ISSN 0288-4534

CODEN: KPPJAD

KONA

Powder andParticle Journal

No.32 (2015)

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

KONA Powder and Particle Journal No. 32 (2015)

About this Journal
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.
The Chinese character “ 粉 ” in the cover is pronounced“KONA” in Japanese, and means “Powder”. The hand written “” is after the late Mr. Eiichi Hosokawa, founder ofthe Hosokawa Micron Corporation (http://www.hosokawamicron.co.jp).
About the Cover:
Nano/micromotors are quite minute engines that can convert
energy into autonomous motion. The cover picture demonstrates the motion of micromotors made by a single component metal (Pt). They are propelled by a catalytic reaction(2H2O2 → 2H2O + O2). A spherical Pt particle, which shows
Brownian motion in pure water, exhibits more vigorous random motion (active Brownian motion) in hydrogen peroxidewater. Furthermore, aggregates of the particles exhibit various
regulated motions such as translation, rotation, and spin, depending on the morphology (see the video at Doi:10.14356/
kona.2015005). This is only one instance among a variety ofnano/micromotors. Please read the full article on pp. 2–22.

http://www.kona.or.jp

Editorial Board
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)
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. Li(Chinese Academy of Science, China)
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)
Secretariat
T. Yokoyama(Hosokawa Powder Tech. Foundation, Japan)
L. Cui(Hosokawa Micron Corp., Japan)
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(Univ. of Seville, Spain)
G. Meesters(Univ. of Delft, Netherlands)
W. Peukert(Univ. Erlangen, Germany)
J.M. Valverde(Univ. of Warsaw, Poland)
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.I. 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.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. 32 (2015) <Editorial>
1

Editor's Preface<Review Papers>
2

Self-Propelled Nano/Micromotors with a
Chemical Reaction: Underlying Physics
and Strategies of Motion Control

Daigo Yamamoto and Akihisa Shioi
23

Role of Acoustic Fields in Promoting theGas-Solid Contact in a Fluidized Bed ofFine Particles

Federica Raganati, Paola Ammendola andRiccardo Chirone
41

Settling Suspensions Flow Modelling:
A Review

Rui Silva, Fernando A. P. Garcia, Pedro M. G.
M. Faia and Maria G. Rasteiro
57

A Review of Aerosol Nanoparticle Formationfrom Ions

Qing Li, Jingkun Jiang and Jiming Hao

Contents

KONA Powder and Particle Journal No. 32 (2015) 

75

Powder-based Additive Manufacturing
for Development of Tailor-made Implants
for Orthopedic Applications

Takayoshi Nakano and Takuya Ishimoto
85 Control of Particle Morphology and Sizein Vapor-Phase Synthesis of Titania,
Silica and Alumina Nanoparticles

Hoey Kyung Park and Kyun Young Park
102

Size-Selective Separation Techniques
for Nanoparticles in Liquid

Yasushige Mori
115

Model-Based Optimization of Industrial
Gas-Solid Reactors

Venkataramana RunkanaContents

KONA Powder and Particle Journal No. 32 (2015) <Original Research Papers>
131

The Kinetics of De-agglomeration ofMagnesium Stearate Dry-Coated Salbutamol
Sulphate Powders

Jiani Shi, Shyamal Das, David Morton andPeter Stewart
143

Effect of Particle Size on Flow Mode andFlow Characteristics of Pulverized Coal

Haifeng Lu, Xiaolei Guo, Yi Liu and Xin Gong
154 Structural and Optical Characterizationof Ni and Al Co-Doped ZnO Nanopowders
Synthesized via the Sol-Gel Process

Amor Sayari and Lassaad El Mir
163 Density-Driven Segregation in Binary
and Ternary Granular Systems

Kit Windows-Yule and David Parker

Contents

KONA Powder and Particle Journal No. 32 (2015) 

176

Surface Chemical Characterization of
Bastnaesite through Electrokinetics

Pradip, Charles C. H. Li and Douglas W.
Fuerstenau
184

Penetration of Diesel Exhaust Particles(DEPs) through Fibrous Filters Produced
Using Melt-Blown Technology

Agata Penconek, Anna Jackiewicz and
Arkadiusz Moskal
196

Formation of Nanoscale Layered Structures
and Subsequent Transformations during
Mechanical Alloying of Ni60Nb40 Powder
Mixture in a Low Energy Ball Mill

Mohammad Hossein Enayati
207 CaCl2-in-Mesoporous Silica Grown on
Superabsorbent Polymer to Enhance
Water Uptake

Ki Woong Ahn, Soo Yeon Jang, Min Hyuk
Hwang and Sun-Geon Kim

Contents

KONA Powder and Particle Journal No. 32 (2015) 

217

Modeling of Magnetic-Field-Assisted
Fluidization: Model Development and CFD
Simulation of Magnetically Stabilized
Fluidized Beds

Antonio Busciglio, Giuseppa Vella, Giorgio
Micale and Stefano Brandani
227

A Spheroid Model for the Role of Shapein Particle Size Analysis

Richard Hogg
236

Discrete Element Method (DEM) for
Industrial Applications: Comments
on Calibration and Validation for the
Modelling of Cylindrical Pellets

Michele Marigo and Edmund Hugh Stitt
253

Direct Laser Forming of Titanium Alloy
Powders for Medical and Aerospace
Applications

Hideshi Miura

Contents

KONA Powder and Particle Journal No. 32 (2015) 

264

Pinning Effect of In-Situ TiCp and TiBw
on the Grain Size and Room Temperature
Strength of (TiC + TiB)/Ti Composites

Lei Jia, Biao Chen, Shu-feng Li, Hisashi Imaiand Katsuyoshi Kondoh
270

Preparation of Composite Powder andProperties by Surface Modification of
Inorganic Pigments for Papermaking

Jun Hyung Cho and Yong Won Lee<Information Articles>
279

Symposium Reports

282

The 21st KONA Award(Awardee: Dr. Yoshio Sakka)

283 General Information

Microstructure of textured β-Si3N4 with high thermal conductivity prepared by slip casting in a strong
rotation magnetic field flowed by sintering. (p. 282)

Contents

KONA Powder and Particle Journal No. 32 (2015) 1/Doi:10.14356/kona.2015019

Editorial

Editorʼs Preface
Sotiris E. Pratsinis,
Chairman of European/African Editorial Board
Swiss Federal Institute of Technology (ETH Zurich), Switzerland

I am delighted to introduce the issue of KONA Powder & Particle Journal No. 32. As every year, this issue is afine blend of review and original research articles. Review articles, in particular, highlight the state-of-the-art incritical fields of particle technology by its leading experts. That way the latest advances in such fields are communicated to practitioners in an easy-to-understand fashion that is essential for their advancement.
What distinguishes, however, KONA from other journals is that here largely all these articles have been invitedcontributions from select research groups across the globe by KONA’s intercontinental editorial boards. For example, in the European Board that I am chairing, the selection of such articles follows a distinct pattern. Throughout
the year, our board members identify leading researchers and “hot” particle technology topics, following the international conference scene. Such researchers are singled out and courted while probing their motivation and availability to write an article for KONA visualizing its predominantly industry-based audience and readership,
worldwide. Such researchers submit a one page abstract that would be discussed by our entire board in its annual
meeting somewhere in Europe hosted by one of our members and generously supported by Hosokawa-Micron anda local university. For example, in January 2014 the European/African contributions of this issue were selected in
Granada, Spain with a board visit to the particle technology programs at the University of Granada hosted by Prof.
Jose Manuel Valverde from University of Sevilla. In 2015, I have them all in Heraklion, Krete, Greece, my birthplace and childhood island with a visit to University of Crete. In these meetings, the qualifications and abstracts ofperspective authors are discussed and formal invitations are extended to those chosen shortly after. Few months
later, the full articles arrive to Hosokawa’s office in Augsburg, Germany from where they are distributed for standard peer-reviewing. This process is administered remotely by board members determined at the above board meeting but not involved in the original invitation of that article. That way quality assurance and quality control are
guaranteed with successful articles making it in the upcoming KONA issue as this one.
In 2014 with the occasion of the World Congress of Particle Technology in Beijing, we had the rare opportunity
for a meeting of all three continental boards of KONA, hosted again by Hosokawa-Mikron. There we committed tointensify the dissemination of our journal through web search machines capitalizing and further advancing it beyond last year’s J-STAGE platform of Japan. This should please both readers and authors of KONA.
In sum, the present issue contains an array of contributions from traditional to novel and most innovative applications of particle technology in both classic and emerging disciplines such as bio and nano with a unique geographical distribution of contributing authors. I sincerely hope that this issue would become another valuableaddition to advancing the knowledge and technology bases of particle science and engineering.
Enjoy!

Sotiris E. Pratsinis
Chairman of European/African Editorial Board
November 6, 2014©2015 Hosokawa Powder Technology Foundation

1

KONA Powder and Particle Journal No. 32 (2015) 2–22/Doi:10.14356/kona.2015005

Review Paper

Self-Propelled Nano/Micromotors with a Chemical Reaction:
Underlying Physics and Strategies of Motion Control †Daigo Yamamoto * and Akihisa Shioi1

Department of Chemical Engineering and Materials Science, Doshisha University, Japan

Abstract
Nano/micromotors exhibiting regulated motions are ubiquitous in nature, with biological ones having highlysophisticated and functional properties. However, nano/micromotors have not been applied in the design of useful
man-made chemical systems to our knowledge. In this article, we review artificial nano/micromotors that havebeen proposed in the last decade. The underlying physics is completely different from that of conventional
macroscale motors. The viscous force dominates the regulated motion of a nano/micromotor, in which thermal
fluctuation converts into active Brownian motion. To overcome the viscous force and sustain motion, motors
driven by various types of mechanisms have been fabricated. We focus on chemical reaction propelled motors
classified into catalytic and self-reacted ones because most biomotors move by consuming chemical energy.
Furthermore, the directional control of artificial nano/micromotors by chemotaxis, external field, and confined
space is also discussed. As this hot and challenging topic is still currently at a fundamental stage, practicalresearch will be required for developing various applications. We expect that chemical systems mimicking elegant
biological ones will be realized in the near future.
Keywords: nano/micromotor, Reynolds number, viscous force, thermal fluctuation, regulated motion, chemical
reaction

1. Introduction
1.1 What is a nano/micromotor?
What kind of scientific principle do we associate withthe phrase “transfer of nano/micro particles”? In this article, the authors suppose that the readers are chemicalengineers, especially engaged in powder technology. As
such, most of us are often reminded of translation along a
chemical potential gradient, such as sedimentation/floatation under a gravitational field (Svarovsky, 1981), electrophoresis under an electric field (Westermeier, 2006),
magnetophoresis under a magnetic field (Lim et al., 2011;
Suwa and Watarai, 2011), and diffusion under a concentration gradient (Cussler, 2009). This translation arisingfrom a chemical potential gradient formed by external
fields is called “passive motion,” where objects movemonotonously from an area or state of higher chemical
potential to that of a lower one.
†

Received 7 July 2014; Accepted 31 July 2014
J-STAGE Advance published online 30 August 20141
39-16 Tatara Nishihirakawara, Kyotanabe-shi, Kyoto 610-0321,
Japan* Correspopnding author: Daigo Yamamoto;E-mail: dyamamot@mail.doshisha.ac.jp
TEL: +81-774-65-6577 FAX: +81-774-65-6803

2

In contrast, self-propelled nano/micro objects that canovercome the chemical potential gradient are ubiquitousin our bodies, and play a crucial role in vital actions (Fig.
1). For example, in a process called “chemotaxis,” a whiteblood cell chases a bacterium by sensing irritants that it
produces, such as N-formylated peptides (Fenteany and
Glogauer, 2004). A kinesin, which is one of the motor
proteins in the cell of living organisms, carries a biogenic
object, and moves toward the destination along a microtubule using chemical energy released by the decompositionof adenosine triphosphate (ATP) (Kanai et al., 2004; Valeet al., 1996). These biological systems can convert chemical energy into mechanical work, leading to more sophisticated functional motions than the “passive motion”
described above. Such a self-motile nano/micro object,
and a spontaneous motion that is independent of a chemical potential gradient are called “nano/micromotor,” and“active motion”, respectively (Romanczuk, 2011).

1.2 A new transport phenomena in chemicalengineering“Active motion” is a common topic in mechanical engineering (Zhang et al., 2010), biology (Campuzano et al.,2011), and non-linear science (Strogatz, 1994); however,most chemical engineers do not seem to be acquainted©2015 Hosokawa Powder Technology Foundation

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

Fig. 1 Examples of autonomous motions of biological systems. (a) Bacterial phagocytosis (meaning “todevour”) of a white blood cell, and (b) mass transfer by a motor protein in a cell cytoplasm.

Fig. 2

Possible applications of nano/micromotors.

with it. To the authors’ knowledge, nano/micromotors
have not been applied to the design of chemical systems
since chemical engineering was established half a century
ago. This is no wonder, since chemical engineering has
developed based on monotonous dynamics to thermodynamic equilibrium (Bird et al., 2007). It has often been
the case for chemical engineers that numerous dynamics
related to chemical processes proceed monotonously
toward thermodynamic equilibrium. However, thedegrees of freedom for chemical system design will
increase dramatically if these new “transport phenomena”
are incorporated into the design. We believe this forecast
by taking into account the recent progresses in biologythat reveal a vast number of wonderful characteristics ofliving matter as a chemical system (Phillips et al., 2009).
We expect a rich variety of possible applications as
depicted in Fig. 2. For example, if catalytic particles canmove against the concentration gradient of reactants likethe chemotaxis of a white blood cell, the catalytic activity
will increase, not by catalyst chemistry, but by its mobility if the overall reaction order is greater than one.
Furthermore, higher-order reactions will yield more re-markable differences from a conventional catalyst because
the reaction rate depends more strongly on concentration.
In addition, these micro motors may be used as a powersource for micromachines and microfactories. For
instance, a motor that shows rotation or spin motion might
be applied to a microcarrier or micropump, which is ubiquitous in our bodies, for example, a motor protein (Valeand Milligan, 2000), or an ion pump in cells (Blausteinand Lederer, 1999).
In this article, we review artificial nano/micromotors
and focus on chemical reaction driven ones because these
seem to exhibit a biomimetic function with potential
applications to chemical systems. For example, these
motors may move by responding to surrounding chemicals and migrating along the concentration gradient.
Firstly, we discuss the underlying physics of nano/micromotors compared to conventional macro motors (Sec. 2).
Secondly, we introduce examples of artificial nano/micromotors propelled by various types of mechanisms (Sec. 3).
Finally, we focus on reaction-propelled motors and theirdirectional control (Secs. 4 and 5).

3

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

2. Physics of nano/micromotors
A moving macro object can maintain its motion without
continuous force for an extended period of time even undera viscous drag, while a nano/micro scale object cannot be
due to the crucial effect of viscous force. Furthermore,
thermal fluctuation, which is caused by collisions with thesurrounding molecules, becomes more significant with adecrease in size. This section presents a theoretical andquantitative discussion of the motions of nano/micro
objects. Although these concepts are rudimentary to physics, explicit descriptions related to nano/micromotors are
rarely seen to the authors’ knowledge. In Secs. 2.2 and 2.3,
theory and equations based on Stokes’ law will fail under
the situation where a solvent behaves as a discontinuous
one, that is, with a smaller nano-sized object or at gas
phase. Nevertheless, they are useful enough to estimate
order of magnitude roughly even under such a situation.

2.1 Reynolds number
In general, inertia and viscous force dominate themotion of macro and nano/micro scale objects. For a moving object of characteristic length L with velocity v, theReynolds number Re, which represents the ratio of inertia
to viscous force, is defined as

Re

v 2
vL vL
Inertia 
Viscous force v L



(2.1)

where ρ, μ, and υ are the density, dynamic viscosity, andkinematic viscosity of the medium, respectively. Table 1
shows examples of Reynolds numbers of self-propelledobjects in water and air. Although water and air have quite

Table 1

2.2 An equation of translational motion
Newton’s second law states that the net force (Fnet) acting upon an object is equal to the time (t) derivative of its
momentum. If the mass (m) of the object is constant, theequation for translation isdv Fnetm(2.2)
dt

Reynolds numbers of self-propelled organisms and artifacts

Motile object
Large submarine
WhalebTunab
Human (swimming)
Sparrow
Killifish
Water flea
Flyb
Paramecium
Sea urchin spermb
Bacteriumb
Artificial nano/micromotorcadifferent densities (ρwater ≈ 1.0 × 103 kg/m3, and ρair ≈ 1.2 kg/m 3) and dynamic viscosities (μwater ≈ 1.0 mPa·s, andμair ≈ 0.018 mPa·s), kinematic viscosities are similar to someextent (υwater ≈ 1.0 × 10 –6 m2/s, and υair ≈ 1.5 × 10 –5 m2/s). In
addition, the smaller object usually has the slower velocity,
as demonstrated by allometric laws, such as the swimming
speed of fish (Bainbridge, 1958), running speed of mammals(Garland, 1983) and cruising speeds of flying animals, andvehicles (Tennekes, 1997). Therefore, the size is a crucial
factor determining which of the two forces is dominant (seeTable 1). If this boundary is set at Re = 1, then a motile
object having a size below the submillimeter scale is influenced strongly by viscosity.
Unlike currently available macro motors, a nano/micromotor should exhibit high performance under the condition that the viscous drag overcomes inertia, i.e., ultralow
Reynolds numbers (Re « 1). In this regime, the strategy ofsimple downsizing of macro motors cannot be applicable.
Even if a conventional mechanical motor is downsized tothe micron size, one has to supply a large amount ofenergy to maintain its mobility. Such a large energy input
into a small system is inefficient and may destroy the system. Of course, assembling a small mechanical system isalso quite difficult from a practical point of view.

Mediuma
Water
Water
Water
Water
Air
Water
Water
Air
Water
Water
Water
Water

Size scale [m]
2

~10
~101
~100
~100
~10 –1~10 –2~10 –3~10 –3~10 –4~10 –4~10 –6~10 –6

Velocity scale [m/s]
1

~10
~101
~101
~100
~101
~10 –1~10 –2~10 –1~10 –3~10 –5~10 –5~10 –5

Reynolds number [–]
~109
~108
~107
~106
~105
~103
~101
~101
~10 –1~10 –3~10 –5~10 –5

Kinematic viscosities (ν = μ/ρ) of water and air are set to ~10 –6 m 2/s and ~10 –5 m 2/s, respectively.
The data are estimated based on the previous book (Vogel, 1994).
c
The data are those of the earliest artificial nano/micromotor (See Secs. 3 and 4) (Paxton et al., 2004). However, note that othermotors have almost the same scales of size and velocity as shown in Table 3.
b4

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

where v denotes the velocity of the object. Here, let us
consider that the driving force (Fd) and drag force (Fη)
constitute the net force. Eqn. 2.2 is described asdv
m  Fd  Fη
dt

(2.3)

For a spherical object of diameter dp, the drag force isdefined as
1
1Fη 
C D   v 2    πd p 2 
2
 4


(2.4)

Here, CD is the drag coefficient, which is a function of the
Reynolds number (i.e., a function of velocity). The relationship between CD and Re for a spherical object was
obtained experimentally (Schlichting, 1955) and approximated byCD 

24(Re < 1, Stokes flow)
Re

10
CD 
Re

(1 < Re < 500, Allen flow)

(2.5)

(2.6)

  0.056 SG  d p / mm [s] (in water)(2.13)

  3.1 SG  d p / mm [s] (in air),(2.14)

22

where SG is the specific gravity (ρp/ρwater) of the sphericalparticle.
The time (t±99%) required to proceed from v0 = 0 to
0.99vf with constant driving force and v0 ≠ 0 to 0.01v0
without driving force are calculated from Eqns. 2.3–7,
and shown as a function of Re in Fig. 3. At Re < 1, thetime required to proceed from v0 = 0 to 0.99vf with theconstant force is
t99% ln100  

Note that Eqn. 2.10 is not applicable to macro motors,
which moves in Allen and Newton flows. Hence, vf andt99% are instead estimated as follows:
A) Allen flow,


vf

3

16Fd 2
 d p 1
25π 2 t99%  7.87 Re 0.5  
5C D  0.44 (500 < Re < 10 , Newton flow)(2.7)(2.15)(2.16)(2.17)B) Newton flow,

Fd
Because nano/micromotors have ultralow Reynolds
(2.18)
 d p 1vf
0.055π
number, the drag force is evaluated from Eqns. 2.4, 5 asfollows:
t99% 143Re 1  (2.19)Fη  3πd pv (Stokes’ law)
(2.8)
According to Eqns. 2.13, 15, if a constant driving force
From Eqns. 2.3, 8, the equation of translational motion for
acts on a particle with a diameter of 1 μm, its motion
a nano/micro object is expressed by
immediately reaches the terminal velocity (< 1 μs). This
is why the translational motion of a nano/micro object is
dv
dv
m  Fd  3πd pv(2.9)
 0 even if the driving force
often discussed with mdt
dt
If Fd is constant, the differential equation can be analytivaries with time (Takinoue et al., 2010), as well as why
cally integrated for a finite change in velocity from v0 at
the terminal velocity, such as sedimentation velocity andt = 0 to v at an arbitrary time t:electrophoretic mobility, plays a more decisive role thantransient velocity (Dueck, 2013).
t
Fig. 3 also demonstrates that the translational motionv(t ) (v0 vf )e   vf
(2.10)
with velocity v0 of a nano/micro object is immediately
The terminal velocity vf and relaxation time τ are given
damped by the surrounding viscosity and transformed
respectively byinto thermal fluctuation, i.e., Brownian motion. Therefore,a sustained motion of nano/micromotor requires a continFd
uous driving force.
vf (2.11)
3πd pFor other types of regulated motion, such as spin orandrotation, a non-zero torque N is required, which is definedas the vector product of the displacement and force vecπ 3d2tors. The equation for rotation is
p
p
d 
m
(2.12)
  6  p p3πd p3πd p18 d
I  Nd  Nη
(2.20)
dt
where ρ represents the density of the particle. When thepvalues are ρwater ≈ 1.0 × 103 kg/m3, μwater ≈ 1.0 mPa·s, andμair ≈ 0.018 mPa·s, Eqn. 2.12 is approximated by

Similarly to Eqn. 2.3, Nd and Nη are the torque arisingfrom the driving force and drag force, respectively. I and

5

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22dyTFd sin    y (t )dtR

(2.22)d  R (t )dt

(2.23)The subscripts T and R denote translation and rotation,
while γT and γR are the friction coefficients. In the case ofa spherical particle with Re « 1, these are given by

Fig. 3

Relationship between Reynolds number and time t±99%
required to proceed from v0 = 0 to 0.99vf (with constantforce), and v0 ≠ 0 to vf = 0.01v0 (without constant force).
The graph was calculated from Eqns. 2.3–7. The relaxation time τ for each flow was given by Eqn. 2.12. Terminal velocities vf were obtained from Eqns. 2.12, 16,
17 for Stokes, Allen, and Newton flows, respectively.

ω represent the moment of inertia and angular velocity.
We assume that rotation is restricted on the x-y plane, andthe torque vector only has a z-component. Essentially thesame discussion can be made in the case of translation.
The important conclusions are as follows, although thedetails are not mentioned:d 0 for a nano/1) Eqn. 2.20 should be calculated as I
dt
micromotor.
2) A spinning nano/micromotor requires continuous
torque to maintain its motion.

2.3 Thermal fluctuation
The smaller particle is strongly influenced by thermal
fluctuation, leading to random Brownian motion. Let us
consider the two-dimensional motion (x-y plane) of anano/micro-sized Janus motor propelled by a constantdriving force Fd toward the azimuth ϕ(t) as shown in Fig.
4a. A typical Janus particle is a spherical one composedof two hemispheres with different properties. Unless thermal fluctuation affects the motion, the particle showstranslation toward the direction of the initial azimuth ϕ(0).
At an ultralow Reynolds number, each acceleration termddv
is neglected ( m 0 ) as discussed in Sec. 2.2.
 0, I
dt
dt
In this case, the equations of motion for translation
and rotation, i.e., Langevin equations (Uhlenbeck and
Ornstein, 1930), are expressed by Eqns. 2.21–23 as follows:dxT
Fd cos    x (t )dt

6

(2.21)


T 
x 
y 3πd p(2.24)

 R  πd p 3(2.25)

ξx(t), ξ y(t), and ξ R(t) are the thermal noise or Gaussian
white noise with zero mean (Horsthemke, 1984). These
are characterized by i (t1)  0(2.26)

 i(t1) i (t 2 ) 2i k BT (t1  t 2 )(2.27)

where the subscript i means x, y, or R. δ is the Kronecker
delta, and ... denotes an ensemble average. k B and T are
the Boltzmann’s constant and absolute temperature,
respectively. According to the Einstein–Smoluchowski
relation (Einstein, 1905, Von Smoluchowski, 1906), thediffusion constant Di is obtained from

Di 

k BT
i

(2.28)

According to a previous paper (Ten Hagen et al., 2011),
the mean-square displacement (MSD) r 2 is given by the
integration of Eqns. 2.21–23 as

r2

2

4 2
 v 
d p DRt  2  f   DRt  1  e  DRt 
3
 DR 

(2.29)

where vf is the terminal velocity given by Eqn. 2.11. Eqn.
2.29 is reduced tor  2

The4 t  2vf 2  t   1  e t  
3reduced(2.30)

parameters

r 

are

r
dp

r x(t  t )  x(t )  y(t  t )  y(t )  (t : arbitraryatime), vf 

a

2

a

a

2

a

vf
, and t* = DRt. Fig. 4b shows the relad p DR

tionships between t* and r  2 at various vf s. When the
Brownian motion dominates (Fd does not work or is negligibly small compared to thermal noise ξ; vf « 1), Eqn.
2.30 approximately becomes the Einstein-Smoluchowski
equation in two-dimensional space as follows:r 2 

44t 
4DTt
 r 2  d p 2 DRt 
3
3(2.31)

With a sufficiently strong constant driving force ( vf  1),

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

a larger diffusion constant than thermal Brownian motion(Jain, 2008). The apparent diffusion constant DA fortranslation is obtained fromDA DT 

vf 2
2DR(2.35)

Here, DR–1 of a spherical particle at 298 K is approximated byDR 1  0.76  d p / μm [s] (in water)
3(2.36)

The above discussion demonstrates that a micro-sized
motor with a characteristic length of 1 μm generally
exhibits random, instead of translational, motion at a timescale greater than 1 s. Thus, long-distance migration isquite difficult because the average distance with thismotion is proportional to t0.5 (see Eqn. 2.34). In such acase, directional motion, where the distance is proportional to t, is preferred for efficient motion.

2.4 Directional control of continuous regulated
motions of nano/micro objects
Fig. 4(a) Model of a Janus motor propelled by a constantforce toward the specific direction. (b) Relationshipbetween reduced time and mean-square displacement(MSD) at various vf *s.

Eqn. 2.26 can be approximated, depending on the timescale as follows:(A) Brownian motionr 2 

4t 
t
3

1 


0  t  ~ v  2 f (2.32)(B) Translational motion (linear motion with constantvelocity r   vft  )2

r  2  vft   t  2

 1
 ~ v  2  t  ~1
 f

(2.33)(C) Active Brownian motion (random walk with active
diffusion constant)4r  2    2vf 2  t   t 
3

~1  t 

(2.34)

Following Eqns. 2.32–34, the dynamical mode changes
dramatically depending on the time scale. At the microscopic scale corresponding to the shorter time scaled p 2 DR t  v 2  , the self-propelled particle shows thermalf
B r ow n i a n m o t io n . A t t h e i n t e r m e d i a t e s c a l e
 d p 2 DR
1 
 v 2  t  DR  , it exhibits translational motion with f
constant velocity vf, which is the terminal velocity without thermal f luctuation. At the macroscopic scale(DR–1 < t), it exhibits active Brownian motion, which has

For a nano/micromotor, the viscous force and thermal
fluctuation have a significant influence on its regulated
motion as mentioned above. The former damps its motion
immediately while the latter converts its motion into
active Brownian motion via thermal rotation. In order tocontrol the direction of its translational motion, not only acontinuous driving force, but also a response to external
stimuli is required. For example, chemotaxis, which isinherent to numerous biological motions, such as a whiteblood cell or bacterium (Adler and Tso, 1974), may play asignificant role in regulated motion toward the destination. In addition, biomotors, such as kinesin and myosin,show directional motion by using a confined space, thatis, walking along a monorail (e.g., microtubule or actin),which hinders the change of direction arising from thermal fluctuation (Van Den Heuvel and Dekker, 2007).
An artificial nano/micromotor can exhibit such biomimetic motion because even a molecular-sized biomotor
can generate regular motion under viscous force andthermal fluctuation effects that are stronger than those inartificial nano/micromotors. Artificial motor systems
propelled by a continuous driving force by various
mechanisms are introduced in Secs. 3 and 4. Furthermore,
directional control has been attempted for artificial nano/micromotor, which is discussed in Sec. 5.

3. Examples of fabricated artificial nano/micromotors
The preparation of nano/micromotor systems has beena hot and challenging topic in nanotechnology and bio-7

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

Fig. 5

Examples of synthetic micro motors propelled by different mechanisms.

technology for the last decade. Pioneers in the field are
Paxton and coworkers, who demonstrated a nano/microsized motor system with catalytic particles in a solution
containing reactants in 2004. The particles exhibit
non-Brownian motion by using chemical energy generated by a catalytic reaction. Motors such as these will be
discussed in detail in Sec. 4. Since then, several researchgroups have attempted to fabricate artificial nano/micromotors that employ other mechanisms such as electric,
magnetic, and ultrasound propulsions (Fig. 5). Here, wesummarize these nano/micromotors before discussing
chemical reaction driven motors.
One of the simplest electric-induced motor is a Janus
particle made by partially coating a polystyrene (PS) latex
sphere with Au (Gangwal et al., 2008). The particle showstranslational motion along the direction perpendicular to
the AC field. The electric double layer on the Au hemisphere is more strongly polarized, and thus drives a stronger induced-charge electro-osmosis (ICEO) slip, than thaton the PS one, resulting in induced-charge electrophoresis(ICEP) motion in the direction of the PS side.
A typical magnetic-driven motor is the composite
nanowire with a flexible thin Ag joint connecting the Au
head and Ni tail (Au/Agflex/Ni nanowire) (Gao et al., 2010).
A rotating magnetic field creates a corkscrew rotation of
the Ni segment, leading to translational motion along therotation axis of the magnetic field. Interestingly, the translational direction was changed by tuning the length of
the Au and Ni segments. Based on a similar mechanism,a helical motor with Ni/Ti bilayers has been reported(Tottori et al., 2012). The translational motion, converted
from rotational motion, mimics bacterial motion by rotatinga flagellar filament (Berg and Anderson, 1973). Note thatthe motion of the above electric/magnetic-driven motor isessentially different from the passive motion of a charged/

8

magnetic particle by electrophoresis/magnetophoresis
because the steady gradient of chemical potential does notexist under these switching fields.
An example of an ultrasound-propelled motor is a
microrod made of two metal species (Au/Ru or Au/Ptrods) (Wang et al., 2012). When an ultrasonic frequency
is tuned to create a vertical standing wave, the rod levitates to the midpoint plane of a cylindrical acoustic cell,and exhibits various kinds of motion (translationalmotion, rotation, and axial spinning). The asymmetricshape of the metal rod can lead to an asymmetric distribution of the acoustic pressure from the scattering of theincident acoustic waves at the metal surface (called self-­
acoustophoresis), which produces the above dynamic mode.
In general, the shape and chemical composition of thesenano/micromotors are designed so as to break the symmetry of the local field to generate a gradient (Wang etal., 2013). Note that the term “symmetry breaking” is a
notion widely accepted in physics (Anderson, 1972).
Therefore, most of these previous nano/micromotors possess compositional asymmetry to gain the continuous
driving force or torque required for motion toward a certain direction.

4. Nano/micromotors driven by chemical
reactions
Nano/micromotors driven by chemical reactions areclassified into two groups: a catalytic motor and a self-­
reacted one. The former shows semi-permanent motion solong as reactants exist in medium, while the latter moves
due to a decrease or increase in size, and sometimes, achange in its composition. In this section, we introduce
these motors in detail.

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Fig. 6

Examples of catalytic motors propelled by consuming hydrogen peroxide as fuel. (a) Macro motor: polymer boat with a Pt-covered stern (Ismagilov et al., 2002; copyright 2002 John Wiley & Sons, Inc.,
reprinted with permission), and nano/micromotors: (b) Pt/Au nanorod (Paxton et al., 2004), (c) L-shaped
Si/Pt particle (Yuping et al., 2007; copyright 2007 American Chemical Society, reprinted with permission), and (d) spherical silica particle with a Pt-coated hemisphere (Gibbs and Zhao, 2009; copyright2009 American Institute of Physics, reprinted with permission).

4.1 Catalytic nano/micromotors
Most catalytic nano/micromotors are propelled by consuming hydrogen peroxide as fuel, while some use hydrazine (Gao et al., 2014, Laocharoensuk et al., 2008).
Because both fuels are thought to propel the motor
through essentially the same mechanism, we focus solely
on catalytic motors with hydrogen peroxide fuel.
In 2002, Ismagilov et al. have demonstrated a macro-­sized (~1 cm) catalytic boat converting chemical intomechanical energy (Fig. 6a). At the air/water interface
with hydrogen peroxide, a polymer boat with its stern
covered with Pt moves for several hours. The decomposition of hydrogen peroxide,
2H2O2 → 2H2O + O2,(4.1)

proceeds on the surface of Pt and generates a recoil force
from oxygen bubbles released from the Pt surface.
Two years later, Paxton et al. (2004) proposed the first
synthetic nano/micromotor system (Fig. 6b). The motor
system is a catalytic nanorod consisting of 1 μm-long catalytic Pt and inert Au segments immersed in a solution
containing hydrogen peroxide. Around the same time,
Fournier-Bidoz et al. (2005) independently demonstrated
a similar nano/micromotor system using a Ni/Au rod.
Very recently, Lee et al. (2014) have fabricated a quite
minute Pt/Au motor with a diameter of 30 nm, whichshows high ballistic speed. Interestingly, these nano/micromotors move toward the direction opposite of thatin the macro motor proposed by Ismagilov et al. (2002),
that is, toward the catalytic Pt or Ni end of the rod.
This unexpected motion can be explained by either
interfacial tension gradient (Fig. 7a) or self-electrophoresis(Fig. 7b). In the former, the decomposition of hydrogen
peroxide occurs only on the Pt surface, leading to a gradient of solute (dissolved oxygen) concentration or temperature along the rod. That is to say, the boundary layer
around the Pt end has a higher oxygen concentration ortemperature, which reduces interfacial tension between
the solution and rod. The interfacial tension gradientcauses convection flow to occur from the Pt to Au side,and the simultaneous movement of the rod toward the
opposite direction according to the law of momentum
conservation. Here, note that the hydrogen peroxide concentration gradient does not affect the interfacial tensionof the metal/water interface because that of hydrogen peroxide water is almost identical to pure water (Phibbs andGiguere, 1951).
In the latter electrokinetic mechanism, the Pt and Au
segments work as the positive and negative electrodes,
respectively, causing an oxidation/reduction reaction ofhydrogen peroxide as follows:
Pt: H2O2 → O2 + 2H+ + 2e –(4.2)

Au: H2O2 + 2H+ + 2e – → 2H2O

(4.3)

The net reaction is identical to Eqn. 4.1. Hydrogen peroxide
is oxidized to generate protons in solution, and electrons on

9Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

Fig. 7

Possible moving mechanisms of catalytic nano/micromotors. (a) Interfacial tension gradient, and (b)self-electrophoresis propel catalytic Pt motors toward the Pt side, while (c) nano/micro bubble, and (d)self-diffusiophoresis propel it away.

the Pt side. On the other hand, protons and electrons are
consumed with the reduction of hydrogen peroxide on theAu side. The resulting ion flux induces the motion of theparticle relative to the fluid, propelling the particle toward
the Pt end (Moran and Posner, 2011, Paxton et al., 2006,
Wang et al., 2006). Self-electrophoresis seems to be moreprobable because the translational direction changes oppositely in the case of a composite particle of catalytic Pt anddielectric material, as will be discussed shortly.
An L-shaped Si/Pt particle immersed in hydrogen peroxide water (Fig. 6c), in contrast to bimetallic rods,
moves or rotates in such a way that the rear side is catalytic Pt (Yuping et al., 2007). Likewise, spherical silica(Gibbs and Zhao, 2009), fluorescent polymer (Ebbens andHowse, 2011), or PS (Baraban et al., 2012) particles with a
Pt-coating hemisphere (Fig. 6d), exhibit translationalmotion away from Pt. These studies imply that a composite particle of a catalytic metal and dielectric material has
a different moving mechanism from a bimetallic one.
There are other possible mechanisms for the self-motionof catalytic particles, such as nano/micro bubble propulsion(Fig. 7c), and self-diffusiophoresis (Fig. 7d). Bubble propulsion seems to be a facile mechanism because the macro-­sized boat proposed by Ismagilov et al. (2002) obviously
moves by ejecting visible bubbles. Although no visible
bubble is observed in all of the above catalytic nano/mi­
cromotor systems, nanobubbles may be created on the
catalytic surface. In fact, elegant designs of a few nano/micromotor systems propelled by ejecting visible microbubbles
or invisible nanobubbles have been reported (Fig. 8).
Solovev et al. (2009) demonstrated that a rolled-up micro-

10

tube made of a thin Pt/Au/Fe/Ti sheet moves like a rocketby bubble ejection through one opening of the tube. Furthermore, a Pt-nanoparticle-encapsulated polymer stomatocyte, which was fabricated by Wilson et al. (2012), swims
through invisible nanobubble ejection through an opening.
In addition, Wang and Wu (2014) demonstrated that a PS
micro dimer with one lobe sprinkled with Pt nanoparticles
moves through the periodic growth, and collapse of bubbles. These bubble-propelled motors commonly have astructure suitable to trap bubbles and promote its growthand coalescence, implying the possibility of bubble propulsion even if the bubbles are invisible.
Self-diffusiophoresis arises from the self-induced concentration gradient of reactants or products (Golestanian
et al., 2005). In this case, the catalytic Pt surface produces
oxygen continuously. As the oxygen begins to diffuse
away from the catalytic surface, a steady concentration
gradient of oxygen is generated as shown in Fig. 7d. This
diffusion results in particle movement toward the inert
side. Here, a spherical geometry is known to have a larger
effect on self-diffusiophoresis than a rod-like one because
velocity depends on the aspect ratio (Howse et al., 2007).
All catalytic motors introduced in this section possesscompositional asymmetry, and sometimes, a complicatedstructure to obtain a continuous driving force and torque,which increases operational factors from the viewpoint oftechnology. In the next section, we discuss simpler catalytic motors made of a single metal, the motion mechanisms of which are quite different from those of the
motors above.

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

Fig. 8

Catalytic nano/micromotors driven by nano/micro bubbles: (a) Rolled-up microtube made of a thin Pt/
Au/Fe/Ti sheet (Solovev et al., 2009; copyright 2009 John Wiley & Sons, Inc., reprinted with permission), (b) Pt-nanoparticle-encapsulated polymer stomatocyte with an opening (Wilson et al., 2012; copyright 2012 Nature Publishing Group, reprinted with permission), and (c) PS micro dimer with one lobesprinkled with Pt nanoparticles (Wang and Wu, 2014; copyright 2014 American Chemical Society,reprinted with permission).

4.2 Simpler catalytic nano/micromotors made of asingle metal
Is the chemical asymmetry of a particle absolutely necessary for its regulated motion? Our answer is “No!” Perhaps literally all catalytic particles may be active in a
medium containing a reactant.
A few research groups including us have investigatedthe motion of catalytic particles made of a single metal(Pt) instead of catalytic composites. The first catalyticmonometallic motor is a Pt nanorod proposed by
Kovtyukho et al. (2008). For a monometallic motor, thecritical factor to determine the motion should be its morphology. However, the rods fabricated by Kovtyukho et al.
(2008) exhibit various types of regulated motions despite
the similar shape. On the other hand, we investigated therelationship between the morphology of Pt particles anddynamical modes (Yamamoto et al., 2013). We demonstrated that even a spherical catalytic Pt particle shows
more vigorous random motion (active Brownian motion)in hydrogen peroxide water than the usual Brownian
motion in pure water, despite the fact that non-composite
particles cannot break the symmetry of the local field,that is, the net driving force is zero. However, we wereable to obtain regulated motions. This suggests that asimple Pt particle causes transient asymmetry of the local
field, although the mechanism of force generation(described in Sec. 4.1) is not elucidated. A stationary
asymmetry induced by chemically anisotropic motors
generates a constant driving force, while a transient force
only generates random fluctuation. Interestingly, theresultant random fluctuation caused by the chemical reaction has a unique characteristic, a ballistic (translational)
motion for a shorter time scale (< 1 s). According to the11Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

Fig. 9 Summary of the authors’ research on catalytic motors made only of Pt (Yamamoto et al., 2013; copyright2013 American Institute of Physics, reprinted with permission).

Second Law of Thermodynamics, regulated motion consine term, which is an odd function, represents the pointverts to thermal fluctuation, but not vice versa. However,symmetry. Therefore, f(ψ) (0 ≤ f ≤ 1) can be defined asa random fluctuation containing ballistic motion at a
microscopic time scale can be converted to various reguan 2lated motions in appropriate situations, such as a particun 1f ( )  (4.6)

lar morphology of the moving object or geometry of thean 2   bn 2
field of particle motions. This was demonstrated in the

n 1
n 1
case of aggregates of Pt particles, which exhibit various
regulated motions depending on morphology as shown in[2] Among all axes (0 ≤ ψ ≤ π), the axis with the highest
Fig. 9.
and lowest axial symmetry were determined and denoted as
fmax and fmin, respectively. The axial symmetry-asymmetry
In our simple motor system, the dynamical modes (i.e.,balance (SAB) of particles was calculated astranslation, rotation, and spin) of particles or aggregates
depend only on their morphology. Our quantitative
f max
degreeof symmetry
analysis of the two-dimensional shape can predict
the
SAB 

(4.7)
 f mindegreeof
asymmetry1
dynamical mode. The analytical procedure consists of thefollowing two steps (Fig. 10):
From the above analysis, we found that the aggregates[1] First, we calculated the ratio of the axial symmetry
tend to show translational motion at SAB > 1. In addition,f(ψ) of the two-dimensional shape with respect to an axis
aggregates exhibit rotational, and spinning modes atthrough the center of gravity, tilted at an arbitrary aziSAB ~ 1, and SAB < 1, respectively (Fig. 11 and Table 2).
muthal angle ψ as follows:
Therefore, we concluded that the motion of aggregates
R(θ), which is the length between the center and edge atwith various shapes could be classified based on SAB,
angle θ (–π ≤ θ ≤ π) from the ψ-axis (0 ≤ ψ ≤ π), is giventhat is, the morphological symmetry.
as
Most recently, Bao et al. (2014) demonstrated that ashape-controlled catalytic motor made only of Pt moves
(4.4)
R(
 ) Rav   R()faster than ours, although its velocity is a little slower
where Rav is the mean radius of a particle. Here, δR(θ) is
compared with other composite motors (see Table 3).
expressed as a Fourier series
A simple motor is the most favorable candidate to
examine the effect of shape on motion because the modesolely depends on morphology. The above-mentioned par
 R()  (an cos n  bn sin n )
(4.5)
n 1
ticle will give us significant insight for a sophisticated
The cosine term represents the axial symmetry of thedesign not only for non-composite motors but also forparticle with respect to the ψ-axis. On the other hand, thecomposite ones.

12

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

Fig. 11

Relationship between dynamical modes, and axial
symmetry-asymmetry balance (SAB) of particles.
The data correspond to those in Table 2.

Table 2

Summary of SAB values, and motion types (unpublished data)

Dynamical
mode

Fig. 10

Typical example of quantitative analysis of particle
morphology (using an imaginary particle with a birdlike shape).

Aggregatea

fmax [–]

1 – fmin [–]SAB [–]

Translation

0.84
0.91
0.95
0.93
0.95
0.91
0.89

0.78
0.81
0.90
0.93
0.90
0.90
0.90

1.08
1.12
1.06
1.00
1.06
1.01
0.99
av. 1.04

Rotation

0.95
0.90
0.90
0.90
0.86
0.88
0.93
0.95

0.96
0.92
0.88
0.89
0.87
0.88
0.92
0.92

0.99
0.98
1.02
1.01
0.99
1.00
1.01
1.03
av. 1.00

Spin

0.92
0.96
0.86
0.84
0.94
0.94
0.82
0.86
0.84
0.86

0.97
0.96
0.92
0.96
0.95
0.92
0.86
0.97
0.89
0.92

0.95
1.00
0.93
0.88
0.99
1.02
0.95
0.89
0.94
0.93
av. 0.95

aSize scale is different among all aggregates, although therange only varies from 3 to 7 μm.

13Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

4.3 Self-reacted nano/micromotors

5.1 Chemotaxis

Several research groups have attempted to fabricate
self-reacted nano/micromotors. These motors movethrough consumption of themselves, or due to a change incomposition; hence, these have limited lifetimes unlike
catalytic motors. In addition, these do not require specificreactants such as hydrogen peroxide, which is incompatible with living organisms. These characteristics are preferable for biotechnological applications such as drugdelivery systems (DDS) (Jain, 2008) because the motors
can be dissipated by exhausting themselves after carrying
drug to the destination.
To the authors’ knowledge, the first self-reacted motor
is a Cu/Pt nanorod in dilute aqueous solutions of Br2 or I2(Liu and Sen, 2011) (Fig. 12a). This motor is driven by
self-electrophoresis, similar to the one proposed by
Paxton et al. (2006). The remarkable difference is that the
Cu segment, working as a negative electrode, is oxidized
to Cu 2+ (etched Cu segment in Br2 solution) or CuI(CuI-coated Cu segment in I2 solution).
Most of the other self-reacted nano/micromotors arethose propelled by microbubbles. Mg-containing Janus
micro motors are propelled by hydrogen gas generated by
the oxidation of the Mg surface

Chemotaxis is the directional movement of an organism along a concentration gradient of chemicals (Adler,1966). For instance, a bacterium swims toward a higher
concentration of food (glucose), while it swims awayfrom an acidic area. Chemicals that attract, and repel anorganism are called a “chemoattractant,” and a “chemorepellent,” respectively. Recently, Lagzi et al. (2010)
demonstrated an interesting artificial chemotactic
system, namely “maze solving by chemotactic droplet.”At the water/air interface, a millimeter-sized oil droplet
moved from the entrance to the goal of the maze (Fig. 13a).
This chemotaxis can be explained as follows. First, an acid-­soaked gel is placed at the goal to form a pH gradient.
Then, a droplet containing surfactants is put at theentrance. Because of the interfacial tension gradientdepending on the pH of the surfactant system, the droplet
shows chemotactic motion toward the chemoattractant(the acid-soaked gel).
The first example of chemotaxis with nano/micromotors
in a laboratory was demonstrated by Hong et al. (2007).
When Pt/Au rods were put under a concentration gradientof hydrogen peroxide, which is generated by an agar gel
containing 30% hydrogen peroxide, these gathered in thevicinity of the gel (Fig. 13b). This is because the active
diffusion constant of the rods became larger with anincrease in the concentration of the reactant. Furthermore,
Baraban et al. (2013) demonstrated that catalytic motors
with different morphologies, a tubular microjet and Janus
particle, also show chemotactic behavior in microfluidic
channels. Although the chemotactic motion demonstratedby Hong et al. (2007) takes a long time (almost 30% and
70% of the rods were accumulated at the gel after 38 h and
110 h, respectively), these motors show collective motion
toward the chemoattractant. This is quite interesting andpromising, not only for biomimetic technology, but also
for reaction technology with catalysts.

Mg(s) + 2H2O → Mg(OH)2(s) + H2.

(4.8)

Although the resultant passivation layer of magnesiumhydroxide hinders the reaction, Gao et al. (2013), and Mou
et al. (2013) succeeded in sustaining it in seawater, andwater-containing sodium hydrogen carbonate, respectively, through different mechanisms (Fig. 12b, c). Gao et
al. also proposed other types of self-reacted motor systems, such as a Janus micro motor composed of a partiallycoated Al–Ga alloy driven in pure water (Gao et al.,2012a), and a Zn-based microtube driven in acidic water(Gao et al., 2012b) (Fig. 12d, e).
A quite challenging research on the application of theseself-reacted motors as DDS began recently (Mou et al.,2014) (Fig. 12f). Such research will accelerate both fundamental and applied studies concerning self-reacted motors.

5. Directional control of artificial
nano/micromotors
For practical uses of the above nano/micromotors, more
highly functional properties, such as directional control,
collective motion (Duan et al., 2013; Kagan et al., 2011),
chemical sensing (Kagan et al., 2009), and transport ofcargo (Sundararajan et al., 2008), are required. These
characteristics are often observed in biological systems.
Here, we focus on attempts at directional control of nano/micromotors.

14

5.2 Control with external field and confined space
As mentioned in Sec. 2.3, thermal fluctuation converts
the directional motion of nano/micromotors into active
Brownian motion at a macroscopic scale. Directional control of nano/micromotors driven by chemical reactions
has been attempted mainly under a magnetic field, forinstance, by Kline et al. (2005) with Pt/Ni/Au/Ni/Au rods
(Fig. 14a). Without the magnetic field, the rods exhibittranslational motion in hydrogen peroxide water with ahigh rotational diffusion constant (0.2 rad 2 s–1). On theother hand, an applied magnetic field aligns the rods along
a direction perpendicular to the field (0.02 rad 2 s–1). Notethat the field serves to orient the rods and does not affectthe motion itself. Furthermore, a catalytic Ni-containing

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Fig. 12 Examples of self-reacted motors. (a) Cu/Pt nanorod in dilute aqueous solutions of Br2 or I2 (Liu and Sen,
2011; copyright 2011 American Chemical Society, reprinted with permission), (b, c) Mg-containing
Janus micromotors in seawater (Gao et al., 2013; copyright 2013 the Royal Society of Chemistry,
reprinted with permission), and in water containing sodium hydrogen carbonate (Mou et al., 2013;
copyright 2013 John Wiley & Sons, Inc., reprinted with permission), (d) Al-Ga alloy driven in purewater (Gao et al., 2012a; copyright 2012 American Chemical Society, reprinted with permission), (e)Zn-based microtube driven in acidic water (Gao et al., 2012b; copyright 2012 American Chemical Society, reprinted with permission), and (f) Mg-containing motor carrying, and releasing drug (Mou et al.,2014; copyright 2014 American Chemical Society, reprinted with permission).

microtube and microrod propelled by microbubbles also
show complicated motions such as stop, turn, and go,
depending on the magnetic field vector (Burdick et al.,2008; Zhao and Pumera, 2012).
The authors’ group is now trying to fabricate a micro
motor system that mimics motor proteins, which exhibits

directional and sustainable motion along a monorail. Our
strategy is to control the motion of a catalytic Pt particle
using a microchannel. Fig. 14b shows our preliminary
data for Pt particle motion in a microchannel, with several
micrometers in width and depth in hydrogen peroxidewater. The particle exhibits directed motion along the15

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

16

Fig. 13

Chemotaxis of artificial objects. (a) Millimeter-sized oil droplet moving from the entrance to the goal ofthe maze (Lagzi et al., 2010; copyright 2010 American Chemical Society, reprinted with permission),and (b) collective motion of Pt/Au nanorods toward an agar gel containing 30% hydrogen peroxidewater (Hong et al., 2007, Sen et al., 2009; copyright 2009 The Royal Society of Chemistry, reprinted
with permission).

Fig. 14

Directional control with external field and confined space. (a) Pt/Ni/Au/Ni/Au rod motion under a magnetic field (Kline et al., 2005; copyright 2005 John Wiley & Sons, Inc., reprinted with permission), anddirectional motion of Pt particles along a microchannel, with trajectory and snapshots of motion shownin (b1), and corresponding spatiotemporal diagram of motion on a vertical line along the microchannel
shown in (b2) (unpublished data).

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

Table 3

Summary of artificial nano/micromotors

Propulsiona

Morphologyb

External field propulsion
Au/PS Janus particleE(D: 8 μm) (see Fig. 5)
Au/Agflex /Ni nanowire
M(L: ~7 μm, D: ~0.3 μm) (see Fig. 5)
SU-8 helical motor with Ni/Ti bilayers
M(L: 35 μm, D: 8.0 μm)
Ru/Au or Au/Pt nanorod
U(L: 2 μm, D: 0.33 μm) (see Fig. 5)

Maximum velocity(Body(μm/s)
length/s)

~30

3.8

6

0.9

~180

5.1

200

100

Chemical reaction propulsion
Polymer boat with a Pt-covered stern
2.2
CR (B)(D: 9 mm, T: 1–2 mm) (see Fig. 6a)
2 × 104(Note that it is not a nano/micromotor!)
Pt/Au nanorod
CR (SE/IT)
7.9 (14?)7.9 (14)(L: 1 μm, D: 0.37 μm) (see Fig. 6b)
Ni/Au nanorod
1.5 rad/s
CR (?)(L: 2.5 μm, D: ~0.2 μm)(rotational speed)
Rh/Au nanorod
CR (SE)
23.8
12(L: 2 μm, D: 0.37 μm)
Pt/(Au 25-Ag75-alloy) nanorod
150
75
CR (SE)(L: 2 μm, D: unknown)
Pt/Au nanorod containing carbon
CR (SE)
nanotubes> 200
100(L: 2 μm, D: 0.22 μm)
Janus-type Au particle with an embed660
2.2 × 104(ballistic speed)
CR (SE)ded Pt particle on one face
(D: 0.030 μm)
Pt/PS Janus particle
CR (SE)~15
7.5(D: 2 μm)
Ni/Au nanorod with high surface area
CR (?)~5
1.6(L: 3.1 μm, D: 0.20–0.25 μm)
L-shaped Si/Pt nanorod
CR (B?)
2 rad/s (rotational speed)(L: ~3 μm) (see Fig. 6c)Rolled-up catalytic microjet
CR (B)
2000
33(L: 60 μm, D: 5.5 μm) (see Fig. 8a)
Pt/SiO2 Janus particle
7.5
3.8
CR (B)(D: 2 μm) (see Fig. 6d)
Pt-nanoparticle-encapsulated polymer
CR (B)
stomatocyte with an opening~30
200(D: ~0.15 μm) (see Fig. 8b)
PS micro dimer with one lobe sprinCR (B)kled with Pt nanoparticles~40
10(L: 4 μm?) (see Fig. 8c)
Pt/Fluorescent-polymer Janus particle
CR (SD)
3.4
1.7(D: 2 μm)
Ir/SiO2 Janus particle
21
17.5
CR (SD)(D: 1.2 μm)
Pt/PS Janus particle
CR (SD)~3.3
2.1(D: 1.6 μm)
Pt nanorod
M-CR (B)15
4.7(L: 3.2 μm, D: 0.35 μm)
Streamlined Pt aggregate
M-CR (SD/B?)~3
0.6(L: 5 μm)

Conditionc

Reference

0.1 mM NaCl under an AC
field (~300 V/cm, 1 kHz)
Under a rotating magnetic
field (5 G, 15 Hz)
Under a rotating magnetic
field (10 G, 60 Hz)
Under an acoustic field(3.7 MHz)(Gangwal et al.,2008)

3% H2O2 water
3.3–5% H2O2 water
H2O2 water (concentration:
unknown)
5% H2O2 water
5% H2O2 water
Water containing H2O2
(2.5%) and N2H4 (0.15%)
1.5% H2O2 water

10% H2O2 water
15% H2O2 water
5% H2O2 water
15%? H2O2 water
6% H2O2 water(Gao et al., 2010)(Tottori et al.,2012)
(Wang et al.,2012)

(Ismagilov et al.,2002)
(Paxton et al.,2004).
(Fournier-­Bidoz
et al., 2005)(Wang et al.,2006)
(Demirok et al.,2008)
(Laocharoensuk
et al., 2008)(Lee et al., 2014)(Brown andPoon, 2014)(Zacharia et al.,2009)
(Yuping et al.,2007)
(Solovev et al.,2009)
(Gibbs and Zhao,
2009)

0.3% H2O2 water(Wilson et al.,2012)

10% H2O2 water(Wang and Wu,
2014)

5% H2O2 water(Ebbens andHowse, 2011)

0.001% N2H4 solution(Gao et al., 2014)

10% H2O2 water
3–30% H2O2 water
1% H2O2 water(Howse et al.,
2007)
(Kovtyukhova,
2008)(Yamamoto etal., 2013)17

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

M-CR (B)SR (SE)SR (B)SR (B)SR (B)SR (B)

V-shaped Pt nanorobot(L: ~5 μm, T: 0.7 μm)
Pt/Cu nanorod(L: 1 μm, D: 0.3 μm) (see Fig. 12a)
Mg particle asymmetrically coatedwith Ti/Ni/Au layers(D: 30 μm) (see Fig. 12b)
Mg/Pt Janus particle(D: 20 μm) (see Fig. 12c)
(Al-Ga-alloy)/Ti Janus particle(D: 20 μm) (see Fig. 12d)
Polyaniline/Zn microtube(L: 10 μm, D: 5 μm) (see Fig. 12e)

~7.3

1.5

23.6% H2O2 water

~20

20

1.0 mM Br2

300

10

3 M NaCl

92

4.6

0.5 M NaHCO3

3000

150

Ultrapure water

1050

105

Acidic water (pH: –0.2)

~10
↓
52
7.4
↓
3.9
~8.3
↓
5.5

5
↓
26
3.0
↓
1.6
1.7
↓
1.1

(Bao et al.,2014).
(Liu and Sen,
2011)
(Gao et al., 2013)(Mou et al.,2013)
(Gao et al.,2012a)(Gao et al.,2012b)For applications (chemical sensing, transport of cargo)
CR (SE)

Pt/Au nanorod (L: 2 μm, D: 0.2 μm?)

CR (SE)

Pt/Au/polypyrrole nanorod(L: 2.5 μm, D: 0.37 μm)

CR (SD)

Pt/PS Janus particle (D: 5 μm)

SR (B)

Mg/(Pt-NIPA) Janus particle for DDS(D: 50 μm) (see Fig. 12f)

35

Pt/Au/Agflex /Ni nanowire (L: 6 μm, D:
0.2 μm)

8 (CR on)
6 (M on)8 (M&CR on)

5% H2O2 water⇒ Adding 0.1 mM AgNO3(Kagan et al.,2009)

5% H2O2 water⇒ With a cargo(Sundararajan etal., 2008)

15% H2O2 water⇒ With a cargo(Baraban et al.,
2012)

0.7

Human plasma(Mou et al.,
2014)

1.3
1.0
1.3

8% H2O2 water or pure water
under/without a rotational(Gao et al., 2011)
magnetic field (10 G, 15 Hz)

Hybrid motor
M & CR (SE)a

E, M, U, CR, and SR denote electric propulsion, magnetic, ultrasound, catalytic reaction, and self-reaction, respectively. M-CR
represents CR by monometallic catalytic motor. IT, SE, B, and SD shows detailed mechanisms of chemical-reaction-propelled
motors, and denote interfacial tension gradient, self-electrophoresis, bubble propulsion, and self-diffusiophoresis, respectively (seeFig. 7).
b
D, L, and T denote diameter, length, and thickness, respectively.
c
The list shows the condition where motors exhibit the motion at the maximum velocity.

channel without rotations, which resembles a molecularmotor walking along a microtubule. Although fluctuations
cause random noise in the motion, the particle can exhibit
an almost linear motion with constant velocity. This result
may be applied to microdevices such as the microcarriersystem shown in Fig. 2.

6. Summary and Conclusions
In this article, we reviewed artificial nano/micromotors,
which are summarized in Table 3. First of all, we discussed the underlying physics of nano/micromotors (Sec.
2). The key terms for the motion of nano/micro objects
are “ultralow Reynolds number,” and “thermal fluctuation.” The former means that viscous force dominates the
motion instead of inertia. Therefore, the motion of nano/micro objects reaches terminal regulated motion or turns
into Brownian motion immediately after the driving force

18

works or ceases, respectively. The latter converts regulated motion to active Brownian motion at a macroscopic
time scale. Because the rotational diffusion constant DRof an object increases with a decrease in size ( DR  d p 3
for a spherical particle with diameter dp), it is difficult forsmaller particles to maintain its regulated motion for along period.
To overcome these effects of viscous drag and thermal
fluctuation, artificial nano/micromotors propelled by various mechanisms, such as chemical reaction, electric,
magnetic, and ultrasound propulsion, have been proposedin the last decade as explained in Sec. 3. Most possesscompositional asymmetry to gain the continuous anisotropic driving force or torque required for motion.
Furthermore, a hybrid nano/micromotor driven by two dif­ferent mechanisms, namely magnetic and chemical reaction propulsions, has been studied recently, although bothpropulsions do not accelerate the translational velocity
dramatically as shown in Table 3 (Gao et al., 2011).

Daigo Yamamoto and Akihisa Shioi / KONA Powder and Particle Journal No. 32 (2015) 2–22

In Sec. 4, we focused on motors driven by chemical
reactions because chemical reactions are one of the mostfamiliar phenomena for chemical engineers, especiallythose who are engaged in powder technology, and because
numerous biomotors are driven by chemical reactions.
Such reaction-driven motors are classified into catalytic
and self-reacted ones. Although some catalytic motors
are similarly driven by the decomposition of hydrogen
peroxide, the driving forces can arise from various
mechanisms, such as interfacial tension gradient,
self-electrophoresis, nano/micro bubbles, and self-­
diffusiophoresis. The self-reacted ones have a limited
lifetime, and a few move by decreasing in size, which ispreferable for biotechnological applications such as DDS.
We feel that there are few reactions that produce the driving force required for the two types of reaction-propelled
motors mentioned above. If the above-mentioned “active
motion” could be observed for other types of chemical
reactions, such as catalytic or solid-state reactions, thisnovel transport phenomenon would have a remarkable
impact on chemical systems proposed by chemical engineers.
In Sec. 5, we introduced some examples of directional
control of artificial nano/micromotors for possible applications. In particular, the chemotaxis of a catalytic motor
can enhance its catalytic activity, not by catalyst chemistry, but by its mobility.
In conclusion, we expect that non-equilibrium phenomena including “active motion” can be applied to chemicalsystems in the future. This is because systems adopting
such phenomena, which are ubiquitous in nature, canaccomplish more functional or efficient work. The “active
motion” of nano/micro objects is often observed in ourbodies, meaning that all organisms are “natural” chemicalengineers! Therefore, control of an artificial nano/micromotor seems to be attainable, although there is still a long
way to go toward its realization. Because such a non-­equilibrium phenomenon sometimes produces chaos,strict control of the motor may be difficult. Now is just thetime for us, “learned” chemical engineers, to show ourskills in process design and control. If we can drive a runaway motor vehicle, the unknown world spreads out
before us!

Acknowledgements
Our research on the active motion of Pt catalytic particles was supported in part by Hosokawa Powder Technology Foundation, and by Grant-in-Aid for Young Scientists(B) (Grant No. 26820341 23656497) from the Japan Society for the Promotion of Science (JSPS).

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Author’s short biography
Daigo Yamamoto
Dr. Daigo Yamamoto graduated from Kyoto University in 2006, and the graduate
school of the same university in 2008. He received a Ph.D. in Engineering from Kyoto
University under the supervision of Prof. Minoru Miyahara in 2011. Then, he worked asa research fellow in the laboratory of Prof. Kazuhiro Mae for a year, where his research
focused on the synthesis of nanoparticles with nano/micro reaction fields using a dendrimer, and micro-mixer. He is currently an assistant professor at Doshisha University,and is involved in non-equilibrium phenomena such as spontaneous motion, and self-­
assembly.

Akihisa Shioi
Dr. Akihisa Shioi, a professor of chemical engineering, and materials science at Doshisha University, has been focusing his research on the dynamics, and pattern formationof non-equilibrium chemical systems. His research interests are the development ofchemical systems that mimic the behavior of living matter, and the determination ofcharacteristics that impart a semblance of life to chemical systems.

22

KONA Powder and Particle Journal No. 32 (2015) 23–40/Doi:10.14356/kona.2015006

Review Paper

Role of Acoustic Fields in Promoting the Gas-Solid Contact
in a Fluidized Bed of Fine Particles †Federica Raganati 1, Paola Ammendola 2* and Riccardo Chirone 2
1
2

Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli Federico II, Italy
Istituto di Ricerche sulla Combustione-CNR, Italy

Abstract
The present work is a review presenting the main results obtained by our research group in the field of soundassisted fluidization of fine particles. Our aim is to highlight the role of acoustic fields in enhancing the gas-solid
contact efficiency, with specific attention to the phenomenological mechanism upon which this technique is based.
In particular, the first section presents the characterization of the fluidization behaviour of four different
nanopowders in terms of pressure drops, bed expansion, and minimum fluidization velocity as affected byacoustic fields of different intensity and frequency. The fluidization of binary mixtures comprising two powders
is also investigated under the application of different acoustic fields and varying the amounts of the two powders.
The second section focuses on the study of the mixing process between two different nanopowders both from a“global/macroscopic” and “local/microscopic” point of view and highlighting the effect of mixture composition,
primary particles density and sound intensity. The last section presents a promising application of sound-assisted
fluidization, i.e. CO2 capture by adsorption on a fine activated carbon, pointing out the effect of CO2 partialpressure, superficial gas velocity, sound intensity and frequency on the adsorption efficiency.
Keywords: sound-assisted fluidization, nanoparticles, CO2 capture, adsorption, nanoparticle mixing, nanoparticle
binary mixture

1. Introduction
Ultraf ine powders, in par ticular nanopar ticles(< 100 nm), have received increased attention in recent
years. Indeed, due to their unique properties arising fromtheir very small primary particle size and very large surface area per unit mass, nanoparticles provide higher contact and reaction efficiencies than traditional materials(Hakim et al., 2005), thus finding application in different
industrial sectors such as in the manufacture of cosmetics, foods, plastics, catalysts, energetic materials, biomaterials, and in micro-electromechanical systems (MEMS)(Nakamura and Watano, 2008). Within this framework, it
has become increasingly important to understand how
these nanoparticles can be handled and processed in large
quantities (i.e. mixing, transporting, coating). However,before the processing of such materials can take place, thenanoparticles have to be well dispersed. In this respect,
gas fluidization is one of the most effective available tech†

Received 6 May 2014; Accepted 25 June 2014
J-STAGE Advance published online 12 September 2014
1,2
P.le V. Tecchio, 80-80125 Napoli, Italy* Corresponding author: Paola Ammendola;E-mail: paola.ammendola@irc.cnr.it
TEL: +39 0817682237 FAX: +39 0815936936©2015 Hosokawa Powder Technology Foundationniques in ensuring continuous powder handling and good
mixing, chiefly because of the large gas-solid contact area
(Zhu et al., 2005; Lepek et al., 2010a). Nevertheless,on the basis of their primary particle size and material
density, nanosized powders fall under the Geldart
group C (< 30 μm) classification, which means that their
fluidization is expected to be particularly difficult (i.e.
characterized by plug formation, channelling and agglomeration) because of cohesive forces (such as van der Waals,
electrostatic- and moisture-induced surface tension forces)
existing between particles and which become more andmore prominent as the particle size decreases (King et al.,2008; Scheffe et al., 2009; Wang et al., 2002; Gündoğdu
and Tüzün, 2006). Despite their Geldart classification,
growing experimental evidence proves that nanoparticles
can be smoothly fluidized for an extended range of gas
velocities, thus implying that primary particle size anddensity are not representative parameters for predicting
their fluidization behaviour. Indeed, due to the interparticle forces mentioned above, nanoparticles are always
found to be in the form of large-sized porous aggregates(King et al., 2008; Scheffe et al., 2009; Saleh et al., 2006),
rather than as individual nanosized particles when packed
together in a gaseous medium. In other words, nanoparticles actually fluidize in the form of nanoparticle aggre-

23Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

gates, and their properties (size/density) have a significant
effect on the fluidization nature. Several experimental
campaigns (Wang et al., 2002) show that highly porous
nanoparticle aggregates can exhibit two distinct fluidization behaviours: APF (agglomerate particulate fluidization)
and ABF (agglomerate bubbling fluidization). The former
is characterized by very large bed expansion, smooth fluidization and very low minimum fluidization velocity; thelatter, instead, shows little bed expansions and bubbling.
All things considered, even though the fluidization ofsuch materials is made possible by agglomeration, thisphenomenon on the other hand also represents a strong
limitation to the exploitation of their full potential
because of the undesired decrease in specific surface area.
Accordingly, it is always preferable to contain the formation of aggregates as much as possible, i.e. the aggregate
size should be relatively small to enable proper exploitation of the potential of nanoparticles. In other words, theachievement of a smooth fluidization regime is closely
related to an efficient break-up of the large aggregatesyielded by cohesive forces. To this aim and to overcome
these inter-particle forces and achieve a smooth fluidization regime, externally assisted fluidization can be used,thus involving the application of additional forces generated, for instance, by acoustic (Ammendola and Chirone,
2010; Raganati et al., 2011a; Raganati et al., 2011b),electric (Lepek et al., 2010b; Valverde et al., 2009) andmagnetic (Zeng et al., 2008; Yu et al., 2005) fields ormechanical vibrations (Nam et al., 2004; Yang et al.,2009a) to excite the fluidized bed, thus avoiding channelling and enhancing the dynamics of the powder. Among
all these available techniques, sound-assisted fluidization
has been indicated as one of the best technological options
to smoothly fluidize fine and ultra-fine powders: under the
influence of appropriate acoustic fields, channelling and/or slugging tends to disappear, the bed expands uniformly
and the minimum f luidization velocity is distinctly
reduced (Ammendola and Chirone, 2010; Raganati et al.,2011a; Raganati et al., 2011b). Besides this, it is worth
noting that this technique also holds advantages from apractical point of view: i) it is not intrusive, since neither
additional equipment nor materials must be inserted in
the bed; ii) the powders to be employed do not need tohave any special property; iii) as widely reported in literature (Russo et al., 1995), the application of acoustic fields
is capable of reducing the elutriation of fine particles froma fluidized bed, preventing problems related to downstream carry-over of fine particles such as clogging ofvalves; iv) last but not least, this technique is extremely
economical and user-friendly, since the extra equipment(signal generator, audio amplifier loudspeaker and oscilloscope) required is very easily available on the market.
This review is not intended to be comprehensive, as itpresents the main results obtained by our research group24

in the field of the sound-assisted fluidization of fine particles. Our focus here is on highlighting the role of acoustic
fields in enhancing the gas-solid contact, with particularattention to the phenomenological mechanism at the basisof this technique.
In particular, the first part of section 2 presents a fundamental study describing the fluidization behaviour offour different nanopowders, Al2O3, Fe2O3, CuO and ZrO2,
as affected by the application of acoustic fields. The effectof sound frequency (50–300 Hz) and sound intensity(SPL) (125–150 dB) on the fluidization quality in terms ofpressure drops and bed expansion across the bed andminimum fluidization velocity is reported. The fluidization of binary mixtures of the two powders (Al2O3, Fe2O3)
is also investigated under the application of different
acoustic fields (130–135 dB, 120 Hz) and varying theamounts of the two powders from 7 %wt to 90 %wt of
Fe2O3. Then, aiming to explain sound-assisted fluidizationfrom a phenomenological point of view, the second partof section 2 focuses on the mixing process between two
different nanopowders. Scanning electron microscopy
with X-ray microanalysis (SEM/EDS) of the captured
samples lets us obtain key information about the dynamics of the mixing process both from a “global/macroscopic” and the “local/microscopic” point of view: thetime dependence of the mixing degree, its asymptotic
value and the mixing characteristic time are evaluated.
The effect of mixture composition, primary particle density and SPL is also studied.
The positive and promising results obtained from thisfundamental and phenomenological study form the basis
for a second research activity aimed to apply and exploit
this experimented capability of the acoustic fields inenhancing the fluidization quality and consequently thegas-solid contact in an actual gas-solid process such as
adsorption on fine solid sorbents. Within the frameworkof the pressing environmental concerns about the escalating level of atmospheric carbon dioxide (Stone et al.,2009; Markewitz et al., 2012; Orr, 2009), CO2 sequestration from flue gas streams by adsorption is recognized as
being one of the most promising capture techniques(D’Alessandro et al., 2010). However, for adsorption to be
used in large-scale applications, two main aspects need tobe addressed. The first is the choice of the adsorbent
materials (Drage et al., 2012). To date, great attention is
focused on the development of highly specific adsorbent
materials, namely materials with great affinity towards
CO2 molecules (Choi et al., 2009). Sure enough, the scientific community is moving in this direction, putting theemphasis on the manufacture of designed nanomaterials
in which a molecular level of control can be achieved as ameans of tailoring their CO2 capture performance (Wanget al., 2009; Dawson et al., 2013; Baxter et al., 2009).
However, although certain attributes of solid sorbents

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

prove to be promising, they must still be integrated into a
viable process (D’Alessandro et al., 2010) which includes
the implementation of equipment that can take full advantage of the sorbent properties and maximize the separation efficiency (Sjostrom et al, 2011; Yang et al., 2009b).
The choice of the reactor configuration is, therefore, thesecond crucial point to be considered. In this regard,
fluidized beds can be a promising solution since they arecharacterized by a very efficient gas-solid contact andintense solid mixing, thus increasing the efficiency ofreactor operation in terms of extremely high mass andheat transfer coefficients (Yang et al., 2009b). So, we proposed sound-assisted fluidization as a viable technologicalsolution which would be capable of matching these two
critical demands, i.e. fine solid sorbents and fluidized
beds (Raganati et al., 2014a; Raganati et al., 2014b;
Valverde et al., 2013).
To this end, section 3 reports the results obtained from
CO2 adsorption tests carried out on fine activated carbon
particles (0.39 μm) in a sound-assisted fluidized bed. All
the adsorption tests are performed at ambient temperatureand pressure in a laboratory-scale reactor under ordinary
conditions and under the effect of acoustic fields of different intensities (125–140 dB) and frequencies (20–300 Hz).
In particular, the effectiveness of CO2 adsorption isassessed in terms of the moles of CO2 adsorbed per unit
mass of adsorbent, breakthrough time and fraction of bed
utilized at breakpoint. Then, the effect of sound frequency
and SPL, the superficial gas velocity (1, 1.5, 2 cm/s) andCO2 concentration in the feed stream (5, 10 and 15 %vol.
in N2) is studied.

2. Sound-assisted fluidization of nanoparticles
2.1 Experimental apparatus, materials andprocedures
All the fluidization tests were performed at ambient
temperature and pressure in a laboratory-scale sound-­assisted apparatus consisting of a fluidization column(40 mm ID and 500 mm high) made of Plexiglas. It isequipped with a porous plate gas distributor, a
300-mm-high wind-box filled by Pyrex rings to ensure aneven distribution of gas flow, a pressure transducer
installed at 5 mm above the gas distributor to measure thepressure drop across the bed, a sound wave guide at the
top of the freeboard, a sound-generation system and adata acquisition system. This experimental set-up was
also designed according to the Helmholtz resonator, i.e.
one of the most-used engineering noise control methods,
in order to reduce the sound insulation even for highintensity acoustic fields.
Nanopowders of Al2O3, Fe2O3, ZrO2 and CuO withp­ rimary particle average sizes lower than 50 nm and densities of about 4000, 4500, 6000 and 6300 kg/m3, respectively, were used.
More detailed information about both experimental apparatus and materials can be found elsewhere (Ammendola
et al., 2011a).
Fluidization of single powders: Aeration tests were carried out with an initial bed of 48, 33, 35 and 75 g for
Al2O3, Fe2O3, CuO and ZrO2, respectively, corresponding
to a bed height of about 15 cm for all the four powders.
Dimensionless pressure drop (ΔP/ΔP0) and bed expansion
curves (H/H0) were obtained both with and without theapplication of acoustic fields of different intensities (125–
150 dB) and frequencies (50–300 Hz), ΔP being the actualpressure drop across the bed, ΔP0 the pressure drop equal
to the buoyant weight of particles per unit area of bed, H
the actual bed height and H0 the initial bed height under
fixed bed conditions
Fluidization of binary mixtures: the fluidization tests ofbinary mixtures were carried out using Al2O3 and Fe2O3
nanopowders. On the basis of the experiments carried out
on the two powders alone, two different sound intensities(130/135 dB) at a fixed frequency (120 Hz) were applied.
In particular, tests were performed by loading 30 g of thebinary mixtures and increasing the amount of Fe2O3 from
7 to 90 %wt, so that the effect of the relative amount of
the two powders on the fluidization quality could be highlighted. Then, the effect of the initial loading order of thetwo powders inside the column was also investigated.
The experimental pressure drops and bed expansion
curves of the single powders were elaborated and threeparameters evaluated as an index of the fluidization quality: 1) the maximum value of dimensionless pressure drop
(ΔPmax /ΔP0); 2) the maximum value of dimensionless bed
expansion (Hmax/H0); 3) the minimum fluidization velocity(umf ).
Mixing: Mixing tests on Al2O3 and Fe2O3 nanopowders
were performed under application of a fixed acoustic field(130 dB-120 Hz). Experiments were carried with an initial
bed height of about 15 cm, corresponding to a bed of 30 g.
The superficial gas velocity was fixed at about 0.45 cm/s,
enough to fluidize the materials under application of theabove acoustic field and to assure a ratio between the
superficial gas velocity and the minimum fluidization
velocity of about 7 for Al2O3, namely the powder characterized by the worse fluidization quality. The effect of therelative amounts of the two powders on the mixing quality was investigated by performing three tests, noted as A,
B and C, with a Fe2O3 amount of 17, 50 and 77 wt%,
respectively. For all experimental conditions, the starting
point was obtained by loading the Al2O3 nanopowder andthen the Fe2O3 one. Each test was carried out for about
120 min.
Two mixing tests on ZrO2 and CuO were also per-25

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

formed in order to study the effect of nanoparticle density.
The relative amounts of the two powders was fixed at
50 wt% of ZrO2 and two different acoustic fields wereapplied (130/140 dB-120 Hz). In particular, the starting
point was obtained by feeding 35 g of the ZrO2 nanopowder and then 35 g of the CuO nanopowder, in order tohave the same bed height used in the tests performed on
Fe2O3 and Al2O3 (15 cm). The superficial gas velocity was
fixed at about 1.5 cm/s, enough to fluidize the materials
under application of the above-mentioned acoustic fields
and so that the ratio between the superficial gas velocity
and the minimum fluidization velocity was about 7 for
ZrO2, that is the powder characterized by the worse fluidization quality. Each test was carried out for about
120 min.
Two possibilities of mixing two powders, both at a
global scale (i.e. mixing between aggregates formed byonly one powder) and at a local scale (i.e. mixing inside
the aggregates, leading to the formation of hybrid aggregates formed by both powders) have been investigated. In
this regard, the study was carried out by means of both avisual observation of the bed and SEM/EDS analysis. The
visual observation of the bed gave some preliminary roughinformation on the uniformity of the mixing and the mixing characteristic times on the global scale. During experiments, samples of the fluidized materials were taken at
different times by means of a non-destructive sampling
procedure (Ammendola et al., 2011b) and then analysed by
SEM/EDS analysis to determine the chemical compositionof aggregates. In particular, a probe made of a silicon tube
linked to the adhesive sample disc used for SEM analysis

Fig. 126

was carefully inserted from the top of the reactor so thatthe fluidized materials present in the upper part of the bed
stuck to it. On this basis, the time dependence of the mixing degree, its asymptotic value and the mixing characteristic time on the local scale were evaluated.

2.2 Results and discussion
2.2.1 Fluidization of single powders

The fluidization quality of Al2O3, Fe2O3, CuO and ZrO2nanopowders is very poor without the application of
acoustic fields, as clearly confirmed by the obtained fluidization and expansion curves (Fig. 1).
In particular, incrementing the gas flow rate causes the
bed to be lifted at first by a slug which then collapses,
giving rise to a structure which—even though displaying
a certain expansion ratio—is characterized by channels,
an uneven surface and a substantial lack of particle
motion. Therefore, the application of an acoustic field isinvestigated to achieve a proper fluidization regime, as
clearly shown in Fig. 2, reporting the dimensionless pressure drop and expansion curves obtained at 140 dB and
120 Hz. Indeed, the dimensionless pressure drop always
reaches the asymptotic value of 1, thus indicating that theparticle bed is completely fluidized, and also the expansion ratio is enhanced.
Fe2O3, on the contrary, is characterized by a good fluidization behaviour also under ordinary conditions asshown by both pressure drop and bed expansion curves(Fig. 1). In particular, a quite stable fluidization regime
was achieved also in this case after the formation and

Dimensionless pressure drop (ΔP/ΔP0) and bed expansion (H/H0) obtained under ordinary conditions.

