerate diameter which, however, should be conIt can be verified that also the theory predicts a result thatfirmed by a larger number of samples.
is independent of the vibration parameters and of the agThe agglomerate breakage phenomenon has beenglomerate diameter as roughly suggested by Figs. 7b andwidely discussed by Antonyuk S. et al. (2005). In particu8b. Furthermore, in agreement with the experiments, thelar, it has been highlighted that the agglomerate undertheory predicts a smaller deformation at breakage for titaplastic deformation breaks because the plasticized nucleus
nium dioxide than for calcium carbonate.
radially expands to the yield of the equatorial band of theparticles. Fig. 3b shows the mechanism. A rough evaluation of the breakage conditions can be carried out as follows. According to Fig. 5b, we assume an inner cylinder
4. Conclusionof material completely plasticized and characterized by adiameter of 2(da sc)0.5 and a height of da-2sc≈da. The mateThe process of agglomerate formation by means of merial in this cylinder pushes radially outwards with a preschanical vibrations was studied by means of agglomerate
sure Kpf, where K is the material vertical to the horizontal
size distributions and uniaxial compression tests on agstress ratio. Eqn. 2 is valid within the Mohr-Coulomb asglomerate deformation and resistance. Results indicate
sumption. The outward pressure of the inner material is
the formation of wide agglomerates’ size distributionsbalanced by the strength of the material in the unplastiand hard and compact agglomerates.
cized external toroid. A force balance allows calculation
Considering the experimental results and the interpreof the collapse condition of this band that occurs when thetation of data, it is suggested that in order to produce agtangential stress equals the material’s unconfined yield
glomerates with this mechanism, powders should havestrength:
flow functions close or smaller than a flow factor value of
3. The same analysis suggests that agglomerates’ consolidation pressures and deformation at breakage is almost(10)
pf K 2 d a sc d a   t 2 At
independent of the agglomerate diameter and of the vibrawhere At is the cross-section of the unplasticized toroid.
tion conditions.
The fact that, in general, it is sc<<da, allows second-orderterms to be neglected and therefore:
πd a2
(11)
 2d a d a sc
4
Furthermore, within the Mohr-Coulomb hypotheses we
have:

2 At

Nomenclatureaacceleration due to vibration (m s –2)

A

vibration amplitude (m)Accontact area (mm 2)

293

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295

Acb contact area at breakage (mm 2)At

cross-section of the unplasticized toroid (mm 2)

D

mold diameter (mm)daagglomerate diameter (mm)dp

particle diameter (μm)

foscillation frequency (s –1)

Fb

force measured at breakage (N)Fpl

force measured in the agglomerate plastic deformation
range (N)fc

unconfined yield strength (Pa)FF

flow factor (–)

g

acceleration due to gravity (m s –2)

IDinternal diameter (mm)K

vertical to horizontal stress ratio (–)

pb

average pressure at the contact at agglomerate breakage
(kPa)

pf

the material strength at the agglomerate contact point
(kPa)

pf b

pressure measured at breakage at the agglomerate contact
point (kPa)saagglomerate deformation (mm)sabagglomerate deformation at breakage (mm)sccontact deformation (mm)scb

contact deformation at breakage (mm)Z

final sample height after consolidation (mm)ρbaverage bed density (kg m –3)

ρpparticle density (kg m –3)

σnormal stress (Pa)σ0applied normal stress on the sample surface in the uniaxial
tester (Pa)σ1major principal stress at consolidation (Pa)σttensile strength, Pa

ϕangle of internal friction (Deg)ϕeeffective angle of internal friction (Deg)ϕwangle of wall friction (Deg)μwmold wall friction (–)ω

vibration pulsation (s –1)

References
Antonyuk S., Tomas J., Heinrich S., Mörl L., Breakage
behaviour of spherical granulates by compression, Chemical Engineering Science, 60 (2005) 4031–4044, DOI:
10.1016/j.ces.2005.02.038.

294

Antonyuk S., Khanal M., Tomas J., Heinrich S., Mörl L., Impact
breakage of spherical granules: experimental study and
DEM simulation, Chemical Engineering and Processing:Process Intensification, 45 (2006) 838–856, DOI: 10.1016/
j.cep.2005.12.005.
Barletta D., Donsì G., Ferrari G., Poletto M., Russo P., Solidflow rate prediction in silo discharge of aerated cohesive
powders, AIChE Journal, 53 (2007) 2240–2253,
DOI:10.1002/aic.11212.
Barletta D., Poletto M., Aggregation phenomena in fluidizationof cohesive powders assisted by mechanical vibrations,
Powder Technology, 225 (2012) 93–100, DOI: 10.1016/
j.powtec.2012.03.038.
Barletta D., Poletto M., A device for the measurement of thehorizontal to vertical stress ratio in powders, Granular
Matter, 15 (2013) 487–497, DOI: 10.1007/s10035-013-04277.
Barletta D., Russo P., Poletto M. Dynamic response of a vibrated
fluidized bed of fine and cohesive powders, Powder Technology, 237 (2013) 276–285, DOI: /10.1016/j.powtec.2012.12.004
Cruz M.A.A., Passos M.L., Ferreira W.R., Final drying of whole
milk powder in vibrated-fluidized beds, Drying technology,
23 (2005) 2021–2037, DOI: 10.1080/07373930500210473.
Janssen H.A., Versuche über Getreidedruck in Silozellen,Zeitschrift des Vereins Deutscher Ingenieure, 39 (1895)
1045–1049.
Kua N., Hare C., Ghadiri M., Murtagh M., Oram P., Haber R.,
Auto-granulation of fine cohesive powder by mechanicalvibration, Procedia Engineering, 102 (2015) 72–80, DOI:
10.1016/j.proeng.2015.01.108.
Kwade A., Schulze D., Shwedes J. Determination of the StressRatio in Uniaxial Compression Tests—Part2, Powder Handling and Processing, 6 (1994) 199–203.
Hencky H., Über Einige Statisch Bestimmte Fälle Des Gleichgewichts In Plastischen Körpern, Zeitschrift für Angewandte Mathematik und Mechanik, 3 (1923) 241–251, DOI:
10.1002/zamm.19230030401.
Ishlinsky A.J., The axi-symmetrical problem in plasticity andBrinell test, Journal of Applied Mathematics and Mechanics (USSR), 8 (1944) 201–224.
Mawatari Y., Tatemoto Y., Noda K., Yamamura M., Kageyy H.,
Vibro-fluidization characteristics for size arranged agglomerates, Proc. of The 12th Int. Conf. on Fluidization—New
Horizons in Fluidization Engineering Vancouver, Canada,
May 13–18, (2007) 424–432.
Molerus O., Theory of yield of cohesive powders, Powder Technology, 12 (1975) 259–275, DOI: 10.1016/0032- 5910(75)85025-X.
Schulze D., Development and application of a novel ring shear
tester, Aufbereitungstechnik, 35 (1994) 524–535.
Parrella L., Barletta D., Boerefijn R., Poletto M., Comparison ofuniaxial compaction tester and shear tester for characterization of powder flowability, Kona Journal, 26 (2008) 178–
189, DOI: 10.14356/kona.2008016.
Van Ommen J.R., Valverde J.M., Pfeffer R., Fluidization of
nanopowders: a review, Journal of Nanoparticle Research,
14 (2012) 1–29, DOI: 10.1007/s11051-012-0737-4.

Hamid Salehi Kahrizsangi et al. / KONA Powder and Particle Journal No. 33 (2016) 287–295

Author’s short biography
Hamid Salehi Kahrizsangi
Hamid Salehi Kahrizsangi is a PhD student at the Department of Industrial Engineering, University of Salerno, Italy. He graduated from Lund University, Lund, Sweden, asa food engineer specialized in powder technology in 2012.

Diego Barletta
Diego Barletta is assistant professor of chemical engineering at the Faculty of Engineering of the University of Salerno. His research interests include powder flow fromstorage units, rheology of powders and biomass particulate solids, fluidization of cohesive powders assisted by vibrations and process systems engineering for renewableenergy. He is the author of more 30 papers on refereed international journals and books
on chemical engineering and of more than 50 papers for international conferences. He
reviews papers for international journals and conferences on chemical engineering andpowder technology.

Massimo Poletto
Massimo Poletto is professor of chemical engineering since December 2012 at the
Department of Industrial Engineering. Since January 2011, he has served as the chairman of the degree programme board of chemical and food engineering. In the period
2008–2013, he served as the chairman of a working party of the European Federation ofChemical Engineering (EFCE) on “Mechanics of Particulate Solids” that he joined in
2004 as a delegate from the Italian Association of Chemical Engineering (AIDIC). He isa member of the editorial board of Chemical Engineering Research and Design, the official journal of IChemE and of EFCE. (ChERD, www.icheme.org/journals).

295

KONA Powder and Particle Journal No. 33 (2016) 296–303/Doi:10.14356/kona.2016013

Original Research Paper

Effects of Mixing Ratio of Binary Fine Particles on the
Packing Density and Filtration Characteristics †Kuo-Jen Hwang * and Iou-Liang Lin
1

Department of Chemical and Materials Engineering, Tamkang University, Taiwan

Abstract
Binary fine particles were dispersed in glycerol aqueous solutions with different mixing ratios to study the effectsof particle size distribution and fluid viscosity on the cake properties in dead-end filtration, such as average
porosity, average specific filtration resistance and compressibility of cake. The average specific cake filtration
resistance increases with increasing the fraction of small particles. However, the lowest cake porosity occurs
under the volume fraction of large particles of 0.75. Comparing different methods for porosity estimations, model
estimation is more accurate for those particles near pure composition, while simulation method is more suitable
for moderate composition of particle mixtures. Furthermore, the cake porosity increases but the average specificcake filtration resistance decreases with increasing fluid viscosity. An 18 % porosity increase and a 30 % filtration
resistance decrease are obtained when fluid viscosity increases from 1 to 10 × 10 –3 Pa·s. The cake properties, suchas the particle packing structure in the cake and the resulting filtration resistance, are affected not only by theparticle size distribution but also by the fluid viscosity. The particle size distribution plays a much more importantrole on the cake compressibility than the fluid viscosity does.
Keywords: particle size distribution, microfiltration, cake properties, fluid viscosity, packing porosity, filtration.

1. Introduction
Microfiltration has been widely used for separating fineparticles from liquids in various industrial processes. In
such a filtration, how to enhance filtration rate and separation efficiency are the most important issues on processengineers concern. In general, the cake properties, suchas porosity and specific filtration resistance, are the mostimportant parameters affecting filtration performance.
The cake properties are determined mainly by many factors, for instance, the particle size distribution, fluid rheology and operating conditions. Therefore, understanding
the effects of particle size distribution and fluid viscosity
on the cake properties is an essential step in grasping afiltration problem.
Tiller F.M. et al. (1972) proposed power-type empiricalequations to correlate cake porosity or specific filtration
resistance with solid compressive pressure. The cakecompressibility could be indicated by the exponent of
those empirical equations. Lu W.M. and Hwang K.J.
†
1*

Received 7 June 2015; Accepted 4 August 2015
J-STAGE Advance published online 12 September 2015
No.151, Yingzhuan Rd., Tamsui Dist., New Taipei City 25137,
TaiwanCorresponding author: Kuo-Jen Hwang;
E-mail: kjhwang@mail.tku.edu.tw
TEL: +886-2-2621-5656 ext.2726 FAX: +886-2-2620-9887

296(1993) employed a force balance model to discuss the critical friction angle for particle deposition on the cake surface in constant pressure filtration. The packing structure
in cake could then be simulated using the friction angle
under various conditions. When the critical friction angle
was larger, the particles were more stable on the cake surface, the packing structure would be looser, and the cakeporosity increased but the specific filtration resistance decreased. To discuss how the properties of cakes formed bydual-sized submicron particles affect the performance ofcross-flow microfiltration, Hwang K.J. and Lin K.P.
(2002) used packing theories of cavern and displacement
effects to estimate the cake porosities with different particle compositions. Although the fluid flow effect in thetangential direction was ignored, the theoretical results
agreed with the experimental data in the region near uniform sized particles.
The basic filtration equation was frequently used for
estimating filtration flux based on the well-known Darcy’slaw, that is,


q

P


 R c  Rm P

 wc   av  Rm 

(1)where q is the filtration flux, ΔP is the filtration pressure,μ is the fluid viscosity, wc is the cake mass, α av is theaverage specific cake filtration resistance, and Rc and Rm
are the filtration resistances of filter cake and medium,©2016 Hosokawa Powder Technology Foundation

Kuo-Jen Hwang et al. / KONA Powder and Particle Journal No. 33 (2016) 296–303

respectively. According to this equation, the fluid viscosity is separated from cake filtration resistance. It implies
that the cake properties are independent on the fluid flow
behavior. The hypothesis is questionable because the particle packing is possible to be affected by the drag force
exerted on the particles. This point will be discussed inthis study.
Although the particle size distribution is believed an
important factor affecting cake properties, few researchers devoted their efforts on the theoretical study of this issue. In this study, the effects of particle size distribution
and fluid viscosity on the cake porosity and specific filtration resistance in dead-end microfiltration were studied.
The cake properties under various conditions were estimated using theoretical models and simulation method,
and were compared with experimental data.

2.1.2 Specific cake filtration resistanceAccording to Kozeny equation, the average specificfiltration resistance of cake can be estimated using thefollowing equation:

 av 

2.1.1 Cake porosity

Many models were derived for estimating the packing
density of binary particles in the past 30 years (German,
R.M., 1989), in which to consider the cavern and displacement effects are the simplest models for random packing.
Cavern effect is used for the condition that few small particles are filled into the vacant space in a packed bed constructed by large particles. The packing porosity can thenbe estimated by a material balance (German R.M., 1989)

 av 

L  1   L
L(2)

where ϕ is the volume fraction of particles, and the subscript L represents large particles. Another effect is considered that some small particles are replaced by few
large particles in a packed bed constructed by most ofsmall particles. The packing porosity in such a condition
can also be estimated by a material balance (German
R.M., 1989)

 av  1 

1 S
1   S L

(5)where k is the Kozeny constant, and So is the specific surface area of particles. The theoretical Kozeny constant is
5.0 for a compact packed bed constructed by smooth
spheres. For a random packing, the relationship between
Kozeny constant and packing porosity can be derived
theoretically using the free cell model (Happel J. and
Brenner H., 1965) as:k

2. Estimation of cake properties
2.1 Models

kS o2  1   av  s   av 32 3

  1  1  1    2 
1     ln   1  1   2 


1


  
  
 

(6)

The mean particle diameter, dp,av for dual-sized particles
can be calculated by the primary sizes and the volume
fractions of particles, that is,

1
L


 S
d p,av d p,L d p,S(7)

Because the specific surface area exactly equals 6/dp for aspherical particle, the value of So for a particle mixture
can be estimated using the mean particle diameter asfollowing:So 

6d p,av(8)

Once the variables in Eq.(5), such as k, So and εav, are
known, the average specific filtration resistance of cakecan be calculated.

(3)

where the subscripts S represent small particles. Because
both cake porosity and particle volume fraction aresmaller than 1, the cakes formed by dual-sized particles
have smaller porosity than that by uniform-sized, and thedifference becomes more obvious when the composition
is far distant from uniform. The lowest porosity can thenbe given from the intersection point of the curves plottedusing Eqs. (2) and (3), that is, av  s   L(4)Fig. 1 Forces exerted on a depositing particle in microfiltration.

297

Kuo-Jen Hwang et al. / KONA Powder and Particle Journal No. 33 (2016) 296–303

2.2 Numerical simulation
A numerical method for simulating the particle packing
structure and cake porosity in dead-end filtration was
proposed by Lu W.M. and Hwang K.J. (1993). It was
modified to be used for the system of this study. Refer toFig. 1, the external forces exerted on a depositing particle, Particle A, include the drag force due to permeate
flow, Fp, the net inter-particle force, Fi, and the net gravitational force, Fg. The drag force can be estimated using
the modified Stokes law (Lu W.M. and Hwang K.J., 1993).
The inter-particle force can be estimated using the DLVO
theory based on a pair-wise assumption. According to thistheory, the net inter-particle force is a summation of vander Waals force and electrostatic force (Hwang K.J. et al.,1997).
The Newton’s second law of motion is integrated to estimate the velocity and the displacement of particle migration during filtration. Particle migrations are simulated
until they can deposit stably. The details can be referred
to Lu W.M. and Hwang K.J. (1993, 1995).

3. Materials and methods
Two uniform-sized polymethyl methacrylate (PMMA)
spherical particles with a density of 1210 kg/m3 were usedin experiments. The particles were purchased from Soken
Co., Japan. Their density was 1210 kg/m3, and their mean
diameters were 0.45 μm (Ca.#: MP-1000) and 5.0 μm(Ca.#: MX-500), respectively. Their particle size distributions were measured using a HORIBA LA-300 laser particle size analyzer and are shown in Fig. 2. Propane-1,2,3triol (glycerol) with a density of 1255 kg/m3 and purity of
99.5 % in volume was purchased from Fisher ScientificCo. in USA (Ca.#: G33-4). Different amounts of glycerol
were added into de-ionized water to prepare aqueous

Fig. 2298

Size distributions of particles used in this study.

Fig. 3 Schematic diagram of the dead-end microfiltration system.

solutions with different viscosities. The fluid viscosity
was measured using a viscometer (Brookfield LVDV-II
CP, USA). The particle suspensions with a concentrationof 3 kg/m3 were prepared by dispersing particles into thoseaqueous solutions under different particle mixing ratios.
The particle size distributions were then adjusted by different mixing ratios of the dual-sized particulate samples.
The pH and temperature of the suspension were kept at 7.0
and 20 °C, respectively. The zeta potential of particles under such a condition was measured as –25 mV. A membrane manufactured by ADVANTEC Co. in Japan (Ca.#:
A010A142C) was used in filtration as filter medium. The
membrane was made of mixed cellulose ester, its meanpore size was 0.1 μm, and its filtration resistance was
2.26 × 1011 m–1 under a filtration pressure of 2 × 104 N/m2.
Dead-end microfiltration experiments were performed
using a bomb filter shown in Fig. 3. The filtration area in
the filter chamber was 8.04 × 10 –4 m2. Suspension was
prepared in the filter and prevented particle settling usinga magnetic stirrer. The filter chamber was installed toorient to the filter wall, as shown in Fig. 3, to prevent the
obstruction or destruction of cake formation by stirring.
The filtration pressure was supplied by a compressed air,adjusted using a regulator, and indicated by a pressuregauge. The filtrate was received into a flask and weighedusing a load cell. The data of filtrate weight was transferred to volume and recorded on a personal computer
during the whole course of filtration. An orifice plate wasinstalled in the filter chamber to indicate the cake growth.
When the growing cake surface reached the orifice plate,the filtration area decreased suddenly, and the tangent
slope of the filtration curve of dt/dv vs. v increased drastically. Therefore, the average cake porosity could be calculated using the received filtrate volume based on a massbalance, and the average specific cake filtration resistancewas determined by the tangent slope of the filtration curve
before the sudden change.

Kuo-Jen Hwang et al. / KONA Powder and Particle Journal No. 33 (2016) 296–303

Fig. 4Filtration curves of dt/dv vs. v under various filtrationpressures.

4. Results and discussion
Fig. 4 shows the filtration curves of dt/dv versus v under various filtration pressures for uniform PMMA particle with a mean size of 0.4 μm. The dispersion medium
was de–ionized water with a viscosity of 1 × 10 –3 Pa·s.
The filtration curves have the same trend; each of themcan be divided into two parts. The first part is a straightline. This implies that the average specific cake filtration
resistance is constant during filtration. When the filter
chamber is filled with cake, the received filtrate volume
reaches vf, and the tangent slope of the filtration curve
suddenly increases due to the decrease in filtration area.
The average specific cake filtration resistance is then calculated by the slope of the straight line before the transition point, as those shown in the figure. The average cakeporosity can be obtained using the mass balance at thetransition point because the cake volume is known. Comparing the values of vf under various pressures, a smaller
vf value under lower pressure indicates a higher cake porosity in such condition. Furthermore, because the valueof dt/dv is the reciprocal of filtration flux, the curves
shown in Fig. 4 also reveal that a lower filtration pressure
results in a lower filtration flux.
Fig. 5 shows the comparisons of cake porosity among
the experimental data, simulated results and model calculated results under various large particle fractions andvarious filtration pressures. The dispersed medium wasde-ionized water with a viscosity of 1 × 10 –3 Pa·s. The
values of cake porosity of dual-sized particles are lower
than those of mono-sized under a given filtration pressure,and the lowest cake porosity occurs around the large particle fraction ϕL = 0.75. When a few large particles exist ina small particle packing, some original packing positionsof small particles are replaced by large particles, and thecake porosity due to this “displacement effect” can be estimated using Eq. (3). The cake porosity decreases with

Fig. 5Comparisons of cake porosity among model estimations, simulated results, and experimental data for various mixing fractions of large particles.

increasing the volume fraction of larger particles. On theother hand, when a small portion of small particles packs
into a cake constructed by large particles, small particles
may pack into the void space among large particles, andthe cake porosity due to the “carven effect” can be estimated using Eq. (2). This effect causes the cake porosity
decreases with increasing the fraction of small particles.
The curves calculated using Eqs. (2) and (3) show that thelowest cake porosities occurring at ε Lε S where is the intersection point of those two curves, i.e, Eq. (4). These results agree qualitatively with the experimental data.
However, the estimated results are always lower thanexperimental data, and obvious deviations between themcan be found under moderate fractions of large (or small)particles. The maximum deviation is as high as 30 %occurring at the lowest cake porosity. This demonstrates
that the models of cavern and displacement effects can beused properly only when a few different-sized particles
are added into a cake formed by uniform-sized particles.
Those models were originally used for estimating the random packing of particles under gravity. All particles haveopportunities to migrate to reach stable positions. This issimilar to the conditions under high filtration rates. However, when filtration rate is not high enough, the depositing particles may have no enough drag force to pushthem to form a compact cake. Thus, the packing porosity
increases with decreasing filtration rate. This is therefore
another reason why the deviation between the model estimations and experimental data becomes larger for a morecompact cake. Furthermore, comparing the cake porosities under various filtration pressures at a fixed volume
fraction of large particles, an increase in filtration pressure leads to lower cake porosity. It is attributed to more299

Kuo-Jen Hwang et al. / KONA Powder and Particle Journal No. 33 (2016) 296–303

Fig. 6Comparisons of average specific cake filtration resistance between calculated results and experimental datafor various mixing fractions of small particles.

severe cake compression under a higher pressure. Anothermethod for cake porosity estimation is computer simulation. Although the simulation results are scattered, thetrend of the simulated curve is much closer to that ofexperimental data. Comparing those estimations obtainedby different methods, model estimation is more accurate
for those particles near pure composition, while simulation method is more suitable for moderate composition ofparticle mixtures, especially for the compositions to resultin the lowest cake porosity.
Fig. 6 shows the effects of small particle fraction, ϕS,
and filtration pressure, ΔP, on the average specific cakefiltration resistance, αav. The theoretical curves were calculated by substituting the calculated average cake porosity into Eq. (5), in which the values of Kozeny constantand specific surface area of particles were estimated using
Eqs. (6)–(8), respectively. Comparing those factors affecting specific filtration resistance, specific surface area andparticle size play the most important roles. The mean particle size decreases from 5 to 1 μm as ϕS value increases
from 0 to 0.3. However, there has only a 50 % decrease in
the mean particle size when ϕS increases from 0.3 to 1.0.
Therefore, the value of αav increases very rapidly withincreasing ϕS when ϕS value is smaller than 0.3. The slightincrease in cake porosity and decrease in mean particle
size with ϕS cause the αav values to become nearly invariant when ϕS > 0.5. The calculated results agree with experimental data except for the overestimations occurringaround ϕS = 0.3. This is attributed to the marked underestimations of cake porosity at that particle mixing fraction,
as shown in Fig. 5.
The experimental data of average specific cake filtration resistance are plotted against filtration pressure fordifferent large particle fractions in logarithm scales inFig. 7. The data can be regressed to a straight line for agiven particle mixing fraction. This reveals that the rela-

300

Fig. 7

Plots of αav versus ΔP under various mixing fractionsof large particles.

Fig. 8

Effects of fluid viscosity and particle mixing fraction
on the average cake porosity.

tionship between αav and ΔP follows a power function
proposed by Tiller et al. (1980). The αav value increases
with increasing pressure and decreasing the fraction oflarge particles. The αav value of pure small particles is ca
two order-of-magnitudes higher than that of pure large
particles under a given pressure. This is because the αav
value is proportional to the inverse square of particle size.
According to the Tiller’s empirical equation, the slope ofa curve represents the cake compressibility. Comparing
the slopes of the curves shown in the figure, the cakecompressibility is very low (around 0.07) for uniform-sized particles and shows the highest value around0.55 occurring at the fraction of large particles of 0.7. This
result indicates that the cake compressibility is sensitive
to particle size distribution and will be discussed in moredetail later.
The viscosity of dispersed fluid was adjusted by adding
different amounts of glycerol into de-ionized water. The
aqueous solutions were measured as Newtonian fluids,
and the values of viscosity were 10 and 5 × 10 –3 Pa·s forKuo-Jen Hwang et al. / KONA Powder and Particle Journal No. 33 (2016) 296–303

Fig. 9Plots of αav versus ΔP under various fluid viscosities.

53 and 45 vol% glycerol solutions, respectively. The same
viscosity values measured before and after filtration experiments demonstrate that no glycerol molecule was retained on or adsorbed onto the particles, cake or filter
media during filtration. Fig. 8 shows the effects of fluid
viscosity and volume fraction of large particles on the average cake porosity. The curve trend for each fluid viscosity is similar to that in Fig. 5. The values of cake porosity
are higher for uniform-sized particles, and a minimum
cake porosity occurs at ϕL = 0.75. An increase in fluid
viscosity leads to higher cake porosity. An 18 % porosity
increase is obtained as fluid viscosity increases from 1 to
10 × 10 –3 Pa·s. This is because a higher fluid viscosity
causes a larger drag force exerted on the depositing particles as well as larger surface friction between particles.
As a result, the particle packing structure in a filter cakeis looser.
Fig. 9 shows the effects of fluid viscosity and filtrationpressure on the average specific cake filtration resistancefor pure large particles. The relationship between αav andΔP can be regressed to straight lines, i.e., power-type
Tiller’s empirical equations, the same as those mentionedin Fig. 7. Because the cake porosity is lower (a more compact cake) for a dispersed fluid with lower viscosity, theαav value is therefore higher. A 30 % filtration resistancedecrease is obtained when fluid viscosity increases from 1
to 10 × 10 –3 Pa·s. This magnitude variation in αav can bereasonably explained by the particle packing structure fora given particle size distribution. Thus, the αav value is
not a sole function of particles themselves; but it is also
affected by the physical properties of dispersed fluid, e.g.,
fluid viscosity. This result is different from those previous
inferences in which the resistance caused by dispersed
medium, fluid viscosity, is separated from cake resistanceterm, e.g., αav. In conclusion, the influence of fluid properties on the overall filtration resistance cannot be separated
to a single term of fluid viscosity. The cake properties, atFig. 10

Effects of particle mixing fraction and fluid viscosity
on the cake compressibility.

least the particle packing structure in the cake and theresulting filtration resistance, are also affected by fluid
viscosity. However, the similar slopes of the curves shownin the figure indicate that the cake compressibility is similar and independent of fluid viscosity.
The cake compressibility can be obtained from the line
slope plotted by αav vs. ΔP in logarithm scales, as thoseshown in Figs. 7 and 9. The effects of particle mixing
fraction and fluid viscosity on the cake compressibility
are shown in Fig. 10. The particle size (distribution) plays
a much more important role on the cake compressibility
than the fluid viscosity does. Although the average specific cake filtration resistance was affected obviously by
the fluid viscosity, the cake compressibility was nearly
constant for a given particle mixing fraction. For those
uniform-sized conditions, the values of cake compressibility are as low as 0.1; and the cakes are only slightly
compressible. The cake compressibility becomes higher
in the conditions of particles with size distributions. Thisis because particles have more opportunity to pack into
the voids to form more compact structure during filtration. The highest cake compressibility is ca 0.56 occurring at the mixing fraction of large particles of 0.70. Thisparticle mixing fraction is a little different from that resulting in the lowest cake porosity (ϕL = 0.75, as thoseshown in Fig. 8). It is because the factors affecting thespecific cake filtration resistance is not only the cake porosity but also the particle size distribution.