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

Fig. 2Dimensionless pressure drop (ΔP/ΔP0) and bed expansion (H/H0) obtained under sound-assisted conditions (140 dB–120 Hz).

break-up of a plug. However, as for the other three powders, the application of a suitable acoustic field canstrongly improve the fluidization quality (Fig. 2).
In order to point out the most effective ranges of soundintensities and frequencies needed to obtain a good fluidization regime, ΔPmax /ΔP0 and Hmax /H0 values were plotted as functions of the SPL at fixed frequency and viceversa for all the powders, as reported in Fig. 3a and b.
The analysis of these curves suggests that, at a fixed SPL,
the ranges of frequencies 100–125 Hz and 90–120 Hz
were able to stabilize an optimum fluidization condition
for Al2O3and CuO, respectively, whereas the best frequencies fall in the range 80–120 Hz for Fe2O3 and ZrO2.
On the other hand, at fixed frequency, Al2O3, CuO andZrO2 need acoustic fields of a SPL higher than 135 dB toobtain a good fluidization quality, whereas for Fe2O3,
intensities higher than 125 dB are enough.
The pressure drop curves were elaborated in order toevaluate the minimum fluidization velocity for all thepowders. The results obtained show that the denser powders (CuO and ZrO2) are characterized by minimum fluidization velocities one order of magnitude higher thanthe lighter ones (Al2O3 and Fe2O3).
The trends obtained for the minimum fluidization
velocities reported in Fig. 4 are consistent with theabove-mentioned evidence concerning the influence ofsound on the fluidization quality. In particular, at fixed
frequency, an increase of the SPL results in a decrease ofumf, whereas at fixed intensity, an optimum range of frequencies exists for all the powders according to what wasFig. 3(a) Effect of SPL ( f = 120 Hz) and (b) effect of soundfrequency (SPL = 140 dB) on ΔPmax /ΔP0, Hmf/H0 for all
the powders.

27Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

that: (i) the particle aggregate’s size is far higher than the
primary particle one (hundreds of microns), thus confirming that fine particle fluidization actually occurs in theform of aggregates; (ii) varying the sound parameters, theaggregate size and densities have opposite trends; in particular, at fixed frequency, aggregate size decreases atincreasing SPL, as observed for umf, whereas aggregate
density increases; at fixed SPL, the ranges of frequencies
which ensure the lowest values of umf give the lowest
values of aggregate size and the maximum values of theaggregate density. This behaviour can be related to thefact that the more efficient break-up mechanism producedby higher values of sound intensity implies a reasonablereduction of aggregate size and in turn, of umf along witha greater bed compactness; in other words, higher soundintensities yield smaller as well as denser aggregates.
As regards the influence of frequency, on the one hand,
optimal frequencies are able to promote the break-up ofparticle aggregates as well, thus determining lower valuesof aggregate size and consequently of umf, whereas on theother hand, they promote a greater bed compactness.
Fig. 4

Minimum fluidization velocity (umf ) as a function of (a)SPL at fixed frequency (120 Hz) and as a function of (b)frequency at fixed SPL (140 dB).

stated above. The beneficial effect generally shown by the
SPL can be explained considering that an increase of thesound intensity implies an intensification of the energy
introduced inside the bed, i.e. the external force exercisedby the acoustic field on the aggregates is amplified. Therefore, large aggregates are more easily broken into smaller
ones, thus consequently determining a reasonable
decrease of umf. On the other hand, sound frequency has a
not monotone effect on the fluidization quality. This isdue to the ability of sound to penetrate the bed as well as
to promote aggregate reduction down to a scale that also
depends on powder structure. In particular, the application of the acoustic field induces a relative motion
between larger and smaller aggregates, thus leading to thebreak-up of the large aggregates originally present in thebed. For frequencies higher than about 125 Hz, the acoustic field is not able to properly propagate inside the bed,
whereas for frequencies lower than about 80 Hz, the relative motion between smaller and larger sub-aggregates ispractically absent. Between these values, there is a range
of optimal frequencies able to maximize aggregate
break-up.
All these considerations on the different fluidization
behaviours were explained and clarified by referring to
the different fluidized particle aggregates’ properties, sizeand density, produced by the application of sound(Ammendola et al., 2011a). The results obtained show

28

2.2.2 Fluidization of binary mixtures

Fig. 5 reports the dimensionless pressure drop and bed
expansion curves obtained for the different binary mixtures of Al2O3 and Fe2O3 under the effect of different
acoustic fields. In particular, on the basis of the results
obtained for the fluidization of single powders, two different sound intensities, namely 130 and 135 dB, wereadopted at a fixed sound frequency of 120 Hz. Indeed,
under these operating conditions the two nanopowders
showed different behaviours: both a poor and a good fluidization quality was achieved for Al 2O3 and Fe2O3,
respectively. The pressure drop and bed expansion curves
obtained during the fluidization of single powders havealso been reported for comparison. Obviously, mixtureswith a high amount of Al2O3 or Fe2O3 behave like thepowders alone, while mixtures with an intermediate composition have an intermediate behaviour in terms of bothpressure drop and bed expansion. The addition of Fe2O3,
the most fluidizable powder, to Al2O3 generally improves
the fluidization quality of the mixture in terms of higher
values of both pressure drop and bed expansion. In particular, the maximum value of dimensionless pressure drop,
ΔPmax /ΔP0, reached at high superficial gas velocity,
approaches unity (i.e. the limit value achievable in a condition of ideal fluidization) as the Fe2O3 amount increases.
In particular, the beneficial effect deriving from anincreasing amount of Fe2O3 becomes relevant from aweight composition of Fe2O3 of 1/3 %wt. Moreover, thisbeneficial effect is clearly stronger at a lower SPL(130 dB), where the Al2O3 nanopowder alone showed avery poor fluidization quality. This evidence is likely due
to the better fluidization quality of Al2O3 at a higher SPL

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

Fig. 5

Dimensionless pressure drop (ΔP/ΔP0) and bed expansion (H/H0) as functions of superficial gas velocity
during aeration for binary mixtures of Al2O3 and Fe2O3. (a) 130 dB–120 Hz; (b) 135 dB–120 Hz.

(135 dB). The initial loading order of the two powders
inside the column has a negligible effect. The experimental values of umf were plotted as functions of the weightcomposition of the mixture in Fig. 6, where the values of
umf obtained for the single Al2O3 and Fe2O3 powder werebeen reported (0 %wt and 100 %wt of the Fe2O3 amount,respectively) for comparison. The analysis of these curves
suggests that the values of umf of binary mixtures areintermediate between those of single nanopowders, andincreasing amounts of Fe2O3 result in lower values of umf.
This evidence is in agreement with the better fluidizability shown by Fe2O3 and it is most likely related to a reduction of the aggregate size. In particular, increasing
amounts of Fe2O3 do not result in a linear decrease of umf:
this decrease is much more relevant for low amounts of
Fe2O3; for Fe2O3 amounts higher than 1/3 %wt, an evident
change of the slope of umf vs Fe2O3 curve was observed. It
is most likely that mixtures with Fe2O3 amounts of
1/3 %wt are already characterized by good fluidization
behaviour, so that further increasing the Fe2O3 amount
can only result in smaller changes of umf. Moreover, it canbe noticed that the influence of SPL obtained for the powders alone is confirmed – in fact, and also in the case ofbinary mixtures, the umf values are higher at 130 dB thanat 135 dB.

2.2.3 Mechanism of nanopowder mixing

The efficiency of the mixing process between different
nanopowders during aeration promoted by the applicationof a suitable acoustic field was verified. In particular,

Fig. 6

Minimum fluidization velocity (umf ) of binary mixturesof Al2O3 and Fe2O3 as a function of %wt of Fe2O3 in
the mixture under different acoustic fields (120 Hz).
Reprinted with permission from Ref. [Ammendola etal., 2011b]. Copyright: (2011) Elsevier B.V.

mixing occurs within different time periods depending onwhether the phenomenon is observed from a macroscopic
or microscopic point of view. The visual observation of
the bed, also recorded by a video camera, gave some preliminary qualitative information on the uniformity of
mixing and the mixing characteristic time from a macroscopic point of view. Fig. 7 refers to the B test: thestarting point was obtained by feeding 15 g of the Al2O3
nanopowder (white powder) and then 15 g of the
Fe2O3 nanopowder, which forms a red layer on the top of
the bed (Fig. 7a). Fig. 7b refers to 1 min bed aeration with29

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

a nitrogen flow rate of u = 0.45 cm/s without the application of sound.
Fig. 7c and d is relative to bed aeration assisted by theapplication of sound (SPL = 130 dB, f = 120 Hz) for 10 s
and 2 min, respectively. Analysis of the figures shows thatwithout the application of acoustic fields, no mixing occurs(Fig. 7b). Channelling is present, namely the formation of
one or more channels through the bed can be noticed. The
application of the acoustic field results in a relatively large
bed expansion and solid mixing (Fig. 7c). After a few
minutes, the entire bed turned brown and appeared to bewell-mixed (Fig. 7d). On a macroscopic scale, the visualobservation of the bed highlighted the effectiveness ofsound application in promoting the fluidization and theglobal mixing of nanopowders within few minutes. Similar
results have been obtained for A and C tests.

Fig. 7

mixing test: (a) bed made of 15 g of the Al2O3 (white)and 15 g of the Fe2O3 (red) nanopowder; (b) bed aerated
for 1 min without application of sound (u = 0.45 cm/s);(c) bed aerated for 10 s with sound application(u = 0.45 cm/s, SPL = 130 dB, f = 120 Hz); (d) bed aerated for 2 min with sound application (u = 0.45 cm/s,
SPL = 130 dB, f = 120 Hz). Reprinted with permission
from Ref. [Ammendola et al., 2011a]. Copyright: (2011)
Elsevier B.V.

Fig. 830

In order to obtain in-depth information, samples of thefluidized materials were taken from the upper part of the
bed at different times during these experiments by meansof the ad-hoc non-destructive sampling proceduredescribed in the experimental section. The different samples were analysed by SEM/EDS analysis in order todetermine the shape and the chemical composition of theaggregates. On the basis of the initial bed composition of
the three tests, the Al weight composition corresponds to79, 43 and 19 % for the tests A, B and C, respectively.
These values represent the theoretical limit corresponding
to a complete mixing both at the global scale (averagecomposition of the bed) and at the local scale (averagecomposition of aggregates). The EDS analysis performedon entire areas of bed samples pointed out that the Al
weight composition was close to the theoretical one
already after 1 min of sound-assisted aeration for all tests.
These results confirm the information obtained by visualobservation of the bed, i.e. at the global scale, the mixingof the bed really does occur within a very short time.
On the other hand, the EDS analysis carried out on the
single aggregates pointed out that the microscopic mixing
is characterized by dynamics developing over longer times.
Fig. 8 reports the SEM images and EDS analysis of
nanoparticle aggregates taken during A, B and C tests at
14, 4 and 1 min, respectively, after the application of sound.
The analysis of Fig. 7 shows that all the aggregates areformed of both alumina and iron oxide, in accordancewith a mixing process also occurring at the local scale.
Their compositions vary over a wide range. Aggregatesricher in both alumina and iron oxide were detected, but
their shapes and sizes appear to be similar to each other;
therefore, the simple morphological analysis is useless todistinguish the different composition of the aggregates.
Fig. 9 reports the cumulative distribution of aggregate
composition at different times obtained for A, B and C
tests. N denotes the number of aggregates with an Al
weight composition lower than the x-axis value, and Nt
represents the total number of aggregates.
The curve named “initial composition of the bed sur-

SEM images and EDS analysis of nanoparticle aggregates. (a) Sample taken after 14 min during the A
mixing test; (b) sample taken after 4 min during the B mixing test; and (c) sample taken after 1 minduring the C mixing test.

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

face” is the first of the experimental curves, namely theone corresponding to the time t = 0; whereas the curve
named “limit value” represents the theoretical limit curve,
achievable in the case of uniform mixing within all aggregates. The analysis of these curves suggests that thechemical composition of the nanoparticle aggregates
actually changes as time goes on. In particular, a generalapproach of the curves towards the limit curve withincreasing time can be observed, even though this evolution is slower in the case of mixtures richer in Al2O3 (Fig.
9a). This evidence is absolutely consistent with the theoryof the nanoparticle aggregate undergoing an actualdynamic evolution (due to a continuous break-up andre-aggregation mechanism) during the sound-assisted fluidization process. As a matter of fact, all aggregates are
mixed even after 1 min of sound-assisted fluidization andtheir composition varies over a wide range. In order toobtain more direct information, these data were elaborated
to obtain the time-dependence of the aggregate’s mixing
degree M(t) for A, B and C tests (Fig. 10a), where M at a
fixed time t was defined as the ratio between the numberof aggregates whose Al composition differs from the theo-

retical one less than 10 % and the total number of aggregates analysed at time t. Each data series was fitted withan exponential rise-to-maximum law M(t) = a(1 – e–t/b).
The analysis of these curves suggests that the mixing
quality in terms of both characteristic time and maximum
mixing degree is strongly affected by the relative amountsof the two powders; indeed, increasing the amount of
Fe2O3 from 17 to 77 wt%, the asymptotic value of M(t)increases from about 50 to 100 % and, at the same time,
the characteristic time of the process decreases from 87 to
10 min. In other words, less time is needed to accomplish
higher values of the mixing degree as the Fe2O3 mixture
content is increased.
The explanation of this behaviour is likely to be foundin the tight link existing between the mixing effectivenessand the nature of the two powders, namely their intrinsic
nature and fluidizability. Indeed, an efficient mixing canonly be achieved by means of an efficient break-up andre-aggregation mechanism promoted by the acoustic field.
In view of that, the increasing Fe2O3 amount, namely the
powder that shows the better fluidization behaviour, promotes an evident improvement of the above-mentioned

Fig. 9Cumulative distribution of aggregate composition at different times for Al 2O3 and Fe2O3 mixing tests. (a)A mixing test; (b) B mixing test; and (c) C mixing test.

Fig. 10(a) Time-dependence of the aggregates’ mixing degree for A, B and C mixing tests; (b) time-­
dependence of the aggregates’ mixing degree for Al2O3 and Fe2O3 A mixing test (130 dB–120 Hz;
17 wt% Fe2O3) and for the Al2O3 and CuO mixing test (140 dB–120 Hz; 14 % CuO).

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mechanism, thus resulting in both the increase of theasymptotic value of the mixing degree and the decrease of
the time needed by the process to reach the steady stage.
This is due to the fact that mixtures with higher contentsof Fe2O3 are logically characterized by aggregates withhigher amounts of Fe2O3 that are much more fluidizable
than the Al2O3 ones; in other words, aggregates withhigher a concentration of Fe2O3 are characterized by amuch more prominent tendency to undergo a dynamic evolution (break-up and re-aggregation) during the fluidization
process, thus making the acoustic field more effective. In
other words, the break-up of the Al 2O3 nanoparticles,characterized, as said before, by a worse fluidization quality, is the limiting stage of the mixing process.
In the light of what has been said so far and in order topoint out how the mixing quality is affected by the soundintensity, results obtained in another experimental campaign (Ammendola and Chirone, 2010) were considered
and compared. Al2O3 and CuO mixing tests were performed under application of an acoustic field of 140 dB
and 120 Hz, at a superficial gas velocity of 0.45 cm/s andwith a CuO weight composition of 14 %, corresponding
to an Al weight percentage of 80 %, that is very close to
the one used for the A test (79 %). Considering that inboth the tests one material, Al2O3, is the same from botha qualitative and quantitative point of view, as well as thesuperficial gas velocity, a comparison between theobtained results is definitely reasonable; however, possible differences arising from the different nature of thesecond powder have to be considered. In Fig. 10b, themixing degree curves of the two tests are shown. The
analysis of this diagram shows that, using a more fluidizable powder, Fe2O3 instead of CuO, the second material
Al2O3 being the same, similar results in terms of bothmaximum mixing degree (50 and 53 %, respectively) andcharacteristic time (87 and 80 min, respectively), can beobtained with the application of an acoustic field of much

Fig. 1132

lower intensity, 130 dB instead of 140 dB, respectively. In
other words, the lower CuO fluidizability is balanced by
the application of a more intense sound level, thus determining more energy introduced inside the bed, in turnresulting in the enhancement of the external force yieldedby the acoustic field on the aggregates. Accordingly, thesound capability to penetrate the bed and promote thebreak-up of the nanoparticle aggregates, namely themechanism behind the mixing process between the two
powders at a microscopic level, is enhanced.
In order to point out the influence of the nanoparticle’s
density on the mixing quality, tests were performed using
two denser powders, ZrO2 and CuO (about 6000 insteadof 4000 kg/m3 of Fe2O3 and Al2O3), with a relative amountof the two powders of 50 wt%, under the application of
the same acoustic field used for the Fe2O3 and Al2O3 mixing tests (130 dB-120 Hz). Also in this case, the bed samples have been analysed by SEM/EDS analysis in order todetermine the shape and chemical composition of theaggregates. Fig. 11a reports the cumulative distributionof the aggregates’ composition at different times, whereas
Fig. 11b reports the time-dependence of the mixing
degree along with the curve obtained for the Fe2O3 andAl2O3 B mixing test, reported for comparison.
The analysis of this diagram shows that with the sound,the bed height and the relative amount of the two powders
being the same, the ZrO2 and CuO mixing quality islower than the Fe2O3 and Al2O3 one, in terms of both themixing degree asymptotic value (44 % instead of 71 %)and characteristic time (64 instead of 38 min). This evidence can likely be attributed to the higher density ofZrO2 and CuO nanoparticles, thus resulting in denser
aggregates which break with more difficulty during the
dynamic evolution. In other words, the ability of thesound to penetrate the bed is reasonably hindered by thehigher density of the bed.

(a) Cumulative distribution of aggregates at different times for the ZrO2 and CuO mixing test
(130 dB–120 Hz); (b) time-dependence of aggregates’ mixing degree for the ZrO2 and CuO mixing test
and for the Al2O3 and Fe2O3 B mixing test (130 dB–120 Hz).

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3. CO2 capture by adsorption on fine powders
in a sound-assisted fluidized bed

3.2 Results and discussion

3.1 Experimental apparatus, materials andprocedures

In Fig. 12a and b, the dimensionless pressure drops
and bed expansion curves obtained under ordinary conditions are reported.
The fluidization quality under these conditions (i.e.
without the application of any acoustic field) is particularly poor (channelling), as clearly confirmed by the factthat the asymptotic value reached by the pressure drops
is lower than 1. Therefore, the application of sound isrequired to achieve a proper fluidization regime, which is
closely related to an efficient break-up of the large aggregates produced by cohesive forces into smaller structuresthat are easier to fluidize. In particular, an in-depth study
was carried out in order to evaluate the most effective
acoustic conditions, namely whether it is possible or not
to find optimal values of SPL and frequency. Fig. 13a andb report the effect of SPL (at fixed frequency, 80 Hz) andfrequency (at fixed SPL, 140 dB) on the fluidization quality, respectively.
In the first place, the analysis of these curves confirmsthat under application of the acoustic field, more regularpressure drops and expansion curves were obtained.
Then, as regards the role played by the SPL, it is clear
from Fig. 13a that sound intensities higher than or equal
to 125 dB are enough to obtain a good fluidization quality. In other words, 125 dB is a kind of threshold value forthis activated carbon. Indeed, all the tests performed at a

An activated carbon DARCO FGD (Norit) was used as
the adsorbent material. According to its cumulative size
distribution (Raganati et al., 2014b), it is characterized bya Sauter mean diameter of 0.39 μm, i.e. it belongs to thegroup C of Geldart’s classification. It is also characterized
by a large surface area (1060 m2/g) and broad pore size
distribution. More detailed information can be found elsewhere (Raganati et al., 2014b).
Preliminary fluid-dynamic characterization: the activated carbon was previously characterized to assess its
fluidization quality both under ordinary and sound-­assisted conditions (12–140 dB, 20–300 Hz). All the fluidization tests were performed at ambient temperature andpressure in the same experimental apparatus describedabove.
Adsorption tests: All adsorption tests were carried out atambient temperature and pressure. The sorbent material
was treated prior to each adsorption test by heating thepowder up to 140 °C in order to remove any trace of moisture. In a typical experiment, the sorbent (110 g) is loadedin the column in order to obtain a bed height of 15 cm.
Then, in a pre-conditioning step of about 10 min, N2 isintroduced into the column in order to stabilize a fluidization regime at fixed operating conditions in terms of superficial gas velocity and sound parameters. This is followedby the adsorption step in which a CO2/N2 gas mixture at a
fixed CO2 concentration is fed through the column. N2 andCO2 flow rates were set by means of mass flow controllers(Bronkhorst) and were subsequently mixed before entering
the bed. The CO2 concentration in the column of effluent
gas is continuously monitored as a function of time (breakthrough curve) by an ABB infrared gas analyser (AO2020)
until the gas composition approaches the inlet gas composition value (99 %), i.e. until bed saturation is reached. CO2
concentration profiles were obtained as a function of time t,which was counted from the time the gas mixture takes toflow from the fluidized bed to the analyser. This transit
time was previously measured for each gas flow rate byflowing the gas mixture through the empty bed (about90 s). Each adsorption test was performed both under ordinary and sound-assisted fluidization conditions. In particular, the effect of sound parameters (SPL and frequency),
fluidization velocity and CO2 partial pressure on adsorption
efficiency was investigated.

3.2.1 Preliminary fluid-dynamic characterization

Fig. 12(a) Dimensionless pressure drops and (b) bed expansion curves obtained in ordinary tests.

33

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

Fig. 13(a) Effect of SPL on pressure drops and bed expansion curves at fixed frequency (80 Hz); (b) effect of
frequency on pressure drops (c) and bed expansion curves at fixed SPL (140 dB).

higher SPL (125, 135 and 140 dB) are characterized byquite similar pressure drops and expansion curves, whichmeans that any additional increase of sound intensity does
not succeed in further enhancing the fluidization quality,
since the break-up mechanism is already efficient at
125 dB. On the contrary, the test performed at 120 dB issignificantly worse in terms of both pressure drops andexpansion ratio. As regards the sound frequency, theresults reported in Fig. 13b show that it has a not monotone effect on the fluidization quality. Actually, it is possible to find an optimum range of frequency (50–120 Hz) toachieve the best fluidization quality. Either too low or too
high frequencies which fall out of this range (20, 300 Hz)
correspond to worse fluidization qualities. All these
remarks regarding the effects of SPL and frequency canbe even more clearly inferred from Fig. 14, which reports
the minimum fluidization velocity, umf, (evaluated fromthe pressure drop curves by means of a graphical method)
as a function of SPL and frequency.
Firstly, all the sound-assisted tests are characterized bya lower umf with respect to the test performed under ordinary conditions, thus confirming the ability of the soundto enhance the fluidization quality. As for the SPL, umf issharply decreased passing from 120 to 125 dB and then it
holds steady, regardless of the further increase of SPL.
Provided the SPL is higher than 120 dB, a reasonably
good fluidization quality can be attained. As for the frequency, the curve is characterized by a minimum value (inthe range 50–120 Hz), corresponding to the best soundfrequencies. All these results are in agreement with thoseobtained from the experimental campaign on the fluidiza-34

Fig. 14

Effect of SPL (at fixed f = 80 Hz) and frequency (atfixed SPL = 140 dB) on umf. Reprinted with permission from Ref. [Raganati et al., 2014b]. Copyright:(2014) Elsevier B.V.

tion of fine powders presented in the previous section ofthis review.

3.2.2 Adsorption tests
3.2.2.1 Effect of sound application

Fig. 15 reports the typical breakthrough curves (i.e. C/
C0 vs time, C and C0 being the CO2 concentration in theeffluent and feed stream, respectively) obtained underordinary and sound-assisted conditions (140 dB-80 Hz).
In order to highlight the most significant portion of thecurve, namely the section before and soon after t b, thegraph has been reported in logarithmic scale. In particu-

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lar, a CO2 inlet concentration of 10 % and a fluidization
velocity of 1.5 cm/s were used in this case.
These curves were worked out to evaluate: (i) the molesof CO2 adsorbed per unit mass of adsorbent, nads, calculated by integrating the breakthrough curves; (ii) the
breakthrough time, t b, or breakpoint, which is the time ittakes for CO2 to reach the 5 % of the inlet concentration
at the adsorption column outlet; (iii) the fraction of bed
utilized at breakpoint (W), namely the ratio between theCO2 adsorbed until the breakpoint and that adsorbed until
saturation.
Analysis of the curves suggests that the application ofsound greatly enhances the breakthrough time, which, asreported in Table 1, in sound-assisted tests (63 s) is morethan four times the value obtained under ordinary conditions (12 s). The application of sound affects also theglobal adsorption capacity. Indeed, the total amount of
CO2 adsorbed until saturation, nads, moves from 0.31 mol/
kg under ordinary conditions, to 0.37 mol/kg undersound-assisted conditions (Table 1). The fraction of bed
utilized at breakpoint (W) is also greatly enhanced bysound, moving from values lower than 3 % in the tests
performed under ordinary conditions up to values morethan five times larger in the sound-assisted tests.
Finally, the application of sound greatly improves the
kinetics of the entire process. Indeed, the application of
acoustic fields makes it possible to speed up the adsorption process: under sound-assisted conditions, the time forCO2 to approach the saturation value is significantly
decreased (60 min for the sound-assisted test against
120 min for the test performed under ordinary conditions,Fig. 15), both the values of nads and average rate of CO2
adsorption being higher than those obtained under ordinary conditions.
It can be observed that soon after t = t b, the CO2 concentration rises abruptly for the tests performed underordinary conditions. The explanation of this evidence islikely to be found in the fluidization quality being

Table 1

extremely poor and unstable under ordinary conditions;
indeed, unable to overcome the cohesiveness of the fine
powder, most of the fluid only manages to flow across the
bed by finding channels of minimum resistance. Spreading across the bed, these channels allow for a bypass of anappreciable volume of gas, thus hampering the quality of
fluid–solid contact, which is the main factor ruling any
adsorption process. Therefore, it is most likely that
adsorption takes place mainly on those aggregates placedat the wall of the gas channels, whereas most of theadsorption surface is almost precluded by the fluid. Soon
after the extremely quick saturation of the adsorption sites
on those easily available aggregates, CO2 is logicallyfound to appear in the effluent gas. Therefore, the rather
steep slope of the CO2 concentration profile is due to theabove-mentioned majority of fluid bypassing the bed
without truly taking part in the adsorption process. After
this sharp rise, a likewise abrupt decrease of slope can beobserved in the breakthrough curve, which very slowly

Fig. 15

Breakthrough curves obtained under ordinary andsound-assisted conditions in logarithmic scale.
u = 1.5 cm/s; C0 = 10 %vol. Reprinted with permission from Ref. [Raganati et al., 2014b]. Copyright:(2014) Elsevier B.V.

Results of CO2 adsorption tests.
CO2 inlet concentration

Superficial
gas velocity

2 cm/s
1.5 cm/s
1 cm/sSound
Parameters

5%

10 %

15 %

tb
s

nads
mmol/gW%

t95-t b
min

tb
s

nads
mmol/gW%

t95-t b
min

tb
s

nads
mmol/gW%

t95-t bmin

Ordinary

15

0.22

3.6

44

8

0.30

2.7

36

7

0.37

2.7

24

140 dB-80 Hz

65

0.26

15

13

51

0.34

12

15

43

0.46

15

12

Ordinary

19

0.23

3

63

12

0.31

2.7

47

10

0.38

2.8

50

140 dB-80 Hz

80

0.27

11

30

63

0.37

15

22

58

0.44

14

19

Ordinary

27

0.23

2.8

78

20

0.31

3

71

15

0.38

2.7

57

140 dB-80 Hz 185

0.28

16

31

165

0.38

20

31

155

0.47

23

31

35

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goes to saturation. The explanation of this behaviour is
twofold: the channels are subjected to perturbation whichbrings fresh aggregates into contact with CO2, and thesmaller portion of fluid actually permeating the bed. In
other words, while most of the inflow CO2 bypasses thebed, only a small fraction takes part in the adsorption,
which is actually very slow, as clearly confirmed by theextremely slow breakthrough curve tail, which practicallyaccounts for the whole adsorption process. In particular,
the extreme slowness of the breakthrough tail is probably
due to the action of two different aspects: the slow
adsorption kinetics yielded by the poor fluidization quality and the fact that, despite a fixed flow of CO2 being fed
to the bed, only a limited fraction takes part in adsorption
while the majority flows through the bed unaltered (i.e.
the CO2 flow actually undergoing adsorption is smaller
than the nominal one because of the bypassing gas), thus
reasonably slowing down the process. Obviously thisslowness is also due to the fact that with time passing, thebed becomes more and more saturated with CO2, thus
making further CO2 adsorption increasingly difficult. In
contrast, the sound-assisted tests are characterized bymore regular breakthrough curves. Indeed, no abruptchange of slope can be noticed. The beneficial effectshown by the sound is probably due to the enhancementof the fluidization quality (which brings better gas-solid
contact and mass transfer coefficients) with respect to thetests performed under ordinary conditions, namely without the aid of any external force.
In particular, the enhancement of the break-up mechanism and re-aggregation of fluidizing aggregates producedby the sound application implies the constant renewal of
the surface exposed to the fluid. In other words, the continuous aggregate break-up and re-aggregation mechanism makes the surface of the activated carbon more
readily available for the adsorption process. In order toverify these considerations, a further test was carried out.
This test was started under ordinary conditions, and only
at a time t = t*, corresponding to the above-mentioned
change of slope typical of ordinary adsorption tests, thesound was switched on (Fig. 16).
Analysis of the obtained breakthrough curve clearlyshows that for t < t*, the CO2 concentration profile isabout the same as that obtained under ordinary conditions(i.e. the bypassing gas makes the CO2 concentration riseabruptly ). Then, at t = t*, the CO2 concentration suddenly
drops before rising again, but now following the typicaltrend of the sound-assisted tests. This behaviour confirms
the ability of the sound to better exploit the adsorption
capacity of the activated carbon. Indeed, as soon as thesound was switched on, that specific surface, precludedby the fluid under ordinary conditions, suddenly becomes
available, causing the CO2 concentration to drop becauseof the renewed activated carbon adsorption capacity.

36

Fig. 16

Breakthrough curve obtained by switching on thesound at t = t*. u = 1.5 cm/s; C0 = 10 %vol. Reprinted
with permission from Ref. [Raganati et al., 2014b].
Copyright: (2014) Elsevier B.V.

3.2.2.2 Effect of SPL and frequency

The effect of SPL and frequency on the CO2 adsorption
efficiency was evaluated by carrying out tests at fixed frequency (80 Hz) and different sound intensity (from 120 up
to 140 dB) and at fixed SPL (140 dB) and varying thesound frequency (from 20 to 300 Hz), respectively. The
comparison among all the tests performed in terms ofmoles of CO2 adsorbed, t b and W are reported in Fig. 17aand b. The data obtained under ordinary conditions werealso reported for comparison.
The analysis of these results is quite clear: the SPL
effect on the CO2 adsorption process reflects what wasobserved in the fluidization tests. Indeed, the adsorption
process undergoes a significant enhancement only when
SPLs that are higher or equal to 125 dB are applied,which is perfectly consistent with the activated carbon
fluid-dynamic behaviour obtained.
Indeed, 125 dB is a sort of threshold intensity beyond
which any further increase of SPL is ineffective, and sureenough all the tests performed at a higher SPL are very
similar in terms of breakthrough curve shape, moles of
CO2 adsorbed, t b and W. Whereas the behaviour observed
at 120 dB is intermediate.
As well as for the SPL, the results obtained on theeffect of sound frequency are also in perfect agreementwith those obtained from the fluidization tests. Indeed,
the best results in terms of CO2 adsorption efficiency canbe achieved when sound frequencies in the same optimum
range (50–120 Hz) are applied. Indeed, the tests performed at intermediate frequencies (50, 80 and 120 Hz)
are characterized by very similar behaviours (breakthrough curves, nads, t b and W). Whereas the adsorption
tests carried out at 20 and 300 Hz are significantly worse.
These results are additional proof of the tight link
existing between the adsorption efficiency and the fluid-­
dynamics of the system.

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

Fig. 17

Effect of (a) SPL (at fixed f, 80 Hz) and (b) f (at fixed SPL, 140 dB) on CO2 adsorption in terms of nads, t b
and W. u = 1.5 cm/s; C0 = 10 %vol.; f = 80 Hz.

Fig. 18

Activated carbon adsorption isotherms in ordinary and sound-assisted test. (a) u = 2 cm/s; (b)u = 1.5 cm/s; (c) 1 cm/s.

3.2.2.3 Effect of CO2 partial pressure

The effect of the CO2 partial pressure on the adsorption
process was highlighted by performing tests at three different CO2 inlet concentrations (5, 10 and 15 %vol. in N2)
for each investigated superficial gas velocity (1, 1.5 and2 cm/s) and both under ordinary and sound-assisted conditions (125/140 dB-80 Hz). The overall results arereported in Table 1. As expected, the CO2 capture capacity of the adsorbent (nads), at fixed superficial gas velocity
and fluidization conditions (with or without the sound), is
increased with CO2 partial pressure. This trend is absolutely consistent from a thermodynamic point of view,
since the CO2 partial pressure represents the driving forceof the adsorption process.
As clearly reported in Table 1, an increase of the CO2
inlet concentration also results in a decrease of the breakthrough time, in spite of the increased adsorption capacity.
This behaviour is probably due to the adsorption process
becoming faster at a higher CO2 inlet concentration;indeed, the higher the CO2 inlet concentration, the more
CO2 molecules enter the bed per unit time, the quicker the
bed saturation, all the other operating conditions being thesame. The obtained experimental values of the moles of
CO2 adsorbed when the bed is completely saturated,namely the sorbent capacity at equilibrium conditions,were elaborated and fitted by the Langmuir equation(Raganati et al., 2014b). Fig. 18 reports the adsorption iso-therms calculated under ordinary and sound-assisted tests.
Analysis of the curves highlights the beneficial effectplayed by the application of the acoustic field on adsorption performance. Under sound-assisted fluidization, theadsorption isotherms move to more favourable adsorption
conditions.

3.2.2.4 Effect of fluidization velocity

The results of the same tests were elaborated in orderto point out the effect of the fluidization velocity on theadsorption process. In particular, the dependence of the
breakthrough time on the contact time, defined as theratio between the mass of adsorbent and the CO2 volumetric flow, was highlighted. The curves obtained for different CO2 inlet concentration are shown in Fig. 19a, b and c.
As a matter of fact, the fluidization velocity is expected toaffect the breakthrough time because the mere increase of
the fluidization velocity results in a decrease of the contact time. However, the dependence of the breakthroughtime on the contact time, i.e. the fluidization velocity, is as
one could expect linear only for the tests performed underordinary conditions. Whereas the breakthrough time isfound to exponentially increase with the contact time,
namely decreasing the fluidization velocity from 2 to
1 cm/s for the sound-assisted tests. This evidence is likely
due to the role played by fluidization velocity in sound-­assisted tests. Indeed, under ordinary conditions, the system is quite insensible to changes of the fluidization37

Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

Fig. 19

Breakthrough time as a function of contact time (u ranging from 2 to 1 cm/s) under ordinary andsound-assisted conditions. (a) C0 = 5 %vol.; (b) C0 = 10 %vol.; (c) C0 = 15 %vol.

velocity, the fluidization quality always being very poor.
Therefore, the observed linear increase of the breakthrough time with the decrease of the fluidization velocity
is only due to the CO2 taking more time to flow throughthe bed. On the other hand, in sound-assisted fluidization
tests, changes of the fluidization velocity greatly affectthe fluid dynamics of the system.
In particular, the decrease of the fluidization velocity
results in a more homogeneous fluidization regime, which
is characterized by a lower bypass of gas through the bed
compared with the tests performed at higher fluidization
velocity. This is confirmed by the bed expansion curves
reported in Fig. 13, which show a quite sharp change ofslope at superficial gas velocities higher than 1 cm/s, thus
confirming the occurrence of bubbles (i.e. bypass of gas).
As a consequence, the breakthrough time is more thantripled, passing from 2 to 1 cm/s. Moreover, the fluidization velocity has a slight effect on the adsorption capacityof the activated carbon and the fraction of bed utilized
until breakpoint, as clearly shown in Table 1.

4. Conclusions
In this work, a review of the main results obtained byour research group in the field of sound-assisted fluidization was performed. The fluidization behaviour of four
different nanosized powders, Al 2O3, Fe2O3, CuO andZrO2, and of binary mixtures of Al 2O3 and Fe2O3 as
affected by the application of acoustic fields of different
intensities (125–150 dB) and frequencies (50–300 Hz) was
studied. The results obtained show that the application ofsound is necessary for all the powders to obtain a smoothand regular fluidization regime. This observed beneficial effect resulting from the application of sound is due tothe fact that it promotes a continuous break-up and re-­aggregation mechanism of the fluidizing aggregates (produced by the cohesive forces) into smaller structures, which
are easier to fluidize. In particular, the parameters of theapplied acoustic field strongly affect the fluidization quality: increasing SPLs generally enhance the fluidization
quality, whereas the sound frequency has a not monotone

38

effect, it always being possible to find an optimum range
giving the best fluidization quality. As regards the binary
mixtures, the addition of Fe2O3, i.e. the powder characterized by the best fluidization quality, to the mixtures generally results in a better fluidization behaviour.
These considerations about the mechanism upon which
the sound-assisted fluidization is based were verified bycarrying out mixing tests of two different nanopowders.
The results obtained show that the fluidizing aggregates
actually undergo a dynamic evolution (break-up andre-aggregation) during the fluidization process. In particular, the mixing occurs in a different time perioddepending on whether the phenomenon is observed froma macroscopic or microscopic point of view. Visual observation shows that the bed was already homogeneously
mixed after a few minutes. In contrast, the local mixing,
namely the mixing occurring among sub-aggregates due
to a continuous break-up and re-forming of single aggregates, leading to the formation of mixed ones, needs
dynamics developing over longer time periods. The maximum value of the mixing degree and the time needed by
the process to reach it are strongly affected by the relative
amount of the two powders. Increasing the amount of
Fe2O3 from 17 to 77 wt%, the asymptotic value of the mixing degree is enhanced from about 50 % to 100 % and thecharacteristic time is decreased from about 87 to 10 min,thus confirming the tight link between the mixing effectiveness and the intrinsic fluidizability of the two powders.
On the basis of this experimented capability of theacoustic fields to enhance the fluidization quality andconsequently the gas-solid contact, CO2 adsorption on afine activated carbon was performed in a sound-assisted
fluidized bed, thus meeting the increasing environmental
concerns and interest in the CCS technologies. CO2
adsorption on a fine activated carbon was performed in asound-assisted fluidized bed. The experimental results
show that the acoustic field positively affects the adsorption efficiency of the powder in terms of a significantly
higher breakthrough time, adsorption capacity, fraction of
bed utilized until breakthrough and adsorption rate. This
beneficial effect shown by the sound is due to enhancement of the fluidization quality (which brings better gas-Federica Raganati et al. / KONA Powder and Particle Journal No. 32 (2015) 23–40

solid contact and mass transfer coefficients) with respect
to the tests performed under ordinary conditions. In particular, the above-mentioned enhancement of the break-up
and re-aggregation mechanism of the fluidizing aggregates constantly renews the surface of activated carbon
exposed to the fluid, thus making it more readily available
to the adsorption process.

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Author’s short biography
Federica Raganati
Federica Raganati received her degree in Chemical Engineering from the University
Federico II of Naples (Italy) in 2010 and her PhD in Chemical Engineering from the same
university in 2014. She has now a postdoc position at University Federico II of Naples.
Her main research topics are the sound assisted fluidization of cohesive powders and theuse of sound assisted fluidized bed reactors for CO2 capture by adsorption on solid sorbents. She is the author of 2 patents and about 20 papers; 80 % of them are published inpeer-reviewed international journals or proceedings of international conferences.

Paola Ammendola
Paola Ammendola received her degree in Chemical Engineering from the University
Federico II of Naples (Italy) in 2003 and her PhD in Chemical Engineering from thesame university in 2006. She has a permanent position as researcher at the Istituto di
Ricerche sulla Combustione of the Consiglio Nazionale delle Ricerche (IRC-CNR). Her
main research topics are the sound-assisted fluidization of cohesive powders, the set-up
of innovative catalytic systems and the use of fluidized bed reactors for clean energy
production and the study of renewable energy sources. She is the author of 2 patents andabout 90 papers; 50 % of them are published in peer-reviewed international journals orproceedings of international conferences.

Riccardo Chirone
Riccardo Chirone obtained his degree in Chemical Engineering from the University
Federico II of Naples (Italy) in 1980 and his PhD in Chemical Engineering from thesame university in 1986. He is the Director of the Istituto di Ricerche sulla Combustioneof the CNR (IRC-CNR). He is an active researcher in the field of chemical reactors,
combustion and gasification processes and process technology of granular solids. Authorof about 110 international publications in ISI Journals, over 250 publications in conference proceedings with international and national peer review committee and 3 patents.
Riccardo Chirone has taken the coordination responsibility and direction of several
working groups and projects. In particular it can be mentioned: the Scientific Board of
the Department for Energy and Transport of the CNR, the Technology Platform for Sustainable Management of Wastes of the Ministry of Education and Internationalization,
the Marie Curie “INECSE” program - European Commission Research Directorate General Human Resources and Mobility, the CANMET’s Service Program (ISEP).

40

KONA Powder and Particle Journal No. 32 (2015) 41–56/Doi:10.14356/kona.2015009

Review Paper

Settling Suspensions Flow Modelling: A Review †Rui Silva 1, Fernando A. P. Garcia 1, Pedro M. G. M. Faia 2 and Maria G. Rasteiro 1*
1

Chemical Process Engineering and Forest Products Research Centre (CIEPQPF), Department of Chemical Engineering,
Faculty of Sciences and Technology, University of Coimbra, Portugal
2
Department of Electric and Computers Engineering, Faculty of Sciences and Technology, University of Coimbra, Portugal

Abstract
In spite of the widespread application of settling suspensions, their inherent complexity has yet to be properly
predicted by a unified numerical model or empirical correlation, and usually industries still possess customized
charts or data for their particular suspension. This is, clearly, rather inefficient and can lead to oversized
dimensioning, low energy efficiency and even operation limitations/difficulties. In this manuscript a review of
empirical correlations, charts and numerical models that have been employed to predict the behaviour of settlingsuspensions is briefly described, providing information on the advantages and drawbacks of each method. Theirevolution throughout the years: from Durand and Condolios correlations, to empirical models by Wasp, single
phase simplifications with mixture properties by Shook and Roco, and to other Euler-Euler or Euler-Lagrangian
numerical models, will be presented. Some considerations on recent particle migration and turbulence
modification publications will be added. In addition, information about some current CFD application of LatticeBoltzmann and Discrete Element Method (DEM) will be given. Lastly, data from CFD modelling employed by
the authors that is able to predict turbulence attenuation in settling flows with medium sized particles for different
concentrations is reported.
Keywords: solid-liquid settling suspensions, pipe flow, Computational Fluid Dynamics (CFD), numerical models,
turbulence modulation

1. Introduction
A solid-liquid settling suspension is a heterogeneous
mixture of solid particles in a liquid, which is a subclassof multiphase flows. Different sized particles, ranging
from micro to millimeters, and having diverse densities,
can be considered when speaking of solid-liquid settlingsuspensions. When suspensions contain medium orcoarser particles with density higher than the liquid theytend to settle and accumulate at the bottom of the vesselor pipe. These are called settling suspensions. One of thefirst recorded settling suspensions flow investigations wasdone in 1906 by Nora Blatch, where pressure drop as afunction of flow, density, and solid concentration was accounted for, in a 25 mm (1 in) diameter horizontal pipe
(Abulnaga, 2002). The mining industry was the first,
amongst many other industries, to have dealt with settlingsuspensions flows in the mid-nineteenth century. Other
†
1
2

*

Received 21 June 2014; Accepted 28 July 2014
J-STAGE Advance published online 20 September 2014
3030-790 Coimbra, Portugal
3030-290 Coimbra, Portugal
Correspopnding author: Maria G. Rasteiro;
E-mail: mgr@eq.uc.pt
TEL: +351 239798700©2015 Hosokawa Powder Technology Foundation

examples include not only classical industrial sectors suchas paints, oil, cement, coal, drugs and foodstuffs, but also
emerging ones as those dealing with “intelligent” materials, biological systems, and also in applications relatedwith environmental remediation processes. Also, in manyindustrial processes the concentrated solid-liquid mixtures, called pastes, are either subjected to molding as in
the case of casting metals, or extrusion, as in the case ofceramics, polymers, or foods, such as pasta. These suspensions are of great practical interest having become
ubiquitous in everyday life, either as a natural or formulated product (Abulnaga, 2002; Balachandar & Eaton,
2010).
In spite of the widespread application of settling suspensions, their inherent complexity has yet to be properly
predicted by a unified numerical model or empirical correlation, and usually industries still possess custom charts
or data for their particular suspension. This is, clearly,
rather inefficient and can lead to oversized dimensioning,
low energy efficiency and even operation limitations/difficulties. When working with settling suspensions a number of variables have to be accounted for such as flow
patterns, transition velocities, the flow behavior in pipesof different geometries, and also particle concentration,
shape, size, and size distribution. For concentrated set-41

Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

tling suspensions, incorporating the modelling of phenomena such as particle-particle interaction, particle-wallinteraction, shear-induced migration, turbulence attenuation and augmentation, and lift forces is paramount for aproper suspension behavior characterization.
As pointed by several authors, selecting the material toinclude in a review paper can become a daunting task,
mainly because existing materials in the literature provide
a thorough and much appreciated job on the matter. So,
adopting a point of view similar to Shook in his review
paper from 1976 (Shook, 1976), rather than providing an
exhaustive literature review, the choice here was to try to
add on existing materials and simultaneously present a
different interpretation. Thus, with this manuscript a review of empirical correlations, charts and numerical models that have been employed to predict the behaviour ofsettling suspensions is done, providing information on theadvantages and drawbacks of each method. Their evolution throughout the years: from Durand and Condolios
correlations, to empirical models by Wasp, single phasesimplifications with mixture properties by Shook andRoco, and to other Euler-Euler or Euler-Lagrangian numerical models, a historical review will be summarized.
Some considerations on recent particle migration and turbulence modification publications will be added. In addition, information about some current CFD application of
Lattice-Boltzmann and Discrete Element Method (DEM)
will be presented. Lastly, results from CFD modelling
employed by the authors that are able to predict turbulence attenuation in settling flows with medium sized particles for different concentrations is reported.

2. Literature Review
2.1 Empirical Correlations
One of the first recorded empirical correlations for pressure drop estimation considering fully suspended heterogeneous flows of solid-liquid settling suspensions inhorizontal pipes was developed by Durand and Condolios
in 1952. This correlation was constructed based on a collection of pressure drop data associated with the flow ofsand-water and gravel-water mixtures with particles ofsizes ranging from 0.2 to 25 mm. and pipe diameters from
3.8 to 58 cm. with solids concentrations up to 60 % by volume (Aziz & Mohamed, 2013). These studies culminatedwith the establishment a relation between the pressure
drops of water and slurry, given by Eqn. 1, where i and iw
are the pressure drop of slurry and of water respectively, k
is a constant, CD is the drag coefficient for the free falling
particle at its terminal velocity, g is the gravity, Di is thepipe internal diameter, Co is the volumetric concentrationof solids and Vm is the average flow velocity.

42i  iw V2
k m
iw C o g.Di


CD 


1.5

(1)

Another important result was the classification of flow
regimes, based on particle size and for particles having
a specific gravity of 2.65 (Abulnaga, 2002; Aziz &Mohamed, 2013):
1) Particles of a size less than 40 μm are transported as ahomogenous suspension;2) Particles of a size between 40 μm and 0.15 mm aretransported as suspension that is maintained by turbulence;3) Particles of a size among 0.15 and 1.5 mm are transported by a suspension and saltation;4) Particles of a size greater than 1.5 mm are transported
by saltation.
Although being quite useful for narrow sized highly
turbulent flows, it fails to account the effect of particle
concentration, size and shape.
In 1967 Zandi and Govatos using an extensive numberof data points, improved Durand’s correlation to different
solids and mixtures (Abulnaga, 2002) and defined an index number, Ne, that defined the limit between saltationand heterogeneous flows. While Durand and Condolios
based their studies on the drag coefficient, Newit based its
work on the terminal velocity as a means to determine thepressure drop (Eqn. 2). In 1955 a comprehensive paper
was published where a thorough study on solid-liquid
flows resulted in several flow regime specific correlations.
These correlations, which were not more than a set ofcriteria, allowed to define the flow regime and their-­specific set of equationsi  iw    L   g.DtVt  k2  S

iw C o  L   Vm3 (2)where i and iw are the pressure drop of slurry and of water
respectively, K2 is a constant, ρs is the density of the solids, ρL is the density of the liquid, Co is the volumetricconcentration of solids, Vt is the terminal velocity of theparticle and Vm is average flow velocity.
Wasp’s correlation from 1977 is based on the assumption total pressure loss is a sum of contributions fromboth a homogeneous distribution of particles (vehicle partof the flow) and from the excess pressure drop resulting
from a heterogeneously distribution of particles (bed formation part of the flow). This correlation can be applied
to solid-liquid settling suspensions with varied sized particles, typically in industrial slurries, by splitting the particles’ sizes into fractions. The procedure, which was
thoroughly described in the literature (Crowe, 2005), forthe pressure drop estimation with this correlation is an iterative one where the correlation’s critical factors determine: i) the particle size, split between the homogeneousand heterogeneous parts of the flow; ii) the equivalent ho-Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

mogeneous vehicle properties (i.e., density and viscosity)
as a function of particle size and concentration. This correlation assumes that the homogeneous vehicle is a Newtonian fluid: in 1980 Hanks extended this approach toaccount for non-Newtonian properties of the vehicle.
Turian and Yuan’s correlation (Crowe, 2005; Peker &Helvaci, 2011), in 1977, extended the pressure loss correlation scheme, by taking into account the fact that various flow regimes are observed depending upon the flow
conditions. Their correlation (Eqn. 3) utilizes regime-­
specific coefficients, K, m1, m2, m3, and m4 to estimate thepressure drop.
Vm2

f  fL K m1 f Lm2C Dm3  g.Di  s  1 

m4

(3)

f and f L are the friction factors for slurry and water, respectively, at the same mean velocity, Vm, ϕ is the volumetricfraction of solids and ρs is the specific gravity of the solids .
To decide on which coefficients to use for each regime theauthors proposed a regime delineation scheme based on aregime transition discretization number (Eqn. 4).
Rij 

Vm2
K t n1 f Ln2CDn3 g.Di  s  1(4)

In Eqn. 4 Kt, n1, n2 and n3 are coefficients for determining
the regime number.
An exhaustive review on the empirical correlations forsolid-liquid settling flows is beyond the scope of this manuscript and additional details have been presented in the literature (Crowe, 2005; Lahiri & Ghanta, 2008; Miedema,2013; Peker & Helvaci, 2011).

2.2 Semi-Empirical Model
Acknowledging the limitations of purely empiricalmethods, researchers devoted their attention to othermethods that incorporated both theoretical and semi-­
empirical knowledge as found in the work carried by
Bagnold (Bagnold, 1966; Shook & Daniel, 1965). These
works had diverse outcomes: one of the most relevant wasan equation for energy loss based on the dispersive stressdefined by Bagnold, in an attempt to describe solid-liquid
settling suspension flow. Some studies (Shook et al., 1968)
added on the work done by Bagnold mechanisms describing particle suspension by dispersive stress, incorporating
the influence of turbulence suspension of particles using
the eddy diffusivity concept together with Richardson-­Zaki equation for settling velocity (Shook et al., 1968),
which allowed to derive an equation for concentration
distribution in steady state.
The Richardson-Zaki equation (Eqn. 5) was introduced
in 1954 (Richardson & Zaki, 1954) and it is the mostwidely employed semi-empirical correlation used to depict concentrated settling velocity, u, of non-Brownian

hard spheres in liquids (0.05 < ϕ < 0.5) where ϕ is the volumetric fraction of solids (Peker & Helvaci, 2011).
u u o 1   n(5)This correlation is a modification of the individual particle settling velocity, uo, based on an empirical parameter, n, dependent on the flow regime, represented by theterminal Reynolds number (Felice & Kehlenbeck, 2000;
Crowe, 2005; Peker & Helvaci, 2011), and also on the ratio
between particle and vessel diameter, dp/D. The empiricalparameter, n, also known as the Richardson-Zaki exponent has been the subject of several publications. Traditionally this parameter was determined using a set of
equations, each defined for a different range of terminal
Reynolds number, however: however, these equations canbe cumbersome to use, since there are regions where
overlapping occurs, and a continuous function was presented as an alternative by Rowe (Rowe, 1987) for the determination of the Richardson-Zaki exponent. Batchelor(Batchelor, 1982; Batchelor & Wen, 1982) extended thework of Richardson and Zaki extending the application of
the previous correlation to dilute suspensions (Eqn. 6)u u o 1  n 

(6)The hindered settling function is given by h(ϕ) = 1 – nϕ
and Eqn. 6 provides the settling velocity of randomly dispersed spheres in suspensions in Stokesian regime(Batchelor & Wen, 1982; Peker & Helvaci, 2011). This expression is valid for dilute suspensions where the volumetric fraction of solids is low enough for flocculation to
occur. In the studies conducted by this author, the empirical parameter, n, was suggested, for suspensions withnegligible interparticular forces, to be 5.5 when Pe number is large and 6.5 for suspensions with very small Pe(Batchelor & Wen, 1982). Additional studies, that the author encourages as further reading, were published by Di
Felice (Di Felice, 1999) where a detailed revision on the
Richardson-Zaki and other equations for the settling velocity and their dependence with the Reynolds Number ispresented. Buscall (Buscall & Goodwin, 1982) conducted
several experiments for dilute and concentrated dispersions of polystyrene latex and compared the results withtheoretical results from the Richardson-Zaki (Richardson& Zaki, 1954) and Batchelor (Batchelor & Wen, 1982)
equations. This study further investigated the empiricalparameter, n. Peker & Helvaci (Peker & Helvaci, 2011)
also provided a thorough assembly of the values for theRichardson-Zaki exponent in their book. The most impressive aspect of Richardson-Zaki equation is how it effortlessly depicts the complex phenomena in particle-fluid
interaction forces and how that is simply compressed into
only two parameters: nevertheless, that over a half cen-43

Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

tury has passed since its inception, it still is used presently by researchers (Kaushal, Seshadri & Singh, 2002;
Baldock et al., 2004; Bargieł & Tory, 2013).
It is well known that the most notorious shortcoming of
the Richardson-Zaki equation, based on experiments withhighly concentrated particles, is failing to take into account the fact that the settling velocity should tend toward
zero at the maximum concentration, ϕmax, (Bürger &Wendland, 2001; Kusuda et al., 2007). To circumvent thisshortcoming, in some works (Kusuda et al., 2007) the
Richardson-Zaki equation has been corrected (Eqn. 7).
uu o 1   

n 1   max

1   

max 

(7)

of solid-liquid suspensions where flocculation of fine particles occurs. This model is dependent on two constitutive
material-specific functions, the Kynch batch flux density
function, f bk(ϕ), and the effective solids stress, σe(ϕ), bothof which depend only on the local solids concentration
and are calculated based on Kynch’s (Kynch, 1952) andRichardson-Zaki’s (Richardson & Zaki, 1954) works. In
Eqn. 10 and 11 Δρ is the solid-fluid mass density difference and pe is the excess pore pressure; other authors employ fractal based equations, for the settling velocity ofaggregates (Eqn. 12), based on empirical drag correlations(Smith & Friedrichs, 2011; Johnson, Li & Logan, 1996),
where θ is a particle shape factor (1 for spherical particles), ρs the particle density, ρl the fluid density dp the particle diameter, D f the f loc diameter, η f the fractal
dimension, µ the fluid dynamic viscosity and Rep the floc
Reynolds number.

Additionally this equation has been extended to thestudy of more complex solid-liquid systems behaviors,such as the settling of cohesive particles (Kusuda et al.,2007; Bürger & Wendland, 2001).
  f bk   e    
Settling of fine cohesive particles are of extreme im(10)
f bk   

t z
z 
 g
z 
portance in a many natural and industrial systems including rivers, estuaries (Ouillon et al., 2010), sewers,pe
treatment plants (Spencer et al., 2011) and sedimentation(11)  g   e 
zztanks, etc. (Cuthbertson et al., 2008). Moreover, complex
behavior can arise for cohesive particles, such as the forn 1
g
Df f
3 n
mation of fractal aggregates (Johnson, Li & Logan, 1996), (12)u s  l d p f
18 
1  0.15Rep0.687
settling transient or permanents gels (Senis, Gorre-Talini& Allain, 2001) or even consolidation (Nasser, Twaiq &Further developments on the use of the Richardson-Zaki
Onaizi, 2012; Manning et al., 2010; Toorman & Berlamont,
equation in depicting the behavior of agglomerates com1993), and thus knowledge of the settling (in case of single
posed of fine micrometric sized particles in a fluidized
flocs) or hindered settling velocity becomes pivotal.
bed have been published by Valverde & Castellanos
For cohesive particles sediments, when flocculation oc(Valverde & Castellanos, 2006). Assuming that the fluidized bed is composed of simple agglomerates, they modicurs and assuming the size and density of the floc arefied the Richardson-Zaki equation for the settling
constant, since some of the effects of concentration on thevelocity:
settling velocity are due to the size of the particle, the volume concentration of flocs, φ, as well as the maximumnconcentration of flocs, φmax, should be used. Eqn. 8, which(13)u u o* 1   *can be reduced to the Richardson-Zaki relationship whenϕ < 0.1, is given for the hindered settling velocity of coheWhere u o* is the settling velocity of an individual agglomsive sediments (Kusuda et al., 2007).
erate and ϕ* is the volumetric fraction of agglomerates in
the fluidized bed, which are defined in Eqn. 14 and Eqn.
  maxn2
15, respectivelyu  u o 1   1   1 (8) max 
N
Another model (Eqn. 9) for the hindered settling velocu *  uo
(14)kity of large concentrations of cohesive sediments is byWinterwerp (Kusuda et al., 2007).

1   1   u  uo1  2.5*  
(9)

Additional models for the settling velocity of aggregated solid particles in a fluid are given in the literature
by Bürger and Concha (Garrido, Bürger & Concha,
2000; Bürger, Concha & Tiller, 2000) who employ a
one-dimensional phenomenological model (Eqn. 10 and
11) for the prediction of the batch sedimentation behavior

44

k3
N

(15)

The number of primary particles is represented by N, uo
is the Stokes settling velocity of a single particle and k isdefined as
k 2

RG
dp

(16)

RG being the radius of gyration, and dp is the particle size.

Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

Using Eqns. 14 to 16, Eqn. 13 can be rewritten asu * uoN  k3 

1 
k 
N 

n

(17)

Finally, for the value of n, since fluidized beds of fineparticles are usually operated in the small Reynolds number regime, the best choice, in our opinion, is to fix n as
5.6 in agreement with the theoretical derivation in the dilute limit (Valverde & Castellanos, 2006).
Presently, the number of publications regarding solid-­
liquid pipe flow of cohesive particles is scarce by comparison to gas-solid pipe flow of cohesive particles andmostly for laminar flow (Vaezi G, Sanders & Masliyah,
2011; Grof et al., 2009). These works involve fundamental
studies of floc and cluster structure interaction. For turbulence studies Toorman (Toorman et al., 2002) published avery interesting paper on turbulence modulation due tothe presence of suspended cohesive sediment, and more
recently Chai (Chai, Yang & Wang, 2014) presented amodel which takes flocculation, sedimentation and turbulent diffusion into account, to analyze the vertical transport of cohesive fine sediment.
Numerical studies are also present in the literature forsolid-liquid sedimentation of cohesive particles. Publications can be found which are dedicated to the simulationof flocculation processes for differential settling of cohesive sediments, which were simulated via the Lattice
Boltzmann method (LBM) in which the hydrodynamics
and attractive van der Waals forces were considered(Zhang & Zhang, 2011).
A more detailed review on cohesive particles is beyond
the scope of this manuscript, however, for further readings on the subject the authors recommend the following
works found in the literature (Concha & Bürger, 2002;
Kusuda et al., 2007; Mantovanelli & Ridd, 2006; Bürger
& Wendland, 2001; Garrido, Bürger & Concha, 2000).
Most studies in the literature are related to the descriptionof tidal and marine sediments.
Other authors obtained the pressure drop using the dispersion coefficient as a function of local distribution ofsolids, also describing the settling phenomena making useof the Richardson-Zaki equation (Rasteiro, Figueiredo &Franco, 1993; Rasteiro, Rebola & Scarlet, 1988). Although
the coupling of these concepts provided somewhat accurate
good comparisons with experimental data, they were only
possible to implement through significant simplifications.
An engineer or researcher examining the literature will
be overwhelmed by the sheer amount of publications on
empirical correlations based on dimension analysis for thecritical velocity and pressure loss in settling and non-­settling suspension pipe flows and listing all of them isbeyond the scope of this manuscript. Each one of these
correlations assumes an enhancement on the quality of
results compared to existing publications. Traditionally,

empirical correlations have been used to effectively design pipelines; nevertheless, these successful predictions
are limited to specific ranges of variables and lack universality since outside the specified range these correlations
produce disappointing results. Moreover, for empiricalcorrelations to be effective predictive tools, their coefficients need to be fine-tuned using experimental data fromtests in the pipeline system. This is in itself, a logical fallacy, since accurate predictions from empirical correlations to properly design a pipeline need data from thatsame pipeline.
Some thorough reviews and books on earlier iterations
on empirical and semi-empirical correlations for bothpressure drop and critical deposition velocity can befound in the literature (Abulnaga, 2002; Crowe, 2005;
Peker & Helvaci, 2011; Shook, 1976).

2.3 Mechanistic Models
The study of solid-liquid settling suspension flows
where a non-homogeneous distribution of particles exists,
has provided us with one certainty: any model or correlation accuracy in predicting flow characteristics is intrinsically related to its capability of incorporating the flow
regime mechanisms (Crowe, 2005).

2.3.1. Two-Layer Model

In 1970, Wilson (Miedema, Riet & Matoušek, 1995;
Wilson, 1970) developed a mechanistic model in which
the flow is divided in two layers. In the first layer of the
Two-Layer model, the upper part of the flow, the suspended particles linger while in the bottom layer, thesecond layer, the particles have settled. This model
development started with experimental results obtained
for narrow particle size distribution of solid-liquid settlingsuspensions; however it is not suited for cases where there
is a low contact of the particles in the bottom layer. In
such cases a homogenous model is preferred. One of theissues with the Two-Layer model is that the existence of
two layers and an interface inside the pipe is purely conceptual and used only for the sake of numerical representation purposes.
The fundamental bases of this model are as follows(Crowe, 2005):- The flow is divided in two hypothetical layers, an upper
layer of particles less than 74 μm and a lower layer containing all particle sizes in the slurry;- Each layer has its own uniform velocity and volumetricsolids concentration and there is no slip between the solids and the liquid within the layers;- Since the suspension in the upper layer behaves essentially as a liquid, as far as the wall shear stress is concerned, then the wall shear stress in the upper layer iskinematic, i.e., velocity-dependent;

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In the lower layer the particles experience a Coulombic
friction force;
Several iterations of the Two-Layer model have been
proposed in the literature that are either simplifications ormodifications of the original model in which a stationaryor moving bed, is in the bottom layer, and in the upper
layer, a heterogeneous suspension with a particle concentration gradient is present (Crowe, 2006). Additional developments were later introduced by other authors (Crowe,
2005; Gillies, Shook & Wilson, 1991; Shook et al., 1982;
Shook et al., 1981). A more detailed description of the
Two-Layer model, which is beyond the scope of this review, can be found in the literature (Crowe, 2005; Peker& Helvaci, 2011).

2.3.2. Three-Layer Model

The Three-Layer model was introduced in 1995 (Doron& Barnea, 1995) and was developed by joining experimental information with the Two-Layer model. Since the
Two-Layer model fails to accurately predict the suspension behavior for low flowrates where a stationary bed ispresent, this model states that in suspension pipe flow
three different flow regimes occur at the same time. In the
top layer a heterogeneous flow, in the middle layer a moving bed and in the bottom layer a stationary bed. The additional complexity of the Three-Layer model equips itwith the capability of predicting flow patterns transitions,however, due to the aforementioned complexity, supplementary expressions and constitutive relations are required for closure of the equation set (Crowe, 2005; Doron& Barnea, 1995; Ramadan, Skalle & Saasen, 2005).
Mechanistic methodologies are a considerable improvement to empirical correlations in the depiction of settlingand non-settling suspension flows. Still, layered models
present difficulties in predicting the flow of particles between layers (Roco & Shook, 1984) and, additionally,
these models’ predictions are still obtained with the helpof parameters that require accurate experimental data.
Again, this defeats the purpose of the “predictive” aspectintended for a model. In addition, some of the assumptions required for the successful application of mechanistic models may not hold in this case since the assumptionthat velocity in each layer is uniform in both Two andThree-layer models (Crowe, 2005).

2.4 Deterministic Models
Traditional approaches for predicting the behavior of
multiphase flows were based on empirical correlations
and mechanistic approaches, as seen in previous sections,which resulted from extensive experimental data compiled by equipment designers. These methods had thedrawback of being case specific, i.e., they failed to produce accurate predictions if any of the conditions, such as

46

particle data, inlet conditions or geometry, were altered.
With the advent of computational modelling techniquesand ever evolving computer hardware, the traditional
approaches have been refined or replaced, providing scientists, engineers and equipment designers with anenhanced predictive capability and lack of restrictions toadjust process conditions to better suit their demands(Massoudi, 2010).
The number of CFD codes and software, either proprietary or open source, has grown considerably through recent years. Although, single phase CFD codes are wellestablished in the literature, for multiphase flows they arestill an open problem (Balachandar & Eaton, 2010;
Borhani, 2010; Sommerfeld, Wachem & Oliemans, 2008),
in spite of extensive research. When categorizing CFD
codes for multiphase flow, more precisely for solid-liquid
settling suspension f lows, the following approaches
have been considered:

2.4.1. Single-Phase Numerical Models

This approach is only suitable for solid-liquid settlingsuspension flows were the solids concentration is quite
low and there is one-way coupling, i.e., where the presence of the particles has little or no impact on the overall
properties of the liquid phase (Crowe, Troutt & Chung,1996). Earlier works derived a turbulent model that used
the properties of the mixture for the calculations of settling suspension flow behavior (Roco & Shook, 1984).
Two equations turbulence models (Wilcox, 2006) became
increasingly popular in two-phase applications, and somerecent works have employed this approach to highly concentrated solid-liquid settling suspension flows with turbulence modulation (Bartosik, 2010; Bartosik, 2011).
In other approaches with two-equation single phaseturbulence models for solid-liquid settling suspension theauthors introduced additional parameters into the equations to incorporate the particle influence on the carrier
phase (Hsu, 2003; Jha & Bombardelli, 2009; Rizk &Elghobashi, 1989).
Single-phase numerical models offer a computational
inexpensive tool for predicting pressure drop, velocity
and turbulence profiles, while providing some insight into
particle-boundary layer information, although in a limited
fashion, namely now when more complex numerical models are available in software packages, either commercial
or open-source. Recently these models have been employed in the study of turbulence modification for concentrated solid-liquid settling suspensions flow (Bartosik,
2011; Bartosik, 2010).

2.4.2. Euler-Euler Numerical Models

In the “Dense Phase approach”, Eulerian or even “twofluid” approach, the two components are interacting witheach other in a way that the behaviour of each phase in-Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

fluences the other and are considered to be at the same location at the same time. The volumetric fraction is ofparamount importance as this variable will dictate theamount of each phase at a given time and place. The Eulerian models provide an averaged depiction of a multiphase system, and in the literature a wide range of
averaging processes can be found, namely time, volume
or ensemble based averaging (Ishii & Hibiki, 2011). With
this averaging methodology two advantages arise, one being that with the averaging process all the forces are inherently present in the model, the other advantage is thatthe computational cost is not dependent on the number ofparticles, making the Eulerian approach more suited forlarge systems with a great number of particles. A drawback of the Eulerian modelling approach, also a consequence of the averaging process, is the loss of detail,which creates the need for closure equations for the turbulence and interaction forces. This approach is widely employed in fluidization, gas-solid flows, pneumatic andhydraulic conveying, and suspension flows (Balachandar& Eaton, 2010; Sommerfeld, Wachem & Oliemans, 2008).
The Eulerian approach has become increasingly popularfor concentrated or dense suspension flows, either using asingle fluid approximation (Mixture Model, Volume of
Fluid, cavitation models, etc.) or two-fluid approximation(Euler-Euler Model or Euler-Granular Models) (Brennen,2005).
The Mixture Model (Manninen, Taivassalo & Kallio,
1996) was used to perform a series of numerical studies
on pipe flows of both zircon-water and silica-water mixtures to a maximum of 20 % solid volumetric fraction: allof them showed good agreement with the experimental
data (Ling et al., 2003); this same model was also used together with the Standard/High Reynolds k-ε Turbulence
Model for highly concentrated solid-liquid flow in pipes(Kaushal et al., 2012) with results far from satisfactory
due to an over-prediction of the pressure drop in the pipe
section, which increased with solids concentration. Another approach, using the Mixture Model and a Low
Reynolds Turbulence closure, was employed to describe
highly concentrated flows of solid-liquid suspensions(Silva et al., 2014; Silva et al., 2013). A new photocatalytic
reactor (XiaoWei & LieJin, 2010) with solar concentrator
for hydrogen production was simulated using an Algebraic Slip Mixture model (ASM) with a catalyst volumetric fraction up to 15 % .
An increasing number of publications where the TwoFluid approach incorporating the Kinetic Theory of Granular Flow is employed to characterize particle-particle
interaction can also be found in the literature to study
highly concentrated solid-liquid settling suspensions pipe
flow (Kaushal et al., 2012; Lahiri & Ghanta, 2008; Lahiri& Ghanta, 2010), slush nitrogen (Jiang & Zhang, 2012),
ice slurry (Wang et al., 2013), with good results in repro-

ducing experimental data. The Kinetic Theory of Granular Flow is an adaptation form the Kinetic Theory ofGases, and in this way the particle-particle interactions
are quantified in the flow. However, although it provides
some good results for concentrated solid-liquid flows fordifferent size and density of the particles, it is a very complex numerical model, with a high computation requirement and often with boundary conditions issues that
require some simplifying assumptions.
The predominant applications of the existing twofluid models exhibit problems hindering their use for more
complex flows of engineering interest. Amongst the main
issues one can point out numerical instabilities, very time-­
consuming, difficulty in dealing with the complex geometries since the calculation time becomes prohibitively
expensive and none of the existing models has shown to beable to determine the minimum in the pressure gradientversus slurry velocity, which characterizes the transition
to bed flows (Messa, Malin & Malavasi, 2014).

2.4.3. Euler-Lagrange Numerical Models

The Lagrangian approach, also known as “Dilute Phase
approach”, is employed when the amount of the dispersed
phase is small and does not disturb the motion of the continuous phase. This approach is predominant in case studies of sprays, atomization and flows with bubbles, where
droplets and particles which are treated as the dispersed
phase. Amongst the Lagrangian approach three major
modelling techniques can be outlined: “Point-Particle Direct Numerical Simulation (DNS)”, “Point-Particle Large
Eddy Simulations (LES)” and “Point-Particle Reynolds
Averaged Navier-Stokes (RANS)”. The DNS modelling
approach requires that the particles must be smaller thanthe Kolmogrov scales, i.e., the time scales of the particle
have to be smaller than the time scales of the smallerscales of the fluid. This requirement limits the DNS application to very low Reynolds numbers or to very small
particles. To overcome this limitation LES modelling canbe used. Both DNS and LES application are limited to dilute systems where collisions and hydrodynamic interactions can be neglected and a one-way coupling between
the dispersed and carrier phases is assumed (Balachandar& Eaton, 2010; Fox, 2012; Hiltunen et al., 2009;
Mashayek & Pandya, 2003).
In recent studies (Adams, Fairweather & Yao, 2011;
Soldati & Marchioli, 2012) one-way coupled Eulerian-­
Lagrangian models where employed in the study of dilutesolid-liquid flows of suspension and re-suspension of particles; a solid-liquid settling suspension flow in horizontal
pipes was investigated (Capecelatro & Desjardins, 2013)
for operating conditions above and below the criticaldeposition velocity. A high-fidelity large eddy simulation
framework is combined with a Lagrangian particle tracking solver to account for polydispersed settling particles

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in a fully developed turbulent flow. Two cases were simulated, the first having a Reynolds number of 85 000 andthe second considers a lower Reynolds number of 42 660.
Since most studies of the Lagrangian properties of turbulence trace point-like particles (Toschi & Bodenschatz,2009) they cannot still be generally applied to all types ofparticles, due to the high computational demand, whichhinders their application for processes with a large number of particles. They will, nevertheless, most likely become standard tools in the future.
Some excellent reviews on Lagrangian-Eulerian Methods on multiphase flows were given by Subramanian(Subramaniam, 2013) and Zhou (Zhou, 2010).

2.4.4. Lattice-Boltzmann Numerical Models

In the last two decades the lattice Boltzmann method(LBM) has been developed into an established CFD approach for solving fluid flow problems. Important developments have been done in LBM’s capability for several
flow problems, containing multiphase flows, turbulence,and microfluidics. Amongst the numerous areas, the solid-­
liquid systems have received special emphasis considering
the unique advantage of LBM in its computational efficiency and parallel scalability. Traditionally, conventional
CFD numerical schemes are based on discretisation’sof macroscopic continuum equations, like finite-­difference, finite-element or finite-volume methods, havebeen used to solve the velocity and pressure field from
Navier-Stokes equations: on the other hand LBM is basedon microscopic models and mesoscopic kinetic equations in which the fluid is described by a group of discrete particles that propagate along a regular lattice andcollide with each other. This scheme is particularly successful in fluid flow applications involving interfacial
dynamics and complex boundaries (Aidun & Clausen,
2010; Chen et al., 2010; Yu & Fan, 2010).
The LBM can serve as an alternative flow solver for different types of incompressible flows. The incompressible
Navier-Stokes (NS) equations can be obtained in the nearly
incompressible limit of the LBM. Pressure, in the LBM, iscalculated using an equation of state. In contrast, in the direct numerical simulation of the incompressible NS equations, the pressure satisfies a Poisson equation with velocity
strains acting as sources and solving this equation often
produces numerical difficulties requiring special treatment,such as iteration or relaxation (Aidun & Clausen, 2010;
Chen et al., 2010; Yu & Fan, 2010). For the modelling ofsolid-liquid systems, the LBM, due to its simple implementation, becomes particularly appropriate for simulations involving large numbers of particles. Furthermore, it can becoupled, if it’s regarded only as a solver for the fluid flow,
with various methods for particles such as Discrete Element Method (DEM) or Lagrangian tracking (Aidun &Clausen, 2010; Balachandar & Eaton, 2010; Borhani, 2010).

48

Among recent publications on LBM application insolid-liquid settling suspensions flows, it is important torefer: Shardt & Derksen (Shardt & Derksen, 2012)
simulations of up to 45 % solids volume fraction of rigid
non-spherical particles with low density ratios at moderate Reynolds numbers (< 1) using the LBM coupledwith DNS studies; two-dimensional (2D) and three-­dimensional (3D) CFD studies of solid-liquid settlingsuspensions flows, by Kromkamp et al. (Kromkamp et al.,2006) where Couette flows of single, two and multi-particle
systems were conducted; Gao et al. (Gao, Li & Wang,2013) particle-resolved simulation method for turbulent
flow laden with finite size particles where the method wasbased on the multiple-relaxation-time lattice Boltzmann
equation. In this case, a maximum of 51200 particles in
3D have been considered in their simulations and the authors note that particle-laden turbulent flow is a multiscale problem that requires state of the art computers toinclude all relevant scales into the simulations with realistic physical parameters.
The Lattice-Boltzmann approach, due to its relatively
simple implementation for parallel computing and hybrid
combinations of the Eulerian lattice with a Lagrangian
grid system (Aidun & Clausen, 2010; Subramaniam,2013), shows great promise relatively to traditional approaches, still it is only possible to employ it in the simulations of suspension flows with dilute concentrations.
Detailed reviews on LBM theory and multiphase applications can be found in the literature (Aidun & Clausen,
2010; Chen et al., 2014; Chen & Doolen, 1998; Yu & Fan,
2010).

2.4.5. Discrete Element Method (DEM) Numerical
ModelsOne of the main challenges in simulating settling suspensions flows derives from their intricate behaviour
brought about by the complex interactions between individual particles and their interactions with surrounding
liquid and wall. Understanding the underlying mechanisms has been the aim of particle scale research which inrecent years has grown worldwide, a result from the intense development of both discrete particle simulation
techniques and computational capabilities. An important
discrete model is the discrete element method (DEM)
originally developed in 1979 by Cundall and Strack(Cundall & Strack, 1979). This method uses the Newton’s
equation of motion to contemplate a finite number of
discrete particles interacting through contact and non-­contact forces moving translationally and rotationally.
Both trajectories and transient forces acting on individual
particles are extremely difficult to obtain by experimental techniques, is the type of information provided by
DEM simulations (Zhu, Zhou & Yang, 2008).
In recent publications DEM has been combined with

Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

CFD techniques to describe solid-liquid settling suspensions: modelling solid-liquid suspension flows in thedensity-driven segregation of a binary particulate suspension incorporating 10 000 particles in a closed container,using a hybrid combination of the discrete element method(DEM) with computational fluid dynamics (CFD) (Qiu
& Wu, 2014); using a new Lagrangian-Lagrangian algorithm, also referred to as the DEM-SPH method, forsolid-liquid flows in both a dam break problem and aquasi-steady solid-liquid flow in a cylindrical tank (Sun,
Sakai & Yamada, 2013); simulating dense medium cyclone separation (DMC), combining DEM with CFD incoal preparation, which is a process with a simple design
but where the flow pattern within is complex, due to thesize and density distributions of the feed as well as theturbulent vortex formed (Chu et al., 2009). In this casestudy, DEM is used to model the motion of discrete particles by applying Newton’s laws of motion and CFD isused to model the motion of the slurry medium by numerically solving the local-averaged Navier-Stokes equations
using the Volume of Fluid (VOF) and Mixture multiphaseflow models; transported in a fluid, for predicting the location of the puncture point location of the particles in an
elbow, DEM was used to describe the kinematics and trajectory of the discrete particles as well as the particle-­particle interaction while the hydrodynamic modelling of
the fluid phase was based on the volume-averaged Navier-­
Stokes equations, and a fluid density-based buoyancy
model was adopted to calculate the solid-fluid interaction
force (Zhang et al., 2012).
Regarding numerical studies involving DEM for solid-­
liquid settling suspensions in horizontal pipe there
seems to be a lack or complete absence of publications on
the subject, which can be attributed to the limitation on
the number of particles that it is possible to simulate evenwith this method that offers an alternative to DNS due toits parallel computing capability (Zhang et al., 2012; Zhu,
Zhou & Yang, 2008). A review on DEM application inparticulate systems was given by Zhu et al. (Zhu, Zhou &Yang, 2008).

2.5 Turbulence Modulation
As pointed out in section 2.4.1, with low solids volumetric fraction the usual assumption is that the turbulenceof the fluid phase is equal or very similar to the single-­phase flow. Yet, as the solids volumetric fraction increases
additional phenomena appear where turbulence augmentation, dissipation and distortion become significant. For
solid-liquid settling suspensions flows the phenomenaof turbulence attenuation is a rather interesting one fordesign engineers, since this would allow solids conveyingof concentrated suspensions at energy expenditures
similar to those of single-phase f lows (Balachandar& Eaton, 2010; Crowe, Troutt & Chung, 1996).
Crowe and Elghobashi have done extensive work on turbulence modification, but mainly for gas-liquid and gassolid flows (Crowe, 2000; Kenning & Crowe, 1997;
Elghobashi & Truesdell, 1993; Truesdell & Elghobashi,1994). In his studies, the following conclusion was attained“small particles will attenuate the turbulence while large
particles will generate turbulence” (Crowe, Troutt &Chung, 1996). And while this seems to hold true for gassolid and gas-liquid suspensions flows, recent studies(Kaushal et al., 2012; Lahiri & Ghanta, 2010; Matoušek,
2005) seem to contradict this statement for solid-liquid settling suspensions flows. In fact, quite the opposite seems tobe the case for solid-liquid settling suspensions but only forhighly concentrated solids volumetric fractions.
In a recent publication Tanaka (Tanaka & Eaton, 2008)
presents a dimensionless parameter, the particle moment
number, Pa, that was derived using dimensional analysisof the particle-laden Navier-Stokes equations. This analysis was based on a set of 80 experimental measurements
where the turbulent kinetic energy was modified by particles. Data for the turbulent kinetic energy augmentationin air and water was included as well as data for the turbulent kinetic energy attenuation in air, but there is a voidof data for the turbulent kinetic energy attenuation forwater. This is a very thorough study that in spite of theabsence of information on turbulence attenuation whenthe medium is water represents a significant step towards
predicting turbulence modification in particle laden flows.
Searching the literature for current numerical studies
trying to characterize on turbulence attenuation for solid-­
liquid settling suspensions, some manuscripts are foundwhere: drag correlations are modified in an attempt to reproduce experimental data where turbulence modulation
occurs (Hadinoto, 2010; Hadinoto & Chew, 2010); single-­phase Low Reynolds turbulence models are modified
to incorporate turbulence modulation (Bartosik, 2010;
Bartosik, 2011); Mixture Models with a Low Reynolds
Turbulence closure for turbulence modulation in highly
concentrated solid-liquid flows (Silva et al., 2013); Euler-­
Euler model is used for pipe flow of concentrated slurries(Kaushal et al., 2012; Lahiri & Ghanta, 2010).
Some thorough reviews and studies on turbulence modulation in particle laden flows are present in the literature(Balachandar & Eaton, 2010; Crowe, Troutt & Chung,1996; Hosokawa & Tomiyama, 2004; Kiger & Pan, 2002).

3. Concentrated Solid-liquid Settlingsuspensions Flow in a Horizontal Pipe:
Mixture Model Studies
The authors of this review have also been working on
numerical studies of solid-liquid settling suspensions

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Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

Fig. 1Pressure drop comparison between experimental, Durand & Condolios correlation and simulated results,
using the Mixture Model with a Low Reynolds k-ε Turbulence model, for settling particles with diameterof 0.44 mm, density of 2470 kg.m –3 and volumetric fractions from 0.1–0.4.

flows in horizontal pipes, with highly concentrated solids
volumetric fractions up to 40 % (v/v), using data from theliterature for validation (Lahiri & Ghanta, 2010). CFD
numerical studies where a Mixture Model, incorporatinga Low Reynolds turbulence closure (Costa, Oliveira &Blay, 1999; Hrenya & Bolio, 1995) and using a Schiller-­
Naumann drag correlation (Pang & Wei, 2011), were used
to describe experimental data from the literature on solid-liquid suspension flow of medium sized particles wherewall turbulence attenuation phenomena is observed (Fig.
1). The focus of our studies was mainly on higher flow
velocities and concentrations, since in an industrial environment working with flow velocities where a stationaryor moving bed is observed is not desirable, and working
in a regime where turbulence attenuation occurs is preferred, since there is an energetic advantage. A recent
publication on energy consumption of solid-liquid settlingsuspensions flows demonstrates the interest on this subject (Jafari, Tanguy & Chaouki, 2012).
Previous numerical studies employing the Mixture
Model with a High/Standard k-ε Turbulence Model including a traditional wall function, which is an empiricalapproximation employed in single-phase models, proved
to be inadequate for the numerical study of highly concentrated solid-f lows as also seen in the literature(Ekambara et al., 2009; Troshko & Hassan, 2001).
Thus, incorporating a Jones-Launder Low Reynolds Turbulence closure in the Mixture Model, for the settling particles studied, can circumvent this issue, since the Low
Reynolds Closures solve the model through the integrationof the turbulence equations until the wall. The numericaland experimental pressure drops for different concentrations and flow velocities are shown in Fig. 1, as well as
the results using Durand and Condolios correlation.
There is a good agreement with the experimental pressure drop data for flow velocities from 3 up to 5 m/s for all

50

solid concentrations. This improved agreement is morenotorious for the highest flow velocities, which is the areaof interest, since for 5 m/s there is a similarity between
this pressure drop and the monophasic pressure drops (theregion where turbulence attenuation is expected as explained in the literature (Lahiri & Ghanta, 2010; Tanaka& Eaton, 2008). A possible explanation for this phenomenon is related with lift-forces resulting from the viscous-­
turbulent interface at the bottom layer of particles closest
to the pipe wall (Kaushal & Tomita, 2007; Matoušek, 2005).
Overall, the tendencies detected in the numerical andexperimental vertical solid volumetric fraction profiles(Fig. 2) are in concordance. The general tendency of thesolid concentration lines is followed by the numericalones, with the exception of the lower flow velocities,
where there is a slight deviation, especially in the bottom
region of the pipe cross-section. This can be again attributed to the moving bed regime that the Mixture Model
does not represent accurately (Ling et al., 2003). With theinclusion of the Jones-Launder Low Reynolds Turbulence
closure in the Mixture Model, the deviations in the solids
volumetric fraction profiles for the highest velocities decrease significantly and overall the deviations are smallerwhen compared with the High Reynolds Turbulence closure, especially for the highest average concentrations.
For the lowest solid volumetric concentration the HighReynolds Turbulence closure behaves well for all the velocities, with a better fit for velocities up to 3 m/s. For
these lower velocities the Mixture Model with the inclusion of the Jones-Launder Low Reynolds Turbulence closure behaves poorly by comparison with the HighReynolds Turbulence closure, which can be attributed to
the fact that the suspension viscosity is higher at the pipebottom and since the Low Reynolds Turbulence closure
integrates the turbulence equations all the way to thewall, pressure drop will rise accordingly, due to an over-Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

Fig. 2Comparison of normalized (using the efflux volumetric fraction, CVF) experimental and numerical (Mixture
Model with the Low Reynolds k-ε Turbulence model) vertical solid concentration profiles for settling particles, (a)0.1 volume fraction, (b) 0.3 volume fraction, (c) 0.4 volume fraction.

shoot of the suspension and turbulent viscosity with thisclosure, which, for this concentration, is not compensatedby the presence of strong lift forces resulting from particle/particle interactions in that region, as happens for thehigher concentrations.

4. Future Directions
In the near future, with the continuous development ofcomputational architectures, more complex DNS numerical studies will be possible allowing the widest application of current Eulerian and Lagrangian numerical models
and providing further information on turbulence modula-

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Rui Silva et al. / KONA Powder and Particle Journal No. 32 (2015) 41–56

tion and particle migration phenomena. Additionally, interphase correlations (drag, lift, etc.) can be further
improved by DNS data.
Euler-Lagrange numerical methods will become morewidespread with enhanced computational structures andalgorithms (LBM and DPM) for more concentrated suspensions and complex flows.
Thus, with the improvement of DNS and Lagrangian
methods, providing microstructural data for settling suspensions flows, as well as further development of computational capability, Euler models coupled with that
information will become more reliable and faster, allowing design engineers to obtain fast and accurate representations of the flow, thereby reducing the cost of empirical
data acquisition and optimizing the energy consumption
in pipe flow, thereafter minimizing the need for empiricalcorrelations and charts.
Finally, it is paramount to have quality experimental
data for settling suspensions, regarding turbulence datafor both dispersed and carrier phases, which requires further development of experimental techniques.

fL

friction factors for waterf bk

Kynch batch flux density function (m.s –1)

ggravitational accelerationislurry frictional pressure loss (m/m)

iwfrictional pressure loss for an equivalent volume of water
(m/m)

k

constant in Eqn. 1

K

regime-specific coefficient in Eqn. 3

Kt

coefficients for determining the regime number in Eqn. 4

K2

constant in Eqn. 2

m1

regime-specific coefficient in Eqn. 3

m2

regime-specific coefficient in Eqn. 3

m3

regime-specific coefficient in Eqn. 3

m4

regime-specific coefficient in Eqn. 3nempirical parameter dependent on the flow regimen1coefficients for determining the regime number in Eqn. 4

n2

coefficients for determining the regime number in Eqn. 4

n3

coefficients for determining the regime number in Eqn. 4

n4

coefficients for determining the regime number in Eqn. 4

Nnumber of primary particles

Pe

Péclet Number

Acknowledgments

peexcess pore pressure (Pa)Rep

particle Reynolds NumberThis work has been financially supported by theFundação para a Ciência e Tecnologia, project PTDC/
EQU-EQU/66670/2006, individual PhD scholarship
SFRH/BD/79247/2011 and Strategic Research Center
Project Pest-C/EQB/UI0102/2013.

RG

radius of gyration (m)Rijregime transition discretization numberVmaverage flow velocity (m.s –1)

Vt

terminal velocity (m.s –1)

usettling velocity of suspension (m.s –1)

uosettling velocity of an individual particle (m.s –1)

u o*settling velocity of an individual agglomerate (m.s –1)

NomenclatureΔρsolid-fluid mass density difference (kg.m –3)

θparticle shape factor

σeeffective solids stress function (Pa)–3

ρL

liquid density (kg.m )ρssolids density (kg.m –3)

φvolumetric fraction of flocs

φmax

maximum volume fraction of flocs

ϕvolumetric fraction of solids

ϕ*volumetric fraction of agglomerates

ϕmax

maximum volume fraction of solids

CDdrag coefficientCovolumetric concentration of solids

CV

local solids volumetric concentration

CVF

efflux volumetric concentrationDi

pipe internal diameter (m)Df

the floc diameter (m)dpparticle diameter or size (m)ffriction factors for slurry

52

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Author’s short biography
Rui Silva
Rui César Costa da Silva received his degree in Chemical Engineering from the University of Porto in 2006. In 2009 received his Master Degree in Chemical Engineering fromthe University of Coimbra. He is presently a Ph.D. student at the research group of Particle
Technology and Multi-Phase Systems in the University of Coimbra. His research interests
are in Multiphase Systems, Process Tomography and Computational Fluid Dynamics.

Fernando A. P. Garcia
Fernando Garcia graduated as a chemical engineer in the Instituto Superior Técnico,Technical University of Lisbon. He obtained an MSc in Biological Engineering and a
PhD in Chemical Engineering in the University of Birmingham, UK. He then became an
assistant professor and latter an associate professor in Coimbra University. His main interests are in transport phenomena and in the areas of biocatalysis and fermentation processes and in treatment of industrial effluents. He served as secretary of the Section
Applied Biocatalysis of EFB, then member of the Executive Board, remaining as a member of the Scientific Board.

Pedro M. G. M. Faia
Pedro M. Faia received his degree in Electrical Engineering (1990), is M.S. in Automation (1994) and is Ph.D in Electronic Materials (2003) from the University of Coimbra. He is an Auxiliary Professor at the Department of Electrical Engineering andComputers of the University of Coimbra where is research interests concern the researchand development of solid state chemical sensors, sensor integration techniques, Electrochemical sensor and biosensors, Impedance Spectroscopy technique and applications.
He is a member of the Portuguese Materials Society.

Maria G. Rasteiro
Maria da Graça Rasteiro graduated in Chemical Engineering at the University of Coimbra, did post graduate studies at Loughborough University, UK, and obtained a PhD
in Chemical Engineering/Multiphase Processes, in 1988, under the co-supervision ofProfessor Brian Scarlett. She is presently an Associate Professor with Aggregation, in
Chemical Engineering, at the University of Coimbra, Portugal. Her research interests
have always been in the field of Particle Technology and she started the first Portugueselaboratory on particle characterization. Presently, she has got research interests in thefields of particle aggregation, rheology of suspensions, solid/liquid flow modelling andon flow visualization techniques.

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KONA Powder and Particle Journal No. 32 (2015) 57–74/Doi:10.14356/kona.2015013

Review Paper

A Review of Aerosol Nanoparticle Formation from Ions †Qing Li 1, Jingkun Jiang 1,2* and Jiming Hao 1,2
12State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, China

Abstract
Ion-induced nucleation (IIN) mechanism has been widely accepted as an efficient source of new particle
formation in the middle and upper troposphere. However, there have been debates on its contribution to the
nucleation events observed in the boundary layer. To reveal the current understanding of IINs, we here summarise
investigations on microphysical mechanisms of aerosol formation from ions, i.e., ion induced homogeneous
nucleation (HN). The basic formation steps of ion-induced HN include primary ion production, charged cluster
formation from ions, and stable nanoparticle formation from clusters. Two essential controlling processes (iongeneration and formation of condensable species) are emphasized in the viewpoint of plasma performance forthree case studies, i.e., galactic cosmic ray travelling in the troposphere, lightning discharges, and charging
processes by aerosol chargers. The first two cases result in IINs due to the simultaneous generation of ions andcondensable species (such as H2SO4 and oxidized organic vapours) from oxidizing trace atmospheric molecules.
The third case leads to ion-induced clustering or nucleation in aerosol chargers. Due to a relatively short residence
time in chargers (normally less than 1 minute), big charged clusters often exist in the outgoing aerosol flows.
Keywords: ion-induced nucleation, charged clusters, plasmas, lightning, cosmic rays, chargers

1. Introduction
Aerosol nucleation events, frequently observed in many
regions of the troposphere (Kulmala et al., 2004; Kulmala
et al., 2013; Deshpande et al., 2014), may significantly influence the earth’s climate (Kazil et al., 2010; Koren et al.,2012; Makkonen et al., 2012) by causing a net cooling andleading to numerous small cloud droplets, which makes
cloud brighter and extends their lifetimes (Solomon,
2007; Nicoll and Harrison, 2014; Yu and Luo, 2014). This
nucleation via gas-to-particle conversion, called as homogeneous nucleation (HN), is the largest source of atmospheric aerosol particles (Kulmala et al., 2004), providing
up to half of global cloud condensation nuclei (Merikanto
et al., 2009; Yu and Luo, 2009). Therefore HN is criticalto achieve a clear physical understanding for improving
our prognostic capability of aerosol influences on climate(Kerminen et al., 2012; Riccobono et al., 2014).
HN is a complex process of forming aerosol particles(solid or liquid particles) from gas-phase species, originating from both natural (such as volcano and plant emissions) and anthropogenic (such as industrial and vehicle†

Received 21 August 2014; Accepted 16 September 2014
J-STAGE Advance published online 10 October 2014
1, 2Tsinghua Yuan, Haidian District, Beijing 100084, China* Corresponding author: Jingkun Jiang;
E-mail: jiangjk@tsinghua.edu.cn
TEL: +86-10-62781512 FAX: +86-10-62773597©2015 Hosokawa Powder Technology Foundation

emissions) sources. Prevailing HN theories include binary
homogeneous nucleation (BHN, e.g., H2SO4-H2O) (Harrington and Kreidenweis, 1998; Kulmala et al., 1998; Vehkamaki et al., 2002; Yu, 2008; Sipila et al., 2010) andternary homogeneous nucleation (THN, e.g., H2SO4-NH3H2O) (Ball et al., 1999; Kulmala et al., 2000; Merikanto etal., 2007). BHN, the nucleation mechanism primarily
used in global aerosol models (Vehkamaki et al., 2002),
has been widely used to explain aerosol formation in theupper troposphere, due to there’s enhanced solar radiation, high relative humidity, and low aerosol surface area
(Brock et al., 1995; Weber et al., 1996). In the middle andlower troposphere, especially in the boundary layer, aerosol nucleation rates have been frequently observed to bemuch higher than that predicted by classical BHN (Weberet al., 1996; Clarke et al., 1998; Weber et al., 2001; Vehkamaki et al., 2002). This discrepancy has led to the explanation by using THN (Marti et al., 1997; Kulmala et al.,2000), whose nucleation rate is much higher than the
BHN rate due to ammonia stabilizing the critical embryos. However, the tertiary compound has not been determined, such as that amines strongly enhance nucleation
rates already in low pptv range (Chen et al., 2012;
Almeida et al., 2013), and oxidation products of biogenicorganics at atmospheric concentrations can reproduce
particle nucleation rates (Riccobono et al., 2014). Recently
experimental researches suggest that the THN of including amines or biogenic organics to stabilize the sulfuric

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acid clusters can successfully explain the dominant nucleation way in the boundary layer (Chen et al., 2012;
Almeida et al., 2013; Riccobono et al., 2014).
The ion-induced nucleation (IIN) (Yu and Turco, 2000,
2001), which predicts that the presence of ions can enhance the formation of aerosol particles, have also beendeveloped to explain the high nucleation rate in the boundary layer. Recent studies (Hanson and Lovejoy, 2006; Yu,
2007; Yu and Turco, 2008) have further updated parameters in IIN such as the bounding energetics of H2SO4(Lovejoy et al., 2004) and revealed that the updated IIN
model can not only predict new particle formation in thecolder upper troposphere (Lucas and Akimoto, 2006), butalso in the warmer lower troposphere including the boundary layer (Yu and Turco, 2008; Yu, 2010). This model has
been confirmed in some conditions of chamber experiments such as low nucleation rates and high ion concentrations (Almeida et al., 2013; Riccobono et al., 2014).
Although significant progress has been achieved forunderstanding nucleation phenomena over last decade orso, there are still uncertainties on dominant nucleation
mechanism in the lower troposphere.

1.1 Debate between IIN and NCN
A lively discussion on the dominant nucleation pathway
in the lower atmospheric layer is the relative importanceof IIN versus neutral cluster nucleation (NCN) under various atmospheric conditions. The argumentation is mainly
among results from the direct charged aerosol ratio measurement of freshly nucleated particles in atmosphericobservations (Iida et al., 2006; Kulmala, 2007; Kulmala etTable 1

al., 2010), predicted results from the IIN simulations (Yuand Turco, 2008; Yu et al., 2008; Yu and Turco, 2011), andresults from laboratory experiments (Enghoff et al., 2008;
Kirkby et al., 2011; Almeida et al., 2013; Riccobono et al.,2014).
Table 1 lists some main supporting evidences for thedebate between IIN and NCN. In a series of studies, NCN
has been claimed to be identified as the main nucleation
pathway, with only a small contribution of about 10 % orless from IIN in a boreal forest (Kulmala et al., 2007;
Laakso et al., 2007; Manninen et al., 2009; Gagne et al.,2010; Kulmala et al., 2010; Kulmala et al., 2013). By contrast, a different conclusion has been made by using a different analytical methodology to the same atmosphericdata, with IIN to be clearly dominant (Yu and Turco,
2008; Yu et al., 2008; Yu, 2010; Yu and Turco, 2011). Furthermore, recently several laboratory experimental results
have simulated the IIN and NCN processes in the upperand lower troposphere, especially in the boundary layer(Kirkby et al., 2011; Chen et al., 2012; Almeida et al.,2013; Riccobono et al., 2014). The relative importance of
IIN versus NCN under different atmospheric conditions
remains unresolved.

1.2 Laboratory experiments on the role of IIN
To address the long-standing controversy, recent
CLOUD experiments in CERN (under well controlledlaboratory conditions) have been performed to reveal therole of IIN and NCN in atmospheric condition (Kirkby etal., 2011; Almeida et al., 2013; Riccobono et al., 2014).
Their results show that ion-induced BHN of H2SO4-H2O

Evidence comparison for the two most likely mechanism candidates of ion-induced nucleation (IIN) and neutral cluster
nucleation (NCN) in the lower atmosphereIon-induced nucleation

Neutral cluster nucleation

Atmosphericobservations:(i) Ions have been confirmed to participate inmany boundary layer nucleation events (Hirsikko
et al., 2007; Laakso et al., 2007; Tammet, 2009;
Manninen et al., 2010).
(ii) Observational parameters in 6-typical nucleation events in the boreal forests have been
reproduced by IIN model (Yu and Turco, 2008;
Yu, 2010).

(i) Field measurements taken in the boreal forests suggestthat the contribution of IIN is about 10 % or less of the total
nucleation events (Kulmala et al., 2010; Kulmala et al.,2013) and less than 5 % during summertime events in
Boulder (Iida et al., 2006).
(ii) Estimated nucleation rates (~1–103 numbers·cm –3·s –1) inthe polluted boundary layer (e.g., Atlanta and Mexico city)were often much greater than typical ion production rates(~2–30 numbers·cm –3·s –1) (Chen et al., 2012).

Laboratory
experiments:(i) An increase in ionization by a factor of 10
increases the production rate of stable clusters bya factor of about 3 in chamber experiments(Enghoff et al., 2008).
(ii) Ions increase the nucleation rate by an additional factor of between 2 and more than 10 atground galactic-­
cosmic-ray level in the Cloudexperiments (Kirkby et al., 2011).

(i) The role of amines in particle formation was examinedin Minnesota laboratory chamber and suggested thatamines enhanced sulfuric acid NCN rates (Chen et al.,2012; Titcombe, 2012).
(ii) Sufficient nucleation rate in the Cloud experiment canbe explained by a base-stabilization mechanism involving
H2SO4 -dimethylamine pairs, with small influence fromgalactic-cosmic-rays (Almeida et al., 2013).

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nucleation proceeds at a significant rate in cool temperatures (such as in the middle troposphere) at atmosphericconcentrations of H2SO4, but may be unimportant when
ternary vapor concentrations are high (such as in theboundary layer) (Kirkby et al., 2011). Comparing to neutral BHN, the nucleation rate of ion-induced BHN can beenhanced roughly two times at 292 K with ground levelgalactic-cosmic-rays. However, both rates from the neutral
and ion-induced BHNs are too low to explain the nucleation rate in the boundary layer, especially in the polluted
regions (Chen et al., 2012).
To explain the high nucleation rate in the boundary
layer, further experimental results reveal that a base-­stabilization mechanism involving pairs or H 2SO 4 -­dimethylamine and H 2 SO 4 -oxidation products ofbiogenic compounds, strongly decreasing evaporation, canprovides high enough nucleation rate in the boundary
layer (Almeida et al., 2013; Riccobono et al., 2014). The
high nucleation rate in the boundary layer is highly sensitive to the trace amine and organic vapor (or other highly
oxidized organic species) levels, which are more tightly
bound than with ammonia for H2SO4. However, in mostof the atmospheric conditions, the nucleation is limited by
the concentration of H2SO4 and not that of amine and organic vapors. Concentrations of H2SO4 and oxidized organic
vapors highly depend on ·OH radical level, because it
accounts for the oxidation both of organics to oxidation
products and of SO2 to H2SO4 (Riccobono et al., 2014).
These Cloud experimental results suggest that IIN can besignificant when (i) the associated neutral particles haveappreciable evaporation, and (ii) the overall nucleation
rates are low (lower than about 0.1 numbers·cm–3·s–1) andbelow the ion-pair production rate (around 2 numbers·cm–3·s–1) (Reiter, 1992).
For the polluted regions, such as in the ambient conditions of cities, a kinetic model based on a sequence ofacid-base reactions (including efficient stabilization of theacids by a tertiary compound such as amines) has been
proposed to explain the high nucleation rates without involving ions (Chen et al., 2012). Laboratory chamber
studies in the University of Minnesota and ambient measurements in Atlanta and Mexico City have shown a reasonable agreement of predicted nucleation rates(Stolzenburg et al., 2005; Iida et al., 2008; Smith et al.,2008; Chen et al., 2012; Kuang et al., 2012; Titcombe,2012).
Suggestions from the overlap between laboratory experiments and atmospheric observations in diverse locations are briefly summarized in Fig. 1. The schematicshows the main nucleation pathways in the troposphere:(i) IIN in the upper and middle troposphere, (ii) IIN (mostreports believed in IIN) or THN in free troposphere, and(iii) IIN or THN (most reports believed in THN) in theboundary layer, where acid-base chemical reaction model

can explain nucleation events in polluted regions.
A clear physical understanding of nucleation processes
is critical in explaining nucleation events and assessing
the influences of aerosols on climate. The precise mechanism of IIN, as one of the most likely candidates for fieldobservations, is still unclear (Svensmark et al., 2007).

2. How do ions induce aerosol formation in
the atmosphere?
Lots of efforts to model HN basic processes have focused on simulating the dynamics of a nucleating aerosol
particle growing through condensation and coagulation(Mcmurry, 1983; Lehtinen and Kulmala, 2003; Korhonen
et al., 2004; Kulmala et al., 2004; Gaydos et al., 2005;
Mcmurry et al., 2005; Yu, 2006; Kuang et al., 2010; Yu
and Turco, 2011; Kulmala et al., 2013). Here we present a
brief analysis for the “development process”, growing
from sub-1 nm ions to stable aerosol particles and partially to cloud droplets (~10–20 μm).

2.1 Development process of nucleation from ionsFig. 2 shows the schematic illustration of development
process for IIN from a negative ion (<0.36 nm, one molecule in mass diameter) to a stable heavy aerosol particle(> 22 nm). The process for originating from a positive ion
would be similar (Yu and Turco, 2011). The new particle
formation in the atmosphere is a dynamic process involving various interactions between precursor gas molecules(including primary ions), clusters (including charged andneutral ones), and aerosol particles (including pre-existingones) (Horrak et al., 2000).
The initial primary ions (or atmospheric ions nearground), concentrations of about several hundreds tothousands up to 5000 numbers·cm–3 in the boundary layers (Dhanorkar and Kamra, 1993; Aplin and Harrison,
2000; Vartiainen et al., 2007), are continuously generated
by galactic cosmic rays at the rate of about 4 numbers·cm –3·s –1 at ground level and up to 40–60 numbers·cm–3·s –1 in the upper troposphere (Thomas, 1974;
Reiter, 1992; Volland, 1995). Below approximately 80 km,
primary ions are mainly lost by the recombination between positive and negative ions (including electrons)with rate constant of about 5 to 8 × 10 –8 numbers·cm–3·s–1(Smith et al., 1981). In the boundary layer, other ion loss
processes are attachment both onto aerosol particles (i.e.,
aerosol charging process) and onto the ground.
The initial primary ions quickly react with common
trace air constituents such as H2O, H2SO4, HNO3, NH3
and organic species to form more stable ions such as
HSO4 –, called as small cluster ions with mass diameter inrange 0.36–0.85 nm (Horrak et al., 2000). The following

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Fig. 1

Schematic of aerosol formation in the troposphere. The red words on the left denote the nucleation pathways including binary homogeneous nucleation (BHN) and ternary homogeneous nucleation (THN),
according to the atmospheric observations and simulation analysis (Yu and Luo, 2009; Kirkby et al.,2011; Chen et al., 2012; Almeida et al., 2013; Riccobono et al., 2014). Heterogeneous nucleation commonly occurs in cases where there are pre-existing nuclei present of high concentration (Mcmurry, 1983;
Mcmurry et al., 2005; Kuang et al., 2010), such as in the top of anthropogenic emission sources. Organic
compounds and iodine oxides emitted from trees and marine are thought to participate in aerosol nucleation and growth (Riccobono et al., 2014).

big cluster ions with mass diameter in range 0.85–1.6 nm
would be typical in the troposphere (Luts and Salm, 1994;
Volland, 1995; Beig and Brasseur, 2000; Eichkorn et al.,
2002; Harrison and Tammet, 2008):
HSO4 – (H2O) n, NO3 – (HNO3) n, HSO4 – (HNO3) n(H2SO4) m,
H3O+(H2O)n, H+(NH3)n(H2O)m, H+(OM)n(H2O)m,where n and m are small integers (about 5~7 for watermolecule in hydrated proton ions), and OM denotes organic matters (such as CH3CO and CH3CN). The size andmobility distributions of these big cluster ions (0.85–
1.6 nm) depend on the minor contaminants present in theatmosphere. Commonly, their lifetime in the lower atmosphere is about one minute (Rosen and Hofmann, 1981).
Most cluster ions will be lost through condensation topre-existing aerosol particles (Mcmurry et al., 2005;
Kuang et al., 2010) or recombination with ambient ions.
Small aerosol particles will be formed when the cluster
ions can successfully continue their growth. This is60

known as ion-induced homogeneous nucleation or IIN for
simplification (Yu and Turco, 2001; Yu, 2006; Yu andTurco, 2011). The formed particles encounter further
small ambient ions, and will be neutralized or recharged
on timescales of minutes (Harrison and Tammet, 2008)by collisions (a proposed charging mechanism by collisions can be seen in Fig. 3) (Pahtz et al., 2010) or ion attachments.
HN is commonly considered to form a “critical nucleus” firstly, and then grows to a larger size (Kulmala etal., 2000; Sipila et al., 2010; Zhang, 2010; Kulmala et al.,2013). Commonly, the lifetime of clusters (0.36–1.6 nm) is
extremely short (a few or ten minutes) (Tammet et al.,2014), and only a few can reach the critical size to form
aerosol particles. Classical nucleation theory reveals thatwhen the critical nucleus forms, the free energy of the nucleating cluster reaches a maximum, called as “the nucleation barrier”, beyond which the formed small aerosol

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Fig. 2

Schematic illustration of the IIN kinetic processes for aerosol formation from a negative ion (as an example).
The terminal aerosol particle can be negatively and positively charged, and can also be neutralized (seeFig. 3). The nucleation is processing in the atmosphere, where concentration of ambient ions is about
several hundreds or thousands of numbers·cm –3. Particles larger than 1.6 nm in mass diameter are
defined as aerosol particles according to their physical nature, while smaller ones are called as clusters
or primary ions (Tammet, 1995; Horrak et al., 2000; Ehn et al., 2010). Clusters and aerosol particles areeasy to be charged or neutralized due to the kinetic processes evolve in the quasi-electroneutrality environment with high charge concentrations (Vartiainen et al., 2007). Condensable species can condense
onto clusters and aerosol particles, but also can evaporate from their surfaces. Thus the kinetic processes
include condensation, evaporation, coagulation, charging, and neutralized reactions (NR), and recombination (Yu and Turco, 2011). A small fraction of freshly formed particles, mostly lost through scavenging
by pre-existing aerosol particles (Mcmurry, 1983; Mcmurry et al., 2005; Kuang et al., 2010), will grow tocloud condensation nuclei (~ 50–100 nm diameter) and further become cloud droplet (~10–20 μm)(Dusek et al., 2006; Kuang et al., 2009; Westervelt et al., 2013).

particle growth become spontaneous (Zhang, 2010; Zhang
et al., 2012). This critical diameter can be calculated andpredicted to be about 1.6 nm at standard atmospheric condition, detail of the calculation as shown in other reviews
such as Harrison R.G. and Tammet H. (2008). This diameter has been identified in the atmosphere observations(Sipila et al., 2010). Thus the small clusters (0.36–
0.85 nm), too small to overcome the nucleation barrier(Mcgraw and Zhang, 2008), need continually growth tobig clusters (0.85–1.6 nm), and then become stable aerosol
particles, whose molecules of more than hundreds areheld together by noncovalent van der Waals interactions(Castleman and Bowen, 1996; Zhang et al., 2012).
The above discussion on the development process ismainly for nucleation and growth, however, loss of clusters
and aerosol particles onto the pre-existing aerosol surfaces
should also be noted, especially in polluted atmosphericboundary layer. An aerosol population balance model,
considering the ratio of the particle scavenging loss rate to
the particle growth rate, has been developed to determine
whether or not aerosol nucleation would occur in diverse
conditions since 1983 (Mcmurry, 1983; Mcmurry et al.,2005; Kuang et al., 2010; Kuang et al., 2012). The model
proposes that aerosol nucleation events in the boundary
layer can occur and be suppressed when the ratio is lowerand higher than 0.7, respectively (Mcmurry et al., 2005;
Kuang et al., 2010).

2.2 Two essential controlling processes
Generations of ions and condensable species are thetwo key controlling processes for IIN in the atmosphere.
Generation of ions, the origin of the subsequent formedclusters and further aerosol particles, is an apparent key
progress. We will mainly focus on generation of condensable species from atmospheric trace gas molecules. The
clustering and condensable species are often trace gas
molecules in the atmosphere, such as H2SO4, HNO3, NH3,
amines, and oxidized organic vapors. These molecules
have large dipoles and hydrogen atoms connected withelectronegative atoms (such as N, S, and O). Their electrostatic properties and hydrogen-­bonding interactions
have been recognized to play a significant role in HN(Zhang et al., 2012). These species also commonly have61

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Fig. 3

Schematic illustration for the proposed charging and neutralizing mechanism of colliding particles in a
local electric field. The local electric field is denoted as E0, which is ubiquitous in the atmosphere,
especially in the free troposphere (Carslaw et al., 2002). The black bold arrow denotes the direction ofparticle movements, while the colors denote the polarity of charges on particles. For simplification, thepolarized charge is assumed to be one unit charge. Initially a pair of particles polarized by an external
electric field collides to neutralize adjoining hemispheres. Once separated, the particles again becomepolarized by the external field. In this way, initially neutral ((a) and (b)) but polarized particles gain one
unit of charge following every collision. The charged ones with opposite polarities collide and lose theircharges (i.e., recombination process). Adapted from Pahtz et al. (2010).

very low vapor pressure at typically atmospheric temperatures, especially H2SO4, whose vapor pressure is further reduced in the presence of H2O (Ayers et al., 1980;
Marti et al., 1997; Sipila et al., 2010). H2SO4 is therefore
considered to be a key precursor for atmospheric HN(Weber et al., 1996; Kuang et al., 2008; Riccobono et al.,2014). Its value higher than 105 numbers·cm–3 (commonly
106 –108 numbers·cm–3) (Nadykto and Yu, 2007; Zhao etal., 2009) has been identified to be necessary condition for
HN in the atmosphere (Nieminen et al., 2009). Thus the
key process for IIN is the generation of such high concentrations of condensable species.
The condensable species should be efficiently produced, since they can rapidly (within about one hour intypical atmosphere conditions) condense onto pre-existing
aerosol particles as heterogeneous nucleation (see Fig. 1),
especially in the polluted boundary layers (Kuang et al.,2010; Chen et al., 2012). Thus geographical regions of low
concentration aerosol particles are the most favorable
locations for HN, such as in the boreal coniferous forest
in Helsinki, Finland. The cold upper troposphere also
tends to be a region for new particle formation and growth
due to relatively lower surface area of pre-existing aerosol
particles and low ambient temperature (Heitmann and
Arnold, 1983; Lee et al., 2003). In the boundary layer,
where HNs are strongly suppressed by both primary aero-62

sol emissions and entrainment of particles nucleated in
the free troposphere (Merikanto et al., 2009; Kuang et al.,2010), however, sometimes HN can still occur as an“aerosol burst”, a brief period of rapid production andgrowth lasting a few hours, such as in the polluted Beijing(Yue et al., 2011), Mexico City (Iida et al., 2008; Smith etal., 2008), and Atlanta (Stolzenburg et al., 2005; Kuang etal., 2010; Kuang et al., 2012). During aerosol bursts, as
shown in Jiang et al. (2011c), the clusters measured by thecluster chemical ionization mass spectrometer (Zhao etal., 2010) overlapping in size with the smallest nano condensation nuclei (Jiang et al., 2011b) have provided valuable new information about the mechanism of neutral
nucleation and growth.
The detailed for the importance of producing condensable species has been given in other reviews (Arnold,
2008; Zhang et al., 2012), and case discussions will bepresented in the next section, based on the two essential
controlling processes.

2.3 Charged ratio of nucleated aerosol particles
Information on the electric charge distribution carriedby the nucleating clusters is one key for identifying therelative contributions of neutral and ion-induced processes. Fortunately, measurements of the charged states

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or fractions of ambient freshly nucleated and undergoingparticles have been available to help resolve the main
pathway of aerosol nucleation in the past several years(Kuang et al., 2012). Some studies (see Table 1) basing on
the atmospheric observations have concluded that theoverall contribution of IIN to the atmospheric aerosol formation is typically much less than 10 % (Kulmala et al.,2007; Kulmala et al., 2010) and less than 5 % during summertime events in Boulder (Iida et al., 2006). Responding
to the direct measurement results of the low charged ratios, some simulation studies have explained by assumingthat IIN aerosol particles neutralized and collided during
growth and movement in the environment with ambientions (Yu and Turco, 2011).
Fis. 2 and 3 show the complex processes for charging
and neutralization reactions in the atmosphere, which isactually a quasi “plasma environment” with non-negligible
concentration of ions and reactive species (Rycroft andHarrison, 2012). Ion-ion recombination has been considered as an important channel for IIN (see Fig. 2 and Fig.
3(c)) (Turco et al., 1998; Yu and Turco, 2008). The importance of IIN will be systematically underestimated if allneutral sub-2 nm particles are attributed to a purely NCN.
Due to these complex evolution processes, it is difficult tomake interpretation for the origin of aerosol particles andassessing the role of IIN directly. However, the size dependent evolution of freshly formed aerosol particles can
carry the electrical signature of the nucleation pathway(Gagne et al., 2010; Yu and Turco, 2011). By considering
complex interactions among ions, molecular clusters ofvarious sizes and charges, vapor molecules, and pre-existing aerosol particles, the relatively low charged ratios ofatmospheric observations from state-of-the-art multi-instrument field measurements have been explained in a recent IIN simulation (Yu and Turco, 2011).
A precise estimation for the contribution of IIN is difficult from directly analyzing observational data in theatmosphere. While evidence for the effect of IIN is
accumulating, the exact mechanism is still not known andmore research is required to understand and quantify thiseffect (Enghoff and Svensmark, 2008; Riccobono et al.,2014).

3. Ion-induced aerosol formation in threemost common cases
The most important trace gas for aerosol nucleation has
been thought to be H2SO4 (sometimes including amine,ammonia, or oxidized organic vapors) (Weber et al., 1996;
Chen et al., 2012; Riccobono et al., 2014). These condensable species are favorable to condense onto the pre-­existing aerosol surfaces, especially in the polluted regions(Kuang et al., 2010). Their pre-existing concentrations are

generally too low to drive nucleation and subsequent
growth for a nucleation event in the lower atmosphere if
they cannot be efficiently generated in local regions. Ion
generation should be together with generation of condensable species for IIN (see Fig. 4).
Fig. 4 shows the schematic illustration for the general
nucleation process including ion generation and the induced generation of condensable species. Input energy(such as cosmic rays and lightning) can create a plasma
region, which contains reactive radical species, electromagnetic radiation, and ions. These plasma species further react with trace gas molecules in the atmosphere to
generate condensable species, such as H2SO4, HNO3, andoxidized organic vapors. These locally generated species
and ions, or stabilized by tertiary species such as amine
and NH3, can continue clustering and nucleation.
Basing on the above discussion on the basic processes
and the two essential controlling processes, we will
briefly review three common IIN cases, i.e., cosmic rays,

Fig. 4 Schematic illustration for the nucleation process including ion generation and induced generation of condensable species in the case of low pre-existing aerosol
concentration. The energy input on air results in a local
plasma region containing electromagnetic radiation,
reactive radicals, and primary ions. The radiation andradicals can induce the oxidation-reduction reactions
with trace gas molecules such as organics, SO2, andNOx to be oxidized into condensable species such asoxidized organics (e.g., organic acids), H 2SO4, andHNO3. The generated primary ions react with the oxidation products to form cluster ions, sometimes stabilized by tertiary species such as amine and NH3 (Chenet al., 2012; Riccobono et al., 2014), and then further
grow to aerosol particles by the generated condensable
species, following the kinetic processes for aerosol formation in Fig. 2. A small fraction of these formed
aerosol particles can even grow to cloud droplets, andcontribute to the cloud formation.

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lightning, and aerosol charger. The first two cases are always linked with the kinetic process of cloud formations
and evolutions (Kirkby, 2007), while the third one resulting in nano-scale clusters is a frequently encounteredproblem for aerosol measurements (Jiang et al., 2011b;
Steiner and Reischl, 2012).

3.1 Cosmic ray-induced nucleation
Intensive studies have been conducted on the correlations between cosmic rays and cloud formation. Excellent
reviews have also been published (Carslaw et al., 2002;
Kirkby, 2007; Bazilevskaya et al., 2008; Williams andMareev, 2014). It has been proposed and confirmed that
the Earth’s climate could be affected by changes in cloudiness caused by variations in the intensity of galactic cosmic rays (Nicoll and Harrison, 2010; Harrison et al., 2011;
Rycroft and Harrison, 2012; Bennett and Harrison, 2013).
Two microphysical mechanisms have been proposed to
explain how cosmic rays could affect clouds: i.e., (i) presence of ions from cosmic rays enhances the production ofcloud condensation nuclei (Yu and Turco, 2001; Yu et al.,2008), and (ii) influence of cosmic rays on the global
electric current flowing between the atmosphere and theground surface affects ice nucleation and other cloud
microphysical processes (Tinsley, 2000; Harrison andCarslaw, 2003; Harrison et al., 2011). In the second
mechanism, the current flowing in the global circuit isgenerated by disturbed weather such as lightning (Rycroft
and Harrison, 2012), which will be briefly reviewed in thenext subsection. Corresponding to the first mechanism,
here we only briefly describe the basic process for cosmic
ray-induced nucleation, since this topic is rather complicated and still unclear.
Cosmic rays contain the lowest energies (~107–1010 eV),mainly attributed to solar cosmic rays, intermediate energies (~1010 –1015 eV) of galactic cosmic rays, and the highest energies (>~1015 eV) of extragalactic cosmic rays. The
low energy rays are deflected by the Earth’s and Sun’smagnetic fields, while only high energy rays (>~1 GeV)
can enter the Earth’s atmosphere. When primary cosmic
ray particles (about 90 % are protons) (Beringer, 2012)
enter the atmosphere, collision with air molecules (nuclear
collision) produces a cascade of lighter particles and leads
to a number of chemical reactions, a so-called air shower(see Fig. 5).
Cosmic ray is an ionizing radiation with sufficient energy to knock electrons out of atoms or molecules, andproduces Cherenkov radiation during secondary particles’
travelling. A plasma region is actually produced by theshower particles. In the air shower, the generated photon
radiations of high energy and reactive radicals have highpotentials to effectively oxidize atmospheric trace species, such as organics, SO2, and NOx (Harris et al., 2013;

64

Schobesberger et al., 2013). Some of these oxidized trace
species turn out to be condensable species. Most of thecondensable species will condense onto pre-existing aerosol particles such as in the top region of aerosol sources,
and result in their growth (Mcmurry et al., 2005; Ehn etal., 2014). However, cosmic rays can lead to the aerosol
formation, especially in the middle and upper troposphere(Kirkby et al., 2011). Although no direct connection between cosmic rays and aerosol nucleation was reported(Kulmala et al., 2010), the above basic analysis indicatesthat the air shower of cosmic rays contributes to the formation of global cloud condensation nuclei (Yu et al.,2008; Kirkby et al., 2011).

3.2 Lightning-induced nucleation and oxidation: anatural way to clean our sky
Lightning is one of the most impressive and commonly-experienced geophysical phenomenon. The current related researches have mainly focused on thunderstorm
electrification and lightning formation as meteorologicalphenomena (Paluch and Sartor, 1973a, b; Bazeli︠a︡ n and
RaĭZer, 2000; Williams, 2005; Williams and Mareev,2014). The electrification of cumulonimbus clouds has
been proposed to be charged by three mechanisms, i.e.,(i) diffusion of air ions to attach onto cloud droplets(Millikan R.A., 1911), (ii) polarization (similar as Fig. 3)(Frenkel J., 1944), and (iii) convective motion (Gish andWait, 1950). In general, the collisions between cloud
droplets transfer negative charge to larger particles, whichthen separate gravitationally to produce intense largescale electric dipoles, carrying negative charge downwards and positive charge upwards (Kirkby, 2007). The
lightning occurring in cloud-to-ground is the main pathway for the carried charges to transfer between clouds
and ground (see Fig. 6). Lots of interesting works havebeen done on the lightning activities and its contribution
to the global atmospheric electric circuit operation. Excellent reviews on this topic have also been reported (Kirkby,
2007; Rycroft and Harrison, 2012; Rakov, 2013; Williams
and Mareev, 2014). However, the most basic questions of
how lightning is initiated, how it then propagates formany tens of kilometers, and what’s effects on atmospheric aerosol particles have only begun to be addressed(Pawar et al., 2011; Dwyer and Uman, 2014). The following discussion is some like a hypothesized process due torare reports on the direct observation of lightning-induced
nucleation.
Fig. 6 shows a schematic illustration for a simplified
lightning process and its-induced nucleation and oxidation reactions. The example shows a descending lightning
with negatively-charged leaders outgrowing from a cloudtoward the ground, though active lightning can be very
complicated (Stolzenburg and Marshall, 2008). The elec-

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Fig. 5

Schematic illustration for cosmic rays-induced aerosol formation via generating ions and condensable species.
Primary cosmic rays interact with air molecules in the atmosphere to create secondary particles, including γ-rays,
leptons (such as muons), and hadrons (such as proton and mesons). These secondary particles continue to collidewith air molecules to produce a cascade of lighter particles and decay to stable particles such as protons and electrons, thereby producing “primary ions”. Ion-pair production rates are about 2 numbers·cm-3·s-1 at ground level,
10 numbers·cm –3·s –1 at 5 km altitude, and 20–50 numbers·cm –3·s –1 at 15 km altitude (Volland, 1995). Secondary
particles can travel faster than the speed of light in air with causing “Cherenkov radiation”, which can effectively
oxidize the atmospheric trace species, such as organics, SO2, and NOx. Thus cosmic rays can induce nucleation
by simultaneously generating ions and condensable species. However, IIN is very hard to occur if the concentration of pre-existing aerosol particles is very high to consume almost all the generated condensable species(Mcmurry et al., 2005).

tric fields of these leader heads are the source of the runaway electrons that produce ultra-violet radiation (Dwyer
et al., 2005). When the leader header contacts their induced electron avalanches (Li et al., 2011), current andluminosity waves transfer through the leader channel.
These self-sustained processes complete the leader step
process (Dwyer and Uman, 2014). Regions of surrounding
leader heads and ionization channels are filled with generated ions (including electrons) and reactive radicals
with high concentrations. These species can effectively
oxidize atmospheric trace gases such as organics, SO2,
and NOx. Since the initiation and dissipation stage oflightning is under some IIN favorable atmospheric conditions, like low temperature, high relative humidity, highion production rate, and sometimes low concentration ofpre-existing aerosol particles (such as after a heavy rainfall) (Pawar et al., 2011). These generated condensable

species and primary ions can result in further condensation to droplets or IINs, depending on the pre-existing
aerosol concentration, or even both processes occur simultaneously.
As discussed above, the lightning may serve as an effective and natural way to oxidize and remove atmospheric trace gases such as organics, SO2, and NOx. The
condensation and nucleated species will be scavenged to
the ground by rains and removed from the sky. Thuslightning is a natural way to clean our sky through induced oxidation, condensation, and nucleation.

3.3 Aerosol charger-induced clustering andnucleation: a limitation for nanoparticle sizing
Charging of nanoparticles by aerosol chargers is an important process in aerosol particle sizing and electrical65

Qing Li et al. / KONA Powder and Particle Journal No. 32 (2015) 57–74

Fig. 6

Schematic illustration for lightning-induced nucleation and oxidation, an example of a descending lightning withnegatively-charged leaders outgrowing from a cloud toward the ground. The leader heads (shadowed in rosinesscolor) represent the initiation and growth of conductive plasma channels for lightning occurring in intra-cloud,inter-cloud, cloud to air, and cloud to ground (Bazeli︠a︡ n and RaĭZer, 2000). Intensive ionizations, changing theneutral air to highly conductive plasmas, occur in the surrounding of the leader heads, which carry its own strong
electric field induced by the space charge (Li et al., 2011). High concentrations of ions, electrons, reactive radicals, and electromagnetic radiation are created in the proximity of the leader heads and in the plasma channels(Dwyer et al., 2005; Li et al., 2011). These generated species can induce the oxidation of the atmospheric tracegases, and further induce the condensation of oxidized species to droplets or IINs, depending on the pre-existing
aerosol concentration (Mcmurry et al., 2005; Yu and Turco, 2011).

mobility analysis (Jiang et al., 2011a; Jiang et al., 2014).
The objective of aerosol chargers (also called as neutralizers) is to impose a known stationary state charge distribution on incoming aerosol particles. Charging efficiency of
nano-scale aerosol particles (especially for diameter between sub-1 nm and 3 nm) is the major limitation for theiraccurate measurements (Mcmurry, 2000; Jiang et al.,2011b; Kulmala et al., 2012), which are required to understand these particles’ property and dynamics (Intra andTippayawong, 2011). The precise determination of thesesmall particles is especially important for fundamental
investigations of nucleation and subsequently growth in
the atmosphere (Winkler et al., 2008).
Charging mechanisms for nanoparticles include diffusion, photoionization, and thermionization (Jiang et al.,2007a; Jiang et al., 2007b). Basing on these mechanisms,
various types of chargers have been developed such as byusing corona discharges (Pui et al., 1988; Romay et al.,
1994; Hernandez-Sierra et al., 2003; Qi et al., 2007; Li L.
and Chen, 2011), surface-discharge micro-plasmas (Kwonet al., 2006; Manirakiza et al., 2013), ionizing radioactive
sources (Adachi et al., 1985; Chen and Pui, 1999), photoelectric effect of UV-lights (Burtscher et al., 1982; Shimada
et al., 1999), and soft X-rays (Shimada et al., 2002; Han etal., 2003; Lee et al., 2005). The basic principle is that66

these ionization sources emit energetic particles, such asα, electrons, and photons, to generate plasmas. The plasmas contain ions, electrons, light radiation, and reactive
radicals (such as ·O and ·OH). In addition to charging theincoming aerosols with electrons and ions, the radiation
and radicals can oxidize trace components (such as SO2
and organics) of the incoming gases, and further induce
the formation of charged clusters (possible neutral clusters in partially) or aerosol particles, which partially condense onto the incoming aerosol particles and partiallysurvive a long time in the downstream of the aerosol
flows (see Fig. 7). The condensation may change the sizeand property of the incoming aerosol particles. The survival clusters and nucleated aerosol particles can cause anerror in aerosol nanoparticle counting by using a differential mobility analyzer (Winklmayr et al., 1990; Romay etal., 1994; Yun et al., 2009; Jiang et al., 2011b), while thecondensation onto the incoming aerosol particles can cause
an error in analyzing aerosol size distribution and property.
The ion-induced clustering in the chargers has been
investigated in cases of corona based charger (Romay etal., 1994), radioactivity based 241Am charger (Luts et al.,2011; Steiner and Reischl, 2012; Steiner et al., 2014), andsoft X-ray photoionization based charger (Yun et al.,2009). Though not reported, in principle induced cluster-Qing Li et al. / KONA Powder and Particle Journal No. 32 (2015) 57–74

Fig. 7

Schematic illustration for possible mechanism of charger-induced clustering and nucleation processes in aerosol
chargers with different ionization sources such as corona discharges, surface-discharge micro-plasmas, radioactive sources, UV-light radiations, and soft X-ray sources. Energetic particles (such as α, electrons, or photons)
emitted from ionization sources generate plasmas inside charger chambers. The plasmas can induce complex
kinetic processes (see Fig. 4). The trace components (such as organics and SO2) of the incoming gases can beoxidized by light radiation and reactive radicals to be condensable species, which can form charged clusters (andpossible neutral ones). Since the residence time in the charger is always much shorter (commonly less than 1 minute) than the stable aerosol formation time (~1 h or more time) in direct atmospheric observations (Spracklen etal., 2008; Kulmala et al., 2013). The formed clusters partially condense onto the incoming aerosol particles, andpartially survive for a long time in the downstream. The ratio of the survived clusters depends on the residence
time (Yun et al., 2009) and pre-existing aerosol concentration (Kuang et al., 2010).

ing or nucleation may also occur in surface-discharge
micro-plasma based chargers.
As an example, Fig. 8 shows a typical induced-clustering
effect of a laboratory soft X-ray charger (model 3087,
TSI Inc.). The incoming ambient air (summer in Beijing)
was filtered by a high efficiency particulate air (HEPA)
filter. There were no aerosol particles and clusters observed at the outlet of the charger when soft X-ray was
turned off. However, cluster ions (both positive and negative ones) with mass diameters between 0.36–1.6 nm (seeFig. 2) existed in the outgoing gas of the charger whensoft X-ray was turned on. The result is consistent with the
kinetic processes of generating charged clusters, as shownin Fig. 7. The clustering effect is sensitive to the residence
time and concentration of the aerosol particles in the neutralizers (Yun et al., 2009). However, basing on the clustering and nucleation effect, the charger is a ready-madeinstrument for investigating IIN in atmospheric condi-

tions, such as a simulated system of SO2/H2O/N2 gas
mixture irradiated by soft X-ray (Munir et al., 2010,2013).
The charger-induced clustering or nucleation is an obstacle for nanoparticle sizing, especially for analyzing
sub-2 nm aerosol particles and for sizing aerosols in fluegases which often contain relatively high concentration of
trace gas species. This effect results in challenges forcharging ultrafine particles and for monitoring the newparticle formation process in the ambient air.

4. Summary
The kinetic processes of ion-induced clustering andaerosol formation are briefly reviewed in the viewpointof plasma physics. The current debates on the IIN con­tribution to nucleation events observed in the lower tro­67

Qing Li et al. / KONA Powder and Particle Journal No. 32 (2015) 57–74(41227805 & 21190054 & 21221004 & 21422703) are acknowledged.

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a common ion source mostly used in aerosol studies. The
charger-induced clustering (or nucleation) leads to challenges for charging ultrafine particles and monitoring newparticle formation events in the atmosphere.

Acknowledgements
We thank Mr. Jianguo Deng and Mr. Runlong Cai from
Tsinghua University for experimental support. Q. Li also
gratefully thank Prof. Kikuo Okuyama from Hiroshima
University (Japan) and Prof. Da-Ren Chen from Virginia
Commonwealth University (USA) for their helpful discussions. Financial supports from the National Key Basic Research and Development Program of China (2013CB228505)
and National Natural Science Foundation of China

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Author’s short biography
Qing Li
Qing Li is currently a postdoctoral researcher in School of Environment at Tsinghua
University. He received his B.Sc in theoretical physics from Lanzhou University, M.Sc
in fluid science from Tohoku University, and Ph.D in plasma science from Tsinghua University. His current research interests are characterizing aerosol nucleation and particle
emissions from stationary sources as well as plasma technology.

Jingkun Jiang
Jingkun Jiang is currently an associate professor in School of Environment at Tsinghua University. He received his B.Sc and M.Sc in environmental science and engineeringfrom Tsinghua University, and Ph.D in aerosol science and technology from Washington
University in St. Louis. Prior to joining Tsinghua, he did postdoctoral research in Particle Technology Laboratory at University of Minnesota. His current research interests areaerosol formation mechanism, aerosol instrumentation, and particle control technology.

Jiming Hao
Jiming Hao is currently a professor in School of Environment at Tsinghua University
and a member of Chinese Academy of Engineering. He received his B.Sc in civil engineering and M.Sc in environmental engineering from Tsinghua University, and Ph.D inenvironmental engineering from University of Cincinnati. His main research area isenergy and environment, including acid rain prevention, vehicle emission control, andregional air quality management.

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Review Paper

Powder-based Additive Manufacturing for Development of
Tailor-made Implants for Orthopedic Applications †Takayoshi Nakano * and Takuya Ishimoto
1

Division of Materials and Manufacturing Science, Graduate School of Engineering, Osaka University, Japan

Abstract
Powder-based additive manufacturing (AM) technology has increasingly attracted attention because it realizes
fabrication of arbitral three-dimensional structures with a great degree of freedom at high speed, which is almost
impossible using conventional manufacturing tools. In early times, AM was known as a rapid prototyping (RP)
which merely aimed at a trial prototyping of products mainly from resin materials. Today, thanks to the progressin the heat source, optical system, raw material production, etc., the expected role of AM has dramaticallychanged into a direct fabrication of final or near-final products made from a variety of materials from resins toceramics, metals, and intermetallic compounds. Because the powder-based AM is a bottom-up manufacturingmethod, it offers a big advantage for the small-lot production of a wide range of products; therefore, tailor-madeproduct portfolio is a potent target of AM. In this review paper, the advantage and possibility of AM in thedevelopment of functional metallic biomaterials for orthopedic usage is described. Medical treatments need to beflexibly done in accordance with individual pathological conditions and constitutions, therefore, the function of a
medical device should be tunable, which is quite a strength of AM.
Keywords: additive manufacturing (AM), powder bed fusion, bone implant, tailor-made, apatite

1. Introduction
Additive manufacturing (AM) technology could knock
the bottom out of the design concepts of the Monozukuri(that denotes manufacturing) in Japan, where it is a
specialty. However, despite the fact that the first principles
of AM were demonstrated in Japan in the early 1980s(Kodama, 1981), research and technical development inthis field is now, ironically, lagging far behind that inother places such as Europe and the U.S. Recognizing thistrend, the Japanese government has embarked on across-ministry program, which is centered around theMinistry of Economy, Trade and Industry, with the aim ofsupporting AM technology for materials ranging fromresins and gels to ceramics and high-melting-point metals
using 3D printers.
AM technology is used to create products of any shapeusing a near net shape process based on computer aided
design (CAD) data, and can be thought of as a system thatencompasses all aspects from the design concept and thento the most advanced processing work. As a result, AM is†
1*

Received 18 September 2014; Accepted 6 October 2014
J-STAGE online 28 February 2015
2-1 Yamada-oka, Suita, Osaka 565-0871 Japan
Corresponding author: Takayoshi Nakano;E-mail: nakano@mat.eng.osaka-u.ac.jp
TEL/FAX: +81-6-6879-7505©2015 Hosokawa Powder Technology Foundation

able to provide products that could create new markets,and the new business models and business opportunities.
However, as AM is a moldless technique, it could also
greatly change the conventional Monozukuri landscape,and as such there is a concern within certain industries
that the use of particular industrial processes could decline when AM transforms the structure of the manufacturing industry.
The emergence of AM technology will change Monozukuri from mass production of a narrow range of
products to the small-lot production of a wide range of
products. This change is expected to foster the advancement of fields concerned with individuality, including
customization and design, and fields that require complex
shapes using highly responsive materials. For example, in
medical fields involved with creating organs of different
shapes and sizes on an individual basis, tailor-made medical care that is capable of achieving the optimal shape andoptimal function for each patient is a priority. Additionally, technology that contributes greatly to a wide variety
of fields such as food, transport, machinery materials, andtailor-made home electronics is also anticipated.
In this paper we describe the current state of researchand development using AM technology pertaining tometal bio-functional materials, focusing on the area of orthopedic surgery.

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2. Types of additive manufacturing
According to ASTM F2792-12a (2009), AM manufacturing is classified into the following seven disciplines: (1)powder bed fusion, (2) binder jetting, (3) directed energy
deposition, (4) sheet lamination, (5) vat photopolymerization, (6) material extrusion, and (7) material jetting.
When divided according to the material used, AM is classified into areas such as: (1) metal AM, (2) ceramic AM,(3) resin AM, (4) gel AM, (5) composite AM, and (6)bio-3D printers (cell 3D printers). A feature of AM technology is that only the portions required are selectively
solidified. One method produces a structure from metal
raw powders by irradiating a laser or electron beam as aheat source. One method produces a structure from photocurable resin, via photopolymerization using light containing UV. Another method creates a ceramic or metal
product by dispersing ceramic or metal powder in resin,fabricating it into any desired shape, and then removing
the resin by heat treatment. These methods can be thoughtof as new fabrication processes that contrast with cuttingwork.
In the early AM era, known as rapid prototyping (RP),AM technology did not extend beyond the design of models using resin. Although resin is relatively easy to use,
the range of AM applications has now expanded to include ceramics and metals, which have high melting
points and are difficult to prototype. Since ceramics andmetals can be employed for the actual structural material
parts, the value of AM technology has undoubtedly increased through its expanded application with such materials. Bio-3D printers are a type of AM technology thathas recently received considerable attention for use in layering cells three-dimensionally. Until now, cells were
mostly cultured two-dimensionally, but to exhibit thefunctions of true organs they need to be layered in three
dimensions with intercellular connections and an extracellular matrix between them (Matsusaki et al., 2013).
Although it will take time before bio-3D printers are usedin vivo as a regenerative medicine technique, it is anticipated that they can be used in ex vivo applications, suchas creating mini-organs to assist in drug design and to assess toxicity and efficacy of cosmetics. Most importantly,
since it is becoming possible to create blood vessels andarrange multiple cells, the fabrication of artificial organs
is now becoming a reality.

3. Summary of metal additive manufacturingmethods (powder bed fusion)
Powder bed fusion, which uses an electron beam or laser beam as a heat source and a metal powder as a starting
material, has gained attention as a metal AM technique,76

and ways of controlling the shape and microstructural parameters of metal materials has been widely studied.
Directed energy deposition is a technique (developed
primarily in Europe) that is used as a method for inserting
metal powder into a laser beam heat source. Both techniques are capable of fabricating any shape by selectively
melting and solidifying metal materials, but the former
technique laminates metal powder and then selectively
melts and solidifies it, whereas the latter method deposits
molten metal at required locations.
Fig. 1 is a schematic diagram illustrating the processused in fabricating a metal product with a lattice structure
by powder bed fusion. In this technique, an electron beam(or laser beam) is scanned based on 2D slice data createdfrom 3D CAD data of the final form, and by repeating theprocess of selectively melting and solidifying one layer ofmetal powder at a time, multiple layers are stacked up to
produce a three-dimensional structure (Murr et al., 2009).
Fig. 2 shows raw metal (Ti-6Al-4V) powders utilized for
powder bed fusion using an electron (a) and laser (b)beam as a heat source. Generally, comparing to the laser
beam, the electron beam generates a larger molten poolbecause of higher energy density (Guo and Leu, 2013),
therefore, a large lamination thickness (~0.1 mm) can beused. Accordingly, raw powder particle used is larger inthe case of usage of the electron beam. The surface precision is influenced by the powder particle size.
Three-dimensional metal AM using powder bed fusion
is capable of creating high-precision surface shape designs
in addition to the structures and porous bodies of complex
shapes, which is difficult to achieve using conventional
casting and cutting work techniques. Fig. 1(f) shows ametal structure fabricated using the powder bed fusion.
This was designed in CAD, and is precisely fabricatedwith 15-mm side lengths and 3-mm lattice spacing. With
metal materials, the control of mechanical functions is
possible from the viewpoints of both structure and materials because the hierarchical microstructure can be controlled in addition to the macroscopic shape.

4. Possible medical applications of additive
manufacturing
The major factors involved in the remarkable adoption of
AM technology are the generalized use of CAD (due toits ease of use), and the formation of networks capable oftransmitting digital data at high-speeds. Three-dimensional
design systems that employ 3D CAD modeling can also
be used in AM devices at remote sites by converting to an
STL format, which is a mode that approximates a 3D freeform surface as a composite of triangular elements.
AM has the following advantages over the productionof 3D structures using conventional mechanical process-Takayoshi Nakano et al. / KONA Powder and Particle Journal No. 32 (2015) 75–84

Fig. 1

Schematic diagram illustrating the powder bed fusion method used in fabricating a metallic product witha lattice structure. Reproduced from Ikeo et al. (2014) with modifications.

ing with cutting tools: (1) it can fabricate 3D shapes with
free-form surfaces and complex internal structures thatare difficult to produce using cutting work; (2) as it is asemi-automated process, specialized knowledge andtraining are not required to operate the machinery; (3) it
is able to produce a desired structure economically and ina short period of time; (4) it does not cause tool wear, generate noise or vibration, or produce shavings; and (5) ahigh percentage of the materials used can be recycled.
These advantages make it tremendously effective in thecustomization of medical devices, and there have been
numerous attempts by various countries (including Japan)
to develop biomaterials using AM technology. For example, 3D bone data obtained from magnetic resonance imaging (MRI) or computed tomography (CT) can be used
to design an implant shape in CAD that fits the bones of apatient, and to actually produce such a device using AM
technology. Novel attempts at using such technology havebeen examined for bone replacement devices such as artificial hip joints including the acetabular cup and head
(Marin et al., 2010), total knee replacement implants(Murr et al., 2011), and for skull bones and jawbones(Parthasarathy et al., 2011; Jardini et al., 2014; Nickels,
2012). Another strength of AM technology, as described
below, is the ability to hierarchically control (Zhang et al.,2013) not only the exterior shape but also the internal
structure, including the porous structure.
Although concerns have been voiced about cited drawbacks, such as the small amounts of voids remaining incompacted portions, and the degradation of fatigue char-

Fig. 2Starting raw metal (Ti-6Al-4V) powders utilized for
powder bed fusion using (a) an electron and (b) laser
beam as a heat source.

acteristics, compaction can be achieved by the optimization of fabrication conditions together with the use of hot
isostatic pressing (HIP) after fabricating (Ye et al., 2010),
and mechanical properties exceeding those of conventional cast materials can be obtained.

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5. Creation of artificial hip joint fit to
medullary cavity
The greatest advantage of metal AM technology is thatit can provide tailor-made implants that conform to thebone shape of individual patients. Because most implants
currently produced are designed based on an averagebone shape, they do not necessarily fit everyone, andtherefore for most patients the bone needs to be cut tomatch the shape of the implant. Although it is possible touse precision processing to customize the implant shapefor a patient, it is rarely worth the cost. In contrast, metal
AM enables fabricating using a near net shape process
based on 3D CAD data, even for complex shapes withmany free-form surfaces, and is extremely effective as amanufacturing process of products that conform to thebone shape of individual patients.
Fig. 3 shows an artificial hip joint fabricated by electron beam melting (EBM) and customized to conform to
the femoral medullary cavity. Both the exterior shape anda porous structure enabling bone ingrowth can also be
formed simultaneously on the surface at specified locations. Guidelines on AM implants are currently being

Fig. 378

A model of a tailor-made artificial hip joint fabricatedby electron beam melting (EBM). The exterior shape ofthe stem is designed to fit to marrow cavity. A porous
structure on the stem surface enabling bone ingrowth
can be fabricated by one-step EBM process.

created under the guidance of the Japanese Ministry of
Health, Labour and Welfare (NIHS, 2013) , and it is considered that implants customized by AM will be commercially available in the near future.

6. Creation of biomimetic hierarchicalstructure using powder metal
When powder bed fusion is used, the layered metal
powder is partially melted and solidified. Therefore, withthe exception of completely solid structures, a residue ofthe starting metal powder is generated inside the structure.
Normally the powder is removed and only the solid part isextracted as a product, but as the structure becomes more
complex and compacted, and as the proportion of the periphery covered with the solid part becomes greater, themore difficult it becomes to extract the unmelted powder.
With AM, however, it is possible to modify the preconceived notion of removing unnecessary metal powder andusing only the structural framework, and to actually utilize the powder that would previously have been discarded(Nakano et al., 2011a). This conceptual transformation
can be used to make it possible to achieve a hierarchicalstructure and to impart novel mechanical functionality.
Hierarchical structures are seen throughout the natural
world, and these hierarchical organizations and structuresorganically unite and function on various scales. Bonetissue and plants are both typical examples of hierarchicalstructures (Bar-On and Wagner, 2013). With EBM, it ispossible to design a structure that contains a small
amount of starting powder at an order of tens of microns.
The fabricated beam (or wall structures) formed by melting this powder are then several hundred microns in size,
and the exterior shape measures several mm to several
cm, or greater. As a result, it is possible to construct a hierarchical structure in which structural elements on different scales work together both structurally andfunctionally.
For example, as shown in Fig. 4(a), a product was created by neck formation between powders that had a low
Young’s modulus and an energy absorbency similar tobone. This was achieved by confining metal powder witha particle volume fraction of approximately 50 % in the
structure, and then performing appropriate heat treatment. In addition, in an attempt to make a bone mimetic
structure, a structure with uni-directional pores was designed to exhibit the uniaxial anisotropy of the mechanical function possessed by long bones (Bonfield andGrynpas, 1977).
Fig. 4(b) shows stress-strain curves of the products inwhich an unmelted powder is packed fabricated by EBM.
The product without heat treatment (as-EBM) exhibits alow energy absorptive capacity and low Young’s modulus.

Takayoshi Nakano et al. / KONA Powder and Particle Journal No. 32 (2015) 75–84

Fig. 4(a) Schematic illustration of an EBM fabricated product in which an unmelted powder is remained andphotograph of the powder part after heat-treated. (b) Nominal stress-nominal strain curves of the products with and without heat treatment that makes powders necked. The product with the necked powder
inside becomes highly energy absorptive due to the stress transmission between powders through thenecks. Reproduced from Nakano et al. (2011a) with modifications.

However, the heat treatment below the α/β phase transformation temperature promotes the necking of unmelted residual powder, which results in the great improvement ofenergy absorptive capacity evidenced by the emergenceof a plateau in the stress-strain curve.
The powder part of as-EBM material behaves likespace and does not affect mechanical properties. However, the necking achieved between powders enables control of the Young’s modulus and the ability to impart anenergy absorptive capacity. The deformation behavior of
the structure differs greatly depending on the neck-size
ratio. In this case, the threshold value of plateau emergence is approximately 0.25. However, if the neck-size ratio is increased to approximately 0.5, compaction of thepowder part progresses, resulting in the disappearance ofthe plateau. Neck-size ratio is governed by volume diffusion, and the heat treatment temperature is its controlling
factor. Therefore, the mechanical function of a structure
depends on the construction of a network between powders that occurs with an increased heat treatment temperature. Bone mimetic structures fabricated in thismanner, with low Young’s moduli and high energy absorptive capacities, are called powder/solid (P/S) composites, and this method is expected to become the new wayof using powder bed fusion (Nakano et al., 2011a).

7. Creation of a bone replacement porous
structure with a low Youngʼs modulus
The appeal of using a titanium alloy as a biomaterial isrelated to its relatively low Young’s modulus (which is

close to that of living bone), high biocompatibility
through osteointegration (by which it adheres directly tobone on a level seen by optical microscope), and its osteoconductivity. Bone replacement materials using titanium
alloys are widely used, and a variety of products that aredesigned using EBM are now beginning to use clinically(Unwin, 2014).
Titanium alloys have a lower Young’s modulus than
general metal materials, but still exhibit a higher value
than the 20 to 30 GPa of long bones. For example, analloy of Ti-6Al-4V has a Young’s modulus of 110 GPa. As
a result, implanting this alloy in bone produces stressshielding, which hinders normal stress transfer to osteocytes (OCYs) as mechanosensors in bone, thereby leading
to reduced bone mass, reduced bone mineral density(BMD), and reduced bone quality around the implant(Noyama et al., 2012).
Reducing the Young’s modulus of a structure by introducing pores is an important technique (Ikeo et al., 2014).
To succeed in reducing the Young’s modulus with thegoal of reducing stress shielding, control of mechanicalfunction focused on pore formation into the structure using various techniques has been attempted. However,since nearly all conventional methods aim at making porous structure using a self-assembly reaction, it is difficult
to impart a certain designed pore diameter or porosity, letalone desired pore configuration. In addition, althoughpore formation by self-assembly can control the internal
structure to a certain extent, it is unable to customize theshape.
However, using AM technology it is possible to freely
control the macroscopic exterior shape using a 3D shape79Takayoshi Nakano et al. / KONA Powder and Particle Journal No. 32 (2015) 75–84

design on a PC. Additionally, simultaneous control of theexterior shape and the internal hierarchical structure ispossible by controlling the irradiation conditions of theheat source beam. Based on this concept, 3D porous
structures with any desired pore diameter and porosity
can be fabricated using layer-by-layer additive manufacturing (Ikeo et al., 2012).
Fig. 5 is a photograph of a structure with uni-directional
pores fabricated by powder bed fusion using an electron
beam as a heat source. By varying the scan interval of theelectron beam, a structure can be produced in which thelattice spacing is controlled. Porosity can be continuously
varied, even in cell structures and the lattice structures ofmore complex 3D shapes (Murr et al., 2012). In such
structures, the Young’s modulus can be reduced by the required amount by reducing the cross-sectional area ratio
while taking fatigue characteristics into consideration. In
addition, the bending strength can be maintained by designing a shape with consideration of the second momentof inertia with respect to a bending moment. Furthermore,by matching the anisotropy of the bone microstructure(collagen/apatite) (Nakano et al., 2002), it is also possible
to control osteoconductivity.

Fig. 5Photograph of porous products fabricated by EBM withvarious-sized uni-directional pores.

Fig. 680

8. Creation of osteoconductive porous metal
implant
In producing a porous metal implant, it is possible toartificially fabricate a porous structure at an order of several hundred microns and above. Pores of the smallest
formable size promote entry of osteoblasts into the structure, and therefore production of a new bone matrix.
Pores on a larger scale function as scaffolds for osteoblasts, enabling free design and fabrication of the exterior
shape, and thus may allow for the creation of tailor-madeimplants suited to the bone shapes and bone defect sizesof individual patients.
Fig. 6 illustrates the appearance of a porous cylindricalmetal implant made of Ti-6Al-4V by EBM (powder bed
fusion) after being implanted for 24 weeks in a rabbit ulna
defect site (Nakano et al., 2011b). The implant was tailor-­
made according to a CAD design, and has a cross-­
sectional diameter of 8 mm ϕ and various lengths of 10 mm,
15 mm, and 20 mm (according to the varying bone defectsizes) (Fig. 6(a)). Fabrication of this porous metal cylindrical implant is not possible without using a 3D AM. Its
design criteria were: (1) to have an apparent Young’s
modulus similar to that of cortical bone, (2) the ability tofunction as a scaffold for constructing the preferential
orientation of collagen/apatite, so that osteoblasts are arranged and migrate preferentially along the long axis of
the bone, (3) creation of a structure that stimulates theformation of the medullary cavity for maintaining hematopoietic function; and (4) creation of a bone implant witha tailor-made exterior shape that conforms to the bone
defect size.
As shown in Figs. 6(b) and (c), the new bone formation
and bone fusion is dependent on whether or not the porous(a) Appearance of porous cylindrical metal implants made of Ti-6Al-4V by EBM. Regeneration of a 10mm bone defect created in a rabbit ulna (b) with and (c) without the cylindrical implant for 24 weeks. (d)Regenerated bone in the pores which forms the tubular structure at the center of the implant, and (d)marrow formed in the tubular bone, indicating regeneration of biological function of hematopoiesis.
Reproduced from Nakano et al. (2011b) with modifications.

Takayoshi Nakano et al. / KONA Powder and Particle Journal No. 32 (2015) 75–84

cylindrical metal implant is inserted. The bone matrix isseen to exhibit a uni-directional orientation of the collagen/apatite crystals along the long axis of the bone, resembling normal bone. Additionally, formation of a
medullary cavity is also seen inside the porous body, andthe porous implant is utilized without hindering bonefunctions such as sustaining mesenchymal stem cells andmaintaining hematopoietic function (Fig. 6(e)). Because
this metal porous implant uses a titanium alloy powder
with a relatively large particle size (averaging approximately 80 μm as shown in Fig. 2(a)) and is formed by
EBM, it has a relatively coarse surface. However, it has
also been reported that such a coarse surface structureleads to promotion of calcification and osteointegration(Alla et al., 2011), and as such this is a major factor thatneeds controlling in AM.

Fig. 7

9. Imparting osteoconductivity by controlling
implant surface shape
Designing the interface between living tissue and an
implant is a critical factor in extracting the maximal functionality of living tissue. The bone/implant interface isno exception. A porous coating or a roughened surface isoften introduced onto the medial part of an artificial hip
joint to provide an anchor effect. However, bone mass,
BMD, and the bone quality including collagen/apatite
crystal orientation varies depending on the OCYs’
stress-sensing (Nakano et al., 2012a). For this reason,
when constructing an implant surface intended to provide
just a simple anchor effect, loosening sometimes occurs at
the interface. Here, note that bone quality as referred to inthis study is related to factors that determine bonestrength other than BMD and bone mass (NIH consensus
2000, 2001).
In short, the surface shape of an implant is critical forearly fixation after implantation, and the long-term main-(a) An artificial hip joint (stem) for beagle with oriented grooves on the proximal surface and (b) simulated principal stress in the grooves by finite element analysis (FEA). The bone with oriented apatite in
the groove depth direction is formed inside the groove fabricated by (c) machining and (d) EBM method.
Reproduced from Noyama et al. (2013) with modifications.

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tenance of bone quality. Developing an implant that actively promotes stress transfer to the bone, rather than
producing stress shielding also enables promotion ofsound bone microstructure, in addition to avoiding thefunctional degradation of the bone after implantation(Noyama et al., 2013). Introduction of oriented pores andgrooves on the implant surface along the principal stressdirection which provides a space that delivers the optimal
environment for bone cells, could serve as an effectivemeans, since the collagen/apatite orientation of bone(which is an index of bone quality) is constructed parallelto the maximum principal stress vector (Nakano et al.,2002). It is of note that in many cementless artificial hip
joints, although the porous coatings are introduced onto
the proximal portions, stress shielding and subsequentdegradation of bone mechanical function is inevitable(Noyama et al., 2012) because the conventional porous
coatings consist of random pores that do not achieve ideal
stress distribution to promote stress transfer and formation of new bone with oriented microstructure similar tohealthy bone.
Fig. 7 shows a titanium alloy artificial hip joint for abeagle dog, into which oriented grooves were introduced
using cutting work or AM. When this type of hip joint
implant with oriented grooves was implanted for 24
weeks (cutting work) or 4 weeks (AM) in the beagle femur, the greatest amount of new bone was seen inside theoriented grooves running along the principal stress vector(60°) (Noyama et al., 2013). The distribution of apatite
crystal orientation in the oriented grooves (as analyzed
using microbeam X-ray diffraction with a transmission
optical system) is expressed as a radar diagram, whichshows that its preferential orientation runs along the oriented groove walls parallel to the principal stress vector.
This means that apatite/collagen creates a uni-directional
orientation along the oriented grooves, and therefore thelong-term fixation of the implant is expected.
Even if cytokines are used to stimulate bone regeneration, the recovery of collagen/apatite orientation as anindex of bone quality is very slow with respect to BMD
recovery, and therefore a long time is required for recovery of the bone’s mechanical function (Nakano et al.,2012b; Ishimoto et al., 2013). Osteoblasts can be aligned
and an oriented bone matrix resembling intact bone canbe produced using anisotropic steps on the metal surface(Matsugaki et al., 2012), or by application of uni-directional
stress in vitro (Matsugaki et al., 2013), which is governed
through a certain mechanisms in which cells, molecules,
and genes are involved in. Improving the bone matrix
surrounding the implant by leveraging the bone orientation mechanism is very useful for enabling long-term fixation of implants.
Metal AM also makes it possible to fabricate complex
surface shapes, and construction of the preferential orien-

82

tation of apatite in bone has been seen where oriented
grooves were formed in the direction of the principal
stress vector. Formation of the oriented bone matrix parallel to the principal stress vector (wall direction of oriented grooves) has been seen, similar to the oriented
grooves formed by cutting work. However, although surface roughness is greater than when using cutting work,oriented grooves on the order of several hundred microns
can be introduced by direct metal fabrication using EBM(Fig. 7(d)). In short, a complex implant surface shape designed in consideration of the stress response function ofliving bone can be realized. It is known that the form andmicrostructure of living bone is primarily controlled in
the direction of loading through bone’s functional adaptation capacity (Tsubota et al., 2009). Therefore, it is important in the development of bio-functional metal
implant devices, to optimize their external shape and internal microstructure for aiming at controlling in vivo
principal stress distribution and inducing the healthy bone
tissue possessing the anisotropic microstructure. AM
technology utilizing powders is one of the most promisingtools for making medical implant devices that are morphologically and functionally tunable based on the individual patient’s conditions.

10. Conclusion
Because individuals have unique bone shapes and bonematrix characteristics, customization of implants is necessary for tailor-made medical care. With AM technology,
the realization of ideas for achieving bone functionalization is possible by designing surface shapes, reducing theYoung’s modulus of the implant (by making a powder/solid composite for limiting stress shielding), as well as
guiding cell migration to make the bone matrix sound.
Using a combination of cell printing and regenerative
medicine it is expected that the importance of such customization will be increasingly realized. Additionally,
AM technology that enables the small-lot production of awide range of products can also be applied to transportmachinery materials, home electronics, and so forth. In
an era that calls for de-individuation, AM technology capable of fabricating individual custom products holds infinite possibilities, and can also create a potential impetus
for new market developments in the pursuit of high added
value.

Acknowledgement
This work was supported by the Grants-in-Aid for Scientific Research from the Japan Society for Promotion of
Science (JSPS) and the Funds for integrated promotion of

Takayoshi Nakano et al. / KONA Powder and Particle Journal No. 32 (2015) 75–84

social system reform and research and development, Support Program to break the bottlenecks at R&D System foraccelerating the practical use of Health Research Outcome, from the Japan Science and Technology Agency(JST).

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Author’s short biography
Takayoshi Nakano
Takayoshi Nakano is a full professor in Division of Materials & Manufacturing
Science, Graduate School of Engineering at Osaka University. He received his Ph.D.
degree from Osaka University in 1996. His major area of specialty is crystal plasticity
and biomaterials, and his research interest includes the evaluation of biological tissues,
especially bone, the formation mechanisms of tissues both in vivo and in vitro and thedevelopment of novel biomaterials which can function as or better than the original biological tissues, based on a materials science point of view. In addition, he develops new
high-temperature heat resistance materials with anisotropic crystallographic structures.
He aims to establish “Anisotropy in Materials”, as a new research horizon in Materials
Science.

Takuya Ishimoto
Takuya Ishimoto is an associate professor in Division of Materials & Manufacturing
Science, Graduate School of Engineering at Osaka University, Japan. He received his
Ph.D. degree from Osaka University in 2008 focusing on analysis of regenerated bone bymaterials scientific methodology and reconstruction of bone’s anisotropic microstructure. His research interest includes the understanding of bone’s adaptive responses tomechanical stimuli and artificial optimization of bone anisotropic microstructure
through the control of mechanical environment around bones.