5. Conclusions
The effects of particle size distribution, fluid viscosityand filtration pressure on the average cake porosity, average specific cake filtration resistance, and cake compressibility in dead-end constant pressure microfiltration were301

Kuo-Jen Hwang et al. / KONA Powder and Particle Journal No. 33 (2016) 296–303

studied. An increase in filtration pressure led to lower
average cake porosity and higher specific cake filtration
resistance. The effect of particle mixing fraction on the
cake porosity was explained using packing models, suchas cavern and displacement effects. The lowest cake porosity occurred under the volume fraction of large particles of 0.75. Model estimation for cake porosity was moreaccurate for those particles near pure composition, while
simulation method was more suitable for moderate composition of particle mixtures, especially for the compositions resulting in the lowest cake porosity. The average
specific cake filtration resistance increased with increasing the fraction of small particles, especially for low fraction of small particles. Furthermore, the cake properties
were affected by fluid viscosity. The cake porosity increased but the average specific cake filtration resistancedecreased with increasing fluid viscosity. An 18 % porosity increase and a 30 % filtration resistance decrease wereobtained when f luid viscosity increases from 1 to
10 × 10 –3 Pa·s. This was attributed to the larger surface
friction causing by higher viscous flow. The cake properties, such as the particle packing structure in the cake andthe resulting filtration resistance, were indeed affected not
only by particle size distribution but also by fluid viscosity. The particle size distribution played a much more
important role on the cake compressibility than thefluid viscosity does, and the highest cake compressibility
occurred approximately at the particle mixing fraction
resulting in the lowest cake porosity.

Acknowledgements
The authors wish to express their sincere gratitude to
the Ministry of Science and Technology of the Republicof China for its financial support (Grant number: MOST
103-2221-E-032-057-MY3).

Nomenclaturedp

particle diameter (μm)

Fg

net gravitational force (N)Fi

net inter-particle force (N)Fpdrag force due to permeate flow (N)302kKozeny constant (–)ΔP filtration pressure (Pa)qfiltration flux (m3/m 2 s)Rc

filtration resistance of cake (m –1)

Rm

filtration resistance of medium (m –1)

So

specific surface area of particles (m 2/m3)

tfiltration time (s)vfiltrate volume per unit filtration area (m3/m 2)

vf

filtrate volume per unit filtration area at which the growing
cake surface reached the orifice plate (m3/m 2)

wc

cake mass (kg/m 2)

αspecific filtration resistance of cake (m/kg)

εcake porosity (–)ϕ

volume fraction of particles (–)μ

fluid viscosity (Pa·s)

Subscripts
av

average cake propertyLlarge particle

Ssmall particle

References
German R.M., Particle Packing Characteristics, Chap. 6, Metal
Powder Industries Federation, Princeton, New Jersey, USA,
1989.
Happel J., Brenner H., Low Reynolds Number Hydrodynamics,
Englewood Cliffs, NJ, Prentice Hall, 1965.
Hwang K.J., Yu M.C., Lu W.M., Migration and deposition ofsubmicron particles in crossflow microfiltration, Separation
Science and Technology, 32 (1997) 2723–2747.
Hwang, K.J., Lin, K.P., Cross-Flow Microfiltration of dual-sized
submicron particles, Separation Science and Technology,
37 (2002) 2231–2249.
Lu W.M., Hwang K.J., Mechanism of cake formation in constant pressure filtrations, Separation Technology, 3 (1993)
122–132.
Lu W.M., Hwang K.J., Cake formation in 2-D cross-flow filtration, A.I.Ch.E. Journal, 41 (1995) 1443–1455.
Tiller F.M., Haynes S., Lu W.M., The role of porosity in filtration
VII Effect of side-wall friction in compression-permeability
cells, A.I.Ch.E. Journal, 18 (1972) 13–20.

Kuo-Jen Hwang et al. / KONA Powder and Particle Journal No. 33 (2016) 296–303

Author’s short biography
Kuo-Jen Hwang
Dr. Kuo-Jen Hwang is now a Professor at Tamkang University, Taiwan. After getting
his PhD degree in National Taiwan University in 1992, he moved to the Department ofChemical Engineering at Tamkang University, as an Associate Professor. He became afull professor at Tamkang University in 1997. He was appointed as the Chairman of the
Department of Chemical Engineering, Tamkang University from 1997 to 2001.
His research interests focus on solid-liquid separation and powder technology, especially for microfiltration of fine and soft particles, and hydrocyclone for separation andclassification of fine particles. He has published over 100 papers in peer reviewed journals and 150 conference papers. He is also the Chairmen of the Taiwan Filtration and
Separations Society and the International Delegates on Filtration since 2012.

Iou-Liang Lin
Mr. Iou-Liang Lin received his MS degree at the Department of Chemical and Materials Engineering, Tamkang University in 2009. He is a System Engineer in INNOLUXCorporation, Taiwan now.

303

KONA Powder and Particle Journal No. 33 (2016) 304–309/Doi:10.14356/kona.2016015

Original Research Paper

Improving Dispersion of Bacterial Endospores
for Enumeration †Vijay B. Krishna 1a, Jue Zhao 2b, Smithi Pumprueg 2c,
Ben L. Koopman 2 and Brij M. Moudgil 1*
1
2

Particle Engineering Research Center, Department of Materials Science and Engineering, University of Florida, USA
Particle Engineering Research Center, Department of Environmental Engineering Sciences, University of Florida, USA

Abstract
Precise enumeration of spores is crucial for accurate evaluation of spore survival in the presence of inactivating
agents or extreme environmental conditions. Bacterial endospores tend to agglomerate, leading to low estimatesof spore numbers. We have shown that addition of SDS to diluent used in counting bacterial endospores better
disperses spores, leading to a ten-fold increase in counts. We attribute this effect to steric hindrance andelectrostatic repulsion between micellar structures of SDS adsorbed at spore surfaces. We have also demonstrated
that use of a low surface energy material (Teflon) improves spread-plate counts, an effect we attribute to lesser
hold-up of liquid on Teflon versus glass.
Keywords: spore, enumeration, plating, agglomeration, surfactant, teflon

1. Introduction
Bacterial endospores have received considerable attention in the last few years. As a result of the anthrax attacks in 2001 (CDC, 2006), researchers are designing
more potent and faster acting sporicidal agents. Survivalof bacterial endospores is being investigated under extreme conditions of pressure, temperature and radiation,such as those encountered in outer space (Mastrapa et al.,
2001; Moeller et al., 2008; Moeller et al., 2007; Nicholson
et al., 2000). Precise enumeration of spores in a given
sample is crucial for accurate evaluation of spore survivalin the presence of inactivating agents or extreme environmental conditions. Bacillus subtilis and Bacillus cereus arecommon bacterial species employed for such investigations. Although B. cereus spores have an outer coat called
exosporium, similar to B. anthracis, both B. cereus and B.
subtilis (lacking exosporium) are employed as surrogates†

Received 21 August 2015; Accepted 27 August 2015
J-STAGE Advance published online 30 September 2015
1,2
University of Florida, P.O. Box 116135, Gainesville, Florida 32611,
USA
Present address:aDepartment of Biomedical Engineering, Lerner Research Institute,
Cleveland Clinic, USA, Cleveland, Ohio 44195, USAb
City of Grand Island Wastewater Treatment Plant, USA, Grand
Island, Nebraska 68801, USAc
Vigo Engineering Co. Ltd., Rayong, 21150, Thailand* Corresponding author: Brij M. Moudgil;
E-mail: bmoudgil@perc.ufl.edu
TEL: +1-352-846-1194 FAX: +1-352-846-1194

304

for Bacillus anthracis (Ivanova et al., 2003; Krishna V. etal., 2005; Krishna Vijay B., 2007; Nicholson et al., 2003;
Read et al., 2003; Vohra et al., 2005; Vohra et al., 2006).
Plate counts are the primary means (gold standard) of
enumerating viable spores and other techniques, such as
optical microscopy or particle counting method, utilize
the plate count method to verify the concentration of viable spores. Spores must be maintained in a dispersed stateprior to plating in order to achieve accurate and reproducible counts. Bacterial endospores, particularly spores of B.
cereus, are hydrophobic and thus tend to agglomerate(Peng et al., 2001; Ronner et al., 1990), leading to low estimates of spore numbers. Inaccurate counting can lead tomisleading conclusions, particularly in spore inactivation
studies. Agglomeration as a potential cause of inaccurate
spore enumeration has been largely ignored. In the lastten years, more than 1000 papers have been published oninactivation of bacterial spores, however, only ten studies
have considered agglomeration of spores during inactivation, and none has taken into account the agglomerationof spores during the enumeration step.
In the spread-plating method, which is one of the mostwidely used plate counting approaches, the spore suspension must be spread uniformly over the surface of the agar
plates. The spreader material employed for spread-platingof spores is crucial in achieving uniform and reproducible
counts (Koch, 1981). Use of a Teflon coating for the
spreader was recommended by Koch because of its low
affinity for bacterial cells (Koch, 1981). None of the literature reviewed mentions the type of spreader material©2016 Hosokawa Powder Technology Foundation

Vijay B. Krishna et al. / KONA Powder and Particle Journal No. 33 (2016) 304–309

employed for spread-plating.
We hypothesize that addition of surfactant to enhancedispersion of spores in suspension will significantly improve spore enumeration. Non-ionic surfactants, such as
Tween80 are sometimes used for dispersion of bacteria.
However, these are not suitable as dispersion aids for
spores in inactivation studies as Tween80 can induce germination of spores thereby exposing the susceptible germinating spores to inactivating agents present along with
spores (Parker et al., 1968). Similarly, cationic surfactants
can induce germination and inactivate spores, and, thus,
are not suitable as dispersion aids for spore enumeration(Pinzon-Arango et al., 2009). Anionic surfactants at lower
concentrations are not known to induce germination or
inactivate spores (Grund et al., 1982). Sodium dodecyl
sulfate (SDS), an anionic surfactant used in washing ofspores treated with lysozyme during purification, wasemployed as a model surfactant in the present study. Because of its anionic nature, SDS can reduce aggregation
by enhancing electrostatic repulsion between spores. The
presence of surfactant structures (e.g., micelles) on spore
surfaces can act as a barrier to close approach of otherparticles, further limiting agglomeration. Teflon and glass
spreaders were employed to investigate the effect of
spreader material. Deionized water and phosphate buffered solution (PBS) were employed for spore storage andserial-dilution.

2. Materials and methods
2.1 Chemicals
Chemicals were obtained from Fisher Scientific(Hampton, NH), except as noted. Solutions and deionizedwater for washing bacterial suspensions were autoclaved
before use. Lysozyme and phenylmethylsulfonyl fluoride(PMSF) solutions were prepared using autoclaved buffers.
Lysozyme powder was stored at 4 °C and lysozyme solution was prepared from powder for each use. The PMSF
powder was stored at 4 °C. PMSF solution was preparedby dissolving the powder in 2 to 3 mL (the minimum possible) ethanol. TEP buffer containing 50 mM Tris.Cl at
pH 7.2, 10 mM EDTA, and 2 mM PMSF was preparedfresh for each use. Phosphate buffered saline (PBS) was
prepared by combining 10.9 g of Na 2HPO 4, 3.2 g of
NaH2PO4, 90 g of NaCl and deionized water to prepare
1 L of solution. The solution was autoclaved and diluted
ten times prior to use.

2.2 Culturing of B. cereus spores
Culture media was prepared according to the American
Society for Testing and Materials ASTM E2111-00 stan-

dard (ASTM, 2001) using Difco Columbia broth powder.
Bacillus cereus (ATCC 2) colonies from Difco Tryptic
soy agar plates were inoculated in 500 mL Erlenmeyer
flasks containing 100 mL of 1/10th strength culture media
fortified with 0.1 mM MnSO4.2H 2O. The flasks werecapped with foam plugs and wrapped in aluminum foil.
The inoculated growth media was incubated for 72 h at
35 ± 2 °C on an orbital incubator-shaker (Model C24,
New Brunswick Scientific, Edison, NJ) with a speed of
200 rev/min.

2.3 Purification of B. cereus spores
The B. cereus spores were purified by lysozyme
method according to Xue and Nicholson (1996). Culture
was centrifuged at 10,000 × g for 10 min at 4 °C and thepellet was resuspended in 20 mL of 1 M KCl/0.5 M NaCl.
This suspension was centrifuged and the pellet was resuspended in 20 mL of 50 mM Tris.Cl (pH 7.2) containing
50 μg/mL lysozyme and incubated at 37 °C for 60 minutes. The suspension was then washed with, respectively,
1 M NaCl, deionized water, 0.05 % (w/v) SDS, TEP buffer, and deionized water (three times). The washed suspension was heat shocked at 80 ± 2 °C for 15 min,followed immediately by cooling to 4 °C. Spores werestored at 4 °C in deionized water for no more than one
week. The purity (proportion of spores relative to the total
number of spores and cells) of the final suspension was
99 %.

2.4 Counting
The spore suspension was serially diluted (ten-fold) andaliquots of the three highest dilutions (either dilution
numbers 2, 3 and 4 or 3, 4 and 5) were plated in triplicate
on Tryptic soy agar in 100 × 25 mm Petri dishes by the
spread-plate technique. The plates were then incubated at
37 °C for 12–16 hours and then the colonies were counted.
Counts between 30 and 300 were used for data analysis(Koch, 1981).

2.5 Statistical tests
Hypothesis testing for the significance of differences
between two means was based on two-tailed Student’s ttest (Sokal et al., 1997). Post-hoc testing of the significance of the differences between three or more means
was carried out by Tukey’s test (Berthouex et al., 1994).

2.6 Surface tension measurements
Surface tension was used to estimate the critical micelleconcentration of SDS in phosphate buffered saline. Surface
tension was determined by the Wilhelmy plate technique,305

Vijay B. Krishna et al. / KONA Powder and Particle Journal No. 33 (2016) 304–309

which measures the maximum force required to pull avertical platinum plate from solution at the liquid-air interface. Measurements were made at SDS concentrationsof 0, 0.25, 0.50, 0.75, 1.00, 1.25, 1.50, 5.00 and 10.00 mM
in PBS at 24 ± 2 °C.

2.7 Particle counting
Spores suspended in PBS with and without SDS werecounted with a MultisizerTM 3 Coulter Counter® (Beckman
Coulter, Inc., Fullerton, CA) using a 5 μm orifice. The
spores were serially diluted to obtain a final concentrationof 103 CFU ml–1 in the sample holder.

2.8 Zeta potential
Zeta potential of B. cereus spores in deionized water(pH = 5.8) was determined with a Brookhaven ZetaPlus(Brookhaven Instruments Corporation, Holtsville, NY).

3. Results and discussion

Chou, 2001) and, thus, are more susceptible to changes inionic strength of diluent. The high ionic strength (0.2 M)
of PBS can effectively screen the net negative charge andaid in agglomeration of spores.
Sodium dodecyl sulfate (SDS) was added to PBS forthe purpose of improving the dispersion of spores. Fig. 2
shows the plot of surface tension as a function of SDS
concentration. The critical micelle concentration (the concentration at which surfactant molecules self-assemble
into micelles) for SDS in phosphate buffered saline wasfound to be 1 mM, which is close to the reported value of0.83 mM at 0.2 M NaCl (Rosen, 2004). The CMC for SDS
in DI water is 8 mM (Rosen, 2004). Thus, the beneficial
effect of SDS is achieved in PBS with a much lower concentration of SDS (e.g., 1 mM) than would be required for
DI water. The effect of SDS concentration on colony
counts with PBS as diluent is shown in Fig. 3. Significant
improvement is obtained at one-half the critical micelleconcentration (CMC). Further improvement was observed
at a concentration equal to twice the CMC. Counts
achieved in PBS with 2 mM SDS were much higher than

3.1 Effect of suspending media and surfactant
Average colony counts of Bacillus cereus spores plated
in triplicate, with DI water and PBS as diluents, are given
in Fig. 1. The colony counts with DI water were two times
higher than the counts obtained with PBS. Since the starting material (bacterial spore suspension) was the same in
the various trials, an increase in spore counts is indicativeof a lesser degree of agglomeration. Thus the lower counts
obtained with PBS versus distilled water suggest a higher
extent of spore agglomeration in the PBS. B. cereus spores
have very low zeta potential (–11 mV in DI water) compared to bacteria (–40 mV) (Li et al., 2004; Peng, Tsai and

Fig. 1 Mean colony counts obtained with deionized water (DI)and phosphate buffered saline (PBS) without and with
2 mM sodium dodecyl sulfate (SDS) as diluents.
(Means are significantly different at alpha = 0.01)

306

Fig. 2Estimation of critical micelle concentration for sodium
dodecyl sulfate in phosphate buffered saline. The error
bars are too small to be seen.

Fig. 3

Effect of sodium dodecyl sulfate concentration on colony counts with phosphate buffered saline as diluent.
Means with different letters are significantly different
at alpha = 0.05.

Vijay B. Krishna et al. / KONA Powder and Particle Journal No. 33 (2016) 304–309

Table 1

Effect of spreader material on spore counts
PBS

SpreaderMaterial

PBS + 2 mM SDS

Mean*
(CFU)

CV

Mean*
(CFU)

Glass

2.4 × 10518 %5.2 × 106

20 %Teflon

5

17 %

6

5%

5.0 × 10

7.5 × 10CV

* Means were significantly different at α = 0.05; one-tailed t-test.

Fig. 4

Effect on particle counts of sodium dodecyl sulfate(SDS) addition (2 mM final concentration) to phosphatebuffered saline (PBS). The inset figure shows the total
spore counts for SDS and SDS + PBS diluents. The error bars are too small to be seen.

counts achieved in DI water with 2 mM SDS (Fig. 1).
Enhanced dispersion with SDS addition was verified byparticle counts (Fig. 4). In the presence of SDS, a peak inthe concentration of B. cereus spores, as indicated by themeasured count frequency, occurred in the range of
1–1.5 μm. Without SDS, counts were generally lower andno peak was observed. Since the length of B. cereus
spores is in the range of 0.95–1.5 μm, it is apparent thatmuch higher numbers of individual spores are found in
the presence of SDS. Since the outer protein coats ofspores have positively and negatively charged sites as wellas hydrophobic sites, it is possible that micelles orhemi-micelles of an anionic surfactant such as SDS canadsorb on spore surfaces and inhibit agglomeration bysteric hindrance and electrostatic repulsion. The observation of higher spore counts in the presence of SDS supports this conjecture.

3.2 Effect of spreader material
Compounding errors in counting due to incomplete
spore dispersion is the potential for liquid hold-up by the
spreader. A thinner film of spore suspension will adhere
to Teflon than to glass because of Teflon’s lower surface
energy (20 vs. 47 mN m–1). Thus, with Teflon, there will
be a smaller hold-up volume of spore suspension remaining on the spreader when it is removed from the agar surface. Indeed, significantly higher counts and moreconsistent counts were obtained with a Teflon spreader
compared to a glass spreader, with or without SDS present (Table 1). However, addition of SDS to the diluent increased the spore counts ten-fold with either spreader
material. The combination of a Teflon spreader and SDS
amended diluent provided the highest counts.

4. Conclusions
We have shown that agglomeration is a significant
problem in enumeration of B. cereus spores. An anionicsurfactant was successful in dispersing spores and increasing the spore counts. The adherence of liquid to the
spreader is another source of error in spread-plate counting. This problem can be ameliorated by use of a low surface energy material, such as Teflon, for the spreader.

Acknowledgments
The authors acknowledge the financial support of theParticle Engineering Research Center (PERC) at the University of Florida, the National Science Foundation (NSF
Grant EEC-94-02989), and the Industrial Partners of the
PERC for support of this research. Any opinions, findings
and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarilyreflect those of the National Science Foundation. Experiments were carried out at the University of Florida’s
Water Reclamation and Reuse Laboratory. The second
author was supported by a University of Florida Alumni
Fellowship.

References
ASTM, Standard quantitative carrier test method to evaluate
the bactericidal, fungicidal, mycobactericidal and sporicidal potencies of liquid chemical germicides., ASTM Designation: E2111-00 (2001).
CDC, Questions and answers about anthrax, http://www.bt.cdc.
gov/agent/anthrax/faq (2006).
Grund A.D., Ensign J.C., Activation of Streptomyces viridochromogenes spores by detergents, Current Microbiology, 7 (1982) 223–227.
Ivanova N., Sorokin A., Anderson I., Galleron N., Candelon B.,
Kapatral V., Bhattacharyya A., Reznik G., Mikhailova N.,
Lapidus A., Chu L., Mazur M., Goltsman E., Larsen N.,
D’Souza M., Walunas T., Grechkin Y., Pusch G., Haselkorn
R., Fonstein M., Ehrlich S.D., Overbeek R., Kyrpides N.,
Genome sequence of Bacillus cereus and comparative anal-

307

Vijay B. Krishna et al. / KONA Powder and Particle Journal No. 33 (2016) 304–309

ysis with Bacillus anthracis, Nature, 423 (2003) 87–91.
Krishna V., Pumprueg S., Lee S.H., Zhao J., Sigmund W.,
Koopman B., Moudgil B.M., Photocatalytic disinfection
with titanium dioxide coated multi-wall carbon nanotubes,
Process Safety and Environmental Protection, 83 (2005)
393–397.
Li B., Logan B.E., Bacterial adhesion to glass and metal-oxidesurfaces, Colloids Surf B Biointerfaces, 36 (2004) 81–90.
Mastrapa R.M.E., Glanzberg H., Head J.N., Melosh H.J.,
Nicholson W.L., Survival of bacteria exposed to extreme
acceleration: implications for panspermia, Earth and Planetary Science Letters, 189 (2001) 1–8.
Moeller R., Horneck G., Rabbow E., Reitz G., Meyer C.,
Hornemann U., Stoffler D., Role of DNA protection andrepair in resistance of Bacillus subtilis spores to ultrahighshock pressures simulating hypervelocity impacts, Appl
Environ Microbiol, 74 (2008) 6682–6689.
Moeller R., Stackebrandt E., Reitz G., Berger T., Rettberg P.,
Doherty A.J., Horneck G., Nicholson W.L., Role of DNA
repair by nonhomologous-end joining in Bacillus subtilis
spore resistance to extreme dryness, mono- and polychromatic UV, and ionizing radiation, J Bacteriol, 189 (2007)
3306–3311.
Nicholson W.L., Galeano B., UV resistance of Bacillus anthracis
spores revisited: Validation of Bacillus subtilis spores as
UV surrogates for spores of B. anthracis Sterne, Appliedand Environmental Microbiology, 69 (2003) 1327–1330.
Nicholson W.L., Munakata N., Horneck G., Melosh H.J., Setlow
P., Resistance of Bacillus endospores to extreme terrestrial
and extraterrestrial environments, Microbiology and Molecular Biology Reviews, 64 (2000) 548–572.
Parker M.S., Bradley T.J., A reversible inhibition of the germination of bacterial spores, Can J Microbiol, 14 (1968) 745–
746.
Peng J.S., Tsai W.C., Chou C.C., Surface characteristics ofBacillus cereus and its adhesion to stainless steel, Interna-

tional Journal of Food Microbiology, 65 (2001) 105–111.
Pinzon-Arango P.A., Scholl G., Nagarajan R., Mello C.M.,
Camesano T.A., Atomic force microscopy study of germination and killing of Bacillus atrophaeus spores, J Mol
Recognit, 22 (2009) 373–379.
Read T.D., Peterson S.N., Tourasse N., Baillie L.W., Paulsen I.T.,
Nelson K.E., Tettelin H., Fouts D.E., Eisen J.A., Gill S.R.,
Holtzapple E.K., Okstad O.A., Helgason E., Rilstone J., Wu
M., Kolonay J.F., Beanan M.J., Dodson R.J., Brinkac L.M.,
Gwinn M., DeBoy R.T., Madpu R., Daugherty S.C., Durkin
A.S., Haft D.H., Nelson W.C., Peterson J.D., Pop M.,
Khouri H.M., Radune D., Benton J.L., Mahamoud Y., Jiang
L.X., Hance I.R., Weidman J.F., Berry K.J., Plaut R.D.,
Wolf A.M., Watkins K.L., Nierman W.C., Hazen A., Cline
R., Redmond C., Thwaite J.E., White O., Salzberg S.L.,
Thomason B., Friedlander A.M., Koehler T.M., Hanna P.C.,
Kolsto A.B., Fraser C.M., The genome sequence of Bacillus
anthracis Ames and comparison to closely related bacteria,
Nature, 423 (2003) 81–86.
Ronner U., Husmark U., Henriksson A., Adhesion of bacillus
spores in relation to hydrophobicity, J Appl Bacteriol, 69(1990) 550–556.
Vohra A., Goswami D.Y., Deshpande D.A., Block S.S.,
Enhanced photocatalytic inactivation of bacterial spores on
surfaces in air, Journal of Industrial Microbiology and Biotechnology, 32 (2005) 364–370.
Vohra A., Goswami D.Y., Deshpande D.A., Block S.S.,
Enhanced photocatalytic disinfection of indoor air, Applied
Catalysis B-Environmental, 64 (2006) 57–65.
Xue Y.M., Nicholson W.L., The two major spore DNA repair
pathways, nucleotide excision repair and spore photoproduct lyase, are sufficient for the resistance of Bacillus subtilis
spores to artificial UV-C and UV-B but not to solar radiation, Applied and Environmental Microbiology, 62 (1996)
2221–2227.

Author’s short biography
Vijay B. Krishna
Dr. Vijay Krishna is an Assistant Professor of Molecular Medicine in the Departmentof Biomedical Engineering at the Lerner Research Institute, Cleveland Clinic. His current research interests include carbon nanostructures, nanomedicine and light-nanomaterialinteractions for biomedical applications. Dr. Krishna received his B.E. in Chemical
Engineering from Bangalore University and Ph.D. in Materials Science and Engineeringfrom University of Florida. He continued as a Postdoctoral Associate at the Particle Engineering Research Center at the University of Florida. Dr. Krishna has 19 peer-reviewed
publications and 11 patents.

308

Vijay B. Krishna et al. / KONA Powder and Particle Journal No. 33 (2016) 304–309

Author’s short biography
Jue ZhaoDr. Jue Zhao is a Plant Operations Engineer at the City of Grand Island Wastewater
Treatment Plant. Her expertise includes hydraulic and biological modeling of wastewater
treatment, process design and pilot to scale development of water and wastewater treatment, and biological nutrient removal operations. Dr. Zhao received her Ph.D. in Environmental Engineering Sciences from University of Florida. Her research interest is
photocatalytic disinfection using nanoparticles. She is registered as a Professional Engineer in the State of Florida and State of Nebraska.

Smithi Pumprueg
Mr. Smithi Pumprueg is the founder of Vigo Engineering Co. Ltd., a company focusing on engineering projects based in Rayong, Thailand. He received his M.E. in Environmental Engineering Sciences from University of Florida.

Ben L. Koopman
Dr. Ben Koopman is a Professor and Undergraduate Coordinator in the Department ofEnvironmental Engineering Sciences at the University of Florida. His research interests include removal of microorganisms by surface-modified filter media and bio films, photocatalytic antimicrobial coatings, microbial water-treatment, one-dimensional modeling ofactivated sludge secondary settling tanks, and biological nutrient removal. Dr. Koopman
received his B.S. in Civil Engineering from Oregon State University and Ph.D. in thesame field from the University of California, Berkeley.

Brij M.Moudgil
Dr. Brij M. Moudgil is a Distinguished Professor of Materials Science and Engineering at the University of Florida. His current research interests are in particulate materials
based systems for enhanced performance in bioimaging, nanomedicine, photocatalytic
degradation of hazardous microbes, polymer and surfactant adsorption, dispersion andaggregation of fine particles and nanotoxicity. Dr. Moudgil received his B.E from theIndian Institute of Science, Bangalore, India and his M.S and Eng.Sc.D degrees from
Columbia University, New York. He has published more than 400 technical papers andhas been awarded 14 patents. He is a member of the U.S National Academy of Engineering.

309

KONA Powder and Particle Journal No. 33 (2016) 310–321/Doi:10.14356/kona.2016016

Original Research Paper

Optical Characterization of Industrial Slurries †Marco Potenza 1,2,3*, Tiziano Sanvito 2,3 and Giuseppe Fazio 4
1

Department of Physics, University of Milan, Italy
CIMAINA, Italy
3
EOS Srl, Italy
4
ST Microelectronics, Italy
2

Abstract
In this work we focus on the characterization of micro- and nano-powders typically adopted for chemicalmechanical polishing, extensively used whenever the global and local planarization of surfaces is required as innanoelectronic fabs. We present an innovative method for the accurate characterization of water suspensions of
nanoparticles. It relies upon the combination of a new approach to extract light-scattering information from single
particles and the recently developed diagnostic tool named Single Particle Extinction and Scattering. It can beused in line. Data interpretation becomes independent of any a-priori assumptions about the samples. The resultsof accurate measurements performed on ceria as well as aluminium oxide slurries are reported. We show thestrong advantages of this method compared with traditional ones by explicitly reporting experimental results on
calibrated spheres made of different materials. We discuss possible applications for in-line characterization ofultrapure water, chemicals, slurries for abrasive processes, for example, as well as the detection of any undesiredparticles – which could be the key for future improvements to advanced process control systems.
Keywords: particle sizing, slurries, ceria, CMP, light scattering

1. Introduction
The worldwide nanoelectronic industrial environmentis currently under pressure to achieve better goals interms of cost reduction and manufacturing efficiency,
while the rate of technological advancement must be preserved. The improvement of fab productivity throughnovel methods or the introduction of in-line monitoring ofprocesses, equipment, facilities (e.g. power, gas, chemicals ...), which will make the manufacturing efficiency
higher is an increasing need in this field.

1.1 Chemical mechanical polishing
One of these applications has been recognized to bemonitoring the Particle Size Distribution (PSD) of theslurries used for the Chemical Mechanical Polishing (CMP)
processes, required by the most advanced technology applied to the production of silicon-based technologies (Li,†

Received 8 May 2015; Accepted 17 July 2015
J-STAGE Advance published online 30 September 2015
1,2
via Celoria, 16 – I-20133 Milan, Italy
3
Viale Ortles 22/4, I-20139, Milan, Italy4
via Olivetti 2, I-20864 Agrate Brianza, Italy* Corresponding author: Marco Potenza;
E-mail: marco.potenza@unimi.it
TEL: +39-02-50317209 FAX: +39-02-50317712

3102008), as well as a number of industrial processes such as,
for example, compact disks, hard disks and optical surface polishing. It is generally known that CMP involves
both chemical and mechanical mechanisms where theabrasive slurries are one of the main actors. Slurries areaqueous dispersions of nanoparticles where the characteristics of the abrasive material and PSD typically play animportant role in determining the removal behaviour(Pate, 2011). Modern slurries are characterized by two
main drawbacks that make them difficult to handle: 1)
they rapidly undergo sedimentation; 2) they are affectedby aggregation phenomena (Basim, 2000; Basim, 2002).
Moreover, slurries are typically handled within closedloop circuits, so that guaranteeing the stability of the suspension is a particularly demanding issue. Finally, aging
effects occur during the process which affect the process
performance (Cumbo, 1995; De Nardis, 2005; Remsenz,2006).

1.2 Common slurries
The common materials available on the market are silica (SiO2) and ceria (CeO2) particle suspensions for themost demanding nanoelectronic applications, and aluminium, copper and other oxides for other cases (e.g. opticalpolishing). Ceria slurries represent the most innovative
application for silicon insulation definition, and are the©2016 Hosokawa Powder Technology Foundation

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

most demanding in terms of characterization due to thetight requirements and the high costs. Notwithstanding
the complicated transformation method of the ceria raw
material into the final slurry product and the fact that it
belongs to the rare earths, with consequently increased
costs and availability problems, ceria slurries are quite
diffuse thanks to their improved local planarization performance with respect to silica. Moreover, they are typically used in addition to chemical additives, thus leading
the high selectivity between oxides and nitrides (Merriks,2008). Because of the natural sedimentation and aggregation behaviour of ceria particles, several studies have been
performed to correlate the PSD with the features of handling and distribution systems, as well as correlation of
the CMP with the material removal rate in order to guarantee the proper process control and quality level in thesemiconductor manufacturing environment.

Fig. 1.1

Non-volatile content (NVC%) vs CMP removal rate.

1.3 Slurry characterization
The particle size specifications in the semiconductor
industry of ceria slurries are based on supplier methodology, generally based on laser diffraction. The Mastersize
laser diffraction tool (Malvern TM) is a reference here.
As already mentioned, one of the most critical drawbacks of ceria slurries is the stratification and sedimentation behaviour that must be taken into account prior toand during use. This is simply caused by the high specificweight of the material. Thus, strong shaking is needed tore-disperse the solids content over the entire drum and aminimum flow speed in piping is required to avoid sedimentation. These are the main topics for the design of the
Chemical Distribution System (CDS). Along the CDS,
continuous shaking is still necessary to avoid sedimentation. As it appears clear from this analysis, the most important parameter to be monitored is the PSD, D99
percentile in particular, and NVC%. In Figs. 1.1 and 1.2,
the effect of the NVC% and D99 particle class on the
CMP process performance is shown in terms of the material removal rate. Since the D99 classification is extremely
dependent on the particle size analysis technique, themonitor is typically performed indirectly on the basis of
the NVC% only, since a correlation between D99 and
NVC% has been determined. No direct measurement of
the PSD is usually performed.
The state of the art for slurries in the semiconductor industry is clearly stated in the ITRS guidelines (ITRS andITRS, 2009), which are briefly summarized and discussed
below.
At present, the characterization of free nanoparticles ingeneral does not meet the criteria for a wide class of applications due to lack of accuracy, high throughput, easeof operation and the actual possibility of operation forroutine, in-line characterization (Zelenyuk, 2006; Ghosal,

Fig. 1.2 D99 concentration vs CMP removal rate.

2014). Optical methods are among the most popular ones
due to the non-invasiveness of the measurement, reliability, flexibility, robustness, but nevertheless they sufferfrom fundamental limitations which make them inapplicable for the case in point here. The main limitation isthat most of these methods operate on collections of highly
polydisperse particles, making it necessary to apply data
analysis with inversion algorithms for extracting the PSD
from the superposition of the signals given by a numberof different particles. This often involves ill-posed problems like in DLS or SALS, and limits the reliability of themethods for the specific applications, especially at nanoelectronic fabs. Alternatively, non-optical methods exist
which can be adopted for characterizing the PSD of densematerials such as ultrasonic spectroscopy (Hipp, 1999).
This method can be considered a valid alternative to optical methods, with the advantage of operation without
need of any dilution.

311

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

As a matter of fact, no in-line monitoring is currently
performed on the slurries at nanoelectronic fabs. Samples
are picked up and sent to the producer for size characterization. This process is extremely far from what would beneeded in terms of an efficient, immediate responsive toolfor deciding within a short time the actions to be taken(Budge, 2009; Budge, 2013).

1.4 Future needs
A clear breakthrough here could be made by means ofan in-line Particle Monitoring (PM) tool that continuously determines the PSD and generates the inputs for a
Decision-Making (DM) tool capable of performing thefollowing actions: 1) foreseeing the requisite actions to betaken during the maintenance stops, based on an effective, rational and sustainable basis; 2) adapting the CMP
to maintain the required specification of the product despite the modified characteristics of the slurries that are
currently present inside the machinery.
To present things in a more schematic way, the current
status and the progress beyond the state of the art which
would dramatically change the fab performance are compared below, referring to the main issues and needs of industry.
1) At present, no PM tool is available for in-line monitoring. A new sensor and the relevant tool with the specifications needed to fulfil the objective must therefore
still be developed, tested and implemented in-line.
2) No DM protocol is available. A PM tool with the specified requirements would enable development of sucha protocol.
3) The common way to employ sensor integration is byadapting the FDC system to match the sensor requirements in terms of physical and logical connection. It
is necessary to develop a sensor interface capable ofmaking the interfacing of the new sensors easy andintelligent.
4) The scattering parameters of nanoparticles can bemeasured with methods that estimate only the size
based upon reasonable hypotheses about the composition. A possibility is to develop an extended databaseof both size and absorption coefficients, independentof the calibration of numerical methods.
Development of a new instrument in view of the PM
tool with the requirements above is challenging simply
because of the intrinsic limitations imposed by both industrial requirements and by basic physics arguments. In
the following text, we report a list of the ITRS target
technology issues for particle counter performance (ITRS
and ITRS, 2009).
• Particle counter performance is strongly dependent on
the number of particles counted per unit of time.

312• The number of particles counted per unit of time determines the precision of the measurement. The precision
is predictable based on particle counting statistics.
• The number of particles counted per unit of time is dependent on the sensitivity of the particle counter andthe volume of the chemical measured.
• Particle measurements (with reasonable precision) at a
specific particle size combined with the knowledge ofparticle distributions in liquid chemicals can be used in
predicting particle concentrations at another size.
• PSD in continuously filtered liquid chemical systems
are usually between 1/D2.5 and 1/D3.5, with 1/D3 being
typical. While a PSD can be estimated from any sample, it is more accurate to use a large number of samples and a large number of instruments in making thisdetermination.
• Elimination or minimization of background counts due
to cosmic rays is necessary to ensure that the measured
particle counts represent real particle events.
In the following section, we introduce a completely
novel approach to the characterization of particles suspended in a liquid, and show experimental evidence that
this is able to overcome the main issues listed above. We
think this method is one of the best candidates to achieve
the PM tool described above.

2. Analysis of the field scattered by single
particles
In this section, we recall the fundamentals of the traditional methods for the sizing of single particles, and introduce the approach we will adopt here based uponmeasurement of the scattered field. As it has been recently
discussed (Potenza, 2014), the knowledge of the complex
field scattered by single particles, meaning either amplitude and phase, or real and imaginary parts, is hugely advantageous in assessing information about the samples.
The knowledge of the whole field is the key ingredient
which enables information that is otherwise not available
with traditional methods to be assessed.
Traditional optical methods for measuring the size of
single particles are based on the fundamentals of lightscattering, typically the dependence of the scatteringcross-section Csca upon the particle radius a and the wavelength λ. A tightly focused light beam is usually exploited
to maximize the intensity falling onto a single particle,
and the scattered intensity (almost isotropic for a<<λ) iscollected over a wide solid angle (Chylek, 1976; Sachweh,
1998; Terrel, 2006; Heim, 2008). The ratio of the total
scattered power and the incident intensity gives Csca. In
order to reduce the uncertainties introduced by the unavoidable shape effects, which can be particularly import-

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

ant at high scattering angles, low angle detection has beenalternatively adopted. A colloidal suspension is hydrodynamically focused into a narrow stream illuminated by aperpendicular laser beam with constant intensity and an
elliptical focus. The case of highly concentrated suspensions has also been addressed successfully (Mavliev,
2004). As the particles pass one by one through the laser
beam spot, the scattered light is collected under a scattering angle of 5°. The signals are processed by a multichannel analyser and delivered to an electronic processing
unit. The particle size is more accurately determined, also
for systems subject to aggregation phenomena (Pelssers,
1990).
An alternative approach has been recently introduced
based upon direct measurement of the transmitted beam
intensity. By definition, this provides the extinction
cross-section Cext. The method was commercialized by
Klotz GmbH, and has been extensively compared to thescattering-based methods in some cases of interest for basic science (Ruth, 2002).
In some cases, the two principles mentioned above
have been adopted in a measurement of both the extinction and the scattering cross-sections of single particles inorder to extend the accessible size range (Wells, 1996).
In any of the previous cases, the size is recovered fromjust one parameter, which is the measured power: theamount of light scattered or removed by the impinging
light beam gives the particle size through the proper
cross-section (either scattering or extinction). Any otherfeatures affecting the scattering of light remain out ofcontrol. Even if size by far determines the amount of extinguished and scattered light, other parameters are also
involved, thus limiting the accuracy of the size recovery(Gebhart, 1991; Liu, 2000; Whitby, 1967). The composition, shape and internal structure of a particle also participate in determining the scattered field and intensity, andrecovering the size from the measured intensity ideallyrequires the knowledge of this information which is usually unknown (see, for example, Bohren, 1983; Mischenko,
1999; Borghese, 2003). Moreover, especially when working at high scattering angles, the specific orientation of agiven non-spherical particle represents an additional parameter that determines the scattered field and intensity,
thus making the data intrinsically affected by the uncertainties introduced by the random orientations the particles have in the sensible region (Tinkea, 2008).
The new approach we adopt here (Potenza, 2014) has
been shown to give strong advantages in the characterization of free particles. This approach was first used tocharacterize ceria slurries (Potenza, 2015) in the submicron range. Here, we report the experimental results obtained, adopting the same approach for a more generalclass of slurries, including CeO2 and Al2O3 slurries, withthe aim of proving the feasibility of easy and reliablecharacterization for these kinds of suspensions.
For the basics of this approach we refer to the literature(see Potenza, 2014). Here, we just recall that for liquidsuspensions of particles with a given composition, even if
unknown, several drawbacks of the traditional methods
have been overcome. Moreover, the presence of any
polydispersity is of advantage here, since a statistical approach can be applied to the data as shown below. Therefore, the typical industrial problem of analysing samples
containing particles of homogeneous composition is particularly promising to facilitate going beyond the state of
the art (Potenza, 2015). Other advantages will be further
discussed. The only drawback of the present approach,which is due to and in common with any other single particle detection method, is the need of highly dilute suspensions. This requires strong dilutions, and in principle
could affect the physico-chemical properties of the suspension. This is the requirement also for other opticalmethods commonly adopted for the off-line characterization of slurries for industries, so that our results are comparable to those obtained with such methods. As discussed
in the introduction, in those cases when just the propertiesof the concentrated sample are of interest, different approaches shall be adopted to avoid dilution (for example,
the ultrasonic spectroscopy mentioned above; Hipp,
1999).

3. Single particle extinction and scattering
Simultaneous measurements of the complex scatteredfield have been attempted with several methods by different groups (Batchelder, 1991; Bassini, 1992; Giglio, 2004;
Giglio, 2005; Potenza, 2010). Here we adopt a new
method recently developed (Giglio, 2005; Potenza, 2015),
which overcomes the main issues encountered by anyother method. Actually, the only method which is really
viable for characterizing single particles in a flux is theone described in Batchelder 1991, which was previously
patented (Batchelder, 1990). Nevertheless, the method we
adopt here is simpler, cheaper and more robust such that
we believe it really can be adopted for in-line measurements of nanoparticles suspended in a fluid of industrial
interest. For this reason, in the following text we will notcompare the new method with those mentioned above tomeasure the complex field.
The method we use here has been developed by the optics group at the University of Milan. It is named Single
Particle Extinction and Scattering (SPES) because of its
ability to access simultaneous measurements of the extinction cross-section and the amplitude of the forward
scattered field for each particle. These parameters are ultimately related to the real and imaginary parts of it. The
SPES method is described elsewhere (Potenza, 2015). It

313

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

relies upon a self-reference interferometric scheme inwhich particles are driven through the focal region of alight beam, and the transmitted light is collected on a sensor placed in the far field. Particles are delivered into thebeam through a flow cell (Hellma 137 series), the dilution
being properly chosen so that just one particle is present
in the beam at a time. The superposition of the intensetransmitted beam and the faint scattered wave gives
time-dependent interference patterns which deliver the
information of both the extinction (power reduction of themain beam) and scattering (intensity modulations due tointerference). Collecting data at high speed with a timebase much smaller than the particle transit time throughthe beam and adopting a segmented sensor allow recoveryof the information on both. The advantages with respect
to current methods for measuring single particles are:high size resolution; simultaneous measurement of size
and refractive index of single particles; calibration-free;very simple optical layout; minimum fake events. Morespecifically, working with slurries, one can rely on 1) the
knowledge of the composition, and 2) the almost isometric shape of the grains (Potenza, 2015). The former is ofparamount importance since the refractive index of compact grains is well known, which allows recognition of
spurious grains and aggregates of grains. The latter is important since data are not affected by any uncertainty
coming from the shape and orientation of the grains in the
scattering volume (see, the simulations in Potenza, 2014).
As a result, a precise measurement of the single particle
sizes is possible that is free from any contribution of spurious events. A precise PSD can then be obtained and followed with time, which is one of the basic requirements
for a routine, in-line process control.
The almost complete absence of fake events warrants a
specific discussion here. It is mainly due to 1) the continuous measurement of the transmitted beam, so that thesystem does not produce any signal at the detection limit
when measuring very small particles, and 2) the fast signal detection, which makes an accurate pulse shape analysis of the signals possible. In both cases fake events suchas cosmic rays, electronic noise, as well as double transits
through the beam can be accurately rejected. Moreover,air bubbles can be easily recognized from the pulse shapeanalysis, thanks to the unique, known relative refractive
index of air with respect to the liquid.
In order to show the specific properties of the SPES
method and the way we show data in the following sections, we report some experimental results obtained withcalibrated polystyrene (PS) spheres suspended in water.
In Fig. 3.1 we report the raw data in the complex field(abscissas and ordinates represent the real and imaginaryparts, respectively). Data are represented in the complex
field by a two-dimensional histogram where the numberof events detected within a given two-dimensional bin

314

is represented in grey tones (white corresponds to zeroparticles, black to the maximum number in the plot). Disturbances in the signals give rise to the spread in the complex plane around the centroid of the distribution, which
is more extended towards the lower real and imaginaryparts. Bin size was chosen in such a way to be close to theultimate sensibility of the method in the intermediate region of the accessible range. Spheres of 290, 430 and
600 nm in diameter, refractive index (in air) n = 1.59 forthe laser source adopted here were measured. The data
show a trend identified by the black continuous line, thesize increasing for larger complex amplitudes. For thesake of clarity we report the expected complex values forceria, n = 2.1 (dotted line). Moreover, we stress here thatfor non-spherical particles, the population is expected to
be characterized by a spread in the vertical direction
which is larger for higher aspect ratios (Potenza, 2014).
By contrast, isometric particles show a distribution that ismuch narrower close to the expected lines in the figure.
In Fig. 3.2 we report the PSD as obtained by the samedata in Fig. 3.1. The size has been recovered by simply
associating the measured field of each particle to the corresponding size, on the basis of the exact Mie theory. Notice that no true inversion is needed here, thanks to thesingle particle measurement. The histogram represents
the distribution, expressed as the fractional abundance(%) of particles within each bin. Each bin was chosen to
be close to the sensibility of the method in the intermediate size range (where noise affects the data at minimum).
The red line is the cumulant distribution, while the blue
triangles show the expected sizes on the basis of the calibration of the samples.
In order to show the specific ability of the SPES method
to give two independent parameters, we show in Fig. 3.3
the results obtained from measurement of a suspension

Fig. 3.1

An example of the complex field data obtained withcalibrated PS spheres. The 2D histogram represents
the number of particles detected within each 2D bin.
The continuous line represents the expected complex
fields for PS spheres. The dotted line represents the
expected fields for n = 2.1, respectively.

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

Fig. 3.2

PSD obtained from the data reported in Fig. 1.1. The
histogram reports the experimental data, the red line
the cumulant curve, the blue triangles are the expected median values.

containing spheres of 600 nm in diameter, made of PS
and PMMA. The two materials present different refractive indexes so that we can distinguish them in the complex field and recover the corresponding refractive index
distribution. Moreover, when inverting data to recover thesize, we still obtain a single distribution cantered at
600 nm. Both results cannot be obtained with any traditional single particle method. The refractive index can be
accurately measured by capitalizing on the statistical parameters of the bimodal distribution, turning out with relative refractive indexes (in water) m = 1.12 and m = 1.19.
The expected values for the wavelength λ = 635 nm are
m = 1.118 for PMMA (Szczurowski, 2014) and m = 1.192
for PS (Sultanova, 2009).
In the following sections we report results obtainedwith ceria slurries under different conditions of interestfor industries, and we also show an analysis of Al2O3
slurries to extend the analysis to perhaps the most diffuse
abrasive materials in the industry worldwide.

4. Ceria slurries

Fig. 3.3 Results obtained with PMMA and PS spheres 600 nm
in diameter. a) The index distribution is as expected(see text for details). b) The size distribution is not influenced by different scattering properties due to thedifferent materials. For comparison, see Fig. 3.2.

We first consider the case of ceria slurries. We haveconsidered a sample of slurry commonly adopted for a
CMP process for global planarization of metal or dielectric layer of semiconductor production. Samples of ceria
have been carefully analysed through the SPES method,
operating in the size range from some 100 nm to approximately 1 micron.
In the first series of measurements, the samples wereprepared by accurately shaking the high-concentration
slurries (carefully preventing the sample from forming air
bubbles), and a small amount of slurry was strongly diluted in ultrapure water. A series of samples was preparedby passing the dilute suspensions through an ultrasonicbath for different time intervals. The aim was to check
whether any differences arise in the size distribution ofsamples treated for different times due to some kind of
aggregation process occurring in the original slurry,which would be broken by sonication. Notice that after
dilution, the characteristic time for aggregation is large
enough to allow measuring for hours without suffering
any aggregation process. Data collected at different times
after the sample preparation confirm this assumption.
Moreover, data from samples sonicated for different times(including no sonication) did not bring any appreciable
difference.
A number of measurements were performed in such a
way to test the reliability of the method, both experimentally and from the point of view of data analysis. We show
here typical examples of the measurements, focusing on
assessing the PSD. Finally, in order to focus on one of themain issues which is the stability of suspensions and the315Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

presence of unwanted large grains, we show results obtained with slurries which suffered aggregation processes
due to instabilities induced by chemical additives.
The results are shown in the complex plane in Fig. 4.1.
One population is evident here, polydispersity determining the extension over the complex plane. The complex
fields scattered by a polydisperse suspension of sphereswith the typical refractive index of ceria is drawn as
the dashed line. Notice that the spread around the line iscompatible with the presence of grains of homogeneouscomposition and almost isometric shape, which is in accordance with the expected case for ceria. The red continuous line was obtained as the line joining the medianvalues of the distributions obtained as the vertical sections of the raw data results. In Fig. 4.2 we show the PSD
obtained as described above from the same data shown in
Fig. 4.1. Abscissas are expressed in nm, ordinates represent the fraction of the whole population contained ineach bin. A total amount of several 104 events is represented in the figure which allows channelling the diameters in narrow bins with an appreciable number of counts
each. This makes the Poisson’s errors associated to thenumber of counts for each bin reasonably small, as indicated by the error bars (which indicate one standard deviation error).
Here we present results obtained with the same slurry
as above, mixed with a chemical (MBIT) adopted for HSS
applications. The same preparation procedure was adopted to ensure full consistency with the previous measurements. Fig. 4.3 represents the results for this sample.
Although the amount of detected particles is much lesscompared to Fig. 4.1, the relative population shows important contributions for the largest sizes (upper right region of the plot). These can be attributed to the presenceof aggregates composed of several particles. In Fig. 4.4

Fig. 4.1

316

Raw data obtained with ceria slurry, expressed as thehistogram in the complex plane. The dashed line isthe expected field distribution for spheres with the refractive index of ceria. The red continuous line describes the median of the distribution obtained by
selecting bins along the vertical direction.

we present the results of measurements performed on thesame sample of Fig. 4.3 after passing through strong sonication. The population in this case shows 1) an evident
depression of the upper size population, and 2) a population which is compatible with the results shown in Fig.
4.1. In this case, the presence of aggregates is evidenced
solely by the PSD. More generally, aggregates can be better recognized if they grew in size to acquire the typical
fractal structure. In such a case, the response of the SPES
measurements permit a clear assessment of the fractal nature of the objects (Potenza, 2014).
Notice that the identification of aggregates is one of the
key issues for the exploitation of slurries for high-quality
applications, as for example in semiconductor fabs. We
then consider this series of measurements as the bench-

Fig. 4.2

The PSD as obtained from the raw data in Fig. 4.1, as
described in the text. Error bars represent the Poisson’s standard deviations for the number of particles
within each bin.

Fig. 4.3

Experimental results obtained with ceria slurry
mixed with chemicals (see text). The measurement
was performed just after the sample preparation. The
difference to Fig. 4.1 is evident in the upper part ofthe diagram, corresponding to the larger particles.

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

Fig. 4.4

Experimental results obtained with the same sample
as in Fig. 4.3 put in an ultrasonic bath. The additional
population evidenced in Fig. 4.3 is drastically depressed, indicating the aggregate nature of the objects
which are broken into their original components.

mark to validate the present approach as a candidate forthe most demanding characterization of slurries.