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KONA Powder and Particle Journal No. 32 (2015) 85–101/Doi:10.14356/kona.2015018

Review Paper

Control of Particle Morphology and Size in Vapor-Phase
Synthesis of Titania, Silica and Alumina Nanoparticles †Hoey Kyung Park and Kyun Young Park *1

Department of Chemical Engineering, Kongju National University, Korea

Abstract
Previous studies on vapor-phase synthesis of titania (TiO2), silica (SiO2), and alumina (Al2O3) nanoparticles werereviewed. Interactions between physicochemical phenomena involved in the particle growth and the operating
variables were investigated. Strategies to produce non-aggregated spherical particles of the metal oxides are
discussed. Model predictions based on the sintering laws without any adjustments in the sintering parameters
rarely agreed with experimental data. There remains more to be understood in reaction and nucleation kinetics,
sintering, and fragmentation mechanisms until the technology is developed to the stage of designing reactors formass production of non-aggregated spherical particles for titania, silica and alumina
Keywords: vapor-phase, synthesis, titania, silica, alumina, nanoparticles

1. Introduction
Titania (TiO2), silica (SiO2) and alumina (Al 2O3)
nanoparticles have been in commercial production for decades. Titania nanoparticles are used as sun blocking
agents and photocatalysts. Silica nanoparticles are used asreinforcing fillers for rubbers and plastics, and ingredientsfor polishing slurries for semiconductor substrates. Alumina nanoparticles are used as catalyst supports forhigh-temperature reaction and ingredients for polishingslurries for semiconductor substrates. Theses nanoparticles are largely produced by vapor-phase oxidation of
metal chloride vapors in flames, as aggregates composedof primary particles, a few to a few tens of nanometer indiameter. The vapor-phase synthesis is different from thespray pyrolysis: precursors are fed as vapor for the vapor-­phase synthesis and as liquid droplets for the spray pyrolysis. The flame oxidation of metal chlorides dates back to1940 when a fumed silica production was commercializedin Germany. Later in 1951, a plant for producing titania
particles for white pigments by flame oxidation of TiCl4
vapor was come on stream. The pigment-grade titania islarger in size, 0.2–0.3 μm because the hiding power is low
if the size is too small. Nano-sized titania particles can beobtained by controlling the operating variables of the†1*

Received 18 August 2014; Accepted 3 October 2014
J-STAGE online 28 February 2015
1223-24 Cheonan-Daero, Subuk, Cheonan, Chungnam 330-717,
Korea
Corresponding author: Kyun Young Park
E-mail: kypark@kongju.ac.kr
TEL: + 82-41-521-9354 FAX: + 82-41-554-2640©2015 Hosokawa Powder Technology Foundation

flame oxidation of TiCl4.
It was not until 1980s that control of particle morphology and size for the vapor-phase synthesis of the oxides
was studied in a systematic way. Tables 1, 2, and 3 show
the selected studies on the investigation of morphology
and particle size for titania, silica, and alumina, respectively. Not only metal chlorides but other precursors wereused. Fig. 1 shows the pathway from the chemical reaction of a precursor to the formation and growth of particles. The first step is a generation of product clusters bychemical reaction. Then, nuclei are formed from the clusters. The nuclei grow to particles by coagulation, condensation, surface reaction, coalescence between neighboring
particles by sintering, and agglomeration. Flame and hotwall reactors were used at various temperatures and precursor concentrations. Fig. 2 shows the types of reactors
used in previous studies. In many cases, particles were
not dispersed but aggregated or agglomerated. Aggregate
is defined as a group of particles joined together strongly
with necking between particles, while agglomerate is agroup of particles or aggregates joined loosely by van derWalls force.
A number of review papers have been published. Gurav
et al. (1993) reviewed the generation of particles by gasphase routes for metals, oxides, and non-oxide ceramics.
This may be the first comprehensive review on the vapor-­phase synthesis of particles for diverse materials, although pioneering articles were published earlier by Ulrich
and his coworkers on the growth of silica particles(Ulrich, 1971; Ulrich and Subramanian, 1977; Ulrich andRiehl, 1982; Ulrich, 1984). A few years later, flame synthesis of nanoparticles for ceramic materials including85

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

Table 1

Selected studies on vapor-phase synthesis of titania particles
Particle size

Precursor

Reactor typePrecursor
Reactorconc., mol % temp., °C

Residence
time, s

TiCl4
TTIP

hot wall

0.01–0.11)

860–1000
350–579

3.1–10.8

21–41
21–50

Yoon et al. (2003)

TTIP

hot wall

1.0–7.0

300–700

1.1–4.4

200–400

Park et al. (2001)

TTIP

hot wall

1.0–7.0

300–700

0.7

25–250Choi and Park (2006)

TTIP

flame (PM)0.041)

1200–1500

45

Yang and Biswas (1997)

TiCl4

hot wall

1.61)

1000–1400

1.2

40–50

TiCl4

hot wall

0.05–1.0

900–1100

0.54–0.94

30–70

Jang and Jeong (1995)

TTIP

hot wall

0.01–2.89

300–700

3.8–10.2

20–1200

Kirkbir and Komiyama(1987)

TiCl4

hot wall

1.0–4.1

850–1100

300–700

Suyama and Kato (1976)

TiCl4

flame (PM)

4.18–5.37

900–14000.2

450–570

Park et al. (1990)

TTIP

hot wall1.68–7.84

170–510

2.2

20–140

Kirkbir and Komiyama(1988)

TTIP
TiCl4

hot wall0.00171)

800–1750

<5

12–17
4–8

TiCl4

hot wall

0.111)

927–1450

0.84–1.87

TiCl4

flame (PM)0.0045–0.31)

1377

0.05–0.23

TiCl4

flame (DF)1427–2727
2)

3)

1356–1568

TTIP

flame (DF)

12.9

TTIP

flame (PM)0.09–0.34

1857–2162

TTIP

flame (DF)0.29–4.62)

900–1650

TiCl4

hot wall

1.0–1.4

850–1100

Primary,nm

1.8

0.32–0.60

Reference

Akhtar et al. (1994)

0.06–0.084
0.041–0.067

Nakaso et al. (2001)

0.13–0.26

Akhtar et al. (1991)

41–63

George et al. (1973)

9.5–145

Formenti et al. (1972)

30–70
0.2–0.24

Agglomerate,μm

0.09–0.15

Akurati et al. (2006)

26–45

Arabi-Katbi et al. (2001)

5–60

Wegner and Pratsinis(2003)

50–80

Morooka et al. (1989)

DF: diffusion; PM: premixed
1)

Estimated using precursor concentration given in mol/L or precursor flow rate.
Estimated using precursor flow rate in the center tube.
3)
Temperature measured in previous work with similar operating conditions.
2)titania, silica, and alumina was reviewed with emphasison the fundamentals of particle formation and growth.
(Pratsinis, 1998) Other reviews followed, most of which
deal with synthesis techniques, application fields, andcharacterization of the particles, with rather shallow discussion on the particle formation and growth (Swihart,
2003; Strobel et al., 2006; Strobel and Pratsinis, 2007;
Biskos et al., 2008; Athanassiou et al., 2010). The fundamentals of particle formation for single and mixed oxides
were reviewed by Roth (2007) in a way similar to that by
Pratsinis (1998) but with a different viewpoint on the importance of the vapor-phase reaction and nucleation

86

kinetics. Recently, the history of the development of commercial aerosol reactors for synthesis of carbon black, titania, silica, zinc oxide, nickel particles and the effects ofprocess variables on particle morphology and size werediscussed to conclude that for most of those materials thecoagulation washes out the effect of nucleation because
precursors are rapidly consumed and product particles attain self-preserving distribution by coagulation. (Pratsinis,
2011)
The present review was confined to the control of particle morphology and size in vapor-phase synthesis of titania, silica and alumina nanoparticles in flame and hot wall

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

Table 2

Selected studies on vapor-phase synthesis of silica particles

Precursor

Reactor typePrecursor
conc., mol%

Reactortemp., °C

Residence
time, s

SiCl4

hot wall

10.0

150

60

2)

Particle size
Primary,nm

Agglomerate,μm

Reference

250–300

Park and Park (2008)

HMDS

flame (DF)

1.2–1.3

1800–2000

18–85

Mueller et al. (2004)

SiCl4

flame (DF)8.92)

1100–1900

15–110Zhu and Pratsinis (1997)

SiCl4
HMDS

flame (DF,PM)

0.19–0.87
13.22)

900–1750

11–97

Briesen et al. (1998)

TEOS

hot wall

0.015–0.151)

500

15–50

Smolik and Moravec(1995)

SiCl4

flame (PM)

0.5–5.0

1527–1827

7–18

Ulrich and Riehl (1984)

SiCl4

flame (DF)0.28–0.7

1900–2100

20–30

SiCl4

flame (DF)0.06–1.2

2000

19–42

Rulison et al. (1996)

SiCl4

flame (PM)

0.8–0.9

1480–1532

7–17

Ulrich et al. (1976)

SiCl4

flame (DM)

0.42–4.31)

794–1901

15–50

SiCl4
SiBr4
HMDS

flame (PM)0.191)
2.01)
0.211)

1447

10
10
10

SiH4

flame (PM)0.019–
0.3653)

1713–2262

20–30

7–26

0.05–0.2

0.025–0.14

Chung et al. (1992)

Cho and Choi (2000)
Ehrman et al. (1998)

0.05–0.25

Zachariah et al. (1989)

DF: diffusion; PM: premixed
1)

Estimated using precursor concentration given in mol/L or precursor flow rate.
Estimated using precursor flow rate in the center tube.
3)
Silane volume loading
2)

Table 3

Selected studies on vapor-phase synthesis of alumina particles
Reactor typePrecursor
conc., mol%

Reactortemp., °C

ATSB

flame (DF)0.461)

927–1727

ATSB

hot wall

0.012

500–900

TMA

flame (DF)0.1 ppm 2)

2000

13–60

AlCl3

flame (DF)1427–2727

10–200

AlCl3

flame (DF)0.21–0.85

1900–2100

20–30

AlCl3

hot wall

7.0

200

60

70

Yoo et al. (2009)

ATI

hot wall0.7

200–2501800

80–100Nguyen et al. (2011)

TMA

flame (PM)2)

0.01–0.1

730–1650

Residence
time, sParticle size

Precursor

0.14–2.81

Primary,nm

Agglomerate,μm

Reference

16–21

0.1–0.2

Johannessen et al. (2000)

15–18

3.0–11.1

Okuyama et al. (1986)
0.03–0.09

Xing et al. (1997)
Formenti et al. (1972)

0.05–0.2

Particles per
agglomerate:
20–250Chung et al. (1992)

Windeler et al. (1997a)

DF: diffusion; PM: premixed
1)
2)

Estimated assuming that the carrier gas was saturated with the precursor.
Particle volume fraction87

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

Fig. 1

Pathways for particle formation and growth in vapor-phase synthesis of metal oxides.

Fig. 2

Schematic drawings of reactors used for vapor-phase synthesis of metal oxides.

reactors. In previous reviews, little attention was paid tothe chemical reaction and nucleation kinetics assumingthat the reaction occurs rapidly in one step to form nucleiof oxide-monomer size. In reality, the reaction may notoccur in one step, but in multiple steps to produce intermediate products significantly different in physical properties from the final oxide products. The chemical
reaction and nucleation kinetics were reviewed more

88

extensively than in others. We start by looking at thephysicochemical phenomena involved in the particle generation and growth: chemical reaction, nucleation, condensation, coagulation, sintering, restructuring andfragmentation. We then discuss the effects of operating
variables on particle morphology and size. Finally, control
strategy of particle size and morphology is discussed with
future works to be challenged.

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

Table 4

Reaction kinetic parameters for vapor-phase synthesis of titania, silica and alumina particles

Oxide Reaction

Activationenergy (kJ/mol)

Pre-exponential factor

Temperature
range (K)

Reference

TiO2

88.8

8.26 × 104 (s –1)

973 to 1273

Pratsinis et al. (1990)

102

2.5 × 105 (s –1)

723 to 973

Kobata et al. (1991)

TiCl4 + O2→TiO2 + 2Cl2

TTIP→TiO2 + 4C3H6 + 2H2O

3

74.8

4.9 × 10 (cm/s)

673 to 1320

Ghoshtagore (1970)

60

13

3

3.0 × 10 (cm /mol s)

500 to 1600

Shirley et al. (2011)

70.5

5

–1

3.96 × 10 (s )40

4.0 × 1011 (cm3/mol s)

126

1.0 × 1011 (cm/s)

Okuyama et al. (1990)–1

Kanai et al. (1985)

TTIP + 2H2O→TiO2 + 4C3H7OH

8.43

3.0 × 10 (s )Kashima andSugiyama (1990)

SiCl4 + O2→SiO2 + 2Cl2

401

8.0 × 1014 (s –1)

French et al. (1978)

14–1

SiCl4 + O2→SiCl3 + 2Cl2

410

1.7 × 10 (s )1373 to 1573

Powers (1978)

SiCl4 + 2H2O→SiO2 + 4HCl

121.4

1.0 × 1012.0 (cm3/mol s)

743 to 973

Kochubei (1997)11–1

SiCl4 + H2O→SiCl3OH + HCl

128

8.2 × 10 (s )1000 to 2000

Hannebauer andMenzel (2003)

TEOS→products

176

2.6 × 109 (s –1)

222

Al2O3

623 to 723
15

Zhang and Griffin(1995)
Tsantilis et al. (2002)

164

SiO2

673 to 873

923 to1023

Satake et al. (1996)

10–1

721 to 820

Chu et al. (1991)

16–1

858 to 968

Chu et al. (1991)

7.4 × 10 (s )TMOS→products

340

1.4 × 10 (s )SiBr4 + O2→SiO2 + 2Br2280

5.0 × 1011 (s –1)

AlCl3 + 3/2H2O→1/2Al2O3 + 3HCl 35.8

91.85 × 10 (LFrench et al. (1978)

2.27

/mol

2.27

s) 423 to 483

Park (2014)

TTIP: titanium tetraisopropoxide; HMDS: hexamethyldisiloxane; TEOS: tetraethoxysilane; TMOS: tetramethoxysilane

2. Physico-chemical phenomena involved ingeneration and growth of particles
2.1 Chemical reaction
The first step to the particle generation of a metal oxide
is the homogeneous reaction of a precursor vapor by oxidation, hydrolysis, or decomposition. The reaction usuallyoccurs in multiple steps. The oxidation of TiCl4 was reported to occur through step-wise elimination of chlorine
and addition of oxygen to form titanium oxy-chlorides,
TiOxCly. The oxy-chlorides then collide with each other toform oligomers, (TiOxCly)n, which are further oxidized
toward pure titania. The reaction is homogeneous before
particles are generated. In the later stage, a heterogeneous
reaction or surface reaction may occur simultaneously on
the particles that have been formed. The surface reaction
contributes to the growth of particles.
Table 4 shows reaction kinetic parameters reported forvapor-phase synthesis of titania, silica and alumina.
Power-law type expressions were proposed for theoverall reaction of TiCl4 with oxygen. (Pratsinis et al.,
1990; Kobata et al., 1991) In those rate expressions, however, the homogeneous and heterogeneous reactions were
not differentiated but combined. The surface reaction rate
expression for the TiCl4 oxidation is available elsewhere.
(Ghoshtagore, 1970) In the flame synthesis of titania, notonly the oxidation but also the hydrolysis by the water
vapor generated from fuel combustion is involved. The
competition between oxidation and hydrolysis was studied in a tubular reactor to conclude that the hydrolysis wasimportant, particularly at high H2O/TiCl4 ratios. (Akhtar
et al., 1994) However, no kinetic data on the sole hydrolysis of TiCl4 are available, although the rate constants forthe initial few steps of the hydrolysis were calculated using the transition-state and RRKM theories (Wang et al,2010). Titanium tetraisopropoxide (TTIP) was used insome studies in place of TiCl4. TTIP is thermally decomposed into TiO2, hydrocarbons and water. As in the oxidation of TiCl4, the decomposition of TTIP may proceed in
multiple steps by successive β-hydride elimination of propene and polymerization (Park et al., 2001). For the over-89

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

all reaction represented by TTIP = TiO2 + 4C3H6 + H2O,
the reaction rates were determined from measured concentrations of propylene under varying operating condition to obtain a rate equation, 164 kJ/mol in activationenergy and a half order with respect to TTIP concentration. (Kanai et al., 1985) A different rate equation was reported later in which the reaction order is one and theactivation energy is 70.5 kJ/mol. (Okuyama et al., 1990)
The hydrolysis rate of TTIP is known to be faster thanthat of the thermal decomposition due to the oxidation
effect of water that leads to a considerable reduction in
activation energy. (Seto et al.,1995) These rate equations
for TTIP decomposition do not differentiate between
homogeneous and heterogeneous reactions. While, a rate
constant assigned to the surface reaction of TTIP was
obtained by Tsantilis et al. (2002) by using a reaction
model and the experimental data of Battiston et al. (1997).
Zhang and Griffin (1995) studied the growth kinetics of a
TiO2 film under the decomposition of TTIP in nitrogenand proposed a rate expression, 4.0 × 1011 exp (–40[kJmol–1]/RT)[TTIP][N2].
SiCl4, SiBr4, tetraethoxysilane (TEOS), and hexamethyldisiloxane (HMDS) were used as precursor for thevapor-phase synthesis of silica particles. In the oxidationof SiCl4 vapor with oxygen, pure silica was not formed
below 1200 K; instead chlorosiloxanes containing up to
50 Si atoms per molecule were formed. (Giesenberg et al.,2004) This implies that the oxidation of SiCl4 to silica is amulti-step reaction. The mechanism of the multi-step reaction is not available in the literature, but is presumed tofollow the step-wise elimination of chlorine and additionof oxygen to form (SiOxCly)n, as in the oxidation of TiCl4
mentioned in the beginning of this section. The oxidation
rate of SiCl4 was studied at 1100–1300 °C. (Powers, 1978)
The reaction was first order in SiCl4 and zero order in oxygen up to a 20-fold excess O2, and the activation energy
was 96 kcal/mol. In an independent study (French et al,1978), the reaction order was one and the activation energy was 98 kcal/mol. The rate constant of the hydrolysis
of SiCl4 vapor was measured to be 1012 exp (–121.4/RT)
cm3 mol–1 s–1, where the activation energy is expressed in
kJ/mol. (Kochubei, 1997) Ignatov et al. (2003) studied thereaction mechanism of the gas-phase hydrolysis of SiCl4
and calculated the rate constants of the elementary reactions in the early stage using quantum mechanics. A similar kinetic study was carried out by Hannebauer andMenzel (2003). A power-law type expression for the thermal decomposition of TEOS was proposed as 7.4 × 1010
exp (–49500/RT) s–1. (Chu et al., 1991) The surface reaction rates of TEOS decomposition were measured attemperatures between 923 K and 1023 K, from which arate constant was obtained with an activation energy of
42 kcal/mol. (Satake et al., 1996) For eighteen elementary
reactions composing the decomposition of TEOS, the90

kinetic parameters were obtained by parameter estimation
to fit experimental data. (Shekar et al., 2012a)
AlCl3, aluminum triisopropoxide (ATI), trimethyaluminum (TMA), and aluminum tri-sec-butoxide (ATSB)
were used as precursor for alumina particles. Kinetic data
for the alumina precursors are very rare. Recently, a rate
expression for the hydrolysis of AlCl3 vapor was reported.
(Park, 2014)

2.2 Nucleation
Nucleation occurs when the supersaturation ratio of a
species produced in the reaction system has increased pasta critical value. The nucleus size for silica by flame oxidation of SiCl4 was calculated to be smaller than a SiO2
monomer using the classical nucleation theory. (Ulrich,1971) In many studies that follow, the assumption of a
product monomer being a nucleus was used in the interpretation of experimental results and in the modeling ofparticle growth. (Landgrebe and Pratsinis, 1989; Tsantilis
and Pratsinis, 2000; Heine and Pratsinis, 2006) The surface tension of the nucleus is required to calculate the nucleus size by the classical theory. However, the surface
tension is not well defined for such a small object and theuse of the classical nucleation theory does not make sense.
The atomistic theory developed by Hoare and Pal (1971)
may be better suited for systems with clusters less than
100 molecules. (Kulmala et al., 1987) In some studies, regardless of the nucleus size, the particle size distribution
approached the self-preserving size distribution. (Vemury
et al., 1994; Spicer et al., 2002) In other studies, however,the nucleus size and the nucleation rate were reported toaffect the final particle size distribution. (Suyama andKato, 1976; Yoon et al., 2003, Park et al., 2014) Using
Ti5O6Cl8, an intermediate on the transition to TiO2, as nucleus in place of TiO2 monomer resulted in a considerably
different particle size distribution in a flame synthesis oftitania. (Mehta et al., 2013) At present, little data are
available on the kinetics of producing the intermediate
species and their physical properties including vapor pressure and surface tension that are required for nucleation
calculation.

2.3 Condensation and coagulation
Once nuclei or smallest particles are formed, they collide with each other to form larger particles. The clusters
condense on the particles while the coagulation between
particles proceeds by Brownian motion. In the classicaltheory of coagulation, coalescence occurs instantaneously
after two particles collide, and a new sphere is formed.
The instantaneous coalescence may not be valid for metaloxides depending upon operating conditions. If the coalescence is incomplete, particles grow as agglomerates

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

comprised of primary particles. Ulrich and Subramanian(1977) first introduced a time for complete fusion of two
SiO2 particles in contact to calculate the average numberof primary particles in an agglomerate. The transition of
fully coalesced particles into agglomerated particles waspredicted using relative values of the collision and coalescence times. (Windeler et al., 1997b) The collision ratesof the agglomerates are faster than those of spherical particles because the collision cross sections are larger. The
collision cross section depends on the shapes of the agglomerates. The shape of an agglomerate is defined as afunction of the fractal dimension (Df ) which is correlated
to the number of primary particles (Np) in the agglomerateR
by N p  A( ) Df , where A is a constant prefactor, R is theR0radius of the agglomerate and R0 is the radius of the primary particle. (Forrest and Witten, 1979) The coagulation
rate expressions for spherical particles and agglomerates
are available elsewhere. (Friedlander, 2000) The primaryparticle size increases with increasing condensation andcoagulation, but is not affected by agglomeration.

2.4 Sintering, restructuring and fragmentation
If the temperature is high enough, the primary particles
composing an agglomerate are sintered to form necks
between particles in contact, resulting in a decrease of
the surface area. Ulrich and his coworkers (Ulrich andSubramanian, 1977; Ulrich and Riehl, 1982) reported thatin the flame synthesis of silica particles the coalescenceof primary particles by sintering played a dominant role indetermining the primary particle size. Seto et al. (1995)
showed that the titania particles obtained from thermal decomposition of TTIP started to change in primary particle
and agglomerate sizes by sintering at 1000 K. They showed
in a later study (Seto et al., 1997) that the temperatures
effective for the coalescence of the particles by sintering
were 50–70 % of the bulk melting temperature for titania
and 90–100 % for silica. The coalescence of primaryparticles by sintering was verified in other studies. (Xing etal., 1997; Cho and Choi, 2000) Koch and Friedlander (1990)
incorporated into the general dynamics equation a sinterda1 (a  af ) where a is theing term represented bydt
instantaneous surface area of an agglomerate, af is thesurface area of the sphere resulted by the full coalescenceof the primary particles in that agglomerate, t is the time,and τ is the characteristic time for sintering. The characteristic
time is defined as the time necessary to reduce by 63 % theexcess surface area. Studies followed to obtain characteristic
times for titania and silica, as listed in Table 5. The presence of water vapor in the sintering process was reported toaccelerate the kinetics of the coalescence. (Goertz et al., 2011)
The sizes of the primary particles and the agglomerates
exiting the reactor were calculated using the characteristic
times, and compared with experimental data. In a heatedwall reactor where titania particles were produced by reaction of TiCl4 vapor with oxygen, the maximum primary

Table 5 Expressions for sintering characteristic time
Material
TiO2
TiO2
TiO2
SiO2
SiO2

SiO2

Characteristic time, sReference

7.4  108Td p4exp(

3.1 10 4)T

Kobata et al. (1991)

1.5  10 3Td p3exp(

3.4  10 4)T

Ehrman et al.(1998)

3.2  10 4)
TYang and Biswas (1997)

6.3  10 10 d pexp(

6.1 10 4)T

Ehrman et al. (1998)

6.5  10 15 d pexp[

8.3  10 4  d p,min 
1 
]
T
dp 


Tsantilis et al. (2001)

1.67 1016Td p4exp(

1(1  )af2
1.16  10 4
e
exp(
)
2/3
2 ks
T

Kirchhof et al. (2012)

ks: 3.5 × 10 –4 m 2/s
af: radius of the completely coalesced sphere, m
dp is the particle diameter in cm, dp,min is the particle diameter below which the sintering isinstantaneous, and T is the temperature in K.

91

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

particle size was calculated to be 15 nm using the grainboundary diffusion coefficient by Astier and Vergnon(1976), whereas experimentally observed size was 55–
65 nm. (Kobata et al., 1991) In a different study by Xiong
et al. (1993b), significant deviations were observed for titania and silica between calculated and experimental sizesof primary particles and agglomerates. Wu et al. (1993)
produced alumina particles by oxidation of trimethylaluminum (TMA) in flames at 890–1140 K. They comparedexperimental data with model prediction to show that the
primary particle sizes calculated based on either grainboundary diffusion or volume diffusion were smaller by
several orders of magnitudes than those experimentallyobserved. In contrast, Windeler et al. (1997b) reported
that good agreement was obtained between experimental
and calculated primary particle sizes for titania and silica
produced in a premixed flame. In their calculation, thesintering law of Koch and Friedlander (1990) was assumed to hold for groups of primary particles in contact,
not the whole agglomerate, as proposed by Lehtinen et al.
(1996). Tsantilis et al. (2001) reported that experimental
data were better fitted by modifying the sintering time totake into account the particle size dependence of melting
temperature. In another study, a melting diameter was introduced, which is defined as half the diameter of particles whose characteristic sintering time is identical to thereactor residence time. Particles smaller than the melting
diameter were assumed to coalesce instantaneously upon
collision, by which the prediction at low temperatures was
improved. (Park and Rogak, 2003)
The agglomerates are restructured by sintering. Not

Fig. 392

only the primary particle size increases but also theagglomerates shrink to decrease the cross sectional area.
Fig. 3 illustrates the restructuring of an agglomerate.
(Eggersdorfer and Pratsinis, 2014) The size of an agglomerate decreases by restructuring and fusion until theagglomerate is converted eventually into a sphere. The
agglomerate sintering may result in a fragmentation to reduce the agglomerate size. The fragmentation was studied
theoretically (Irisawa et al., 1995; Lando et al., 2006;
Thouy et al., 1997; Hawa and Zachariah, 2007) and experimentally verified for silver (Bréchignac et al., 2002),
but not yet for titania, silica and alumina. The fragmentation may result in producing smaller primary particles.

3. Effects of operating variables on particle
morphology and size
3.1 Precursor type and concentration
TiCl4 and TTIP were used as precursor for titania particles. SiCl4, SiH4, TEOS, HMDS, and octamethylcyclotetrasiloxane (OMCTS) were used for silica particles andAlCl3, ATSB, TMA and ATI for alumina particles. The
properties of the precursors are shown in Table 6. Under
the same precursor concentration in a hot wall reactor operated at temperatures between 800 °C and 1600 °C, boththe primary particles and the agglomerates of titania werelarger with thermal decomposition of TTIP in nitrogenthan with oxidation of TiCl4 in a mixture of nitrogen andoxygen. (Nakaso et al., 2001) Titania particles from TTIP

Illustration of the evolution of an agglomerate toward a sphere by sintering. Df represents fractal dimension. The lines showing the variation of rm and rva with residence time or temperature were taken fromthe work of Seto et al. (1997). rm represents the average of the mobility equivalent diameters of the aggregates and rva represents the average diameter of the primary particles composing the aggregates.
(Adapted from Eggersdorfer and Pratsinis, 2014.)

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

Table 6

Properties of precursors for vapor-phase synthesis of titania, silica and alumina particles

Oxide

Precursor

Molecular formula(molecular weight)

State

Boiling point, °C

Mass of metal oxide
per mass of precursor

TiO2titanium tetrachloride

TiCl4 (189.7)

liquid

136.4

0.42

titanium isopropoxide(TTIP)

TiC12H28O4 (284.2)

liquid232

0.28

silicon tetrachloride

SiCl4 (169.9)

liquid

57.65

0.35

hexamethyldisiloxane(HMDS)

Si2C6H18O (162.4)

liquid100 to101

0.74

tetraethoxysilane (TEOS)

SiC8H20O4 (208.3)

liquid166 to 169

0.29

silicon tetrabromide

SiBr4 (347.7)

liquid153

0.17silaneSiH4 (32.1)

gas

–112

1.87

aluminum chloride

AlCl3 (133.3)

solid

178 (sublime)

0.38

aluminum tri-isopropoxide(ATI)

AlC9H21O3 (204.3)

solid

135 (10 torr)

0.25

aluminum tri-sec-butoxide(ATSB)AlO3C12H27 (246.3)

liquid200 to 206(30 mmHg)

0.21

trimethylaluminum (TMA)

Al2C6H18 (144.2)

liquid125 to 130

0.35

SiO2

Al2O3

decomposition were spherical, while those from TiCl4 oxidation were faceted. (Yoon et al., 2003) The faceted particles from TiCl4 turned spherical in the presence ofwater. (Akhtar et al., 1994) The crystalline structure of
the titania from TiCl4 in oxygen was a mixture of anataseand rutile (Kato and Suyama, 1974); the rutile content
was higher in H2O than in oxygen. While, the crystalline
structure of the titania from TTIP was anatase in oxygen
and a mixture of anatase and rutile in water vapor. (Choiand Park, 2006) Operating temperature is an importantfactor in controlling the ratio of anatase to rutile, as discussed in the succeeding section. Three silica precursors,
SiCl4, HMDS, and OMCTS, were compared in a diffusion
flame to study the effect of precursor on the primary particle size. (Briesen et al., 1998) At the same precursor
concentration, the particle size was the largest with
OMCTS and the smallest with SiCl4. The larger particle
sizes with the organometallic precursors were attributed
to the higher flame temperatures resulted from the combustion of the organic compounds. Ehrman et al. (1998)
reported, for a precursor concentration lower by an orderof magnitude, that there was little difference in primaryparticle size between SiCl4 and HMDS. No crystalline
silica was produced from any precursor. As produced alumina particles by flame oxidation of AlCl3 vapor were
amorphous or contained small amount of δ and θ phases,
which are meta-stable. (Pratsinis, 1998)
An increase in precursor concentration increases theamount of precursor available for surface reaction, theamount of condensable product clusters, and the numberof nuclei. The increases in the amounts of condensable

product clusters and the precursor vapors for surface reaction act to increase primary particle size, while the increase in the number of nuclei acts to decrease the size.
The opposing effects compete in determining primaryparticle size. Which effect will dominate depends on theoperating condition and the properties of the precursor. In
those studies shown in Tables 1 to 3, primary particle
sizes increased with increasing precursor concentration,
but for a few. The rare events in which primary particle
size decreased with increasing precursor concentration
are described. In a synthesis of titania particles by thermal decomposition of TTIP in nitrogen at 500 °C, an increase in precursor concentration decreased the primaryparticle size and increased the number of particles.
(Moravec et al., 2001) In a synthesis of silica particles byoxidation of SiCl4 in oxygen at 1200 °C with precursor
concentration varied between 1 % and 8 %, the primaryparticle size decreased with increasing precursor concentration in the range of 4–8 %. (Suyama and Kato, 1976) Ina flame synthesis of alumina with TMA as precursor, theprimary particle size was rather insensitive to the precursor concentration. (Windeler et al., 1997a) In contrast withthe contradictory effects on primary particle size, aggregate size increased consistently as precursor concentration was increased. (Chung et al., 1992; Windeler et al.,1997a)

3.2 Temperature and residence time
The operating temperature may be the most influential
factor in controlling particle morphology and size. An in-93

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crease in temperature will increase the reaction rate, thenucleation rate, the collision rate and the sintering rate.
The temperature profile in a reactor for vapor-phase synthesis of particles could be represented by one of the three
types shown in Fig. 4. The figure is more representativeof hot wall reactors, but is adaptable in concept to flame
reactors in which the temperature gradients are usually
stiffer than in hot wall reactors. In Type I the temperature
is below the sintering temperature throughout the reactor
and the sintering of particles is neglected. If the nucleation is fast enough, the particle sizes for both primaryparticles and agglomerates are determined by the collision
rates to exhibit self-preserving size distributions in thelong run (Friedlander and Wang, 1966; Vemury et al.,
1994; Spicer et al., 2002). Otherwise, the primary particle
size is determined by competition between nucleation andsurface growth. Examples of Type I can be found in syntheses with hot wall reactors, in most of which primaryparticle size decreased with increasing reactor set temperature. (Suyama and Kato, 1976; Kirkbir and Komiyama,
1987; Morooka et al., 1989; Jang and Jeong, 1995; Park etal., 2001; Park et al., 2014) By preheating the reactants
before they get in contact or placing the location of reactants mixing at a distance from the inlet toward the centerof the reactor, the particle size was decreased. (Jang andJeong, 1995; Park et al., 2014) In Type II there exists ahigh-temperature zone in which the temperature exceeds
the sintering temperature, but the residence time in the
hot-temperature zone is not sufficient, resulting in aggregates in which the boundaries between primary particles
are unclear. (Ulrich and Subramanian, 1977; Ulrich andRiehl, 1982; Windeler et al., 1997a; Cho and Choi, 2000)
The specific surface area decreased with increasing reactor set temperature. In Type III the residence time in theFig. 4

94

Schematic drawing of axial temperature profiles for
producing aggregated particles or loosely agglomerated spherical particles. Three types of temperature
profile are shown. Loosely agglomerated particles areproduced with types I and III, while aggregated particles are produced with Type II.

high temperature zone is long enough for the primaryparticles in the agglomerates to fully coalesce into larger
particles (Xing et al., 1997; Arabi-Katbi et al., 2001; Lee
and Choi, 2002; Camenzind et al., 2008). In most studies
in Type III, the effects of reaction and nucleation rates inthe early stage were washed out by the dominating sintering effect and primary particle size increased with increasing temperature.
In some studies that fall in Type III, primary particle
size rather decreased with increasing temperature. In a
study on the effect of sintering on the growth of TiO2 particles in a hot wall reactor, the primary particle size increased with increasing temperature, showed a maximum
at 1200 °C, and then decreased as the temperature wasfurther increased to 1600 °C. (Nakaso et al., 2001) In asynthesis of silica particles in a counter-flow diffusion reactor with silane (SiH4) as precursor, the primary particle
size in the hotter flame was smaller than that in the colder
flame. (Zachariah et al., 1989) Similar phenomena wereobserved in other flame syntheses. (Cho and Choi, 2000;
Akurati et al., 2006) The decrease of the primary particle
size was attributed to an insufficient sintering time
(Mueller et al., 2004) or to a lower precursor conversion(Akurati et al., 2006), due to a reduction in flame size. We
raise a conjecture that the agglomerates admitted into
the sintering-coalescence zone were probably smaller, as
reported for a synthesis of iron particles by thermal
decomposition of iron pentacarbonyl (Moniruzzaman etal., 2007), or the fragmentation rate of the aggregates washigher. Further studies are necessary to clarify the reason
for the decrease of primary particle size under the sintering-­
coalescence regime.
Aggregate sizes or the number of primary particles
composing an aggregate increased with increasing reactor
temperature before sintering effects set in, but decreased
thereafter due to restructuring. (Camenzind et al., 2008;
Chung et al., 1992) In flame reactors, the particles wererapidly cooled to prevent aggregation and growth by sintering. Wegner and Pratsinis (2003) reported that virtually
non-aggregated and spherical titania particles as small as
5 nm were produced by placing a quenching nozzle. By
comparison, the particle size was 20–60 nm without thenozzle. The particle size increased with increasing the distance of the nozzle from the burner mouth. The nozzle
quenching effect was confirmed later in a similar study by
Okada et al. (2011) in whose work nearly non-agglomerated
titania particles 6.8 nm in diameter were produced byboth blowing in a –70 °C argon gas and using a expansion
nozzle.
The rutile content of the titania produced by oxidationof TiCl4 vapor was reported to increase with increasing
reaction temperature. (Suyama et al., 1975) In the oxidation of TiCl4, initially anatase particles are formed andthen grow as anatase particles or transformed into rutileHoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

particles before they grow. Not only the temperature but
also the time that the particles remain small in size isknown to be an important factor. Morooka et al. (1989)
reported that the rutile content showed a maximum at
1100 °C and then decreased as the temperature was further increased to 1200 °C. This decrease in rutile content
with temperature increase was attributed to the faster
growth of the anatase particles to the size at which thetransformation into rutile is remarkably slowed down. By
irradiating titania particles with a CO2 laser, the rutile
content decreased against the expectation that the rutile
content would increase because the temperature was increased by the laser irradiation. (Lee and Choi, 2002) The
silica particles produced by the vapor-phase synthesis inflame reactors were all amorphous (Formenti et al., 1972;
Ehrman et al., 1998), while the alumina particles were
amorphous or a mixture of amorphous and metastable
crystalline phases. (Pratsinis, 1998) By calcining an
amorphous alumina for two hours at 1200 oC, the phase
was completely transformed into alpha. (Yoo et al., 2009)

3.3 Reaction medium
The precursors for vapor phase synthesis of the oxides
were reacted or decomposed in different media: nitrogen,oxygen, water vapor or a mixture. The morphology andparticle size showed differences between reaction media.
In a hot wall reactor at 1000–1400 °C, titania particles
were synthesized from TiCl4 with varying ratios of H2O
to O2 and with five injection points of H2O located at various distances from the inlet of the reactor. (Akhtar et al.,1994) At a reactor set temperature of 1200 °C, the primary particles obtained with the injection point at 13 cm
inward from the inlet were faceted, while those with theinjection point at the inlet were spherical. With the injection point at 13 cm, there was no H2O present in the earlystage and the oxygen determined the morphology that isFig. 5consistent with those faceted shapes observed in earlier
studies in pure oxygen. (Suyama and Kato, 1976; Yoon etal., 2003) With the injection point located at the inlet,
H2O was present from the beginning and played to change
the particle shape from faceted to round. As the amountof H2O was increased, the rutile content was invariable up
to 1000 °C but decreased at higher temperatures. In asynthesis of titania particles by thermal decomposition of
TTIP at 300–700 °C, three different reaction media wereused: N2, O2, and a mixture of N2 and H2O. (Choi andPark, 2006) As shown in Fig. 5, particle morphology andsize was significantly different between reaction media.
In N2, agglomerates composed of very small particles
whose individual boundaries are not clearly distinguishedwere produced. In O2, the primary particles were spherical and larger. As the medium was changed to nitrogenplus water, much larger spherical particles were obtained.
The crystalline phases were amorphous in N2, anatase inO2, and a mixture of rutile and anatase in N2 plus water,
respectively. A follow-up study may be necessary to understand how the reaction media affect the morphology,
size, and crystalline structure.
In most studies of synthesis of silica particles with
SiCl4 as precursor, the flame reaction was used in which
both oxygen and water vapor were present. However, in asynthesis by Park and Park (2008), SiCl4 was reacted with
H2O only at 150 °C to produce spherical particles, as
shown in Fig. 6, which are quite different from thosechain-like aggregates resulted by the flame reactions. A
similar method was used to produce non-aggregatedspherical particles by hydrolysis of AlCl3 vapor at 300–
700 °C. (Park et al., 2014) Those particles obtained by the
low temperature hydrolysis were not pure but contained
residual chlorines which were removed downstream in a
calciner. The low-temperature hydrolysis of metal chloride
vapor appears to be an attractive route to non-aggregatedspherical particles of metal oxides when a calcination isComparison of TEM images of titania particles between reaction media. The particles were synthesized in atubular reactor by thermal decomposition of TTIP at 700 °C with the precursor concentration at 7 mol % andthe residence time at 0.7 s. (Adapted from Choi and Park, 2006.)

95

Hoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

Fig. 6

Scanning electron microscopic images of silica particles produced at two different SiCl4 concentrations: (a)10 mol % and (b) 20 mol %. The silica particles were produced in a batch reactor at 150 °C by hydrolysis of
SiCl4 vapor. The residence time in the reactor was 60 s and the initial molar ratio of H2O to SiCl4 was 2.
(Adapted from Park and Park, 2008.)

necessary as for the transformation of crystalline phase.

3.4 Additives and external forces
The presence of additives during vapor-phase synthesisof metal oxides is known to affect the particle morphology and size significantly. (Suyama and Kato, 1985) In
the oxidation of TiCl4 vapor in a hot wall reactor, the titania particles formed in the presence of FeCl3, AlBr3, SiCl4
or ZrCl4 were 1/2 to 1/5 the size of those without additives. The reduction in particle size was attributed to a retardation of TiO2 deposition on the particles. An additionof AlCl3 in the flame oxidation of TiCl4 increased the rutile content. (Mezey, 1966) While, other additives such as
SiCl4 and POCl3 decreased the rutile content. (Akhtar etal., 1992) The addition of an alkali salt, sodium or potassium carbonate, to a flame synthesis of silica reduced theprimary particle size. (Wu et al., 1993) The alkali salts
dissociated into ions, some of which adsorbed on the particle surface to create repulsive forces between particles
to retard coagulation. (Xiong et al., 1992) Ferrocene wasfound to be an effective additive to decrease the primaryparticle size of silica particles by flame oxidation of SiCl4.
(Fotou et al., 1995) Silica particles by flame synthesis
were made smaller by applying electric fields. (Hardesty
and Weinberg, 1973) The presence of a gaseous electric
discharge (corona) in a flame synthesis of titania particles
decreased the primary particle size and the rutile content.
(Vemury and Pratsinis, 1995) The corona was applied to asilica flame synthesis; it was as effective as in titania synthesis and negative electric field was more effective in reducing particle growth. (Vemury and Pratsinis, 1996) The
control of particle size by external electric field was successfully applied to a scaled up process of flame synthesisof silica particles. (Kammler and Pratsinis, 2000)
In parallel with experimental studies on the effects of96

the operating variables, numerous modeling studies havebeen made on the basis of first principles. (Landgrebe andPratsinis, 1990; Xiong and Pratsinis, 1993b; Pope andHoward, 1997; Lee et al., 2001; Nakaso et al., 2001; Parkand Rogak, 2003; Morgan et al., 2006; Moniruzzaman
and Park, 2006; Ji et al., 2007; West et al., 2009; Sander etal., 2009; Zaitone et al., 2009; Shekar et al., 2012b; Mehta
et al., 2013). However, there remains more to be understood in reaction and nucleation kinetics, sintering, andfragmentation mechanisms until the technology is developed to the stage of designing reactors for mass production of non-aggregated spherical particles for titania,
silica, and alumina.

4. Control strategy of particle morphology
and sizeAs discussed in the preceding sections, various physicochemical phenomena are involved in determining particle morphology and size and those phenomena are
influenced by temperature and residence time in the reactor, precursor concentration, reaction medium, and additives and external forces. Since the metal oxides inaggregated form have already been in commercial production, the control strategy in this work is focused on the synthesis of non-aggregated spherical particles varying in size.
As shown in Fig. 4, non-aggregated spherical particles
can be produced either with temperature and residence
time maintained high enough for any aggregates formed
earlier to coalesce later by fusion into spheres or with thetemperature low enough for any serious necking between
particles in contact not to occur. With the low-temperaturescheme, the nucleation rate is a key factor in determining
primary particle size; the particle size decreases with increasing reactor temperature. Smaller particles can beHoey Kyung Park et al. / KONA Powder and Particle Journal No. 32 (2015) 85–101

produced by either preheating the reactants before they
get in contact or placing the location of reactants mixing at
a distance from the inlet toward the center of the reactor.
An additional heat treatment or calcination may be necessary if the conversion of the precursor is incomplete because of the low temperature. With the high-temperaturescheme, sintering of particles in agglomerates is a dominant factor. It is a key how to control operating variables
to obtain a high-temperature zone for full coalescence ofthe particles into spheres.
Increasing precursor concentration results in an increasein particle size except for very limited cases. Reaction
medium is a factor in controlling particle morphology andsize in hot wall reactors. In flame reactors, the control ofparticle size and morphology by reaction medium is lessuseful because water vapor is always present in the flame.
The particle size and morphology is sensitive to theburner type, premixed or diffusion, and to the configuration of injection nozzles for fuel and oxidizer in diffusion
burners. (Akurati et al., 2006) The particle size can also
be controlled either by injecting additives into the reactor
or by applying electric fields. The use of additives may
contaminate the particles, and their uses are limited to thecases where the contamination poses no problem inproduct specification as in the use of AlCl3 to control thephase of titania particles. Cooling the particles rapidly
before the sintering sets in is a way to obtain small
non-agglomerated particles. One needs to be cautious,
however, not to freeze the conversion of precursor whenusing rapid cooling.
The choice of a precursor is based on the volatility, thestoichiometric mass of product per mass of precursor, andthe cost of the precursor. Once a precursor is chosen, thenext step is to decide reactor type, hot wall or flame reactor. The control of operating variables is simpler with hotwall reactors. In flame reactors, there exist interactions
between operating variables to make it difficult to decouple the effect of a variable from those of other variables
and to achieve optimal operating conditions. However,there is an advantage with flame reactor that the energy
needed for the synthesis of particles is supplied in situ by
combustion of fuels.

allel as well, and are influenced and controlled by various
operating variables: precursor type and concentration,
temperature and residence time, reaction medium, andadditives and external forces. By investigating the interactions between the physico-chemical phenomena and theoperating variables, two approaches were derived to produce non-aggregated spherical particles of the metal oxides. One approach is to have the temperature andresidence time high enough for any aggregates formed in
the earlier stage to fuse and coalesce into spherical particles before they exit the reactor. The other is to have thetemperature low enough for any necking between neighboring particles not to occur. If the conversion is notenough with the low-temperature approach, an increase inresidence time or an extra step of calcination may be necessary in order to complete the conversion and to remove
any residuals in the particles.
The configuration of reactants mixing is an importantfactor in controlling the morphology and size. By preheating the reactants before they get in contact or placing thelocation of reactants mixing at a distance from the inlet
toward the center of the reactor, the particle size can be
decreased particularly for the low-temperature synthesis.
In flame reactors, particle size and morphology are sensitive to the burner type, premixed or diffusion, and to theconfiguration of injection nozzles for fuel and oxidizer in
diffusion burners. The particle size increases with precursor concentration except for limited cases. By using additives, applying electric fields or rapid cooling with aquenching nozzle, both the primary particle size and thedegree of aggregation decreased significantly.
It appears that the role of nucleation has been underestimated. The conversion of precursor needs to be checked
more carefully in the analysis of early-stage particle
growth in flame reactors. Model predictions based on thesintering laws without any adjustments in the sinteringparameters rarely agreed with experimental data. Thereremains more to be understood in reaction and nucleation
kinetics, sintering, and fragmentation mechanisms until
the technology is developed to the stage of designing reactors for mass production of non-aggregated sphericalparticles for titania, silica, and alumina.

5. Conclusion

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Author’s short biography
Hoey Kyung Park
Hoey Kyung Park received her B.S. (2005), M.S. (2007), and Ph.D. (2014) degrees inchemical engineering from Kongju National University in Korea. Her main research
focus was on gas-phase synthesis of nano-materials (silica and α-alumina etc.). She iscurrently a post-doctoral researcher at Department of Chemical Engineering, Kongju
National University. Her current research interests include gas-phase synthesis and CFD
simulation.

Kyun Young Park
Kyun Young Park is Professor of Chemical Engineering at Kongju National University, Korea. He holds a B.S. (1972) from Seoul National University, an M.S. (1982) and a
Ph.D. (1984) from the University of Texas at Austin. He was a process engineer at SK
Energy from 1975 to 1980 and a researcher at Korea Institute of Geoscience and Mineral
Resources from 1985 to 1992 before he joined Kongju National University in 1993. His
research interests are in the area of vapor-phase synthesis of particles of metals andmetal oxides and gas-solid reactions.

101KONA Powder and Particle Journal No. 32 (2015) 102–114/Doi:10.14356/kona.2015023

Review Paper

Size-Selective Separation Techniques
for Nanoparticles in Liquid †Yasushige Mori
1

Department of Chemical Engineering and Materials Science, Doshisha University, Japan

Abstract
The many characteristics of nanoparticles (NPs) depend on their size and size distribution. When novel functionsof NPs are attempted, it is necessary to monodisperse or nearly monodisperse NPs. Many synthesis methods havebeen proposed from solution-based chemistry to satisfy this demand. Despite such special techniques to prepareNPs, it is often the case that an additional classification procedure is needed to a certain extent to obtain
monodispersed NPs, similar to other general precipitation methods used in NP preparation. Additionally, NPs
must be purified post-synthesis to remove by-products. Hence, size-selective separation as post-synthesis
operation is expected to become increasingly important. In this review, some recent developments of sizeselective separation methods for NPs based on using external fields, filtration, and the stability of the colloid
system are highlighted.
Keywords: nanoparticles, classification, separation, fractionation, size-selective precipitation, gas-expanded liquid

1. Introduction
The size-selective separation of particles commonly
called size classification or simply classification, which isone of the most important unit operations in powder technology. For controlling the particle size distribution or
preparing nearly monodispersed materials dispersed inliquid, several techniques, such as hydraulic cyclone, elutriation, gravity separation, magnetic separation, screening, and sieving, have been proposed for the classificationof particles and powders whose size is on the order of micrometers; these techniques enable control over particle
size distribution and afford nearly monodisperse materials, which are required in several industrial applications(Masuda H. et al., 2006). Based on their principles, thesetechniques are classified into two categories; one involves
the use of forces generated from external fields (i.e., gravitational, centrifugal, electrical, and magnetic), and theother involves the use of physical holes (i.e., sieving).
Until now, the size classification techniques are still developing for sub-micrometer particles to enhance theirproperties.
The numerous characteristics of nanoparticles (NPs)can depend on their size and size distribution; for exam-†
1

Received 3 October 2014; Accepted 10 November 2014
J-STAGE online 28 February 2015
1-3 Tatara Miyakodani, Kyotanabe, 610-0321 Japan
E-mail: ymori@mail.doshisha.ac.jp
TEL: +81-774-65-6626 FAX: +81-774-65-6847

102

ple, silicon NPs of size 2–16 nm have photoluminescenceof wavelength 500–800 nm (Liu S.M. et al., 2006;
Sugimoto H. et al., 2013). When particle size is controlled,
NPs can be applied in sensors (Anker J.N. et al., 2008;
Chan W.C.W. and Nie S., 1998; Klostranec J.M. and Chan
W.C.W., 2006), drug carriers (Peer D. et al., 2007; Roy I. etal., 2003), super-capacitors (Nakanishi H. and Grzybowski
B.A., 2010), diodes (Bliznyuk V. et al., 1999; Park J.H. etal., 2004), data storage media (Chon J.W.M. et al., 2007;
Tseng R.J. et al., 2006), photocatalysis (Chin S. et al.,2011), or photonic (Barnes W.L. et al., 2003; Teranishi T.
et al., 2005; Xie Z. et al., 2011) and photovoltaic (Hasobe
T. et al., 2005; Sun B. et al., 2003) cells. Attempts havealso been made to obtain novel materials through regulararrangement structure (e.g., colloidal crystal, and monoor multi-layer) of NPs. Several synthesis methods (Buck
M.R. and Schaak R.E., 2013) have been proposed for themonodispersed or near-monodispersed NPs, which is essential. For example, reversed-micelle (Fontes Garcia
A.M. et al., 2011; Mori Y. et al., 2001; Yang P. et al., 2011),
hot-soap (De Mello Donega C. et al., 2005; Delalande M.
et al., 2007; Murray C.B. et al., 1993), solvated metal atom
dispersion (Cingarapu S. et al., 2009), and thermal decomposition methods (Murray C.B. et al., 1993), rapid expansion of supercritical solution (Vemavarapu C. et al., 2009),
and synthesis in polymer gel (Sun Y. and Xia Y., 2002),
clay gel (Arao Y. et al., 2009) or surface modification
agent solution (Zhang H. et al., 2006) were applied tocontrol NP size and shape.
Despite these techniques, similar to conventional pre-©2015 Hosokawa Powder Technology Foundation

Yasushige Mori / KONA Powder and Particle Journal No. 32 (2015) 102–114

cipitation methods used in NP preparation, additionalclassification procedures are often required for obtaining
monodispersed NPs. In addition, purification of particles
is required post-synthesis to remove byproducts. Therefore, size-selective separation as a post-synthesis operation is becoming increasingly important. In this review,
recent developments in size-selective separation methods
using external force fields, sieving and stability of colloidal systems are highlighted to focus NPs whose size is
sub-micrometer, especially below several tens nanometer.

2. External force field
2.1 Field flow fractionation
Field flow fractionation (FFF), proposed by Giddings
in 1966 (Giddings J.C., 1966), is a conventional methodthat employs several external force fields (Rübsam H. etal., 2012; Wahlund K.G., 2013). A field is considered effective if its strength and selectivity is sufficient toachieve separation (Messaud F.A. et al., 2009). Typicalfields include cross-flow streams, temperature gradients,
electrical potential gradients, centrifugal force, dielectrophoretic force, and magnetic force. These fields give riseto several FFF techniques, including flow (FlFFF), thermal (ThFFF), electrical (ElFFF), sedimentation (SdFFF),
dielectrophoretic (DEP-FFF), and magnetic (MgFFF)
field flow fractionation.
Fig. 1 shows the schematic of the separation principleof FFF and gives an overview of the experimental equipment (Mori Y., 1994). The separation occurs inside a narrow channel, in which the carrier solution flows as alaminar flow between the parallel walls. When an external field is applied perpendicular to the flow, i.e., x-axis,
the particles concentrate at the accumulation wall. The
formed concentration gradient induces particle diffusionin the reverse direction. After a certain period, the steadystate concentration profile is reached; this profile depends
on the chosen FFF method, that is, whether mass, electron
charge, or magnetic property acts to make this profile. Incase of SdFFF, that is, mass is the chosen property,
heavier particles are located near the accumulation wall,
while lighter particles are distributed near the center ofthe channel comparing with heavier particles. The average transport velocity of lighter particles is therefore
higher than that of heavier particles because of the parabolic flow profile in the channel.
FFF operations usually proceed in three stages. The
first stage is the injection period, where the sample is injected into the channel inlet under the external field.
During the relaxation period, after stopping the carrier
solution flow, the concentration profile starts to reach thesteady state. In the analysis period, the carrier solution

Fig. 1

Schematic of FFF and the principle of particle separation (Mori Y., 1994).

flows again. Particles move in the channel at different velocities under the external field.
Among these techniques, FlFFF, ThFFF, ElFFF, andMgFFF are currently available for NPs. Two types of
FlFFF are currently used: symmetrical and asymmetrical(A4F). A4F is popular due to its commercial availability
and has been employed in most recent research. For example, Hagendorfer et al. (Hagendorfer H. et al., 2011)
demonstrated the fractionation of gold NPs of size
5–84 nm by using A4F. CdTe NPs were separated into
relatively monodispersed fractions using ElFFF (Ho S. etal., 2009). Successful fractionation was evidenced by fluorescence spectra. The original sample with average diameter 4.7 nm had the maximum fluorescence peak at
615 nm. In the separated sample, red CdTe NPs fluoresced
at 627 nm, which corresponds to a particle size of 5.1 nm,
whereas the green fraction fluoresced at 564 nm, whichcorresponds to a particle size of 2.9 nm. For MgFFF,
Latham et al. (Latham A.H. et al., 2005) used the capillary type and demonstrated that magnetic NPs can beseparated according to not only size (6 and 13 nm) but
also material composition (Fe2O3 and CoFe2O4).

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2.2 Continuous field flow fractionation
Although FFF has many variations and is used to separate particles of sizes ranging from a few nanometers to
100 μm, it is essentially a batch operation, and the amountof sample that can be processed at a time is very small (onthe order of milligrams to micrograms). The developmentof new methods for continuous FFF of NPs is becoming
more important in solving certain industrial problems.
One such new method is split-flow thin fractionation(SPLITT) (Katasonova O.N. and Fedotov P.S., 2009).
Fig. 2 illustrates the principle of the SPLITT system.
Fractionation is performed using a system of thin channels, each of which has splitters at the input and output
within a separate column. The suspended particles andthe pure carrier solution are simultaneously introduced
into the column through the top and lower inlets, respectively. The flow velocity of the pure carrier solution is always higher than that of the sample suspension; therefore,
the incoming particle stream is confined to a narrow layer
in the upper portion of the column. Along with the carrier
solution flow, the external force field perpendicular to theliquid flow also affects the particle movement in the channel. For example, when the external force field is gravity,
large particles more rapidly settle under the effect ofgravity and are collected from the lower outlet (Fig. 2).
Smaller particles concentrating in the upper part of thechannel is collected from the upper outlet. Thus, two particle fractions differing in size is obtained. The stability ofboth flows in the narrow channel is the critical parameter.
Contado et al. (Contado C. et al., 2000) the continuous
separation of wheat starch particles using SPLITT andgravity as the external field. Because the dimensions of
the system is relatively large, the sample size is over
1 μm. Under a 100 gravities field in the centrifuge of the
SPLITT system, however, the magnetic particles in thesample, with size ranging from 60 to 1000 nm, were separated into two fractions at a cutoff size of 660 nm (Jiang
Y. et al., 1999). Fuh and Chen (Fuh C.B. and Chen S.Y.,
1999) reported the separation of magnetic particles by
SPLITT applied magnetic field. They compared the horizontal setup of SPLITT with the vertical setup for separation efficiency. Magnetic and gravitational forces wereused for separation in the horizontal setup, where the twoforces were applied in opposite directions to facilitate
separation; only magnetic force was applied in the vertical setup. They concluded that the horizontal setup was
more effective than the vertical one for separation ofmagnetic particles with high densities and/or large diameters, because gravitational forces acting on particles
tended to counteract the magnetic forces in the horizontal
setup.
An electrical field also was applied to the SPLITT system. Narayanan et al. (Narayanan N. et al., 2006) designed
a SPLITT system with various inlet, outlet, and splitter arrangements based on the simulation of two-dimensional
computational fluid dynamics (Fluent commercial package). Excellent separation was achieved for a mixture of
108 nm and 220 nm amino-coated particles using the offset splitter model. Yamamoto et al. (Yamamoto T. et al.,2009; Yamamoto T. et al., 2011) classified silica particles
using an electrical SPLITT system. Although the dimensions of their instrument was much larger than that ofNarayanan et al. (Narayanan N. et al., 2006), cut-off size
was between 45–80 nm depending on the experimental
conditions, because the absolute value of zeta-potential
increased with decreasing particle size, which assisted
separation.
SPLITT affords continuous separation but has low
throughput; further, there is difficult to find reports of its
application to size-selective separation of NPs in the nm
size ranges.

2.3 Centrifugal field
Use of centrifugal force is a common technique insize-selective particle separation. A hydrocyclone, whereparticles move toward the centrifugal field at different velocities depending on their size, is used for micrometer-­size particles in a liquid system. The particle motion in
the centrifugal field is described by following equation:v d
r dt s 2 r ,where v is the particle settling velocity, r is the distancefrom the axis of rotation, t is time, ω is angular velocity,and s is the sedimentation coefficient. When the frictionalcoefficient is applied in the Stokes region, s can be expressed ass x 2  p   f  18  ,

Fig. 2

104

Schematic of SPLITT (Katasonova O.N. and Fedotov
P.S., 2009).

(1)

(2)

where x is particle diameter, ρp and ρf are particle and liquid densities, respectively, and η is viscosity of liquid.
Nano-diamonds were separated by an ultracentrifuge according to the principle represented in Eqn. (2) (Morita Y.
et al., 2008). On changing the rotation speed and application period, the average size of the particles in the upper
solution (supernatant solution) was found to be controlledYasushige Mori / KONA Powder and Particle Journal No. 32 (2015) 102–114

down to 4 nm using a 30 nm feed suspension size.
However, when particle size decreases to such a nanometer range, the Brownian motion of NPs becomes dominant, meaning that NPs cannot settle as described in Eq.
(1). The diffusion (Brownian motion) of NPs was accounted for in the sedimentation behavior as follows(Fujita H., 1975):
1   2 2
 C   C  s  r C  D r 

 
 ,
r r 
 t  r r  t (3)where C is particle concentration, D is diffusion coefficient of NPs. The exact analytical solutions of this Lamm
equation cannot be obtained, but currently numericalsolutions can be obtained using several software programs (Brautigam C.A., 2011; Schuck P., 2010). The datameasured by the analytical ultracentrifugation have been
analyzed by these software programs to obtain the macromolecular weight distribution of proteins (Zhao H. et al.,2010) or the particle size distribution of ZrO2 or SiO2
(Mittal V. et al., 2010). This effect of particle diffusion results poor separation if particle was small enough to apply
Eqn. (3). This means that more powerful techniques are
required.
One successful technique, reported by Sun et al. (SunX. et al., 2009), is the density gradient centrifugation(DGC) method. They used aqueous solutions with different concentrations of OptiPrepTM (60 % (w/v) iodixanol,
1.32 g·cm–3, Sigma-Aldrich, Inc.). Density gradient solutions of 10 %, 20 %, 30 %, and 40 % iodixanol were created directly in the centrifuge tubes, and a suspension ofcore(FeCo)–shell(C) NPs with average diameters of approximately 4 nm was placed on top of the density gradient solution. After centrifugation at approximately
240,000 g for 3.5 h, the sample was separated by size into
27 fractions. Fig. 3 shows the particle size of each frac-tion, which increased with fraction number (from top tobottom), measured by transmission electron microscopy.
The size-selective separation from a gold NP mixture wasalso successfully performed in the same manner (Qiu P.
and Mao C., 2011; Sun X. et al., 2009).
The separation mechanism of this DGC method can beunderstood by the difference of the apparent densities ofNPs. For ideal spherical NPs with core density ρc, radiusa, shell thickness of the capping agents or surface modification molecules δ, and apparent shell density (mixture ofshell material and solvent molecules) ρs, the net density ρacan be estimated as


1

3


 .

1
a




 a  s   c   s  (4)

It can be deduced from Eqn. (4) that the net density of
NPs would increase when the core size increases with respect to shell thickness, and the net density is equal to thecore material density when the NPs are large enough.
This means that small NPs would folate in a low-density
solution and large NPs would settle to the bottom in ahigh-density solution using the DGC method. Thismethod was proposed as one of the methods to preparemonodispersed silicon NPs from polydisperse alkyl-­
capped silicon NPs (Mastronardi M.L. et al., 2011).
The DGC method was also applied to the shape-selective
separation of gold nanorods (11.5 ± 1.8 nm diameter,36.9 ± 5.4 nm length) from a mixture with 24 nm diameter spherical gold NPs (Xiong B. et al., 2011). They calculated the force balance of a Brownian rod falling in a
Stokes flow and introduced the quantitative dependencyof the nanorod sedimentation rates on their mass andshape. Chen et al. (Chen G. et al., 2009) demonstrated theseparation of dimers and trimmers of gold NPs covered
with polystyrene-block-poly(acrylic acid) using thismethod.
The attempted continuous separation of NPs by centrifugal force was performed using a micro-channel (Kwon
B.H. et al., 2013), which resulted in the reported separation of particles with a diameter of over 100 nm. This
technique requires a great deal of effort for in the separation of NPs.

2.4 Electric field

Fig. 3

The average particle size as functions of fraction numbers for FeCo NPs (Figure was taken from Fig. S2 in
Supporting Information of the literature (Sun X. et al.,2009)).

Electrophoretic techniques can be used widely in biological and biochemical research to separate charged objects in a uniform electric field. In electric fields, charged
molecules or particles migrate toward the electrode of opposite charge. The migration speed of a particle at steadystate v is linearly proportional to the applied electric fieldstrength E, asv  e E ,

(5)

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where μe is the electrophoretic mobility, which depends
on the charge, size, and shape of particles. As a result,particles are ultimately separated into distinct bands inthe electric field (Zhu X. and Mason T.G., 2014). One of
the most popular methods using the electrophoretic force
is gel electrophoresis (GE) using agarose gel (AGE) or
polyacrylamide gel (PAGE). The particles in GE migrate
through a gel matrix. Because the drag force of particles
from the gel matrix increases with particle size, GE canachieve excellent size separation.
Hanauer et al. (Hanauer M. et al., 2007) performed
shape separation experiments with AGE using a mixtureof spherical, triangular, and rod shaped gold or silver NPs.
They found the mixture ratio of shape differed dependingon gel position, but complete separation was not achieved.
Wu et al. (Wu W. et al., 2013) reported the size and shape
separation of gold NPs. They performed their first size
separation using DGC and a subsequent shape separation
using AGE. CdTe NPs were classified and purified by
PAGE (Hlavacek A. and Skládal P., 2012). The separated
fraction of CdTe NPs revealed narrower emission peaks(72 % of the original width) and improved quantum yield(two-fold).

2.5 Magnetic field
Magnetic field can separate NPs according to theirmagnetic susceptibility. This technique can only apply tomagnetic NPs. Yavuz et al. (Yavuz C.T. et al., 2006) reported the potential and usefulness of magnetic separation
under field gradients lower than 100 T/m. The efficient
separation of Fe3O4 NPs of different sizes on a column
packed with steel wool was demonstrated. The particles
apparently did not act independently in the separation, butrather they reversibly aggregated through the resulting
high-field gradients present at their surfaces. The size dependence of magnetic separation permitted mixtures of 4
and 12 nm Fe3O4 NPs to be separated by the applicationof different magnetic fields.

3. Sieving
Sieving, which is the use of physical holes or barriers,
is an alternative method for the classification of particlesof micrometer size. For NPs, application of a membranecorresponds to using physical holes, and some chromatographic techniques apply such physical barriers for the
separation of NPs.

3.1 Membrane
Filtration through a membrane can be used for the purification and size separation of NPs. The retention and106

elution behaviors of NPs depend on the size of the membrane pores. Generally, microfiltration refers to processes
used to remove particles larger than 20–500 nm. Ultrafiltration are available to filtrate macromolecules and colloidal particles of 2–50 nm size (Fedotov P.S. et al., 2011).
The disadvantage of membrane filtration is the blocking
and the adsorption of NPs on the membrane surface andpore walls, which is due to particle larger than pore size
and the aggregation of NPs on the membrane surface. The
advantages of membrane filtration are the easy scale-up
and the recyclability of the feed solution, especially incross-flow filtration.
Sweeney et al. (Sweeney S.F. et al., 2006) performed
continuous ultrafiltration experiments to separate Au NPs
by a hollow-fiber type dialysis membrane where the feed
pressure is about 100 kPa. When a 70 kDa dialysis membrane was used, 1.5 nm Au NPs passed through the membrane, while 3 nm Au NPs remained in the feed solution.
They also performed a fractionation experiment ofpolydispersed Au NPs by a four-cascade filtration set up
using 10, 30, 50, and 70 kDa dialysis membranes, whichyielded four fraction samples with average diameters of
2.0, 2.5, 2.6, and 2.9 nm, respectively. Dead-end ultrafiltration was attempted for the preparation of Ag NPs witha narrow size distribution from a polydispersed sample(1–100 nm size range). When 5, 10, 50, and 100 kDa
membranes were used, NPs with sizes less than 4, 6, 15,
and 25 nm passed through the respective membrane(Palencia M. et al., 2014). Another example was performed by Xie et al. (Xie Q.L. et al., 2009). They used
cellulose acetate microfiltration membrane with pores of
5 μm in size for the separation of 100–150 nm and 300–
450 nm Fe2O3 particles.
Novel materials for the separation of NPs have beendeveloped. Porous nanocrystalline silicon (pnc-Si) wasused as a 15-nm-thin free-standing membrane (Gaborski
T.R. et al., 2010). The pnc-Si membranes could be used indead-end filtration to fractionate gold NPs sized 5–30 nm.
Mekawy et al. (Mekawy M.M. et al., 2011) synthesized
hexagonally ordered mesoporous silica on the anodic alumina membranes (AAM). The hexagonal mesoporous silica was vertically aligned in the AAM channels with apredominantly columnar orientation. The hollow meso-­
structured silica had tunable pore diameters varying from
3.7 to 5.1 nm and was used to separate Ag NPs, whose
size range was changed from 1–17 nm (before filtration)
to 1–4 nm (after filtration).

3.2 Chromatography
In column chromatography, porous materials arepacked in a chromatography column and macromolecules
or particles pass through these materials. The pass line
depends on particle size and the interaction between theYasushige Mori / KONA Powder and Particle Journal No. 32 (2015) 102–114

particles and porous materials.
Gel permeation chromatography (GPC), also known assize exclusion chromatography (SEC), has been developed
for the separation of macromolecules and NPs. Smaller
particles elute more slowly because they can more easily
enter pores of many sizes and thus interact with the surface of porous materials. GPC can be used for the size
separation of CdSe NPs (Shen Y. et al., 2013). Aspanut etal. (Aspanut Z. et al., 2008) demonstrated the size separation of CdS, ZnS, and silica colloids by GPC. Au NPs
covered with 1,1'-binaphthyl-2,2'-dithiol have been fractionated by SEC (Gautier C. et al., 2008). Sodium dodecyl
sulfate is sometimes useful to stabilize NPs in SEC operation (Liu F.K., 2007).
Hydrodynamic chromatography (HDC) proposed by
Small (Small H., 1974) is a technique for the size fractionation of colloids in a size range from a few nm to a few
μm. There are two subtechniques of HDC; packed column
HDC and capillary tube (open tubular) HDC. Particles are
injected into a mobile phase either through a column
packed with nonporous materials or through a long capillary tube. The migration rate of the particles depends ontheir size and the size of the packing material or capillary
tube. In either case, the variation of flow across the interstitial voids (in the case of the packed column type) or
across the capillary (capillary tube type) causes larger
particles to travel more quickly and smaller particles totravel more slowly. Packed column HDC with a columnof spherical nonporous silica of 1.9 or 3.1 μm in size wasapplied to the separation and size characterization of col-loidal silica sized 5–78 nm (Takeuchi T. et al., 2009).
Fischer and Giersig (Fischer C.H. and Giersig M., 1994)
used capillary tube HDC with a wide-bore polyether ether
ketone capillary to separate CdS NPs and gold NPs in the
diameter range between 3 nm and 27 nm.
A unique continuous-flow separation device was proposed as shown in Fig. 4 (Huang L.R. et al., 2004). The
array of micro-columns in each chamber of this device isslightly off set from the previous one. When a sample ispumped through the device, small particles are able topass by the columns in a relatively straight line in the direction of the flow. Larger particles that are above a critical size are unable to follow the flow streams upon
encountering the columns and are instead bumped laterally, resulting in size-based separation. This device
showed continuous separation of the mixture of 0.8, 0.9,
and 1.03 μm microspheres.

4. Control of stability and dispersivity
4.1 Size-selective precipitation
The surfaces of most NPs are modified by adsorbed ligands to increase their solubility or dispersivity in a solution. When a well-dispersed NP solution is mixed withother miscible materials, the stability condition of the ligands absorbed on the surface of the NPs sometimes decreases, resulting in the aggregation or precipitation of
the NPs, because the interaction among the NPs changes
from repulsive to attractive because of the change in ligand condition. If this interaction depends on the size ofNPs, size-selective separation occurs. This idea, i.e., thecontrol of the stability or dispersivity of NPs, was realized
as the size-selective precipitation (SSP) or size-selective
fractionation techniques where gas, liquid, or salt are used
as other miscible materials.
The stability of NPs is described by DLVO theory,
where the interparticle potential VDLVO consists of thevan der Waals potential VA, the electrostatic potential VR(Israelachvili J.N., 2011);
VDLVO
 VA  VR .

Fig. 4 Schematic of a microfluidic device for continuous-flow
separation. The microfluidic channels and the matrix
were fabricated on a silicon wafer using conventional
photolithography and deep reactive ion etching techniques (Huang L.R. et al., 2004).

(6)

At very small interatomic distances, the electron clouds
of atoms of the particle surface overlap, and there arises astrong repulsive force which is called as the Born repulsion VB (Israelachvili J.N., 2011). Furthermore, in the caseof the ligand-stabilized particle system, the steric interactions provided by adsorbed ligands are considered. For
the steric interaction potentials, Vincent et al. (Vincent B.
et al., 1986) proposed osmotic and elastic terms in the location where the surface ligands overlap each other. An
osmotic potential VO that comes from the high density of
the ligand at the surface of the particles represents the in-107

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teraction potential between ligands in the solvent. On theother hand, the elastic potential VE arises from the entropy
loss when the particles approach each other and the compression of the ligand occurs. Total interaction potential
between NPs VT can be expressed asV
T VDLVO  VB  VO  VE .

(7)

These steric repulsion potentials, i.e., the osmotic andelastic potentials, are only effective when the ligands
overlap each other and are basically described by analogical equations for polymer stabilized particles based on the
Flory−Huggins theory (Anand M. et al., 2008; Shah P.S.
et al., 2002).
There have been experimental findings of 1-thioglycerol-­capped ZnS NPs dispersing well in water but precipitatingin ethanol. In this case, water and ethanol were labeled“good” and “poor” solvents, or “solvent” and “antisolvent”, respectively (Komada S. et al., 2012; Nanda J. etal., 2000). Fig. 5 shows the calculation results of particle
interaction potentials between 1-thioglycerol-capped ZnS
NPs of 2 nm in diameter in (a) water or (b) ethanol, using
Eqn. (7) with parameters listed in Table 1. The total interaction potential in water works as repulsion in all distances between NPs because the osmotic and Born
repulsion interactions are stronger than the van der Waals
attraction interaction. On the other hand, the pure ethanol
system has a potential valley resulting from the domination of the van der Waals attraction over the steric interactions, although the Flory-Huggins parameter χ of pureethanol is less than 0.5, which means that ethanol is also agood solvent for 1-tioglycerol-capped ZnS NPs. Thus,
ethanol works as poor solvent, and this calculation suggests that ZnS NPs flocculate in a pure ethanol solution,
which is consistent with the experimental results (Komada
S. et al., 2012; Nanda J. et al., 2000).
When mixture solvents of water and ethanol were usedas the solution with dispersed 1-tioglycerol-capped ZnSNPs, it was experimentally found that a portion of theNPs was precipitated and larger NPs aggregated morethan smaller NPs (Komada S. et al., 2012). Fig. 6 shows
the calculation results of the size-dependent particle interaction potentials when the particle diameter of 1.5, 2.0,
and 2.5 nm in a mixture solution of 40 wt% water and
60 wt% ethanol. According to the calculation results, thesmall particles have a larger potential barrier and shallower potential depth than the large particles. This meansthat small particles are more stable because of the large
potential barrier and easy redispersion due to the shallow
potential depth. This size dependency agrees with the
SSP concept that larger particles first flocculate when
adding miscible material to the well dispersed colloidal
suspension.

108

Fig. 5 Individual and total interaction potentials of 1-thioglycerol-­capped ZnS nanoparticles calculated for pure (a) waterand (b) ethanol systems. All the calculation parameters
were as listed in Table 1, and the particle size was 2 nm.
Table 1 Parameter values used for the calculation of particle
interaction between two spherical 1-thioglycerol-capped ZnS
NPs in water or ethanol systems.
WatersystemEthanolsystem1.65 × 10 –19

1.53 × 10 –19

Refractive index of solvent

1.33

1.36

Relative dielectric
constant of solvent

78.3

24.6

Surface potential of NPs [mV]

–16

+35

Debye-Hückel parameter [nm –1]

1.04 × 10 –3

5.52 × 10 –5

Solvent molar volume [m3/mol]

1.81 × 10 –5

5.86 × 10 –5

Parameter
Hamaker constant [J]4.2 Gas addition
A mixture of an organic solvent and a compressed gas,
where the gas dissolves into the liquid phase at an appliedpressure less than the vapor pressure of the gas, is called ina gas-expanded liquid (GXL) (Saunders S.R. and Roberts
C.B., 2012). GXLs have highly tunable physico-chemical
properties between those of the liquid organic solvent andYasushige Mori / KONA Powder and Particle Journal No. 32 (2015) 102–114

4.3 Solvent addition

Fig. 6

Total particle interaction potential for the particles of
1.5, 2.0, and 2.5 nm diameter at 60 wt% of the ethanol
concentration in water.

Table 2 Reports of SSP using CO2 gas as the miscible material
NPs

LigandSolvent

Reference

Semiconductor and Si NPs were successfully fractionated by SSP where a miscible (poor) solvent was added toa (good) solvent dispersed NPs well. Table 3 listed some
reports using SSP with two-solvents system. In case of
semiconductor NPs, there are two categories from thepoint of a good solvent; aqueous (water) and nonaqueous
solvent (alcohol, toluene, chloroform). Whatever water or
nonaqueous solvent was used as a good solvent, alcohol
was usually chosen as a poor solvent. Murray et al. (Murray
C.B. et al., 1993) demonstrated size separation of CdE(E = S, Se, or Te) NPs capped with TOPO using SSP with
1-butanol as a good solvent. Vossmeyer et al. (Vossmeyer
T. et al., 1994) reported that narrower-sized samples of
CdSe NPs capped with 1-thioglycerol were obtained by
SSP using water as a good solvent. Mastronardi et al.
(Mastronardi M.L. et al., 2012) classified Si NPs by size,
and reported that the PL quantum yield of the NPs decreased with decreasing size.
Segets et al. (Segets D. et al., 2013) evaluated the particle size distributions of 1-tioglycerol-capped ZnS NPs before and after SSP and quantitatively discussed theclassification efficiencies of SSP, such as cut size, separation efficiency, and sharpness of classification from thepowder technology field.

Ag, AuDDT

chloroform(Shah P.S. et al.,
2002)

Ag

DDT

hexane(Mcleod M.C. et al.,
2005)

Au

DDT

hexane(Bhosale P.S. andStretz H.A., 2008)

Ag

DDT

hexane(Von White G. andKitchens C.L., 2010)

4.4 Salt addition

Au

DDT, HThexane–acetonemixture(Saunders S.R. andRoberts C.B., 2011)

Au

DDT

hexane(Vengsarkar P.S. andRoberts C.B., 2013)

Au

OA SA

DMSO

(Duggan J.N. andRoberts C.B., 2014)

CdSe/ZnS TOPO

hexane(Anand M. et al.,2007)

Zhao et al. (Zhao W. et al., 2010) performed separationsof oligonucleotide-capped Au NPs mixture of different
sizes by salt-induced aggregation. The larger sized NPs
aggregated at lower salt concentration, while the smaller
NPs were in supernatant due to the combination of vander Waals attraction and electrostatic repulsion by negatively charged oligonucleotides. They demonstrated theseparation performances of 10/20 nm mixture at 0.3 M
NaCl with 10 mM sodium phosphate buffer (SPB) solution and of 20/40 nm mixture at 0.1 M NaCl with 10 mM
SPB solution.
Wang et al. (Wang C.L. et al., 2010) reported the SSP of
CdTe NPs stabilized 3-mercaptopropionic acid or thioglycolic acid in an aqueous solution by adding MgCl2 solution. They compared this with SSP by adding 2-propanol
as a poor solvent. They concluded that SSP added salt was
simpler and more rapid than SSP added poor solvent because the condensation and heating processes were difficult for the latter SSP operation. Moreover, the cost
efficiency of SSP with an added salt was higher, making it
possible for use on a large production scale.

DDT: dodecanethiol, HT: hexanethiol, OA: oleic acid, SA:steric acid, TOPO: trioctyl phosphine oxide, DMSO: dimethyl
sulfoxide

those of the gas based on temperature and gas pressure.
The use of GXLs provides several processes such as thesynthesis or thin film preparation of NPs. SSP of NPs is
one application of GXLs.
Table 2 summarizes the reports of SSP of NPs using
CO2 gas as a miscible material. Although most reports
concerned novel metals, it is clear that this method has
several advantages, i.e., easy control of the mixture properties by adjusting gas pressure and easy gas recycling bydepressurization.