5. Aluminium oxide slurries
In this section we present an example of the results obtained with exhaust AlO3 slurries for polishing plastic
ophthalmic lenses. The expected sizes are appreciably
larger than for ceria slurries, so that the accessible rangeof fields with the instrument exploited for ceria is notsuitable for these particles. We then adopted a different
realization of the SPES method, an instrument capable ofmeasuring real and imaginary parts more than 10 times
larger. Measurements performed with the SPES instrument adopted for ceria confirmed this choice. In Fig. 5.1
we show the raw data in the complex plane. Notice thatthe real and imaginary parts are 10 times larger than in
the previous figures. The fields of about 20,000 particles
are represented, accurately selected on the basis of stringent validation procedures mentioned above. The continuous blue line is the expected field distribution for sphereswith the expected refractive index (n = 1.75 in vacuum),
obtained through a simplified model (Van de Hulst, 1957).
No free parameters are present in this plot. The data arein good accordance with the expected values for spheresin the small size range (left-hand bottom part of the plot;
we stress that the plot is represented on a logarithmic
scale), while the larger sizes present an evident discrepancy. Here we give evidence that this is in accordancewith what was expected in the case that fractal aggregatesare present in the suspension. The black, dashed line is
the field distribution obtained on the basis of the model
describing the scattered fields from fractal aggregates,
obtained following a simplified model (Van de Hulst, 1957)
for the Rayleigh-Gans approximation of the Mie theory

Fig. 5.1

Experimental results obtained with more than 20,000
particles detected by the SPES instrument. The histogram represents the raw data in the complex plane.
The blue, thick, continuous line is the expected distribution of fields scattered by spherical particles withthe expected refractive index (n = 1.75 in vacuum),based upon a simplified Van-de-Hulst model calculated for a fractal dimension d = 1.8. The black,dashed line represents the expected values for aggregates (see text for details).

adapted to the case of fractal aggregates (Potenza, 2014).
It considers the effect of the reduced amount of matterwithin an aggregate with a given fractal dimension which
determines an increase of the imaginary parts with respect to the values for compact particles. Here the smallest particles have been considered to form aggregateswith a fractal dimension d = 1.8, a typical value for aggregates formed by instabilities due to electric activity between small grains. This is the case for small mineral
grains like those studied here. Notice that a precise valueof the fractal dimension does not overly affect the behaviour of the field distribution. Irrespective of the specific values for the parameters in this model (monomer
size, fractal dimension), we stress that in the complex
plane, a behaviour like that is only compatible with thepresence of aggregates. In fact, imaginary parts larger
than those for spheres of a given index solely indicate
particles with a smaller index, which can only be obtained
for non-compact objects.
By exploiting again the simplified model for the forward scattered field (Van de Hulst, 1957), expanding at
the second order the real and imaginary parts, and by inverting the outcoming relations in terms of the reduced
particle radii, we can give an estimate of the PSD of theparticles represented in Fig. 5.1. In Fig. 5.2 the diameter
distribution is represented for the same 20,000 particles
as above. We stress that this approach to the inversion, al-317

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321– The scattering parameters give access to information
about the presence of aggregates– Performance is not dependent on the number of particles counted per unit of time
– The results and the quality of the results are not obtained on the basis of any statistical approach– Background counts due to cosmic rays and electronicnoise do not affect the measurements– Air bubbles are rigorously got rid of on the basis of adeterministic algorithm

Fig. 5.2

PSD obtained from the raw data in Fig. 5.1. The particles in the upper part of the distribution are mainlycomposed of aggregates, while the smallest ones arecompact objects.

though roughly describing the particles as spheres, takes
advantage of the complete independence of the result on
the knowledge of the particle refractive index. It is therefore particularly suitable for analysing heterogeneous dataas the one of interest here, which contains both compact
particles and aggregates. Within the simplified model
adopted here, each particle is associated to its diameter
without any free parameter.
From the statistical analysis of this distribution we obtain an average size of 780 nm and a median of 711 nm.
The 10th, 25th, 75th, 90th percentiles are 250 nm,
547 nm, 1021 nm, 1363 nm, respectively.

6. Discussion and conclusions
The data presented so far clearly show the superior
ability of the complex field approach to give insights into
the details of real, polydisperse suspensions of small particles like those in slurries. In connection with the SPES
technology, several advantages are introduced, and theireffectiveness makes this approach capable of overcoming
the present status and the most important ITRS target
technology issues for particle counters listed above. We
can sum up the progress beyond the state of the art whichis achievable through the SPES method by discussing
each item mentioned above, taken from the ITRS target
technology issues.
– Capable of operating in-line, and integrated into a DM
tool as described above– Suitable for specific applications in nanoelectronic fabs
– The scattering parameters give access to precise sizing
and classifies the composition

318

We stress that thanks to the given composition and very
simple structures of the single grains, the ceria slurries
are well described solely on the basis of the PSD. This isstrongly supported by the 2D histograms, which show thecorrectness of the choice of one refractive index to recover the PSD. Differences like that shown above canthen be fully understood in terms of the PSD and properly
determined thanks to our approach. Nevertheless, in thecase of Al2O3, the presence of aggregates can be clearlyrecognized in the complex plane.
An additional advantage of the complex field approachis to recognize events clearly attributable to spurious particles such as water impurities (Potenza, 2015). This is
typically not the case of very dense slurries such as thosemeasured here, where proper handling protects them fromthe presence of impurities, but could be of importance inconnection with stringent requirements in process control
such as the presence of unwanted grains suspended in theslurry.
Moreover, the opportunity to monitor slurries easily
and in-line will enable industries to better decide about
the use of these materials. Increasing interest is given to
the possibility of reusing exhaust slurries, which is currently difficult simply due to the impossibility of adequately characterizing these materials. The slurry market
for applications less demanding than nanoelectronic fabs
could be fruitfully integrated by introducing reused slurries, once properly characterized (Seo, 2007; Testa, 2011;
Testa, 2014).

Acknowledgements
This scientific work was partially financed from the
ENIAC Joint Undertaking IMPROVE project JU Grant
Agreement No. 120005. Ceria slurries were supported by
ST Microelectronics. The devices used in this work werebuilt with the collaboration of F. Cavaliere and D. Viganò
of the Mechanical Workshop of the Department of Physics. We acknowledge the anonymous referee for usefulcomments.

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

Nomenclature
2D

Two-dimensional

CMP

Chemical Mechanical Polishing

CDS

Chemical Distribution System

DM

Decision Making

HSS

High Selective Slurry

FDC

Fault Detection System

IC

Integrated Circuit

ITRS

International Technology Roadmap for Semiconductors

NVC% Non-Volatile Content
PM

Particle Monitoring

PMMA Polymethilmetacrylate
PS

Polystyrene

PSD

Particle Size Distribution

SPES

Single Particle Extinction and Scattering

STI

Shallow Trench Isolation

ULSI

Ultra-Large-Scale IntegrationaParticle radius

Cext

Extinction cross-section

Csca

Scattering cross-sectiondFractal dimension

D

Particle diameterm

Relative refractive index (with respect to the surrounding medium)nRefractive index (with respect to the vacuum)S(0)Complex amplitude of the forward scattered fieldλLight wavelength

References
Basim G.B., Adler J.J., Mahajan U., Singh R.K., Moudgil B.M.,
Effect of particle size of chemical mechanical polishingslurries for enhanced polishing with minimal defects, Journal of The Electrochemical Society, 147 (2000) 3523–3528.
doi:10.1149/1.1393931
Basim G.B., Moudgil B.M., Effect of Soft Agglomerates on
CMP Slurry Performance, Journal of Colloid and Interface
Science, 256 (2002) 137–142.
Bassini A., Musazzi S., Paganini E., Perini U.U., Ferri F., Giglio
M., Optical particle sizer based on the Chahine inversion
scheme, Optical Engineering, 31 (1992) 1112–1117. doi:
10.1117/12.56158
Batchelder J.S., Taubenblatt M.A., Measurement of the size
and refractive index of a small particle using the complex
forward-scattered electromagnetic field, Appl. Opt., 30(1991) 4972–4979.

Batchelder J.S., Taubenblatt M.A., Measurement of the size andrefractive index of particles using the complex forward-­
scattered electromagnetic field, US Patent (1990) 547735.
Bohren C.F., Huffmann D.R., Absorption and Scattering by
Small Particles, Wiley, N.Y., 1983.
Borghese F., Denti P., Saija R., Scattering from Model Nonspherical Particles, Springer-Verlag Berlin-Heidelberg,
2003.
Budge J., Benefits and Limitations of Slurry Particle Analysis
and the Need for Next Generation Capabilities, 5th Annual
Levitronix CMPUG Symposium, 2009.
Budge J., Challenges of Large Particle Size Analysis in CMP
Slurries, and Opportunities for Improved Reliability, 29th
European CMP Users Symposium Spring, 2013.
Chylek P., Grams G.W., Pinninck R.G., Light Scattering by
Irregular Randomly Oriented Particles, Science, 193 (1976)
480–482
Cumbo M.J., Fairhurst D., Jacobs S.D., Puchebner B.E., Slurry
particle size evolution during the polishing of optical glass,
Applied Optics, 34 (1995) 3743–3755. doi: 10.1364/AO.34.
003743Denardis D., Choi H., Kim A., Moinpour M., Oehler A., Investigating the effects of diluting solutions and trace metal
contamination on aggregation characteristics of silica-based
ILD CMP slurries, MRS Online Proceedings Library, 867(2005) W7.9. doi:10.1557/PROC-867-W7.9.
Gebhart J., Response of Single-Particle Optical Counters to
Particles of irregular shape, Particle & Particle System
Characterization, 8 (1991) 40–47.
Ghosal S., Weber P.K., Laskin A., Spatially resolved chemical
imaging of individual atmospheric particles using
nanoscale imaging mass spectrometry: Insighs into particle
origin and chemistry, Analytical Methods, 6 (2014) 2413–
2792.
Giglio M., Potenza M.A.C., A method for measuring propertiesof particles by means of interference fringe analysis andcorresponding apparatus, Patent IT (2004) TO20040100;
PCT/IB2005/000411
Giglio M., Potenza M.A.C., A method for measuring propertiesof particles and corresponding apparatus, PCT Patent(2005) PCT/IT2005/00362
Heim M., Mullins B.J., Umhauer H., Kasper G., Performance
evaluation of three optical particle counters with an efficient “multimodal” calibration method, Journal of Aerosol
Science, 39 (2008) 1019–1031. doi:10.1016/j.jaerosci.2008.
07.006Hipp A.K, Storti G., Morbidelli M., Particle Sizing in Colloidal
Dispersions by Ultrasound. Model Calibration and Sensitivity Analysis, Langmuir, 15 (1999) 2338–2345.
ITRS (International Technology Roadmap for Semiconductors),
ITRS Industry Goals for Particle Measurements in UPW
and Liquid Chemicals, <www.itrs.net> accessed 2.09.2015
ITRS (International Technology Roadmap for Semiconductors),
ITRS Yield Enhancement 2009 Edition, <www.itrs.net>
accessed 2.09.2015
Li Y., Microelectronic Applications of Chemical Mechanical
Planarization, Wiley and Sons, 2008.
Liu Y., Daum P.H., The effect of refractive index on size distri-319

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

butions and light scattering coefficients derived from optical particle counters, J. Aerosol Sci., 31 (2000) 945–957.
Mavliev R., Method and apparatus for detecting individual particles in a flowable sample, US Patent (2004) 6710874 B2
Merricks D., Santora B., Her B., Zedwick C., Evolution and
Revolution of Cerium Oxide Slurries in CMP (2008) doi:
http://www.ferro.com/nr/rdonlyres/b92195ab-8d53-4bb48f49-078c6763f859/0/istc2008.pdf
Mishchenko M.I., Hovenier J.W., Travis L.D., Light Scatteringby Nonspherical Particles: Theory, Measurements, andApplications, Academic Press, San Diego, 1999.
Pate K., Tregub A., Characterization of abrasive particle distribution in CMP slurries, ICPT2011 Conference, 2011.
Potenza M.A.C., Sabareesh K.P.V., Carpineti M., Alaimo M.D.,
Giglio M., How to measure the optical thickness of scattering particles from the phase delay of scattered waves:
Application to turbid samples, Physical Review Letters, 105(2010) 193901.doi: 10.1103/PhysRevLett.105.193901
Potenza M.A.C., Milani P., Free nanoparticle characterization
by optical scattering field analysis: opportunities and perspectives, JNR, 16 (2014) 2680.
Potenza M.A.C., Sanvito T., Pullia A., Accurate Sizing of Ceria
Oxide Nanoparticles in Slurries by the Analysis of the
Optical Forward Scattered Field, JNR, 17 (2015) 110.
Potenza M.A.C., Sanvito T., Pullia A., Measuring the Complex
Field Scattered by Single Submicron Particles, submitted to
Applied Physics Letters (2015b)
Pelssers E.G.M., Cohen M.A., Fleer G.J., Single Particle OpticalSizing (SPOS): I. Design and improved SPOS instrument
and application to stable dispersions, Journal of Colloid andInterface Science, 137 (1990) 350–361.
Remsen E.E., Anjur S., Boldridge D., Kamiti M., Li S., Johns T.,
Dowell C., Kasthurirangan J., Feeney P., Analysis of large
particle count in fumed silica slurries and its correlation
with scratch defects generated by CMP, Journal of The
Electrochemical Society, 153 (2006) G453–G461. doi:
10.1149/1.2184036
Ruth U., Concentration and size distribution of microparticles
in the NGRIP Ice Core (Central Greenland) during the lastglacial period. PhD Dissertation at Department of Geosciences of the University of Bremen. Published as Report n.
428 of the Berichte zur Polar- und Meeresforschung (2002)(ISSN 1618-3193). Available at: http://www.ub.uni-heidelberg.

320

de/archiv/2291
Sachweh B., et al., In situ optical particle counter with improved
coincidence error correction for number concentrations up
to 107 particles cm–3, Aerosol Science, 29 (1998) 1075–1086.
Seo Y.J., Methodological Study on the Recycle of Oxide-­
Chemical Mechanical Polishing Slurry, Journal of the
Korean Physical Society, 50 (2007) 700–707.
Sultanova N., Kasarova S., Nikolov I., Dispersion properties of
optical polymers, Acta Physica Polonica A, 116 (2009)585–587.
Szczurowski M., (2014) private communication.
Terrell E., Beal D., Gromala J., Understanding Liquid Particle
Counters. PMS Inc. Boulder, Colorado, 2006.
Testa F., Coetsier C., Carretier E., Ennahali M., Laborie B.,
Serafino C., Bulgarelli F., Moulin P., Retreatment of silicon slurry by membrane processes, Journal of Hazardous
Materials, 192 (2011) 440–450. doi: 10.1016/j.jhazmat.2011.
05.016
Testa F., Coetsier C., Carretier E., Ennahali M., Laborie B.,
Moulin P., Recycling a slurry for reuse in chemicalmechanical planarization of tungsten wafer: Effect ofchemical adjustments and comparison between static anddynamic experiments, Microelectronic Engineering, 113(2014) 114–122. doi: 10.1016/j.mee.2013.07.022
Tinke A.P., Carnicer A., Govoreanu R., Scheltjens G., Lauwerysen
L., Mertens N., Vanhoutte K., Brewster M.E., Particle
shape and orientation in laser diffraction and static image
analysis size distribution analysis of micrometer sized rectangular particles, Powder Technology, 186 (2008) 154–167.
doi: 10.1016/j.powtec.2007.11.017
Van de Hulst H.C., Light Scattering by Small Particles, DoverN.Y, 1957.
Wells D., Nicoli D., Single-particle optical sensor with improved
sensitivity and dynamic size range, US Patent (1996)
5835211 A
Whitby K.T., Vomela R.A., Response of Single Particle Optical
Counters to Nonideal Particles, Environmental Science andTechnology, 10 (1967) 801–814.
Zelenyuk A., Cai L., Imre D., From agglomerates of spheres toirregularly shaped particles: Determination of dynamic
shape factors from measurements of mobility and vacuum
aerodynamic diameters, Aerosol Sci Technol, 40 (2006)
197–217.

Marco Potenza et al. / KONA Powder and Particle Journal No. 33 (2016) 310–321

Author’s short biography
Marco Potenza
Marco Potenza obtained his PhD in physics at the University of Milan with a thesis
about the applications of nonlinear optics. Since 2002, he has been a permanent researcher at the University of Milan, where he now leads the Optics Group, developing
novel instrumentation with scientific and technological applications. Since 2004, he has
been working for the European Space Agency developing optical instrumentation forspace, and he was the instrument scientist for the COLLOID experiment aboard thespace station in 2010. He is the author of more than 50 papers and 5 patents.

Tiziano Sanvito
Tiziano Sanvito obtained his PhD in physics in 2013 within the Optics Group at the
University of Milan with a thesis on the development of a novel technique for depolarized dynamic light scattering. He has been working on development of the SPES method
and its applications in different fields of physics and science in general. He is the authorof several research papers and a patent.

Giuseppe Fazio
Giuseppe Fazio is a physics graduate (application bearing, electronic branch) from
Milan University. He has significant experience in industrial electronic devices and since
2000, he has been working in the semiconductor industry. In the semiconductor field,
Giuseppe has significant experiences in advanced process and equipment control. He
was the APC/AEC group leader in ST Microelectronics. He is the author and co-authorof several publications and patents.

321

KONA Powder and Particle Journal No. 33 (2016) 322–332/Doi:10.14356/kona.2016018

Original Research Paper

Decoration of Carbon Nanotubes
by Semiconducting or Metallic Nanoparticles
using Fluidized Bed Chemical Vapour Deposition †Pierre Lassègue 1, Nicolas Coppey 1, Laure Noé 2,
Marc Monthioux 2 and Brigitte Caussat 1*
1
2

Université de Toulouse, Laboratoire de Génie Chimique, FranceCentre d’Élaboration de Matériaux et d’Etudes Structurales (CEMES), UPR-8011 CNRS, France

Abstract
Multi-Walled Carbon Nanotubes (MWCNTs) have promising properties that make them potentially useful in awide variety of applications. The decoration of MWCNTs by metallic or semiconducting nanoparticles aims tointensify some of their properties, in particular thermal and electrical conductivity. Fluidized Bed Chemical
Vapour Deposition (FBCVD) is an efficient process to uniformly coat powders by various materials. The coatingby SnO2, Fe and Si nanoparticles of MWCNTs (Graphistrength®) tangled in balls of 360 microns in mean
diameter using the FBCVD process has been studied. The influence of some deposition parameters with andwithout oxidative pre-treatment is analysed on the nucleation and growth of nanoparticles. The various results
obtained indicate that the intrinsic surface reactivity of MWCNTs is high enough for CVD precursors involving
the formation of highly reactive unsaturated species such as silylene SiH2 formed from silane SiH4 pyrolysis in
the case of Si deposition. But it must be enhanced for less reactive CVD precursors such as tin tetrachloride SnCl4
which needs the presence of oxygen-containing groups at the nanotube surface to allow Sn nucleation. So,
provided the reactivity of the powder surface and that of the CVD precursors are well tuned, the FBCVD process
can uniformly coat the outer surface of MWCNTs by metallic or semiconducting nanoparticles.
Keywords: fluidized bed, chemical vapour deposition, multi-walled carbon nanotubes, nanoparticle deposition,
silicon, tin oxide, iron

1. Introduction
Multi-Walled Carbon Nanotubes (MWCNTs) are nowadays a popular nanoform of graphenic carbon in whichgraphenes are arranged more or less parallel to the nanofilament axis. They have promising properties that makethem potentially useful in a wide variety of applications
such as electronics, renewable energy and composite materials. They exhibit a large surface area, high mechanicalstrength, and unique electrical properties, and are efficient thermal conductors (Monthioux et al., 2007). The
decoration of MWCNTs by semiconducting or metallic
nanoparticles (NPs) aims to intensify some of their physical properties. For instance, the deposition of silicon NPs
†
1
2

*

Received 13 May 2015; Accepted 17 July 2015
J-STAGE Advance published online 10 October 2015
ENSIACET/INP Toulouse/UMR CNRS 5503, 4 allée Émile Monso,
BP84234, 31432 Toulouse Cedex 4, France
29 rue Jeanne Marvig, BP94347, 31055 Toulouse Cedex 4, France
Corresponding author: Brigitte Caussat;
E-mail: brigitte.caussat@ensiacet.fr
TEL: +33-53-432-3632 FAX: +33-53-432-3697

322

on MWCNT networks allows the formation of anodes of
Li-ion batteries with improved electrochemical performance due to the high specific capacity of silicon andgood mechanical strength and structural flexibility of
MWCNTs (Cui et al., 2010). Composites made of SnO2
NPs deposited on MWCNTs present excellent sensing responses for gas detection at room temperature (Mendoza
et al., 2014). Metal-coated MWCNTs can be used as reinforcing and conductive fillers for high-performance polymer composites by combining the electrical conductivityof the metal and the mechanical properties of the nanotubes (Babal et al., 2014).
There are many methods of depositing nanoparticles
onto MWCNTs, each offering various degrees of controlof particle size and distribution along the MWCNTs(Wildgoose et al., 2006; Bacsa and Serp, 2012). They are
generally classified in two groups: the wet methods including the electrochemical, sol-gel and impregnation
methods, and the gas processes such as Physical Vapour
Deposition (PVD), Atomic Layer Deposition (ALD) andChemical Vapour Deposition (CVD) (Kuang et al., 2006;
Bacsa and Serp, 2012; Goulas et al., 2014). The wet im-©2016 Hosokawa Powder Technology Foundation

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

pregnation method is the most widely used techniqueeven if a multi-step method, because of its simplicity(Bacsa and Serp, 2012). However, the size of the coatedparticles is difficult to control precisely (Kuang et al.,2006). The gas processes yield products purer than thewet methods. They are single-step methods and generally
involve high coating rates (Bacsa and Serp, 2012). CVD
on MWCNTs has often been used to deposit metallic (Pt,Pd, Ru, Rh) or semiconducting (InP, CdSe) nanoparticles(Bacsa and Serp, 2012), but most often at the milligram
scale, on nanotubes either deposited or supported on a planar substrate or put into a crucible as a fixed powder bed.
CVD can be adapted to the large-scale production ofcoated MWCNTs provided a good gas-solids contact canbe achieved. Gas-solids fluidisation consists in flowing a
gas upwards through a vertical bed of particles, thus generating an intense gas-solids mixing and subsequently
high thermal and mass transfer rates between the gas andthe powders (Kunii and Levenspiel, 1991). Fluidisation
can be combined with the CVD technology, giving rise to
the Fluidized Bed Chemical Vapour Deposition (FBCVD)
process. If the MWCNTs can be fluidized, this technology
allows multi-gram-scale beds of nanotubes to be uniformly coated with a high conversion rate of the gaseous
precursor into solids, high versatility, good homogeneityof products and the possibility of continuous operations(Vahlas et al., 2006). This technology is also easy to use
and to rescale, and requires low equipment costs (Vahlas
et al., 2006).
Whatever the process used, a bottleneck in the coatingof MWCNTs by metallic or semiconducting materials often concerns their surface reactivity. Nanotubes areformed of graphenes often arranged in a concentric manner with both ends ideally capped by fullerene-like structures. Due to the seamless arrangement of hexagon rings
without any dangling bonds, carbon nanotube walls arerather unreactive (Lin et al., 2003). Experimental observations have clearly shown that surface defects which are
inherently present play an important role in the adsorptionof atoms on MWCNTs. But as the density in defects isgenerally low, a surface activation in the form of functionalization or creation of defects at the carbon nanotube
surface is essential to achieve a high coating density(Feng and Puddephatt, 2007; Bacsa and Serp, 2012). The
most common method at the lab scale is the oxidative acidreflux that purifies and functionalizes the nanotubes in
one step by grafting hydroxyl (C-OH), carbonyl (C = O)and carboxyl (O = C-OH) groups on the nanotube surface(Bacsa and Serp, 2012; Vennerberg et al., 2014). However,the liquid wastes generated by these wet oxidation methods are not suited to industrial use (Kim and Min, 2010).
Some works about gas-phase oxidation of CNTs using
either air (Ajayan et al., 1993; Behler et al., 2006) or nitricacid vapours (Xia et al., 2009; Li et al., 2012; Rong et al.,2010) at high temperatures have led to mixed results: theCNTs were structurally damaged or far worse, even partially burnt. Gas-phase oxidation from ozone (O3) is anenvironmentally friendly and low-cost route to form oxygen-containing groups on the CNT surface, even for mass
production (Vennerberg et al., 2014). This route will betested in the present work. By a pre-deposition of a titanium carbide (TiC) layer by CVD acting as a surfacefunctionalization, Feng and Puddephatt (2007) have successfully increased the surface coverage of CNTs by Ni,
Pd and Pt films deposited by CVD.
The present article aims to detail some examples of
MWCNT coating by FBCVD in order to provide new insights concerning the interaction between a reactive gaseous phase leading to nanoparticle deposition and thenanotube surface. Three deposits were selected involving
CVD precursors of very different reactivity and decomposition pathways. Tin oxide deposition from tin tetrachloride SnCl4 and water vapour will be first presented,
and then silicon deposition from silane SiH4, and finallyiron deposition from ferrocene Fe(C5H5)2. In particular, it
will be shown that surface activation is not always necessary to coat MWCNTs by metallic or semiconducting
particles.

2. Experimental set-up
The experimental set-up (Fig. 1) is composed of a vertical stainless steel FBCVD reactor (internal diameter of
8.3 cm and 1 m in height) heated by a two-zone external
furnace. The top of the reactor is water-cooled to ensure
gas tightness. A high-performance HEPA13 filtration cartridge is placed at the exhaust of the reactor to collect any
elutriated particles.
At the bottom, a perforated steel plate provides a homogeneous gas distribution and the conical zone of the gas
inlet is pre-heated by either an oil bath or heating ribbons.
A differential pressure sensor (Unik5000; Druck Ltd.)
with taps under the distributor and at the top of the reactor measures the pressure drop across the MWCNT bed.
An absolute pressure sensor (PR21; Keller) allows monitoring the total pressure below the distributor. Temperature is measured by several K-type thermocouples: three
in a small tube along the vertical axis of the reactor tocheck the MWCNT bed isothermicity (T1 at 6 cm above
the distributor, T2 at 21 cm and T3 at 28 cm for the SnO2
and Si depositions, T1 at 6 cm, T2 at 12 cm and T3 at
21 cm for the Fe deposition), two on the outer reactor
walls to control the temperature furnace and one in the
bubblers when used.
Ferrocene Fe(C5H5)2 (98 %, Strem Chemicals) and tin
chloride SnCl4 (anhydrous, 98 %, Strem Chemicals) areused as received. Deionised water is mixed to SnCl4 to323

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

Fig. 2

Fig. 1FBCVD reactor.

deposit SnO2. Each is loaded into a specific bubbler
placed in a thermostatically controlled oil bath and connected to the reactor entrance by thermostated controlled
gas lines. The bath temperature is fixed at 50 °C for SnCl4and H2O and at 155 °C for ferrocene. All the gas lines areheated by heating ribbons to the same temperature as the
precursor bubblers to avoid any premature condensation.
Gas flows (N50 N2 and H2, Air Liquide) are regulated bymass flow controllers (MFC-7700 type; Aera), except silane (Electronic grade SiH4, Air Liquide) whose flow rate
is regulated by a ball rotameter (GT1355, Brooks Instrument). The ozone generator (Lab2B, Ozonia) is connectedto the bottom of the FBCVD reactor and is fed with oxygen (Alphagaz oxygen, Air Liquide), ensuring an ozone
concentration of 10 g.h–1. All the experiments are performed at atmospheric pressure.
Multi-walled carbon nanotubes (Graphistrength® C100;
Arkema) were used without any purification treatment.
As can be seen in Fig. 2a and 2b, they are entangled in
the form of coarsely spherical balls of 388 μm in Sauter
diameter.
Nanotube walls are made of 10 –15 concentric
graphenes with a mean external diameter of 12 nm. Some
amorphous carbon is present on the walls and internal defects are visible (Fig. 2c). Their intrinsic iron content is
1.35 wt%. Their skeleton density is 1.984 kg/m3, whereas
the grain density of the MWCNT balls was estimated at~180 kg/m3 and their untapped density is equal to 90 kg/

324(a, b) SEM and (c) TEM views of the raw MWCNTs.

m3 (Coppey, 2013) As found by other authors in the caseof nanotubes (Yu et al., 2006; Dasgupta et al., 2011), these
MWCNT balls display very good fluidization performance. They exhibit agglomerate particulate fluidization(APF) characteristics between 0 and 3.3 Umf (minimum
fluidization velocity) and agglomerate bubbling fluidization (ABF) features beyond 3.3 Umf with high bed expansion (Coppey, 2013). This behaviour is characteristic of
the fluidization of nanoparticle agglomerates (Zhu et al.,2005; Wang et al., 2007). It should favour excellent gassolid contact and then high conversion rate of the CVD
precursors and also uniform deposition. The minimum
fluidization velocity Umf of the MWCNT balls measured
at ambient temperature under N2 is equal to 1.2 cm.s–1(Coppey, 2013).
MWCNTs were analysed before and after ozone
pre-treatment and CVD by several characterization techniques. The powder morphology was observed by Field
Emission Gun Scanning Electron Microscopy (FEGSEM) on a Jeol JSM-7100F linked to an Energy Dispersive X-ray (EDX) detector (X-MAX N Oxford Instrument)
and High-Resolution Transmission Electron Microscopy(HRTEM) using a CM30 (Philips) with LaB6 gun and operated at 150 kV to minimise damaging irradiation effects. The SEM and TEM images presented in the article
were selected from many as the most representative onesof each sample. X-ray Diffraction (XRD) results were obtained using a D8-2 (Drucker) with a monochromatic Kα
copper (Cu) source (λ = 1.5418 Å). The iron weight percentage deposited on MWCNTs was measured by
ICP-MS (XSeries2 Thermo Scientific).
A dimensionless bed pressure drop ΔP* was calculated
as the ratio between the bed pressure drop and the theoretical one (the latter being equal to the bed weight perunit section area). The bed pressure drop was obtained bysubtracting the pressure drop of the distributor from thePierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

differential pressure measured.
The operating conditions tested are detailed in Table 1.
100 g of MWCNTs were used for each experiment, corresponding to a ratio between the fixed bed height and thereactor diameter of 2.3. Such a ratio is necessary to ensure
good thermal transfer between the reactor walls and the
FB and isothermal bed conditions, which are both wellsuited to thermal CVD. The fluidization ratio U/Umf was
fixed to 4 to ensure a good compromise between thegas-particle mixing and the residence time of the gas in
the bed. For liquid and solid precursors, the gaseous precursor molar fractions entering into the reactor were estimated by assuming the equilibrium in the bubblers andusing the vapour pressure laws of Yaws (1999) for SnCl4
and of Emel’yanenko et al. (2007) for ferrocene.

3. Results
3.1 Process behaviour
Fig. 3 presents the dimensionless bed pressure drop
and thermal profile measured during run nCNT3. They
are characteristic of the overall results obtained. The thermocouples T1 and T2 are located in the fluidized bed
whereas T3 is just above the FB. Indeed, in a preliminary
study conducted in a glass column, the fluidized bed
height was measured at U/Umf = 4 as varying between 25
and 27 cm (Coppey, 2013).
This explains that the temperatures measured by T1
and T2 are very close to each other, thanks to the goodgas-solids mixing and particle-wall contact existing in thefluidized bed. The bed thermal gradient did not exceed
5 °C for all the CVD experiments. The temperature meaTable 1
Run

sured by T3 is lower because the thermocouple is locatedin a region where the fluidized particles are more diluted
in the gas and then it is directly in sight of the reactor
walls.
The T1 and T2 temperatures were not adversely affected by the deposit, probably due to the quite low inlet
molar fractions of the precursors tested. The T3 temperature decreases with run duration because the fluidized
bed height tends to decrease due to the grain density increase induced by the deposit.
The dimensionless bed pressure drop ΔP* progressively increased during deposition, which is indicative of
the good fluidization of the bed. This allows the increaseof the bed weight due to deposition to be followed in realtime. The agreement between the deposited masses deduced from the bed pressure drop and those obtained byweighing the bed before and after deposition is good on
average, confirming that the bed of MWCNTs remained
fluidized during the whole experiment.
The elutriation of the powder was low, on average lessthan 1 wt% of the bed after the CVD experiments. This isdue to the fact that the MWCNT balls were not broken by
the FBCVD treatment: the Sauter diameter of the powders
after CVD remained very close to that before treatment,
as measured by laser size scattering analysis (not shown).

3.2 SnO2 deposition
Possible CVD precursors of SnO2 are SnCl4, Sn(CH3)4,(CH3)2SnCl2 and SnH4 (VanMol, 2003; Coppey, 2013). By
oxidation with O2 or hydrolysis with H2O, these precursors form a SnO2 layer. The most commonly used precursor is tin tetrachloride SnCl4 mixed with water, for which
the deposition conditions are quite well established (LiOperating conditions tested and experimental results obtained
Substrate

Target Average bed Run duration
deposit temperature
(min)
(°C)

Precursor andinlet molar %Deposited mass
Conversion
deduced fromrate of the main
bed weighing (g) precursor (%)

T02

Raw CNTs

SnO2

500

90

SnCl4 —0.9
H2O—1.8

27

78

T05

Raw CNTs

SnO2

550

125

SnCl4 —0.5
H2O—1.9

17

90

T06

H2O-CNTs

SnO2

550

120

SnCl4 —0.5
H2O—1.9

15

79

S30

Raw CNTs

Si

500

60

SiH4 —7

40

100

W6

H2O-CNTs

Si

500

60

SiH4 —7

38.6

100

nCNT3

Raw CNTs

Fe

650

180

Fe(C5H5)2—2.3

57

87

nCNT9

O3-CNTs

Fe

650

180

Fe(C5H5)2—2.3

52.5

100

nCNT10O3-CNTs

Fe

550

180

Fe(C5H5)2—2.3

37.8

90

nCNT13

Raw CNTs

Fe

650

180

Fe(C5H5)2—2.3

40

84

325

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

Fig. 3

Bed thermal profile and dimensionless pressure drop
during run nCNT3 (the dotted lines indicate the beginning and the end of the deposit).

and Hua, 1997; VanMol, 2003; Coppey, 2013). This routehas been used here.
Fig. 4a presents SEM images of the MWCNT balls after
run T02 was performed at 500 °C as detailed in Table 1.
The deposit appears as micron-size particles at the ballsurface, predominantly in the interstices and cracks
within the balls. At higher magnification (Fig. 4b), someparticles of the deposit can be distinguished. They present
a pyramidal form, characteristic of crystalline materials.
Their size ranges from a few hundred nanometres to several microns. The largest ones might be made up of aggregates of primary particles of the deposit. So, thesurface coverage of the MWCNTs is low.
Some SEM-EDX analyses on MWCNT balls which had
been opened along their hemisphere (Fig. 5) reveal that
the SnO2 particles are present everywhere inside the balls.
This means that the mass transfer rate inside the porous
MWCNT balls is higher than or equal to the heterogeneous
reaction rate, probably thanks to the quite low reactivityof the SnCl4 precursor on the nanotube walls. Table 2
summarizes the chemical reactions and pathways available in the literature about SnO2 CVD from SnCl4/H2O. It
appears that at our deposition temperature (500 °C), seeds
can be formed in the gas phase and can then be capturedby the particles of the bed to act as nuclei for the deposit(Li and Hua, 1997). A direct heterogeneous deposition
pathway also exists. The two pathways could co-exist in
our conditions.
In order to increase the reactivity of the precursors andthen the direct nucleation on the MWCNT surface, ahigher temperature was imposed for run T05. The H 2O/
SnCl4 ratio was also increased in order to favour thewater adsorption on the MWCNTs and then to intensify
the MWCNT surface reactivity. H 2O adsorption on thesubstrate is also necessary to allow Sn (Wartenberg et
Ackermann, 1988) to be deposited.
Fig. 4c shows that the number of deposited particles
seems to have decreased in comparison with run T02.

326

Fig. 4

SEM images of MWCNTs after (a, b) run T02, (c, d)
run T05, (e, f) run T06 (SnO2 deposition).

Fig. 5(a) SEM image of a cut MWCNT ball after SnO2 deposition, (b) corresponding EDX cartography of Sn (runT02).

This has been confirmed by the entire SEM analyses performed for these runs. Their formation still occurs predominantly in the interstices of the MWCNT balls. At
higher magnification (Fig. 4d), the size of the particles
seems larger than for run T02. As detailed in Table 1, theconversion rate of SnCl4 is higher than for run T02 (90 %Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

Table 2

Chemical reactions and pathways involved in the CVD of SnO2, Si and Fe

Deposit Homogeneous chemical reactions

Heterogeneous chemical reactions References

SnO2

Unknown reaction pathway leading to SnO2 From 200 °C:
fines formation above 400 °C at sufficiently SnCl4 + 2 H2O → SnO2 + 4 HClhigh SnCl4 and H2O concentrations
Necessity of H2O adsorption on
the substrate to deposit Sn

Li and Hua (1997)–Van
Mol (2003)–Wartenberg
and Ackermann (1988)

Si

SiH4 ↔ SiH2 + H2
SiH2 + SiH4 ↔ Si2H6
SiH2 + Sin H2n + 2 ↔ Sin + 1H2n + 4

Reuge et al. (2009)–Liu
and Xiao (2015)

Fe

Possible homogeneous formation of Fe3C
Under N2:
particles and of CNT at temperatures higher Fe(C5H5)2 → Fe + 2 C5H5than 500 °C
Under H2:
Fe(C5H5)2 + H2 → Fe + 2 C5H6

SiH4 → Si + 2 H2
SiH2 → Si + H2
Sin H2n + 2 → n Si + (n + 1) H2

Lubej and Plazl (2014)–
Wasel et al. (2007)–
Senocq et al. (2006)

Under H2O:
4 Fe(C5H5)2 + 45 H2O → 4 Fe +35 CO + 65 H2 + 5 CO2instead of 78 %), meaning that the reactivity of the SnCl4/
H2O mixture was increased but the surface reactivity of
the MWCNTs still remained too low. It can be concluded
that the concomitant increase of the temperature and of
the H2O/SnCl4 ratio was adequate to increase the precursor reactivity on already formed nuclei since growth wasintensified, as found by Li and Hua (1997). However, thisincrease of precursor reactivity was not high enough toenhance SnO2 heterogeneous nucleation on the graphenic
surface of the MWCNTs, which is clearly the limiting
step of the deposit, as found by Feng and Puddephatt(2007) for Ni deposition on CNTs.
In order to increase the MWCNT surface reactivity, apre-treatment of the nanotube bed using water vapour was
finally applied during 60 min at 650 °C at a fluidization
ratio U/Umf of 4, just before proceeding with the deposition using conditions close to those of run T05. Fig. 4eshows that after run T06, the deposit is mainly formed ofnanosized particles even if some larger particles are present in which MWCNTs seem to be embedded, as shown
on Fig. 4f.
So, the surface reactivity of the MWCNTs towards the
SnCl4/H2O precursors has been intensified by the water
pre-treatment since the nucleation is clearly higher thanfor the previous runs. The fact that some MWCNTs havebeen trapped by the deposit in the largest deposited particles confirms the higher wettability of the MWCNT surface by the deposit.
The conversion rate of SnCl4 is lower than for run T05,
in fact quite close to that of run T02. This could mean thatthe nucleation rate on the graphenic surface remained
lower than the growth rate on the already formed nuclei.
An autocatalytic behaviour could exist, as it is often thecase in the CVD of metals (Feng and Puddephatt, 2007).

Fig. 6 TEM images of MWCNTs after (a, b) run S30, (c, d)run W6 (silicon deposition).

3.3 Si deposition
Silicon CVD precursors are most often chosen among
the silanes (silane SiH4, disilane Si2H6), the silicon chlorides (SiHCl3, SiH2Cl2) or the organosilanes (in particulartrimethyl silane SiH(CH3)3) (McEvoy et al., 2013). Even ifpyrophoric, silane presents the advantages of producing
pure silicon deposits at temperatures lower than 700 °C
without any corrosive by-products, which explains its
massive use in the microelectronic and photovoltaic industry (Braga et al., 2008; Reuge et al., 2009; McEvoy etal., 2013).
Figs. 6a and 6b present TEM views of the MWCNTs
after run S30 performed at 500 °C, as detailed in Table 1.

327

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

Silicon is present everywhere in the MWCNT balls, taking the form of nanoparticles of 20 nm in average diameter. This indicates that the mass transfer into the porous
balls is not a limiting step of the process, as for the SnO2
deposition. The nanoparticles are predominantly present
on structural defects of the MWCNT surface.
The conversion rate of silane was 100 %, probably due
to the high surface area of the MWCNT powder and to
the quite high reactivity of silane.
The silicon nanoparticles are clearly more numerous
and smaller than the SnO2 particles. These results meanthat the reactivity of silane is higher than that of the
SnCl4/H2O mixture. However, a nucleation limitation still
exists for Si deposition since the surface coverage of the
MWCNT surface is not 100 %. It is well-known that, asdetailed in Table 2, when silane is heated to temperatureshigher than 370 °C, pyrolysis reactions occur in the gas
phase leading to the formation of very reactive unsaturated molecules such as silylene SiH2. It is likely thatthese unsaturated molecules are the origin of the first formation of nuclei on the MWCNT surface.
They are probably much more reactive than the species
formed during the depositions from SnCl4/H2O. However,they are not reactive enough to chemisorb and form nuclei
everywhere on the inert MWCNT surface. Once the firstSi nuclei are formed on the nanotube surface, the growth isprobably easier as shown by Zahi et al. (2011) on SiO2 planar substrates, explaining the 100 % conversion of silane.
In order to intensify the surface reactivity of the
MWCNTs, a pre-treatment using water vapour at 650 °C
was applied during 60 min at a fluidization ratio of 4 justbefore the deposition using the conditions of run S30, as
detailed in Table 1. The conversion rate of silane for run
W6 was still 100 %. As can be seen in Figs. 6c and 6d, no
significant difference can be seen regarding the morphology of the deposit between this run and run S30. The size,
localisation and surface density of the Si nanoparticles areunchanged. It can be concluded that for the conditions
tested, the water pre-treatment has not modified the surface reactivity of the MWCNTs for the silicon deposition
from silane.
If we compare with the SnO2 deposition results, thesame pre-treatment was applied to the MWCNTs. In thecase of SnCl4/H2O, an increase of the deposit nucleation
had occurred, whereas this was not the case for silane. As
previously explained, SnCl4 needs a preliminary H 2O
adsorption step for allowing the chemisorption of Sn tooccur. It is then logical that the SnO2 deposition is intensified by an H2O pre-treatment. On the contrary, the surface
reactivity of the chemical species responsible for silicon
deposition is not intensified by the H2O pre-treatment.
Previous works (Zahi et al., 2011) about the first steps of
nucleation and growth during silicon CVD from silane onplanar substrates have shown that substrates covered by328hydroxyl (OH) groups involve a quite low surface reactivity of unsaturated species such as SiH2 due to the highelectronegativity of the oxygen atom. For the Si FBCVD
process, the creation of more defects on the MWCNT surface would probably have been more efficient to increase
Si nucleation.

3.4 Fe deposition
Among the possible CVD precursors of iron, ferrocene(Fe(C5H5)2) is an organometallic compound stable in air
and non-toxic, quite cheap and easy to sublimate, forming
stable vapour to feed the CVD reactor (Philippe, 2006;
Senocq et al., 2006) and able to deposit pure iron (Stauf etal., 1987; Dormans, 1991).
As detailed in Table 1, the deposition temperature was
fixed at 650 °C, except for nCNT10 (550 °C) because of
the reactivity increase of ferrocene induced by the presence of hydrogen (Wasel et al., 2007; Qian et al., 2001).
Under an inert atmosphere (N2), Figs. 7 and 8a showthat the deposit occurs in the form of discrete iron-based
nanoparticles of 50–500 nm in diameter confined in agraphenic carbon shell and present everywhere in the
MWCNT balls. This has been confirmed by SEM-EDX
analyses (not shown) at the ball scale.
This uniform distribution indicates that the mass transfer
into the porous balls is not a limiting step of the process,as for the previous deposits. But the surface coverage ofthe MWCNTs is low. So, a nucleation limitation also exists for this deposit. X-Ray Diffraction (XRD) analyses(Fig. 9) indicate that the nanoparticles are composed ofiron and iron carbide Fe3C for all runs. A possible explanation is that the homogeneous decomposition of ferrocene under N2 is auto-catalytic beyond 500 °C (Senocq etal., 2006) and forms Fe3C particles (cementite) which canthen be captured by the nanotubes of the bed. Some
nanoparticles could also directly nucleate on the MWCNT
surface, probably on surface defects like for silicon. SomeFig. 7SEM image of MWCNTs after run nCNT3 (deposition
from ferrocene).

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

of the nanoparticles seem to catalyse the formation of
nanofibres (CNFs) or of large CNTs.
Ferrocene is indeed known to be a precursor of bothiron and carbon nanomaterials (Bhatia and Prasad, 2010).
Carbon coming from the ligands lost by ferrocene during
its decomposition could saturate iron nanoparticles toform Fe3C and then CNTs and/or carbon nanofibres(CNFs).
The conversion rate of ferrocene into iron deposited
within the MWCNT balls was deduced from ICP-MS
measurements and bed weighing. It is 87 %, as detailed inTable 1. ICP-MS measurements indicate that the powder
after run nCNT3 is composed of 11.8 wt% of iron (with-

Fig. 8 TEM images of MWCNTs (a) run nCNT3—insert: viewof a Fe nanoparticle forming (a) CNF, (b) run nCNT9,(c) run nCNT10, (d) run nCNT13 (deposition fromferrocene).

out removing the initial iron content).
The same deposition conditions were tested after an O3
pre-treatment (nCNT9) in order to increase the surface
reactivity of MWCNTs and then the nucleation of iron.
TEM images (Fig. 8b) indicate that the deposit produces
10–100-nm-size iron-based particles more uniformly distributed at the TEM scale than previously. The depositedparticles are then more numerous and smaller than previously. These two effects and the more uniform presenceof nanoparticles on the nanotube surface could be a consequence of ozonation. By grafting oxygen-containing
groups (hydroxyl or carboxyl) onto the nanotube surface,ozonation increases the nucleation of iron-based nanoparticles and then the surface reactivity of MWCNTs towards
ferrocene and its gaseous products of decomposition. Thisis confirmed by the ICP-MS iron content of 17.6 wt% in
the powder and by the higher ferrocene conversion rate
(close to 100 %), as detailed in Table 1. However, the deposited nanoparticles are still covered by graphenic shells,
CNFs/CNTs are still formed and the deposit is still composed of Fe and Fe3C (Fig. 9). Moreover, a large amountof amorphous carbon was deposited everywhere.
The addition of H2 at a volume percentage higher than
25 % is known to limit CNF/CNT growth and amorphous
carbon formation (Wasel et al., 2007; Qian et al., 2001).
Consequently, H 2 was added with a H 2 /N2 ratio of
67/33 vol.% during run nCNT10. TEM images (Fig. 8c)reveal less numerous iron-based nanoparticles which areless uniformly distributed at the TEM scale. These Fe/
Fe3C particles are smaller (20–50 nm) and still covered bygraphenic shells but no CNFs/CNTs were observed andthe large amount of amorphous carbon previously reported is absent. So, the addition of H2 seems to have inhibited the formation of both the amorphous carbon and
the CNT/CNFs, but seems also to reduce the nanoparticle
nucleation and growth, as confirmed by the ICP-MS ironcontent of 13.7 wt% and by the ferrocene lower conversion rate (90 %).
According to Senocq et al. (2006), the addition of water

Fig. 9XRD spectra of MWCNTs after deposition from ferrocene.

329

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

vapour to ferrocene could favour the formation of pureiron films under conditions close to ours. The runnCNT13 was then performed in the presence of water vapour. TEM images are shown in Fig. 8d. Less numerous
and less uniformly distributed Fe3O4/Fe3C nanoparticles
are covered by graphenic shells and have catalysed theformation of large CNFs/CNTs. Their diameter is related
to the nanoparticle size, ranging between 10 and 100 nm.
The ICP-MS iron content is 12.3 wt%, which is similar tothat of run nCNT3. This means that under the conditions
tested, the addition of water vapour does not favour thedeposition of pure iron nanoparticles as expected. It even
seems to lower the ferrocene reactivity in proportion close
to that observed in the presence of hydrogen, as indicatedby the ferrocene conversion rate of 84 % given in Table 1.
When compared with the two previous deposition experiments (SnO2 and Si), the use of ferrocene involves not
only a nucleation limitation on the nanotube surface asstrong as for the SnCl4/H2O system, but also a difficulty
to form pure iron deposits, which was not the case of thetwo previous deposits studied. When compared with otherworks performed on steel, quartz or Si planar substrates(Stauf et al., 1987; Dormans, 1991; Senocq et al., 2006),
this difficulty to deposit pure iron is clearly reinforced by
the chemical nature of the MWCNT surface. A pre-depositof TiC as done by Feng and Puddephatt (2007) could improve both the surface reactivity of the MWCNTs and theselectivity of ferrocene decomposition to produce pureiron deposits.

4. Conclusion
Multi-walled carbon nanotubes were decorated by threekinds of nanoparticles from precursors of very different
reactivity using the fluidized bed chemical vapour deposition process.
When comparing the results obtained in terms of nucleation of nanoparticles on the nanotube surface, it
clearly appears that the intrinsic reactivity of the CVD
precursors is a key factor. The most reactive precursor westudied, silane SiH4, forms many silicon nanoparticles ofseveral tens of nanometres on the nanotube surface, without any nanotube pre-treatment. This is not the case withthe less reactive SnCl4/H2O system for which the nucleation of SnO2 nanoparticles was favoured by an H2O
pre-treatment. A third situation was observed using ferrocene, for which not only a nucleation limitation on the
nanotube surface exists, but also a difficulty to obtainpure iron deposits which seems reinforced by the chemical nature of the nanotube surface. However, an ozone
pre-treatment of the nanotubes made it possible to increase the iron deposition.
So, the FB-CVD process can successfully decorate car-330

bon nanotubes by metallic or semi-conducting nanoparticles provided that the reactivity of the precursors towards
the nanotube surface is high enough. If this is not thecase, a dry mode pre-treatment of the nanotubes by ozone
or water vapour can be efficient if the surface modification involved is well-suited to the precursor reactivity.
The reactive pathways of the precursor must then be analysed accurately before deciding whether to apply a
pre-treatment and what kind of pre-treatment.
In comparison with the wet methods, a main advantageof the dry mode pre-treatments in fluidized beds and of
the FB-CVD process is that they can treat large amountsof carbon nanotubes, i.e. 100 g at lab-scale and tons at theindustrial scale, opening the way for a mass production of
decorated MWCNTs for applications such as nano-fillers
of innovative multi-functional composite materials or
high-capacity anodes for Li-ion batteries.

Acknowledgements
The part of work concerning iron deposition was supported by the French Midi-Pyrénées region and by the
Waspe FUI project. The authors thank M. Molinier and E.
Prévot from LGC for technical help and ARKEMA for
providing the carbon nanotubes.

References
Ajayan P.M., Ebbesen T.W., Ichihashi T., Iijima S., Tanagaki K.,
Hiura H., Opening carbon nanotubes with oxygen andimplications for filling, Nature, 362 (1993) 522–525.
Babal A.S., Gupta R., Singh B.P., Singh V.N., Dhakate S.R.,
Mathur R.B., Mechanical and electrical properties of highperformance MWCNT/polycarbonate composites preparedby an industrial viable twin screw extruder with back flow
channel, RSCAdv., 4 (2014) 64649–64650.
Bacsa R., Serp P., Decorated (coated) carbon nanotubes, in:Monthioux M. (Ed.), Carbon meta-nanotubes, synthesis,
properties and applications, Wiley, 2012, pp. 163–207.
Behler K., Osswald S., Ye H., Dimovski S., Gogotsi Y., Effect ofthermal treatment on the structure of multiwall carbon
nanotubes, J. Nanopart. Res., 8 (2006) 615–625.
Bhatia R., Prasad V., Synthesis of multiwall carbon nanotubes
by chemical vapour deposition of ferrocene alone, Solid
State Commun., 150 (2010) 311–315.
Braga A.F., Moreira S.P., Zampieri P.R., Bacchin J.M.G., Mei
P.R., New processes for the production of solar-grade polycrystalline silicon: a review, Solar En. Mat. Solar Cells, 92(2008) 418–424.
Coppey N., Nanotubes de carbone décorés par CVD en lit fluidisé: application en batterie lithium-ion, PhD thesis, INP
Toulouse, France, 2013.
Cui L.F., Hu L., Choi J.W., Cui Y., Light-weight free-standing
carbon nanotube-silicon films for anodes of lithium ion

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

batteries, ACSNano, 4 (2010) 3671–3678.
Dasgupta K., Joshi J.B., Banerjee S., Fluidized bed synthesis ofcarbon nanotubes—A review, Chem. Eng. J., 171 (2011)
841–869.
Dormans G.J.M., OMCVD of transition metals and their silicides
using metallocenes and (di)silane or silicon tetra-bromide,
J. Cryst. Growth, 5 (1991) 806–816.
Emel’yanenko V.N., Verevkin S.P., Krol O.V., Varushchenko
R.M., Chelovskaya N.V., Vapour pressures and enthalpies
of vapourization of a series of the ferrocene derivatives, J.
Chem. Thermodyn., 39 (2007) 594–601.
Feng M., Puddephatt R.J., Chemical Vapour Deposition of nickel-group metals on multiwall carbon nanotubes, Can. J.
Chem., 85 (2007) 645–650.
Geldart D., Types of gas fluidization, Powder Techn., 7 (1973)
285–292.
Goulas A., Ruud van Ommen J., Scalable production of nanostructured particles using atomic layer deposition, Kona
Powder and Particle Journal, 31 (2014) 234–246.
Kim J.H., Min B.G., Functionalization of multi-walled carbon
nanotube by treatment with dry ozone gas for the enhanced
dispersion and adhesion in polymeric composites, Carbon
Lett., 11 (2010) 298–303.
Kuang Q., Li S.F., Xie Z.X., Lin S.C., Zhang X.H., Xie S.Y.,
Huang R.B., Zheng L.S., Controllable fabrication of SnO2coated multiwalled cabon nanotubes by Chemical Vapour
Deposition, Carbon, 44 (2006) 1166–1172.
Kunii D., Levenspiel O., Fluidization engineering, second edition, John Wiley & sons Inc., New York, 1991.
Li C., Hua B., Preparation of nanocrystalline SnO2 thin film
coated Al2O3 ultrafine particles by fluidized chemicalvapour deposition, Thin Solid Films, 310 (1997) 238–243.
Li C., Zhao A., Xia W., Liang C., Muhler M., Quantitative studies on the oxygen and nitrogen functionalization of carbon
nanotubes performed in the gas phase, J. Phys. Chem. C,
116 (2012) 20930–20936.
Lin T., Bajpai V., Ji T., Dai L., Chemistry of carbon nanotubes,
Aust. J. Chem., 56 (2003) 635–651.
Liu S.S., Xiao W.D., CFD-PBM coupled simulation of silicon
CVD growth in a fluidized bed reactor: Effect of silane
pyrolysis kinetic models, Chem. Eng. Sci., 127 (2015)
84–94.
Lubej M., Plazl I., Theoretical and experimental study of ironcatalyst preparation by chemical vapour deposition of ferrocene in air, Chem. Eng. J., 242 (2014) 306–312.
McEvoy A., Markvart T., Castaner L., Solar Cells: Materials,
Manufacture and Operation, Elsevier, second edition, 2013.
Mendoza F., Hernandez D.M., Makarov V., Febus E., Weiner
B.R., Morell G., Room temperature gas sensor based on tin
dioxide-carbon nanotubes composite films, Sensors Actuat.
B, 190 (2014) 227–233.
Monthioux M., Serp P., Flahaut E., Laurent C., Peigney A.,
Razafinimanana M., Bacsa W., Broto J.M., Introduction tocarbon nanotubes, in: Brushan B. (Ed.), Springer Handbookof Nanotechnology, Second revised and extended edition,
Springer-Verlag, Heidelberg, 2007.
Philippe R., Synthèse de nanotubes de carbone multi-parois pardépôt chimique en phase vapeur catalytique en lit fluidisé.