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Table 3

Report of SSP using poor solvent
NPs

ligandgood solvent

poor solvent

Reference

CdE E = S, Se, Te

TOPO

1-butanol

methanol(Murray C.B. et al., 1993)

CdSe, CdTe, InP

TOP, TOPO

toluene, n-hexane, methanol
chloroform(Talapin D.V. et al., 2002)

InAs:Mn

TOP

chloroform

ethanol(Stowell C.A. et al., 2003)

GaSe

1-HDA

methanoloctane(Chikan V. and Kelley D.F., 2002)

CdS

1-TG

water

ethanol, acetone,
2-propanol(Vossmeyer T. et al., 1994)

ZnS

cysteinewater

ethanol(Bae W. and Mehra R.K., 1998)

CdTe

1-TG, TGA, 2-ME water

2-propanol(Gaponik N. et al., 2002)

ZnSexTe1-x

TGA

water

2-propanol(Li C. et al., 2008)

ZnS:Mn

1-TG

water

2-propanol(Komada S. et al., 2012, Segets D.
et al., 2013)Si

octadecene

chloroform

methanol(Li X. et al., 2004)Si

1-octene

toluenemethanol(Liu S.M. et al., 2006)Si

allylbenzene

toluenemethanol(Mastronardi M.L. et al., 2012)

TOPO: trioctylphosphine oxide, TOP: trioctylphosphine, 1-HDA: 1-hexadecylamine, 1-TG: 1-thioglycerol, TGA: thioglycolic acid, 2-ME: 2-mercaptoethanol

5. Others

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Author’s short biography
Yasushige Mori
Dr. Mori is currently Professor of Department of Chemical Engineering and Materials
Science at Doshisha University, Japan. He received his Doctor of Engineering from
Kyoto University in 1980. His research interests include the formation of nanoparticles
of metals and/or semiconductors in liquid system, measurements of interaction forces
between particles and plates, particle size analysis, and application of titania nanoparticles for solar cells.

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KONA Powder and Particle Journal No. 32 (2015) 115–130/Doi:10.14356/kona.2015025

Review Paper

Model-Based Optimization of Industrial Gas-Solid Reactors †Venkataramana Runkana
1

Tata Research Development and Design Centre, Tata Consultancy Services Limited, India

Abstract
Heterogeneous non-catalytic gas-solid reactors are commonly used in the production of chemicals, metals andmetal oxides, for example, rotary drum reactor for producing quick lime; blast furnace, a moving bed reactor for
producing iron, etc. Industrial processes involve multicomponent mixtures of solids as most of the naturally
occurring materials such as minerals are multicomponent in nature. Generally the raw materials areheterogeneous and their physical and chemical characteristics vary from one source to another. The solid particles
are also usually moist, the moisture content varying from season to season. The physico-chemical phenomenathat take place in these reactors include flow of gases through porous media, heat transfer between the gases,
solids, equipment and the environment, evaporation and condensation of moisture, reactions between gases andsolids and within a single phase, melting and solidification depending on the temperatures inside the reactor, etc.
The flow of particles is also of great importance especially in rotary drum and moving bed reactors. Generalfeatures of the mathematical models for non-catalytic reactors are described along with models for the phenomena
mentioned above. Case studies from the iron and steel industry on model-based optimization of production of
direct reduced iron in a rotary kiln and induration of wet iron ore pellets on a moving packed bed reactor are
discussed here.
Keywords: multiphase reactors, modeling, optimization, soft-sensors, iron and steel, pelletization

1. Introduction
Production of many commodity chemicals, metals, ceramics and polymers involve reactions between gases andsolid particles, which may act as catalysts in some cases
and in other cases, participate in the reaction with thegases directly. The reactors commonly used in the industry include stationary and moving packed bed reactors,
rotary drum reactors or rotary kilns, vertical shaft kilns
or moving bed reactors, fluidized bed reactors, rotary
hearth furnaces, etc. For example, vertical shaft or rotarykilns are used for the production of quick or burnt lime inthe soda ash, pulp and paper (Fernandes and Castro, 2000),
and iron and steel industries (Watkinson and Brimacombe,
1982; Davis and Englund, 2003; D’Abreu et al., 2008).
Rotary kilns are also used for producing sponge iron or
direct reduced iron (Feinman and Mac Rae, 1999), cement(Peray, 1984; Mastorakos et al., 1999; Majumdar et al.,2007), for waste incineration (Marias, 2003; Yang et al.,2003), and for combustion or pyrolysis of coal (Patisson
et al., 2000a, 2000b; Li et al., 2003), maize (Klose and†1

Received 7 October 2014; Accepted 14 November 2014
J-STAGE online 28 February 2015
54-B, Hadapsar Industrial Estate, Pune, India, 411013
E-mail: venkat.runkana@tcs.com
TEL: +91-20-66086268 FAX: +91-20-66086399©2015 Hosokawa Powder Technology Foundation

Weist, 1999), etc. Moving packed bed reactors are employed for sintering of iron or zinc ores (Chatterjee et al.,
1993; Nakano et al., 2010; Ahn et al., 2013), for indurationof iron ore pellets on a moving or traveling grate or strand(Thurlby et al., 1979; Barati, 2008; Sadrnezhaad et al.,2008), and for gasification of coal (Yoon et al., 1979;
Hobbs et al., 1992). The blast furnace, a moving bed reactor, is utilized for making hot metal or liquid iron in an
integrated steel plant (Omori, 1987; Ueda et al., 2010a).
Fluidized bed reactors are employed for roasting of zincand sulfide ores (Nyberg, 2004; Shu et al., 1999), for the
production of polycrystalline silicon (Ranjan et al., 2011)and polyethylene (Kiashemshaki et al., 2006), etc. Rotary
hearth furnaces are employed for reduction of iron oresby volatiles (Sohn and Freuhan, 2005, 2006a, 2006b). In
several cases, besides gases and solids, the reactor too is
non-stationary. For example, the rotary kiln revolves
around its axis whereas sintering (see Fig. 1) or induration reactors move continuously on a traveling grate. The
industrial scale reactors are also generally large in size
because of the large tonnage production of materials. For
example, the reactors used for sintering of wet iron or
zinc ore granules and for induration of wet iron ore pellets
are rectangular in shape and have volumes in excess of
500 m3 whereas the blast furnace is irregular in shape andhas volumes in excess of 5000 m3 (Ueda et al., 2010a).
The focus of this paper is on the non-catalytic gas-solid

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Venkataramana Runkana / KONA Powder and Particle Journal No. 32 (2015) 115–130

Fig. 1

Schematic of a traveling grate reactor for sintering ofiron ore granules.

reactors only.
As the process involves particulate solids which areheterogeneous in nature and usually have a wide size distribution, process monitoring, optimization and control
are relatively complex. The main objectives of processoptimization are enhancement of reactor productivity, efficient utilization of raw materials and fuels, productquality improvement, reduction of energy consumption,
environmental compliance, maintaining the equipment ingood health and ensuring its availability.

1.1 Challenges faced in process optimization
The process engineers face several challenges in process monitoring, optimization and control. Firstly, as theearth’s natural resources are getting depleted, availabilityof good quality raw materials from a single source has
become relatively rare. Hence, the plant operators have toblend raw materials from different sources which invariably leads to inhomogeneity and variability in quality.
Secondly, the production as well as product quality demands keep changing depending on the market demands.
Thirdly, as the effect of industrial operations on climatechange is well established, there is a need to optimize theoperations to ensure environmental compliance.
It is evident that one needs to undertake multi-objective
optimization in order to achieve these goals. However,
because of difficulties or uncertainties with numericalconvergence and because it is computationally intensive,
single objective optimization is commonly carried out toachieve the highest priority goal while ensuring that thevariables representing the other objectives are within thedesired limits, as stipulated by either upstream or downstream process requirements.
In the case of industrial process optimization, there arestringent requirements for the mathematical models. The
key requirements of these models and related softwaretools include validity over diverse operating conditions,stringent prediction accuracy, ability to handle abnormal
or missing values of variables, interactions between dif-116

ferent input variables, uncertainty in input data, error anduncertainty of model predictions within and beyond theoperating regimes for which the model was tuned or customized, etc. This necessitates development of reasonably
comprehensive and accurate models, such as those mentioned above, and testing their validity and accuracy withcurrent and past data. This, in turn, requires extensive application of data mining and analysis techniques, identification of key process variables from a large set (consistingof several hundreds of variables in some cases), and development of relationships between variables. One of theimportant steps here is the prediction of variables thatcannot be measured online, using fundamental models
and establishing relationships between measured and unmeasured variables using data collected over several
years. Hence, there is a need for high fidelity lumped or
distributed parameter models which could be deployed as
soft-sensors for on-line optimization as well as for advanced process control. Moreover, these models could beincorporated into general purpose simulation softwaretools such as SolidSim for processing of particulate solids(Hartge et al., 2006) for designing processes.

1.2 Need for soft-sensors and data analytics
It is difficult and in some cases impossible to measure
many key intrinsic process variables either because of lackof suitable sensors or instruments or because the particles
and/or the equipment may be in motion which makes it
difficult to make measurements. The sensing element may
also get damaged due to the impact of solids. Moreover,quality parameters of products that are in particulate
form, for example, strength, reactivity or chemical analysis, have to be determined through off-line laboratory
analyses as it is not possible to determine them in-line inmost cases. Hence, there is a necessity to develop and install soft-sensors which could be employed for on-line optimization and control.
As is the case with most of the manufacturing processes
which involve complex interplay of several physico-chemical
phenomena, it is always not feasible to represent all thephenomena using first principles and relate them to properties of the end-products. For example, the strength ofiron ore sinter and pellets depend strongly on the mineralphases formed and reactions that occur at high temperatures but a representative model for these phenomena is
still elusive. In such cases, the usual practice is to resort todata-based modeling techniques (Ge et al., 2014; Kaneko
and Funatsu, 2014) for soft-sensor development. However,
it would be more appropriate to address this problem using a hybrid phenomenological-statistical modeling approach, employing the mathematical model of the process
to predict internal condition of the reactors, for example,
temperature, chemical composition, etc., and then use theVenkataramana Runkana / KONA Powder and Particle Journal No. 32 (2015) 115–130

simulated and raw process data to predict the propertiesof the end-product (Runkana and Majumder, 2013).
Gas-solid reactors are very common in several industries and fundamentals of reaction kinetics and reactor
design have been described in detail elsewhere (Szekely
et al., 1976; Smith, 1981; Levenspiel, 1988). Modeling andoptimization of gas-solid reactors has been studied extensively (Schaefer et al., 1974; Cumming and Thurlby, 1990;
Rovaglio et al., 1994; Cross and Blot, 1999; Sadri et al.,2007) and it is difficult to review the entire subject in detail here. The author has been involved in the developmentof mathematical models for industrial scale gas-solid reactors, related software tools (Virtual SinterTM, Virtual
InduratorTM and DRIKSTM) and their application for various industrial problems, for example, sintering of iron ore
fines (Venkataramana et al., 1997, 1998, 1999, 2002;
Gupta and Runkana, 2000; Kapur and Runkana, 2003), induration of iron ore pellets on a traveling grate (Majumder
et al., 2009, 2012; Mitra et al., 2009; Cavalcante et al.,2012; Runkana and Majumder, 2013), direct reduction ofiron ore by coal in a rotary kiln (Runkana et al., 2007;2010a, 2010b, 2010c), and calcination of limestone in a
vertical shaft kiln, etc. The objective here is to share thisknowledge of model development incorporating important physico-chemical phenomena, simulation and optimization techniques employed and details of theirdemonstration for a couple of industrial cases. However,appropriate references are provided so that the interested
reader can explore the subject in greater detail.
This paper is organized as follows: Mathematical modeling of gas-solid reactors is described in detail in Section
2. The simulation and optimization techniques applied are
discussed in Section 3. Industrial case studies on direct
reduction of iron ore by coal in a rotary kiln and induration of iron ore pellets on a traveling grate are presented
in Sections 4 and 5, respectively. Finally, concluding remarks and suggestions for future research are provided in
Section 6.

2. Mathematical modeling of gas-solid
reactors
The industrial reactors could be co-current, cross-current
or counter-current in nature with respect to the flow ofgases and solids. The process is heterogeneous in naturesince particulate solids are involved and it is appropriate
to represent gas-solid reactors using distributed parametermodels. However lumped or distributed parameter models
are formulated, depending on the intended application of
the model. Mass and energy balance models are formulated as nonlinear algebraic equations, especially in the
preliminary stages of development, to determine unmeasured process variables and for data reconciliation. The

one-dimensional models are usually formulated as a set ofordinary differential equations whereas two- andthree-dimensional models are formulated as partial differential equations employing the Cartesian or cylindricalcoordinates, depending on the geometry of the reactor.
The basic physico-chemical phenomena that take placein gas-solid reactors include: charging of particles andformation of particulate bed, flow of particles and gases
and their mixing, heat transfer between gases, solids,
equipment and the environment, mass transfer between
gases and solids, drying or evaporation of moisture fromwet particles or agglomerates, reactions within or between
gas and solid phases, partial melting at high temperaturesand subsequent solidification as temperature decreases,
condensation of moisture from the gas phase, etc. Whilethe individual phenomena occur based on the thermodynamics or kinetics that govern them, there will be interactions amongst the phenomena that make the processcomplex to understand. Because of the interactions
amongst the underlying phenomena, it is important to incorporate all the representative phenomena, to the extent
possible, and solve the equations in a coupled manner.
From the process systems engineering perspective, thegas-solid reactors are represented in terms of process
variables (flow rate, chemical composition, temperature,pressure, particle size distribution, etc.), raw material
characteristics (chemical composition, particle size distribution, surface roughness, reactivity, strength, etc.),
equipment or reactor design parameters (length, diameter,height, type of refractory, refractory wall thickness, speedof revolution, traveling grate speed, material used forshell and shell thickness, etc.), environmental conditions(pressure, temperature, humidity, chemical composition),
product quality indices (strength, reactivity, porosity),
physical or thermodynamic properties of the materials involved as functions of temperature and pressure (density,
viscosity, specific heat, thermal conductivity, etc.), andmodel parameters (heat and mass transfer coefficients, reaction rate constants, etc. as functions of temperature
and/or pressure).
Porosity of individual particles or agglomerates and theparticulate bed voidage play an important role in the heatand mass transfer between the gases and the solids. The
bed voidage especially is a crucial parameter and depends
on the size and shape distribution of particles, deformation and consolidation behavior in the case of agglomerates, reactor geometry, and on the mechanism of charging
of the particles into the reactor.

2.1 Charging and flow of solids
Permeability of the particulate bed has a strong influence on the key performance indicators of the reactor,
namely, productivity, product quality, energy or fuel effi-

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ciency and environmental compliance. The bed permeability in turn is a function of the bed voidage which isrelated to the shape and size distributions of the particles,
the manner in which the particles are charged into the reactor, and deformability and consolidation behavior incase of wet granules and pellets (Hinkley et al., 1994;
Venkataramana et al., 1999).
Generally the particles are irregular in shape and havea wide size distribution which leads to segregation andnon-uniform bed voidage in the axial and radial directions. The particle shape is commonly represented using
the sphericity and the flow of particles is modeled bytreating the bed of particles as a continuum. More recently, however, the discrete element method (DEM)(Cundall and Strack, 1979) has been applied to simulate
flow of particles wherein not only the particles can betreated as individual entities but the particle shape canalso be taken into account by creating irregular shaped
particles using the sphere as a basic building block.
The structure of the particulate bed that forms inside
the reactor depends quite a lot on the mechanism of
charging, the device used, and segregation behavior andphysical properties of the particles. For example, chutes
or inclined plates are used for charging of particles fromsilos or bins onto the moving grate in the case of sinteringand induration of iron ore pellets. More complex devices
such as the bell-type or a rotating chute are employed forcharging of particles in vertical shaft kilns and blast furnaces (Omori, 1987; Mio et al., 2008, 2009). Since particles are heterogeneous in nature, segregation is a common
phenomenon. It could happen due to differences in particle size, density, shape, surface roughness and also due tointeractions between particles (Hogg, 2009). The bed
voidage is the main parameter that represents the structure of the bed. Since it is difficult to measure this in large
scale reactors, it is either determined through experimentson the laboratory scale or used as an adjustable parameterin mathematical models. However, simple empirical expressions were derived for the bed voidage as a functionof the parameters of the particle size distribution such as
normalized inter-fractile range, mean or median particle
diameter, normalized inter-fractile coefficient of skewness, dispersion of the distribution, and moisture content incase of granules or pellets, and applied for laboratory scale
or industrial scale packed bed reactors (Venkataramana etal., 1999; Majumder et al., 2012). Theoretical models forparticle packing have also been proposed in the literature(Lee, 1970; Bierwagen and Saunders, 1974; Gan et al.,2004) and were applied to predict voidage or maximum
packing fraction as a function of the particle size distribution (Servais et al., 2002; Muralidharan and Runkana,2009). The DEM was also applied for simulation of packing of spherical particles in a cylinder (Siiriä and Yliruusi,2007).

118

The flow of particles inside the reactor depends on thereactor geometry, size and shape distributions of the particles and the fluid flow, especially in the case of fine particles and fluidized bed reactors. Kunii and Levenspiel(1997) described a number of gas-solid contacting patterns depending on the gas velocity and bed structure in a
fluidized bed. The flow of heterogeneous particulate solids in rotary drums was analyzed in detail by Abouzeid
and Fuerstenau (2010) along with experimental evidenceof the effect of particle properties on residence time distribution inside the drum.
Since the computational time required for simulation ofindividual particles is enormous, the continuum approach
was followed earlier to simulate flow of particles. Saeman(1951), and Kramers and Croockewit (1952) first developed a steady state model for transport of solids. Saeman(1951) used experimental observations of Sullivan et al.
(1927) to develop a mathematical expression for solids
residence time and volumetric transport rate in the kiln as
a function of kiln rotation speed and its angle of inclination. Kramers and Croockewit (1952) developed an expression for bed height variation along the kiln axis as afunction of volumetric flow rate of solids, kiln inclination,
angle of repose of solids, kiln internal radius and speed ofrotation. Henein et al. (1983) developed a diagram to represent several of modes of operation of a rotary kiln suchas slipping, slumping, rolling, cascading, cataracting andcentrifuging. Boateng and Barr (1996) developed an analytical model to predict particle segregation within a rotary kiln as a function of diameter and speed of rotationof kiln and bed depth. Khakhar et al. (2001) developed acontinuum model to predict the extent of mixing and size
segregation for binary mixtures of different sized particles but of same density. Liu et al. (2005) developed ananalytical expression to calculate maximum active layer
thickness and mass flow rate in the layer as function ofkiln geometry and operating parameters.
With the advent of computationally efficient processors
and high performance computing systems, the DEM isalso employed nowadays for particle flow simulations.
Moysey and Baird (2009) studied segregation of Nickel
pellets in the surface flow of a packed bed whereas Liu etal. (2013) applied DEM for studying the transverse mixing of wet particles in a rotary drum. Mio and coworkers(2008, 2009) studied the effect of chute angle on charging
behavior of sintered iron ore in bell-less type charging
system of a blast furnace and validated simulations ofparticle segregation on the chute with laboratory experimental data. Ueda et al. (2010b) simulated descent of particles and its effect on the stress distribution within thepacked bed of a blast furnace. Discrete element simulations for particulate systems and their industrial applications were reviewed in detail elsewhere (Zhu et al., 2008).

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2.2 Fluid flow and heat transfer
Intimate mixing of gases and particulate solids is crucial for efficient heat and mass transfer, and reactions between the two phases. In case of rotary drum reactors, thefreeboard gases flow mostly over the solids. On the other
hand, in case of packed or moving bed reactors, the gases
have to pass through a porous bed of particles and also
through the particles. Not only the bed voidage but also
the particle voidage play an important role here. The
Navier-Stokes equations are commonly applied for flow ofgases in rotary kilns and in fluidized bed reactors whereas
the Ergun equation or the Darcy’s law is employed forflow through porous media.
The heat transfer process in multiphase reactors iscomplex and involves transfer of heat through radiation,
convection and conduction between gases, solids, reactor
wall and the environment. The dominant mode of heattransfer between the two phases depends on the nature offlow of solids and gases. Convection is generally the more
dominant mechanism for heat transfer between phases
and also when the particles are porous in nature, whereas
heat transfer occurs mainly through conduction within
the solid particles.
Gorog et al. (1981) studied the radiation phenomenon inrotary kilns by evaluation of the fundamental radiative
exchange integrals with a modified reflection method using numerical analysis. Barr et al. (1989a, 1989b) conducted experiments with limestone, Ottawa sand andpetroleum coke to obtain net rates of heat transfer between solids bed, gas, refractory wall and radial flux forthe inside refractory surface as a function of circumferential position. They have also developed a cross-sectionalmodel consisting of a finite difference approximationof conduction in the kiln wall, together with the radiative and convective heat transfer coefficients for setting
up the boundary condition. Boateng and Barr (1996) developed a quasi-three-dimensional model for heat transfer
between gases and solids in a rotary kiln from a two-­
dimensional representation of bed transverse plane and one-­dimensional conventional plug flow model. The model
was shown to predict the temperature distribution in therolling bed, which tends to be isothermal at higher rotation rates, accurately. Dhanjal et al. (2004) investigated heattransfer in the transverse plane of solids bed in a rotarykiln and developed a model to scale up the experimental
results. They used mixing conductivity to simulate theparticle mixing effects in the active layer of heat transfer
between the two phases which was evaluated by fitting
temperature predictions to the experimental data. They
have shown that the segregation or particle size has little
effect on heat transfer within the bed.
Computational fluid dynamics (CFD) models are applied quite commonly now for simulation of fluid flow,

especially because of the availability user-friendly
simulation software such as Fluent TM , CFX TM , andCOMSOLTM. CFD models have been applied, for instance,
for the simulation of fluid flow as well as heat transfer inlime kilns, cement kilns (Mastorakos et al., 1999), for incineration of hazardous waste in a rotary kiln (Yang et al.,2003), for sintering of iron ores (de Castro et al., 2012), forgas flow in induration of iron ore pellets on a moving packed
bed reactor (Cross et al., 1990); and for the production ofpolyethylene in a fluidized bed reactor (Kiashemshaki et al.,2006). Recently, Verma and coworkers (2013, 2014) developed a two-fluid continuum model based on kinetic theory of granular flow to investigate bubble formation in a
gas-solid fluidized bed and validated the model with experimental data obtained using ultrafast electron beam
X-ray tomography. Since flow of gases and solids influence each other, especially for moving and fluidized bed
reactors, efforts have been directed towards coupling theDEM for flow of particles with the CFD model for flow ofgases (Tsuji et al., 1992; Tsuji, 1993; Zhou et al., 2008;
Yuu et al., 2010; Ueda et al., 2010b). Marias (2003) utilized the process simulation software gPROMSTM formodeling solids processes and coupled it with the CFD
tool FluentTM for modeling fluid flow to simulate a rotarykiln incinerator.

2.3 Evaporation and condensation of moisture
In case of many industrial processes, raw materials are
stored in the open and transported through open conveyors. Several materials are hygroscopic in nature and capture moisture during storage and transport, and during
rains. Water is sprayed in dry seasons to prevent dustingand it is also used commonly as a liquid binder in many
agglomeration processes such as balling and granulation.
The kinetics of drying is important because the moisturepresent in the solids not only delays the onset of reactions
between the gases and solids but also causes additionalpressure drop, especially in packed bed reactors. Considerable amount of fuel is consumed for evaporation of the
moisture from the particles. Similarly, when the productparticles are cooled with ambient air, moisture present in
the air condenses on the particles and leads to additionalpressure drop. The kinetics of drying was represented asa two-step process involving constant rate of drying up to
the critical moisture content in the first stage and thenfalling rate of drying in the second stage (Patisson et al.,1990) in the case of drying of iron ore granules, or as asimple first order rate process (Patisson et al., 2000a) in
the case of drying of coal. Although this is an importantphenomenon, it has not received as much attention andthere is a need to incorporate appropriate models for drying and condensation kinetics in the industrial reactor
models for the reasons stated above.

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2.4 Reaction kinetics
Both the solid and gas phases are usually multicomponent in nature and unlike controlled experiments in thelaboratory, industrial scale reactors involve series as wellas parallel reactions both within a phase and between
phases. For example, coal gasification and combustion, as
well as direct reduction of hematite by carbon monoxideand hydrogen occur simultaneously during the productionof sponge iron (Donskoi and McElwain, 2003). The coalgasification reactions are:C (s) + CO2 (g) → 2 CO (g)C (s) + H2O (g) → CO (g) + H2 (g)The direct reduction process involves the following
series-parallel reactions involving CO and H2: conversionof hematite (Fe2O3) to magnetite (Fe3O4):
3Fe2O3 (s) + CO (g) → 2Fe3O4 (s) + CO2 (g)
3Fe2O3 (s) + H2 (g) → 2Fe3O4 (s) + H2O (g)Conversion of magnetite to wustite (FeO):
Fe3O4 (s) + CO (g) → 3 FeO (s) + CO2 (g)Fe3O4 (s) + H2 (g) → 3 FeO (s) + H2O (g)Conversion of wustite (FeO) to metallic iron (Fe):FeO (s) + CO (g) → Fe (s) + CO2 (g)FeO (s) + H2 (g) → Fe (s) + H2O (g)The reactions can be exothermic or endothermic in nature. Combustion of gaseous or solid fuels to provide energy for the reaction is a common feature because manygas-solid reactions occur at high temperature. On theother hand, endothermic reactions such as decompositionof CaCO3 or MgCO3 to CaO or MgO and CO2 are common in the case of processes which involve utilization oflimestone or dolomite as a fluxing material. The shrinking core model or its variants are commonly used for gassolid reactions (Omori, 1987; Levenspiel, 1988; Homma etal., 2005). However, since particle porosity or pore size
distribution plays an important role in gas-solid reactions,the random pore model has also been employed (Bhatia
and Perlmutter, 1980, 1981; Kajitani et al., 2002). Heatand mass transfer and chemical reactions within a single
particle or pellet have also been modeled and incorporated
into the global reactor model (Kucukada et al., 1994;
Patisson et al., 2000a).

2.5 Melting and solidification
In some cases, the high internal temperature in a gas-120

solid reactor leads to partial or complete melting of theparticles depending on the melting point and chemicalcomposition of the materials present. For example, in thecase of iron ore sintering and induration, the internal temperature is on the order of 1400 °C which results in partial
melting of iron oxides. The molten material solidifies as
the temperature decreases during cooling with gases orambient air before the product is discharged from the reactor. The internal temperature inside a blast furnace canbe as high as 2000 °C and results in complete melting of
the iron oxides. The melting and solidification phenomena
have been incorporated in mathematical models in an approximate manner only (Patisson et al. 1991; Firth andManuel, 2005). They depend strongly on the temperatureand chemical composition of the materials involved.
There is a necessity to develop accurate thermodynamic
and kinetic models as well as appropriate experimental
studies to understand these phenomena.

2.6 Numerical solution and tuning of models
Depending on the nature of the models, that is, linear
or nonlinear algebraic equations, and ordinary or partial
differential equations, appropriate numerical techniquessuch as the Gaussian elimination, Newton-Raphson
method, Runge-Kutta method, Gear’s method, Finite difference or element or volume method, are commonly employed to solve the equations (Constantinides, 1987; Liuand Bhatia, 2001). For example, the mathematical model
for a rotary kiln can be cast as a two-point boundary
value problem, with properties of the solids at the feed
end and those of inlet gas at the discharge end of the kiln
as boundary conditions. The set of equations can besolved either simultaneously or iteratively (Martins et al.,
2001; Majumdar et al., 2007). The simultaneous solution
is computationally efficient but requires a good initial estimate of the dependent variables. Convergence problems
may also arise because a large number of nonlinear equations are solved simultaneously. The iterative technique ismore robust and does not require a good initial guess.
In the case of moving packed bed reactors such as traveling grates employed for sintering or induration of ironores, alternate direction integration schemes are utilized
for solving the mass, momentum and energy balance
equations (Majumder et al., 2009) from top to bottom orbottom to top for the gas phase, and from left to right forthe particles, in line with the motion of the two phases.
As mentioned earlier, mathematical models for industrial gas-solid reactors involve quite a large number ofmodel parameters such as heat transfer coefficients, reaction rate constants, emissivities, etc., which are dependent
on the nature of materials and on the reactor operating
conditions. It is necessary to tune or customize the model
parameters with actual plant measurements so that theVenkataramana Runkana / KONA Powder and Particle Journal No. 32 (2015) 115–130

models can be employed in a predictive mode. While it issimple or straight forward to minimize the error between
model predictions and plant data for the process variablesof importance through a parameter estimation algorithm
to obtain numerical values of model parameters, it does
not always lead to meaningful values of parameters. Thisis due to the highly nonlinear nature of the equations involved and due to mutual compensation (Runkana et al.,2005). On the other hand, a staged approach, comprisingof testing individual sub-models with representative plantor experimental data to obtain numerical values of the parameters relevant for that phenomenon in the first stage,
and then fine-tuning these parameters subsequently whilecustomizing the global model for the reactor is likely toyield realistic and robust model parameters. It is also important to note that the models have to be tested extensively with plant data obtained under different operating
conditions and for raw materials having different characteristics. In some cases, it may become necessary to develop look-up tables for model parameters for different
raw materials and for widely differing operating conditions so that the end-users can easily choose appropriate
numerical values and use the models for predictive simulations and for process optimization.

3. Framework for optimization
The framework for model-based on-line optimization,
shown schematically in Fig. 2, is described here. The key
components of this framework are a phenomenologicalmodel of the process, a statistical or phenomenological or
hybrid phenomenological-statistical model for productquality indices and an optimization algorithm. As mentioned in the Introduction, the optimum solution needs tobe arrived at by taking cognizance of the characteristicsof the raw materials, process operating conditions, equipment and environmental constraints, production targets
and product quality indices. The framework described

Fig. 2

here was implemented for on-line optimization as well as
advanced process control of industrial scale gas-solid reactors used for induration of wet iron ore pellets on a
traveling grate (Majumder et al., 2012; Cavalcante et al.,2012).
In order to undertake real-time or on-line optimization,
it is necessary to integrate the optimization system withthe plant’s supervisory control and data acquisition system (SCADA) or the distributed control system (DCS),
the laboratory information and management system(LIMS) and if necessary the manufacturing execution
system (MES) or the manufacturing operations management (MOM) system so that optimum solutions are obtained using the real-time or near real-time process andlaboratory analyses data. Interfacing of the optimization
system with the plant automation and information systems is done through an object linking and embedding forprocess control (OPC) compliant gateway. A real-time database acts as a data buffer for the data transfer between
the optimization system and the automation network of
the plant.
The phenomenological process model acts as a virtual
plant. It accesses data from the real-time database and isused to simulate the process to predict the state variablesor the internal conditions of the reactor, for example, temperature, and chemical composition of the gas and solid
phases, pressure and velocity of the gas phase, throughout
the reactor. This information, along with other data suchas raw material characteristics and chemical composition,
is used to predict product quality parameters. In otherwords, the process model and the product quality modelact as soft-sensors. The predictions from these models arethen utilized by the optimization algorithm along withconstraints such as limits on the quality indices, bounds
on the manipulated variables, equipment design parameters, and emission control measures, to determine the optimum values of the decision variables. The optimum set
points are either downloaded automatically to the SCADA
or the DCS or provided as an advisory to the plant engi-

General framework for model-based optimization.

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neers so that they can take informed decisions.
The optimization algorithm may be based on rigorous
nonlinear programming techniques such as sequential
quadratic programming (SQP) (Edgar and Himmelblau,
1989) or based on repeated simulations which may be
warranted in case of difficulties in convergence for constrained optimization. In case of multi-objective optimization, it may be necessary to invoke techniques such asgoal programming or genetic algorithms (Deb, 2001; Mitra
et al., 2009).
We have been involved in the development of mathematical models and their application for process optimizationand control of industrial gas-solid reactors. Two industrial
case studies are described in the following sections.

4. Direct reduction of iron ore by coal in arotary kiln
Direct reduced iron (DRI) or sponge iron is the preferred raw material for secondary steelmaking. India isthe largest producer of sponge iron in the world and majority of the sponge iron plants in India utilize non-coking
coal as the reductant. Rotary kilns are widely used in India for DRI production because of the ease of operationand control. We have developed DRIKSTM, a simulator
for DRI production in a rotary kiln. This software tool isbased on a fundamental mathematical model for direct reduction of iron ore by coal in a rotary kiln (Runkana etal., 2010b). The mathematical model was customized andvalidated with data from one of the industrial scale rotarykilns at Tata Sponge Iron Limited (TSIL) and was utilized
for process analysis and optimization. Complete details of
the mathematical model and the simulation software tool
are provided elsewhere (Runkana et al., 2010a, 2010c) andonly brief description of the process, mathematical modeland results of optimization are presented here.

4.1 Brief process description
The rotary kiln is a refractory lined steel cylinder inwhich the direct reduction process takes place in acounter-current fashion with iron ore, coal and dolomite
fed from the feed end of the kiln while air enters from thedischarge end (referred to as primary air). Secondary air
is introduced along the kiln length to provide additional
oxygen for the chemical reactions occurring inside thekiln and to improve gas mixing. Additionally, air is used
to inject coal pneumatically through a blower from thedischarge end. Coal acts as the reducing agent as well as
the source of energy.

122

4.2 Iron ore direct reduction model
The mathematical model developed was one-dimensionalin nature and it was meant for steady-state simulation of
direct reduction of iron ore. The overall kiln model includes appropriate sub-models for kinetics of reactions
involving hematite, magnetite, wustite, carbon, oxygen,carbon monoxide and hydrogen, and the flow of solids inside the kiln (see Fig. 3). The model consists of a set ofordinary differential equations, which includes overallmaterial and energy balances for the gas and the solid
phases, material balances for individual species in eachphase and height of the solids bed along the kiln length.
In order to predict gas and solid phase temperature andconcentration profiles along the kiln length, the rotarykiln is divided into a number of computational elements
over which material and energy balances for the gas andthe solid phases and mass balances for the individual species are written. In the direct reduction process various
physico-chemical and thermal phenomena occur inside
the rotary kiln, which result in transfer of material acrossphases, either due to phase change, such as evaporation ofmoisture or volatilization of species present in coal, or
due to chemical reactions, such as reduction of various
mineral phases or carbon combustion. All such mechanisms were considered in the mass transfer model.
Heat transfer between gases, solids, the kiln refractorywall and the shell occur by different mechanisms, depending on the temperature and the transport medium.
The mechanisms of heat transfer that were incorporated
in the model are: radiation and convection between gases
and the kiln refractory wall; radiation and convection between gases and solids; radiation and conduction between
the kiln refractory wall and solids; conduction in the kiln
refractory layer; conduction in the kiln shell layer; andconvection (natural or forced) and radiation from the kilnshell to the ambient air.
The kinetics of the following reactions or mass transfer
processes were incorporated in the model: Drying of particulate matter (solids) like iron ore, coal and dolomite,
which happens mainly in the pre-heating zone; Coal
devolatilization and gasification; Step-wise reduction ofiron ore, from hematite to magnetite, magnetite to wustite
and wustite to metallic iron by carbon monoxide and hydrogen, as described in Section 2. The volatile matter incoal was assumed to consist of tar, methane, carbon monoxide, carbon dioxide, hydrogen and chemically-boundwater.
The variation of the bed height along the kiln length
was related to the total flow rate of solids, kiln internal diameter, angle of inclination of the kiln with the horizontal
and the dynamic angle of repose of the solids. This information is used to calculate the angle and degree of filling,which in turn, are used to compute the axial velocity andVenkataramana Runkana / KONA Powder and Particle Journal No. 32 (2015) 115–130

Fig. 3

Schematic of the sponge iron or direct reduced iron kiln model showing sub-models including and inputs
and outputs of the model.

mean residence time of the solids inside the kiln. The angle of filling is useful in computing the interfacial area forheat transfer between gases, solids and the kiln wall. Additionally, the model is also capable of predicting the refractory wall and shell temperatures along the kiln length.

4.3 Model validation & process optimization
The model was tuned for the operating conditions in
one of the rotary kilns at TSIL. The gas and solid temperature profiles, measured using the K-type quick response thermocouples (QRTs) and the metallization (% Fein DRI), were used for tuning and validation of the model.
Typical results for validation of the model are shown in
Fig. 4 and Fig. 5 for gas and solid phases, respectively.
The model was used subsequently in a predictive mode
to identify optimum conditions of key process variables
to increase kiln throughput, to minimize coal consumption and to enhance kiln campaign life. The air profile andthe total carbon-to-iron (C/Fe) ratio were found to be critical to achieve the above objectives. The optimum valuesof these variables were determined through repeated simulations and they were maintained at their set points on aregular basis, which resulted in the reduction of coal consumption by about 55 kg/ton of DRI produced. As a resultof this model-based optimization exercise and additional
process optimization and control measures undertaken by
the plant engineers, the kiln campaign life has also increased beyond 300 days, from approximately 150–180
days for previous kiln campaigns (Runkana et al., 2010b).

Fig. 4Comparison of predicted kiln internal gas temperature
profile with measured profile at an iron ore flow rate of
22.2 tph and kiln speed of 0.46 rpm (taken from Runkana et al., 2010b).

Fig. 5Comparison of predicted kiln internal bed temperature
profile with measured profile at an iron ore flow rate of
22.2 tph and kiln speed of 0.46 rpm (taken from Runkana et al., 2010b).

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5. Induration of wet iron ore pellets on a
traveling grate
Pelletization is an important process in the iron andsteel industry. It is essentially a process for converting
raw iron ore fines into hard agglomerates or pellets, which
are fed to a blast furnace or a reduction furnace to makehot metal (liquid iron). Pelletization involves two steps,
balling of fine particles on a rotating disc into wet pellets,
which are then converted into fired pellets on a travelinggrate induration furnace (Fig. 6). The quality of fired pellets has a strong influence on the productivity of the reduction furnace and on the quality of hot metal. Hence,
maintaining pellet quality within the specifications is
critical while optimizing induration to enhance furnace
productivity and energy efficiency. One of the key problems encountered by plant personnel is that pellet quality
is analyzed off-line through laboratory analyses. Secondly, it is not possible to measure any intrinsic process

Fig. 6

Fig. 7124

parameters such as the time-temperature history of pellets
because the furnace moves continuously on a travelinggrate.
We have developed a software tool, Virtual InduratorTM
to address the above problems. The framework for model-based optimization and control of induration of wet
iron ore pellets is shown schematically in Fig. 7 (Runkana
and Majumder, 2013). This tool consists of a rigorous
model of the induration process (Fig. 8) (Majumder et al.,2009) which acts a soft-sensor for properties of pellets
and gases, a set of statistical models derived through data
analytics that act as soft-sensors for pellet quality and has
an in-built optimization algorithm based on sequential
quadratic programming.

5.1 Brief process description
A schematic of the typical straight grate iron ore induration furnace is shown in Fig. 6. The wet pellets are first

Schematic of the traveling grate reactor used for induration of wet iron ore pellets.

Components of the model-based optimization system for induration of iron ore pellets.

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Fig. 8

Schematic of the model for induration of iron ore pellets along with input and output variables.

charged onto the moving grate and pass through updraught drying (UDD) and down draught drying (DDD)
sections of the reactor for moisture removal from the pellets through evaporation. Subsequently, the temperatureof the bed increases slowly in the pre-heating zone (PHZ)
using recycled gases. In the firing zone or ignition zone
(FZ), the pellet bed is subjected to firing by a set of burners located in the control zones of the ignition hood at therecuperation section of the furnace. As the temperature of
the pellet bed increases further, partial melting takes
place, leading to fusion within the pellets. After the firing zone, as the pellet bed moves into the after-firingzone (AFZ), the bottom section of the pellet bed is ignited
primarily by re-utilizing the enthalpy of the recycled gas
stream from first stage cooling (CZ1). After the recuperation section, the pellet bed is cooled in the first (CZ1) andthe second stage (CZ2) cooling zones sequentially. To increase the energy efficiency of this energy intensive process, spent gas streams with high enthalpy content arerecycled to appropriate stages of the furnace.

5.2 Induration model
The induration process model is based on the fundamental principles of heat and mass transfer between gases
and pellets, chemical reaction kinetics for combustion ofcoke, calcination of limestone (CaCO3), kinetics of drying
and condensation of moisture, and partial melting and solidification of the solids. Complete details of the mathematical model can be found elsewhere (Majumder et al.,2009). The model consists of overall energy and materialbalances for the gas and solid phases, gas pressure drop
across the particulate bed, and material balances for individual species in the gas and solid phases. Forced convection is the dominant mode of heat transfer in this reactor.
The energy balance for the gas phase includes terms forconvective heat transfer between the gas and solid phases
and the heat transfer due to moisture evaporation/condensation. The energy balance for the solid phase includes
terms for the convective heat transfer, latent heat of moisture drying or re-condensation and enthalpy due to cokecombustion, limestone calcination and pellet melting. The
mass balances of the individual components in the gas
and the solid phases are formulated appropriately considering moisture evaporation or re-condensation, coke combustion and limestone calcination.
The moisture drying from the pellet was assumed totake place at a temperature midway between solid phaseand gas phase temperatures and is represented by twostages, constant and falling rates of drying. During theconstant drying rate stage, drying takes place at a constant rate till the moisture reaches the critical moisturelevel of 5 % (Patisson et al., 1990), after which drying by
falling rate stage follows. The coke combustion andlimestone calcination reactions within the pellets wereassumed to take place according to the shrinking coremodel (Levenspiel, 1988). The rate expression for kinetics
of coke combustion was taken from Wynnyckyj andBatterham (1985) and that for limestone calcination was
taken from Clixby and Young (1992).
One of the important inputs to the induration processmodel is the cold bed voidage which is used for calculating the bed permeability as well as the pressure drop
across the particulate bed. This is usually treated as an
adjustable parameter in the induration models. In ourmodel, cold bed voidage was calculated using the indicesthat represent the wet pellet size distribution, namely,
mean pellet diameter, normalized inter-fractile range andnormalized inter-fractile coefficient of skewness, andmoisture content of pellets (Venkataramana et al., 1999).
The quality of fired pellets obtained after induration isrepresented using parameters such as cold compressive125

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strength (CCS), tumble index (TI) and abrasion index(AI). These parameters depend not only on the characteristics of the raw materials used but also on the operating conditions during balling and induration, forexample, time-temperature history during induration whichcannot be measured. Hence, we have developed hybrid
phenomenological-statistical models for these parameters,
using raw material properties as well as time-temperature
history during induration, obtained through simulations
using the rigorous induration model (Runkana andMajumder, 2013).

5.3 Model validation & process optimization
The phenomenological induration model was tuned appropriately and validated with sufficiently large sets ofprocess data collected over several months of process operation at two industrial scale pellet plants in India andBrazil (Majumder et al., 2012; Cavalcante et al., 2012),
covering diverse operating regimes and raw materials.
Model parameters such as heat transfer coefficients, reaction rate constants, etc. were estimated either throughtrial and error or through nonlinear parameter estimation
techniques by minimizing the error between the model
predictions and the plant measurements. The accuracy ofthe phenomenological induration model was verified bycomparing the predicted temperature of gases in thewind-boxes below the induration reactor with plant measurements. A typical result for induration process model
validation with waste gas temperatures in the wind-boxes
is shown in Fig. 9.
After validating the model, process optimization was
carried out using an appropriate optimization algorithm.
Initially, multi-objective optimization was carried out using a genetic algorithm (Mitra et al., 2009) but since it
was computationally intensive, a single objective optimization problem was formulated and solved using the SQP(Majumder et al., 2012). The induration model and the
optimization algorithm were tested extensively with sev-

Fig. 9126

Comparison of the predicted and measured waste gas
temperatures during induration of iron ore pellets(taken from Majumder et al., 2009).

eral months of process operating data to check numericalconvergence, robustness of the model and accuracy ofmodel predictions and optimization solutions obtained.
The model-based optimization system was then implemented on-line, as described earlier, by integrating it with
the DCS and the LIMS through appropriate softwareinterfaces. Process optimization was then carried out todetermine optimum values of manipulated variables toenhance plant production while maintaining productquality within pre-specified constraints. As a result of thison-line optimization exercise, it was possible to improverate of production by about 3 %.

6. Concluding remarks and suggestions for
future research
Heterogeneous non-catalytic gas-solid reactors areprevalent in many diverse industries such as chemicals,
minerals and metals, polymers and ceramics. Although
the fundamentals are well established, application ofmathematical models for industrial processes is not very
common due to complexity of the multiphase phenomena
involved and their interactions and due to the heterogeneous nature of the materials.
Important physico-chemical phenomena that take place
during heterogeneous multiphase industrial gas-solid reactors were elucidated in this article. Development ofphenomenological process models and their applicationfor on-line process optimization was described along with
two industrial case studies on induration of wet iron orepellets on a traveling grate and direct reduction of ironore by coal in a rotary kiln. The concepts of hybrid
phenomenological-statistical models for product quality
and application of rigorous models as soft-sensors for online optimization were discussed.
An integrated model coupling charging of the particles,
segregation and flow of particles, formation of particulate
bed, flow of gases through the particulate bed, heat andmass transfer between the two phases, reactions within
and involving the two phases, heat transfer between
gases, solids, equipment and the environment, evaporation and condensation of moisture, melting and solidification of the particles has still not been developed. Such amodel will be of immense value for optimization of processes such as sintering, induration, direct reduction, calcination, etc.
Formulation of process optimization problems usually
incorporate targets for maximizing plant productivity andminimizing energy consumption with constraints on
quality or equipment or process variables. There is a needto include constraints on emissions and personnel safety
in the problem formulation.

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Acknowledgments
The author thanks the management of Tata Consultancy
Services for the permission to publish this paper and hiscolleagues, Sushanta Majumder, Viswanadh Bandla andPradeep V. Natekar for their contributions during the implementation of industrial projects. He is also grateful toProf. E. C. Subbarao, Prof. P. C. Kapur, Prof. Mathai
Joseph, Dr. Pradip and Mr. K. Ananth Krishnan for theirhelpful suggestions and encouragement.

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Author’s short biography
Venkataramana Runkana
Venkataramana Runkana is currently a Principal Scientist at Tata Research Development and Design Centre, a division of Tata Consultancy Services. Venkat received B.
Tech (Chemical Engineering) from Sri Venkateswara University College of Engineering,
Tirupati (India), M.Tech (Chemical Engineering) from Indian Institute of Technology,
Kanpur (India) and Ph.D. (Earth and Environmental Engineering) from Columbia University, New York (USA). Venkat has more than 23 years of experience in developing
and implementing industrial research projects on process modeling, simulation and optimization, process data analytics, and process development, scale-up and design. His current areas of research are nanomaterials and their applications, colloidal suspensions,drug delivery systems and process data analytics.

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Original Research Paper

The Kinetics of De-agglomeration of Magnesium Stearate
Dry-Coated Salbutamol Sulphate Powders †Jiani Shi 1, Shyamal Das 2, David Morton 1 and Peter Stewart 1*
1
2

Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Australia
New Zealand’s National School of Pharmacy, University of Otago, New Zealand

Abstract
The objective was to investigate the effect of dry coating of salbutamol sulphate (SS) with magnesium stearate(MgSt) on the kinetics of powder de-agglomeration. The relative de-agglomeration of the MgSt coated SS
powders was higher than uncoated SS at all air flow rates; the SS coated with 2 % MgSt showed the highest extent
of de-agglomeration (> 5 % MgSt coated SS > 1 % MgSt coated SS). Rate of de-agglomeration was described bya cumulative de-agglomerated versus time profile. Profiles fitted a mono-exponential model and the deagglomeration rate constant (kd) was estimated. No significant differences existed between any of the uncoatedand coated powders. The significance of this study relates to the improved aerosolization and de-agglomeration
performance of the MgSt coated SS powders with optimum performance of 2 % MgSt coated SS. More significant
is the finding that no change existed in de-agglomeration rate constants between the coated and uncoatedpowders, with the potential implications that their aerosol plume concentration and deposition patterns weresimilar.
Keywords: kinetics of de-agglomeration, dry coated powders, extent of de-agglomeration, salbutamol sulphate,magnesium stearate

1. Introduction
Dry powder inhalers (DPIs) have received increasing
attention for use in respiratory drug delivery in recent
years. In order to reach the lower respiratory tract, thedrug particle size needs to be less than 5.0 μm (Qui et al.,1997). These small particles form agglomerates due to thebalance of attachment and detachment forces favoringcohesion/adhesion. Detachment forces are mainly gravitational or centrifugal and decrease as the mass of theparticle decreases. Adhesion and agglomeration occur
when the attachment forces, such as electrostatic, intermolecular and capillary forces, exceed detachment forces(Visser, 1989). Agglomeration results in poor efficiency ofaerosolization of dr y powder inhalers. Different
approaches have been investigated to increase the de-­agglomeration properties of the drug powders by decreasing the interaction between particles. The use of ternary†

Received 28 February 2014; Accepted 22 April 2014
J-STAGE Advance published online 25 June 2014
1
381 Royal Parade, Parkville, Victoria 3052, Australia
2
Adams building, 18 Frederick street, P.O. Box 56, Dunedin 9054,
New Zealand* Corresponding author: Peter Stewart;
E-mail: peter.stewart@monash.edu
TEL: +61-3-9903-9517 FAX: +61-3-9903-9583©2015 Hosokawa Powder Technology Foundation

components such as fine lactose (Louey and Stewart,
2002; Zeng et al., 1998), force control agents such as magnesium stearate (MgSt) and leucine (Begat et al., 2009),
and modification of carrier surface (Islam et al., 2004) area few examples of the approaches explored. A comprehensive list of these approaches can be found in a review(Xu et al., 2011).
One recent approach was to change the surface properties of powders by dry coating or mechanofusion of powders with hydrophobic materials to modify the surface
energy and reduce particle interaction (Pfeffer et al.,2001). Mechanofusion is the process of mechanical dry
coating in which submicron-sized guest particles directly
attach onto relatively larger, micron-sized host or core
particles; the process uses shear and compression to produce a thin continuous film of the guest material on thesurface of the host. Mechanofusion has attracted attention
because it is solvent free, relatively simple, cheap andcapable of being scaled up. Previous studies have demonstrated that the de-agglomeration behavior of a range ofdifferent powders such as fine lactose and Pharmatose®
450 M (P450, median particle size approximately 20 μm)
could be increased substantially via mechanofusion with
MgSt as the coating material (Zhou et al., 2010a). In
another study, three drugs (e.g., salbutamol sulphate, triamcinolone acetonide and salmeterol xinafoate) showed131

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greater extents of de-agglomeration, determined by laser
diffraction particle sizing of the aerosolized plume at asingle flow rate of 60 L/min after mechanofusion with 5 %(w/w) MgSt (Zhou et al., 2010b). The respiratory patternsof patients during inhalation may affect the depositions of
the inhaled particles, because the flow rates in different
regions of the respiratory tract are affected by the lungcapacity and the frequency of breathing (Gonda, 1990;
Martinez and Amidon, 2002). Thus, the extent of de-­agglomeration and resultant delivery of the drug is usually dependent on the air flow rate.
While the extent of de-agglomeration of coated powders has been studied to some degree, the kinetics ofde-agglomeration of mechanofused powders from inhalers has not been studied. The rate of de-agglomeration
describes how quickly the respirable fractions will be
produced by the device and thus relate to the concentration of the powder of the plume in the airways of the lung.
The aerosol concentration may influence the deposition
patterns in the lung which may modify dissolution andabsorption. Generally, few studies have described the
kinetics of de-agglomeration. Kinetic studies have beenreported on the de-agglomeration of particles of size
range of 250–1000 μm (deVilliers, 1997), the detachmentof drug from carriers in carrier based formulations (deBoer et al., 2004) and the de-agglomeration of particles insuspensions (Ding and Pacek, 2008). Our group has
recently reported the kinetics of powder de-agglomeration
where the rate of de-agglomeration of two cohesive powders salbutamol sulphate (SS) and Lactohale 300 werestudied from three different inhalers (Behara et al.,2011a). The de-agglomeration rate constants (kd) of both
SS and Lactohale 300 were estimated and were related tothe material, air flow rate and device.
Several studies have also addressed the influence of
MgSt concentration on surface properties of a coatedmaterial (Das et al., 2011b; Zhou et al., 2011). Increasing
MgSt concentrations increased surface coverage until an
optimum concentration was reached (1–2 % w/w MgSt).
Further coating adversely affected surface properties bychanging morphology and increasing surface energy (Daset al., 2011b).
It was hypothesized that due to mechanofusion and theresultant surface modifications, the extent and rate ofde-agglomeration of coated powders would be dependent
on the concentration of coating material used in the mechanofusion. Therefore, a study was designed to determine
the comparative extent and rate of de-agglomeration of an
uncoated model drug for respiratory delivery (SS) and SS
mechanofused with different concentrations of MgSt. The
study was designed to mechanofuse SS with 1 %, 2 % and
5 % (w/w) MgSt. The de-agglomeration behavior was
defined using relative de-agglomeration versus air flowrate profiles obtained between air flow rates of 45–120 L/

132

min. The percentage de-agglomeration obtained from theaerosolized plume obtained in these profiles was normalized against the actual proportion of respirable fines fromthe primary particle size distribution to obtain the relative
de-agglomeration. A non-linear least square model wasused to fit relative de-agglomeration versus air flow rate
profiles. The regression parameters provided an understanding of powder cohesive structure. The use of aerosol
concentration versus time profiles and the emitted doseversus time profiles allowed the construction of cumulative de-agglomeration versus time profiles from which
de-agglomeration rate constants were obtained.

2. Materials and methods
2.1 Mechanofusion or dry coating of the materials
The salbutamol sulphate (SS, Cambrex Profarmaco,
Milan, Italy) was dry coated with magnesium stearate(MgSt, Mallinckrodt Chemicals, Phillipsburg, USA) in a
Nobilta mechanofusion system (AMS-Mini, Hosokawa
Micron Corporation, Osaka, Japan). SS (about 10 g) was
mixed with 1 % (w/w), 2 % (w/w) and 5 % (w/w) MgSt
prior to mechanofusion treatment. Each mixture wasadded into the process vessel (process volume 80 ml). The
mechanofusion processing was performed for 10 min atprocessor rotation speeds of 5000 rpm. In order to prevent
vessel temperatures exceeding 25 °C, cold water was circulated through the water jacket.

2.2 Dispersion of powders
The Spraytec ® laser diffraction system (Malvern
Instrument, Worcestershire, UK) provided real time spraycharacterization of the powders. Mechanofused powders(20 ± 1 mg) were filled into No. 3 gelatine capsules(donated by Capsugel, NSW, Australia). Filled capsules
were dispensed by using Rotahaler® (GSK, Middlesex,
UK). Measurements were carried out under flow rates of
45 L/min, 60 L/min, 90 L/min and 120 L/min of the inhalation cell of the Spraytec in the horizontal position. The
flow rate was calibrated by an external flow meter andcontinuously monitored by the digital flow meter (DFM
2000, Copley Scientific Limited, Nottingham, UK) connected to a Spraytec. Rapid real time measurements werecarried out, capturing 100 measurements per second overa 5 second period. Data acquisition were made using a
300 mm lens, which was suitable to measure particles inthe size range of 0.1–900 μm. Measurements were performed with data collection triggered manually. All thedevices, capsules, powder and capsule retentions were
weighed by a five figure balance (model XS105 dual
range, Mettler-toledo AG, Greifensee, Switzerland). The

Jiani Shi et al. / KONA Powder and Particle Journal No. 32 (2015) 131–142

results were the mean of five replicates.

2.3 Primary particle size distributions
The primary particle size distributions of mechanofused SS were determined by laser diffraction (Mastersizer® 2000, Malvern Instruments, Worcestershire, UK)
using 300RF lens equipped with 150 ml dispersion unit.
Approximately 100 mg samples were dispersed in 5 ml ofpropane-2-ol and then sonicated for 5 minutes. An imaginary refractive index of 0.01 was used in this project. The
particle size analyses of the samples were performed
using 2000 sweeps and analyzed with refractive indicesof mechanofused SS as 1.553 and of isopropyl alcohol as
1.378. The mean particle size distribution was characterized by derived parameters Dv10, Dv50 and Dv90. The
experiments were carried out on five replicates.

2.4 Surface energy
Surface energies of both uncoated and MgSt coated SS
powders were determined using an Inverse Gas Chromatography (IGC, Surface Measurement Systems Ltd, andLondon, UK). Approximately 0.40 g of each powder was
filled in presilanised glass columns (300 mm long × 3 mm
internal diameter) and both the ends were closed by glass
wool. The powder filled column was then fitted in IGC.
Helium was passed through the column for 2 hour at
273 K to remove any impurities in the powder. Helium
run at 10 sccm (standard cubic centimeter per minute)was used to carry all the probes. The dead volume wascalculated from the retention time for methane while theretention times were detected by a flame ionization detector. Methane was run at a concentration of 0.1 p/p0 (wherep denotes the partial pressure and p0 the vapor pressure).
The detailed methodology for determination of total surface energy distribution calculation can be found in theliterature (Das et al., 2011a). Non-polar surface energy(γ NP) was determined from the retention volumes of GC
grade hexane, heptane, octane, nonane and decane (Sigma-­
Aldrich GmbH, Steinheim, Germany) and the polar surface energy (γP) was determined from the retention volumes of two polar probes (i.e., dichloromethane and ethyl
acetate). Combining non-polar (γ NP) and polar surface
energies (γP), the total surface energy (γT) was calculated(van Oss et al., 1988). The experiments were conducted intriplicate on the same batch.

2.5 Statistical modelling and analysis
The Spraytec data calculations were modelled using a
non-linear least square regression analysis in the SigmaPlot
12.0 software (Systat Software, Inc., IL, USA). The statistical significance was carried out using one-way analysis ofvariance with Tuckey’s post-hoc analysis at a p-value of
0.05 using SPSS (version 19.0, SPSS, Inc., IL, USA).

3. Results
3.1 Characterising de-agglomeration behaviour of
powders using de-agglomeration profiles
3.1.1 Changes in the particle size distribution of theaerosol plume with magnesium stearate coating

The extent of de-agglomeration of the model cohesive
powders, SS and MgSt dry coated SS, was determined by
the laser diffraction particle sizing of the aerosol plumefor two batches of the powder using the Malvern Spraytec. The powders were aerosolized from a commercial
inhaler device (Rotahaler) at air flow rates between
45–120 L/min. The particle size distributions following
the aerosolization at 60 L/min were selected to demonstrate the change in particle distribution in the aerosol
plume that occurred with uncoated and MgSt coated SS(60 L/min was a common flow rate used in previous studies to calculate the relative de-agglomeration (Coates etal., 2005; Srichana et al., 1998; Chew and Chan, 2001)).
The comparative particle size frequency distributions of
uncoated and coated SS particles in the aerosol plume at
60 L/min and in the fully dispersed form are presented inFig. 1.
The primary particle size distribution of SS was smaller
than the particle size distribution of the aerosolized plumedispersed from the Rotahaler at an air flow rate of 60 L/
min (Fig. 1). The SS powders aerosolized from the
Rotahaler were not fully dispersed and all uncoated andcoated powders contained agglomerates following aerosolization, demonstrated by either a shoulder on the distribution or a bi-modal distribution. The 2 % (w/w) MgSt dry
coated SS powder showed the greatest extent of de-­agglomeration following aerosolization, with the rankorder being 2 % (w/w) MgSt > 5 % (w/w) MgSt > 1 % (w/w)
MgSt > uncoated SS (data not shown). The relevant de-­agglomeration of the powders at other air flow rates
showed a similar rank order of aerosolization performanceand, in general, the relative de-agglomeration increased
with air flow rate. The importance of observing the fullparticle size distributions of the aerosol plume was toobserve the changes over the whole of the particle size
range. In particular, the data in the full particle size distribution provide an indication of the cohesive nature of thepowders, the agglomeration behavior and the change inaerosolization performance of the powder with MgSt coating. The multi-modal nature of the full particle size distribution demonstrated the resilience of agglomerates of theuncoated and coated cohesive drug to comminution.

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Fig. 1

Particle size distributions of the aerosol plume of uncoated (B), 2 % w/w magnesium stearate (MgSt)
coated salbutamol sulphate (SS) batch 1 (C) and batch 2 (D) determined using the Malvern Spraytec®
when dispersed from a Rotahaler ® at 60 L/min and their primary particle size distributions determinedby laser diffraction (Malvern Mastersizer ® 2000) in liquid medium. The variability of uncoated SS
de-agglomeration in Spraytec determination has been shown with error bar in (A). (n = 5, data represent
mean ± standard deviation).

3.1.2 Relationships between relative deagglomeration and flow rates

De-agglomeration behavior of model cohesive powders,
SS and MgSt dry coated SS, was compared by constructing
percent relative de-agglomeration versus air flow rate profiles (Behara et al., 2011b) of the cohesive powders in theaerosolized plume, dispersed from a commercial inhaler
device (Rotahaler) at air flow rates between 45–120 L/
min. The relative de-agglomeration was the percentage
ratio of the extent of de-agglomeration of particles lessthan 5.4 μm following aerosolization and the extent ofde-agglomeration of particles less than 5.4 μm of the drug
powder in its fully dispersed state (Fig. 2).
As the flow rates increased (45 L/min, 60 L/min, 90 L/
min and 120 L/min), the percent relative de- agglomeration
significantly increased for the uncoated, 1 % (w/w), 2 %134(w/w) and 5 %(w/w) MgSt coated SS (p ≤ 0.009, p ≤ 0.001,
p ≤ 0.012 and p ≤ 0.024 respectively). In general, SS
coated with MgSt had higher efficiencies of aerosolisation
than the uncoated SS, demonstrated by the percent relative de-agglomeration.
At all four flow rates, the percent relative de-agglomeration of 1 % (w/w), 2 % (w/w) and 5 % (w/w) MgSt coated
SS was significantly higher than the uncoated SS(p ≤ 0.009). The 2 % (w/w) MgSt coated SS displayed asignificantly increased percent relative de-agglomerationin comparison with the other MgSt coated powders atflow rates of 45 L/min, 60 L/min and 90 L/min (p = 0.001,
p = 0.001 and p = 0.002 respectively). However, at theflow rate of 120 L/min, there was no significant difference
between the 2 % (w/w) and the 5 % (w/w) MgSt coatedpowders (p = 0.137); this could be due to the fact that both

Jiani Shi et al. / KONA Powder and Particle Journal No. 32 (2015) 131–142

Table 1

Fig. 2The percent relative de-agglomeration versus air flowrate profiles for uncoated and two batches of 1 %, 2 %and 5 % magnesium stearate (MgSt) coated salbutamol
sulphate dispersed from Rotahaler ® and determinedusing laser diffraction particle sizing of aerosol plume(Malvern Spraytec®) at flow rates of 45, 60, 90 and
120 L/min. (n = 5, data represent mean ± standard
deviation).

of the powders approached their plateaus of percent relative de-agglomeration).
The 5 % (w/w) MgSt coated SS showed a significant
increase in percent relative de-agglomeration compared to
the 1 % (w/w) MgSt coated powder at all flow rates(p ≤ 0.001). The de-agglomeration percentage of the 1 %(w/w). MgSt coated powder was significantly higher thanthe uncoated SS at all flow rates (p ≤ 0.009), but significantly lower than both 2 % (w/w) and 5 % (w/w) MgSt
coated SS at all flow rates (p ≤ 0.001, p ≤ 0.000 respectively).

3.1.3 Modelling the profiles of relative deagglomeration versus flow rates

The de-agglomeration data in the profiles were empirically modelled in order to characterize the powder behavior in the relative de-agglomeration versus air flow rate
profile (Behara et al., 2011b). Since the shape of the de-­agglomeration-air flow rate profiles, including the lagphase at lower air flow rates and the plateau at higher
rates, suggested sigmoidal relationship between relative
de-agglomeration and air flow rates (Behara et al., 2011b),these data were fitted to 3 and 4 parameter sigmoidal
equations using a non-linear least squares regression
algorithm (Marquardt, 1963) using SigmaPlot 12.0 software. The fitting was performed on the mean of the five
replicates. The requirements to test the goodness of fitting
have been described elsewhere (Draper and Smith, 1981).
The statistics used to distinguish and determine the good-

Non-linear least squares estimated parametersof the 3-parameter fitting of the relative de-­agglomeration versus flow rate profile foruncoated and 1 %, 2 % and 5 % (w/w) magnesium stearate (MgSt) coated salbutamol sulphate(SS). The maximum relative de-agglomeration,difference in flow rate between 75 % and 25 %of the relative de-agglomeration and air flow
to achieve 50 % of the maximum relative
de-agglomeration have been presented by (a),(b) and (x0)Materials

a

b

x0

R2

Norm

Uncoated

50.0

25.3

93.6

0.99791.11

1%batch1

57.4

12.2

63.7

0.99871.38

1%batch2

62.5

17.9

70.3

0.9993

1.01

2%batch1

82.2

17.7

49.9

0.99791.64

2%batch2

80.9

14.1

47.7

0.99990.37

5%batch1

81.5

14.2

64.3

0.99951.14

5%batch2

74.0

16.0

60.3

0.99762.06

ness of fit were: R2 (correlation co-efficient), F-statistic
and Norm. The relative de-agglomeration versus air flowrate profile data for the MgSt coated SS powders could be
fitted to both 3 and 4 parameter sigmoidal equations. For
uncoated SS, only the 3 parameter sigmoidal equation
could be fitted as the fitting did not converge within the
maximum number of iterations for the 4 parameter sigmoidal equations. The 3 parameter sigmoidal equation(Eqn. 1) was therefore selected as an appropriate model
since it provided good fits of the data for both coated anduncoated drug powders and, therefore, difference between
powder behavior could be determined.
y

a

1 e x  x0 

b 

(1)

where ‘a’ represented the upper asymptote (or maximum
relative de-agglomeration), ‘x0’ was the x-value or air
flow at which 50 % of the maximum relative de-agglomeration occurs, ‘b’ represented the width of the transition
and was the difference in flow rate between which 75 %and 25 % of the relative de-agglomeration occurred. The
estimated parameters of ‘a’, ‘b’, and ‘x0’ and some of thefitting statistics to demonstrate the goodness of fit are
shown in Table 1.
These estimated parameters (‘a’, ‘b’ and ‘x0’ (Table 1))
were used to describe the relative de-agglomeration
behavior. The maximum extent of relative de-agglomeration (a) was an indication of the capacity of each powder
to fully de-agglomerate within the air flow field of thespecific device (Behara et al., 2011b). This parameter varied from about 50 % to 82 % depending on the presence

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and extent of coating of the SS powders (Table 1). Thechange in relative de-agglomeration (b) with air flow rate
also provided another measure of the ability of the powders to aerosolize. For example, in comparing 1 % batch 2
and 2 % batch 1, while the change in relative de-agglomeration (b) with air flow rate was similar (17.9 and 17.7
respectively), both the maximum extent of relative de-­agglomeration (a) and the air flow rate to achieve 50 %relative de-agglomeration (x0) were different, suggesting
different de-agglomeration behavior.
The flow rate to achieve 50 % relative de-agglomeration
was also an important parameter indicating the ability ofthe powder to de-agglomerate. It provided an indication of
the dispersion “energy” required to overcome the internalinteractions within the powder (Behara et al., 2011b) andthe 2 % (w/w) mechanofused SS required least input“energy” to disperse the powder (Table 1).

3.2 De-agglomeration kinetics from Spraytec
3.2.1 Cumulative fine particles mass

The cumulative fine particle masses were determinedby the method outlined in Behara et al., 2011a. Firstly,using the data inherent in the particle sizing of the aerosol
plume, a measure of the extent of de-agglomeration (i.e.
the percentage of particles less than 5.4 μm) at specifictimes of aerosolization for uncoated and coated SS at four
air flow rates was determined. These data show the timecourse of the aerosolization events for the uncoated andcoated SS powders. The data were truncated to the time
when the aerosol concentration of one of the replicates(among five replicates) approached the base-line value.
The time of aerosolization varied, and the data were collected for no more than about 4 seconds.
Secondly, the calculated emitted masses at specifictimes (EMt) were obtained from the product of the fractional concentration at a specific times by the total emitted mass. The EMt then was multiplied by the fractional
volume less than 5.4 μm at specific times to calculate thefractional fine particle mass (FPMt), i.e. the mass of particles less than 5.4 μm at a specific time available for respiratory deposition. The cumulative fine particle mass
(CFPMt) was determined and the CFPMt versus time profiles for the uncoated SS and the coated SS aerosolized
from Rotahaler, at 45, 60, 90 and 120 L/min were presented in Fig. 3.
For both uncoated and MgSt coated SS, the time toreach maximum cumulative fine particle mass was
reduced with increased flow rates. A clear difference inthe extent of cumulative fine particle mass was observed
between low and high air flow rates for both uncoated andcoated SS powders. This behavior was expected due toincreased aerosolization energy with increased air flowrate (Chew et al., 2002; Coates et al., 2005). The rate con-136

stant for de-agglomeration was determined from thecumulative plots by modelling and parameter estimation
as described in section 3.2.2.

3.2.2 Estimation of de-agglomeration rate constants

The shape of the cumulative fine particle mass versus
time profiles suggested that a sigmoid or mono-exponential
rise to maximum (MERM) model may be appropriate
and the data were fitted to both equations. Both thesemodels have parameters that relate to the rate of de-­agglomeration which were the focus of the current investigation. Individual data replicates were fitted in order toobtain mean and standard deviations for the selection of
the model and to find the actual variability in the rate ofde-agglomeration. The coefficient of determination andNorm for both the sigmoidal and MERM models for thedata in the cumulative fine particle mass versus time plots
for uncoated and MgSt coated SS at the flow rate of 60 L/
min are presented in Table 2; these statistical parameters
favored the MERM as the most appropriate model compared with the sigmoid. The fitting statistics shown at
60 L/min were typical of the statistics at other air flow
rates. While the data were best fitted to the MERM
model, the goodness of fit may be further improved by fitting the data to higher order exponential fits. Hence, thefitting process was extended to bi-exponential rise to
maximum 4 parameter (BERM) equation. However, someof the replicates did not converge with BERM. Therefore,
calculated cumulative fine particle mass data were modTable 2

Modelling statistics for the fitting of replicatesat 60 L/min of cumulative fine particle masstime profiles for the Rotahaler®, to sigmoid 3
parameter, mono-exponential rise to maximum
2 parameter (n = 5; mean ± SD).
Sigmoidal 3
parameters

MERM

R2

Norm

R2

Norm

Uncoated

0.9813± 0.0092

1.9 ± 0.7

0.9988± 0.0010

0.8 ± 0.4

1%
Batch1

0.9828± 0.0083

1.3 ± 0.9

0.9972± 0.0024

1.1. ± 0.6

1%Batch20.9800± 0.0091

1.5 ± 0.6

0.9965± 0.0031

0.9 ± 0.7

2%
Batch10.9662± 0.0105

1.6 ± 1.1

0.9959± 0.0039

1.2 ± 0.8

2%Batch20.9900± 0.0096

1.5 ± 0.9

0.9942± 0.0040

1.3 ± 0.6

5%Batch1

0.9932± 0.0075

1.3 ± 0.7

0.9946± 0.0043

1.2 ± 0.7

5%Batch20.9839± 0.0100

1.9 ± 0.8

0.9947± 0.0047

1.4 ± 0.8

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Fig. 3Cumulative fine particle mass versus time profiles for salbutamol sulphate (SS) powders emitted from the
Rotahaler ® aerosolised at 45, 60, 90 and 120 L/min (n = 5). Uncoated SS shown with error bar representing standard deviation (A), and without error bar (B); 2 % (w/w) magnesium stearate (MgSt) coated SS
batch 1 (C) and batch 2 (D) without error bar

elled using a MERM model and the de-agglomeration rate
constants (kd) were estimated (Eqn. 2).
CFMPt = CFPMmax(1 – e –kd*t )(2)where ‘CFPMt’ is calculated cumulative fine particle
mass, ‘CFPMmax’ is the maximum calculated cumulative
fine particle mass predicted by the model, ‘kd’ is de-­agglomeration constant in relation to particles less than
5.4 μm and ‘t’ is the time.

3.2.3 Influence of air flow rate on de-agglomeration
rate constant for uncoated and magnesium
stearate coated salbutamol sulphate powders

The rate constants of de-agglomeration, kd, from modelling the data using Eqn. 2, plotted against the flow rates

for the uncoated and MgSt coated SS powders (Fig. 4)
provided an understanding of the influence of MgSt coating and air flow rate on the kinetics of de-agglomeration.
Some important observations were:

3.2.3.1 Difference in kd between materials

The kd versus air flow rate profiles for the uncoated andMgSt coated SS powders were not significantly different
between uncoated and coated SS powders nor among any
coated SS powders (0.985 ≤ p ≤ 1.000 at the flow rate of
45 L/min; 0.559 ≤ p ≤ 0.997 at 60 L/min; 0.011 ≤ p ≤ 0.935
at 90 L/min; 0.876 ≤ p ≤ 1.000 at 120 L/min). Therefore,
there was no evidence that MgSt coated SS powders
increased the rate of de-agglomeration or that there wasany direct relationship between the percentage of MgSt
used in coating and the de-agglomeration rate constant.

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3.2.3.2 Difference in kd between air flow rates

While the estimated kd versus air flow rate plots in Fig.
4 showed a positive trend, there was no difference between
the kd values at the different air flow rates for the uncoatedSS powder (0.116 ≤ p ≤ 0.999). However, there were differences in some kd for the MgSt coated SS powders. Morespecifically, for 1 % (w/w) MgSt coated SS, the kd at 45 L/
min was significantly lower than the kd value at 120 L/min(p ≤ 0.001); for 2 % (w/w) MgSt coated SS, the kd valuesof the 45 L/min and 60 L/min were significantly lower
than the kd values at 90 L/min and 120 L/min (p ≤ 0.001
and p ≤ 0.001, respectively) and for 5 % (w/w) MgSt coated
SS, the kd values at 45 L/min and 60 L/min were lower
than the kd values at 90 L/min and 120 L/min (p ≤ 0.001
and p ≤ 0.001, respectively).

3.2.3.3 Variability of kd values

The kd values showed considerable variability in Fig. 4.
The variability could be associated with the laser diffraction particle sizing of the aerosol plume, the uniformity of
the cohesive powder samples in their capacity to aerosolize and the variability associated with aerosolization
capability of the inhaler device. The cause of the variability in kd is unknown; however, the authors would assess
the variability associated with laser diffraction methodology to be small. In addition, the aerosolizability variability associated with the cohesive powders (not mixtures)
was likely to be related to particle size, packing fraction
and surface energy properties. The particle size differences between samples of the powders were small; however, depending on the effectiveness of the intensive dry
coating, the packing fraction and surface energy may
change from sample to sample. It is noteworthy that theuncoated powder possessed variability which was at leastas great as the coated samples. The more likely cause ofvariability was the aerosolizability reproducibility of theRotahaler with variability possibly associated with its
design including its large chamber and random movementof the capsule within the device.

4. Discussion

Fig. 4138

The de-agglomeration rate constant (kd) of the uncoatedand 1, 2 and 5 % (w/w) magnesium stearate (MgSt)
coated salbutamol sulphate for cumulative fine particle
mass as a function of flow rate profiles (n = 5;
mean ± Standard Deviation) aerosolized by Rotahaler®.

The relative de-agglomeration versus flow rate profiles
were generated to describe and compare the aerosolization properties. MgSt dry coated SS demonstrated betterde-agglomeration behavior than the uncoated SS. The 2 %(w/w) MgSt coated SS demonstrated best performance
followed by the 5 % (w/w) MgSt coated SS and the 1 %(w/w) MgSt coated SS. The modelling of these profiles
using the three parameter exponential equation allowed
the estimation of these parameters which described theaerosolization behavior of the uncoated and coated powders. The ideal powder for respiratory delivery would
possess a high maximum percentage of de-agglomeration(a), low change in de-agglomeration with flow rate values(b) and a low flow rate to achieve 50 % de-agglomeration(x0) (Behara et al., 2011b). In such a case, the aerosolised
dose of drug would only need the minimum effort toreach a high percentage of de-agglomeration. The relationship between the estimated modelling parameters andthe uncoated and coated powders is summarised in Fig. 5.
The modelling showed that the 2 % (w/w) MgSt coated
SS exhibited the greatest extent of de-agglomeration witha relative de-agglomeration of about 80 %. Thus, 80 % of
the available particles were de-agglomerated to particles
less than 5.4 μm. The estimated parameter ‘x0’ showed aminimum value of about 48 L/min when the SS powders
were coated with 2 % (w/w) MgSt. The estimated parameter ‘b’ was low for all powders and did not change sig-

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Fig. 5

The relationship between estimated modelling parameters (a = maximum relative de-agglomeration, b = difference in flow rate between 75 % and 25 % of the relative de-agglomeration and x0 = air flow to achieve
50 % of the maximum relative de-agglomeration) withthe uncoated (0 % Magnesium stearate, MgSt) andcoated (1, 2 and 5 % MgSt) salbutamol sulphate (SS)powders (n = 2)

nificantly for the coated powders. The coating of MgSt
onto the SS powders, therefore, changes the aerosolization
behavior and the modelling demonstrates that the 2 % (w/w)
MgSt coated SS produced the greater de-agglomeration
properties. The study also shows that there were goodcorrelations in performance between the two batches of
the coated material indicating the reliability and reproducibility of the dry coating process. The parameters
derived from modelling these profiles provided valuable
information in understanding powder de-agglomeration
behavior.
The reasons for the difference in aerosolization behavior between the uncoated and differently coated SS powders were not fully understood. The presence of the MgSt
surface coating altered the surface energy of the powders.
Fig. 6 shows the total surface energy changes of theuncoated and coated powders; these data demonstrated
that the 2 % (w/w) MgSt coated SS exhibited the lowest
total surface energy. Thus, the interaction between par­ticles would have been reduced based on the powder
adhesion models (Kendall and Stainton, 2001) and aerosolization of the powders would be expected to increase.
However, de-agglomeration also can be related directly to
packing fraction and indirectly to particle size distributions through their relationship with tensile strength(Kendall and Stainton, 2001; Das et al., 2012). In thisstudy, the particle size distributions of all powders were
not significantly different. Conversely, the packing fraction distributions demonstrated increased consolidation
with coating and this would suggest a decreased aerosolization performance for the coated powders. Thus, the im­proved performance of the 2 % MgSt coated SS must be
related to some balance between the surface energy andpacking fraction effects to ensure better de-agglomeration

Fig. 6

Surface energy distributions of uncoated and 1, 2 and
5% (w/w) magnesium stearate (MgSt) coated salbutamol sulphate (SS) determined by inverse gas chromatography using finite dilution technique.

and aerosolization.
The results obtained in this study were consistent withother studies which show that coating of particulate surface resulted in improved performance. The 1 % (w/w)
MgSt coated SS presented with the lowest relative de-­agglomeration among the coated powders; this could be due
to incomplete surface coverage of the MgSt. The 5 % (w/w)
MgSt coated SS may likely have an excess amount of MgSt
which could lead to uneven multilayer coating or excess
MgSt particles mixing with the coated SS in the powder
bed. In this study, the use of 2 % (w/w) MgSt provided theideal concentration for improved de-agglomeration.
The CFPMt versus time profiles showed exponential
increases in de-agglomeration with time which were consistent with the extent of coating and air flow rates. The
modelling of the CFPMt versus time using a non-linear
regression modelling estimated first order de-agglomeration
rate constants, kd. The kd versus air flow rate profiles
described the relative rates of de-agglomeration of theuncoated and coated powders. Generally, as the air flow
rates increased, an increasing kd was observed for all
powders. However, the air flow rate did not affect the rate
constant of de-agglomeration of the uncoated powder.
There was no significant difference in kd values observed
between uncoated and MgSt coated SS. There was also no
obvious relationship between the amount of coating usedin the mechanofusion process and kd value.
The lack of relationship between the de-agglomeration
rate constant and the extent of MgSt coat on the SS powder points to the fact that the device is more important incontrolling the rate of de-agglomeration of powders thanthe powder characteristics. The large variability associated with the rate constants may relate to the use of thespecific device (Rotahaler) in dispersing the SS powders.
The mechanism by which the Rotahaler aerosolizes powders involves less controlled and more chaotic movement

139

Jiani Shi et al. / KONA Powder and Particle Journal No. 32 (2015) 131–142

and orientation of the open capsule in the chamber of thedevice.

5. Conclusion
The dry coating of salbutamol sulphate powders using
magnesium stearate was seen to modify the kinetics of de-­agglomeration by improving the extent of de-agglomeration
but not the rate of de-agglomeration. In this study, theoptimum coating was achieved using 2 % magnesium
stearate during the mechanical dry coating process; inthis case, using the Rotahaler device the extent of de-­agglomeration was about 80 %. The rate constant for de-­agglomeration ranged between about 0.8 and 1.4 s–1 for
all powders depending on the air flow rate. For example,
at 60 L/min, the rate constant was about 0.9 s –1 with thehalf-life of de-agglomeration being about 1.3 s. The extent
of de-agglomeration was dependent on the formulation,
but the rate of de-agglomeration was independent of theformulation.
A knowledge of the rate of de-agglomeration is important, because the rate of de-agglomeration will be related
to the plume concentration which may influence the deposition pattern of the drug in the lungs and may, therefore,
influence the dissolution and bioavailability of the drug.

Nomenclatureaupper asymptote of sigmoidal equation or maximum relative de-agglomerationbwidth of the transition and the difference in flowrate between which 75 % and 25 % of the relative
de-agglomeration occurred.