Nouvelle classe de catalyseurs, étude cinétique et modélisation, PhD thesis, INP Toulouse, France, 2006.
Qian D., Dickey E.C., Andrews R., Jacques D., Kinetics of carbon nanotube growth by pyrolysis of ferrocene/xylene,
Carbon, 1 (2001) 1117–1119.
Reuge N., Cadoret L., Caussat B., Multifluid eulerian modellingof a silicon fluidized bed Chemical Vapour Deposition process: analysis of various kinetic models, Chem. Eng. J., 148(2009) 506–516.
Rong H., Liu Z., Wu Q., Lee Y.H., A facile and efficient gas
phase process for purifying single-walled carbon nanotubes, Curr. Appl. Phys., 10 (2010) 1231–1235.
Ruan S.L., Gao P., Yang X.G., Yua T.X., Toughening high performance ultrahigh molecular weight polyethylene using
multiwalled carbon nanotubes, Polymer, 44 (2003) 5643–
5654.
Senocq F., Duminica D., Maury F., Delsol T., Vahlas C., Iron
thin films from Fe(CO)5 and FeCp2/H2O under atmosphericpressure, J. Electrochem. Soc., 153 (2006) G1025–G1031.
Stauf G.T., Driscoll D.C., Dowben P.A., Barfuss S., Grade M.,
Iron and nickel thin film deposition via metallocene decomposition, Thin Solid Films, 153 (1987) 421–430.
Vahlas C., Caussat B., Serp P., Angelopoulos G., Principles andapplications of CVD powder technology, Mat. Sci. Eng.:
Reports, 53 (2006) 1–72.
Van Mol A.M.B., Chemical Vapour Deposition of tin oxide thinfilms, Technische Universiteit Eindhoven, 2003, ISBN90-386-2715-7.
Vennerberg D.C., Quirino R.L., Jang Y., Kessler M.R., Oxidation behaviour of multi walled carbon nanotubes fluidized
with ozone, Appl. Mater. Interfaces, 6 (2014) 1835–1842.
Wang X.S., Rahman F., Rhodes M.J., Nanoparticle fluidization
and Geldart’s classification, Chem. Eng. Sci., 62 (2007)
3455–3461.
Wartenberg E.W., Ackermann P.W., Electrical and mechanicalproperties of quenched SnO2 films on glass substrates,
Glastechnische Berichte, 61 (1988) 256–262.
Wasel W., Kuwana K., Reilly P.T.A., Saito K., Experimental
characterization of the role of hydrogen in CVD synthesis
of MWCNTs, Carbon, 45 (2007) 833–838.
Wildgoose G.G., Banks C.E., Compton R.G., Metal nanoparticles and related materials supported on carbon nanotubes:
methods and applications, Small, 2 (2006) 182–193.
Xia W., Jin C., Kundu S., Muhler M., A highly efficient gasphase route for the oxygen functionalization of carbon
nanotubes based on nitric acid vapour, Carbon, 47 (2009)
919–922.
Yaws C.L., Chemical Properties Handbook, McGrawHill, 1999.
Yu H., Zhang Q., Gu G., Wang Y., Luo G., Wei F., Hydrodynamics and gas mixing in a carbon nanotube agglomerate
fluidized bed, A.I.Ch.E. Journal, 52 (2006) 4110–4123.
Zahi I., Mur P., Blaise Ph., Esteve A., Djafari Rouhani M.,
Vergnes H., Caussat B., Multi scale modelling of silicon
nanocrystals synthesis by Low Pressure Chemical Vapour
Deposition, Thin Solid Films, 519 (2011) 7650–7658.
Zhu C., Yu Q., Dave R.N., Gas fluidization characteristics ofnanoparticle agglomerates, A.I.Ch.E. Journal, 51 (2005)
426–437.

331

Pierre Lassègue et al. / KONA Powder and Particle Journal No. 33 (2016) 322–332

Author’s short biography
Pierre Lassègue
Pierre Lassègue is a PhD student in the Innovative Multiphase Reactor Engineering
Department of the Laboratory of Chemical Engineering at the National Polytechnic Institute of Toulouse. He received his BSc degree in chemistry and physico-chemical processes in 2011 and his two MSc degrees in process engineering and in environment andprocesses in 2013 from Toulouse University. His research interests concern carbon nanotubes and fluidized bed chemical vapour deposition.
Nicolas Coppey
Dr. Nicolas Coppey is currently working as a laboratory manager at SNAM, a European player collecting and recycling metals from batteries. He is taking part in the development of hydrometallurgy treatment units to optimise recycling performance. He
received his PhD. in process engineering and environment from the University of Toulouse in 2013 under the supervision of Prof. Brigitte Caussat. He studied the decorationof carbon nanotubes by silicon nanoparticles and the application as an electrode material
in lithium-ion batteries.
Laure Noé
Mrs Laure Noé has been tenured at the French National Research Institute (CNRS)
since 1991, where she is currently working as an assistant engineer. She is dedicated tothe synthesis and/or characterization of materials (mostly carbon nanoforms, but othernanomaterials as well, and more recently archaeological crafts), formerly regarding theirchemical composition by means of chemical analytical routes, nowadays regarding theirstructural, textural, and chemical features by means of TEM-related methods (HRTEM,
electron diffraction, X-EDS). She has co-authored 22 publications in international journals and 37 papers in colloquia and conferences.
Marc Monthioux
Dr. Marc Monthioux is currently working as a research director at the French National
Research Institute (CNRS). He has been working on the synthesis and/or structural characterisation of carbon and carbon-containing materials of any kind since 1979. He is currently co-editor of CARBON journal (Elsevier), former chairman of the French Carbon
Group (GFEC) and former chairman of the European Carbon Association (ECA). He has(co)authored 18 book chapters, more than 340 papers in conferences, and more than 130
papers in international journals cited more than 5000 times (h = 30).
Brigitte Caussat
Brigitte Caussat is a professor of chemical engineering in the Innovative Multiphase
Reactor Engineering Department of the Laboratory of Chemical Engineering at the National Polytechnic Institute of Toulouse. She is an engineer in chemical engineering andreceived her PhD in 1994. Her research interests concern the chemical vapour deposition
processes either on planar substrates to form graphene and thin films, or in fluidized bed
to decorate carbon nanotube balls and to coat powders. She is co-author of more than 80
publications, 6 patents and 90 papers.

332

KONA Powder and Particle Journal No. 33 (2016) 333–339/Doi:10.14356/kona.2016019

Original Research Paper

Nanostructure Design on Porous Carbon Powders under
Chemical Process and Their Physical Properties †Tomoya Nakazono* and Takahiro Morishita
1

Performance chemicals Div., Advanced Carbon Technology Center, ToyoTanso Co. Ltd, Japan

Abstract
The carbon pore-structure formed by MgO-templated method consists of nano-carbon wall and mesopore madewith MgO. From MgO and its mixtures with resin that have high yield of carbon, micro-mesoporous carbons wereobtained through their carbonization at 900 °C, followed by the dissolution of MgO. The carbon powder preparedby this method possesses high surface area without any activation process. The mesopore size of carbon powders
is controlled by the crystallite size of MgO, and micropore amount depends on resin structure. Crystallinities of
their carbon powders with heat-treatment up to 2500 °C were very uniform.
Keywords: MgO-templated carbon coating process, mesoporous carbon, crystallinity, nano structure

1. Introduction
Various kinds of porous carbon materials are produced,
which can be used as adsorbents, energy storage electrodes, and catalyst supports, etc. (Derbyshire F. et al.,1995). In order to get a high surface area, the oxidationand vapor activation process of carbon are usually employed. Through this process various activated carbons
are prepared and applications developed (Derbyshire F. etal., 1995; Sanada Y. et al., 1992; Inagaki M, 2000).
On the other hand, there have been reported various
precursors and processes to prepare porous carbons without any activation process. Pore size controlled carbons
were prepared by using hard templates, such as zeolites
and silicates (Kyotani T. et al., 2003; Shi Z-G. et al.,2003). For these template processes, however, it waspointed out that the template had to be dissolved out bystrong acids after carbonization and that large amount of
production was not easy, although the pores with a specific size were easily obtained. On the other hand, without
template use, carbonization of organic aerogels was also
reported to give mesoporous carbons (Hanzawa Y. et al.,
1998; Tamon H. et al., 1997; Yamamoto T. et al., 2001).
We have developed easy process to coat metal-oxide
ceramic particles by carbon; the powder mixtures of thermoplastic resin, such as poly(vinyl alcohol) and phenol
resin dispersion, etc., and ceramics were heat-treated at a†
1*

Received 1 July 2015; Accepted 17 September 2015
J-STAGE Advance published online 10 October 2015
5-7-12 Takeshima, Nishiyodogawa-ku, Osaka 555-0011, Japan
Corresponding author: Tomoya Nakazono;
E-mail: t_nakazono@toyotanso.co.jp
TEL: +81-6-6472-5914 FAX: +81-6-6471-0617©2016 Hosokawa Powder Technology Foundationhigh temperature in an inert atmosphere. The carbon
layer coated on the surface of a ceramic particle wasfound to be porous, and 3D-nano dimensions when using
MgO as substrate metal oxide and dissolving it out by
sulfuric acid (Inagaki M. et al., 2004). This result suggested us a new preparation process of porous carbons
without any stabilization and activation processes evenstarting from thermoplastic precursors.
Following this idea, MgO-templated carbonization process was developed for preparing mesoporous carbons byheat-treatment of mixtures of carbon precursor and MgO
powder or through the thermal decomposition of magnesium citrate (Morishita T. et al., 2006). Characterizationof heat-treated MgO-templated carbons derived frommagnesium citrate was reported. The obtained carbons
show high BET surface area and superior electric conductivity even after the heat-treatment at around 2000 °C(Orikasa H. et al., 2012). These carbons also show highperformance as negative electrode of a Li-ion capacitor(Soneda Y. et al., 2013).
In this study, we prepared the porous carbons derived
from three different carbon precursors and MgO powders,and characterized them. Each carbon precursor’s difference is reported.

2. Experimental
2.1 Non-crystalline mesoporous carbon
Magnesium oxide (MgO) was used as a template because of its chemical and thermal stability, no structuraland compositional changes, no reduction with carbon, and333

Tomoya Nakazono et al. / KONA Powder and Particle Journal No. 33 (2016) 333–339

Fig. 1

Fig. 2Illustration of preparation method of porous carbons.

N2 adsorption isotherms of the carbons derived from (a), PVA (b) phenol and (c) imide.

easy dissolving to dilute acidic aqueous solutions. MgO
powders were analyzed by XRD and these crystallite
sizes were calculated by a Scherrer equation. In thisstudy, MgO powders with an average crystallite size of
11 nm were used. Carbon precursors were poly(vinyl alcohol) (PVA), Kapton-type polyimide (Imide), and phenol
resin dispersion (Phenol). MgO powders/carbon precursors were mixed 4/6 by weight. Its mixtures of Phenol or
Imide were cured in inert atmosphere and then ground to
powders. Its mixtures thus prepared were heated at a temperature of 900 °C for 1 h in a flow of N2. Heating rate
was 5 °C/min. After carbonization, the mixture wasstirred in diluted sulfuric acid (3.5 mol/L) to dissolve out
MgO. Its slurry was repeated suction filtration and waterrinse until the pH of its filtrate showed 5 to 7. Finally carbons were recovered as powders after drying at 150 °C for
96 h in a flow of air. Illustration of the preparation method
is shown in Fig. 1.

2.2 Highly crystalline mesoporous carbon
As-prepared carbon powders were heat-treated at 1800,
2200, 2500 °C for 1 h in Ar atmosphere for graphitization.
The obtained carbons were characterized by XRD,
TEM, Thermogravimetry, and N2 adsorption analysis. BET
surface area was determined from the adsorption isothermsof N2 gas measured at 77 K. Pore size distribution was
evaluated by BJH method and HK method. Oxidation loss
temperature was estimated by thermogravimetry in air up
to 900 °C.

334

Table 1

BET surface areas and pore volumes
BET surface area
(m 2/g)Vtotal(ml/g)

Vmeso(ml/g)

Vmicro(ml/g)

PVA

1315

2.12

1.64

0.48

Phenol

1189

2.15

1.70

0.45

Imide

837

1.20

0.88

0.32

3. Results and discussion
3.1 Non-crystalline mesoporous carbon
The carbons derived from PVA and Phenol had similar
N2 adsorption isotherms as seen in Fig. 2. BET surface areas and pore volumes are listed in Table 1. The pore size
distributions by BJH method are shown in Fig. 3. The
as-prepared carbons have high mesopore volume of 0.88–
1.70 ml/g, with micropore volume of 0.32–0.48 ml/gslightly depending on the carbon precursor. The pore sizeat the peak distribution is about 11 nm on each carbon precursor. It’s the same about crystallite size of MgO. This result suggests that carbons were coated along crystallite of
MgO. TEM images of carbons derived from phenol andimide are shown in Fig. 4(a) and (b). Pores are formed withamorphous carbon layers. The porous structure seemed
like inter-connected three dimensionally. Thus, pore volume parameters depend slightly on three different carbon
precursors, but the pore size at the peak distribution depends on crystallite size of MgO, not carbon precursors.

Tomoya Nakazono et al. / KONA Powder and Particle Journal No. 33 (2016) 333–339

3.2 Highly crystalline mesoporous carbon
BET surface area and pore volumes of mesoporous carbons are shown in Fig. 5 and Table 2. Pore size distributions measured by BJH method are shown in Fig. 6. The
heat-treatment at 1800 °C gave only a small change in pore
structure; no change in mesopore volume, a slight decreasein micropore volume, and consequently small decreases intotal pore volume and BET surface area. Pore size profiles
also nearly unchanged after heat-treatment at 1800 °C.
However, it has to be pointed out that these changes show a
sharp contrast to some commercially available activatedcarbons, where marked collapsing of micropores has been
observed (Inagaki M. et al., 2014). After the heat-treatment
above 1800 °C, all pore parameters decreased markedly.

Fig. 3

Pore size distribution profiles by BJH method.

Fig. 5

But the peak positions at around 11 nm didn’t shift even after pore volume decreased relatively. These results suggestthat mesopore structures didn’t collapse by heat-treatment
despite decreasing mesopore volumes for some reasons
such as forming closed pore by shrinking of micropores.
Thus their heat-treatment temperature dependences arevery similar.

Fig. 4 TEM images of carbons derived from (a) phenol and (b)imide.

BET surface areas and pore volumes on each carbon precursor.

335

Tomoya Nakazono et al. / KONA Powder and Particle Journal No. 33 (2016) 333–339(002) X-ray peak profiles of carbons derived from PVA
are shown depending on each heat-treatment temperatures in Fig. 7. Obvious (002) peak was observed only the
carbons heat-treated at 2500 °C. This peak profile can beseparated into several peaks. These peak profiles suggest
the formation of structural components having different
crystallinity. The similar peak profile was also observedon the carbons derived from magnesium citrate(Orikasa
H. et al., 2012). (002) peak profiles of carbons heat-treatedat 2200 °C and 2500 °C are shown in Fig. 8(a) and (b).

Table 2

BET surface areas and pore volumes on each carbon
precursor and heat-treatment temperatures
BET
surfacearea (m 2/g)

PVA

PhenolImide

as-prepared

1315

2.12

1.64

0.48

1800 °C

1146

2.09

1.68

0.41

2200 °C

476

1.12

0.95

0.17

2500 °C

212

0.61

0.53

0.08

as-prepared

1189

2.15

1.70

0.45

1800 °C

870

1.99

1.67

0.33

2200 °C

523

0.99

0.81

0.19

2500 °C

242

0.76

0.67

0.09

as-prepared

837

1.20

0.88

0.32

1800 °C

590

1.11

0.91

0.21

2200 °C

337

0.77

0.65

0.12

2500 °C

123

0.34

0.30

0.04

Fig. 6336

Vtotal Vmeso Vmicro(ml/g) (ml/g) (ml/g)

On the same heat-treatment temperature, (002) peak profiles depend on carbon precursors.
Thermogravimetry profiles in air are shown in Fig. 9.
The beginning temperatures of oxidation of as-preparedcarbons depended on carbon precursors. When reachingat about 600 °C, as-prepared carbons were almost entirely
oxidized. But the carbons heat-treated at 1800 °C didn’t
start oxidation at about 600 °C. In addition, the TG profiles were almost the same on each carbon precursor. The
starting temperatures of oxidation of carbons heat-treated
above 2200 °C almost didn’t shift to high temperatures
despite the crystalline growth as shown in Fig. 5.
TEM images of carbons derived from polyimide andphenol resin are shown in Fig. 10 and Fig. 11, respectively. As seen in these images, before the heat-treatment,
amorphous layers were observed. And pores were seemed
like inter-connected in three-dimensionally. After theheat-treatment at 1800 °C, stacked layers were observed.

Fig. 7(002) peak profiles of the carbons derived from PVA.

Pore size distribution by BJH method on each carbon precursor.

Tomoya Nakazono et al. / KONA Powder and Particle Journal No. 33 (2016) 333–339

On the other hand, pore structures were just a littlechanged. The carbons heat-treated above 2200 °C weremore graphitized and pores were still inter-connected.
Thus, the inter-connected pore structures still existed
continuously after heat-treatment despite carbon walls
was crystallized.
Pore volumes of carbons derived from MgO-templated
carbonization process didn’t change after the heat-treatment
at 1800 °C. Crystal growth of carbons wasn’t observed by
XRD analysis. But the results of TEM and TG analysis
suggest that carbon wall was crystallized. These results
suggest that both the high porosity and good characteristics by crystallization of carbon walls can be obtained byheat-treatment at 1800 °C.

Fig. 9Fig. 8(002) peak profiles of the carbons heat-treated at (a)2200 °C and (b) 2500 °C.

(002) peak profiles of the carbons heat-treated at (a) 2200 °C and (b) 2500 °C.

Fig. 10TEM images of carbons derived from imide.

337

Tomoya Nakazono et al. / KONA Powder and Particle Journal No. 33 (2016) 333–339

Fig. 11

TEM images of carbons derived from phenol.

4. Conclusions
Carbon coating on MgO was successfully processedusing the mixtures of carbon precursors with MgO precursors prepared by physical mixing. BET surface area ofthe carbons isolated from MgO by dissolving out wasvery high, such as over 1000 m2/g, even though activation
processes were not applied. The carbons obtained throughten-odd crystallite size MgO were rich in mesopores, ofwhich sizes were almost the same as MgO particles.
Furthermore, we found that pore structure of as-­prepared MgO-templated carbon depends slightly on the
carbon precursor, but their heat-treatment temperaturedependences are very similar. And MgO-templated carbons heat-treated at 1800 °C satisfy both high porosityand oxidation resistance on each carbon precursor. The
surface area was kept to 70–80 % even by the 1800 °C
treatment. This would be due to the characteristic morphology and structure of the MgO-template carbon.
These results will be useful to pursue the method of
preparation for highly porous carbon materials with ahigh thermal stability. The porous carbon having hugemesopores will also provide an unprecedented new function to various applications.

References
Derbyshire F., Jagtoven M., Thwaiter M., Activated Carbons-­
Production and Application, in Porosity in Carbons, Patrick
J.W. (Ed.), Edward Arnold. 1995.
Hanzawa Y., Kaneko K., Yoshizawa N., Pekara R.W.,

338

Dresselhaus M.S., The Pore Structure Determination of
Carbon Aerogels, Adsorption, 4 (1998) 187–195.
Inagaki M., Porous carbons, in New Carbons, -Control of Structure and Functions, Elsevier, 2000.
Inagaki M., Kobayashi S., Kojin F., Tanaka N., Morishita T.,
Tryba B., Pore structure of carbons coated on ceramic particles, Carbon, 42 (2004) 3153–3158.
Inagaki M., Toyoda M., Tsumura T., Control of crystalline structure of porous carbons, RSC Adv., 4 (2014) 41411–41424.
Matsuoka K., Yamagishi Y., Yamazaki T., Setoyama N., Tomita
A., Kyotani T., Extremely high microporosity and sharp
pore size distribution of a large surface area carbon prepared in the nanochannels of zeolite Y, Carbon, 43 (2005)
876–879.
Morishita T., Soneda Y., Tsumura T., Inagaki M., Preparation of
porous carbons from thermoplastic precursors and theirperformance for electric double layer capacitors, Carbon,
44 (2006) 2360–2367.
Orikasa H., Morishita T., Nano structure of MgO-­templated
carbon and its change induced by heat-treatment, TANSO,
254 (2012) 153–159.
Shi Z-G., Feng Y-Q., Xu L., Da S-L., Preparation of porous
carbon-silica composite monoliths, Carbon, 41 (2003)
2653–2689.
Soneda Y., Suzuki M., Fujimoto K., Activated Carbons, Fundamentals and Applications, Koudannsha, 1992.
Soneda Y., Yamaguchi T., Imoto K., Kodama M., Morishita T.,
Orikasa H., Performance of MgO-templated mesoporous
carbons as electrode materials in lithium-ion capacitor,
TANSO, 256 (2013) 57–59.
Tamon H., Ishizaki H., Mikami M., Okazaki M., Porous structureof organic and carbon aerogels synthesized by sol-gel polycondensation of resorcinol with formaldehyde, Carbon, 35(1997) 791–796.

Tomoya Nakazono et al. / KONA Powder and Particle Journal No. 33 (2016) 333–339

Author’s short biography
Tomoya Nakazono
Mr. Tomoya Nakazono is a researcher of Performance chemicals Div. in ToyoTanso
Co., Ltd. in Japan. He received his master’s degree from Kyoto Institute of Technology
in 2012. His research focuses on the crystallinities and the nanostructure of MgO-­
templated porous carbons.

Takahiro Morishita
Dr. Takahiro Morishita is performance chemicals division manager at ToyoTanso Co.,
Ltd. in Japan. He received his Ph.D. degree from Aichi Institute of Technology in 2006.
His research field is a synthesis of fine carbon particles and application of the energy device. His research interest process of fully nano-pore size controlled on carbons and its
three-dimensional microstructure.

339

KONA Powder and Particle Journal No. 33 (2016) 340–353/Doi:10.14356/kona.2016025

Original Research Paper

Preparation of Monodispersed Nanoparticles ofTransparent Conductive Oxides †Atsushi Muramatsu 1*, Kiyoshi Kanie 1, Takafumi Sasaki 2 and Masafumi Nakaya 1
1
2

Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, Japan
Mitsui Mining & Smelting Co., Ltd., Japan

Abstract
Generally, indium-tin-oxides (ITO) thin film is prepared by the sputtering process with ITO target, but only 20 %of ITO yielded from the target is deposited on the substrate. Namely, about 80 % ITO is exhausted by thedeposition elsewhere far from the substrate. The recycling process of indium is limited so that ca. 20 % ITO of thestarting material is lost without any recovery. Even if the recycling of ITO has been carried out in this process, weshould prepare ITO target of 5 times more than apparent use of ITO on film. If we change it to printing processfrom the sputtering, the reduction in ITO use is expected as ca. 50 %, considering the increase in film thickness
by printing. Our target technology also includes ITO nanoink for the project. As a result, monodispersed ITO
nanoparticles (NPs) with a cubic shape were fabricated by using quaternary ammonium hydroxide-assisted metalhydroxide organogels. These NPs have perfect uniformity in size with beautiful shape, and perfect single
crystalline structure including Sn. As we were attempted to make thin film with ITO nanoink, it was successfully
fabricated below 200 nm in thickness and the resistivity was drastically decreased below 1.0 × 10 –3 Ω cm after
heat treatments. GZO nanoink as substitute of ITO has also been developed.
Keywords: uniform nanoparticles, transparent conductive oxides, indium tin oxide, shape control.

1. Introduction
Indium is well known to use as indium-tin-oxides(ITO) materials for transparent conductive oxides (TCO),
which is often used in devices such as a flat panel display,
touch-panel, solar cell, etc. Japan has an excellent advantage to produce ITO-relating devices, since Toyoha Minein Sapporo, Japan, had supplied Indium as a top of world.
But it was closed on March 31, 2006 mainly due to theeconomical reason. Before that, METI (Ministry of Economy, Trade and Industry, Japan) and Tohoku Univ. hadstarted to discuss the project on substitute materials development of indium from 2004.
ITO thin film for devices is prepared by the sputtering
process with ITO target, but only 20 % of ITO yieldedfrom the target is deposited on the substrate. Namely,
about 80 % ITO is exhausted by the deposition elsewherefar from the substrate as shown in Fig. 1. The recycling

process is limited to be recovered so that only ca 80 %ITO of the starting materials can be recycled but 20 % islost without any recovery. Even if the recycling of ITO
has been carried out in this process, we should prepare
ITO target of 5 times more than apparent use of ITO on
film. If we change it to printing process from the sputtering, the reduction in ITO use is expected as ca. 50 %,considering the increase in film thickness by printing.
Principally, the printing process results in the approximately 100 % yield of film production from ITO nanoink,
as shown in Fig. 2. As a result, our target technology included the development of ITO nanoink for this project.
ITO nanoink was the next important technology for Japan, since the concept of printed electronics by use ofnanoink has recently accepted as a novel engineering to
make a functional thin film and/or advanced electronic
circuits. Moreover, ITO film prepared by ink-jet of its†

Received 16 May 2015; Accepted 10 August 2015
J-STAGE online 28 February 2016
1
Katahira 2-1-1, Aoba-ku, Sendai 980-8577, Japan2
1-1-1 Hikoshimanishiyamacho, Shimonoseki-shi, Yamagachi,
750-0093 Japan* Corresponding author: Atsushi Muramatsu;
E-mail: mura@tagen.tohoku.ac.jp
TEL: +81-22-217-5163 FAX: +81-22-217-5165

340

Fig. 1ITO loss in sputtering process.

©2016 Hosokawa Powder Technology Foundation

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

Fig. 2Printing process of ITO nanoink.

nanoink has been expected as the transparent electrode of
the next-generation solar cell. Namely, indium is well recognized as one of the most important resources for the future devices in Japan.
METI has started “Japan Rare Metal Substitute Materials Development Project” from the financial year of 2007,
where Tohoku Univ. is the main university for research sothat our project was also included as “Reduction of Indium
use in transparent conducting electrodes.” The sub-project,“Development of high performance ITO nanoparticles(NPs) for nano-ink,” was composed of Tohoku University,
ULVAC, Inc., Mitsui Mining & Smelting Co., Ltd., DOWA
Electronics Materials Co., Ltd., and Sharp Co., Ltd. This
project has been carried out until FY2011. During it, several patents on ITO nanoink has been submitted. Besides,
U.S.—Japan Roundtable on Rare Earth Elements Researchand Development for Clean Energy Technologies, washeld on November 18–19, 2010 on U.S. Department of Energy Lawrence Livermore National Laboratory so that wereported the investigation of ITO nanoink as a reduction
technology of indium use.
The key technology of ITO nanoink is the productionof ITO NPs precisely controlled in size, shape, and crystalline properties. However, ITO particles could not be prepared by hydrothermal synthesis because Sn-containing
InOOH or In(OH)3 crystalline particles were once formed
during aging even at higher temperature and at rather
higher pressure but they could not be converted into ITO
phase anymore. Considering the fact that alumina cannot
be also prepared by the hydrothermal synthesis method,InOOH and In(OH)3 must be too stable to convert into
ITO in aqueous media. Namely, we have reported thesize and shape control of In(OH)3 nanoparticles by amino-­acid assisted hydrothermal method. (Sasaki et al., 2009).
Cubic-shaped In(OH)3 nanoparticles with the size of 50 nm
were formed by addition of aspartic acid in the reaction
solution. Hence, size- and shape-controlled In(OH)3
nanoparticles has been successfully prepared; however,the further thermal treatment is inescapable to obtain ITO
nanoparticles.
We have found a novel solvothermal method to directly
obtain ITO NPs with cubic morphology on 2008. (Endo etal., 2008). Remarkably, only by use of this procedure,highly crystalline cubic-shaped ITO NPs with narrowsize distribution were successfully prepared, for the firsttime, through one-step process from a mixed ethylene
glycol solution of indium and tin salts. (Sasaki et al., 2010)

In order to enhance the uniformity of as-prepared ITO,
quaternary ammonium hydroxides were first applied forthe solvothermal synthesis of monodispersed ITO NPs
controlled in cubic morphology. Formation of a gel-likeintermediate was a decisive factor for the precise control
in size and shape. (Kanie et al., 2013) We also will showthat their blue-colored compacts exhibits low resistivity
as a transparent conductive oxide.

2. Concept
The unique properties of highly optical transparencyof ITO in the visible region and controllable low resistivity have been utilized (Hartnagel, 1995) (Hamberg and
Granqvist, 1986) so that ITO electrode is industrially prepared by sputtering or laser deposition method followedby chemical etching process. (Chiou et al., 1993) (Lin etal., 2008) (Gessert et al., 2009) On the other hand, since
ITO is used in tandem with the penetration of a flat panel
display, the world-wide demand for In as a TCO has beenremarkably increased so that the saving and utilization ofindium becomes task of pressing urgency. Furthermore,from the environmental point of view, such an energy-consumed system, including high-temperature andgas-phase deposition manufacturing process, fundamentally deserves a new look for the preparation of ITO electrode with low resistivity. In this regard, ITO nanoink,
obtained by extremely stable dispersion of ITO NPs into
medium, has been paid attention as an alternative to theconventional processes (Hong and Han, 2006) (Hong etal., 2008) (Bühler et al., 2007) However, TCO manufacture based on ITO nanoink contains many problems toward its practical use, as shown in Fig. 3. One of the mostserious problems is the high resistivity of TCO electrodes
from the nanoink, mainly because of the interfacial resistance between the particles, even if highly conductive
ITO NPs are synthesized. As mentioned in Fig. 3, in order to reduce this resistance, the shape of the ITO particles can be designed as a cubic so that the face-to-facecontact, 2-dimensional one, between particles is expected
to decrease the resistance, compared with the point-topoint one between uncontrolled particles. In addition, forthe high transparency and low haze, the particle size of
ITO is to be below 100 nm.
However, single-nanometer particles are not target materials in this study, as shown in Fig. 4. Even if monodispersed single-nanometer ITO particles are succeeded to
produce, they are tremendously aggregated to form aggregates. Since the particle characteristics obey the apparent
particle size, the size and shape of aggregates determines
the quality and property. The control of size and shape of
aggregates is theoretically impossible. Because of thehigher surface reactibility, the corner become rounded341

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

Fig. 3Fig. 4

Concept and design to synthesize ITO nanoparticles to be applied to the transparent conductive oxide
electrodes.

Basic concept for uniform ITO particles synthesis.

off, even if cubic nanoparticles are initially formed. In
addition, since the particle size is nearly equal to nuclei
size, they are not well grown. The precise control in shapefor single-nanoparticles are basically impossible. Since
the dispersant is strongly and chemically adsorbed on thesurface, the TCO properties get worse after calcination
treatment. However, the dispersant is indispensable forwell-dispersion of single-nanoparticles.
As a result, in order to achieve both the requirement of
shape and size with higher cubic surface as well as lessthan 100 nm over 10 nm, the preparation method of ITO
NPs with high crystallinity, uniform composition, andwell-defined particle morphology with monodispersed
size distribution should be established.

3. Gel-Sol method: Synthesis of uniform
anatase-type TiO2 Nanoparticles
The liquid-phase synthesis has much more advantages

342

for the precise shape and size control of NPs, where theshape can be controlled by the addition of shape controller
adsorbing selectively onto the specific surface and thesize be by the strict separation of nucleation step, determining nuclei number, that is, the final particle size, fromthe particle growth, compared with other methods such asgas or solid-phase synthesis. (Sugimoto et al., 1998)(Kanie and Sugimoto, 2004) (Niederberger, 2007)
Owing to DLVO theory (Derjaguin and Landau, 1941)(Verwey and Overbeek, 1948), the dispersion and aggregation of fine particles have been estimated in rather diluted solution system. The concentrated solution leads tothe tremendous aggregation between particles because of
the compression of electrical double-layer, which contributes the dispersion of the particles through the electrostatic repulsion. In this regard, the dilute solution system
is required for the monodispersed particle synthesis until
our discovery of Gel-Sol method in the concentrated solution. The features of Gel-Sol method can be listed as follows. (Sugimoto et al., 2003) 1) Gel is a reservoir of metal
ion so as to gradually supply it to the system under therather low concentration of metal ions in solution phase. 2)
Gel makes condensed viscous network in order to preventthe motion of particles to be aggregated each other by theanchoring particles in it. 3) Particles grow at the expenseof the Gel until its complete consumption.
Here, as we illustrate a typical example of the Gel-Sol
method in Fig. 5, the preparation method of monodispersed anatase-type TiO2 nanoparticles will be introduced
as follows, since it is well-established procedure, based on
the Gel-Sol transformation as above mentioned. This outstanding technology for the preparation of anatasenanoparticles precisely controlled in size and shape in-Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

cludes 3 steps in the most important procedure. Namely,
they are 1) the formation of rather stable Ti complex from
Ti iso-propoxide with triethanol amine to inhibit the uncontrolled hydrolysis after the addition of water, 2) the
fabrication of well stable gel network to avoid the tremendous aggregation between growing particles, 3) the strict
control in the process temperature to strictly separate thenucleation stage from the particle growth one.
Established procedure will be shown in Fig. 5 in detail.
The Ti stable complex composed of Ti and triethanol
amine with 1/2 in molar ratio. Since the formation rate ofthis complex is too slow to complete, it takes at least awhole day after Ti mixing with triethanol amine. If thecomplexation reaction is incomplete, the uncontrolled hydrolysis is promoted in the addition of water to the system. In that case, both the structure and composition must
be inhomogeneous so that the monodispersed particles
cannot be obtained.

In order to apply them to a photocatalyst, the precise
control of shape is the most important, because photocatalytic activities strongly depends on crystal facets (Kimijima
et al., 2010),. In Gel-Sol methods, the shape control can bealso carried out simultaneously as the particle formationin their growth stage. Namely, the shape controller isadded in the particle synthesis system. It is strongly adsorbed on the specific plane to disturb the crystal growthnormal to the plane. As a result, the particles are formed,
bounded by the plane adsorbed by the shape controller.
Fig. 6 illustrates TEM photos of shape-controlled TiO2
nanoparticles. Basic reagents such as amines and amides
were found effective to the shape control for anatase-type
TiO2 nanoparticles, in comparison with oleic acid to shaping as cube. This synthesis procedure has been applied to
the Ti-perovskite particles, such as SrTiO3 and BaTiO3,
which showed characteristics unique in photocatalytic activities. (Kimijima et al., 2014)
The development of the synthesis method for monodispersed Ti-based oxide nanoparticles precisely controlled
in size and shape directly led to the success in the production of uniform NaxKyNbO3 fine particles as a precursorof lead-free piezo materials for industrial use. (Kanie et al2011) The niobate particle synthesis is basically the sameas TiO2 preparation. In addition, it finally affects the development research for TCO nanoparticles, as described
below.

4. Application to direct ITO Synthesis
Fig. 5Synthesis procedure of uniform TiO2 particles.

Fig. 6

Preparation methods of In 2O3 or ITO NPs have beenreported based on the combination of coprecipitation of

TEM photos of as-prepared uniform TiO2 particles.

343

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

metal precursors and successive thermal treatment (Song etal., 2005) (Kim and Park, 2004), laser-induced fragmentation (Usui et al., 2006), solvothermal synthesis (Lee et al.,2005) (Yang et al., 2008a)), microwave-assisted synthesis(Okuya et al., 2007), emulsion techniques (Sujatha Devi etal., 2002), sol-gel synthesis (Aoki et al., 2006), and one-pot
preparation of colloids (Seo et al., 2003) (Liu et al., 2005
(Lee et al., 2006) (Zhang et al., 2005) (Kanehara et al.,2009) (Gilstrap Jr. et al., 2008) (Gilstrap Jr. and Summers,2009) However, the precise control of ITO NPs in size,
shape, and structure simultaneously has never been reported, in particular, for monodispersed particle synthesis
system. Namely, there is the technological difficulty fortin-doped indium oxide in the strict separation of nucleation and growth, and crystallization at rather low temperature below 300 °C. Recently, we have reported sizeand shape control of In(OH)3 NPs by amino-acid assistedhydrothermal method(Sasaki et al., 2010). In this report,
cubic-shaped In(OH)3 NPs with the size of 50 nm wereformed by addition of aspartic acid in reaction solution.
Since the thermal treatment is indispensable so as to obtain ITO particles at rather higher temperature, as-prepared ITO is aggregated and/or sintered so that the sizeand shape is changed. Even though monodispersed In(OH)3 NPs are obtained, finally formed ITO NPs is not
uniform. As a result, the monodispersed ITO NPs should
be obtained by one-step synthesis.
ITO particles cannot be prepared directly by the hydrothermal synthesis, because In(OH)3 is not converted into
ITO phase by solution-reprecipitation mechanism due tothe extremely stable state of In(OH)3 phase. In this regard,
the solvothermal system with use of an organic solvent,such as methanol, ethanol, and polyols, is applicable tothe ITO synthesis by place of hydrothermal one, wherewater content can be reduced into the stoichiometric one
for ITO formation. In the present paper, the direct synthesis of ITO NPs will be described by the combination ofgel-sol and solvothermal method. Also, the present
method will be applicable to the preparation of GZO, analternative TCO for ITO.

5. Experimental
5.1 First trial
The first trial is unique in use of NaOH as a base as follows (Muramatsu, 2010). The reagent-grade indium (III)chloride tetrahydrate (InCl3·4H2O), tin (IV) chloride pentahydrate (SnCl4·5H2O), sodium hydroxide (NaOH), ethylene glycol (EG), diethylene glycol (DEG), and 1-butanol(BuOH) were used as received. The established procedure
is as follows: InCl3 and SnCl4 were dissolved in EG with
0.50 and 0.050 mol L–1, respectively. In order to clarify the344

Fig. 7

Schematic drawing of the direct preparation of ITO
nanoparticles by the solvothermal method with using
ethylene glycol (EG) as a solvent.

effect of Sn concentration on the electric resistivity, SnCl4
concentration was varied in the range of 0–0.050 mol L–1
under the same InCl3 concentration at 0.50 mol L –1. An
ethylene glycol solution of NaOH was also prepared andadded to the same volume of InCl3 and SnCl4 ethylene
glycol solution at 0 °C under agitation. The concentrationof NaOH was adjusted to 1.0, 1.5, and 2.0 mol L –1. After
15 min stirring, the 10 mL suspension was transferred
into a Teflon-lined autoclave (Parr Instrument, volume:
23 mL) and aged at 250 °C for 4 days. The standard conditions are molar ratio [In 3+]/[Sn 4+] = 10/1, [In 3+]/
[OH–] = 1/3, and 250 °C for 12 h. As-obtained productwas purified by centrifugation (18,000 rpm, 10 min),washed 3 times with ethanol, and dried at 60 °C in an
oven. Fig. 7 illustrates the standard procedure with use of
NaOH as an alkaline reagent, we have developed first, toobtain ITO NPs by the present method.

5.2 Established procedure
As a result of all our attempts to prepare ITO, the established procedure is composed of the solvothermal synthesis and the Gel-Sol method as shown in Fig. 8 (Kanieet al., 2013). Namely, an EG solution of tetramethylammonium hydroxide (TMAH) in place of NaOH was preparedby using a 10 wt% TMAH methanol solution as follows.
Initially, 100 mL of the TMAH solution was mixed with
25 mL of EG. Then, methanol was roughly removed by arotary evaporator at 40 °C under 4 kPa, and the residue
was evacuated at room temperature with stirring. Removal of methanol was confirmed by 1H-NMR measurement. Then, the TMAH concentration was determined byacid-base titration, and the concentration was adjusted
from 1.0 to 2.5 M by the dilution with EG. The synthesis
procedure of ITO NPs with a cubic shape will be shownas follows. Initially, indium chloride tetrahydrate (InCl3·4H 2O, 0.50 M) and tin chloride pentahydrate (SnCl4·5H 2O, 0.050 M) were dissolved in EG at roomAtsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

Fig. 8

Schematic drawing of the direct preparation of ITO nanoparticles by the combination of the solvothermal
and Gel-Sol method with use of ethylene glycol (EG) as a solvent and TMAH as an alkaline reagent.

temperature. Then, 5.0 ml of 2.5 M TMAH solution in
EG was poured into the same volume of as-prepared solution mixture of indium and tin under agitation at 0 °C.
The resultant solution was stirred at the temperature for
15 min, transferred into a Teflon-lined autoclave (Parr Instrument Company, Model 4749) and aged in an oven at
250 °C for 96 h. As-obtained blue-coloured precipitates
were collected by centrifugation (18,000 rpm, 10 min) andwashed three times with ethanol and one time with
ion-exchanged water.

5.3 Characterization
The crystallographic structures of the dried powders
were identified by a Rigaku Ultima-IV X-ray diffraction(XRD) measurement system using Cu Kα radiation (40 kV,
40 mA) equipped with a D/teX Ultra detector. As-preparedparticles were observed by a transmission electron microscope (TEM) using a Hitachi H7650 system with an acceleration voltage of 100 kV. High resolution TEM (HRTEM)
analysis was carried out on a FEI TITAN 80–300. The
particle surface was characterized by X-ray photoelectron
spectroscopy using an ESCA-3400 (SHIMADZU Corporation).

6. Results and Discussion
6.1 First trial with use of NaOH
In our first trial to prepare ITO directly by the solvothermal procedure, (Muramatsu et al., 2010) initially, theeffect of NaOH concentration on the particle morphology
and size in this system was investigated. The particle size
was successfully controlled in the range from 10 to
50 nm, as the initial NaOH concentration was decreased,

Fig. 9Selected-area electron diffraction (a), respective TEM
images (b), and HRTEM (c), of ITO nanoparticles
shown in Fig. 10 (a).

possibly due to the increase in number of nucleus formed.
Generally speaking, the rather higher alkaline conditionis advantageous to the formation of the monomer, precursor complex, of metal oxide. ITO NPs were also preparedwith different initial molar ratios of Sn/In from 0 to 0.15.
In all cases, In2O3 and ITO NPs with cubic crystal structures were obtained. Trace amount of SnO2 was also
formed only in the case with Sn/In = 0.15, confirmed by
XRD measurement. On the other hand, for Sn/In = 0.10,
Sn/In molar ratio in as-prepared one ITO particle wasfound to keep in the same as the initial feed one, estimated by the energy dispersive X-ray (EDX) analysis.
Fig. 9 shows conventional TEM and high-resolution
TEM (HRTEM) images with the electron diffraction (ED)spots. The ED spots as well as HRTEM analyses of theone particle shown in Fig. 9 (a) revealed that the crystal
structure of the cubic shaped ITO NPs is basically single
crystalline with the lattice parameter of the a-axis as1.014 nm, which is consistent with the a-axis of In 2O3
with a cubic crystal structure (JCPDS No 06-0416) of
1.012 nm. Thus, the incorporation of Sn ions into the
crystal structure of In 2O3 is not influenced to the change
in the lattice parameter.
As a result of the effect of solvents, Figs. 10 and 11
show the TEM images and XRD patterns of the particles

345

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

Fig. 10TEM images of particles obtained in (a): BuOH; (b):
DEG; (c): EG; (d): 80 wt% EG aqueous solution.

Fig. 11 XRD patterns of particles obtained in (a): BuOH; (b):
DEG; (c): EG; (d): 80 wt% EG aqueous solution.

obtained in (a): EG; (b): diethylene glycol (DEG); (c):80 % EG aqueous solution; (d): butanol (BuOH), respectively. Here, the molar ratios of In3+/OH– and Sn/In were
fixed as 1/3 and 0.10, respectively, with otherwise thestandard condition, but aging time was 12 h. As shown in
Fig. 10 (a), irregular-shaped ITO NPs are obtained in EG
aged for 12 h, and the particle size is ca. 10 nm. In thecase of DEG, ITO NPs are also formed as a single phase,and the particle shape is basically cubic and the size isabout 20 nm. In contrast, the diffraction peaks of colorless powder obtained in an 80 wt% EG aqueous solution
system (Fig. 11 (c)) are indexed to be an indium oxyhydrate

346(InOOH).
As a result of the comparison of EG-water and EG
alone system, the addition of water seems inhibitory to
the formation of ITO crystal. In the aqueous solution system of In salts, the formation of InOOH was strongly enhanced. One might consider that InOOH once formed
during the reaction is hardly converted into In 2O3 phase,because of the extremely low solubility of InOOH. In
other word, InOOH cannot play a role in an intermediatefor In 2O3 formation. In contrast, as has been reported
elsewhere (Sugimoto et al., 1993) (Sugimoto et al., 2001),
monodispersed hematite, α-Fe2O3, particles in the concentrated Gel-Sol system, are formed through the 2-stepphase transformation, namely, Fe(OH)3 to akaganeite,
β-FeOOH, and β-FeOOH to α-Fe2O3, via a solution phasein the dissolution-reprecipitation process. In this case,β-FeOOH plays a role in the intermediate for α-Fe2O3, because of the higher solubility.
The solid particles obtained by using BuOH as a solvent consist of In 2O3 and InOOH. It shows stepwise phasetransition from metal hydroxides to give oxides by way ofoxyhydroxides by the dissolution-precipitation is seen in
the BuOH system. This behavior is commonly observedin aqueous systems to form metal oxides. In contrast, inthe solvothermal system as shown here, direct formationof ITO solid particles is observed starting from amorphous-like indium hydroxides, In(OH)3 and InOOH
phases are not detected as intermediates.
The temperature at aging in the autoclave was changed
from 200 to 250 °C in ethylene glycol as a solvent, whereaging period was fixed at 96 h. Fig. 12 shows the effect ofaging temperature on the ITO formation. Left-side of Fig.
12 illustrates TEM observation while right-side XRD.
Judging from the TEM images, only an amorphous like
product was formed at 200 °C, but the shape-distinct solid
particles were clearly observed over 220 °C. XRD patterns of the products also indicate not crystalline structure for 200 °C aging sample, in contrast to crystalline
ITO phase over 220 °C. Thus, the crystallinity was obviously improved with increase in the aging temperature.
Taking this remarkable temperature effect into consideration, the activation energy of the particle formation may
be rather large so that the promoted formation rate canlead to the brisk nucleation and then the growth.

6.2 Established synthesis method of ITO
nanoparticles
As has been reported (Kanie et al., 2013), Fig. 13 shows
TEM images of the NPs obtained with different concentration of TMAH, (a) 0.50 M, (b) 0.75 M, (c) 1.0 M, and (d)1.25 M under 0.25 M [In3+] 0.025 M [Sn4+]. XRD measurements revealed that all products could be assigned asan In2O3 with a cubic system of Ia3 (unit cell parameter:Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

Fig. 12Fig. 13

Effect of the aging temperature of ITO particle synthesis under otherwise the standard condition. The
temperature was varied in the range from 200 to 250 °C. (a) TEM observation, (b) XRD patterns.

TEM images of ITO NPs synthesized at 250 °C for
96 h. The TMAH concentrations are (a) 0.50 M; (b)0.75 M; (c) 1.0 M; (d) 1.25 M. The scale bar in (d) is
common for all images.

a = 1.012 nm) as a single-phase. As shown in Figs. 13 (a)and (b), inhomogeneous- and pseudocubic-shaped ITO
NPs with round edges are formed at TMAH concentrationof 0.50 and 0.75 M in the solutions. In contrast, ITO NPs
with a cubic shape with sharp edges are obtained for the
TMAH concentration of more than 1.0 M (Figs. 13 (c) and(d)). The average sizes of ITO NPs at different TMAH
concentration are 26.1 ± 6.5 nm (0.50 M), 20.9 ± 3.4 nm(0.75 M), 15.0 ± 1.6 nm (1.0 M), and 17.3 ± 1.9 nm(1.25 M). Nucleation for metal oxide particle formation is
generally promoted at higher alkaline condition in hydrothermal systems; since the formation of the metal hydroxide monomers precursory to oxide particles is accelerated.
In the present solvothermal system, the initial mixing molar ratio of [TMAH]/[In3+] was fixed to 2/1, 3/1, 4/1, and5/1. In this regard, the decrease in the particle size by in-

Fig. 14

XRD patterns of the precipitates formed by aging for(a) 1 h; (b) 3 h; (c) 24 h; (d) 96 h at TMAH concentration of 1.0 M in the solution.

crease in the TMAH concentration might be due to thepromotion of metal hydroxides monomers in the initial
reaction solution. The size distribution of particles obtained at 1.0 and 1.25 M TMAH was found extremely
narrow; actually, the standard deviation of the average
size divided by the average size is calculated as 11.0 %.
As a result, monodispersed ITO NPs were prepared with
precise control in size and shape only by the present
method.
Time evolution of the products at 250 °C under TMAH
concentration of 1.0 M was investigated. As shown in Fig.
14, only broad halo-peaks in XRD analyses indicated theformation of an amorphous matter until 1 h aging. After
3 h aging, crystalline ITO phase was confirmed as a single phase, in contrast to no peak of crystalline In(OH)3and InOOH phases. The diffraction peaks of ITO became
sharpen with aging time so that crystallite size of the NPs

347

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

was increased. TEM images of the products obtained at
different aging time with 1.0 M TMAH are shown in Fig.
15, of which Fig. 15 (a) indicates an amorphous-like gelfor the products obtained at 1 h aging. Judging from Fig.
15 (b), irregular-shaped ITO NPs are seemed to be formedin the amorphous-like gel matrix after 3 h aging. The particles grow at the expense of the amorphous gel, as shownin Fig. 15 (c). After 96 h aging, monodispersed ITO NPs
with the cubic shape are finally formed as shown in Fig.
15 (d).
Fig. 16 illustrates a macroscopic change of the reactant
mixture with aging time, including TEM images of the
products obtained at each step. The reaction solution justFig. 15 TEM images of the precipitates formed by aging for (a)1 h; (b) 3 h; (c) 24 h; (d) 96 h at TMAH concentrationof 1.0 M in the solution. The scale bar in (a) is common for all images.

Fig. 16

348

after mixing both EG solutions of In/Sn and TMAH
seemed homogeneous without any precipitates (Fig. 16(a)). A viscous gel with pale yellow color was formed after
1 h aging at 250 °C (Fig. 16 (b)). As increasing aging
time, the gel was gradually converted into a blue coloredsol (Fig. 16 (c)), which suggests the formation of ITO
NPs. The gel formed 1 h aging was further characterized
by ICP, IR, and NMR analyses, after the gel was separated from solid and liquid parts by centrifugation. As aresult of ICP measurement of the dried gel, the molar ratioof the Sn/In was estimated as 0.038, while indium ion
concentration of the supernatant solution was 0.060 M.
Namely, 75 mol% of indium was used in the gel-intermediate transformed from the initial solution mixture. The
actual concentration of the indium ions in the solution
was kept at low state under aging. In this period, only
particle growth became a predominant process to form
monodispersed ITO NPs. FTIR analysis resulted in the
distinct adsorption bands assigned to TMAH and EG.
Since the amounts of TMAH and EG in the xerogels were
quantitatively analyzed by 1H-NMR, 2.2 wt% of TMAH
and 22.8 wt% of EG was included. As a result, the molar
ratio of In:EG:TMAH was estimated as 1.0:0.8:0.05, respectively. The gel must be formed via the complex formation of indium hydroxides with EG and TMAH withhydrogen bonding network, confirmed by IR measurement of the xerogel. Yang et al also mentioned such chelation behavior of indium ions with polyols (Yang J. et al.,2008b). Thus, it is to be noted that TMAH may play a
critical factor for the formation of the gel because such
gel was not obtained by using NaOH as a basic agent (Sasaki et al., 2010). ITO NPs were formed, obeying the following gel-sol transformation mechanism, as a principleof the Gel-Sol method established in our laboratory. Initially, mixed solution of indium and tin salts dissolved in

A schematic illustration of time change of reaction solution at 250 °C in 1.0 M TMAH. (a) A transparent
precursor solution of the ITO NP synthesis. (b) A pale yellow viscous gel formed by aging for 1 h at
250 °C. (c) A blue-colored ITO NP sol obtained by the present method.

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

the EG solution with TMAH was transformed into metalhydroxides at the early stage in aging time at 250 °C.
During aging, the viscous gel network was shaped via hydrogen bonding, as shown in Fig. 16 (b). Maybe, the nucleation of ITO were processed in the period of the gelmatrixes formation. The growing particles were very
tightly held on the gel matrixes so that NPs were grownthrough dissolution and recrystallization process at theexpense of the gel. Such a gel-sol transformation of reaction solution was only observed at TMAH of 1.0 and1.25 M, while the gel was not formed at lower TMAH
concentration. Thus, the gel-sol transformation must be a
key factor for synthesis of monodispersed ITO NPs.
The effect of Sn/In ratio on the resultant particle sizeand shape was investigated so that cubic-shaped ITO NPs
were also formed in the Sn/In range from 0 to 0.20. Predominant In2O3 and ITO NPs with the cubic crystal structures were obtained at Sn/In from 0 to 0.15 without theformation of tin oxides such as SnO and SnO2. However,for Sn/In = 0.20, a trace amount of SnO2 was identified.
In this investigation, the average sizes with size distributions were 26.1 ± 2.7 nm (Sn/In = 0) and 23.9 ± 2.8 nm(Sn/In = 0.020). The particle size was decreased with increase in Sn/In ratio, possibly due to the acceleration of
nucleation by tin. Such the size change with increase in
Sn content was good agreement with the literature (Ba etal., 2006). The influence of species of the quaternary ammonium hydroxides on the ITO synthesis. Tetrabutylammonium (TBA) and tetrahexyl-ammonium (THA)
hydroxides was also investigated. They also played thesame role of promoting the formation of the cubic-shaped
ITO NPs with sharp edges by way of gel-intermediates.
The particle mean size and the distribution of the ITO
NPs obtained by using 1.0 M TBAH and THAH were24.0 ± 3.3 and 25.5 ± 4.6 nm, respectively.
HRTEM and Fourier transform (FT) image of the cubic-shaped ITO NP (Fig. 15 (c)) are illustrated in Fig. 17.
They clearly show the single-crystalline structure for the
cubic-ITO NP. The lattice spacing parallel to exposing
crystal plane calculated from Fig. 17 (b) is 0.50 nm, corresponding to an interplanar distance of (200) planes of
ITO with the cubic crystal system of Ia3. The FT patternof Fig. 17 (b) means the particle growth perpendicular to{200} plane. In general, particle morphology is controlled
by the specific adsorption of additives, which regulates
the growth rate of the particle in this direction. Thus, thereduction in the growth rate perpendicular to {200} planeby specific adsorption of EG and TMAH leads to form
the cubic-shaped ITO NPs in this system. The streaks,
observed among each spots in the FT in the inset of Fig.
17 (b), suggested homogeneous substitutional doping of
tin ions in the In 2O3 crystal structure. Resistivity measurements of compacts prepared with ITO NPs with different Sn/In molar ratio of 0, 0.04, 0.10, and 0.15 were

17.34, 1.06, 1.93, and 2.67 Ω cm, respectively. Thus, theoptimized range in the tin doping amount was foundabout 0.04–0.10 mol%, and the minimal value of the powder resistivity was reached at 1.06 Ω cm. It should be
noted that the resistivity of the particles with the Sn/In ratio of 0.10 was drastically decreased to 8.4 × 10 –2 Ω cm
after the heat treatment at 300 °C for 30 min under air
followed by 300 °C for 30 min under a 1 % H2 in N2 atmosphere. As a result, the cubic-shaped monodispersed
ITO NPs with low resistivity synthesized in the present
method, have large potential for the development of printable TCO nano-inks.
Finally, we were attempted to make thin film with ITO
nanoink. As a result, ITO film below 200 nm in thickness
was successfully fabricated and the resistivity of the particles (Sn/In ratio of 0.10) drastically decreased to below
1.0 × 10 –3 Ω cm by various heat treatments 1 % H2/N2 atmosphere. The value seems enough to apply to somespecific region. Now, some companies are preparing commercial ITO nanoink after so many examinations for inkjet as shown in Fig. 18.