CFPMmax maximum calculated cumulative fine particle
mass predicted by the model (mg)
CFPMtcumulative fine particle mass (mg)EMtcalculated emitted masses at specific times (mg)FPMt

fractional fine particle mass, i.e. the mass ofparticles less than 5.4 μm at a specific time forrespiratory deposition (mg)kd

de-agglomeration constant in relation to particles
less than 5.4 μm (s−1)

ttime (s)x0x-value or air flow at which 50 % of the maximum
relative de-agglomeration occurs (L/min)γ NP

non-polar surface energy (mJ/m 2)γPpolar surface energy (mJ/m 2)

γT

total surface energy (mJ/m 2)

140

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Author’s short biography
Jiani Shi
Jiani Shi is a dry powder formulation research scientist and a pharmacist. After graduating from the Monash Pharmacy College, she found a new interest in respiratory drugdelivery. Therefore she spent two and half years studying at Monash Institute of Pharmaceutical Science. During her Master degree study, she focused on researching the performance of the dry powders that have altered surface properties and made some interesting
discoveries. Now she is working in a pharmaceutical company that develops pulmonary
medications.

Shyamal Das
Dr Shyamal Das is a Senior Lecturer of Pharmaceutical Sciences in the New Zealand’s
National School of Pharmacy in the University of Otago. His research interests revolvearound drug delivery, in particular, respiratory drug delivery of powder formulations fortreating chronic lung conditions such as COPD, asthma, tuberculosis and other lunginfections. His particular focus is on the (i) approaches to develop high efficient and stable powder, (ii) the dissolution of drugs in the lung and (iii) solid state characterization
for understanding processes such as milling, mixing, spray drying, coating and storage
related to the development of solid dosage forms

David A.V. Morton
David is a chemist, focussed on development of pharmaceutical inhalers, and associated science. David was previously Pulmonary Research Head at Vectura, responsible
for the PowderHale® formulation technologies, and contributed to several inhaler products now marketed.
In 2007, David joined Monash University, with research interests in drug delivery andparticle surface modification, and also building strategic “Open Industrialisation”
university-industry collaborations. He Heads the GSK-Monash Centre of Innovation
and Industrialization, which won the Australian BHERT Awards for both “Outstanding
Collaboration” and “Best R&D Collaboration” in 2013. David is co-inventor of the Monash
Oxytocin inhaler technology, winner of the Australian Innovation Challenge 2013.

Peter StewartPeter Stewart is Professor of Pharmaceutics at the Faculty of Pharmacy and Pharmaceutical Sciences at Monash University in Australia.
Professor Stewart’s research interests are associated with particle interactions andagglomeration in powders related to solid drug delivery systems. His major interests lie
in applications for respiratory and gastrointestinal drug delivery. He has focused onadhesion in powder formulations for inhalation with a particular interest in improving
the de-agglomeration of micronised drugs. A mechanistic and modelling approach to
improving the dissolution of poorly water-soluble drugs has resulted in a better understanding of drug agglomeration processes in solid systems and strategies to minimise
agglomeration.

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KONA Powder and Particle Journal No. 32 (2015) 143–153/Doi:10.14356/kona.2015002

Original Research Paper

Effect of Particle Size on Flow Mode and Flow
Characteristics of Pulverized Coal †Haifeng Lu, Xiaolei Guo, Yi Liu and Xin Gong *1

Key Laboratory of Coal Gasification and Energy Chemical Engineering of Ministry of Education,
Institute of Clean Coal Technology, East China University of Science and Technology, China

Abstract
This paper reports on an extensive experimental campaign aimed at studying on effect of particle size and flowmode on the flow characteristics of pulverized coal. A series of pulverized coal changing the mean particle size inthe range 18–224 μm were used as the experimental materials and three flow modes including gravity-induced
flow, downwards and upwards mechanically forced flow were investigated. The gravity-induced flow wascharacterized by discharging pulverized coal from the Perspex hopper gravitationally, while the mechanicallyforced flow was performed inside a cylindrical vessel where the blade rotated and moved downwards or upwards
to displace the pulverized coal. Furthermore, the relationship between different flow modes was discussed and theforce analysis was used to reveal the flow mechanisms of gravity-induced flow.
Keywords: flow behaviors, pulverized coal, particle size, flow mode, predominant force

1. Introduction
Powders are probably the least predictable of all materials in relation to flowability because of the large numberof factors that can change their flow behaviors (Peleg,
1978; Fitzpatrick et al., 2004). Unlike the relatively wellunderstood constituent phases from which they arederived, powders are more complicated materials. They
are comprised of: solids in the form of particles; gas, usually in the form of air between the particles; and water,
either on the surface of the particle or within its structure.
The interaction of these phases contributes to the changeability of powders, and as a consequence the processing
and handling of powders is certainly not straightforward
or easily predictable.
Physical properties, including particle size, shape, surface texture, size distribution, moisture content, etc.
determine the flow behavior of the powder to a large
extent and external factors, such as humidity, conveying
environment, vibration, consolidation and aeration, etc.
compound the problem (Freeman et al., 2010). The powder flowability is a consequence of the combined effectsof various variables and a more fundamental and physicalmeasurement of powder flow properties is needed.
†
1*

Received 12 March 2014; Accepted 7 May 2014
J-STAGE Advance published online 25 June 2014
Shanghai 200237, PR China
Corresponding author: Xin Gong;
E-mail: gongxin@ecust.edu.cn
TEL: +86-21-6425-2521 FAX: +86-21-6425-1312©2015 Hosokawa Powder Technology Foundation

The influence of particle size in terms of powder flowability has often been described. Beverloo et al. (1961)
studied on the f low of a number of coarse seeds(dp ≥ 1000 μm) through orifices as early as 1961. Spinkand Nedderman (1978) carried out an experimental andtheoretical study on the gravity discharge of fine particles(50 μm ≤ dp ≤ 500 μm) from a hopper. Cannavacciuolo etal. (2009) t r ied to discharge cohesive powders(dp < 50 μm) from an aerated silo. It confirms that relatively coarse powders, such as plastic pellets and certain
grains, rarely present discharge problems. Reliable
discharge by gravity is reasonably assured without sig­
nificant investment in engineering or equipment. Finerpowders, starting at about 1000 μm mean particle size,
frequently will not discharge reliably from silos without
careful engineering and selection of discharge equipment
and silo geometry (Ferrari and Bell, 1998).
Furthermore, considering processes used to manipulate
powders are diverse and subject materials to a wide rangeof conditions, there is more and more attention paid to the
influence of flow mode in terms of powder flowability.
Freeman et al. (2008) showed that fine powders tended tobehave more like a fluid when subjected to forced flow,
unlike coarse powders that were more likely to become
solid like and become very resistant to flow. Latter,
Krantz et al. (2009) found that different characterization
techniques could provide different results based upon differences in how they tested powders. Thus, care must be
taken to select the appropriate characterization technique.
The background of this paper is entrained-flow pulver-143

Haifeng Lu et al. / KONA Powder and Particle Journal No. 32 (2015) 143–153

ized coal gasification and pulverized coal was used as
experimental materials. Handling coal is a major processconcern for situations of adherence containing surfaces,
excessive aeration in pneumatic conveying systems,
bridging in hoppers, electrostatic charging during handling and consolidation during transport or storage etc.
(Liang et al., 2012). Flow properties are the foundation of
powder technology, which determine the behavior of
powders in bins, hoppers, feeders, and other handlingequipment. Therefore, the characterization of flow properties of pulverized coal is an important requirement forengineers to deal with flow problems and design of equipments. However, due to the different geographical environment, production techniques and applications, there isa wide range of particle size distribution, different moisture contents and coal types, all of which contribute to thechangeability of pulverized coal, and as a consequence
the processing or handling coal is certainly not straightforward or easily predictable. A comprehensive study on
the flow properties of pulverized coal is urgently required.
The purpose of this work is to investigate the flow
behaviors of pulverized coal with different particle sizes
in both gravity-induced flow and mechanically forced
flow in order to verify how the particle size and the flowmode can affect the powder flowability.

2. Experimental
2.1 Materials
Coal from Tianba mine in Yunnan province was usedas the experimental material in this work. In order to form
a series of samples having different particle sizes, coal
was first pulverized by a pulverizer and then sieved by anelectric vibrating screen. Seven samples of pulverizedcoal were obtained.
Table 1 shows the physical properties of pulverized
coal. The particle size distribution of pulverized coal wasmeasured by a laser diffraction particle size analyzer(Malvern Mastersizer 2000) and shown in Fig. 1. The
pulverized coal has a mean particle size (dp) ranging from18 μm to 224 μm. According to Rosin-Rammler, uniformity coefficient (n) was used to characterize the size distribution, where the smaller n corresponds to the wider
size distribution. As shown in Table 1, all samples havean approximate n within the range of 0.91–1.21, indicatingsome uniformity in the particle size distribution. In addition, all samples were dried to the same moisture content(wt) 1.25% to exclude the water effect. Therefore, the differences of seven samples of pulverized coal mainly focus
on the mean particle size.
Particle density (ρp) was determined by the pycnometer
method. It was about 1500 kg/m 3 for pulverized coal

144

Table 1

Physical properties of pulverized coal
a

b

c

d

e

f

g

dp (μm)

18

44

56

75

94

141

224

n

1.2

1.06 1.21 1.12 1.11 1.02 0.91

2

S (m /g)

0.79 0.36 0.33 0.29 0.26 0.22 0.19

wt (%)

1.25 1.25 1.25 1.25 1.25 1.25 1.25

ρb (kg/m3)

353

510

547

580

624

696

727

3

779

816

831

848

881

876

801

ρT (kg/m )HR
AOR (°)

Fig. 1

2.21 1.60 1.52 1.46 1.41 1.26 1.10
51.5 43.0 42.6

4039.0 38.4 36.8

Particle size distribution of pulverized coal.

and showed little difference between seven samples.
Hosokawa Micron Powder Tester (PT-X) was used tomeasure bulk density (ρb), tap density (ρT) and angle ofrepose (AOR) of samples. Hausner Ratio (HR) was calculated by dividing bulk density by tap density (Hausner,1967). It can be seen clearly in Table 1 that both AOR
and HR decrease gradually as the mean particle size
increases, which lead to a better powder flowability.
Specific surface area (S) was provided by Malvern
Mastersizer 2000, which assumes that particles are dense
spheres (Shu et al., 2011). As shown in Table 1, the specific surface area of pulverized coal decreases graduallyas the particle size increases. Scanning electron microscopy (SEM) images were further taken to inspect the surface morphologies of pulverized coal. Fig. 2 shows theSEM images of smallest and largest pulverized coal,respectively. For the smallest one with a mean particle
size of 18 μm, particles have a great tendency to form
agglomerates and clumps, typical of cohesive powders.
On the contrary, for the largest one with a mean particle
size of 224 μm, particles exist separately in the form ofindividual particles.

Haifeng Lu et al. / KONA Powder and Particle Journal No. 32 (2015) 143–153(a) dp = 18 μm
Fig. 2

Fig. 3(b) dp = 224 μm
SEM images of pulverized coal.

Schematic diagram of hopper discharge.

2.2 Instruments and methods
Discharging form a Perspex hopper was used to characterize the gravity-induced flow. Fig. 3 shows the schematic
diagram of hopper discharge process. The hopper madefrom transparent allowing a visual inspection inside, has ahalf opening angle 15° and an outlet diameter 32 mm. In
each experiment, the hopper was first loaded with about
700 g pulverized coal from the top through a funnel orwith a spoon to keep a relatively loose state. The pulverized coal was then discharged immediately to avoid any
de-aeration or consolidation phenomena. During the discharge, a weighing equipment was used to calculate thedischarge rate and a digital camera was used to ‘freeze’
the motion of the discharging powder.
FT4 Powder Rheometer was used to characterize themechanically forced flow. The detailed description of this
instrument has been provided in the paper (Freeman,2007). In the experiment, all tests were carried out in a
160 mL borosilicate test vessel with a internal diameterof 50 mm. The sample of pulverized coal was first pre-­conditioned using the instrument ‘conditioning’ methodol­
ogy to produce a homogeneously packed powder bed, andthen subjected to forced flow. That means, the volume ofsample charged in the FT4 cell was kept constant, 160 ml,
and the mass of sample could be obtained by multiplying

the conditioned bulk density, with value of 83.2 g, 97.6 g,
99.2 g, 102.4 g, 110.4 g, 115.2 g and 118.4 g. The blade
rotates simultaneously and moves both downwards andupwards with a constant tip speed of 100 mm/s and a constant up/down speed of 50 mm/s as shown in Fig. 4. The
resistance experienced by the blade represents the difficulty of this relative particle movement. Dynamic methodology of FT4 is employed to measure both rotationaland vertical resistances and the flow energy is the composite of these two signals that quantifies the total resistance to forced flow. As shown in Fig. 5, the total flowenergy can be obtained by integrating the energy gradient, which is automatically calculated by FT4 for both
downwards and upwards testings.
In this work, two kinds of flow energies were used todescribe the forced flow characteristics, Basic Flow
Energy (BFE) used for downwards testing (Fig. 4a) andSpecific Energy (SE) used for upwards testing (Fig. 4b).
Their definitions and descriptions are shown in Table 2.

3. Results
3.1 Gravity-induced flow
Gravity-induced flow is one of the most common flow

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Fig. 4

Mechanically forced flow in FT4 Powder Rheometer.

Fig. 5Illustration for flow energy calculation.
Table 3Fig. 6

Effect of particle size on hopper discharge.

Table  2 Dynamic parameters from FT4 Powder Rheometer
SymbolDefinition or description

BFE (mJ)The energy needed to displace conditioned powder sample during downwards testing at specificconsolidating conditions.

SE (mJ/g)

The energy per gram needed to displace conditioned powder during upwards testing using anegative 5° helix.

146

Discharge parameters for pulverized coal with different particle sizes
a

b

c

d

e

f

g

dp (μm)

18

44

56

75

94

141

224

Ws (g/s)

0

25.3

33.0

81.7

128.6 211.9 284.7

Std Dev

—

3.91

7.8

4.6

2.96

8.03

7.15

RSD (%)—

15.46 23.63

5.63

2.30

3.79

2.51

modes, where gravity is relied upon to recover powders
from storage and to cause flow in process (Wolf and
Schwarz, 1991). In most industrial situations where hoppers are used, it is of interest to know how the powder
will flow from the hopper as it empties under gravitational
forces only (Medina et al., 2000). Fig. 6 reports the resultsof pulverized coal discharge from the Perspex hopper.
Each discharge experiment was repeated three times. The
average discharge rate (Ws), standard deviation (Std Dev)
and relative standard deviation (RDS) are provided inTable 3 to show the fluctuation of data in the Fig. 6.
As shown in Fig. 6, the discharge rate of pulverized coalincreases gradually as the particle size increases. However,Haifeng Lu et al. / KONA Powder and Particle Journal No. 32 (2015) 143–153(a) Arching, dp = 18 μm(b) Unstable-flow, dp = 56 μm(c) Mass-flow, dp = 224 μm
Fig. 7

Discharge characteristics of pulverized coal in different regions.

the actual discharge process is far more complicated:unstable flow or blocking occurs for small pulverized coal,and only those larger than a certain size can perform a stationary flow. The digital camera was used to freeze the discharge of pulverized coal from the Perspex hopper, andfound that pulverized coal with different particle sizes
would show different flow modes. Based on analyzing theflow behaviors, three regions including arching, unstable-­
flow and mass-flow were found and reported as a functionof mean particle size of pulverized coal.
In arching region (sample: a), a static arch forms acrossthe hopper outlet which completely stops the powder flow.
As shown in Fig. 7a, pulverized coal with the mean particle size 18 μm can not be discharged at all due to thearching phenomenon. It has been proved in Fig. 2a thatfine pulverized coal trends to form agglomerates andclumps and has strong cohesive forces. The cohesive
forces acting in pulverized coal can give rise to arching
phenomena, thus the discharge is notoriously unreliable.
It is further found that there is amount of pulverized coaladhered to the hopper wall even if the arch was broken
artificially.

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Haifeng Lu et al. / KONA Powder and Particle Journal No. 32 (2015) 143–153

In unstable-flow region (sample: b–e), dynamic archforms and collapses alternately across the hopper outlet,
which does not block the powder flow but results in the
non-steady flow. As shown in Fig. 7b, pulverized coalwith the mean particle size 56 μm shows irregular shapesof the solid stream (dense → loose → dense) under thehopper outlet. A periodical collapsing phenomenon wasobserved, particles falling down within the hopper losttheir velocities at the location of the arch and formed
blocks, then dropped freely down with a certain time foran alternate formation and collapsing of the block.
In mass-flow region (sample: f–g), powder discharges
smoothly just as a liquid. As shown in Fig. 7c, pulverizedcoal with the mean particle size 224 μm shows a typicalmass flow pattern; all the particles can move simultaneously without any trouble. It also confirms the fact that
powder discharge rate is independent of the quantity ofmateriel in the hopper.
The experimental results show that with the increase ofparticle size, the discharge rate increases, the flow stability improves and a progressive transition from blocking tounstable flow and to mass flow can be found.

Fig. 8

Effect of particle size on BFE.

Fig. 9Effect of particle size on SE.

3.2 Mechanically forced flow
Mechanically forced flow is another common flow
mode, where powders are driven to flow by the mechanical force (Bruni et al., 2007; Klausner et al., 2000). In thissection, pulverized coal was subjected to forced flow in
FT4 Powder Rheometer and the flow behaviors associatedwith the blade moving downwards and upwards were discussed, respectively.
In downwards testing, the pulverized coal was forced toflow by moving the blade through the powder from the topof the vessel to the bottom. The movement of blade caused
many thousands of particles to interact, or flow relative to
one another, named as bulldozing action. BFE was used todescribe the forced flow characteristics of pulverized coal.
It is a key flowability parameter that is highly sensitiveand differentiating in relation to small differences in flow
properties (Freeman and Fu, 2008). As shown in Fig. 8,
the BFE of pulverized coal increases exponentially as theparticle size increases. Eqn. (1) expressed the relationshipbetween the BFE and the mean particle size with the correlation coefficient R2 as high as 0.99.

BFE 
2402  2373exp(d p / 98)   R2 = 0.99

(1)

Generally, the more difficult it is to move the blade, themore remarkably the particles resist motion and the harder
it is to get the powder to flow. Therefore, the low BFE
represents good flowability and the high BFE corresponds
to poor flowability. The experimental results indicate thatfine pulverized coal exhibits very little resistance andbehaves more like a fluid, while coarse pulverized coal148

experiences substantial amount of resistance and it
becomes more difficult to get the pulverized coal to flow.
In upwards testing, the pulverized coal was forced toflow by moving the blade through the powder from thebottom of the vessel to the top, which establish a particular flow pattern in a conditioned, precise volume of powder. During the measurement, this flow pattern is an
upward clockwise motion of the blade (Fig. 4b), generating gentle lifting and low stress flow of the powder. SE
was used to describe the forced flow characteristics ofpulverized coal and it is a measure of how easily a powder
will flow in an unconfined or low stress environment. Fig.
9 reports the SE of pulverized coal as a function of themean particle size. Eqn. (2) expressed the relationshipbetween the SE and the mean particle size with the correlation coefficient R2 as high as 0.99.

SE 
8.83  36.4exp(d p / 21)   R2 = 0.99

(2)

It can be seen that, SE decreases gradually as the particle

Haifeng Lu et al. / KONA Powder and Particle Journal No. 32 (2015) 143–153

size increases and tends to be a fixed value about 9 mJ/g
when the mean particle size is larger than about 100 μm.
It means that 100 μm is a critical size above which powders flowability changes little; it is only below this criticalsize that powders flowability improves as the particle size
increases.

4. Discussion
4.1 Relationship between different flow modes
The experimental results show different flow behaviorsof pulverized coal under different flow modes. In this section, relationship between gravity-induced flow and me­
chanically forced flow will be discussed.
Fig. 10 shows the relationship between BFE and discharge rate of pulverized coal. In Fig. 10, the increase
trend of discharge rate is slow at beginning and thenquicken along with BFE. Especially, the near-perfect linear relationship later indicates that these two flow modes
may show some similarities. Eqn. (3) expressed the relationship between the BFE and the discharge rate with thecorrelation coefficient R2 as high as 0.99.

Ws 0.22BFE  192.94   R2 = 0.99

Fig. 10

Relationship between BFE and discharge rate.

Fig. 11

Relationship between SE and discharge rate.

(3)

Submitting Eqn. (1) into Eqn. (3), then a critical value
43 μm of mean particle size can be obtained corresponding the zero discharge rate.
In BFE testing, the blade simultaneously rotates anticlockwise and moves downwards. The resistance experienced by the blade represents the difficulty of this relative
particle movement, or the bulk flow properties of thepowder bed. In discharge testing, discharge rate from ahopper can be expressed as Beverloo’s model, which islinearly related to the bulk density of powder. It means,
both BFE and discharge rate are closely related to thebulk properties of the powder bed. As we know fine particles have a greater tendency to stick to one and another;
they are more prone to forming agglomerates and tend toentrain a great deal of air, reducing its bulk density andmaking it potentially highly compressible. On the contrary, coarse particles always pack in a denser conditionand have a higher bulk density as the interparticle force
between particles becomes weaker (Abdullah and Geldart,1999). Therefore, both discharge rate and BFE increase
with the particle size, and a positive correlation between
discharge rate and BFE was obtained.
Fig. 11 shows the relationship between SE and discharge rate of pulverized coal. The discharge rate de­creases rapidly with SE until it reaches a certain value. In
SE testing, the blade simultaneously rotates clockwise
and moves upwards. The energies measured are more
dependent on the cohesive and mechanical interlocking

forces between the particles. Considering the demonstration of cohesive force on discharge above, it is concluded
that both SE and discharge rate are closely related to thecohesion of powders. For fine particles, they have larger
specific surface areas and stronger agglomerations, andthe cohesive forces are more appreciable in comparison to
the relatively large-size particles. With the rise of particle
size, powder changes from cohesive state to more free
f lowing. Therefore, discharge rate increases but SE
decreases with the particle, and a negative correlation
between discharge rate and BFE was obtained.

4.2 Predominant force between particles
In this section, dominated force between particles will
be discussed to reveal the flow mechanism based on force
analysis. However, considering the complicated forces
during the flow in FT4 Powder Rheometer which may
depend on the blade path, only the gravity-induced flow
will be focused on here.

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During the gravity-induced flow, a particle is acted
upon by gravitational force (Wg), buoyancy force (Wb),drag force (Fh) and interparticle force (Finter) (Qian et al.,2001). It should be noted that, discharge experiments werecarried out with the pulverized coal ranging the mean
particle size from 18 to 224 μm. Spink and Nedderman(1978) considered that, for discharge in air, the motion ofparticles was significantly influenced by fluid resistancewhen the size smaller than 500 μm. Therefore, the dragforce was taken into account.
In this work, pulverized coal is regarded as a collectionof spherical particles. The gravitational force and buoyancy force on a spherical particle are1
Wg  πd p 3 p
6

(4)

1
πd p 3 f
6

(5)

Wb 

The “effective weight” of the particle can be therefore
given byWe  Wg  Wb 1
πd p 3( p  f )6

(6)

If the relative velocity between a single particle and thesurrounding fluid is u, then the drag force on the particle
is defined by
f u 2 πd p224Fd  C D(7)

At low Reynolds number, the drag coefficient CD isgiven by
CD 

24
Re

(8)

and the drag force follows fromFd  3πu d p

(9)

The dominant interparticle force between two particles
is the Van der Waals force. The Van der Waals force
between two spherical and rigid particles of diameter d1
and d2 can be expressed as

Finter 

A
d1d 2
2
12 d1  d 2(10)

Where A is Hamaker’s constant and δ is the distance
between the two particles. Assuming that the particles
have the same size, d1 = d2 = dp, Eqn. (10) becomes
Finter 

Ad p
24 2

(11)

Fig. 12 shows the influence of particle size on effective
weight, drag force and interparticle force. It is a qualitative concept because it is hard to accurately calculate the150

Fig.  12 Influence of particle size on effective weight, dragforce and interparticle force.

drag force and the Van der Waals force. On one hand, therelative fluid-particle velocity is unknown. On the other
hand, Eqn. (10)(11) are only applicable to the sphericaland rigid particles, while the pulverized coal used isirregular. The calculation value of Van der Waals force
seems much larger. Thus, we have not provided these dataand only qualitatively discussed the change trend of eachforce. According to Eqn. (6)(9)(11), the effective weight is
proportional to the third power of the particle size whileboth the drag force and the interparticle force are linearly
related to the particle size. It can be seen clearly that, theeffective weight increases steepest with increasing particle size and becomes the dominant force above a certain
particle size (d’). On the contrary, for fine powder with aparticle size smaller than d’’, the interparticle force willplay a vital role and govern the powder properties. Whenthe particle size falls in between d’ and d’’, the drag force
will be predominant.
Fig. 12 and the discussion above can be used to well
explain the region division in Fig. 6. In the gravity-­
induced flow, gravity is the driving force while drag force
and interparticle force are resistance and contribute a
more significant portion for fine powders. For large particles in mass-flow region, the gravity is large compared tothe drag force and the interparticle force, and so large
particles will have an individual mobility that will resultin mass flow pattern. As confirmed in Fig. 2(b) that, theweight and the inertia of individual particles are muchlarger than the very weak Van der Waals forces in coarse
pulverized coal. For intermediate particles in unstable-­
flow region, the fluid drag is dominate and can no longer
be neglected, and the powder motion will be retarded by
air resistance significantly. For small particles in archingregion, the interparticle force between particles is large
enough, resulting in high structural strength and poor
particle mobility (Forsyth et al., 2002). As confirmed inFig. 2(a) that, the Van der Waals force that causes indi-Haifeng Lu et al. / KONA Powder and Particle Journal No. 32 (2015) 143–153

vidual particles to cling to one another is predominant infine pulverized coal. In a word, discharge behaviors in
Fig. 6 can be well explained by the model proposed by
Qian et al. The predominant force is different in eachregion and responsible for the various flow behaviors ofpulverized coal.
Combining Fig. 12 and experiments as well as discussions above, the characteristic particle sizes d’ and d’’ forpulverized coal might be obtained. From Fig. 9 and Eqn.
(2), we can define a critical particle size of about 100 μm
dividing powder flow into two types. From Fig. 8, Fig. 10
and Eqn. (1)(3), another critical particle size of about
40 μm (exact solution is 43 μm) can be calculated. It isconsidered that the characteristic particle sizes d’ and d’’for pulverized coal are about 100 μm and 40 μm, respectively. These values would be very valuable and could
provide a reference basis for the size selection ofentrained-flow pulverized coal gasification.

5. Conclusions
In this paper, gravity-induced flow and mechanicallyforced flow were carried out, and the effect of particle
size and flow mode on the flow behavior of pulverizedcoal was investigated.
From experimental observations it has been shown thatthe flow behavior of pulverized coal changes drasticallywithin the broad range of particle sizes and different flowmodes. With the increase of particle size, a progressive
transition from blocking to unstable flow and to mass flow
was found in the gravity-induced flow, while the flow
resistance increased exponentially (BFE) for downwards
testing and decreased gradually (SE) for upwards testingin the mechanically forced flow.
The relationship between different flow modes was discussed. BFE and discharge rate are closely related to thebulk properties of the powder bed, and a positive correlation between them was obtained. SE and discharge rate
are closely related to the cohesion of powders, and a negative correlation between them was obtained. The flowmechanism of gravity-induced flow was revealed and well
explained based on force analysis. The force dominating
the flow properties of pulverized coal varies as the particle
size increases. Further, two characteristic particle sizes ofpulverized coal were obtained from experiments and discussions, which can provide a reference basis for the size
selection of entrained-flow pulverized coal gasification.

Acknowledgements
This work was supported by the National Natural Science Foundation of China (21006027, 21206041), the Fun-damental Research Funds for the Central Universities(WB1214012) and China Postdoctoral Science Foundation
funded project (2012M520847). Especially, the authors
thank the anonymous reviewers for their helpful suggestions on the quality improvement of this article.

Symbols and Abbreviations
A

Hamaker’s constantAOR

angle of repose [°]BFE

basic flow energy [mJ]dp

mean particle size [μm]d1, d2

particles diameter defined in Eqn. (7) [μm]d’, d’’

characteristic particle size [μm]CDdrag coefficient

Finterinterparticle force [N]Fddrag force [N]HR

Hausner ratio

SE

specific energy [mJ/g]urelative fluid-particle velocity [m/s]Wbbuoyancy force [N]We

effective weight [N]Wggravitational force [N]Wsdischarge rate [kg/h]

μfluid viscosity [Ps·S]δ

distance between the two particles [μm]ρbbulk density [kg/m3]

ΡT

tap density [kg/m3]

ρf

fluid density [kg/m3]

ρpparticle density [kg/m3]

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Author’s short biography
Haifeng Lu
Haifeng Lu received his B.S and PH.D. from East China University of Science andTechnology in 2007 and 2012, respectively. His research interests include powder technology and multiphase flows.

Xiaolei Guo
Xiaolei Guo received his B.S, M.S and PH.D. from East China University of Scienceand Technology in 2001, 2004 and 2010, respectively. His research interests include
pneumatic conveying and coal gasification.

Yi LiuYi Liu is a graduate student in the Institute of Clean Coal Technology. He received his
B.S from East China University of Science and Technology in 2012. His current research
focuses on powder technology.

Xin Gong
Xin Gong, a Professor at East China University of Science and Technology and an
expert on entrained-flow coal gasification. She received her B.S and M.S. from East
China University of Science and Technology in 1982 and 1986, respectively. Her researchinterests include dense-phase flow, pneumatic conveying and coal gasification.

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KONA Powder and Particle Journal No. 32 (2015) 154–162/Doi:10.14356/kona.2015003

Original Research Paper

Structural and Optical Characterization of Ni and Al Co-DopedZnO Nanopowders Synthesized via the Sol-Gel Process †Amor Sayari 1,2* and Lassaad El Mir 3,4
1

Department of Physics, Faculty of Science, King Abdulaziz University, North Jeddah Branch, Saudi Arabia
Equipe de Spectroscopie Raman, Département de Physique, Faculté des Sciences de Tunis, Campus Universitaire, Tunisia
3
Al Imam Mohammad Ibn Saud Islamic University (IMSIU), College of Sciences, Department of Physics, Saudi Arabia
4
Laboratoire de Physique des Matériaux et des Nanomatériaux appliquée à l’Environnement, Faculté des Sciences de Gabès, Tunisia
2

Abstract
We have successfully synthesized (Ni,Al) co-doped ZnO nanostructured powders via the sol-gel technique at
low temperature. The elemental analysis confirms the incorporation of the Ni and Al ions into the ZnO matrix.
The structural study revealed that the nanopowder samples are assembled in flower-shaped Zn0.9-xNi0.1AlxO
nanostructures with average crystallite sizes ranging from 39 to 53 nm. The XRD patterns show that theZn0.9-xNi0.1AlxO nanopowders have a hexagonal wurtzite polycrystalline structure. Weak diffraction peaks related
mainly to nickel oxides are also detected in the samples. The highest crystallite size, lowest lattice parameters andunit cell volume are obtained for the nanopowder samples that contain 1.5 at.% of aluminum. The decomposition
process of the dried gel system is investigated by thermogravimetric analysis (TGA). Raman scattering and FT-IR
measurements confirm the wurtzite structure of the synthesized Zn0.9-xNi0.1AlxO nanopowders. The energy band
gap of the synthesized nanopowders (~3.32 eV) was estimated by using the Brus equation and the crystallite sizes
obtained from XRD data, for comparison. The strain in the nanopowder samples (~2.7 × 10 –3) was also calculated
according to the Stokes-Wilson equation.
Keywords: ZnO, sol-gel, Ni and Al co-doping, SEM, XRD, Raman scattering

1. Introduction
Recently, semiconductor nanoparticles have attracted
much attention due to different physical and chemical
properties compared to their bulk materials1). When thesize of a nanostructure approaches the Bohr radius of
exciton, optical, electronic and structural properties are
greatly affected by the quantum confinement effect,
meaning that nanosized materials are very different fromtheir bulks2–5). Therefore, semiconductor particles are
viewed as promising candidates for many important technological future applications1). Among various semiconductor nanoparticles, nanosized zinc oxide (ZnO) particles
are the most frequently studied due to their frequent
applications in industrial areas. ZnO nanoparticles whichhave great potential for use as efficient UV light absorbers
were recently applied to improve the finishing of textile
†
1
2
3
4

*

Received 15 January 2014; Accepted 12 May 2014
J-STAGE Advance published online 25 June 2014
P.O. Box 80203, Jeddah 21589, Saudi Arabia
El-Manar, 2092 Tunis, Tunisia
Riyadh 11623, Saudi Arabia
Cité Erriadh Manara Zrig, 6072 Gabès, Tunisia
Corresponding author: Amor Sayari;
E-mail: amor.sayari@laposte.net
Tel: +966(2)6952287 Fax: +966(2)6952278

154

products6) and are being utilized for improved cosmetic
products7). To synthesize uniform nanosized ZnO particles and control their sizes and morphology, a variety oftechniques has been used such as thermal decomposition8), chemical vapor deposition 9), sol-gel10–13), spraypyrolysis14), and precipitation15).
It is well known that adding impurities into a wide gap
semiconductor such as ZnO induces great changes in the
optical, electrical and magnetic properties16). Thus doping
certain elements into ZnO has become an important route
to control and optimize its optical, electrical and magnetic
performance. It was reported that ZnO has been dopedwith elements of the groups IA such as Li, IIIA such as
Al, VA such as N and VIII such as Ni, Co, etc.17,18). Doping of ZnO with magnetic ions such as Ni introduces
magnetic properties ultimately forming dilute magnetic
semiconductors, which are interesting materials for application in the spintronic domain. In addition, nanosized
nickel oxide has demonstrated other excellent properties
such as catalytic19), electrochromic20), optical16,21) andelectrochemical properties22). There is considerable interest in the development of zinc-oxide-based (ZnO: transition metal) diluted magnetic semiconductors because of
their high Curie temperature which is essential for spintronic devices3). Recently, co-doped ZnO nanomaterials©2015 Hosokawa Powder Technology Foundation

Amor Sayari et al. / KONA Powder and Particle Journal No. 32 (2015) 154–162

with Al and transition metals have been investigated forscientific and practical interests. It was found that for theNi and Al co-doped ZnO (Zn(Ni,Al)O) film, the ferromagnetic behavior was enhanced by increasing the Ni
content23–25). However, the co-doping of ZnO with metallic ions such as nickel (Ni2+) and aluminum (Al3+) is notwidely investigated. The Zn(Ni,Al)O semiconductor is aninteresting material due mainly to its ferromagnetic
behavior with a high Curie temperature.
It is a significant task to prepare Zn(Ni,Al)O nanomaterials with better electrical, optical and ferromagnetic
behavior for potential applications in magnetoelectronic
and optoelectronic devices. The aim of the present contribution is the elaboration as well as the structural andoptical characterization of Zn(Ni,Al)O nanoparticles synthesized by the sol-gel process where many environmental parameters such as temperature, pressure and doping
concentration can be varied to control the growth of thecrystal26). This study is a prerequisite to investigate theferromagnetic behavior in these powdered samples. The
prepared Zn0.9-xNi0.1AlxO samples have an Ni composition
fixed at 10 at.% and various Al contents ranging from 0.0
to 2.5 at.%. The composition and structure of theZn(Ni,Al)O nanopowders are studied using energy dispersive X-ray spectroscopy (EDX), X-ray diffraction(XRD) and scanning electron microscopy (SEM). The
optical property and thermal analysis characteristics ofthe nanopowder samples are measured by using an FT-IR
spectrophotometer and a thermogravimetric analyzer. The
vibrational properties are studied by Raman scattering atroom temperature in back-scattering configuration.

2. Experimental details
Zn(Ni,Al)O nanocrystals were prepared by the sol-gel
method using 16 g of zinc acetate dehydrate as a precursor in 112 ml of methanol. After 10 min magnetic stirring
at room temperature, 2.8 g of nickel chloride hexahydratecorresponding to [Ni]/[Zn] = 10 at.% and an adequate
quantity of aluminum nitrate-9-hydrate corresponding to[Al]/[Zn] ratios of 0.0, 1.5 and 2.5 at.% were added. Afteran additional 15 min magnetic stirring, the solution wasplaced in an autoclave and dried under supercritical conditions of ethyl alcohol (EtOH).
The phase identification of nanopowders was analyzed
by X-ray diffraction using a Philips X’pert diffractometerequipped with copper X-ray tube (λ kα1 = 1.5406 Å), nickel
filter, graphite crystal monochromator, proportionalcounter detector, divergence slit 1° and 0.1 mm receiving
slit. The working conditions were 40 kV and 30 mA for
the X-ray tube, scan speed 0.05° and 2 s measuring time
per step. The morphology of the Zn(Ni,Al)O nanoparticles
was observed by scanning electron microscope (SEM)

equipped with an energy dispersive X-ray spectrometer(EDX). FT-IR spectroscopy (Thermo Scientific Nicolet
iS10 FT-IR Spectrometer) was used at room temperaturein the range of 500–4000 cm–1 to study the optical properties of the synthesized Zn 0.9-x Ni0.1Al xO nanopowders.
Thermal gravimetric analysis (TGA) curves of the (Ni,Al)
co-doped ZnO nanopowders was performed using a TGA
apparatus (Perkin Elmer). Approximately 2.9 mg of a sample was placed in a platinum crucible on the pan of the
microbalance and heated from 35 to 950°C at a rate of
10°C/min–1. The Raman spectra were performed at room
temperature with a Labram system equipped with a microscope in back-scattering configuration. The excitation line
was at 514.5 nm from an Ar+ laser.

3. Results and discussion
3.1 Composition and structure
Fig. 1 shows typical EDX spectra of the (Ni,Al)
co-doped ZnO nanopowders. The spectra in Fig. 1 show
the existence of Zn, Ni, Al and O in the powdered samples, indicating the incorporation of Ni and Al ions into
the ZnO matrix. It is clear from the elemental analysis thatthe percentage of Ni atoms is almost constant (around 11at.%) for all the doped samples (Table 1). The intensity of
the characteristic Al peak increases with increased dopantlevel. The average Al concentrations in the doped nanopowders are 0.0, 2.04 and 3.38 at.% for 0, 1.5 and 2.5 at.%
Al doped Zn(Ni)O nanocrystals, respectively (Table 1).
Fig. 2 shows the X-ray diffraction spectra (verticallyoffset for clarity) of the prepared Zn(Ni,Al)O nanopowder
samples. The crystal structure of Zn 0.9-x Ni 0.1 Al x O(x = 0.00, 0.015 and 0.025) was wurtzite with preferredorientation along [101], [100] and [002] directions for all
the samples. The reflection peak positions of the products
coincide well with JCPDS data for the wurtzite structureof bulk ZnO27,28). With Ni and Al incorporation, the dominant peaks of ZnO are retained but are accompanied byextra peaks marked by asterisks. The intensity of these
extra peaks is seen to decrease with Al concentration due
to incorporation of Al ions in the ZnO lattice. The occurrence of extra peaks and the intensity dependence on the
Al concentration signifies that these peaks are due to thepresence of additional phases in wurtzite ZnO mainly
related to metallic Ni and NiO-like crystalline structures.
It is known that Ni is very unsTable in the ZnO matrix
and has the tendency to form clusters of metallic Ni or
NiO29). The dominant phase is, however, the wurtzite
because of smaller concentrations of Ni (10%) and Al(0–2.5%) atoms in the prepared samples. The results showthat the addition of Ni and Al atoms as dopants did notaffect the lattice patterns of ZnO nanocrystals.

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Table 1 The atomic content of the different elements ofthe three Zn0.9-xNi0.1AlxO nanopowders obtained
from the EDX measurements
Nominal Al
concentration Oxygen(at.%)(at.%)

Fig. 2

Fig. 1

Energy dispersive X-ray analyses of the Zn0.9-xNi0.1AlxO
nanoparticles synthesized by the sol-gel process. (a)x = 0 at.%, (b) x = 1.5 at.% and (c) x = 2.5 at.%.

The Zn0.9-xNi0.1AlxO crystallite sizes (D), calculated
from the widths of the major diffraction peaks observedin Fig. 2 using the Scherrer formula 30), are given in
Table 2. The average crystallite sizes range from 39 to
53 nm. The slight changes in the lattice parameters(Table 3) are expected due to the ionic radii mismatch.
Both ionic radii of Al3+ (0.57 Å) and Ni2+ (0.69 Å) aresmaller than the ionic radius of Zn 2+ (0.74 Å)24). Fig. 3
illustrates the shift occurring in the (101) peak of ZnO
with increase in Al concentration. It is clear from Fig. 3that the (101) peak of ZnO shifts to higher angles for156

EDX Results
Nickel(at.%)

Aluminum Zinc
(at.%)(at.%)

0

47.39

9.26

—

43.35

1.5

51.68

13.19

2.04

33.09

2.5

51.49

12.64

3.83

32.05

XRD patterns of the three Zn0.9-xNi0.1AlxO nanopowder
samples synthesized by the sol-gel method: (a) x = 0
at.%, (b) x = 1.5 at.% and (c) x = 2.5 at.%.

x = 0.015 and 0.025 compared to x = 0. However, for thehigher doping concentration (x = 0.025), a smaller shift is
detected. The lattice parameters (a and c) and the unit cellvolume decrease (Table 3) with the introduction of Ni andAl ions; this can be attributed to the larger ion radius of
Zn2+ compared with that of Ni2+ and Al3+ 31). Therefore,
Al atoms are more incorporated into the ZnO lattice at
x = 0.015. The lattice strain of the Zn0.9-xNi0.1AlxO nanopowder samples is also calculated according to the
Stokes-Wilson equation. The different values of the strain
are 2.7 × 10 –3, 2.3 × 10 –3 and 2.9 × 10 –3 for x = 0, 0.015
and 0.025, respectively.
Fig. 4 shows SEM images of the Zn(Ni,Al)O nanopowders prepared with different nominal concentrations ofaluminum. The nanoparticles have flower-like shapes
consisting of a large number of primary nanocrystallites
which are detected by XRD. No appreciable change either
in shape or in size of the ZnNiO particles could beobserved on Al-doping (Fig. 4(a–c)). From SEM micrographs, a non-uniform distribution of particles isobserved. It consists of either several single particles or acluster of particles. The magnified image shows that flower-­
shaped structures are constituted by the accumulation of

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Table 2 Peak position, full width at half maximum (FWHM) and the estimated crystallite sizes from XRD data along
the major reflection planes of the three Zn0.9-xNi0.1AlxO nanopowders
Nominal Al
concentration(at.%)

(101)

(100)

Peak positionand (FWHM)(degree)

Crystallite
size D(nm)Peak positionand (FWHM)(degree)

Crystallite
size D(nm)Peak positionand (FWHM)(degree)

Crystallite
size D(nm)036.34 (0.199)

41

31.86 (0.181)

45

34.51 (0.198)

42

1.5

36.43 (0.173)

48

31.96 (0.154)

53

34.59 (0.169)

49

2.5

36.38 (0.204)40

31.89 (0.209)

39

34.54 (0.197)

42

Table 3 Lattice parameters, unit cell volume and energy
band gap of the Zn0.9-xNi0.1AlxO nanoparticles
Nominal Al
Unit cell Lattice parameters (Å)
Energyconcentration
volumegap (eV)(at.%)(Å3)
acFig. 3

(002)

0

3.322

47.2

3.241

5.194

1.5

3.325

46.8

3.231

5.181

2.5

3.321

47.1

3.237

5.189

Enlarged view of the diffraction angle ranges
2θ = 35.6°–37.2° for Zn 0.9-x Ni0.1Al xO nanopowders:(a) x = 0.00; (b) x = 0.015 and (c) x = 0.025.

several hundred sharp sheets of Zn(Ni,Al)O nanoparticles. The typical diameters of these individual nanosheets
are in the range of 0.5–1 μm with a thickness of
50–100 nm. We note that similar morphologies of thenanostructured materials, consisting of nanometric homogeneous subunits aggregated into larger particles, havebeen reported previously for materials prepared by controlled precipitation 32) or thermal decomposition of
metalorganic precursors33).
In order to compare the energy band gap (Eg) of thedifferent Zn0.9-x Ni0.1Al xO samples, we used the Brus
equation derived from the effective mass model34) and the
crystallite sizes obtained from XRD data. The parametersof ZnO bulk used in the calculations were taken from Ref.
35. The different values of Eg are given in Table 3. We
note that Eg is almost constant (~2.2 eV) when the Al content increases from 0 to 2.5 at.%.

3.2 Thermal properties
The thermal behavior of dried Zn(Ni,Al)O gel has been
investigated by thermogravimetric analysis (TGA). Fig. 5
depicts the results of the TGA measurements on the nanosized Zn0.9-xNi0.1AlxO particles. It can be seen that the
sample weight decreases continuously with increase intemperature in the interval 25–450°C. However, for temperatures up to 450°C, the weight loss decreases albeit ata small rate (Fig. 5), which corresponds to the release ofentrapped gases formed during the decomposition of acetate ions36). The TGA curves show four main regions. The
first weight loss is from room temperature up to 150°C
due to the dehydration of Zn0.9-xNi0.1AlxO nanopowders.
The second weight loss is from 150 to 220°C, which is
attributed to the decomposition of chemically boundgroups. The third step from 220 to 350°C is related todecomposition of the organic groups. The last weight loss
from 350 to 450°C is attributed to decomposition of the
impurity ions from the ZnO lattice and the formation of
ZnO pure phases. The TGA study also shows the stabilityof the Zn0.9-xNi0.1AlxO nanopowders. We note that for
x = 0, the weight loss is more important compared to samples with x > 0; this can be related to the addition of Al
atoms.

3.3 Raman scattering
Room temperature Raman spectra of Zn(Ni,Al)O
nanopowders, synthesized by the sol-gel process, arereported in Fig. 6. The intense Raman peak located atabout 502 cm–1 could be assigned to multiphonon scattering of the transverse optical (TO) E1 and E2 (low) modes.
The peak at 708 cm–1 can be assigned to combinations of
longitudinal acoustic (LA) and TO modes at the M point.
In the low-frequency region, the peaks at 190, 330, 384
and 409 cm–1 are attributed to 2E2 (low), 2E2 (M), A1 (TO)157

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Fig. 4

SEM images showing the morphology of nanocrystalline Zn0.9-xNi0.1AlxO nanopowders at different magnifications: (a) x = 0 at.%, (b) x = 1.5 at.% and (c) x = 2.5 at.%.

and E1 (TO) modes37), respectively. Phonons in a nanocrystal are confined in space and all types of phonons
over the entire Brillouin zone will contribute to theRaman spectrum. The intense peak at ~434 cm–1 is theE2(high) mode, characteristic of the ZnO crystallinity38).
Observation of the later modes indicates that theZn(Ni,Al)O product has the wurtzite structure as confirmed by XRD analysis. The broad Raman peak ataround 573 cm –1 may be due to the A1(LO) mode39).
Recent reports related the appearance of this mode to lattice defects, namely either oxygen vacancies or zinc inter-158

stitials or their combination40,41).

3.4 FT-IR measurements
Fig. 7 illustrates the FT-IR spectra of nanosized
Zn0.9-xNi0.1AlxO powders in the range 500–4000 cm –1.
FT-IR spectra of the synthesized products show a significant spectroscopic band at around 500 cm–1, which is thecharacteristic band of ZnO42). The broad OH band (3574,
3447) cm–1 appears in all the FT-IR spectra43). Theoretical
calculations predict O-H vibrations in ZnO ranging from

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Fig. 6

Room temperature Raman backscattering spectra of
the nanocrystalline Zn 0.9-x Ni 0.1Al xO nanopowders.
(a) x = 0.00, (b) x = 0.015 and (c) x = 0.025.

Fig. 7

FT-IR spectra of nanosized Zn0.9-xNi0.1AlxO powderedsamples: (a) x = 0.00, (b) x = 0.015 and (c) x = 0.025.
The spectra are recorded at room temperature and vertically offset for clarity.

4. Conclusions
Fig. 5

TGA curves of nanosized Zn0.9-xNi0.1AlxO powders at aheating rate of 10°C/min. (a) x = 0.00, (b) x = 0.015 and(c) x = 0.025.

3216 to 3644 cm–1, depending on the configuration andnumber of hydrogen atoms in the complex44). The transmission bands at ~1576 and 1412 cm–1 in all the samples
are due to the carbonyl groups of the carboxylate ionswhich might remain adsorbed on the surface of nanoparticles45). The peak at 887 cm–1 is probably due to thenitrate (NO3–) group which is not completely removed
during the sol-gel process46). Inclusion of Ni and Al ionsin the ZnO lattice is confirmed by the emergence of the
bands at 1038 and 582 cm-1 45).

In summary, Zn0.9-xNi0.1AlxO nanopowders with crystallite sizes ranging from 39 to 53 nm were successfully
synthesized by the sol-gel process and investigated by avariety of techniques for changes in microstructure andoptical properties. The XRD patterns indicate high crystallinity in the hexagonal lattice of Zn 0.9-x Ni0.1Al xO
nanoparticles; such a result is also confirmed by Raman
measurements. The lowest lattice parameters and unit cellvolume are obtained for the nanopowder samples that
contain 1.5 at.% of aluminum, which indicates that Al isbetter incorporated into the ZnO lattice at this Al content.
The main weight losses showed by TGA curves occurringin the range between room temperature and 450°C areassociated with water emission and the decomposition oforganic groups and impurity ions. The TG trace shows

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the formation of ZnO pure phases up to 450°C. The estimated band gap (~3.322 eV) from the Brus equation andthe average crystallite sizes deduced from XRD data is
not strongly dependent on the Al composition of theZn0.9-xNi0.1AlxO nanopowders in our range of Al content(x = 0–0.025). The synthesized materials have potential
applications in photocatalytic, optoelectronic and magnetoelectronic devices.

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Author’s short biography
Amor Sayari
Amor Sayari received his bachelor’s degree in physics in 1989 and his Master of Science and PhD graduate degree in solid-state physics in 1991 and 1998, respectively, fromAl Manar University, Tunisia. He obtained the habilitation qualification in physics (HDR)at Al Manar University in July 2012, and became an associate professor in 2013. Afterobtaining his PhD on “Study of folding, confinement and interface effects in GaAs/AlAs
superlattices and quantum wells by Raman spectroscopy”, Dr. Sayari combined teachingactivities with experimental research in the field of solid-state physics at Al Manar University. He is a member of the Raman Group, Faculty of Science Tunis.
His research interest is vibrational, optical and structural properties, especially for III/V
and II/VI semiconductors heterostructures.
In 2006, he joined the King Abdulaziz University, Saudi Arabia, where he became
interested in ZnO nanoparticles and their applications in nanotechnology.

Lassaad El Mir
Lassaad El Mir completed his PhD in 1995 at the College of Sciences in Tunis in collaboration with the University of Paris VI in the field of electronic transport in semiconductors and his “HDR” (accreditation to supervise research) in the field of
nanotechnology in 2007 from the College of Sciences in Sfax, Tunisia. From 1992 to2010 he spent several periods of research at the University of Paris VI and in the Instituteof NanoSciences in Paris (France). Since 2012 he has been a full professor of physics.
From 1997 to 2002 he was head of the Physics Department of the College of Sciences inGabes University. His main research interest covers the synthesis and characterization of
nanoparticles, thin films and nanocomposites for a variety of applications such as solar
cells, transparent electrodes, advanced catalyst supports, water treatment, energy storage
and gas sensors. Since 2012 he has been a guest professor at the College of Sciences at
Al-Imam Muhammad Ibn Saud Islamic University, Saudi Arabia.

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Original Research Paper

Density-Driven Segregation in Binary and Ternary
Granular Systems †Kit Windows-Yule * and David Parker
1

School of Physics and Astronomy, University of Birmingham, UK

Abstract
We present a first experimental study of density-induced segregation within a three-dimensional, vibrofluidised,ternary granular system. Using Positron Emission Particle Tracking (PEPT), we study the steady-state particle
distributions achieved by binary and ternary granular beds under a variety of differing system parameters. In
doing so, we determine the extent to which the segregative processes in ternary systems resemble theircomparatively well-researched binary counterparts. We examine the influence of particle elasticity on the system,
demonstrating the existence of a significant parameter range for which the effects of inelasticity-induced
segregation may be safely neglected. Finally, we investigate the existence of a causal link between convective
motion within a system and the segregative behaviours observed.
Keywords: granular, segregation, binary, ternary, convection, buoyancy

1. Introduction
Granular materials, systems comprising multiple discrete, macroscopic components or ‘grains’, display a multitude of interesting physical phenomena both similar toand distinct from those observed in conventional ‘molecular’ materials (Jaeger et al., 1996). Of these myriad phenomena, perhaps the most pertinent to real-world
applications (Muzzio et al., 2002; Mohabuth et al., 2007)is that of granular segregation, the process by which anenergised granular system composed of two or more distinct particle ‘species’ may spontaneously separate into its
individual constituents. Having no analogy in classical,
molecular materials, this partitioning process of granularsegregation remains incompletely understood. This lackof understanding is due, in part, due to the significant
number of distinct mechanisms by which segregation canoccur: particles may separate from one another due todifferences in size (Rosato et al., 1987), density (Hong etal., 2001), elasticity (Serero et al., 2006; Brito and Soto,2009) or shape (Li et al., 2013) to name but a few. Nonetheless, thanks to the considerable volume of research on
the matter, a reasonable level of comprehension of thesegregative processes within simple, bidisperse systems
has been achieved. However, in many real-world applica†
1*

Received 2 April 2014; Accepted 3 June 2014
J-STAGE Advance Published online 30 June 2014
Edgbaston, B15 2TT, UK
Corresponding author: Kit Windows-Yule;
E-mail: windowsyule@gmail.com
TEL:+44-0-414-4708©2015 Hosokawa Powder Technology Foundation

tions, particulate systems possess more than two distinct
particle types. This poses an important question—when
the condition of bidispersity is relaxed, is our knowledge
based on these simple, two-component systems still relevant? In this paper, we take an incremental yet important
step towards answering this question by comparing
results obtained from an experimental, ternary-by-density
granular system to new and existing data concerning similar, binary systems. In our experiments, the energy necessary to produce segregation is provided by verticalvibration of the experimental granulate. Vertical vibration
is widely used in industrial processes requiring the separation and mixing of powders, grains and other particulatematerials. Such vibrations can also occur inadvertently,
for example during the transport of granular materials,
leading in many cases to unwanted separation or mixing
behaviours. Despite the clear importance of a better
understanding of vibrated granular mixtures, there exist
very few prior studies of these systems in the ternary casewith, to the best of the authors’ knowledge, no existing
literature regarding the rôle of differing particle densities
within these systems.
In this paper, we begin by describing the experimental
system from which our results are acquired (Section 2)
and detailing the manner in which data is extracted fromthis system (Section 3). We then provide information
regarding the discrete particle simulations used to supportand verify our findings (Section 4) before presenting andinterpreting our results for both non-convective (Section
5.1) and convective (Section 5.2) binary and ternary systems. Finally, in Section 6, we summarise the major

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results and conclusions arising from this work.
We demonstrate that while non-convective systems
exhibiting spatially homogeneous packing densities andtemperatures1) undergo a relatively simple segregative process reminiscent of Archimedean buoyancy in classicalfluids, spatially non-uniform and convective systems display significantly more complex segregation phenomena.
We show that, in both cases, these segregative behav­
iours remain qualitatively similar in both two- and three-­component systems, providing support for the possibility
that our understanding of binary systems may, with further research, be successfully extended to the ternary case.

2. Experimental Details
Our experimental set-up consists of a granular bed of
N = 6000 3 mm diameter, spherical particles housed
within a cuboidal container which is vibrated sinusoidally
in the vertical (z) direction. Two main types of bed areinvestigated: binary, in which NA = NB = 3000, and ternary, in which NA = NB = NC = 2000, where A, B and C
denote individual particle species. In our experiments three
species of particle, each of equal size but differing in theirmaterial properties, are used: steel (ρ = 7900 kgm –3),
glass (ρ = 2500 kgm–3) and polyurethane (ρ = 1200 kgm–3).
For brevity and ease of comprehension, the steel, glass,and polyurethane particles are referred to throughout the
paper as heavy (H), medium (M) and light (L) particles,
respectively.
In both the binary and ternary case, the total number ofparticles, N, and hence the dimensionless resting bed
height, H, are held constant. The consistency in total bed
height between binary and ternary systems is important,as H is a key control parameter in determining the state of
the system (Luding et al., 1994); the fixed H-value for thevarious systems explored thus allows a more direct comparison between the two cases. Similarly, the key control
parameters relating to the mechanical excitation of thesystem (Eshuis et al., 2005), namely the frequency,
ω = 2πf, and peak amplitude, A0, of the vibrations withwhich the system is driven, are also held equal for binary
and ternary systems for all data sets.
In order to produce and observe a variety of behaviours
within the experimental system, the dimensionless accel-

1)

The temperature to which we refer here, and throughout this manu,
script, is the granular temperature (Ogawa, 1978), defined as T = m〈c2〉
where c is the fluctuation velocity of a particle about its mean value.
The ‘ordinary’ or thermodynamic temperature can safely be assumed
negligible when considering the behaviour of granular systems due tothe large sizes, and hence high energies, of the particles involved(Jaeger et al., 1996). The granular temperature is an important quantity
to consider, as it affects many aspects of a granular system’s behaviour,
including segregative processes (Lim, 2010).

164

2 A
, is varied in the range Γ ∈ (5, 17) tog
produce varying degrees of fluidisation. Two differing
container widths Lx = Ly = 40 mm and Lx = Ly = 80 mm
are also utilised, providing bed heights H = 33.8 and
H = 8.4 for the narrower and wider systems respectively.
The variation of the container width not only varies theresting bed height, altering once again the density state of
the system, but also determines the importance of wall
effects, allowing us to investigate both convective andnon-convective systems (see Fig. 1). For both system
widths, Lz = 200 mm. The considerable height of the container results in a minimal probability of particle collisions with the upper boundary, allowing the system to beconsidered effectively open. The relatively large size ofthe particles allows us to neglect air effects within thesystem (Zeilstra et al., 2008). The size of the particles
combined with the use of a steel-based container will also
act to reduce the influence of electrostatic effects within
the system (Shi et al., 2007). The absence of significant
effects due to interstitial air and the build-up of static
charge is supported by the strong agreement observed
between experiment and simulation, in which no interstitial air or electrostatic effects are included.
In this study, our main interest is in the equilibrium
distribution of particles, or in other words the steady-state
degree of segregation exhibited by the granular bed. For
each data set, the system is initially vibrated for a periodof 1000 s to ensure that a steady state is reached. The
duration of this initial excitation was chosen based on

eration,  =Fig. 1Comparison of experimentally acquired velocity fields
for a) convective and b) non-convective ternary systems of widths Lx = Ly = 40 mm and Lx = Ly = 80 mm
respectively, each driven with a dimensionless acceleration Γ = 17. The lengths and orientations of the arrows
in the vector plots represent, respectively, the magnitude, |v|, and direction of the average velocity for a particle travelling through a given spatial region of thesystem. The images are scaled such that |v| is directlycomparable for the two systems shown. Note the considerably reduced magnitudes and random orientationsof the velocity vectors in image b), indicative of thechaotic motion of particles within this system and its
spatial homogeneity.

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preliminary tests in which ternary beds of height H = 33.8
and H = 8.4 were driven with a constant acceleration
Γ = 5 over a period of 3600 s. By utilising both the weakest driving and most polydisperse systems used in experiment, we can safely assume that these ‘test runs’ will
provide an upper limit for the time required to reach asteady state (Windows-Yule et al., 2014). The point at
which the system may be considered to be in a steadystate is determined by subdividing the data set acquired
into a series of 200 s segments and, for each of these segments, analysing the distributions of particle density andtemperature. If the form of these distributions is found toremain consistent over multiple adjacent segments, thenthe system can be assumed to have reached a steady state.
Over several repeated tests, both systems were typicallyfound to achieve a non-equilibrium steady state in lessthan 200 s, which was then maintained for the remainderof the 3600 s run. However, in order to thoroughly ensure
reliability, an initial excitation period of a considerably
longer duration was used when obtaining the main data
sets. For all data sets presented, the steady-state nature of
the system was confirmed as described above.
Preliminary tests were also conducted to investigate
the influence of a system’s initial particle distribution on
the final steady state reached. Various systems—both
convective and non-convective, and using both the highest and lowest excitation strengths explored in experiment—were investigated. In each case, three different
preparation protocols were used: in the first protocol, thegranular bed is stirred to produce a random distributionof particles, i.e. a mixed bed. In the second, a pure layerof heavy (H) particles rests atop a pure layer of light (L)particles, and in the third a layer of L particles lies above
a layer of H particles. Although, clearly, the time required
for each system to reach its equilibrium distribution wasaffected by the system’s initial conditions, the resultant
steady state was not found to be affected in any of thetested cases. For the main experiments, each system was
prepared using the first protocol.
The definitions of all symbols used in the manuscript
may be found in Table 1 below.

3. Data Acquisition—Positron Emission
Particle Tracking
Information is acquired from the experimental systemusing the Positron Emission Particle Tracking (PEPT)
technique. This technique uses a modified dual-headed
gamma camera to track, in three dimensions, the motionof a single particle which has been radioactively ‘labelled’
with a positron emitting radioisotope. By placing a granular bed containing such a radioactive ‘tracer particle’
between the twin detectors of the gamma camera andTable 1

Definitions of symbols used

SymbolDefinition

Units

A0

Amplitude of base vibrationmdParticle diameterm

H

Dimensionless resting bed height—heightof the static bed normalized by the particle
diameter—Is

Segregation intensity, as defined in Section
5.1.

—

f

Frequency of base vibration

Hz

Li

Dimensions of system in the i-direction of
the Cartesian coordinate system (i = x, y, z)mN

Total particle number—

Ni

Number of particles of a given species i—T

Granular temperature, as defined in
Section 1.

JΓDimensionless acceleration of basevibration—εEffective elasticity—

εiEffective elasticity for a collision between
like particles of species i—

εij

Effective elasticity for a collision between
unlike particles of species i and j—μ

Coefficient of friction—ρMaterial densityϕFractional concentration of a given particle
species in a multi-component system

kgm–3
—

determining the points at which the back-to-back 511 keV
γ-rays emitted by the tracer are detected, it is possible totriangulate the spatial position of this particle to an accuracy of up to 1 mm (Parker et al., 2002). For an adequately
active tracer, several thousand of these triangulation
events may be recorded each second, allowing the motionof the particle to be tracked with a temporal resolution of
the order of milliseconds. Since the γ-rays used are highly
penetrating, particle motion may be accurately recorded
deep within the interior of even large, dense and/or
opaque systems. A simple visual representation of theprocess described above may be seen in Fig. 2.
For systems in a non-equilibrium steady state (NESS),
such as those discussed here, the long-time averaged
motion of a single particle may, through the principle ofergodicity, be considered representative of the behaviourof the system as a whole (Wildman et al., 2000). For
example, the average fraction of time spent by a single
particle in a given region of the system (i.e. the tracer’s
residence time in this region normalized by the total duration of the data set) is directly proportional to the local

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For full details pertaining to the PEPT technique andits application to composite systems, the reader may referto the works of Parker et al. (2002), Wildman et al. (2000)
and Wildman and Parker (2002).

4. Data Acquisition—Simulations

Fig. 2

Simple schematic of the experimental system. Red
dashed lines represent the paths of γ-rays emitted by
the tracer particle (marked ‘T’) and detected by the
gamma camera’s dual detector heads, illustrating themanner in which the tracer’s position may be triangulated. Other than its radioactivity, the tracer particle isphysically identical to all other particles of its species.

packing fraction for this region. By subdividing theexperimental volume into a series of cells and determining the residence time of the tracer within each of thesecells, it is possible to recreate the steady-state spatial distribution of particles. For binary, ternary or polydisperse
systems, an individual run is conducted using a tracer of
each particle species, and the resultant data combined 2) togive information regarding the distributions and concentrations of the various particle types, as exemplified in
Figs. 4–10. Taking as an example the case of a bidisperse
system, the steady state obtained by the system may besignificantly segregated to the point that an individual
tracer will have a residence time of zero in certain regionsof the experimental volume. However, by combining theappropriately time-averaged data from two identical runs
conducted using two tracers (one of each particle species)
into what is effectively a single data set, the entire system
will still be explored. The regions of zero residence time
for a single particle species therefore simply represent
areas of the system devoid of a particular particle type.
This principle may be extended to an arbitrary number ofparticle species. From a known particle distribution, it ispossible to calculate the segregation intensity, IS, of a system. IS is a measure of the extent to which a given system
exhibits separation or mixing, which is discussed in fulldetail in section 5.1.

2)
Due to the fact that the systems investigated here exist in a steadystate and the residence times for each particle species are normalisedby the duration of their respective runs, the comparison and combination of data corresponding to two or more differing tracers in otherwise
identical systems is a trivial matter.

166

Additional information and verification of experimental
results is provided through the use of the MercuryDPM
simulation code developed by the University of Twente(University of Twente, 2014; Weinhart et al., 2012;
Thornton et al., 2012, Krijgsman et al., to be published).
Although the bulk of the discussion presented in thismanuscript pertains to experimentally acquired results,
the ancillary inclusion of discrete particle method simulations allows certain parameters (e.g. coefficients of restitution), which are highly difficult to isolate in anexperimental situation, to be individually varied andhence their effects studied. The high level of control provided by these simulations therefore allows us to test andverify hypotheses based on our experimental results.
Simulations were conducted using experimental values off, A, N, d, H, Lx, Ly, Lz, ρ and ε. For collisions between
particles of differing species, an inter-species coefficientof restitution was defined as εAB = (εA + εB)/2, where A andB represent individual particle species. This definition ofεAB as the geometric average of the particles’ individual
elasticity values arises naturally from the spring-dashpot
model of particle restitution (Cundall, 1979; Luding,2008). For all instances in which simulated data is presented or discussed, unless specifically stated otherwise,
the value of the frictional coefficient, μ, may be assumed
equal to 0.1 (Louge, 1999). Simulations were, however,conducted for a variety of μ values μ ∈ (0, 1.0) in order toassess the impact of this parameter on the system’s
behaviour; this variation in the bed’s frictional properties
was found to have no significant impact on the system’s
segregative behaviour, simply acting as an additionalsource of dissipation, analogous to an increase in ε.
For full details of the MercuryDPM code, please refer
to Thornton et al. (2013).

5. Results and Analysis
5.1 Non-Convective Systems
In a vibrofluidised granular bed where convection andother instabilities are absent, one can consider the system’s temperature and packing density to be approximately uniform in the horizontal direction. This situation
greatly simplifies the behavior within such a system. As
such, we shall analyse first our observations for systems

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wherein the driving conditions are such that these instabilities are frustrated, before moving on to discuss themore complex, convective case.
Previous studies of bidisperse systems have shown thatfor systems whose temperature and density may also betaken as homogeneous in the vertical direction, segregative processes within granular fluids are governed simply
by Archimedean buoyancy, much like their molecularcounterparts (Huerta et al., 2005). In other words, a particle within a system whose conditions match thosedescribed above will experience an upward force proportional to the mass of material displaced by said particle3).
Thus, within such systems, one may expect particles
lighter and/or larger than the average to rise towards theupper regions of the system and vice-versa. However, due
to the non-uniformity with which they are driven and thedissipative nature of the particles of which they are composed, granular systems excited by vertical vibration
often display density- and temperature-gradients in thez-direction (Helal et al., 1997), removing the condition ofvertical homogeneity. Due in part to the presence ofenergy non-equipartition between the different components of a granular mixture (Feitosa and Menon, 2002),
these gradients may significantly affect the segregation ofa system (Luding et al., 2000; Garzó, 2006). Through thecareful choice of system parameters, we create vibrofluidised granulates which display near-homogeneous temperature fields in both horizontal and vertical directions,as well as systems displaying more prominent verticaltemperature gradients. Thus, we can explore each of thesegregative mechanisms described above, and hence
determine whether they might be applicable also to ternary systems. Fig. 3 provides visual representations of
the temperature distributions discussed above. The experimental temperature profiles illustrated in Fig. 3 are created, as with the density profiles discussed previously,
through a subdivision of the experimental volume into
multiple, equally-sized cells. For all data points fallingwithin a given cell, the instantaneous particle velocity isdetermined, and thus the mean velocity within this particular cell calculated by averaging over the number of data
points recorded. The deviations of the individual velocities from this average value can then be used to computethe local temperature, and the process repeated for eachof the system’s cells, allowing the spatial variation of the
granular temperature to be visualised. Fig. 3(a) shows atypical case for which one may assume a constant temperature, i.e. vertical temperature homogeneity. Although
the profile displays a non-trivial gradient at large z, com3)

In granular systems, of course, one must account not only for thematerial density of the particles themselves but also the solids fractionof the system, which will both influence the ‘effective density’ of thedisplaced material.

Fig. 3

Experimental vertical packing fraction, η, (blue) andtemperature, T, (red) profiles for ternary systems
excited with dimensionless accelerations of a) Γ = 11
and b) Γ = 17.

parison with the system’s packing profile clearly demonstrates the fact that only a small fraction of particles exist
in this region of temperature non-uniformity. Fig. 3(b),meanwhile, shows an example of a system in which temperature gradients may be expected play a significant role.
Here, it can be seen that T-gradients at both large andsmall z extend far into the bulk of the system.
Plots showing the variation of the fractional concentrations, ϕ, of individual particle species for different regions
within the experimental system can be seen in Figs. 4–6.
Due to the symmetry of the system, the data shown here
in the x-z plane is identical to that observed in the y-z
plane. Comparison of the binary system shown in Fig.
6(a) to the equivalent ternary system presented in Fig. 4
demonstrates a qualitatively similar behavior in bothcases: the lightest particles predominantly occupy thehigher regions of the bed, while the heaviest species typically sink to the bottom of the system. In other words, ourresults (both those shown in the Figures provided as wellthose which, for brevity, are not explicitly presented)
strongly imply that ternary systems, like binary systems,
exhibit Archimedean buoyancy in the absence of strong
temperature gradients. Similarly, the behavior observedin Fig. 6(b) shows a qualitative similarity to that demonstrated in Fig. 5. This is a highly pleasing—and potentially important—result, as the observed similarities inbehavior between bi- and tri-disperse systems demon-

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Fig. 4Two-dimensional particle concentration distributions
for an experimental ternary system. Data is shown fora) steel (H) particles, b) glass (M) particles and c) polyurethane (L) particles in a binary system with H = 8.4
particle diameters and Lx = Ly = 80 mm. The system isdriven with a dimensionless acceleration Γ = 11.

strates the possibility that theories developed for simple
two-component systems may be relatively easily extended
to ternary and, potentially, even higher-order systems.
However, considerable further research is required inorder to substantiate this postulation. The findings
described above are also found to hold in simulation for
arbitrary combinations of particle density; although segregation is less complete for cases in which differences
between particles’ densities are less pronounced, thelightest component will invariably possess the highest
vertical centre of mass and the heaviest the lowest.
Of course, the extension of our understanding of binary
systems to the ternary or polydisperse case is still by nomeans trivial. Nonetheless, data for the relatively uncomplicated case of purely buoyancy-driven segregation
seemingly demonstrate a remarkably straightforward correspondence between the segregative behaviors exhibitedby a ternary system and those of the equivalent binary
systems comprising the same components. An example ofthis can be seen in Fig. 7, which shows the vertical distributions of individual particle species for a ternary (LMH)system of steel (H), glass (M) and polyurethane (L) particles alongside the corresponding binary steel/glass (MH)and glass/polyurethane (LM) systems. In order to allow aquantitative comparison between the binary and ternary

168

Fig. 5Two-dimensional particle concentration distributions
for a) steel (H) particles, b) glass (M) particles and c)polyurethane (L) particles in an experimental ternary
system driven at Γ = 17. All other parameters are identical to those given in Fig. 4.

Fig. 6

Two-dimensional particle concentration distributions
for the lighter component of a binary glass/steel mixture, corresponding to experimental data. Results areshown for the cases a) Γ = 11 and b) Γ = 17.

cases, we define a quantity IS, the segregation intensity,
which serves as a measure of the degree to which a single
component of a system becomes segregated from theother species (Khakhar et al., 1997). IS is measured by
dividing the experimental volume into a series of NC

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Fig. 7

Experimentally acquired one-dimensional verticalpacking fraction profiles showing the distribution ofindividual particle species for a) a binary system ofsteel (blue squares) and glass (orange triangles) particles, b) a binary system of glass (orange triangles) andpolyurethane (red circles) particles and c) a ternary
system consisting of steel, glass and polyurethane particles. In all cases, the dimensionless acceleration
Γ = 14.

equally sized ‘cells’ and, for each cell, calculating the
local concentration, ϕi, of an individual particle species,
which can then be compared to the mean concentration,
ϕm, of the system as a whole. Specifically, the segregation
intensity is defined as: i  N C (i  m ) 2 
I S   i 1
NC



1/ 2

(1)

The specific maximal value of IS (achieved when a system’s components are entirely separated from one
another) is dependent on the number of species within thesystem, and the relative volume fraction of each species.
Therefore, for all data presented, the calculated segregation intensity is normalized by the appropriate maximal
value such that a value IS = 1 corresponds to a completely
segregated system, while IS = 0 denotes the case of perfect mixing. For the binary systems shown in Figs. 7(a)and 7(b), we find that IS ≈ 0.62 for the H component of theMH system, and IS ≈ 0.82 for the L component of the LM
system. For the ternary system, meanwhile, we findIS ≈ 0.62 for the H component and IS ≈ 0.86 for the L component. In order to test whether this close correspondence
between IS values for binary and ternary systems is arobust physical phenomenon or merely coincidence, aseries of simulations were conducted using various valuesof particle density. The resultant data was found tostrongly suggest that if the particle densities for all three

species are adequately disparate, the values of segregation
intensity for the L and H particles in the ternary system
are approximately equal to the IS values of their respective binary counterparts. However, if the density ratio
between heavy and intermediate (H and M) or intermediate and light (M and L) particles is small enough that significant mixing occurs between the two species, theresultant segregation of the ternary system is found todiffer. It should be noted that the precise density difference for which a correspondence in the IS values of binary
and ternary systems may be expected to vary dependent
on the strength with which the system is driven and its
resting height. Nonetheless, for the range of parametersinvestigated here it is found, from our simulations, that adensity ratio above H  2 is adequate to produce theLabove-described situation. However, in certain experimental systems, differences in particle elasticity may also
be expected to play an important role in the system’s mixing behaviours, and hence significantly alter this ‘threshold value’. Once again, this is a topic worthy of further
research.
In order to explain these findings, we consider two
hypothetical systems: in system 1, the mass of an L particle is considerably smaller than that of an M particle
which, in turn, is considerably smaller than that of an H
particle; in system 2, meanwhile, the mass of an L particle
is considerably smaller than that of an M particle, while
the mass differential between M and H particles is somewhat reduced. In system 1, due to the large mass differences, mixing between any two species will be limited,resulting in a ternary system with relatively pure interfaces between H and M particles and between M and L
particles. Thus, the buoyancy force acting upwards on the
L particles in this three-component system will be simply
equal to a certain displaced volume of pure M particles.
Hence, the forces acting on the L particles in such a ternary system are identical to those in the binary ML system, resulting in a similar degree of segregation. In
system 2, however, the smaller disparity in mass between
M and H particles will result in a higher degree of mixing, thus removing the condition of pure boundaries
between species. In this case, a mixture of M and H particles will be displaced by the L particles in the ternary
system, resulting in an increased buoyancy force acting
on the L component of the bed, and hence a correspondingly increased IS compared to the equivalent binary LM
case, as is observed in simulations. Through a similar
process, for the case in which the mass difference
between the M and L species becomes small enough toallow significant mixing in the upper region of the bed,
one observes a decreased IS for the H component of a ternary system when compared to the equivalent binary MH
system. In order to test this theory, simulations were per-169

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formed for a variety of combinations of particle density,with comparison being drawn between ternary beds corresponding to the situation of ‘system 2’ as describedabove and similar binary beds for which the density of the
L component is held constant, while the density of a secondary, ‘MH’, species is set equal to the average of the M
and H components. In these binary beds, the particle
number ratio NL:NMH was held equal to 1:2 to approximate the ternary situation. The observed similarities
between the two systems provides qualitative support forour hypothesis.
It is hoped that, through further work stemming fromthis finding, it may be possible to construct a generalized
theoretical framework capable of predicting, for the casein which there exists no gradient of granular temperaturein the direction of gravity, the behavior of a ternary granular system based only on the knowledge of the segregative behaviors of the relevant binary systems.
Another interesting feature of Fig. 7 is the considerablyincreased average density for the L particles compared tothe other two species. This observation is particularly
surprising as one would expect the particles on the freesurface of a system to be more easily able to expand andthus, if anything, exhibit a reduced density. The simple
explanation for this observation is that the L particles
used in experiment are considerably less elastic than theother species used. This lower elasticity leads to anincreased degree of dissipation, i.e. a greater loss ofenergy during intra-species collisions. This energy loss
will lead to a smaller average separation between these
inelastic particles, and hence the augmented packing fraction observed (Hopkins and Louge, 1991).
Since differences in particle elasticity have, both insimulation and experiment, been shown to significantly
influence the segregative behaviours of granulates (Serero
et al., 2006; Brito and Soto, 2009; Windows-Yule et al.,2014), simulations were conducted using a variety of particle elasticities, ε, in order to assess the role of dissipationon the various systems under investigation. It is interesting to note that, for the range of parameters explored inthis study, the elasticity of particles has no significant
impact on the degree of segregation observed, despite
altering the specific form of the particle distributions
observed, as exemplified in Fig. 8. It is thought that thislack of influence due to elasticity effects is due simply tothe large disparities in material density between particles
and hence the dominance of segregation mechanisms due
to mass differences.

5.2 Convective Systems
We now consider systems exhibiting the commonly
observed convective instabilities caused by frictional
interactions between particles and the boundaries by

170

Fig. 8 Simulated density profiles for a binary system of polyurethane (L, red circles) and steel (H, blue squares) particles vibrated with dimensionless acceleration Γ = 8.
The systems in images a) and b) are identical other thanthe elastic coefficient, ε, of the L component of the system, which is implemented as ε = 0.9 in case a) andε = 0.3 in case b). Despite this considerable difference inε, and the resultant differences in the forms of the profiles, the values of segregation intensity, IS, remain strikingly similar in both cases—specifically, IS = 0.83 incase a) and 0.84 in case b). This effective invariance in IS
with ε is observed over a range of driving accelerationsΓ∈ (8, 17) and particle elasticities ε ∈ (0.1, 0.9).

which they are constrained (Brown, 1939; Knight, 1997).
In order to investigate the effect of convective motion on
the distribution of particles within bi- and tri-disperse
beds, we now study a narrower, taller system whereinwall effects are considerably more prevalent, rendering
the system more susceptible to convective instabilities. As
is immediately obvious from Figs. 9 and 10, the equilibrium distribution of particles achieved by the convective
system significantly differs from the case in which thesystem is approximately spatially homogeneous in termsof its granular temperature and packing density. At first,
the particle distribution seems considerably more complex
and difficult to explain than that of the previously discussed non-convective case; for instance in the systems
shown in Fig. 10, the vertical centre of mass position of
the heavy species is in fact slightly higher than that of thelight or intermediate particles, in seeming defiance of theprinciple of minimum energy. However, when one compares the particle distributions of Figs. 9 and 10 to thevelocity fields presented in Fig. 11 and the granular temperature profile shown in Fig. 12, the mechanisms underlying these particular configurations become clearer.

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Fig. 9Two-dimensional particle concentration distributions for an experimental binary system showing thefractional concentrations of a) steel (H) particles, b) polyurethane (L) particles. Here, the resting bed
height H = 33.8 and the container width Lx = Ly = 40 mm. The system is driven with a dimensionlessacceleration Γ = 11.