6.3 GZO as a substitute ITO
The substitute materials synthesis of ITO has beenstudied. Antimony-doped tin oxide (ATO), aluminum-doped zinc oxide (AZO), and gallium-doped zincoxide (GZO) has been focused on in our laboratory. Gallium doped zinc oxide (GZO) has been expected as a substitute material of ITO for TCO. We also have beendeveloped the synthesis method of GZO based on thesimilar one to ITO. For established procedure, 50 mL of
0.30 M Zn(NO3)2·6H2O and x mol% Ga(NO3)3·5.8H2O
solution (x = Ga/Zn = 0–30) in methanol was mixed wellwith the same volume of 0.60–1.8 M NaOH. The mixed
solution was aged at 200 °C for 1–48 hours. For ATO,
mixed solution of SbCl5 and SnCl4 After washing process with ethanol and distilled water, dried sample was
evaluated with TEM.
Fig. 19 shows TEM photos GZO formed with 1,8 M
NaOH after aging at 200 °C for 48 hours. GZO NPs were

Fig. 17 (a) A HRTEM image of an ITO NP shown in Fig.15 (c);(b) A magnified and a FT images (inset) of the ITO
NP exhibited in Fig. 15 (c).

349

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

Fig. 18

ITO nanoink for industrial use.

recrystallization process of the gel formed at early stageof aging. The initially formed gel consisted of indium hydroxide, EG, and TMAH played an important role for formation of monodispersed NPs. The HRTEM and FT
analyses showed that the single crystalline cubic-shaped
NP was formed by the reduction in the growth normal to{200} plane. The FT analysis indicated Sn ions were substituted homogeneously in the ITO NP lattice. ITO
nanoink was also prepared by the dispersion of ITO NPs
into EG solution. Also, this combined method was applied
to the synthesis of the other TCO NPs so that uniform
GZO nanoparticles and their nanoink have been successfully prepared.

Acknowledgements
Fig. 19

GZO formed with 1.8M NaOH, aging time 24 h.

successfully synthesized with narrow size distribution
and precise control in Ga content. Judging from transmittance and resistivity of GZO thin film prepared by inkjet
of GZO nanoink and then the thermal treatment with different temperature under air and then H2 for 1 h each, it isconcluded that resistivity was monotonously decreased
with increase in temperature, but the transmittance andhaze was independent from it. As a result, our GZO
nanoink can be applied for TCO as a substitute ITO.

7. Conclusions
Highly-crystalline monodispersed ITO NPs with a cubic shape were successfully synthesized by way of the
combination of Gel-sol and solvothermal method by useof TMAH as a basic agent and EG as a solvent. The monodispersed ITO NPs were formed through dissolution and

350

This work was financially supported by METI &NEDO Rare Metal Substitute Materials Development
Project and Industrial Technology Research Grant Program in 2011 (No. 11b15004d) from NEDO.

References
Aoki Y., Huang J., Kunitake T., Electro-conductive nanotubular
sheet of indium tin oxide as fabricated from the cellulose
template, Journal of Materials Chemistry, 16 (2006) 292–
297.
Ba J., Fattakhova Rohlfing D., Feldhoff A., Brezesinski T.,
Djerdj I., Wark M., Niederberger M., Nonaqueous synthesisof uniform indium tin oxide nanocrystals and their electrical conductivity in dependence of the tin oxide concentration, Chemistry of Materials, 18 (2006) 2848–2854.
Bühler G., Thölmann D., Feldmann C., One-pot synthesis ofhighly conductive indium tin oxide nanocrystals, Advanced
Materials, 19 (2007) 2224–2227.
Chiou B.-S., Hsieh S.-T., R.F. Magnetron-sputtered indium tin

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

oxide film on a reactively ion-etched acrylic substrate, Thin
Solid Films, 229 (1993) 146–155.
Derjaguin B.V., Landau L., Theory of the stability of strongly
charged lyophobic sols and of the adhesion of strongly
charged particles in solutions of electrolytes, Acta Phys.
Chim. URSS, 14 (1941) 633–662.
Endo Y., Sasaki T., Kanie K., Muramatsu A., Direct preparationand size control of highly crystalline cubic ITO nanoparticles in a concentrated solution system, Chemistry Letters,
37 (2008) 1278–1279.
Gessert T.A., Yoshida Y., Fesenmaier C.C., Coutts T.J., Sputtered In 2O3 and ITO thin films containing zirconium, Journal of Applied Physics, 105 (2009) 083547.
Gilstrap Jr R.A., Summers C.J., Synthesis and analysis of an
indium tin oxide nanoparticle dispersion, Thin Solid Films,
518 (2009) 1136–1139.
Gilstrap Jr R.A., Capozzi C.J., Carson C.G., Gerhardt R.A.,
Summers C.J., Synthesis of a nonagglomerated indium tinoxide nanoparticle dispersion, Advanced Materials, 20(2008) 4163–4166.
Hamberg I., Granqvist C.G., Evaporated Sn—doped In 2O3
films: Basic optical properties and applications to energy—
efficient windows, Journal of Applied Physics, 60 (1986)
R123–R160.
Hartnagel H., Semiconducting transparent thin films, Instituteof Physics Pub., Bristol [England]; Philadelphia, PA, 1995.
Hong S.-J., Han J.-I., Indium tin oxide (ITO) thin film fabricatedby indium–tin–organic sol including ITO nanoparticle,
Current Applied Physics, 6, Supplement 1 (2006) e206–
e210.
Hong S.-J., Kim Y.-H., Han J.-I., Development of ultrafine
indium tin oxide (ITO) nanoparticle for ink-jet printing by
low-temperature synthetic method, Nanotechnology, IEEE
Transactions on, 7 (2008) 172–176.
Kanehara M., Koike H., Yoshinaga T., Teranishi T., Indium tinoxide nanoparticles with compositionally tunable surface
plasmon resonance frequencies in the near-IR region, Journal of the American Chemical Society, 131 (2009) 17736–
17737.
Kanie K., Numamoto Y., Tsukamoto S., Takahashi H., Mizutani
H., Terabe A., Nakaya M., Tani J., Muramatsu A., Hydrothermal synthesis of sodium and potassium niobates fineparticles and their application to lead-free piezoelectricmaterial, MATERIALS TRANSACTIONS, 52 (2011)
2119–2125.
Kanie K., Sasaki T., Nakaya M., Muramatsu A., Quaternaryammonium hydroxide-assisted solvothermal synthesis ofmonodispersed ITO nanoparticles with a cubic shape,
Chemistry Letters, 42 (2013) 738–740.
Kanie K., Sugimoto T., Shape control of anatase TiO2 nanoparticles by amino acids in a gel-sol system, Chemical Communications, 14 (2004) 1584–1585.
Kim K.Y., Park S.B., Preparation and property control of nanosized indium tin oxide particle, Materials Chemistry andPhysics, 86 (2004) 210–221.
Kimijima T., Kanie K., Nakaya M., Muramatsu A., Solvothermal synthesis of SrTiO3 nanoparticles precisely controlledin surface crystal planes and their photocatalytic activity,

Applied Catalysis B: Environmental, 144 (2014) 462–467.
Kimijima T., Sasaki T., Nakaya M., Kanie K., Muramatsu A.,
Photocatalytic activity of Ni-loaded TiO2 nanoparticles
precisely controlled in size and shape, Chemistry Letters,
39 (2010) 1080–1081.
Lee C.H., Kim M., Kim T., Kim A., Paek J., Lee J.W., Choi
S.-Y., Kim K., Park J.-B., Lee K., Ambient pressure syntheses of size-controlled corundum-type In 2O3 nanocubes,
Journal of the American Chemical Society, 128 (2006)
9326–9327.
Lee J.-S., Choi S.-C., Solvent effect on synthesis of indium tinoxide nano-powders by a solvothermal process, Journal of
the European Ceramic Society, 25 (2005) 3307–3314.
Lin Y.C., Li J.Y., Yen W.T., Low temperature ITO thin film
deposition on PES substrate using pulse magnetron sputtering, Applied Surface Science, 254 (2008) 3262–3268.
Liu Q., Lu W., Ma A., Tang J., Lin J., Fang J., Study of quasi-monodisperse In 2O3 nanocrystals: Synthesis and optical
determination, Journal of the American Chemical Society,
127 (2005) 5276–5277.
Muramatsu A., Sasaki T., Endo Y., Doi Y., Kanie K., Solvothermal Synthesis of ITO Nanoparticles Precisely Controlled inSize and Shape, Advances in Science and Technology, 62(2010) 50–55.
Niederberger M., Nonaqueous sol–gel routes to metal oxide
nanoparticles, Accounts of Chemical Research, 40 (2007)
793–800.
Okuya M., Ito N., Shiozaki K., ITO thin films prepared by amicrowave heating, Thin Solid Films, 515 (2007) 8656–
8659.
Sasaki T., Endo Y., Nakaya M., Kanie K., Nagatomi A., TanoueK., Nakamura R., Muramatsu A., One-step solvothermal
synthesis of cubic-shaped ITO nanoparticles precisely controlled in size and shape and their electrical resistivity,
Journal of Materials Chemistry, 20 (2010) 8153–8157.
Sasaki T., Nakaya M., Kanie K., Muramatsu A., Amino acidassisted hydrothermal synthesis of In(OH)3 nanoparticles
controlled in size and shape, MATERIALS TRANSACTIONS, 50 (2009) 2808–2812.
Seo W.S., Jo H.H., Lee K., Park J.T., Preparation and opticalproperties of highly crystalline, colloidal, and size-controlled indium oxide nanoparticles, Advanced Materials, 15(2003) 795–797.
Song J.E., Lee D.K., Kim H.W., Kim Y.I., Kang Y.S., Preparation and characterization of monodispersed indium–tinoxide nanoparticles, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 257–258 (2005) 539–
542.
Sugimoto T., Chapter 8—control of particle characteristics, in:
Sugimoto T. (Ed), Monodispersed particles, Elsevier,
Amsterdam, 2001, pp. 376–388.
Sugimoto T., Khan M.M., Muramatsu A., Itoh H., Formation
mechanism of monodisperse peanut-type α-Fe2O3 particles
from condensed ferric hydroxide gel, Colloids and Surfaces
A: Physicochemical and Engineering Aspects, 79 (1993)
233–247.
Sugimoto T., Wang Y., Itoh H., Muramatsu A., Systematic control of size, shape and internal structure of monodisperse351

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

α-Fe2O3 particles, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 134 (1998) 265–279.
Sugimoto T., Zhou X., Muramatsu A., Synthesis of uniform
anatase TiO2 nanoparticles by gel–sol method: 4. Shapecontrol, Journal of Colloid and Interface Science, 259(2003) 53–61.
Sujatha Devi P., Chatterjee M., Ganguli D., Indium tin oxide
nano-particles through an emulsion technique, Materials
Letters, 55 (2002) 205–210.
Usui H., Sasaki T., Koshizaki N., Optical transmittance ofindium tin oxide nanoparticles prepared by laser-induced
fragmentation in water, The Journal of Physical ChemistryB, 110 (2006) 12890–12895.
Verwey E.J.W., Overbeek J.T.G, Theory of the stability of lyo-phobic colloids. The interaction of particles having an electric double layer. Elsevier, New York-Amsterdam, 1948.
Yang J., Li C., Quan Z., Kong D., Zhang X., Yang P., Lin J.,
One-step aqueous solvothermal synthesis of In 2O3 nanocrystals, Crystal Growth & Design, 8 (2008a) 695–699.
Yang J., Li C., Quan Z., Zhang C., Yang P., Li Y., Yu C., Lin J.,
Self-assembled 3D flowerlike Lu 2O3 and Lu 2O3:Ln3+ (Ln =Eu, Tb, Dy, Pr, Sm, Er, Ho, Tm) microarchitectures: Ethylene glycol-mediated hydrothermal synthesis and luminescent properties, The Journal of Physical Chemistry C, 112(2008b) 12777–12785.
Zhang W., Huang Z., Li T., Tang Q., Ma D., Qian Y., A novel
route to octahedral In 2O3 particles exhibiting near band
emission, Chemistry Letters, 34 (2005) 118–119.

Author’s short biography
Atsushi Muramatsu
Dr.Atsushi Muramatsu is the Director and Professor of the Institute of Multidisciplinary Research for Advanced Materials, Tohoku University. The strategy of his laboratory, named as the Hybrid-Nanoparticle Lab., is to develop the novel synthesis method ofhighly functional nano-materials.
He graduated from the University of Tokyo in 1984 and then took the degree of Doctor of Engineering at the same university in 1988. He had a post of Research Associate at
Tohoku University in 1988 and then Professor in 2001.
He was given the present position of the Director last spring.

Kiyoshi Kanie
Dr. Kiyoshi Kanie, born in 1971, received his Bachelor’s deg. from Nagoya Inst. of
Tech. in 1994 and Master’s deg. from Tokyo Inst. of Tech., 1996.
Then, he was appointed to an Assistant Prof. at the Univ. of Tokyo in 1998.
His research was exploration of materials with novel organic functionality throughself-organization. In 2000, he received his Ph.D. degree from Kyoto Univ. He moved tothe present institution as an Assistant Prof.in 2002 and promoted to an Associate Prof. in
2008. His current research includes the development of liquid-phase synthetic methodsof functional inorganic nanoparticles preciously controlled in size and shape. He also focus
on design and synthesis of functional organic materials.

352

Atsushi Muramatsu et al. / KONA Powder and Particle Journal No. 33 (2016) 340–353

Author’s short biography
Takafumi Sasaki
Dr. Takafumi Sasaki, born in 1979 in Sendai, received his Ph.D. degree from Tohoku
Univ. in 2008. Then, he worked in the Univ. as a post-doc researcher for 3 years. His
main research subject was synthesis of functional inorganic nanoparticles and development
in size control of the particles. In 2011, He moved his occupation to MITSUI MINING &SMELTING CO., LTD.. He’s engaged in work in Shimonoseki at present. His hobby istour of hot springs in western Japan.

Masafumi Nakaya
Dr. Masafumi Nakaya, born in 1978, received his Bachelor’s deg. from Kanazawa
University, in 2002, Master’s deg. from Japan Advanced Institute of Science and Technology, 2004, and Ph.D. degree from University of Tsukuba, 2007. Then, he worked in
University of Tsukuba as a post-doc researcher for a year. He has worked in Tohoku University as an Assistant Professor since 2008. His main research theme is “Preparation ofiron-based magnetic nanoparticles with tunable size and shape and control of their magnetic properties”.

353

KONA Powder and Particle Journal No. 33 (2016) 354–355/Doi:10.14356/kona.2016028

Infomation Article

The 49th Symposium on Powder Technology
The 49th Symposium on Powder Technology was held at Senri Hankyu Hotel, Japan on Monday, September 7,
2015. It was organized by Hosokawa Powder Technology Foundation with the sponsorship of Hosokawa Micron
Corporation. More than 160 people from the industries and universities attended this symposium. The theme of thesymposium this year was “Powder Technology, advancing in response to the requirements of the era”. There were
six lectures with questions and answers followed by a get-acquainted party for further free discussions.
The first lecture was given by Prof.Otani, who was presented the KONA Award last year, on his lifework researchin the field of air filters including the recent trends of new advanced filters. Four lectures were carried out by theprofessors with the diversity of the fields including the electrode/solid electrolyte composite of secondary batteries,
the functional devices using 3D micro/nano stereolithograph and the tailored composite particles as well as its processing. The other two lectures were from the industrial fields introducing the casting technology, which has beendeveloped since the ancient days supporting the manufacturing industry, and the new-type vacuum dryer with theagitating mechanism which is capable of handling heat-sensitive materials like pharmaceuticals and food staff.
The contents of the symposium are shown in the followings.

The 49th Symposium on Powder Technology
Theme: Powder Technology, advancing in response to the requirements of the era
Opening address Prof. emeritus Minoru Takahashi (Nagoya Institute of Technology)

Session 1 Chaired by Prof. emeritus Fumio Saito (Tohoku University)• Lecture 1“Present Status of Air Filters and Exploration of Their New Applications”
Prof. Yoshio Otani (Kanazawa University)• Lecture 2“Room Temperature Fabrication of Electrode-Solid Electrolyte Composite for All-Solid-State
Rechargeable Lithium Batteries”
Prof. Yasutoshi Iriyama (Nagoya University)
Session 2 Chaired by Prof. Hirofumi Takeuchi (Gifu Pharmaceutical University)• Lecture 3“Development of Functional Devices Using Three-Dimensional Micro/Nano Stereolithography”
Prof. Shoji Maruo (Yokohama National University)• Lecture 4“Design of Tailored Composite Fine Particles and Its Processing”
Prof. Satoru Watano (Osaka Prefecture University)354©2016 Hosokawa Powder Technology Foundation

Session 3

Chaired by Prof. Makio Naito (Osaka University)• Lecture 5“Casting Technology, Supporting Manufacturing Industry over 5,000 Years”
Dr. Haruyoshi Hirano (Takeyama Foundry Co., Ltd.)• Lecture 6“New Dyer for the Efficient Drying of Heat-Sensitive Materials”
Mr. Takashi Ochiai (Hosokawa Micron Corporation)
Closing address

Mr. Yoshio Hosokawa (President of Hosokawa Powder Technology Foundation)

The 49th Symposium on Powder Technology

Get-acquainted Party

355

KONA Powder and Particle Journal No. 33 (2016) 356/Doi:10.14356/kona.2016029

Infomation Article

The 22nd KONA Award
Dr. Yoshio Otani, Professor of Kanazawa University, was selected as the winner of the 22nd KONA Award. The
KONA Award is sponsored by Hosokawa Powder Technology Foundation and presented to scientists or groups who
have achieved distinguished research work in the field of particle science and technology.
Dr. Otani received his B.E. in 1977, M.E. in 1979, from Kanazawa University, and Ph.D. in 1982 from Syracuse
University, New York. After completion of his Ph.D., he joined Kanazawa University as a research associate in1982. He became a professor of the Faculty of Engineering in 1998. Since then, he has steered the particle technology lab to be the one of the leading labs in the field of particle technology and aerosol science in Japan. In 1999–
2000, he took a sabbatical leave to the School of Public Health, Harvard University, where he worked on theirreversibility of inhaled air in the periphery of the lung.
He has served on the executive board of the Society of Powder Technology, Japan, the Japan Association of Aerosol Science and Technology, and Japan Air Cleaning Association. He hosted the 2015 Asian Aerosol Conference(AAC2015) in June, 2015, inviting more than 500 attendees from abroad and Japan. At the AAC2015 he was appointed as President of Asian Aerosol Research Assembly to lead in the collaboration of Asian aerosol societies.
In over thirty years of his career at Kanazawa University, Dr. Otani has made outstanding contributions to aerosol science and technology through research on aerosol generation, measurement and collection. He is one of theresearch authorities making substantial achievements in the development of air cleaning technology with air filters.
He has supervised over ten Ph.D. students and more than 200 master course students. Most of his students now
work actively in companies related to particle technology. His contribution is not limited to academics. He hasserved as the chairmen of ISO/TC24/SC4 and ISO/TC142 Japanese mirror committees and contributed to the international standardization of particle measurement instruments and air cleaning equipment.
He has over 150 published research papers and more than 50 review articles, book chapters and textbooks. In recent five years, he has been an invited lecturer of six Asian and international conferences. His research field covers(i) influence of filter structure on the collection performance, (ii) evaluation method of air filter performance, (iii)
generation of test aerosol, (iv) charging of aerosol particles, and (v) aerosol instrumentation. He was the first to apply an air filter to classify aerosol nanoparticles by passing aerosol particles through an air filter at an extremely
high filtration velocity, which is now commercialized as “Nanosampler” for the measurement of atmosphericnanoparticles.
He has conducted much collaborative research work with companies dealing with powder products, industrial hygiene, air filter
and aerosol measuring instruments, since air filtration is an immediate research topic closely related to the problems which
many manufacturing companies are now confronted with.
In summary, his contributions to academics and societies areoutstanding and his progress in networking the international research collaboration is making great strides forward.
On March 9, 2015, Mr. Kiyomi Miyata, Vice-President of theFoundation, presented the 22nd KONA Award to Dr. Yoshio
Otani at the presentation ceremony held at Hosokawa Micron
Corporation in Hirakata.

356©2016 Hosokawa Powder Technology Foundation

KONA Powder and Particle Journal No. 33 (2016) 357/Doi:10.14356/kona.2016030

Infomation Article

General Information
History of the Journal
In the history of KONA Powder and Particle Journal, twoorganizations have been playing an important role. The one is theCouncil of Powder Technology, Japan (CPT) and the other is the
Hosokawa Powder Technology Foundation (Hosokawa Foundation).
The CPT was established in 1969 by Hosokawa Micron Corporation
as a nonprofit organization to enhance the activities of research anddevelopment on powder science and technology. The Hosokawa
Foundation was established in 1991 as a public-service corporation
approved by the Ministry of Education, Culture, Sport, Science andTechnology of Japan. The issues from No.1 (1983) to No.12 (1994)
of KONA were published by CPT and the issues from No.13 (1995)
by the Hosokawa Foundation.
The aim of KONA in the early days was to introduce excellent
Japanese papers to the world and thus KONA consisted of papers
recommended by some Japanese academic societies and translated
from Japanese to English. From the issue of No.8, the CPT changed
its editorial policy to internationalize the KONA and to incorporate
papers by authors throughout the world in addition to translated
papers. In response to this change, three editorial blocks have beenorganized in the world; Asian-Oceanian, American and European.
The policy and system have not changed even after the Hosokawa
Foundation has taken over from the CPT. From the issue of No.27(2009), publication of translated papers has been terminated andonly original papers have been published. The CPT is active still
today and collaborates with the Hosokawa Foundation.
Aims and Scope
KONA publishes papers in a broad field of powder science andtechnology, ranging from fundamental principles to practical applications. The papers describing technological experiences and critical reviews of existing knowledge in special areas are also welcome.
The submitted papers are published only when they are judged
by the Editor to contribute to the progress of powder science andtechnology, and approved by any of the three Editorial Committees.
The paper submitted to the Editorial Secretariat should not havebeen previously published.
Category of Papers•	Invited articles
	
Review papers and special articles invited by the KONA
Editorial Committees.
• Contributed papers
	Original research and review papers submitted to the KONA
Editorial Committees, and refereed by the reviewers and editors.
Submission of Contributed Papers“Inquiry Letter” for submission on the KONA website (http://www.
kona.or.jp) needs to be sent to the Editor-in-Chief (mail to:contact_zasubmit@hmc.hosokawa.com) prior to the submission of themanuscript. After then, papers will be guided to each KONA
Editorial Secretariat as follows.
• Asian/Oceanian Editorial Secretariat
Dr. T. Yokoyama or Dr. L. Cui
Hosokawa Powder Technology Foundation
1-9, Shodaitajika, Hirakata-shi, Osaka, 573-1132 Japan• European / African Editorial Secretariat
Dr. S. Sander or Ms. L. Kneisl
Hosokawa Alpine AG
Peter-Dörfler-Straße 13-25, D - 86199 Augsburg, Germany• American Editorial Secretariat
Dr. C. C. Huang
Hosokawa Micron Powder Systems
10 Chatham Road, Summit NJ 07901 USA
Publication in KONA is free of charge.
Publication Schedule
KONA is published annually. The publication date is around
January 10th.
Subscription
KONA is distributed free of charge to senior researchers atuniversities and laboratories as well as to institutions and libraries
in the field throughout the world. The publisher is always glad toconsider the addition of names of those, who want to obtain thisjournal regularly, to the mailing list.

Instructions to Authors(1) Manuscript format•	Electric files should be submitted to the Editorial Secretariat byonline. Authors’ short biography with less than 100 words perperson and color photographs of all the authors should be
attached to the final version.
•	The structure of manuscripts should follow the following order;
title, authors, affiliations, abstract, graphical abstract, keywords,
main text, (acknowledgement), (appendix), (nomenclature), references. The items with parentheses are not mandatory.
•	Full postal addresses must be given for all the authors. Indicate
the corresponding author by the mark“*” after the name. Telephone and fax numbers and e-mail address should be provided
for the corresponding author.
•	Abstract should not exceed 200 words.
•	G raphical abstract should be a concise, visual summary of thearticle which will be displayed in the contents list both online
and print.
•	The appropriate number of keywords is 5 or 6.
•	The maximum pages printed in KONA are supposed to be:
15 for an original paper and 25 for a review paper.
•	Symbols and units should be listed in alphabetical order with
their definition and dimensions in SI units.
•	The color figures will appear in color both on the KONA
Website (http://www.kona.or.jp) and also in the paper version.
•	Concerning references, the alphabetical system should be
adopted. Please use reference management software such as
Endnote to manage references as far as possible.
List: References should be arranged first alphabetically andthen further sorted chronologically if necessary. More than one
reference from the same author(s) in the same year must be
identified by the letters “a”, “b”, “c”, etc., placed after the yearof publication.
Examples:- Reference to a book:Strunk Jr. W., White E.B., The Elements of Style, fourth ed.,Longman, New York, 2000.
- Reference to a chapter in an edited book:
Mettam G.R., Adams L.B., How to prepare an electronicversion of your article, in: Jones B.S., Smith R.Z. (Eds.),
Introduction to the Electronic Age, E-Publishing Inc., New
York, 2009, pp.281–304.
- Reference to a journal publication:
Tsuji Y., Tanaka T., Ishida T., Lagrangian numerical simulationof plug flow of cohesionless particles in a horizontal pipe,
Powder Technology, 71 (1992) 239–250.
Text: All citations in the text should refer to:1. Single author: the author’s name (without initials, unlessthere is ambiguity) and the year of publication;
2. Two authors: both authors’ names and the year of publication;
3. Three or more authors: first author’s name followed by “etal.” and the year of publication.
Citations may be made directly (or parenthetically). Groups ofreferences should be listed first alphabetically, then chron­ologically.
Examples: “as demonstrated (Hidaka J. et al., 1995; Tsuji Y.,
1992a, 1992b, 1993). Mori Y. and Fukumoto Y. (2002) haverecently shown ....”(2) Copyright and permission•	The original paper to be submitted to KONA has not been
published before in any language or in any journal or media; it
is not submitted and not under consideration for publication inwhole or in part elsewhere.
•	Authors are responsible for obtaining permission from the
copyright holders to reproduce any figures, tables and photos
for which copyright exists.
•	The KONA Journal applies the Creative Commons Attribution
License to all works published by the Journal. Copyright stays
with the agreed copyright owner, and the Hosokawa Powder
Technology Foundation is granted the exclusive right to publish
and distribute the work, and to provide the work in all forms
and media.
•	Users of the journal will be able to reuse the contents in any
way they like, provided they are accurately attributed. No
permission is required from either the authors or the publisher.

Free electronic publication of KONA is available at http://www.kona.or.jp©2016 Hosokawa Powder Technology Foundation

357