Instantly notable from Figs. 9(a) and 10(a) is the tendencyof the H particles to largely occupy the regions corresponding to the centres of convection rolls. The light particles, meanwhile, are generally excluded from theseareas, occupying instead the relatively fast-flowing outerregions of the convective streams in the centre and at theedges of the experimental system. This behavior isobserved for all convective systems investigated, acrossthe entire range of driving accelerations used. In all cases,
an energetic ‘gas’ comprising almost entirely L particles
may be observed in the uppermost section of the bed; alarge proportion of lighter (L and M) particles is also
found near the base of the system, where the proximity to
the vibrating base creates a region of relatively highenergy (see Fig. 12). In the ternary systems, the M particles, as one might predict, show somewhat of a composite
behavior; in the bulk of the bed, the M particles occupy
the intermediary regions of the convective stream, where
the convection centres are largely dominated by theheavier particles, and the fastest-flowing regions mainlycomprise L particles. In the energetic upper regions of thebed, where particle motion more closely resembles that ofa disordered fluid, we also find M particles to be present.
In this region, the system’s behavior is more reminiscentof that described in section 5.1, with the lightest particles
simply tending to rise to the top, and the heaviest sinking.
It should be noted that the general behaviours noted here
were consistently observed over a range of driving parameters, specifically 8 ≤ Γ ≤ 17. It is also worth noting that all
possible binary combinations of particles (i.e. glass/steel,polyurethane/steel, polyurethane/glass) were tested; foreach pairing, the resultant granulate was found to behave
in a manner qualitatively similar to that described aboveirrespective of the specific density combinations used, providing evidence for the generality of our observations.
Although similar behaviour has been observed previously in constrained, bidisperse beds (Yang, 2006; Tai etal., 2010), the current study provides several new insights
into this segregation phenomenon: firstly, and perhaps
most importantly to the aims of this work, we show thatthe migration of heavy particles towards a system’s convection centres occurs in ternary as well as binary systems. Secondly, we demonstrate that the observations
made previously in quasi-two-dimensional systems apply
also in fully three-dimensional systems such as thosedescribed here. We also show the behavior to be applicable for a variety of material densities and elasticities and,indeed, combinations thereof. Another important findingof the current study is the strong evidence of cause andeffect in granular systems exhibiting both convection andsegregation: although we have so far demonstrated thecoexistence of segregation and convective motion in oursystem, and a correspondence between the two, we are
yet to provide specific evidence as to a causal link
between these phenomena. For instance, is the particle
distribution within the system produced by the convective
flow present, as one might intuitively expect? Alternatively, is it possible that an initial segregation of heavier
particles may produce a localised region of higher density
and lower temperature, as has been observed in previous
studies (Rivas et al., 2011a,b), causing an instability
within the system which subsequently leads to the171Kit Windows-Yule et al. / KONA Powder and Particle Journal No. 32 (2015) 163–175

Fig. 10

Panels a)–c): Experimental results showing two-dimensional particle concentration distributions for a)steel (H) particles, b) glass (M) particles and c) polyurethane (L) particles in a ternary system with
Γ = 11. All other parameters are identical to those given in Fig. 8. Panel d) shows the corresponding
one-dimensional packing profiles, with H particles represented by blue squares, M by yellow triangles
and L by red circles. The centres of mass of the H, M and L species are indicated by solid, dotted anddashed vertical lines respectively.

observed convective motion? Fig. 11 shows velocity vector fields for equivalent unary, binary and ternary systems. From these plots, the orientations of the velocity
vectors make clear the presence of convection in each of
the three cases. For all degrees of polydispersity, themajor features of the convective flow—the height of therolls, their orientation, the horizontal and vertical positions of the convection centres, the width of the central
flow region etc.—remain remarkably similar. This consistency in spite of the markedly different compositions of
the systems in question strongly implies convection to be
the cause and segregation the effect, rather than the convective motion observed being somehow a result of theparticle separation. In other words, the fact that convection
remains even in a monodisperse system, where segregationis, by definition, impossible, clearly demonstrates that thisconvective flow cannot be driven by, or indeed instigated172

by, segregative behaviours. Although this finding may not
be particularly surprising, it is nonetheless important, as itprovides substantial support for the idea of convection as amajor mechanism in segregative processes.
Knowing that it is convection that causes segregation,and not the inverse, we now attempt to address the issueof how convective motion affects the distribution of particles within the system. Since a full theoretical treatment
is beyond the scope of this current paper, we propose
instead a simple, qualitative explanation. We posit that the
distribution of particles within a convective granular bed
is determined by the tendency of a physical system
towards its lowest energy state, where mechanical stability
may be achieved. Thus it seems reasonable that, in a given
binary or ternary system, the heaviest particles migrate
towards the slowest-moving regions, corresponding here to
the areas at the centres of the convection rolls (see Fig. 11);

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Fig. 11 Experimentally acquired velocity vector fields demonstrating the presence of convective motion in a) ternary, b) binary and c) unary systems. In all cases, thesystems are driven with an acceleration Γ = 11.

this localization of the H particles to low-velocity regions
clearly results in a reduction of the system’s averagekinetic energy compared to, for instance, a homogeneous
distribution of particles. Similarly, the light particles minimize kinetic energy through their domination of thedilute, high-velocity upper region of the system (see Fig.
12). It is interesting to note the parallel between ourobservations and those of Luding et al. (2000), who foundthat in granulates which were polydisperse by size, thelarger—and hence, by definition, more massive—particles demonstrated a similar tendency to segregate into the‘colder’ or lower-energy regions of a system.
An alternate view may be provided using the work of
Rivas et al. (2011a, b) as a basis. Rivas et al. describe aclustering process whereby an increased pressure exerted
by the more highly excited L particles will act to ‘push’heavier particles away from regions of stronger agitation,
thereby creating dense clusters of H particles. Since, forthe systems described in the current section, the areas oflowest agitation correspond to the convection centres, theH-clusters naturally form in these regions.
Further work, both experimental and simulational, isneeded to verify which, if either, of the above-describedmechanisms accurately describes and explains theobserved behavior.
Whatever the underlying cause, a knowledge that theheaviest particles display a tendency to occupy the lowest-­
velocity regions and, conversely, the lightest particles
the highest-velocity areas allows the prediction, at least ina qualitative manner, of the likely arrangement of differently massive particles in systems displaying higher
degrees of polydispersity—i.e. the 2nd heaviest species islikely to populate the 2nd slowest region, the 3rd heaviest
the third slowest etc., until the positions of all particle
species have been accounted for. The knowledge that sucha correlation between local velocity and particle concentration exists, and the implication of its generality to ternary and higher-order granulates, may prove highly

Fig. 12Experimental vertical granular temperature profile fora ternary system driven with an acceleration Γ = 14.
The form of the profile is typical for the convective
systems investigated here—a relatively high temperature for small z, i.e. near the energizing bottom plate of
the container, decreasing to a roughly constant value
throughout the main bulk of the system before increasing sharply at large heights where the particles form adilute granular gas.

useful in the future construction of theoretical frameworks for polydisperse granular systems.

6. Summary and Conclusions
Through the experimental observation of a variety of
monodisperse, binary and ternary granular systems, andthe application of discrete particle simulations, we havestudied the segregative behaviors of monosized granular
beds in both convective and non-convective states.
We have provided a clear indication that a transition
from a binary to a ternary system does not inherently
change the mechanisms underlying the segregation of distinct particle species; this is an important result, as it suggests that existing findings relating to the segregation ofbidisperse systems may be relatively simply extended toprovide explanations and predictions of the behaviours of
the comparatively less-researched ternary systems. The
similarities in segregative behavior between bi- and tridisperse systems have been found to persist over a rangeof driving parameters, varying bed heights and aspectratios and for systems both in the presence of and absenceof convective motion.
We have demonstrated that, for the range of parameters
explored here, segregative effects due to differences inparticle elasticity are negligible; this direct demonstrationof the ability to avoid inelasticity-induced segregation
through careful choice of system parameters is potentiallyof use both in research and in industry.
Finally, for systems in which both convection and segregation are present, we have addressed the open questionof cause and effect, demonstrating—through comparisonof convective unary, binary and ternary systems—that it

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is convective motion which leads to segregation, and not
the inverse.

Acknowledgements
The authors would like to thank Nicolás Rivas for hishighly valuable input. The authors would also like togratefully acknowledge the financial support of the
Hawkesworth scholarship, provided by the late Dr.
Michael Hawkesworth. Finally, we thank Prof. Stefan
Luding and the University of Twente for granting accessto the computer facilities on which the simulations in thispaper were performed.

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Author’s short biography
Kit Windows-YuleKit Windows-Yule is a doctoral researcher in the University of Birmingham’s Schoolof Physics and Astronomy. His current research concerns the characterisation of the various segregative processes exhibited by granular systems, with the eventual aim ofunderstanding the fundamental mechanisms underlying these processes.

David Parker
David Parker graduated in Mathematics from Cambridge University and was thenemployed for 10 years as an experimental nuclear physicist at the UKAEA’s Harwell
Laboratory. During this period he was awarded his DPhil from Oxford University forstudies of nuclear reaction mechanisms, and also worked on industrial applications of
nuclear physics. Since 1989 he has been in the School of Physics and Astronomy at the
University of Birmingham, where he is currently Professor of Physics and Director of the
Positron Imaging Centre. He was responsible for developing the PEPT technique, and has
applied it to study a wide range of granular and fluid flow phenomena. He also supervises
the Birmingham MC40 Cyclotron, used for a variety of research and commercial work.

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KONA Powder and Particle Journal No. 32 (2015) 176–183/Doi:10.14356/kona.2015007

Original Research Paper

Surface Chemical Characterization of Bastnaesite throughElectrokinetics †Pradip 1*, Charles C. H. Li 2 and Douglas W. Fuerstenau 2
1
2

Tata Consultancy Services Ltd., Tata R&D Design Centre, India
Department of Materials Science and Engineering, University of California, USA

Abstract
The surface properties of the synthesized Ce-bastnaesite were determined by the electrophoresis technique. The
point of zero charge (PZC) of this mineral occurs at pH 7.8. A sample of natural bastnaesite [(Ce, La) FCO3] from
the Mountain Pass deposit in California, USA was also studied through electrokinetic measurements. The mineral
was found to behave essentially like a carbonate with its PZC occurring at pH 9.2 and pCO3 6 in aqueous
solutions. The isoelectric point of synthetic Ce-bastnaesite as obtained by equilibrium pH measurements occurs
around pH 7.8, which agrees reasonably well with the equionic point of pH 7.3, computed on the basis ofthermodynamic data and pH of minimum solubility for Ce bastnaesite, namely pH 7.6. Measurements reported in
the literature on relatively pure samples of bastnaesite do exhibit PZCs closer to the value of pH 7.8, as measured
by us for synthetic Ce-bastnaesite.
Keywords: bastnaesite, electrokinetics, adsorption, lignin sulfonate, synthesis of Ce bastnaesite, Mountain Passrare-earth deposit, electrophoretic mobility, zeta potential

1. Introduction
Bastnaesite [(Ce, La) FCO3], a rare-earth fluocarbonate,
is an important commercial mineral constituent of majorrare-earth ore deposits in the world, such as at Mountain
Pass in California, USA and Baiyan Obo in Inner Mongolia, China. Rare-earths have become critical to society
because of their advanced technology applications such as
smart phones, medical devices and jet engines (Tien,
2013). China holds about 50 % of the world’s totalreserves of rare-earths and until recently was responsible
for nearly 95 % of global supplies. Due to recent cutbacks
in the Chinese exports of rare-earths, there is a renewedinterest in the discovery, development and exploitation ofrich rare-earth ore deposits in the world (Jordens et al.,2013; Zhang and Edwards, 2013; Gleason, 2011).
Bastnaesite is selectively floated using fatty acids andalkyl hydroxamates as collectors and sodium carbonate
and ammonium lignin sulfonate as depressants from such
associated gangue minerals as calcite and barite (Pradip,
1981; Fuerstenau et al., 1982; Pradip and Fuerstenau,
†
1
2

*

Received 3 June 2014; Accepted 10 July 2014
J-STAGE Advance published online 12 September 2014
54B Hadapsar Industrial Estate, Pune 411013 India
Berkeley, California 94720, USA
Corresponding author: Pradip;
E-mail: pradip.p@tcs.com
TEL: +91-20-66086209 FAX: +91-20-66086399

176

1991; Pradip and Fuerstenau, 2013). Bastnaesite belongs
to and occurs in nature along with the family of sparingly
soluble minerals such as fluorite, calcite, barite, fluorapatite and dolomite. The selectivity of flotation separation of
bastnaesite in such systems is controlled by the surface
chemistry of sparingly soluble minerals and the aqueous
solution chemistry consisting of soluble ions contributedby the minerals and the added reagents. The surface
chemistry of sparingly soluble minerals, including rareearth minerals like bastnaesite, has been reviewed
recently (Zhang et al., 2013). We present in this paper our
results on the surface-chemical characterization of bastnaesite with the help of electrophoretic mobility measurements both on a natural mineral sample from Mountain
Pass, California and also on a synthetic Ce-bastnaesite
synthesized by us in our laboratory.

2. Materials and methods
A relatively rich sample of natural bastnaesite wasselected for the electrokinetic experiments. It was possible to handpick pure bastnaesite particles from a crushed
ore sample with a UV lamp since bastnaesite fluoresces
with a distinctive green color under unfiltered UV light.
The hand-picked sample was then washed and ground tominus 37 μm in an agate vibratory mill. The chemical
analysis of the powder assayed 57.4 % REO (as compared©2015 Hosokawa Powder Technology Foundation

Pradip et al. / KONA Powder and Particle Journal No. 32 (2015) 176–183

to 75 % for pure bastnaesite). The impurities in the sample were analyzed to be 8.8 % BaO, 1.6 % CaO and 0.4 %SrO.
The synthetic Ce bastnaesite used in this study was
prepared in the laboratory as per the method described inour earlier publication (Pradip, 1981; Li, 1982; Pradip etal., 2013).
A suspension containing 0.05 % of the finely groundmineral was ultrasonically dispersed and conditioned
overnight (unless otherwise specified) for equilibration
before making the electrokinetic measurements. The electrophoretic mobility of bastnaesite particles was measured
using a Riddick Zeta Meter (Model 3.0). The pH was controlled by an appropriate addition of HNO3 and NaOH.
Unless stated otherwise, we have reported our results
after 24 hours of equilibration.
For investigation of solubility/pH effects, air saturated,
triple distilled water was adjusted to various initial pH
values using HNO3 and NaOH. Then 0.5 g of synthetic
Ce-bastnaesite was added to 250 ml of these solutions
contained in polyethylene bottles and conditioned in a
Environ-Shaker at 25 °C for one minute. The pH measured
at this point was recorded as initial pH. Mixing was continued for 28 hours for equilibration. The solids were centrifuged out and the total concentration of carbonate,fluoride and cerium in the supernatant were determined
as described previously (Pradip et al., 2013).

3. Results and Discussion
3.1 Estimation of the PZC of synthetic bastnaesite
by pH measurements
Starting at eight different initial pH’s, the change insolution pH in presence of synthetic bastnaesite as a function of conditioning time was measured. The results are
summarized in Fig. 1. For initial solution pH’s above 7,
the pH was observed to decrease with conditioning time
whereas for starting pH’s below 7, the pH increased
towards 7. The results thus suggest that the PZC of synthetic Ce bastnaesite lies close to pH 7. It is interesting tonote that this pH is also close to the pH range of minimum
solubility for Ce-bastnaesite, as illustrated in our earlier
paper (Pradip et al., 2013).

3.2 Electrokinetic measurements on synthetic
bastnaesite
The electrophoretic mobility of synthetic Ce-bastnaesite
was measured as a function of pH and conditioning time.
For all sparingly soluble minerals, it requires some conditioning time before the surface reaches an equilibriumwith the solution phase.

Fig. 1

Change of pH with conditioning time for the synthetic
cerium bastnaesite/water system.

In order to delineate the potential determining ions forbastnaesite, the electrophoretic mobilities were measured
in the presence of NaF and Na 2CO3 in solution. The
results presented in Fig. 2 for one minute conditioning
indicate that the addition of NaF has negligible influenceon the electrophoretic mobility as compared to Na2CO3 insolution (Fig. 3), particularly at alkaline pH. NaF does
seem to adsorb specifically at acidic pH. With the additionof 1 mM Na2CO3 in solution, the IEP (isoelectric point) of
bastnaesite shifts by one pH unit (Fig. 3). It is interesting
to note that the electrophoretic mobility-vs-pH curves forsynthetic Ce-bastnaesite for NaNO3 and Na2CO3 in solution are identical and the IEP is at pH 7.8 after 24 hours ofconditioning (Fig. 4).
It is indeed noteworthy that the experimentally determined values for the PZC of synthetic Ce-bastnaesite,namely pH 7.8, agrees well with the equal ionic point of
pH 7.3 (Fig. 5) of the solution phase in equilibrium withbastnaesite. This point was computed using the thermodynamic data provided in our earlier publication (Pradip
et al., 2013). In terms of pH, the equal ionic point of thesolution can be regarded as the PZC of the corresponding
solid, provided that all ionic species have equal affinity
for the surface. Assuming that the free ions and thehydroxy complexes adsorb at the surface with similaraffinity, it is possible to compute the PZC from such thermodynamic calculations. Chander and Fuerstenau (1979,177

Pradip et al. / KONA Powder and Particle Journal No. 32 (2015) 176–183

Fig. 2

Fig. 3

Electrophoretic mobility of synthetic cerium bastnaesite as a function of pH in the presence and absenceof NaF.

Electrophoretic mobility of synthetic cerium bastnaesite as a function of pH in the presence and absenceof sodium carbonate (1 minute conditioning).

1982) predicted the PZC for fluorapatite by this method.
Their predicted values for fluorapatite agreed well withexperimental results. Similar calculations for oxides andcomplex silicates reported by Parks (1965, 1967) also
indicated good agreement with experimentally determined PZC values.

3.3 Electrokinetic measurements on natural
bastnaesite
Electrophoretic mobility results obtained with a natural
bastnaesite (57.4 % REO) sample from Mountain Pass are
summarized in Fig. 6. The isoelectric point for natural
bastnaesite was observed to occur at pH 9.2 as compared to
pH 7.8 for synthetic Ce bastnaesite. In this case also, fluoride ion seems to strongly adsorb at the bastnaesite interface in the acidic pH range. The electrokinetic behavior in
the presence of NaF suggests a surface reaction is occur-

178

Fig. 4

Electrophoretic mobility of synthetic cerium bastnaesite as a function of pH in the presence and absenceof sodium carbonate (24 hour conditioning).

Fig. 5

Change of net charge with pH in the synthetic cerium
bastnaesite/water system in equilibrium with the atmosphere at 25°C (room temp.)

ring at the interface. Detailed computations on the solution
equilibria of Ce bastnaesite as reported by Herrera-Urbina
et al. (2013) clearly indicate that the bastnaesite surface isconverted into cerium fluoride (CeF3) in the acidic pH
range, in particular below pH 6. Our observations suggesting strong adsorption and surface reaction leading toeventual conversion of the bastnaesite surface into cerium
fluoride both with natural bastnaesite as well as synthetic
Ce bastnaesite in the presence of NaF, is consistent withthe conclusions drawn by Herrera-Urbina et al. (2013) on
the basis of theoretical computations.
The addition of 1 mM of Na2CO3 on the other handresults in a shift of one pH unit in the IEP of natural bastnaesite (Fig. 6). At lower carbonate concentrations, theeffect is not significant. It is of course well understoodthat for sparingly soluble minerals, the interpretation ofelectrokinetic data is a little more involved since a sub-

Pradip et al. / KONA Powder and Particle Journal No. 32 (2015) 176–183

Fig. 6

Electrophoretic mobility of natural bastnaesite as afunction of pH in the presence of 1 mM sodium nitrate,sodium carbonate and sodium fluoride

stantial fraction of the potential determining ions are contributed to the solution phase as a consequence of mineraldissolution. For example, the contribution of Ce3+, F– andCO 3 due to dissolution of bastnaesite can be as high as
10 –3 M to 10 –1 M. This is perhaps the reason why no significant influence of addition of electrolytes containing
potential determining ions has been observed for concentrations less than 10 –3 M. Moreover, it is difficult to perform electrophoretic mobility measurements at higher
ionic strength. It is therefore preferable to conduct surface
titration studies on such systems for determining the PZC(Fuerstenau, 1970; Fuerstenau and Pradip, 2005).
Since F– and CO 3 ions are the lattice constituents ofbastnaesite, the electrophoretic mobilities were measured
as a function of the concentration of their corresponding
soluble salts (NaF and Na2CO3). As illustrated in Fig. 7,
the results suggest that carbonate ions are potential determining ions for bastnaesite. The effect of fluoride ions is
not significant at around pH 9, the natural pH for natural
bastnaesite. As we had discussed earlier, the bastnaesite
surface responds to fluoride ions in the acidic pH range.
In fact below pH 6 the surface is converted into CeF3.
It is also important to note that as compared to the PZC
of synthetic bastnaesite (pH 7.2), the PZC of natural bastnaesite is found to occur at pH 9.2. The discrepancy
between the two might result from two main factors,
namely (i) the presence of calcite and barite in the natural
bastnaesite sample used in this work and (ii) differences
arising from the presence of other rare-earths in the natural bastnaesite as compared to synthetic Ce-bastnaesite.
More detailed investigations are needed to delineate theseeffects.
Sodium carbonate and lignin sulfonate are used as
depressants in the flotation separation of bastnaesite fromassociated gangue minerals (barite and calcite). We havemeasured the electrokinetics of bastnaesite in the presence of an increasing concentration of ammonium lignin

Fig. 7 Electrophoretic mobility of natural bastnaesite as afunction of concentration (bottom) and the corresponding aqueous solution pH (top) in the presence of sodium
nitrate, sodium carbonate, sodium fluoride and ammonium lignin sulfonate solutions, respectively

sulfonate at pH 9, and the results are presented in Fig. 7.
The electrophoretic mobilities of bastnaesite in thepresence of lignin sulfonate are comparable to those measured with sodium carbonate. While the addition of
sodium carbonate results in charge reversal since carbonate is a potential determining ion for bastnaesite, thecharge reversal caused by lignin sulfonate is due to something else, namely that it adsorbs specifically at the bastnaesite/water interface. It is interesting to note that lignin
sulfonate does have strong affinity for the bastnaesite surface. Earlier we published our work on the role of lignin
sulfonate in the selective flotation of bastnaesite fromassociated gangue minerals in Mountain Pass Ore(Pradip, 1981, Pradip and Fuerstenau, 1991).
There are only a few publications that report the resultsof measurements of the electrokinetic behavior of bastnaesite. Houot et al. (1991) and Zhang et al. (2013) haveattempted a comparison of published pH values for the
PZC’s of bastnaesite from different sources. We present
an updated version of their compilation in Table 1.
In addition to the source, we have also included thecorresponding purity, wherever available, of the bastnaesite samples used for electrokinetic studies along with
the PZC values reported by various researchers. The

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Pradip et al. / KONA Powder and Particle Journal No. 32 (2015) 176–183

Table 1

Point of Zero Charge (PZC) values for bastnaesite as reported in the literature

Source of bastnaesite
Synthetic Ce-bastnaesite

Purity of samples
100 % pure, synthesizedin the laboratory

Reference

7.8

Li, 1982

Handpicked samples of pure
57.4 % REO, 8.8 % BaO,
bastnaesite from Mountain
1.6 % CaO and 0.4 % SrO
Pass, USA9.25 (also pCO3 6)

Pradip, 1981

Bastnaesite sample from
Mountain Pass, USA

Purity not specified

5.3 (< 30 min), 6.8 (2 h) and7.2 (24 h) ageing in water

Smith and Steiner, 1980

Bastnaesite sample from
Mountain Pass, USA

Purity not specified

4.6 (2 h ageing in water)

Smith and Shonnard, 1986

Bastnaesite sample from
Baiyan Obo, China

Purity not specified

7.0

Luo and Chen, 1984

Bastnaesite sample from
Maoniuping mine, Sichuan,
China

98.1 % pure

8.0

Ren et al., 1997

Bastnaesite sample from
Haoniuping mine, China

96.5 % pure

7.8

Ren et al., 2000

Bastnaesite sample from
Brazil45.3 % RE2O3, 15 % Fe,
4.9
12 % SiO2 and 10 % Al2O3

Pavez et al., 1996

Bastnaesite sample from
Vietnam

Purity not specified

4.7

Kim et al., 2010

Bastnaesite sample from
Zagi Mountain, Pakistan

Purity not specified

8.1

Zhang et al., 2013

results show an interesting trend. It is remarkable to notefrom the results summarized in Table 1 that relatively
pure bastnaesite samples do exhibit a PZC closer to pH
7.8, the PZC reported for the pure synthetic Ce-bastnaesite,
prepared in our laboratory.

3.4 Mechanism of surface charge generation
Bastnaesite belongs to the family of sparingly solublesalt-type minerals, such as fluorite, calcite, barite, apatiteand scheelite. A distinctive feature of these minerals isthat both the solvent constituent ions (H+ and OH–) andthe lattice ions, as well as their complexes with the solvent
species, are found to be potential determining. For example, for calcite, aqueous species of the kind, HCO 3 , CO 3,Ca++, CaHCO3, and CaOH+ have all been shown to determine the potential at the mineral/water interface(Somasundaran and Agar, 1967). These ionic species may
be produced at the solid/solution interface or may form inbulk solution and subsequently adsorb at the interface. In
the case of calcite, based on the available thermodynamic
data, the point of zero charge, PZC, was calculated tooccur at pH 8.2 assuming that the equal ionic point of thesolution corresponds to the PZC of the mineral. Thisapproach was first suggested by Parks and deBruyn(1962), who also pointed out that the equal ionic point180

PZC (pH)

would also correspond to the point of minimum overallsolubility of the solid at which the total concentration ofnegative potential determining ions is equal to the total
concentration of positive potential determining ions in thebulk and at the interface.
All soluble minerals exhibit a point of zero charge withrespect to pH and hence H+ and OH– ions are consideredas the potential determining ions for sparingly soluble
minerals also.
Foxall et al. (1979) had however questioned the assumption that H+ and OH– ions can be regarded as potential
determining ions for the salt type minerals in the same
way as Ag+ and I– are for AgI. According to these authors,
the lattice constituent ions alone can be considered to be“potential determining” since the Nernstian relationshipof the kind
Ψ0  

2.303RT( pM  pM PZC )
z F

(1)

is valid for these lattice ions only. Here ψ0 is the surface
potential, R the gas constant, T the temperature, z thevalence of the ion and F, the Faraday constant. A changeof pH can have the important but secondary effect of producing species of the kind described above, altering thebalance of the ions present and thus the particle charge. In
so far as pH determines the concentration of Ca++ concen-Pradip et al. / KONA Powder and Particle Journal No. 32 (2015) 176–183

tration in solution in equilibrium with calcite, it may havesubstantial influence on the surface potential of calcite.
The mechanism of surface charge generation for minerals that are tri-ionic crystals, such as bastnaesite (or fluorapatite) is even more complex. As pointed out by Saleeb
and deBruyn (1972) for apatites, the concentration of any
two ions can be varied independently for such solids andone can still maintain a constant solubility product. The
ionic equilibrium for Ce-bastnaesite in aqueous solution,for example, can be represented ask(2)

CeFCO 3  Ce   F   CO 3

for which
K sp  [Ce  ][F  ][CO 3 ](3)where the brackets denote the activities of corresponding
ions in aqueous solution. If the concentration of thesethree potential determining ions, namely Ce+++, F– andCO 3 are plotted on three mutually perpendicular axes, a“solubility surface” may be constructed. The coordinatesof all points lying on this surface will satisfy the conditions for heterogeneous equilibrium given by Eqn. 2. On
this surface one may then locate a line or curve connecting, in principle, an infinite number of PZCs. Chander
and Fuerstenau (1979) had analysed the PZC of hydroxyapatite along these lines. In contrast, for a di-ionic solid
like AgI, this surface is only a line and therefore only one
PZC is observed, if specific effects are excluded (Parks,
1965).
Furthermore, since the concentration of CO 3 ions isstrongly dependent on pH, H+ and OH– ions indirectly
determine the potential of the bastnaesite/water interface.
The solution equilibria can be represented as follows
k1

CO 3  H 2O  HCO 3  OH 

(4)

k2

(5)

HCO 3  H 2O  H 2CO 3(Ce +++ )RT(F  )
RT

ln
ln
3F
(Ce +++ ) PZCF(F  ) PZC


0


RT
(CO 3 )ln
2F(CO 3 ) PZC

Table 2

(6)

Thus the double layer surface potential ψ0 for bastnaesite/water interface may be defined by the Eqn. 6,
where the brackets denote the corresponding activities of
the ions in solution.
Our electrokinetic studies show that in NaNO3 solutions, the isoelectric point of natural bastnaesite occurs at
pH 9.25. In the presence of Na2CO3 solutions, charge
reversal takes place at 3.7 × 10 –5 M Na2CO3 with ionicstrength and pH variable in these experiments. Knowing
the pH and the total carbonate concentration CT, the concentration of carbonate ions can be calculated by the following Eqn. (Pradip, 1981; Fuerstenau et al., 1992)

CT[CO 3 ]  

10.3
16.7
2


 [1  10 [H ]  10 [H ] ] (7)where [CO3– –] is the concentration of carbonate ions in
the bulk under these conditions. Table 2 summarizes
these calculations.
A plot of zeta potential-vs-pCO3 exhibits a straight linewith a slope of –9.4 mV as compared to the theoretical
Nernstian slope of –29.5 mV for carbonate ions. The zeta
potential reversal takes place at pCO3 = 6 that is, [CO3– –]= 10 –6 M.

4. Summary
The surface properties of synthetic cerium bastnaesite
as well as a sample of natural bastnaesite sample from
Mountain Pass, USA were studied by the electrophoresis
technique. The PZC of synthetic bastnaesite was measured to occur at pH 7.8 as compared to pH 9.25 and pCO3
6 for natural bastnaesite. The differences in the PZC canbe attributed to the impurities present in the natural sample. A comparison with PZC values for bastnaesite
reported in the literature indicates that relatively puresamples of bastnaesite do exhibit PZC closer to pH 7.8, as
measured by us for pure synthetic bastnaesite. The PZC
of synthetic bastnaesite was found to be pH 7, as determined through equilibrium pH experiments. This value
correlates well with those predicted on the basis of thermodynamic calculations. The equal ionic point for thesolution phase in equilibrium with the bastnaesite surface

Calculation of zeta potential vs log C slope for natural bastnaesite in carbonate solutions

Total sodium
carbonate added

Equilibrium pH of thebastnaesite suspension[ CO 3]

pCO3

Electrophoretic Mobility
in μm/s per V/cm

Zeta Potential
(mV)

10 –5

7.7

2.4 × 10 –8

7.6

+1.16

+14.9

9.3

–6

5.0

–0.73

–9.4

–4

3.3

–1.98

–25.4

2.1

–2.2

–28.2

10

–4

10

–3

10.3

5 × 10

10 –2

11.0

8.3 × 10 –3

9.1 × 10181

Pradip et al. / KONA Powder and Particle Journal No. 32 (2015) 176–183

is computed to be at pH 7.3. Bastnaesite was found tobehave more like a carbonate mineral with fluoride ionsspecifically adsorbing only in the acidic pH range.

Acknowledgements
We thank the Molybdenum Corporation of America,
Mountain Pass Laboratories, USA for providing us the
sample of natural bastnaesite mineral and ascertaining
the purity of the mineral by XRD and chemical analysisof the pure mineral particles handpicked by us in UV
light. Our research program on bastnaesite mineral characterization and flotation was partially supported by agrant from Molybdenum Corporation of America and the
National Science Foundation. Pradip and Charles Li also
acknowledge the financial support provided by the University of California in the form of fellowship awards
during the course of part of this work. Pradip would liketo acknowledge the constant encouragement and support
from Mr. K. Ananth Krishnan, CTO of Tata Consultancy
Services (TCS) during the preparation of this paper.

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Author’s short biography
PradipPradip is currently the Vice President (Technology) and the Head of Process Engineering Innovation Lab at Tata Research Development & Design Centre (TRDDC), Pune a
Division of Tata Consultancy Services (TCS). Pradip received his B.Tech from Indian
Institute of Technology, Kanpur in 1975 and M.S. & Ph.D. from University of California
at Berkeley in 1977 and 1981 respectively. His main areas of research include particle
technology, mineral processing, nanotechnology, design and development of performance chemicals based on molecular modeling, applied surface and colloid chemistry,mathematical modeling & simulation of particulate processing systems, colloidal processing of advanced ceramics, waste recycling and cement chemistry.

Charles C. H. Li
After graduating from Central South Institute of Mining and Metallurgy in Changsha,
Charles Li entered the University of California at Berkeley to pursue graduate studies inmineral processing, and obtained his M.S. degree in 1982 and Ph.D. degree in 1986. He
then joined CRA (now Rio Tinto) in Australia where he worked as a research engineer
on mineral processing problems until his retirement.

Douglas W. Fuerstenau
After receiving his Sc.D. degree at MIT, Dr. Fuerstenau spent a six year period teaching at MIT and working in industry, after which time he joined the faculty of the University of California. At Berkeley he established an extensive program of teaching andresearch in minerals processing, applied surface chemistry and particle technology. With
his graduate students and postdoctoral researchers, he has published a wide range ofseminal papers in this field. He currently is P. Malozemoff Professor Emeritus of Mineral
Engineering in the University of California.

183

KONA Powder and Particle Journal No. 32 (2015) 184–195/Doi:10.14356/kona.2015008

Original Research Paper

Penetration of Diesel Exhaust Particles (DEPs) through Fibrous
Filters Produced Using Melt-Blown Technology †Agata Penconek, Anna Jackiewicz and Arkadiusz Moskal *1

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

Abstract
It is well known that diesel fumes are carcinogenic to humans and may cause inflammation of the respiratory
tract, cardiovascular problems, etc. Diesel exhaust particles (DEPs) occurring in diesel fumes are fractal-likeaggregates with diameters ranging between 10 and 2000 nm, with various compounds adsorbed on their surface.
The fractal-like structure of DEPs and their nanosize leads to the problem of theoretically predicting the DEPs’
filtration efficiency. Thus, experimental studies on effective protection against DEPs as well as theoreticalmodeling are essential. This study analyzes both experimentally and theoretically the mechanical and electretfilters and their potential effectiveness in the filtration of DEPs. The theoretical analysis of the filtration process
on fibrous fabrics was performed using classic filtration theory, assuming that the filtration layer can be treated asa system of a single fiber located in the Kuwabara cell. The experimental results show that the fibrous filters,
produced using melt-blown technology, provide a high level of protection against DEP aerosols (93 %–99 %). The
obtained filtration efficiencies are (in some cases) even higher than requested for particulate respiratory half-mask
type 2 (made from a fibrous filter). However, the theoretical results obtained using classic filtration theory indicate
lower filtration efficiencies than the experimental ones.
Keywords: diesel exhaust particles (DEPs), fibrous filters, aerosol aggregates filtration efficiency

1. Introduction
The quality of ambient air in large cities has been monitored for the last decades due to a profound correlation
between air pollution levels and human health (Murena,
2004; Kyrkilis et al., 2007). The technological development strongly influences the air quality by increasing thepollution emission. One of the highest sources of particle
pollution in the atmosphere is the diesel engine (Weinhold,2002). In 2010, half of all cars were equipped with diesel
engines (ACEA, 2010). The populations of large cities are
therefore exposed to diesel exhaust fumes (Groves andCain, 2000; Kauppinen et al., 2000; Lewne et al., 2007),
which are a complex mixture of gas and particulates (diesel exhaust particles—DEPs). DEPs are the result of an
incomplete combustion of fuel. Exposure to diesel fumes
causes inflammation of the respiratory tract, cardiovascular problems, asthma, allergies (Fujieda et al., 1998; Salvi
and Frew, 1998; Norris et al., 1999). The International
Agency for Research on Cancer (IARC) has classified†
1*

Received 6 May 2014; Accepted 23 June 2014
J-STAGE Advance published online 20 September 2014Waryńskiego 1, 00-645 Warsaw, Poland
Corresponding author: Arkadiusz Moskal;
E-mail: a.moskal@ichip.pw.edu.pl
TEL: +48-22-234-6415 FAX: +48-22-825-1440

184

diesel fumes as ‘carcinogenic to humans’ (Group 1). In
addition, DEPs form fractal-like aggregates with diameters ranging between 10 and 2000 nm, with various inorganic and organic compounds (often mutagenic andcytotoxic) adsorbed on their surface. Thus DEPs are easily inhaled and penetrate the respiratory system (Moskal
et al., 2006; Penconek and Moskal, 2013b) and adversely
affect human health (Pope et al., 2002). The common
solutions for this problem are filtration systems mounted
in the car engines or personal protection equipment represented by a half-mask. Particulate half-masks produced
using fibrous fabrics are the most widespread form of respiratory protection equipment due to their low price(Huang et al., 2007). Fibrous filters can ensure a high filtration efficiency for a given aerosol characteristic withsimultaneously a low pressure drop (Cena et al., 2012;
Kim et al., 2009).
Testing and certifying standards for half-masks are different in Europe and the USA (Rengasamy et al., 2009),
however, in both cases a certification test is conducted
and provided using compact particles (NaCl or DOP—
dioctyl phthalate) which are very different in morphology
compared to the DEPs. Recently, Penconek et al. (2013a)
showed that commercially available half-masks did notsatisfy the quality norm (EN149:2004) when operated
with DEPs. These results show that complex extensive©2015 Hosokawa Powder Technology Foundation

Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

experimental studies should be carried out, and a theoretical analysis of the filtration of DEPs using fibrous filters
was performed to obtain an efficient half-mask for personal respiratory protection. The characteristics of DEPs
depend on many factors such as engine type, load, fuelquality, and it has been the subject of scientific works formany years (Lapuerta et al., 2008; Zhang et al., 2009). In
this work we focused on the process of DEPs filtration
using fibrous structures. The fundamental objective of thepresent work was to evaluate the filtration efficiency offibrous fabrics produced using melt-blown technology,
applied to real DEP particles created in the diesel engine
and compare the efficiency of filtration using electrets andelectrically neutral fibers. The point was examined experimentally as well as theoretically.

1.1 Modeling of DEP penetration through filters
layers
The theoretical analysis of the filtration process on fibrous fabrics can be performed using the classic filtration
theory (Davies, 1973). It assumes that the filtration layer
can be treated as a system of a single fiber located in the
Kuwabara cell (Kuwabara, 1959). The particles, depending on their diameter and process conditions, may depositon the fiber’s surface of the fibrous filters due to various
mechanisms, such as diffusion (Brownian motion), interception, inertial impaction, gravitational settling andelectrical interactions. The total collection efficiency of
the filter layer composed of many fibers, η, is related tothe single fiber efficiency, E, as follows: 4 EL  πd f 1      1  exp (1)where L is the filter layer thickness, α denotes the filter
average packing density and df is average fiber diameter.
Let us introduce fundamental dimensionless numbers to
describe the process as follows:

i. Peclet number: Pe 

Ud f
D

2
ii. Knudsen number for fiber: KnF 
dfiii. Kuwabara number:
Ku

ln( ) 3
2
  
2
4
4iv. Interception parameter: N R dpdf

where U denotes the gas face velocity, D is the coefficientof Brownian diffusion for particles, λ is the gas mean freepath, and dp is the particle diameter. For neutral fibers andneutral nanosized and submicrometer particles (such con-ditions are satisfactory for DEPs in a first approach), theprimary mechanisms of deposition are diffusion and direct interception. The single fiber filtration efficiency due
to diffusion can be described by the correlation proposedby Stechkina and Fuchs (1966):
1

2

1  3  3ED  2.9  Pe Ku 

(2)

However, the single fiber filtration efficiency due todiffusional mechanism can also be obtained using the following correlations:


1



2

(Natanson 1957a) (3)ED 1.71(2  lnRe) 3 Pe 3


1



2

ED  4.46Ku 3 Pe 3
1

(Brown 1993) (4)2(Natanson 1957b) (5)ED  2.32Ku 3 Pe 3121  3  3ED  1.6  Pe CdCd'
 Ku 

(Payet 1991) (6)

with

Cd' 

1

1

2

1-  3  3
1+1.6 
 Pe Cd Ku 

and1

1    Pe  3
Cd  1  0.388Kn  Ku andRe 

Ud f  gwhere ρg is the gas density, μ is the gas viscosity.
The single fiber efficiency due to the direct interception
mechanism can be described using the formula proposedby Lee and Gieseke (1980):

1 ER  
 Ku 
where N R 

N R2(1  N R2) 3(1α)

(7)

dp
df

Kanaoka et al. (1987) proposed the expression for thefiltration efficiency due to electrical interaction as follows:2
5
EE  0.06N Q

(8)

where
NQ 

Ccq 2d p2   p  1 
3π 2 0 d f3U   p  2 

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Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

where εr is the dielectric constant of particle, ε 0 is the permittivity of the vacuum, q is the charge density of electrets fiber.
Assuming that Brownian diffusion, interception andelectrical interactions are independent mechanisms ofdeposition, the total single fiber efficiency, E, resulting
from them can be calculated as follows (Podgórski et al.,2006):
E 1  1  E D 1  E R  (1  E E )(10)

1.2 Modeling of the DEP properties
The DEPs form aggregates composed of many fine particles (primary particles) attached together. The numberof particles in an aggregate ranges from 2, in the simplest
case, to several thousand. The size of primary particles isusually between a few nm and 50 nm (Bałazy et al.,2006). The overall size of such aggregates ranges fromnanometers to a few micrometers (Fridlander, 2000). The
aggregates emitted by diesel engines (DEPs) contain asoot core with volatile organic and sulfur compounds adsorbed on their surface (Maricq, 2007). The structuralparameters of the aggregates influence the dynamic processes such as diffusion, coagulation or sedimentation,
especially for nano-size structures; hence, they can also
affect filtration of the aggregates with fibrous filters.
DEPs have complex structures which depend on the process of their creation and type of fuel used.
To characterize the complex irregular structure of thefractal-like aggregates, the concept of the “equivalent
sphere” is used in many practical and theoretical works aswell as in this work. The aggregate is treated as a spherewith a characteristic radius, which is a function of thegeometrical properties of the cluster. In literature of thesubject, various radii may be found. The most common
radii are:-radius of circumscribed sphere, sometimes calledouter maximum radius of aggregate, Rmax—the radius of
the smallest sphere that fully encompasses the aggregate(Bushell et al., 2002);-dynamic radius, called mobility radius, Rdyn—the radius of an impermeable sphere moving at the same speed
and experiencing the same drag force as the aggregate

1861

 2 1 N2
Rg 
ri  rG  
 ap

N i1



(9)

Introducing Eqn. 9 into the Eqn. 1 allows estimation of
the filtration efficiency for a material with a known thickness, average porosity and average fiber diameter.
To calculate the pressure drop across a filter, the theoretical equation reported by Brown’s handbook (Brown,1993) can be used:P
U (1  1.996Kn F ) 2Ld f Ku  1.996Kn F Ku  α  0.5 1   2(Rogak and Flagan, 1992);-radius of gyration, Rg—the definition of this radius istaken from classic rigid body mechanics and is definedusing the mean square of the distance between centers of
the primary particle and the geometrical center of mass of
all aggregates:

rG 

1 N
ri
N i=1

(11)

(12)

where ap is a radius of primary particle, r i and rG define
the position of the ith primary particle center and center ofgravity of the cluster composed of N identical particles,
respectively (Moskal and Payatakes, 2006);-mass-equivalent radius, Rm—the radius of a nonporous sphere composed of the bulk particle material thathas the same mass as the aggregate (Baron et al., 2001):
Rm  a p 3 N (13)The complex structure of the fractal-like aggregates
can also be characterized by its porosity or permeability.
In this case the aggregate is treated as a porous sphere inwhich the porosity (and permeability) changes with its radius (Veerapaneni and Wiesner, 1996). The radial variation of the porosity, solids concentration and number ofparticles within an aggregate of radius Ragg can be related
to its fractal dimension as follows (Cohen and Wiesner,
1990; Wiesner, 1992):
DfR N Ragg   k  agg   ap 

(14)
3

 a 
 Ragg   N Ragg   p   Ragg (15)

 Ragg   1   Ragg (16)where ε is a aggregate porosity, ρ is a solid concentrationin the cluster, k is a coefficient dependent on Df. Vanni(2000) reported that the fractal-like aggregates are densest in the center and less dense in their outer parts.
In the present work, the mobility diameters measured
using a FAPES device (Fast Automotive Particles Emission
Spectrometer) were used as the “equivalent diameter”
during calculations. The FAPES device is described in thenext section (Experimental methods). The DEP density wasestimated for aggregates characterized by a high value offractal dimension using the following formula:

dp 
dp 


qe  1.00exp    1.53exp    0.10
 173.70  21.14 (17)Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

which is based on the observations and results obtainedby Maricq and Xu (2004) (Fig. 1).
The effective density of motor vehicle exhaust particulates in Maricq and Xu (2004) studies was obtained froma diesel engine (manufactured in 2003; 4 cylinders;
1,8TDI) running on low-sulfur diesel (< 50 ppm sulfur)
under idle-speed conditions.
Using Eqn. 14, the particle number distribution obtained during measurements using the FAPES device canbe transformed into the particle mass distribution (Fig. 2).

2. Experimental methods
2.1 The fibrous filter’s media
Six fibrous filters made of polypropylene using a modified melt-blown technology were investigated. This entirely controlled method described in detail in a work by
Jackiewicz et al. (2013) allows us to produce the desired
material with the defined porosity and mean fiber diame-

ter. Three pairs of two filter types were manufactured—amechanical filter (i.e. electrically neutral) and an electretwith charged fibers—with a similar internal structure tocheck how the additional capture mechanism, which iselectrostatic force, besides conventional mechanical ones
such as Brownian diffusion or interception affects thepenetration of DEPs through the fibrous media. In orderto give electrical properties to the polymer fibers, thecharging system utilizes a corona discharge in the air between the corona comb electrode and the grounded collecting electrode placed at the outlet of the extruder head.
The experiments were carried out at a constant voltage.
Its value was determined as 90 % of the voltage at which
a jump between the electrodes at a given geometry occurred. The corona comb electrode was composed ofthirty-three needles mounted in a row on a metal plate,while the collecting electrode was cylindrical. The
fibers’ charging efficiency depends greatly on how longthey are in the area of corona discharge—the faster thefiber moves, the less charge will be implemented.
The experiments were carried out at a constant voltage.
The structural characteristics of all analyzed materials
were determined, see Table 1.
The filter porosity, εF, and the base weight, qs, were determined by a gravimetric method measuring the thickness, L, of the filter sample with the surface, A, and thefilter mass, m, according to the following formula: F 1 qs 

Fig. 1

Effective density of diesel exhaust particles obtained
from a diesel engine operated at idle-speed conditions.
The dashed lines display the best fits to the experimental data.

m 
100%
 pp AL 

m

A

(19)

The density of the polypropylene, ρpp, is 900 kg·m–3.
Analysis of the fibrous filter images taken using a scanning electron microscope Hitachi TM-1000 made it possible to obtain fiber size distributions of the tested fabrics.
All of them could be precisely fitted by the log-normal
distribution with the geometric mean fiber diameter, dFg,
and the geometric standard deviation, σgdF, presented inFig. 3.
Table 1

Fig. 2 Particle number distribution vs. mass distribution. A
commercial diesel biofuel was used to generate particles.

(18)

Structural characteristics of the tested fibrousfilters

Filter no.

Filterthickness,
L [mm]

Filterporosity,ε [%]Filter baseweight,qs [g m–2]

Filter 1
Filter 2
Filter 3
Electret 1
Electret 2
Electret 3

2.13
3.39
3.63
1.76
3.69
4.22

97.5
96.3
93.9
97.3
96.2
94.9

48.1
114.4
197.9
42.6
125.4
193.8

187

Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

Fig. 3 Fiber size distributions of the fibrous electret and mechanical filters: (a) Electret 1 and Filter 1, (b) Electret
2 and Filter 2, (c) Electret 3 and Filter 3.

2.2 Experimental set-up
The experimental set-up was based on the previous
ones (Penconek et al., 2013abc). The experimental set-up
is presented in Fig. 4.
The fibrous filter being tested was placed in a special
holder in the chamber (chamber volume is 1 m3) where
diesel exhaust fumes from the engine were directed. The
diesel engine (manufactured in 1982, 4 cylinders, engine
power 53 kW at 4200 rpm; torque 137 Nm at 2400 rpm;
engine displacement 2399 cm3) was situated outside thebuilding. Diesel fumes were stabilized in a buffer tank.
The air flow was generated by a pump (SC10A 055S Venture Industries, Poland) and controlled by an electronic
flowmeter (4040 model nano, TSI Inc., USA). The pres-188

Fig. 4

Experimental set-up.

Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

sure drop across the sample was measured using a DPCCALC 8710 micromanometer (TSI Inc, USA).
The number distribution of the DEPs was obtained using the Fast Automotive Particles Emission Spectrometer—FAPES (Model 5.601, Grimm GmbH, Germany).
The FAPES measurement technique is based on 10 DMAs
(Differential Mobility Analyzer) with in-line FCEs (Faraday Cup Electrometers), and the measured particle size
was in the range of 5 nm–640 nm with a frequency up to
5 Hz. In this study, a sampling rate of 0.2 Hz was used.
The DEP samples were not diluted prior to measuring asa special integrated sample conditioning system with aheated dilution function (temperature up to 500 °C) is apart of FAPES. This dilution rate was 1:32.
Motor emissions were generated continuously throughout the test under idle-speed (800 rpm) conditions. The
humidity (40–50 %) and temperature of the air around thediesel engine (20–25 °C) were identical during each test.
The experiment started when the diesel engine oil temperature reached 86 °C (the temperature did not change
during the test). The filtration efficiency of the DEPs andthe pressure drop across fibrous filters were measured at aconstant flow rate of 30 dm3·min–1 (air velocity 0.064 m·s–1)
during a 20-minute test. The pressure drop across the filter and the DEP number distribution downstream of thefilter were measured continuously throughout the test.
The filtration efficiency of the DEPs was determined
gravimetrically (by weighing the absolute filter at 5minute intervals during the test and by weighing the tested
fibrous filter at the beginning and at the end of the test) as:EF 

Mf
100% 
M f  M af

(20)

where EF is the mass filtration efficiency, Maf, Mf is themass of DEPs deposited on the reference absolute filterand on the tested fibrous filter after 20 min.

2.3 Aerosol (DEPs) analysis
The diesel exhaust particles (DEPs) were generated using a real source, namely a Mercedes-Benz diesel engine.
Commercially available diesel fuel was used to generate
the DEPs. Table 2 summarizes the physical characteristics of the biofuel provided by manufacturers.
The average mass and particle number distribution of
the DEPs were shown in Fig. 2. The DEPs’ cumulative
frequency count of experimental data and their fitting by
the log-normal distribution were shown in Fig. 5.
The characteristics of the DEPs are given in Table 3.
The fractal dimension of the DEPs was assumed after
photo inspections without using any sophisticated algorithms. However, Lapuerta et al. (2006) determined thefractal dimension of compact DEP aggregates as 2.32–
2.73. Therefore, based on a comparison of our DEP photos
and Lapuerta et al.’s (2006) study, a fractal dimension of~2.5 was assumed for our DEPs.

3. Results and discussion
Six different fibrous filters (three electrets and threemechanical filters) were examined with natural DEP aggregates and were found to have a mass experimental filtration efficiency higher than 93 %. A fibrous filter before
and after filtration is shown in Fig. 6.
The theoretical prediction of mass filtration efficiency
is in the range of 52–99 %. Table 4 presents the results ofthe mass filtration efficiencies obtained in experiments
and calculated for U = 0.064 m s–1 (Q = 30 l min–1) for thefilters with characteristics given in Table 1 for uncharged
filters and when the fibers have a charge density of 1.5·10 –13
C m–1 (electret filters). The DEPs aggregates deposition is

Table 2

Characteristics of diesel biofuel

Parameter.
Number of compounds*
Density at 15 °C, kg·m –3
Sulfur content, mg·kg–1

20
860–900
0* The number of compounds was determined by theauthors of this study using the gas chromatography-mass
spectrometry method.
Table 3Fig. 5

Cumulative frequency count of DEPs vs. particle diameter, and TEM (transmission electron microscopy)
photo of DEPs.

DEP characteristics

Geometric particle diameter [nm]Arithmetic particle diameter [nm]Size range of particle diameters [nm]Fractal dimension
Observed shape

64.86
88.57
10–640~ 2.5
Compact

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Fig. 6
Table 4FilterFilter 1
Filter 2
Filter 3
Electret 1
Electret 2
Electret 3

Fibrous filter before (left side) and after (right side) filtration of diesel exhaust particles.

Mass filtration efficiencies (%) and pressure drop (Pa)obtained using classic filtration theory correlations andfrom experiments (n = 4, SD—standard deviation).
D + I + E*

D

D+I

Pressure
Experiment SD
drop

89.82
79.90
90.36
98.89
91.08
52.36

82.15
63.02
85.72
93.74
86.24
44.18

89.82
69.82
90.36
96.50
91.08
45.52

13.51
7.08
14.92
23.09
18.27
3.14

94.02
97.54
95.33
99.30
93.38
98.03

0.42
1.01
2.84
0.27
6.90
0.49

Table 5FilterFilter 1
Filter 2
Filter 3
Electret 1
Electret 2
Electret 3

Mass filtration efficiencies obtained using various
correlations for diffusional mechanism
Natanson
(a)

Brown

33.84
33.45
55.43
67.66
53.87
20.67

89.66
80.45
95.38
98.77
95.71
63.45

Stechkina Natanson& Fuchs
(b)
85.21
67.62
88.84
95.56
89.37
49.38

79.82
60.39
83.88
92.47
84.51
42.55

Payet
86.46
67.48
88.70
95.81
88.98
45.20

* D—diffusion, I—interception, E—electrical interception

assumed to be driven by diffusion and direct interception
for uncharged filters, with additional electrical interaction
for electrets. The theoretical filtration efficiencies were
calculated using Eqns. 1–9, taking into account the experimental number distribution of DEPs and the function ofeffective density (Eqn. 17). The filtration efficiency due tothe diffusional mechanism was obtained considering thefive correlations described earlier, and showed as an average filtration efficiency calculated using those five equations (see Table 5).
Theoretical analysis showed that the diffusional mechanism is a primary mechanism of DEPs depositing on fibrous filters. However, experimental data showed that the
number of aggregates of a diameter smaller than 120 nm
is higher after the filtration process than before this process. However, aggregates of a greater diameter are still
effectively filtered. This may show that in the case of nanoaggregates, the diffusional mechanism is ineffective.
Nevertheless, this phenomenon requires further research.
The differences between experiments and theoreticalpredictions may be caused by several factors, e.g.:
non-homogeneity of the filter structure and shading of
one fiber by another one (Bałazy et al, 2006).
The experimental mass distribution curves in Fig. 7
depict that filtration efficiencies for nanoaggregates arevery low (shape of the curve which describes the experi-

190

mental mass distribution after filtration is similar to thecurve which describes the mass filtration efficiency before
filtration). On the other hand, experimental mass filtration
efficiencies (given in Table 4) are higher than 93 %. This
discrepancy may indicate the presence of substantial
quantities of DEPs with diameters higher than 640 nm
and with large mass. The filtration efficiency of large aggregates is believed to be almost 100 %, the presented explanation therefore seems to be reasonable.
Fig. 8 presents the mass of DEPs that penetrated thetested fibrous filters during experiments. One can observethat the penetrated mass did not increase monotonically.
A similar phenomenon was observed in the work ofPenconek et al. (2013a) and may be explained by the process of reemission of previously deposited particles in thefilter, which is more visible for long exposition of filters
on DEPs. This phenomenon needs to be more carefully
investigated.
The mass of DEPs was obtained by weighing the absolute filter placed downstream of the tested material (seeFig. 4) in 5-, 10-, 15- and 20-minute intervals during thetest. The total mass of DEPs deposited in the absolute filter after 20 min is 1.61 (±0.99) mg for electrets and 2.40(±0.06) mg for uncharged filters.
The experiment for the electret filter indicates thatcharging the fibers increases the mass of DEPs that deposit on the filter. The higher the mass of DEPs depositedon the fiber, the higher is the pressure drop across the fi-Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

Fig. 7

Distribution of DEPs’ mass ratio vs. particle diameter: (a) Electret 1 and Filter 1, (b) Electret 2 and Filter
2, (c) Electret 3 and Filter 3.

brous filter observed after the test. Such a relationship canbe seen if electrets and uncharged filters with similar
characteristics are compared. Fig. 9 presents the pressure
drop across filters during experiments.
The data presented in Fig. 7 revealed that the maximum DEP penetration through electret filters tested inthis study occurred at dp = 180 nm, while for uncharged
filters it was dp = 117 nm, 180 nm and 226 nm (filter 1, 2,
and 3, respectively), and does not significantly differ fromtheoretical predictions. There is lack of data in the literature showing the most penetrating DEP aggregate’s size
through fibrous filters. However, Bałazy et al. (2006)
showed that the most penetrating NaCl particle size
through uncharged particulate respirator filters is 300 nm,
while for electrets it is 40–50 nm. Martin and Moyer(2000) indicated that the maximum penetration of DOP
particles (dioctyl phthalate) through the charged filters
occurs at 50–100 nm, but when the electrostatic charge

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tion efficiency even higher than 93 % cannot ensure anadequate level of protection against DEPs, since small aggregates of DEPs (< 120 nm) are still present in the air after the filter.
Although the mass of penetrated nanoaggregates
through the tested fibrous filters do not cause health problems, the health effects associated with nanoaggregatesare still being investigated and for now, remain largely
unknown and do not necessarily relate to the mass.

Acknowledgements
Fig. 8Mass of DEPs that penetrated fibrous filters in the 5th,
10th, 15th and 20th minute of the test. The symbols represent the mean values and error bars represent thestandard deviations.

This scientific work was partly financed from the budget for sciences in the years 2010–2013 as Research Project No. NN209023739, and was also supported by
Cummins Filtration Ltd. This work has been partially
supported by the European Union in the framework ofEuropean Social Fund through the Warsaw University ofTechnology Development Programme.

Nomenclatureap

radius of primary particle, (μm)

Asurface of filter sample, (mm 2)

CC

Cunningham slip correction factor, (–)

Cd, Cd’ parameters, (–)

Fig. 9Pressure drop across fibrous filters. Lines represent
the mean values and error bars represent the standard
deviations

was reduced, the maximum penetration DOP size shifted
up to even 400 nm. The maximum fractal-like aggregates(DEPs) penetration through electret filters occurred at
higher diameters compared with DOP or NaCl particles,
however, slight differences between electrets and mechanical filters, also for DEP aggregates, can be noticed.

4. Conclusions
This study showed that the fibrous filters produced using melt-blown technology provide a high level of protection against DEP aerosols (93 %–99 %). The obtained
filtration efficiencies are (in some cases) even higher thanrequested for particulate respiratory half-masks type 2(made from a fibrous filter). In addition, the benefits froman electrostatic filter can be great (higher mass filtration
efficiency than uncharged filters). However, mass filtra-

192

df

average fiber diameter, (μm)

dpparticle diameter, (μm)

Dcoefficient of Brownian diffusion for particles, (m2 s–1)

DEP

diesel exhaust particles

Df

fractal dimension, (–)

E

single fiber efficiency, (–)

ED	single fiber filtration efficiency due to diffusion mechanism, (–)
EE	single fiber efficiency due to electrical interaction
mechanism, (–)
EF

mass filtration efficiency, (%)

ER	single fiber efficiency due to direct interception mechanism, (–)
kcoefficient in Eqn. 14, (–)

L

filter layer thickness, (mm)mfilter mass, (kg)Maf, Mf	mass of DEPs deposited on the reference absolute filter and on the tested fibrous filter after 20 min, (kg)N

number of identical particles, (–)

NRinterception parameter, (–)

P

pressure, (Pa)Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

qcharge density of electret fiber, (C m –1)

qeparticle effective density, (g cm –3)

qs

filter base weight, (g m –2)

ri, rG	position of the ith primary particle center and center ofgravity of the cluster, (–)
Ragg

radius of aggregate, (μm)

Rdyndynamic radius, called mobility radius, (μm)

Rgradius of gyration, (μm)

Rmmass-equivalent radius, (μm)

Rmax	
radius of circumscribed sphere, sometimes calledouter maximum radius of aggregate, (μm)
U

gas face velocity, (m s –1),

αfilter average packing density, (–)η	total collection efficiency of the filter layer composedof many fibers, (–)λ

gas mean free path, (m)μgas viscosity, (Pa s)ρsolid concentration in the cluster, (–)ρggas density, (kg m –3)

ρpp

density of the polypropylene, (kg m –3)

εaggregate porosity, (–)εFfilter porosity, (–)εpdielectric constant of particle, (–)ε0

permittivity of the vacuum, (F m –1)

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Agata Penconek et al. / KONA Powder and Particle Journal No. 32 (2015) 184–195

Author’s short biography
Agata Penconek
PhD, assistant professor at the Faculty of Chemical and Process Engineering Warsaw
University of Technology, Poland.
2008: MSc in biotechnology, 2009: BCH in biology; 2014 PhD in chemical engineering;
Scientific interests: anthropogenic aerosols (e.g. diesel exhaust particles), their filtration,
deposition in human respiratory system, and interaction with eukaryotic/prokaryotic
cells, aerosol particle transfer through mucus layer, aerosol particle mechanics.
Author and co-author of 8 scientific papers and chapter in one book.

Anna Jackiewicz
PhD, assistant professor at the Faculty of Chemical and Process Engineering Warsaw
University of Technology, Poland
2004 MSc in chemical Engineering; 2010 PhD in chemical Engineering; 2006 course at
the Technische Universitat Berlin (Berlin), 2008 - Institute for Experimental Physics,
University of Vienna (Austria).
Scientific interests: processes of separating solid and liquid particles from fluids, filtering
media design, modeling of filtration in fibrous filters, techniques for aerosol generationand detection, materials science.
Author and co-author of over 47 papers published in periodicals and conference proceedings.
Recipient of awards and scholarships: Award of the Ignacy Łukasiewicz Grant Fund of
PGNiG S.A., Fiat grant, two scholarships for distinguished Ph.D. in the field of scienceand research; an individual Award of the Rector of the Warsaw University of Technology
for scientific achievements, the presentation of achievements at the exhibition entitled“Maria Skłodowska-Curie in the Service of Science Yesterday and Today” at the European Parliament in Brussels, winner of the prestigious program for young scientists“LEADER” from the National Centre for Research and Development, under which she
received funding for the creation of her own research team.

Arkadiusz Moskal
PhD, DSc, professor at the Faculty of Chemical and Process Engineering Warsaw University of Technology, Poland.
1997: MSc in chemical engineering; 2002: PhD in chemical engineering; 2011: DSc in
chemical engineering; 2002 – 2003 postdoc at The Institute of Chemical Engineering
Sciences (ICE-HT), Patras, Greece.
Scientific interests: aerosol particle mechanics, aerosol filtration, and deposition of aerosol particles in human respiratory system, drug delivery, mathematical modeling, andaerosol aggregate dynamics, diesel exhaust particle filtration and interaction with humancells.
Author and co-author of over 150 scientific papers, 2 patents, 1 book.

195

KONA Powder and Particle Journal No. 32 (2015) 196–206/Doi:10.14356/kona.2015010

Original Research Paper

Formation of Nanoscale Layered Structures and SubsequentTransformations during Mechanical Alloying of Ni60Nb40
Powder Mixture in a Low Energy Ball Mill †Mohammad Hossein Enayati
1

Department of Materials Engineering, Isfahan University of Technology, Iran

Abstract
Elemental Ni-Nb powder mixture containing 40 atom % Nb were mechanically alloyed in a low energy ball mill,allowing slower processing and easier progressive observation at intermediate milling times. The evolution ofmorphology and structure of the powders were investigated as a function of milling time by transmission andscanning electron microscopy and X-ray diffractometry. The results revealed that an ultrafine Ni/Nb layered
structure with a typical thickness of ~30 nm, containing nanoscale size grains with a typical size of ~15 nm,
develops in powder particles during mechanical alloying. This microstructure provides numerous high speed
diffusion paths such as sub-grain boundaries and dislocation networks, allowing a high diffusion rate at low
temperature and therefore permits different solid-state reactions to take place kinetically. Under mechanicalalloying conditions used here continued milling led to a fully amorphous structure. Because of non-uniform
plastic deformation the kinetic requirements for the amorphization reaction at the edges of particles is satisfied
prior to the centres, resulting in an inhomogeneous progress of amorphization reaction from the edges towards
the centres of particles.
Keywords: nanostructure, multilayers, mechanical alloying, Ni-Nb, amorphization

1. Introduction
Mechanical alloying is a solid-state synthesis route formaterials using ball milling process. The starting powder,often a mixture of elemental constituent powders, ischarged into a suitable mill and processed for several
hours depending on experimental conditions as well as
desired microstructure and properties. Often, mechanicalalloying has been performed in ball mills including planetary ball mills, centrifugal ball mills and vibratory ballmills, but several other types of mills such as rod mills
can also be used. The foremost restriction for mechanicalalloying is a sufficiently high energy which is provided by
the kinetic energy of the balls on impact with the powder
particles. The energy of milling machines depends not
only on the power supplied to drive the milling chamber,but also on the internal mechanics of the specific mill.
The final powder produced by mechanical alloying can be
subsequently either consolidated by standard powder metallurgy techniques into bulk materials with desirable†
1*

Received 13 July 2014; Accepted 21 August 2014
J-STAGE Advance published online 30 September 2014
Isfahan 84156-83111, IranE-mail: ena78@cc.iut.ac.ir
TEL: +98-311-3915730 FAX: +98-311-3912752

196

properties or deposited on surfaces of engineering parts
using various thermal spraying methods.
From the viewpoint of mass transfer the ball milling
process can be divided into two major categories: a) Milling processes which involve mass transfer between thecomponents. Such cases can occur during milling ofmulti-component powders (e.g. a mixture of elemental ordissimilar alloy powders) and are associated with compositional changes of powder particles. This category is
known as “mechanical alloying” (MA). Material transfer
by diffusion of components during MA is significantly
accelerated by lattice defects and by a momentary
increase in temperature of particles trapped between colliding balls. Modeling as well as inferences drawn fromthe product structure suggests modest temperature rises(~100–200 °C) result from the kinetic energy of the milling balls. However, if a large exothermic reaction is
involved much higher local temperatures can be producedby milling (Takacs, 2002). b) Milling processes thatinvolve no mass transfer and change the structure of asingle composition powder (e.g. an elemental or intermetallic compound powder). These cases have often beentermed “mechanical milling” (MM) or “mechanicalgrinding” (MG).
In MA experiments not only the milling time but also
further parameters such as the type of mill, the milling©2015 Hosokawa Powder Technology Foundation

Mohammad Hossein Enayati / KONA Powder and Particle Journal No. 32 (2015) 196–206

tool materials, the energy of milling, the ball-to-powderweight ratio, the milling temperature and the milling
atmosphere may have significant influence on phasetransformations occurring during MA and therefore, on
the final structure. Hence, by controlling these parameters
a large variety of materials ranging from stable to metastable structures can be synthesised by MA.
MA/MM leads to the significant refinement of microstructure, which is often accompanied with transformation to metastable structures. These techniques increase
the range of obtainable microstructures and can therefore
lead to significant improvements in properties of materials. There are lots of papers published on synthesis andprocessing of various materials by MA/MA during thepast decade showing improved microstructures and properties. In this paper the progressive development of
nanoscaled layered structure during MA process isreported in detail in case of Ni-Nb alloy, as a typicalductile-ductile system. The MA of Ni-Nb system using
high energy ball mills has been previously reported (Kochet al., 1983; Petzoldt, 1988; Zhang, 2004). However, theMA behaviour has not been followed in detail because of
the rapid processing times used in high energy ball mills.
In the present study a low energy ball mill was used toallow slower processing and easier progressive observation
at intermediate milling times. Principally it is possible
to achieve similar results in a high energy ball mill bycontrolling the milling speed but one should note thatthe type of microstructure, structural evolution, kinetics
and thermodynamics of transformations, etc., depend not
only on level of energy of mill machine but also on the internal mechanics of the specific mill (the movement modeof balls in the bowl). In planetary ball mills the most common phenomenon is the impact of balls on powder particles while in a centrifugal ball mill the dominate event isrolling of balls inside the bowl.

2. Materials and methods
99.9 % pure Ni powder and 99.8 % pure Nb powder,from Johnson Matthey, were used as starting materials.
Fig. 1 (a) and (b) shows scanning electron microscopy(SEM) images of the elemental Ni and Nb particles.
As shown, the Ni particles had a nearly uniform size of~25 to 50 μm with a morphology consisting of clusters ofseveral small spherical particles. The Nb particles wereangular in shape with a wide size distribution from ~5 to110 μm. All elemental powders were stored in a vacuum
chamber. The powders were weighed and mixed manually
to give nominal atomic composition of Ni60Nb40.
Mechanical alloying was carried out using a laboratory
Fritsch Pulverisette 6 centrifugal ball mill. Pulverisette 6
is a low energy type mill, capable of achieving up to 8

times gravitational acceleration. Higher energy ball millsystems, as exemplified by a planetary ball mill, are capable of producing a higher acceleration (e.g. 45 times gravitational acceleration). A bowl containing five balls was
fitted on a supporting disc. The drive mechanism rotatedthe supporting disc on a circular trajectory. The milling
media consisted of five 20 mm diameter balls confined in
an 80 ml volume bowl. The bowl and ball materials were
hardened chromium steel. For each MA run 17 g of powder was used. To prevent oxidation or nitridation of thepowder, MA was performed under an Ar atmosphere. The
powder mixture and bowl were placed into a glove bag.
To reduce the oxygen content, the glove bag was flushed
out with a high-purity Ar gas and then fully filled. The
powder mixture was loaded into the bowl then sealed
using a teflon O-ring, covered with a thin layer of sealing
grease, and sealing tape. The bowl was removed from theglove bag and mounted on the mill machine. In all MA
runs the ball-to-powder weight ratio was about 10:1 andthe angular velocity of the supporting disc was approx.
600 rpm. MA was performed nominally at room temperature although a small increase in temperature of the bowl
exterior (ΔT≈20 °C), measured using a digital, portable
thermometer, was observed during process.
The MA was interrupted after selected time intervals
and the bowl was opened in air. A small amount of powder
was removed for further characterization and then the bowl
was resealed in the Ar-filled glove bag and MA continued.
The structure of MA samples were investigated by
X-ray diffraction (XRD) analysis in a Philips vertical
X-ray diffractometer (PW1710) with a scanning speed of
0.03° s–1 in a step-counting mode. Ni-filtered CuKα radiation (λ = 0.1542 nm) produced by a generator voltage of
35 kV and a generator current of 50 mA was used.
The grain size of the MA powder was estimated byanalysing the X-ray diffraction peak broadening. Sincestrain in the crystal lattice also contributes to broadeningof the XRD peaks, the approach of Williamson and Hall
(1953) was used in order to separate the two effects ofgrain size and strain after correction for the instrumental
broadening.
The particle morphologies were investigated in a Hitachi S-530 SEM operating at 25 kV. Powder samples were
fixed in small quantities on a sample holder by Ag conductive paint. At least 40 separate particles were chosen
for the measurement of powder particle size.
A Philips CM20 transmission electron microscope(TEM) operating at 200 kV was used for observation andcharacterization of the internal structure of as-milled
powders. The sample preparation for TEM consisted of
mixing powder with a small amount of a TEM suitable
epoxy glue (Gatan G-1). The mixture was then transferred
into a 3 mm diameter, 1.5 mm high, thin wall metal discand cured for 10 minutes at 130 °C on a hot plate. Both197

Mohammad Hossein Enayati / KONA Powder and Particle Journal No. 32 (2015) 196–206

Fig. 1 SEM micrographs of as-received Ni and Nb powder particles and Ni60Nb40 powder particles after different
milling times.

sides of each disc were then ground on a 1200 grit paper
until a thickness of 90–100 μm was obtained. A 400 meshCu grid was then stuck on one side of the disc and the
opposite side was dimpled to about 40 μm using a 515
SBT dimple grinder with 6 μm diamond paste. Finally thedimpled side of the disc was thinned in a Technoorg-­
Linda ion beam miller. A problem with this technique
was that the glue tended to be sputtered away preferential­

198

ly during ion milling. To reduce the differential thinning,ion milling was conducted at low incident angles (~10°).
Moreover, the Cu grid was used to support powder samples weakened by glue removal from thinning, allowing
further thinning to continue. The resulting specimens
showed relatively large thin areas.
The variation of the hardness of powder particles withmilling time was determined by microhardness measure-Mohammad Hossein Enayati / KONA Powder and Particle Journal No. 32 (2015) 196–206

ments using a Vickers indenter (HV) at a load of 10 g anddwell time of 10 s. This produced a square impression
with an average diagonal length of 7–3 μm, depending on
milling time, which is at least twenty times smaller thanthe powder particle size. To eliminate errors caused bycold working of the surrounding area of indentations, theindentations were never made any closer than three times
the diameter of an impression to an existing impression.
5–10 indentations were made on each sample to obtain an
average value of microhardness.

3. Results
3.1 Morphological observations
Fig. 1 shows SEM micrographs of as-received Ni andNb powders, together with Ni60Nb40 powder particles
after different milling times.
Elemental Ni and Nb powder particles had a size distribution of ~25 to 50 μm and ~5 to 110 μm respectively.
The average powder particle size increased to 280 μm
after MA for 2.5 h, Fig. 1 (c). As shown in Fig. 1 (d),increasing milling times to 5 h led to a further increase inparticle size. After 5 h of milling time the particles werevaried in size, with an average of 430 μm, and also variedin shape. As illustrated in Fig. 1 (e–g), for milling times
longer than 5 h the particle size decreased and their shape
became progressively more uniform so that the final product after 85 h of milling, Fig. 1 (h) had a nearly sphericalmorphology and a narrow size distribution with a mean
size of 45 μm. A higher magnification SEM micrographof the particle surface, after 85 h of milling time, is shownin inset of Fig. 1 (h).

3.2 Structural changes
Fig. 2 shows XRD traces from Ni60Nb40 powder as-­received and after different milling times.
The XRD traces of the as-received powders showed
diffraction peaks from pure crystalline Nb and Ni. The
sharp crystalline diffraction peaks broadened and theirintensities decreased progressively during MA. As is evident in Fig. 2, after a milling time of about 20 h, a halo,
which is attributed to the amorphous phase, developed on
the XRD traces at a position between the crystalline
Nb(110) and Ni(111) peaks. After a milling time of 30 h
the high angle crystalline diffraction peaks disappeared
into the background and only broadened Nb(110) andNi(111) peaks along with an amorphous halo were apparent
on the XRD traces. Further milling resulted in the crystalline Ni and Nb peaks gradually decreasing in size andfinally vanishing. At the same time the amorphous halo
gradually grew until the XRD traces appeared to show a

Fig. 2

XRD traces from Ni60Nb40 powder as-received andafter different milling times.

completely amorphous structure without any indication of
additional phases. The crystalline diffraction peaks did
not shift during MA even at the longer times. Also XRD
showed no intermediate crystalline phase as a precursor
to the amorphous phase.
A problem with MA process, in particular for long processing time, is contamination. The contamination of
powders during MA can arise from the milling media (Fe,Cr) and the atmosphere (O2, N2). The contamination by
oxygen and nitrogen is of more importance during MA
processing of reactive powders such as Zr, Nb, and Ti.
The contamination of powder by oxygen and nitrogen in
MA can be due to the oxygen and nitrogen absorbed on
powder surfaces, residual oxygen and nitrogen in thebowel and oxygen and nitrogen picked up by leakage of
air into the bowl during milling due to inadequate sealingof the bowl.
The XRD traces from as-milled samples did not show
any indication of an additional phase (e.g. an oxide or
nitride) as a result of reaction of powders by O2 or N2.
Contamination by wear of milling media is not a serious
problem during MA of metallic powders because of adhesion of powder particles on milling surfaces which limits
their wear.

3.3 Microstructural evolutions
Fig. 3 shows cross sectional SEM images of Ni60Nb40
powder particles after various milling times. As shown in
Fig. 3 (a) the MA process first produced a layered microstructure consisting of cold welded Ni (dark area) and Nb(bright area) layers. After 2.5 h of milling time the thickness of the layers was quite non uniform from one particle

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Fig. 3

Cross-sectional SEM images of Ni60Nb40powder particles after different milling times.

to another and across the cross section with an average
thickness of ~11 μm. Increasing processing time to 5 hand then 10 h resulted in a finer and more uniform layered
structure as shown in Fig. 3 (b) and (c). The average layer
thickness after 5 h and 10 h of milling times were about 8
and 3 μm respectively. The microstructure after 15 h is ofparticular interest. As shown in Fig. 3 (d) the microstructure at this stage of MA consisted of two zones. Zone I200

was at the edges of the particles where no layered structure appeared within the resolution of the SEM. Zone II
was in the internal part of the particles, with a clear andfine scale layered structure. The average layer thicknessof ~3 μm after 10 h of milling time was reduced to a valueof ~1.5 μm after 15 h of milling time. Most of the particles
showed such a two zone microstructure, although in someparticles the layered structure continued to be observedMohammad Hossein Enayati / KONA Powder and Particle Journal No. 32 (2015) 196–206

throughout the cross section. Milling for 25 h extended
the size of zone I, however, the internal part still continued to exhibit a layered structure with a layer thickness
which did not decrease further, and remained similar withthat for 15 h. Fig. 3 (f) shows a higher magnification SEM
of a particle after MA for 25 h. Traces of the layered
structure were still evident in zone I as darker inhomogeneities. As MA proceeded further, the zone I gradually
grew further inwards as shown in Fig. 3 (g), and the layer
thickness in zone II did not refine significantly. Apartfrom a few particles the microstructure was completely
featureless in the SEM after 85 h of milling time when
XRD traces suggest a fully amorphous structure.
Fig. 4 shows the average layer thickness of particles
centres versus milling time. The average layer thickness
progressively decreased on continued milling and thenreached a constant value after ~20 h of milling time corresponding to the start time of amorphisation reaction.
It was also noted that there is a tendency to form a finer
microstructure at the edges of particles because of
non-uniform deformation occurring in MA.
To provide detail of microstructural evolution at theedges of particles during MA process TEM observations
was used. Fig. 5 (a–c) shows bright field (BF) images andselected area diffraction patterns (SADPs), in the insets offigures, from individual Ni and Nb layers after MA for
5 h. Generally the dark (or white) areas on TEM images
indicate the crystallineregions with the same orientations.
A heavily deformed structure characterized by arrays oflattice defects (mainly dislocations) was observed for both
Ni and Nb after 5 h of milling time. The original grainboundaries are still visible at this time of milling. The Nb
grains in Fig. 5 (b) were long in one direction in contrast,
Ni had equiaxed grains as shown in Fig. 5 (a). The corresponding selected area diffraction SADP for both Ni andNb exhibited the Debye-Scherrer rings characteristic of afine grain structure.
XRD peak broadening suggested grain sizes of 163 nm
and 66 nm for Ni and Nb respectively after 5 h of milling
time which are several order of magnitude smaller thanthe original Ni and Nb grain sizes. It appears that theoriginal grains were split into the several sub-grains byarrays of dislocations after 5 h of milling time. This subgrain structure can be also seen in Fig. 5 (c) in which thearrays of defects formed a cell structure within a large Nb
grain. These small sub-grains, in fact, contribute towards
the broadening of X-ray diffraction peaks and also contribute to the continuous Deby-Scherrer rings in the
SADP. Grain boundaries within original grains were notwell-defined after 5 h of milling time. This did not allow
direct measurements of the Ni and Nb grain sizes on
TEM micrographs.
A typical TEM image from edges of powder particles
after 15 h of MA is shown in Fig. 6 (a). A fine layered

Fig. 4 Variation of average layer thickness as a function of
milling time for Ni60Nb40 powder particles.

structure with an average thickness of ~60 nm wasobserved.
The corresponding SADP included the Debye-Scherrer
rings of fcc (Ni) and bcc (Nb) structures without any indication of a third phase (e.g. an intermetallic compound oran amorphous phase) which is consistent with the XRD
results. Arrays of dislocations within the layers were stillpresent after 15 h of milling time however, the original
grain boundaries disappeared. The sub-grain boundaries
in layers were not well-developed after 15 h of milling,making measurements of the Ni and Nb grain sizes
impossible. XRD peak broadening however, suggested agrain size of 31 nm for Ni and 18 nm for Nb after 15 h ofmilling time which are considerably smaller than the
average layer thickness, ~60 nm. Such a nanosized layered structure was found for most of the particles after
15 h of milling time, although occasionally regions with acoarser layered structure were also found at the edges ofsome particles for this sample.
A typical TEM image of these regions is presented inFig. 6 (b). An extensive Ni layer with a well-defined
nanocrystalline structure was observed in bright field(BF) and dark field (DF) images. The black areas on DF
image indicate the grains with the same orientations. The
dark field (DF) image using the crystalline Ni(111) diffraction ring, suggested that this area had an averagegrain size of ~25 nm which accords with the averagegrain size 31 nm estimated using XRD peak broadening.
After 20 h of MA, the amorphisation reaction starts. As
stated earlier, amorphous phase develops at the edge ofparticles and proceeds inwards as milling time increases.

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Fig. 5

TEM images and corresponding selected area diffraction patterns of individual Ni and Nb layers after 5 h ofmilling time.

At this stage the particle structure was inhomogeneouswith respect to the progress of the amorphous phase. For
instance Fig. 7 shows an area of this sample with agreater fraction of amorphous phase at the interface
between the crystalline Ni and Nb layers.
On continued milling the amorphisation reaction proceeded further. After 50 h of milling the microstructure
at the edges of particles consisted of a large number ofvery small, unreacted Ni and Nb particles with a size of~3 nm embedded in an extensive amorphous matrix, Fig.
8. The structural inhomogeneity observed in the earlystages of milling was much reduced after 50 h of milling.

202

Fig. 6

TEM images and corresponding selected area diffraction patterns of Ni60Nb40 powder particles edges after
15 h of milling time.

Finally Fig. 9 shows the microstructure of a Ni60Nb40
powder sample after 85 h of milling. The uniform contrastof the BF and DF images along with a single diffuse ringon the SADP all suggested a fully amorphous structure at
the edges of particles in agreement with the XRD results.
This fully amorphous structure was found everywhere atMohammad Hossein Enayati / KONA Powder and Particle Journal No. 32 (2015) 196–206

Fig. 7

TEM images and corresponding selected area diffraction patterns of Ni60Nb40 powder particles edges after
20 h of milling time.

the edges of all the particles.

3.4 Microhardness
The dependence of hardness on the microstructure of amaterial makes it a useful tool to study the microstructuralchanges occurring during mechanical alloying. Fig. 10
plots the average value of microhardness at the edges andcentres of Ni60Nb40 powder particles after different milling times.
In the early stage of MA, up to 5 h, the microhardness
values at the edges and centres of particles were identical.
In contrast, the edges and centres of particles start toshow different values of microhardness, 1160 and 814 Hv
respectively, after 10 h of milling time. After 15 h of milling time, the value of microhardness at the particle edges,
increased to 1280 Hv, and that for particle centres
increased to 910 Hv. After 25 h of milling time the microhardness value of the particle edges increased even further, to 1670 Hv, and that for the particle centres also
increased further to 1070 Hv. This remarkable increase in
microhardness at the edges is consistent with the presenceof an amorphous phase, as detected by TEM. The large
difference in microhardness between the edges and cen-Fig. 8TEM images and corresponding selected area diffraction patterns of Ni60Nb40 powder particles edges after
50 h of milling time.

tres of the particles was also observed after 50 h of milling time. At this time the microhardness value of theparticle edges had increased to 1820 Hv as a result of thecontinued gradual amorphisation process while that forparticle centres increased only slightly to 1080 Hv. After
85 h of milling, when XRD and TEM suggested a fully
amorphous structure, the edges and centres of the particles both had the same, very high microhardness value of
1960 Hv.

4. Discussions
Changes in particles morphology and microstructureduring MA of ductile metal powders are produced by two
simultaneous processes; cold welding and fracturing.
During mechanical alloying clusters of particles aretrapped between colliding balls and undergo a high levelof impact. If the impact stresses are sufficient, the powder
particles plastically deform and flatten. As the powder
particles are pressed together their surface area increases
and the surface oxide films rupture, consequently exposing clean underlying metal. When these fresh surfaces ofparticles come in contact a metal bond is formed.

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Fig. 9Microhardness value at the edges and centres of
Ni60Nb40 powder particles after different milling times.

Fig. 11

Schematic illustration of rate of cold welding andfracturing of powder particles during mechanicalalloying process.

TEM images and corresponding selected area diffraction patterns of Ni60Nb40 powder particles edges after
85 h of milling time.

After a period of milling, particles deform to the extent
that cracks initiate, propagate and ultimately fracture theparticles.
The extent of these two events is determined by themechanical properties of the elemental powders, such as
ductility, yield stress and hardness, as well as the magnitude of the impact provided by colliding balls.
Three stages were observed in analysing the powder
particle size during MA of Ni60Nb40 and powder mixture.
The initial particle size first increased reaching a maximum after 5 h of milling time. In the second stage theparticles rapidly decreased in size. This was followed by athird stage; steady-state stage for milling times longer
than ~20 h in which the particle size remained almost
constant. These three stages and associated microstructural changes can be analysed by considering the relative
rates with which the cold welding and fracturing processes occur, as shown schematically in Fig. 11.
In first stage (0–5 h) the cold welding process dominates and as a result the powder particle size continuously
increases. This stage can be termed the agglomeration
stage. The flattening and cold welding of powder particles
during this period of MA result in the development of alayered structure consisting of Ni and Nb layers. The

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Fig. 10cross sectional SEM images showed that in the first few
hours of MA the layers are coarse and vary in thickness
over the particle cross sections. As MA proceeds further,Mohammad Hossein Enayati / KONA Powder and Particle Journal No. 32 (2015) 196–206

the layer thickness progressively refines and becomes
more uniform as a result of the repeated cold welding andfracturing of particles as shown in Fig. 3 and Fig. 4. The
ultrafine layered microstructure developed during ball
milling provides extensive interfaces suitable for any
potential reaction (e.g. amorphisation, formation of crystalline compounds, ...) between constituents at longer
milling times or during subsequent processing (e.g. hotpress, hot extrusion, thermal spray).
Microhardness measurements on Ni60Nb 40 particles
showed a progressive increase in hardness value during
the first stage of MA as a consequence of work hardening.
This leads to a decrease in the ductility of powder particles and therefore, an increased tendency for particle
fracture. Thereby, the first stage is followed by the fragmentation stage which lasts from ~5–20 h of milling time.
During the fragmentation stage the fracturing of particles
occurs more readily than cold welding and as a result thepowder particle size decreases. The layered structure also
refines further in the second stage. TEM images from
Ni60Nb40 particles after 15 h of milling time revealed ananoscale size layered structure, with a typical thicknessof ~60 nm. The faster refinement of the layered structure
at the edges of particles implies that the edges are subjected to greater plastic deformation than the centres,
consistent with high deformation rates in MA. The microhardness measurements provided further support for thisnon-uniform plastic deformation. After 15 h of milling
time the edges of the particles attain a higher hardness(1280 Hv) compared with the centres (910 Hv) because ofhigher work hardening. As will be discussed next section
this inhomogeneity in plastic deformation results in theedges amorphising first, before the centres.
After around 20 h of milling time there is a steady-state
stage in which the powder particle size does not change
significantly. Interestingly the commencement of thesteady-state stage, after ~20 h of milling time, coincides
with the development of an amorphous phase. The amorphisation reaction was observed to start at the edges ofparticles, where a nanoscale size layered structure is first
formed, and continues inwards. It is believed that during
the steady-state stage there is a balance between the frequencies of cold welding and fracturing processes so thatthe average particle size remains unchanged (Dunlap etal., 2000). This mechanism of continuous equal amount of
the fragmentation and cold welding of particles in thesteady-state stage should lead to a random distribution of
the amorphous phase throughout the whole of the particle
volumes. The observed progress of the amorphisation
reaction from the edges towards the centre of particles in
the present work however, suggests that no significant
fracturing or cold welding occur during the steady-state
period. The start time of the steady-state stage and the formation of an amorphous phase at the edges of particles

appeared to be similar, after ~20 h of milling time, indicating that the amorphous shell at the edges of powder particles prevents further fragmentation. Amorphous alloys are
extremely hard, but also can deform substantially in compression, therefore the amorphous particle edges provide astrong and resilient layer preventing fracture during milling. The earlier mechanism based on the balance between
the cold welding and fracturing processes could be
encountered in high energy mills where a much higher
force is imposed on particles and in other cases where aresilient amorphous outer layer is not formed.

5. Conclusions
MA of Ni60Nb40 alloy in low energy centrifugal ballmill consists of three stages; agglomeration, fragmentation and steady-state. During the first stage of agglomeration successive cold welding of powder particles leads toa continuous increase in particle size. In this stage a layered structure is formed, with a progressively refinedlayer thickness with increasing milling time. The agglomeration stage is followed by the fragmentation stage aswork hardening limits ductility and further cold welding.
During fragmentation stage fracturing of particles dominates, leading to a rapid decrease in particle size as well
as a continuing refinement of layered structure. The grainsize, microstructure and microhardness value at edges
and centres of particles were different at early stage of
MA which is consistent with the non-uniform deformation occurring in MA. This causes the particle edges toamorphize prior to the centres. The amorphous phase at
the edges of particles gradually proceeds inwards withincreasing milling time until the whole particles became
uniform with respect to the microstructure and microhardness value i.e. the edges and centres of the particles
both had the same features. However, the amorphous shellprovides a strong and resilient layer, preventing further
fracture. Thereby, the fragmentation stage is followed bya third stage; steady-state stage in which powder particle
size remains constant. Even though the impact force is nothigh enough to rupture the amorphous layer at the edges,
the regions underneath can be plastically deformed andthen transform to the amorphous phase. Continued milling progressively refines the grain size and increases theinternal strain introduced by increasing density of crystal
defects. An ultrafine layered structure with a typical
thickness of 30 nm, containing nanoscale size grains witha typical size of 15 nm and a high density of dislocations,develops prior to amorphization reaction. This observation suggests that numerous high speed diffusion paths
are necessary to allow a high diffusion rate at low temperature and therefore permits the amorphization reaction
to take place kinetically.

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References
Dunlap R.A., Small D.A., MacKay G.R., O’Brien J.W., Dahn
J.R., Cheng Z.H., Materials preparation by ball milling,
Canadian Journal of Physics, 78 (2000) 211–229.
Koch C.C., Cavin O.B., Mckamey C.G., Scarbrough J.O., Preparation of amorphous Ni60Nb40 by mechanical alloying,
Applied Physics Letters, 43 (1983) 1017–1019.
Petzoldt F., Synthesis and process characterization of mechani-

cally alloyed amorphous Ni-Nb powders, Journal ofLess-Common Metals, 140 (1988) 85–92.
Takacs L., Self-sustaining reactions induced by ball milling,
Progress in Materials Science, 47 (2002) 355–414.
Williamson G.K., Hall W.H., X-ray line broadening from filedaluminium and wolfram, Acta Metallurgica, 1 (1953),
22–31.
Zhang D.L., Processing of advanced materials using high-energy mechanical milling, Progress in Materials Science,
49(2004) 537–560.

Author’s short biography
Mohammad Hossein Enayati
M. H. Enayati (PhD, CEng, MIMMM) is a full Professor of materials science at Isfahan University of Technology. Prof. Enayati holds a bachelor’s degree in metallurgicalengineering from the Isfahan University of Technology,-Iran, a master’s degree in materials engineering from Shiraz University-Iran, and a doctorate in materials science fromthe University of Oxford-UK. He is the former Dean of Office for Research Affairs,
Graduate Program Advisor, Deputy of Research Affairs, member of editorial board of
Journal of Advanced Materials in Engineering and founder director of Nanocenter at
Isfahan University of Technology. Prof. Enayati’s research focuses on the nanostructured
and amorphous materials, mechanical alloying and synthesis of advanced materials forthermal spray coating. He has authored several books and numerous articles, is a frequent presenter at professional conferences, has served in editorial capacities for several
leading journals in his field, and has received research funding from a wide variety ofagencies. His research has been recognized with numerous awards, most recently fromthe Iran Nanotechnology Initiative Council and Iranian Nanotechnology Society.

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KONA Powder and Particle Journal No. 32 (2015) 207–216/Doi:10.14356/kona.2015011

Original Research Paper

CaCl2-in-Mesoporous Silica Grown on Superabsorbent
Polymer to Enhance Water Uptake †Ki Woong Ahn, Soo Yeon Jang, Min Hyuk Hwang and Sun-Geon Kim *1

School of Chemical Engineering and Materials Science, Chung-Ang University, Korea

Abstract
Super absorbent polymer (SAP) sorbs copious amount of liquid water but its sorption power for water vapor is quite
low. On the other hand, mesoporous silica loaded with CaCl2 (MPS-CC) has high sorption capacity for water
vapor. However, this is determined by the salt loading, which is limited due to its corrosiveness. Even by simple
powder blending of 75 mass% of SAP and the balance of MPS-CC (SAP75/MPS-CC25), the 3-hour maximum
specific water uptake (g H2O/g sorbent) reached the maximum, 3 times the equilibrium uptake of MPS-CC. The
sorption property was further enhanced by developing a new sorbent in which mesoporous silica was grown on thesurface of swelling SAP and then impregnated by CC in ethanol. In the new sorbent (SAP-iMPS-CC), MPS greworganized, straight and lengthwise by the help of SAP. Owing to such intimate contact between MPS and SAP, the3-hour specific water uptake of the new sorbents grown for more than 24 h (SAP-iMPS-CC24) was kept constant atthe value of 2 times that of SAP75/MPS25. The SAP mass% of SAP-iMPS-144 could reach 25 without the sacrificeof sorption capacity.
Keywords: mesoporous silica, superabsorbent polymer, template growth, calcium chloride, water uptake

1. Introduction
It is attractive to develop the adsorbents having high capacity and high rate of water vapor uptake for gas drying,water production from atmosphere, seasonal energy storage and adsorption cooling cycle (Restuccia et al., 2004;
Saha et al., 2009; Gong et al., 2011). So far the mesoporous
silica impregnated with calcium chloride (MPS-CC) has
been exclusively studied (Aristov et al., 2002; Aristov,2007; Zhang and Qiu, 2007; Tokarev et al., 2002; Xin etal., 2010; Glaznev et al., 2011; Ponomarenko et al., 2010;
Espinosa et al., 2008; Glaznev and Aristov, 2008; Aristov
et al., 2006; Aristov et al., 2008; Ovoshchnikov et al.,2011). This is because its sorption properties can be adjusted in a wide range by varying the porous structure of
the host matrix, chemical nature of the impregnated salt,
and the amount of the salt inside pores. However, the water uptake of such composite sorbent is limited by theamount of salt loading, which cannot be so high as tokeep the corrosive salt inside the pores. On the other
hand, superabsorbent polymer (SAP) has the ability to absorb the liquid water up to 1000 times its mass (Jin et al.,†1*

Received 23 April 2014; Accepted 27 August 2014
J-STAGE Advance published online 30 September 2014
221, Huksuk-Dong, Dongjak-Ku, Seoul, 156-756, Korea
Correspopnding author: Sun-Geon Kim;
E-mail: sgkim@cau.ac.kr
TEL: +82-2-820-5272 Fax: 82-2-824-3495©2015 Hosokawa Powder Technology Foundation

2000) but its sorption for water vapor is quite low.
This study was motivated with the expectation of thesynergistic effect on the water vapor uptake when the twoconstituents were well blended. For such blended sorbents, water vapor would be taken by MPS-CC and then
the water would migrate to SAP, until the SAP saturates
with water. The study started with the preparation of suchsorbents by simply mixing the MPS-CC as prepared with
the SAP as received. The water sorption capacity and rateof such simple blends were investigated. Based on the results, new sorbents having the intimate contact between
MPS-CC and SAP were developed to keep both the portion of SAP as low as possible and the water vapor sorption much higher than that of MPS-CC alone. The
preparation was based on the controlled growth of MPS
on swelling SAP. The mechanism of the SAP-assisted
MPS formation was pursued by probing the changes in
the composition and morphology of the blends prepared
under various conditions. The CC impregnation and water
uptake of the new sorbent was then investigated.

2. Experimental
All the materials used were reagent grade. Tetraethoxyorthosilicate (TEOS), hydrochloric acid, ethanol(95 %) calcium chloride (CaCl·6H 2O, 98 %) were purchased from Samchun Chemicals. Pluronic P123

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Ki Woong Ahn et al. / KONA Powder and Particle Journal No. 32 (2015) 207–216(PEO20PPO70PEO20) was purchased by Sigma-Aldrich.
Superabsorbent polymer (K-SAM) was gratefully giftedby LG Chemicals Co.

2.1 Preparation of mesoporous silica
The precursor solution was prepared for pure MPS
with the molar ratio of TEOS:H2O:HCl:P123 = 1:195.8:
6.09:0.0169. It was magnetically stirred for 24 hours at
35 °C for 24 h, aging at 90 °C for 24 h, filtering and finally calcining at 600 °C for 20 min to remove P123.

2.2 Impregnation of calcium chloride and simple
mixing of MPS and SAP
One gram of MPS was then soaked in 100 mL of aqueous solution having 30 wt% calcium chloride (CC) withmagnetic agitation for 24 h at room temperature. The impregnated MPS was filtered, washed three times with water and dried at 90 °C for 2 h. The average CC loading
was 0.186 ± 0.12 g CC/g MPS. The CC-loaded MPS are
called MPS-CC. For simple blending, SAP as received
and MPS-pCC as prepared were simply mixed in the different mass ratios. These were called SAP/MPS-CC. The
mass fraction of each constituent was often given in theparenthesis in the notation. For instance, SAP50/
MPS-CC50 represents the simply mixture having theequal mass of SAP and MPS-CC.

2.3 Sorbent preparation by the controlled growthof MPS on SAP and impregnation of calcium
chlorideA s ol u t i o n h a v i n g H 2 O: H C l : P12 3 = 19 5. 8:
0.00609:0.0169 was prepared. SAP was then added to thesolution at 1 wt% with respect to the water in it. It took
hours for the SAP to be well slurred. TEOS was then added
to the slurry at TEOS:H 2O molar ratio of 1:195.8. The
molar composition of the final slurry was TEOS:H2O:HCl:P123 = 1:195.8:0.00609:0.0169 plus SAP at 1 wt%of the water in it. The resulting solution was the same asthat of the MPS precursor solution except both adding
SAP and reducing the amount of the acid by 1/1000(reference condition). The solution was stirred for 48 h (re­ference condition) at room temperature. In preliminary
experiment, the amount of HCl varied as 1/10, 1/100 and
1/1000 times 6.09, the concentration used for the pure MPS
preparation, both to control the rates of silica formationand make the SAP swelling. The time of preparation was
also varied from 6 h to 144 h to search for the preparation
mechanism. The specimen prepared so was called SAPiMPS. Here, ‘i’ represents in-situ preparation. The pre­paration time in hours was often included in the abbreviationsuch as SAP-iMPS24. After some pre-experiments the208

CC impregnation to the blends was carried out with ethanol solutions to prevent the salt from washing out. The
impregnation took place in 30 wt% CC ethanolic solution with magnetic stirring for 24 h at room temperature.
The sorbents were filtered, washed three times with ethanol and dried at 90 °C for 24 h. The specimen was thencalled SAP-iMPS-CC.

2.4 Characterization
The porous structures were observed by Transmission
Electron Microscope (TEM, JEM-2000EX, JEOL) and
Scanning Electron Microscope (SEM, Hitachi, S-3400N)
with Energy Dispersive X-ray Spectroscopy (EDX), andanalyzed by Small Angle X-ray Spectroscopy (SAXS,GADDS, Brucker). The silica formation and CC impregnation were measured by Inductively Coupled Plasma-­Atomic Emission Spectrometer (ICP-AES, Jobin Yvon,
JY-Ultima-2) and the EDX. In order to see the effect of
the CC impregnation precisely, it is measured every time
the CC impregnation was carried out, instead of using acalibration chart. X-ray Photoelectron Spectroscopy(XPS, PHI 5000 VersaProbe, ULVAC-PHI) was used toanalyze the surface chemical state of the sorbents. Surface silica complex was identified by 29Si Solid-State Nuclear Magnetic Resonance Spectrometer (NMR, Bruker,
Bruker Avance II). The relative proportions of each species were obtained by Magic Angle Spinning (MAS) associated with 1H→29Si cross-polarization (CP/MAS).

2.5 Water sorption in porous structures
Fifty milligram of the sorbents was spread on petri dishin 1-L chamber at 17 °C. The humid air at 30 °C and28.9 % relative humidity entered the chamber at 1 L/min.
These conditions are usually obtained in the adsorber belonging to a typical adsorption chiller (Restuccia et al.,
2004; Saha et al., 2009). The water uptake of the sorbents
was measured by microbalance up to 3 h.

3. Results and discussion
3.1 Sorbent prepared by simple mixing of SAP
and MPS-CC powders
Fig. 1 shows the FE-SEM image of the sorbent madeby simply mixing ~100-μm SAP powder as received with
~1-μm MPS-CC powder as prepared in the same mass.
Lumps of the small MPS-CC particles were dispersed
among large SAP powders with some segregation. Fig. 2
shows the isotherms of both pure MPS-CC and SAP inhumid air at 17 °C. It is confirmed that SAP has much
lower sorption for water vapor than MPS-CC particularly

Ki Woong Ahn et al. / KONA Powder and Particle Journal No. 32 (2015) 207–216

Fig. 1 FE-SEM image of sorbent prepared by simply mixingof SAP with MPS-CC (SAP50/MPS-CC50).

in the low to moderate humidities. It is noted that
MPS-CC used did not have unique CC loading but this
could not affect the comparison between the two materials. In Fig. 3 the specific water uptake of the simply
mixed sorbents was plotted with respect to sorption timeat 17 °C and 75 % relative humidity, as a parameter of themass fraction of SAP in the sorbent. The water uptakes ofpure SAP and MPS-CC were already saturated at 0.0285
and 0.184 g H2O/g sorbent, respectively in the first 1 hour.
The water uptakes of the sorbents, SAP/MPS-CC, werestill increasing even at 3 h, irrespective of SAP content.
The water uptake recorded the maximum at 75 mass% of
SAP. The water uptakes of the sorbent for 3 h were0.658 g H2O/g sorbent, respectively, while those of pure
MPS-CC were 0.123, 0.184 and 0.184 g H2O. The water
uptake of the mixed sorbent reached 3.5 times the equilibrium water uptake of the pure MPS-CC in the first 3 h
and the difference will increase with the time. For thesesorbents the segregation between MPS-CC and SAP
would become minimized in which it was expected that
small MPS-pCC particles would well surround each polymer particle in a thin layer. These clearly supported thatwater vapor would be taken up by MPS-CC and the water
would then migrate to SAP. The sorption-migration process would continue until SAP saturates with water. However, as shown in Fig. 3, there were some sorbents whosewater uptakes for 3 h were lower than the corresponding
values of pure MPS-CC. They were the mixtures having asmall amount of either SAP or MPS-CC, which suffered
the migration resistance due to the poor contact between
MPS-CC and SAP. For example, in case of SAP10/MPS-CC90 its early water uptakes were lower than thoseof pure MPS-CC. The sorption would momentarily reduce since, in addition to the decrease in the portion of
MPS-CC, the water sorbed in MPS-CC is hard to find the
SAP for migration. Of course, it would be a matter oftime that the sorption of the sorbents surpasses that ofFig. 2

Adsorption isotherms of MPS-CC, SAP and SAPiMPS-CC in humid air at 17 °C.

Fig. 3

Dynamic sorption curve for simply mixed sorbent(SAP/MPS-CC) as a parameter of mass ratio of SAP to
MPS-CC at 17 °C and 75 % relative humidity.

pure MPS-CC. Nevertheless, it is required to enhance theearly water uptakes since the usual adsorption period in a
typical adsorption chiller is no longer than 20 min. Therefore, the additional mass transfer resistance caused by the
migration from MPS-CC to SAP should be reduced. The
use of SAP can give a rise to the environmental problems.
Since the conventional adsorption chiller has been using
silica gel or zeolites as adsorbents, it is not so desirable toreplace these inorganic materials with the polymer. Morethan these, it is highly recommended to enhance the efficiency of SAP usage in short period of adsorber operation. Therefore, it is better to include SAP as small as
possible.

3.2 Sorbent preparation by assisted growth of
MPS on SAP swelling
The simply mixed sorbents could not afford to satisfy
such demands of keeping both the water uptake as highand the SAP fraction as low as possible. Therefore, it is209

Ki Woong Ahn et al. / KONA Powder and Particle Journal No. 32 (2015) 207–216

necessary to develop a new structure of the sorbents inwhich MPS-CC is well exposed outside to water vapor,and the intimate contact is maintained between MPS anda minimal amount of SAP. The desirable structure wasaimed by the controlled growth of MPS on the surface ofthe swelling SAP. We will discuss separately the effect ofhydrochloric acid concentration and growth time on themorphology of the new hybrid sorbents.

3.2.1 Effect of HCl concentration

Hydrochloric acid plays an important role as a catalyst
to speed up the formation of the hydrolysis of TEOS.
However, in the low pH the sodium carboxylate group on
the polymer network was protonated. This in turn decreased the degree of ionization and the charge density on
the network hence decreasing the swelling ratio (Elliott,
2004). In order to swell SAP and then grow MPS on it, itis necessary to keep pH low at the expense of reducing
the rate of MPS formation. At the HCl/TEOS of 6.09, thereference ratio for the pure MPS, SAP did not swell at all,but both the SAP precipitation and silica phase formation
immediately took place. As the acid-to-TEOS molar ratio
decreased to 0.000609, 1/1000 of the reference value, SAP
began to swell (be slurried) in water while the silica wasformed slowly throughout the slurry in milky appearance.
Fig. 4 shows the SAXS data on the SAP-iMPS preparedat the different HCl concentrations. A peak representing
mesophase first appeared for SAP-iMPS-1/100. For SAPiMPS-1/1000, a peak 2θ = 1.04° clearly shows the evidence of a weak mesophase. At low pH, the sodium ionsare liberated by protonation of the carboxyl group (Elliott,
2004). The effect of NaCl on the formation of silica has
been studied widely before (Pan et al., 2007; Lin et al.,
2001; Yu et al., 2001). The hydrothermal stability of mesoporous materials after calcination can be directly enhanced considerably by adding a small amount of theadditional salt. However, The mesophase transformations

Fig. 4210

SAXS data on SAP-iMPS prepared with different HCl
concentrations.

occur in the sequence MCM-41→KIT-1→MCM41→KIT1→amorphous phase as the salt concentration increases.
At high concentration of NaCl the regularity of the mesoporous materials was lost due to the perturbation of the
double layer potential (Yu et al., 2001). When SAP was
fully combusted with oxygen, the combustion residue wasobtained at 40 wt% of the initial SAP mass. By EDX, thehalf of the residue belonged to Na. This strongly justified
the amorphous nature of the silica obtained at HCl/TEOS
greater than 0.0609. On the other hand, the sodium ionsbecome less free and more bound to the polymer networkand the SAP begins to swell at low acid concentrations.
Mesoporous silica would grow under this condition.

3.2.2 Effect of preparation time

Our reference SAP-iMPS was therefore prepared at
HCl/TEOS = 0.00609. Now it was necessary to know
deeply how the silica formed on SAP and how the morphology of SAP-iMPS evolved with the time of preparation. The time evolution of their morphology is shown in
Fig. 5. Silica was gradually being deposited on SAP astime went by. Fig. 6 shows the magnified FE-SEM andTEM images of MPS in SAP-iMPS-144. The mesophase
was not observed in the SEM image until 48 h, after
which the mesophase grew organizing, straight andlengthwise. The extremely organized, straight and longmesophases appeared in 144 h as shown in Fig. 5(d) and
Fig. 6. Each long mesophase was composed of many unitsof the MPS segment glued at the ends like bamboo joint.
It has been reported that the salts such as NaCl act as abridging ion between two colloidal silica particles (Pan etal., 2007; Barrabino, 2011). The small Na+, usually surrounded by the oxygen atoms of six water molecules ofhydration, is adsorbed on the silica surface. One or moreof the oxygen atoms of hydration water are then displacedby the oxygen of the surface silanol groups. This directly
links the two silica particles via Na+ ions. Therefore, it ishighly probable the sodium from the SAP glued the endsof the mesoporous cylindrical segments to help themgrow in length. The wall-to-wall distance is about 10 nm
as shown in Fig. 6. Fig. 7 shows the mass of silica deposited on SAP for the different times of preparation. The
silica deposition took place rapidly up to 6 h, then slowed
down to 72 h and finally accelerated. Fig. 8 shows the
SAXS data on SAP-iMPS growing with the different
ages. SAP-iMPS younger than 48 h showed no meaningful peaks since the one at 0.5° was considered as that inherent to the machine itself. Clear mesophase peak
appeared for SAP-iMPS-96. The mesophase grew further
thereafter at the d value of 11 nm unchanged. The d value
was coincided with the wall-to-wall distance observed in
the TEM image as described before but larger than that of
MPS-CC (~8.9 nm). This reflects the two mesophase weregenerated in the different environments. Fig. 9 shows theKi Woong Ahn et al. / KONA Powder and Particle Journal No. 32 (2015) 207–216

Fig. 5

FE-SEM images of the reference SAP-iMPS prepared for (a) 6 h; (b) 12 h; (c) 48 h; (d) 144 h.

Fig. 6(a) FE-SEM and (b) TEM images of reference SAP-iMPS growing for 144 h.

isotherms of SAP-iMPS with the different ages obtained
from BET analysis. Table 1 shows the results of BET
analysis on them. The youngest, SAP-iMPS-6, belonged
to a typical H4 hysteresis loop classified by IUPAC(Thommes, 2010), indicating complex materials containing both micropores and mesopores. SAP-iMPS-48 wasclassified in between H1 and H3 but biased to H3. In otherwords, it was composed of mostly non-rigid aggregates ofplate-like particles or assemblages of slit-shaped pores

while it had a trace of big cylindrical pores. Young SAPiMPS had much lower BET surface area than the pure
MPS (~375 m2/g) since they were low in silica deposition
and in the early stage of mesophase formation. SAPiMPS-144 was also in between of H1 and H3, but now
had a lot of smaller cylindrical pores and some plate-likeparticles. The appearance of H3 type would be relatedwith the layered growth of silica on the surface of SAP. It
is noted that SAP-iMPS-48 had higher pore volume and211

Ki Woong Ahn et al. / KONA Powder and Particle Journal No. 32 (2015) 207–216

Table 1

BET data for SAP-iMPS-CC prepared for different times
SAP-iMPS-CC: 6 h

SAP-iMPS-CC: 48 h

SAP-iMPS-CC: 144 h

90.86683.6183

152.4

Pore Volume (cm /g)

0.153906

0.772226

0.607289

Average Pore Diameter (nm)4.662136.9405

15.9394

BET Surface Area (m 2/g)
3

Table 2

Proportions of Q2, Q3, and Q4 (estimated from 29Si NMR) for SAP-iMPS-CC prepared for different times
SAP-iMPS

Fig. 7

Fig. 86h

Q2Si(OSi)2(OH)2

14.6 %Q3

Si(OSi)3(OH)49.8 %Q4

Si(OSi)4

35.6 %

48 h
64.4 %

Deposition of silica on SAP with respect to time of
preparation.

SAXS data on reference SAP-iMPS-pCC prepared in
the different reaction times.

15.4 %
36.7 %
48.9 %

144 h
52.1 %

13.1 %
25.9 %
61.0 %

Fig. 9 Isotherms of SAP-iMPS-CC prepared for different times.

these findings indicate that the mesophase became matured in the period from 48 to 144 h. This was reflectedby the slope of silica deposition with respect to the synthesis time as shown in Fig. 7. After the active depositionof silica to 12 h, its deposition slowed down during thecreation of mesophase to 72 h and then the mesophasegrew rapidly lengthwise accompanying with the accelerated silica deposition. Table 2 shows the proportions of
Q2 (Si(OSi)2(OH)2), Q3 (Si(OSi)3(OH)) and Q4 (Si(OSi)4)
on the silica surface groups obtained from 29Si NMR
spectroscopy. Surface silanol (-SiOH) reduced graduallyas the preparation time increased. This would be in relevancy to the exhaustion of silanol groups by sodium ions,described before.

3.2.3 Calcium chloride loadinglarger average pore diameter than SAP-iMPS-144. It issaid that H3 types in principle should not be expected toprovide a reliable assessment of either pore size distribution or the total pore volume (Thommes, 2010). However,212

39.0 %

In preliminary experiments the loading of calcium
chloride on SAP-iMPS-48 was tried from its aqueous
solution. However, the salt loading was found quite low.
When the SAP-iMPS were immersed in the CC aqueous
solution, the bulk water would direct to SAP through theKi Woong Ahn et al. / KONA Powder and Particle Journal No. 32 (2015) 207–216

Fig. 10Specific salt loadings based on g sorbent and g silica
in it with respect to time of preparation.

mesoporous phase, which would bring about high convectional flow of the salt solution. Therefore, the salt could
not help moving to SAP with the water, having little
chance to diffuse into mesoporous silica. Therefore, thesalt would only smear on the SAP surface and be easily
removed by the subsequent water washing. Since SAP
was confirmed not to sorb ethanol in our separate experiment, water was replaced by ethanol as an alternative solvent for the calcium chloride.
Fig. 10 shows two curves on CC loadings of the different aged SAP-iMPS in the ethanol: one is the loading perunit mass of SAP-iMPS itself and the other is the loading
per unit mass of MPS deposited on SAP. The salt loading
increased with the preparation time up to 6 h accordinglywith the silica deposit, and then varied very slowly. The
specific salt loading calculated based on the silica deposit
increased up to 12 h and then even slightly decreased.
Such an almost invariant salt loading would be relatedwith the decrease in Q2 and Q3 of silanol groups and thus
the formation of a bamboo joints between mesoporous
segments. In glass the addition of 25 % sodium oxide(soda, Na2O) to silica reduces the viscosity and lowers themelting point from 1723 °C to 850 °C. Sodium oxide also
increases the tendency of silicon dioxide to form networks
rather than crystals. The sodium-oxygen bridges may interrupt the regular silicon-oxygen bonding and/or sodium
ions may intersperse among the silica molecules to prevent the formation of regular crystals (Best, 1990). Like
the glass, the inclusion of the sodium ions was believed tolower the percentage of the silanol groups available to theloading of calcium chloride. In these SAP-iMPS, the increase in the surface area made up for this silanol grouploss. Therefore, the salt loading resulted in a slight increase with the time of preparation

3.2.4 Water uptake

Fig. 11 shows the dynamic sorption curves for SAP,

Fig. 11

Dynamic sorption curves for SAP, SAP75/MPS25 and
SAP-iMPS-CC aged 48 h at 17 °C and 75 % relative
humidity.

Fig. 12Specific sorption of water vapor for sorbent and CaCl 2
in it with respect to preparation time under adsorption
chiller condition (30 °C and 28.9 % relative humidity).

SAP75/MPS-CC25 and SAP-iMPS-CC aged for 48 h at
17 °C and 75 % RH. It is noted that the sorptions of thelatter two were still increased at 2 and 3 hours, respectively. As shown in Fig. 11, SAP-iMPS-CC48, consistingof 44 % SAP and 56 % SiO2, had much more sorption forwater vapor than SAP75/MPS-CC25 for given sorption
time. Water uptake was also measured under adsorption
chiller condition. Fig. 12 shows the variation in the
3-hour specific water uptakes of SAP-iMPS-CC with respect to preparation time in the adsorption chiller condition (30 °C and 28.9 % relative humidity). Since
mesophase of silica was about to form for SAP-iMPS-CC
grown for 24 h, the specific water uptake of the sorbent
remained almost constant from 24 h to 144 h. It is realized that SAP-iMPS-CC-144 has 75 % SiO2 and 25 %SAP. This is contrasted with the result of SAP/MPS-CC
where the maximum water uptake occurs with SAP75/
MPS-CC25. As also shown in Fig. 12, all the specific water sorptions of SAP-iMPS-CC grown for more than 24 h,based on CaCl2, were higher than 3.5 g H2O/g CaCl2,

213

Ki Woong Ahn et al. / KONA Powder and Particle Journal No. 32 (2015) 207–216

which was much higher than the water uptake of bulk
CaCl2 of 1.22 g H2O/g CaCl2 (Elliott, 2004). This alsostrongly supported that the sorption of SAP-iMPS-CC
was not limited by the CaCl2 loading in MPS since water
vapor was sorbed by the salt and subsequently migrated
to SAP. In addition, the template growth of MPS on SAP
kept the contact between MPS and SAP in good condition
for water migration, even though the SAP mass% decreased up to 25 %. As described above, CaCl2 loading
was almost constant, irrespective of the preparation time,
so was the initial sorption, since the SAP in the sorbent
could never be saturated for 3-hour sorption. In otherwords, the initial water uptake of the sorbent was enhanced, due to the intimate SAP-MPS contact.

4. Conclusion
MPS-CC sorbs water vapor via CC in its pores, but its
water uptake is determined by the amount of the salt loading, which has to be limited due to its corrosiveness. On
the other hand, SAP is extremely high sorbent for liquidwater but its uptake for water vapor is very low. Maximum 3-hour water uptake of the sorbents obtained bysimple powder blending of the two reached more than 3times that of the pure MPS-CC at 75 mass% of SAP. It isbelieved that the water vapor was taken by calcium chloride in MPS-CC and then migrated to SAP. In both thetechnological and environmental viewpoints, SAP addition should be as small as possible by its efficient use.
Therefore, it is required to reduce the resistance of migration from MPS-CC to SAP through the intimate contact
between MPS. New preparation method was developed inwhich the MPS grew slowly at low acid concentration on
the surface of the swelling SAP and the salt was subsequently loaded from its ethanol solution. Silica deposited
rapidly in the first 6 h and then linearly with the preparation time. Mesophase began to grow after 48 h. The mesoporous silica grew well ordered, straight and lengthwise
by the help of sodium ions. However, salt loading was not
so improved due to the unfavorable surface condition.
Therefore, the MPS grown on SAP template for morethan 24 h had almost constant specific water uptake. Nevertheless, the sorption reached two times that of SAP75/
SAP25. This enhancement of SAP utilization wasachieved by the considerable improvement of the contact
between MPS and SAP.

Acknowledgement
This research was supported by Basic Science Research
Program through the National Research Foundation of
Korea (NRF) funded by the Ministry of Education

214

Supplementary Fig. 1 Pore diameter distributions of SAP-iMPS-­
CC prepared for different times.

(No.2013-011294) and the Chung-Ang University Research Scholarship Grant in 2014.

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Author’s short biography
Ki Woong Ahn
2014.2 Ph.D. in Chemical Engineering, Chung Ang University
Ph.D. Dissertation: “Development of High Water Sorbing Materials Based on CaCl2-in
Porous Media”
2008.2 M.S. in Chemical Engineering, Chung-Ang University
2006.2 B.S. in Chemical engineering, Chung- Ang University
2014.2~present Researcher Research & Development Division, Nexentire

Soo Yeon Jang
2014.2 B.S. in Chemical Engineering, Chung Ang University
2014.3~present MS. Student in School of Chemical Engineering, Chung Ang University

Min Hyuk Hwang
2009.2 B.S. in Chemical Engineering, Chung -Ang University
2011.2 M.S. in Chemical Engineering, Chung-Ang University
2014.1~present Researcher, School of Chemical Engineering and Materials Science,
Chung-Ang University

Sun-Geon Kim
1985.12 Ph.D. in Chemical Engineering, University of Texas at Austin
1976.2 M.S. in Chemical Engineering, Korea Advanced Institute of Science and Technology
1974.2 B.S. in Chemical Engineering, Seoul National University
1979. 9~present Professor, School of Chemical Engineering and Materials Science,
Chung Ang University, Seoul, Korea
Research field interested: Nanoparticle formation and aerosol processing216

KONA Powder and Particle Journal No. 32 (2015) 217–226/Doi:10.14356/kona.2015012

Original Research Paper

Modeling of Magnetic-Field-Assisted Fluidization:
Model Development and CFD Simulationof Magnetically Stabilized Fluidized Beds †Antonio Busciglio 1*, Giuseppa Vella 2, Giorgio Micale 2 and Stefano Brandani 3
1

Dipartimento di Chimica Industriale “Toso Montanari”, Alma Mater Studiorum – Università di Bologna, Italy
Università degli Studi di Palermo, DICGIM, Italy
3
School of Engineering, University of Edinburgh, United Kingdom
2

Abstract
Magnetic-field-assisted fluidization is starting to be considered as a viable alternative to standard fluidized beds
for those operations (such as particle separations, filtration, adsorption) in which the solid phase can be made ofmagnetic particles or, alternatively, the fluidizing agent is a ferro-fluid; thus the fluid bed responds to the action ofmagnetic fields, and stabilized fluidization regimes can be generated.
One of the major difficulties to be tackled is the development of a predictive model capable of estimating thestabilized-to-bubbling transition velocity for a given magnetic field or, on the other hand, the magnetic field
intensity required to stabilize the bed to a quiescent condition. The fluid dynamics prediction of a stabilized bed isalso a challenging task at the moment.
On this basis, a very simple model for the description of MSFB was derived in this contribution starting frombasic fluid dynamics and magnetodynamics equations. The model was implemented in a commercial CFD code inorder to simulate the effect of the magnetic field onset on a freely bubbling fluidized bed.
Keywords: gas fluidization, magneto-fluidization, mathematical modeling, CFD, MSFB

1. Introduction
Magnetic-field-assisted fluidization (MFAF) is a technology that, in the last decades, has started to be considered as a possible alternative to standard fluidized beds
for those operations in which magnetic particles or ferro-­
fluids can be employed. The Magnetic Stabilized Fluidized Bed Reactor (MSFBR, or simply MSFB) is a
fluidized bed in which the solid phase is composed ofmagnetic particles or, alternatively, the fluidizing agent isa ferro-fluid; the whole system is surrounded by an arrayof coils that generates a magnetic, usually axial, field.
A large number of applications for MSFBs have been
proposed in the past. The main fields in which the MSFB
has been applied includes particle separations, filtration,
fluidized bed reactors, fluidized bed adsorption, biochemical applications, fluid bed chromatography. In fact, in its
†
1
2
3

*

Received 14 May 2014; Accepted 9 July 2014
J-STAGE Advance published online 30 September 2014
Terracini 34, 40131 Bologna, Italy
Viale delle Scienze Ed.6, 90128 Palermo, Italy
Edinburgh, United Kingdom
Correspopnding author: Antonio Busciglio;
E-mail: antonio.busciglio@unibo.it
TEL: +39-051-2090295©2015 Hosokawa Powder Technology Foundation

simplest form, the magnetic field action eliminates gas
bubbling from a fluidized bed (i.e. magnetically ‘stabilizes’), giving rise to smooth fluidization at higher gas
flow rates than those achievable in the non-stabilized bed.
An MSFB has the primary advantage of combining thelow pressure drop of a fluidized bed with the bubble-free
operation of a fixed bed (Rosensweig, 1978; Saxena andShrivastava, 1991; Webb et al., 1996).
A systematization of the operating modes of MFAF has
been done by Hristov (2002). The fluid flow and the magnetic field can be applied independently, hence two principal magnetization modes are possible: magnetization
FIRST and magnetization LAST.
The term magnetization FIRST was introduced bySiegell (1987). This mode implies the application of themagnetic field on the fixed bed with an isotropic structureand static particle arrangement and the fluidization afterwards (Kirko and Filippov, 1960; Rosensweig, 1979;
Hristov, 2002). In this case inter-particle forces play animportant role. As a result of particle magnetization, thecohesive force of magnetic nature emerges. After the magnetic field application, an increase in flow rate gives rise toa deformation of the initial static bed with simultaneousorientation of particles along the field, leading to the socalled stabilized bed as suggested by Rosensweig (1979).

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The bed appears like a fixed bed with anisotropic particle
arrangement induced by field lines. This state has been
considered as a transitional state between fixed bed andfluidization or as homogeneous fluidization without bubbles. Further increase of the flow rate leads to fluidizationof the particle aggregates and then the formation of bubbles, with the hydrodynamic forces progressively overcoming the magnetic forces until eventually particle aggregatesare destroyed and fully bubbling fluidization occurs.
In magnetization LAST mode, the magnetic field is applied to a fully fluidized bed. The magnetic force causes
the particles to reduce their motion and form aggregates
which are aligned along field lines. At high field intensity,
the bed will ultimately collapse resulting in a fixed anisotropic structure of particle aggregates. As in the previous
case, the regime of homogeneous fluidization of strings isobserved.
Experimental investigation in magnetization LAST is
scarce compared to magnetization FIRST. Hristov andIvanova (1999) performed different experimental tests,
obtaining similar regimes to those obtained in magnetization FIRST mode. In magnetization LAST mode, a similar
sequence of fluidization regimes is observed, increasing
the magnetic field intensity for a given value of the fluid
velocity.
However, even if the technique of MSFBs is almost 40
years old, it has not been applied yet on a large scale. Oneof the main problems is the choice of a suitable magnetic
system. Another one is the ability of predicting with acceptable accuracy the behavior of the full-scale equipment.
The recent development of mathematical modeling ofparticulate solids behavior, together with the increased
computing power, enables researchers to simulate the behavior of fluidized powders (with and without appliedmagnetic fields) and to link fundamental particle properties directly to the powder behavior. In this regard, computational fluid dynamics (CFD) modeling provides afundamental tool to support engineering design and research in multiphase systems. In general, many authors
recognize that computational modeling in multiphase systems has the potential to increase process efficiency andreduce the number of scale-up steps in the design of reliable commercial plants.
Among the many aspects of research in this field, there
is a substantial interest in trying to develop a model capable of estimating the regime transitions occurring in the
MSFB, as well as to correctly simulate the fluid dynamics
of the bed itself.

2. Literature review
Mathematical modeling of fluidized beds can be regarded as a multiphase flow problem. In a fluid bed, solid

218

particles are suspended in a fluid, the former having a discrete nature while the latter is considered a continuum.
Thus, fluidization is described as a dispersed particulate
two-phase flow. The great variety of different flow regimes
in fluidization (Kunii and Levenspiel, 1991; Geldart, 1973)
outlines the complexity of multiphase flow. This in turnimplies a major difficulty for the general formulation of
governing equations. Complete descriptions of Eulerian-­
Eulerian models derivation and additional information canbe found in Ishii (1975), Anderson and Jackson (1967) andJackson (2001). The Eulerian-Eulerian modeling approach
can be extended to the mathematical modeling of magneto-­
fluidized beds. This implies the inclusion of additional
magnetic body forces in the momentum balance equations,which have to be coupled with Maxwell’s magneto-static
equations. This can be done within the framework of existing models. However, two very important aspects haveto be noted: the Eulerian-Eulerian approach considers thesolid phase as a continuum fluid, hence magnetic inter-­particle force and magnetic torque cannot be directly considered, hence these models cannot predict typicalphenomena of MSFBs such as the formation of strings andaggregates at high field intensities. However, the relevant
macroscopic effects can in principle be accounted for, ultimately allowing the simulation of large-scale units.
Conversely, in order to simulate these phenomena,
Eulerian-Lagrangian approaches would be required, able
to directly account for the strong inter-particle forces,
intense local polarization and complex structures deriving from intense magnetic field action (Rosensweig andCiprios, 1991).
The first rigorous mathematical description of fluidization in the presence of a magnetic field was proposed by
Rosensweig (1985). The model developed by Rosensweig
is based on that proposed by Anderson and Jackson(1967). The latter follows a semi-fundamental approachbased on the definitions of space average quantities on
a volume which respects the scale separation condition.
Rosensweig extends the analysis of Anderson and Jacksonby also applying the averaging procedure to the magneto-­
static equations. Another model for the description ofmagneto-fluidized beds was developed by Brandani andAstarita (1996). The model is based on Foscolo andGibilaro’s Particle Bed Model (Foscolo and Gibilaro,1987), which has proved to give qualitative and quantitative agreement with experimental results. In this newmodel, magnetic forces acting on particles are included in
the one-dimensional equations of change of Foscolo andGibilaro.
Within the field of magneto-stabilized fluidized beds
(MSFB), very few contributions in the pertinent literature
dealt with the CFD simulation of this class of equipment:
Li et al. (2010) simulated the flow behavior of gas and solids
in a two-dimensional magnetically assisted bubbling flu-

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idized bed with magnetic balls under a vertical-gradientmagnetic field. In this case, the motion of the gas was
simulated by computational fluid dynamics (CFD), while
the particles were simulated using the discrete element
method (DEM), thus allowing for the simulation of particle chain formation; thanks to DEM modeling it is also
possible to assess the decrease of particle diffusion coefficient with the increase of magnetic field intensity.
The classic multi-fluid modeling approach was also
used in the relevant literature: even if some phenomena
can only be partially simulated (at most, their macroscopic effect), the relative computational easiness allows
the simulation of large-scale equipment. Wang et al.
(2002) carried out a numerical simulation (based on Two
Fluid Model and Kinetic Theory of Granular Flow) andrelevant experimental validation of a cylindrical magneto-­
fluidized bed. The numerical results indicated that below
nil or a weak magnetic field, the typical bubbling fluidization was obtained, while under a moderate magnetic field,
the particles display stable fluidization by restraining
bubble formation. The numerical results were found to bein agreement with the experimental data, reflecting theability of CFD codes to tackle the simulation problem. Xu
and Guan (2003) simulated an Air-Dense Medium Fluidized Bed in which a magnetic medium was used in orderto further stabilize the fluidized bed and increase theequipment efficiency.
In the present work, a first attempt to develop and implement a simple mathematical model in a commercial CFD
code has been made in order to perform 2-dimensional
simulations of a magnetically stabilized fluidized bed.
The commercial CFD code CFX 4.4 was adopted, whichallows substantial flexibility in including additional terms
in the momentum balance equation.

3. Mathematical model
In this section, the mathematical model to be used for
the CFD simulation of magneto-fluidized beds is presented. This model is an extension of the fluidization
model proposed by Brandani and Zhang (2006).
In general, the basic equations describing the momentum balance in a fluidized bed are usually in a symmetricform and always include a term for drag force, for gravity
force and for the pressure gradient. However, this formulation is unable to predict the existence (experimentallyobserved) of the regime of homogeneous fluidization, so
the model has to be suitably extended (by means of additional terms) in order to be able to predict the transition
between homogeneous and bubbling fluidization.
In general, three types of additional forces can be
added: fluid-particle interactions (other than the normaldrag force), particle-particle interactions, and forces re-

sulting from the averaging procedure to obtain continuum
formulation. In any case, these additional terms lead to anelasticity of the bed or particle pressure which allows theexistence of stable, smooth fluidization.
In the Particle Bed Model by Foscolo and Gibilaro(1987), subsequently revised in Gibilaro (2001), a fluid-­
particle force term is added to the solid momentum balance. However, in accordance to Newton’s third law, aforce arising from fluid-particle interactions should cancelout over summation and not be present in the overall
momentum balance. In the model by Brandani and Zhang(2006), the discretized momentum balance equations areformulated in such a way that if a term is added to theparticle-phase equation, a similar term appears also in thefluid-phase equation.
The basic relationships proposed by the authors are assumed as the basis for the present work. In this contribution, the momentum balance equations are extended herewith the addition of a magnetic force term:u  u p up p  FD  E mp +E p 
z 
z
 t

 p P    p g 

z 

uf
u

 uf f  FD  Emf  Ef 
z 
z
 t

P

   f g 

z 


(1)

 f 

(2)

Ep  g 1  2   f  2 p

(3)

Ef  g 1  2   p  2 f (4)

Notably, the momentum balance equations include theusual drag force term (FD), and two additional elastic
terms (dependent on the volume fraction gradient) appear
for each phase: the fluid dynamic elasticity (Ep, Ef ) as derived by Brandani and Zhang (2006), and the magnetic
force terms (Emp, Emf ). The factor δ that appears in thelast two equations is a characteristic length of the order ofthe particle’s diameter which arises from the formulationof a discretized balance. These equations are obtained
without any simplifying assumption except for neglecting
higher-order terms when shifting from the discrete form
to the differential form (Brandani and Zhang, 2006).
A more complex problem is that of calculation of theadditional body forces due to the magnetic field. The basic relations linking the magnetic field, H, the induction
field, B0, and the magnetization, M, are:M   M f   M p   f H f   p H p(5)


H  Hf Hp

(6)

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M  H(7)
B0  0 M  H (8)the speed at which voidage perturbations run through anelastic or compressible medium, resembling, e.g. sonicwaves in a gas. This fundamental result was applied by
Brandani and Zhang (2006) to the limiting case withoutmagnetic forces and results in:

Also, Maxwell’s equations hold under the assumptionthat anywhere within the flow, a local region exists thatis large compared to the size of any particle but small
4.8U
 U 
compared to the distance over which the field wouldu k 
C D Red Equilvary significantly:2
dRe C D
(9)
 B 0and the dynamic wave velocity:
Following the procedure employed by Rosensweig andCiprios (1991), and by Brandani and Astarita (1996), theuD V 2  G  Vfollowing general constitutive equations for the magnetic
force can be derived:
where, under the quasi-equilibrium approximation:
E mp  Ψ mp

Ψ mp

B02

 2 p 0

V

(11)

 fU 2   f   p  g
2G  p   f2

 
    
 1

 
 1      1     

E mf  Ψ mf

B022
  f 0

(12)

2

 
    
(13)
 1

 
 1      1     
These equations are perfectly symmetrical, but are
valid only for the two limiting cases of χf = 0 (the solid ismagnetizable, Eqns. 10–11) or χp = 0 (the fluid is magnetizable, Eqns. 12–13). The corresponding expression forthe magnetic forces is obtained by combining these equations with a constitutive relation for the bed susceptibility.
It can be shown that, using the Clausius-Mossotti relationship (Banhegyi, 1986), the equations by Rosensweig
and Ciprios (1991) are obtained:
Ψ mf

Emp 

Emf 

18 χ p2

B02

3   p  2 p3  0
2

2εχ f2 2 f  3
B02
2
3
1   f  3  2 f   0

(14)

(15)

4. Stability criterion
The kinematic and dynamic wave velocities can be derived following the linearization procedure of Wallis(1969) and Gibilaro (2001). Wallis’ theory states that
voidage perturbation velocities in the bed are bounded by
the kinematic wave velocity and the dynamic wave velocity. The kinematic wave velocity is obtained when thesteady state flow depends on the voidage, and in a fluidized bed it is only a function of the drag equation (Wallis,
1969). On the other hand, the dynamic wave velocity is

220


 fU
  p   f

(10)

(16)

(17)

(18)

(19)

The superficial velocity U is calculated from the bed
equilibrium relationship and the drag force is expressed
as Gibilaro (2001), with the Dallavalle expression for CD:


FD

3 f
4.8  2
CDU 2 3.8 ;
CD 0.63  (20a, b)
4 dp

Re 

The criterion for minimum bubbling can be expressedin terms of a kinematic and a dynamic wave velocity as
Wallis (1969): the bed is stable when the kinematic velocity is smaller than the dynamic wave velocity. In the presence of additional magnetic forces acting on the particles,
the kinematic velocity remains unchanged, while only the
G term is modified to the following expression that has tobe adopted in Eqn.17

GM= G −

18φχ p2(3 + χ p + 2φχ p)

3

B02
ε
μ0 ερp + φρf(21)

For the investigated system, the prediction results arereported in Fig. 1 in terms of wave velocities (kinematicand dynamic) as a function of the fluid-phase volume
fraction and magnetic field intensity.
When the magnetic field intensity is set to B0 = 0.088 Tor below, the minimum bubbling voidage εmb is below the
voidage at minimum fluidization conditions ε mf = 0.4(typical of random particle arrangement): this is a characteristic of all Geldart Type B particles (always bubblingwhen fluidized).
Above B0 = 0.088 T εmf > εmb, the particles behave as
Geldart Type A powders, e.g. conditions exist in which homogeneous fluidization is feasible before bubbling occurs.
For B 0 = 0.1 T the minimum bubbling point is
εmb = 0.423 and Umb = 0.084 m/s, for B0 = 0.15 T the mini-Antonio Busciglio et al. / KONA Powder and Particle Journal No. 32 (2015) 217–226

is adopted for estimating the rheological properties ofthe fluidized solid phase (Gidaspow, 1994) and standard
drag models are adopted to estimate the momentum exchange between phases at the phase boundaries (as done by
Brandani and Zhang, 2006). Complete details on the model
equation implemented within the code can be found in the
CFX documentation.
The additional forces due to the bed hydrodynamic elasticity (Eqns. 3–4: Brandani and Zhang, 2006; Busciglio etal. 2010) and the magnetic forces (Eqns. 14–15) were expressed as follows:
Fig. 1 Kinematic (continuous line) and dynamic wave velocities(dashed lines) as a function of the magnetic induction.


Ff , y ; Fp, y 
 Ef
 Ep
y
y



Fmp, x E
; Fmp, y Emp

mp
x
y
mum bubbling point is εmb = 0.531 and Umb = 0.21 m/s.
Notably, above B0 = 0.188 T, the system is always stable
since ud>uk at all flow rates.
Fmf , x E; Fmf , y Emf

mf
x
y

5. CFD model
A set of test simulations were carried out to assess
qualitatively the effect of the magnetic field on the fluidization regime. The aim is to verify the ability of the codein simulating whether the magnetic field is capable of
suppressing the formation of bubbles in a bed that would
normally be operating in the bubbling regime. In particular, a fluidized bed stabilized in LAST mode (i.e. magnetic
field is applied to a freely bubbling fluidized bed) was
simulated by CFD.
In the Eulerian-Eulerian approach to multiphase modeling, continuity and momentum balance equations in vector
forms have to be solved for each phase (Eqns. 22–25):

 f    f uf   0
t

(22)

 p
    pup   0
t

(23)

 pupT     pup  up   up  up  


t







  B  Pp  FD  Fp

(24)

 f ufT     f uf  uf   uf  uf  
t







  B  Pf   FD  Ff

(25)

Of course, closure relations are also needed in order toproperly model the particle phase and its interactions withthe gas phase: for this purpose, the standard GKT model(26a, b)(27a, b)(28a, b)

The additional hydrodynamic forces, Ff,y and Fp,y,operate in vertical direction. Conversely, the magnetic
body forces possess components in both the x and y directions. Maxwell’s model equation has been used to obtain
the additional terms reported in Eqns. 27–28. These are
implemented in the code, since all the parameters except
the void fraction gradients are constants.
As far as the numerical aspects are concerned, CFD
simulations were performed in a 2D fashion, choosing atime step interval in the range between Δt = 10 –5 s andΔt = 10 –4 s and a computational grid consisting of 0.005 m
square cells, with 200 cells along the vertical direction
and 30 cells along the horizontal direction, thus resulting
in a vertical extension of the domain equal to 1.0 m and ahorizontal extension equal to 0.15 m. Grid independence
was assessed, checking to make sure that cells only half
the size (0.0025 m square cells) of those actually adopted
did not change the simulation results.
The initial condition for the particle bed height is equal
to 0.5 m, with the solid volume fraction within the bed set
equal to 0.60. The lateral walls were modeled using thestandard no-slip boundary condition. The upper sectionof the simulated geometry, or freeboard, was considered
to be occupied only by gas. A simple pressure boundary
condition was imposed at the top of the freeboard (i.e.
fully developed flow condition). A Dirichlet boundary
condition was employed at the bottom of the bed to specify uniform vertical gas inlet velocity throughout the distributor. Symmetry planes were imposed on the front andrear faces of the simulated bed in order to perform thesimulation in a proper 2D fashion. Symmetry planes
placed at the boundaries along the width direction of thecomputational domain causes all variables to be mathematically symmetric with thus no diffusion across the221Antonio Busciglio et al. / KONA Powder and Particle Journal No. 32 (2015) 217–226

boundary except for the component of velocity normal tothe boundary which is anti-symmetric.
A typical Geldart type B system fluidized by air inmagnetization LAST mode was simulated (dp = 300 μm,
ρp = 2500 kg/m3, χp = 10). The fluid velocity was equal to
2.5 Umf, i.e. 0.22 m/s for the investigated system. The
magnetic force is expected to cause particles to reduce
their motion and form aggregates which are aligned along
field lines. At high field intensity, the bed should eventually collapse, resulting in a fixed, anisotropic structure ofparticle aggregates.
A magnetic field, B, in the range between 0.05–0.6 T,
was applied to a fully fluidized bed in order to stabilize
the bed.
Each simulation was subdivided into the following
three stages:• Starting from the initial steady condition of a settled
bed, the gas inlet was simulated for 4s at Δt = 10 –4 s.
During this time interval, the bed evolves from theinitial condition to freely bubbling in pseudo steadystate condition. Because of the inlet gas velocities
adopted, a freely bubbling regime generally develops.
• The freely bubbling condition at 4s is used as theinitial condition for a second simulation stage, inwhich the magnetic field is present. Because of thevery rapid transient dynamics expected, a reduced
time step was adopted, i.e. Δt = 10 –5 s. In general, after 0.5 s subsequent to magnetic field application, thefinal steady state is practically achieved for the MSFB.
• Finally, the simulation was carried out for a further 1s
at a time step value of Δt = 10 –4 s: this allows simulation of the stabilized regime in a reasonable time.
Typical CPU running times for this reference case wereequal to about 4h for 1s of real time simulated with a fixed
time step interval Δt = 10 –4 s on a Dell Dimension 8300
Personal Computer.

6. Data analysis
Data obtained from the simulations were post-processedin order to quantify the effect of the superimposed magnetic field. The following instantaneous parameters werechosen:• the instantaneous polydispersity index of volume fraction distribution within the bed, PI, was chosen tocharacterize the uniformity of bed voidage. This parameter is defined as:10  ( x, y, t )j
 y H  ( x, y, t)
i

PI 21  1 ; ij (t )  y  H 0(29a, b)0where the general moment ratio ϕij(t) is defined on thebasis of the solid volume fraction values of the com-222

putational cells placed below the initial bed height
H0(t = 0). Notably, it was decided to analyze all thecomputational cells below the initial bed height toavoid measurement of the properties of the freeboard
region. The larger the polydispersity index PI(t), theless uniform is the instantaneous volume fraction distribution inside the bed.
• the instantaneous mean value of bed height, H0, waschosen to characterize bed expansion below the initial
bed height. The freeboard extent is computed as theaverage height if the upper domain region has a solid
volume fraction below 0.15 (this is equivalent to theconventional transition value between the emulsion
phase and bubble phase in fluidized beds).

7. Results
The first qualitative evaluation of the simulation ability
to capture the typical behavior of a MSFB can be performed easily by observation of the particle volume fraction map sequences reported in Fig. 2. The maps report
data about the glass ballotini simulation, dp = 300 µm;
fluidized at U = 2.5 Umf with a magnetic field intensity
ranging from B = 0.05 T to B = 0.6 T.
Taking as an example the sequence simulated at
B = 0.2 T (Fig. 2b), which is immediately above the minimum stabilization field, it is possible to observe that thesequence embraces all stages from the initial condition to
fully developed bubbling regime conditions (from 0.0 to
4.0 s), and the subsequent transitory due to application of
the magnetic field (i.e. from 4.0 to 5.5 s) until steady state
conditions are achieved.
It is worth noting that the code is able to simulate bubble formation correctly, at least in a qualitative way, andgrowth along the bed appears to be correctly predicted.
Notably, after application of the magnetic field, the fluid
dynamic regime suddenly changes toward a stabilizedstate. Under these conditions, all bubbles disappear in allcases simulated.
As it can be observed in Fig. 2a, when the magnetic
field is set at values lower than 0.2 T, the stabilizing effectis able to partially damp out bubbles within the bed, but
local in homogeneities are still evident. In this case, theapplied field intensity was not sufficient to achieve a fullystabilized state. In all likelihood, the transition from partial to full bed stabilization can be found at magnetic fieldvalues close to B = 0.2 T as discussed earlier. In the caseof higher field values (B = 0.4 and 0.6 T shown in Fig.
2d–e), the bed is fully stabilized but a longitudinal
voidage gradient appears parallel to the field direction.
Notably, this increases the bed volume occupied by thesolid phase and the magnetic field also increases.
This effect can be theoretically predicted taking into ac-Antonio Busciglio et al. / KONA Powder and Particle Journal No. 32 (2015) 217–226

Fig. 3

Profiles of void fraction in the proximity of the freeboard region as computed by Eqn. 30 at different magnetic field intensities.

count the one-dimensional momentum balances (Eqns.
1–2). At equilibrium, if there are no gradients in the void
fraction of the bed, the bed weight must equate the dragforce, hence it is possible to calculate the void fraction of
the homogeneous bed, which for U = 0.22 m/s yields
ε = 0.537. Most of the bed will be at this condition, except
in the vicinity of the freeboard where a gradient in the void
fraction is inevitable. At equilibrium, we can assume thatup = 0, uf = U/ε and all time gradients are nil. If the fluid
and particle momentum balance are combined to eliminate
pressure, the following differential equation is obtained:FD   (1   )( p   f )d f 1   U 2dz
  E  E  (1   )E

2Fig. 2

Snapshot sequences of the simulated freely bubbling
fluidized bed (0–4 s) and subsequent onset of magnetic-­­field-assisted fluidization (4–5.5 s). dp = 300 µm;
U = 2.5 Umf; from top to bottom: a) B = 0.05 T; b)
B = 0.1 T; c) B = 0.2 T; d) B = 0.4 T; e) B = 0.6 T

 MP

P



F

(30)

Eqn. 30 clearly shows that there will be a region
between ε 0 = 0.537 and ε1 = 1—where the RHS of theequation is zero—where there will be a transition. Eqn.
30 can be used to estimate the stratification at the freeboard as a function of the magnetic field as shown in the
figure below (Fig. 3)
Clearly, further computational and experimental investigations are needed to investigate this behavior in detail,but the full simulations are clearly in qualitative agreement with the predicted effect. In order to quantify andcompare the simulation results after the change in the applied magnetic field, in Fig. 4a the time evolution of thepolydispersity index is reported for all the simulations
shown in Fig. 2.
Before the onset of the magnetic field (t = 0–4 s), it ispossible to observe high PI values with a markedly oscillating behavior due to the bubbles rising through the bed.
After application of the magnetic field, a sudden decreaseof the polydispersity index underlines the stabilization of
the system for all cases investigated.
Notably, the effect of the applied magnetic field is practically instantaneous. Moreover, the quantitative analysis
of PI shows the stabilizing effect that the magnetic field

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Fig. 4

Time evolution of the polydispersity index at different
magnetic field intensities (a) and relevant time-averaged
values (b). dp = 300 µm; U = 2.5 Umf.

has on the bubbling bed: in the case of B < 0.2 T, the PI
value decreases but still oscillates after the magnetic field
onset, while higher magnetic field intensities give rise to asmooth and stable PI value. This is further confirmed by
Fig. 4b, in which time-averaged PI values are reported asa function of the superimposed magnetic field intensity. It
is possible to observe that at B values below 0.2 T, highpolydispersity values are found because of incomplete
bubble suppression, while at values higher than B = 0.4 T,
even if complete bubble suppression occurred, the PI
value increases slightly because of bed stratification.
Hence, these simulations confirm the ability of themodel to predict the existence of a critical B value toachieve a fully stabilized bed.
Clearly, the onset of a diffused freeboard (qualitatively
observed in Fig. 2 and further discussed in Fig. 3) can bequantified by showing the bed height H0 as a function oftime (as reported in Fig. 5a) and relevant time-averaged
values (reported in Fig. 5b). Notably, the increase in the
average bed height becomes important at a high magnetic
field intensity (especially above B = 0.4 T).
Concluding, it is possible to point out that even if some
limits are still present, because of intrinsic limitations of224

Fig. 5Time evolution of average bed height at different magnetic field intensities (a) and relevant time-averaged
values (b). dp = 300 µm; U = 2.5 Umf.

the multifluid approach (Eulerian-Eulerian) and magnetic
model simplification, the CFD model adopted is able tocapture the main characteristics of the MSFB with reasonable accuracy and within acceptable simulation times
from an engineering point of view.

8. Conclusions and future aims
An Eulerian-Eulerian CFD model to simulate the behavior of the MSFB was successfully developed. The
CFD model correctly predicts the onset of different stabilization levels depending on the intensity of the magnetic
field applied and provides further insights on the behaviorof MSFBs.
Even if work still has to be done with regard to the implementation of more comprehensive mathematical models (including inter-particle forces as an example), thisresult may be regarded as very promising, being among
the first CFD simulations of MSFBs with Eulerian-­
Eulerian models.

Antonio Busciglio et al. / KONA Powder and Particle Journal No. 32 (2015) 217–226

NotationBbody forces per unit volume [kg m –2 s –2];

B

magnetic field strength [A m –1];

dp

particle diameter [m];FDdrag force per unit volume, [kg m –2 s –2];

Ff

fluid-dynamic forces (fluid phase) [kg m –2 s –2];

Fp

fluid-dynamic forces (solid phase) [kg m –2 s –2];

Fmf

magnetic forces (fluid phase) [kg m –2 s –2];

Fmp magnetic forces (solid phase) [kg m–2 s –2];gacceleration due to gravity [m s –2];

H

magnetization [A m –1];

H0

bed height [m];PIpolydispersity index [–];

Th

threshold value for bed height calculation [–];uphase velocity [m s –1];

U

superficial gas velocity [m s –1];

Umf minimum fluidization velocity [m s –1];µ0vacuum magnetic permeability [A m –2];

δaveraging length in momentum balance [m];εfluid-phase volume fraction [–];

ηviscosity [Pa s];ρdensity [kg m –3];

ϕparticle-phase volume fraction [–];

χmagnetic susceptibility [–];

References
Anderson T., Jackson R., A fluid mechanical description of fluidized beds: Equation of motion, Ind. Eng. Chem. Fundamentals, 6 (1967) 527–539.
Banhegyi G., Comparison of electrical mixture rules for composites, Colloid & Polymer Sci., 264 (1986) 1030–1050.
Brandani S., Astarita G., Analysis of the discontinuities in magnetized bubbling fluidized beds, Chem. Eng. Sci., 51(20)(1996) 4631–4637.
Brandani S., Zhang K., A new model for the prediction of the
behaviour of fluidized beds. Powder Technol., 163(1–2)(2006) 80–87.
Busciglio A., Micale G., Vella G., Rizzuti L., Linear stability
analysis of gas-fluidized beds for the prediction of incipient
bubbling conditions, Chem. Eng. J., 157 (2010) 489–500.
Foscolo P., Gibilaro L., Fluid dynamic stability of fluidised suspensions: the particle bed model, Chem. Eng. Sci., 42 (1987)
1489–1500.
Geldart D., Gas Fluidization Technology, John Wiley & sons,
1973.
Gibilaro L., Fluidization Dynamics, Butterworth-Einemann,

Oxford, UK, 2001.
Gidaspow D., Multiphase flow and fluidization, Academic
Press, San Diego, 1994.
Hristov J., Magnetic field assisted fluidization—a unified
approach. Part 1. Fundamentals and relevant hydro-dynamics
of gas fluidized beds (batch solids mode), Reviews in
Chemical Engineering, 18(4–5) (2002) 295–509.
Hristov J., Ivanova V., Magnetic field-assisted bioreactors, in:Kawai F., Sasaki K. (Eds.), Recent Research Advances inFermentation and Bioengineering, vol. 2, SignPost
Research, Trivadrum, 1999, pp. 41–94.
Ishii M., Thermofluid dynamic theory of multiphase flow,
Eyrolles, Paris, France, 1975.
Jackson R., The dynamic of fluidised particles, Cambridge
University Press, New York, USA, 2001.
Kirko I., Filippov M., Standard correlations for a fluidized bedof ferromagnetic particles in a magnetic field, Zn Tekhniko
Fizika, 30 (1960) 1081–1090.
Kunii D., Levenspiel O. (Eds.), Fluidization Engineering,
Butterworth-Heinemann, Massachusetts, USA, 1991.
Li X., Sun D., Chen J., Wang S., Bai Y., Lu H., Numerical simulation of magnetic particles flow behaviour in a gas-solid
fluidized bed with gradient magnetic field, Journal ofChemical Engineering of Chinese Universities, 24(1) (2010)
52–57.
Rosensweig R., Process for operating a magnetically stabilized
fluidized bed, U.S. patent, number 4115927, 1978.
Rosensweig R., Magnetic stabilization of the state of uniform
fluidization, Ind. Eng. Chem. Fundam., 18(3) (1979) 260–
269.
Rosensweig, R., Ferrohydrodynamics, Cambridge University
Press, USA, 1985.
Rosensweig R., Ciprios G., Magnetic liquid stabilization of fluidization in a bed of non-magnetic particles, Powder Technol., 64(1–2) (1991) 115–123.
Saxena S., Shrivastava S., Some hydrodynamic investigationsof a magnetically stabilized air-fluidized bed of ferromagnetic particles, Powder Technol., 64(1–2) (1991) 57–67.
Siegell J., Liquid-fluidized magnetically stabilized beds, Powder
Technol., 52(82) (1987) 139–148.
Wallis G.G., One-Dimensional Two-Phase Flow, McGraw-Hill,
New York, 1969.
Wang Y.H., Gui K., Liu L., Shi M., Numerical simulation ofgas-solid flows in MFBs and experimental demonstration,
Journal of Southeast University (Natural Science Edition),32(6) (2002) 936–940.
Webb C., Kang H., Moffat, G., Williams, R., Estevez A., Cuellar
J., Jaraiz E., Galan M.A., The magnetically stabilized fluidized bed bioreactor: A tool for improved mass transfer in
immobilized enzyme systems, Chem. Eng. J., 61(3) (1996)
241–246.
Xu S., Guan Y., Numerical simulation and experimental validation of magnetic medium performance in air-dense medium
fluidized bed. Int. J. of Coal Preparation and Utilization,23(1–2) (2003) 57–65.

225

Antonio Busciglio et al. / KONA Powder and Particle Journal No. 32 (2015) 217–226

Author’s short biography
Antonio Busciglio
Antonio Busciglio was born in 1982. He took the PhD in “Chemical and Materials
Engineering” at Palermo University in 2011, where he continued his activity as a Research
Assistant until 2014. Since September 2014, he is Assistant Professor at the University of
Bologna, Department of Industrial Chemistry. His scientific activity focuses on experimental and computational analysis of multiphase flows (fluidized beds, unbaffled stirredtanks and sparged gas-liquid stirred tanks). He developed several experimental techniques based on image analysis for the characterization of multiphase flows. As a result,he authored of about 50 papers in international journals or conference proceedings.

Giuseppa Vella
Giuseppa Vella graduated in chemical engineering in 2001 and received her doctorate
with a PhD thesis on the dynamics of fluidized beds. Her scientific activity focuses on thefollowing research topics: CFD modelling of single and multiphase flows in stirred vessels, modelling and experimental investigation of photo-catalytic soli-liquid reactors forpollutant abatement, experimental analysis and CFD modelling of fluidized beds, fluid
analysis of equipment for the production of thermal energy by pressure-retarded osmosis.
As a result of this activity, she authored 36 papers in international journals or in conference proceedings.

Giorgio Micale
Giorgio Micale joined University College London as a lecturer in chemical engineering in 2000, promoted to senior lecturer in 2002. From 2005, he has been associate professor of chemical engineering at Università di Palermo. His current research interests
are: 1) mixing and multiphase flows in stirred tanks, 2) fluidization, 3) conventional andunconventional desalination processes coupled with renewable energy, 4) salinity gradient energy production. Contributions were made to developing novel experimental techniques and original simulations based on CFD methods. In 2001, he was awarded the“Young Researcher’s Award” by the Working Party on Mixing of EFCE and in 2014, the“Senior Moulton Medal” by the IChemE.

Stefano Brandani
Stefano Brandani holds the chair of the chemical engineering department at the University of Edinburgh. He is a member of the Directorate of the Scottish Carbon Captureand Storage Centre and is the UKCCS research area champion for adsorption and membranes. He joined the University of Edinburgh in 2007 where he leads the carbon capturegroup in the school of engineering. His research is aimed at the development of novel
materials and processes for next-generation carbon capture applications. In 2001 he received a Philip Leverhulme Prize and a Royal Society-Wolfson Research Merit Award on
Reducing Greenhouse Gas Emissions. In 2013 he was conferred the title of Cavalieredell’Ordine della Stella d’Italia from the President of Italy. He has refereed over 100 publications in the fields of adsorption, fluidization and phase equilibria.

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KONA Powder and Particle Journal No. 32 (2015) 227–235/Doi:10.14356/kona.2015014

Original Research Paper

A Spheroid Model for the Role of Shapein Particle Size Analysis †Richard Hogg
1

Department of Energy and Mineral Engineering, The Pennsylvania State University, USA

Abstract
Standard procedure in particle size analysis is to express size as an equivalent sphere diameter. As a consequence,
results obtained by procedures based on different kinds of response give different results. An alternative approachis presented based on a spheroidal shape which, depending on elongation can represent shapes ranging from rods,
through spheres to discs. Particle size and shape are then presented as volume diameter and elongation.
Applications to commonly used procedures are presented. For particles of similar shape, i.e., elongation
independent of size, the results of procedures such as sieving, microscopy and sedimentation show a simple shift
in apparent size with the form of the distribution unaffected. In the case of methods based on radiation scattering,orientation effects lead to an apparent size distribution even for identical particles. Experimental results on glass
spheres, crushed quartz particles and fine kaolin are shown to be consistent with the spheroid model. Analysis of
the quartz data by a variety of techniques gave consistent values for the volume diameter and elongation. Light
scattering measurements on a series of narrow sieve fractions of the quartz particles showed no variation inelongation with size.
Keywords: particle size and shape, spheroidal shape, volume diameter, elongation, sieving, image analysis,
sedimentation, light scattering, optical and electrical counters

1. Introduction
It is well known that particle size measurements are affected by particle shape. The results of such measurements are generally expressed in terms of an “equivalent
sphere diameter” defined as the diameter of a sphere thatwould have the same response to the measurement procedure as the actual particle. In other words the procedure
involves the assumption that the particles are spherical inshape. In practice, of course, real particles are rarely truly
spherical and often depart significantly from that idealized shape. Departures can include relatively small-scale
irregularities and macroscopic differences in axial dimensions—length, breadth and thickness. The effects of surface irregularities presumably have some effect on size
measurements, but such effects are likely to be small andvery difficult to quantify. Axial ratios, on the other hand,for shapes varying from needles to platelets can be expected to have major effects.

†
1

Received 29 July 2014; Accepted 30 September 2014
J-STAGE online 28 February 2015
232 Hosler Building, University Park, PA 16802, USA
E-mail: rxh19@psu.edu
TEL: +1-814-865-3802 FAX: +1-814-865-3248©2015 Hosokawa Powder Technology Foundation

An important consequence of the effects of shape isthat different measurement procedures typically yield different results, sometimes significantly. For routine applications such as material characterization and productspecification, it is commonly sufficient to avoid the problem by the consistent use of the same analytical procedure. However, if shape is important to an application or
if two or more procedures are required for complete analysis of a broad size distribution, the shape effect should
be taken into account.
While the discrepancies between size distributions obtained using different methods can lead to problems inspecification and the comparison of results, these differences also contain information, albeit indirect, on shapeitself. The differences in apparent mean size between two
methods can be used as a measure of departure from theidealized spherical shape. Comparison of the complete
distributions from several methods may provide further
information on the kinds of shapes involved.
The analysis presented in this paper is based on the useof the spheroidal shape as an alternative to the simple
sphere to evaluate the role of shape in some widely-usedprocedures for particle size analysis and to investigate thepotential for using differences in apparent size distributions to extract shape information.

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Richard Hogg / KONA Powder and Particle Journal No. 32 (2015) 227–235

2. Spheroid GeometryAlso, from Eqn. 1

A spheroid is simply a sphere that has been stretched orcompressed along one axis and is represented by the rotation of an ellipse about that axis. The particle profile is a
circle of radius R along that axis and an ellipse of radius B
perpendicular to the axis as illustrated in Fig. 1. The elliptical profile is described by.
x2
y2


1
R2 B2

(1)

Defining the elongation E as the ratio B/R the shape is thatof an oblate (flattened) spheroid for E < 1, a prolate (elongated) spheroid for E > 1 and a sphere for E = 1.
Particle volume is a unique measure of size, independent of shape or orientation and is commonly represented
by the volume diameter dv, defined as the diameter of asphere with the same volume V as the particle. For a spheroidal particle
4
4 3

V 
πR 2 B
πR E
3
3

(2)

1/3

6V 

d v 
2RE 1/3

  

(3)

Surface area As can be represented by the surface area
diameter ds, defined as the surface area of a sphere withthe same surface area as the particle.
Referring to Fig. 1, the area ds of a surface element at
x is given by

dy
1 

ds 2πy  2π  1 
 ydy
sin
tan 2 

2πds 
 2  E 2 B 2  x 2 1  E 2  dx
E

Integration of Eqn. 8 leads to the following expression for
the total surface areaS  2πR 2 f (E)

(9)

where
f  E  f 0  1 

1  1  E 2 
E2
ln 

E
1 E 2 (10)for oblate, E < 1 andf  E  f p  1 

 E 2 1 
E2
arcsin E 2 1
 E (11)for prolate, E > 1.
The surface diameter is defined byds  Sπ(12)so thatd s  R 2 f E

(13)

(5)

All size analysis procedures are affected in some way
by particle shape. Effects on some widely used procedures
are evaluated below.

3.1 Sieving

1
xdx
E2

(6)

Sieve size is generally defined as the limiting size at
which a particle can pass through a square aperture withside dimension dA. A prolate spheroid can pass through asieve opening provided its smaller dimension R is lessthan half the opening size, i.e.,d A  2R

Fig. 1228(8)

3. Application to Particle Size Analysis

from whichydy  Substitution from Eqns. 5, 6 and 7 into Eqn. 4 leads to(4)

By differentiation of Eqn. 1dyxtan 
2dx E y(7)

E 2
y B2  x2

Profile of a spheroid.

(14)

In this case, the sieve size is, in principle, independent of
the larger dimension B. In practice, of course, the probability of achieving the appropriate orientation by a random shaking operation becomes increasingly small forhighly elongated particles.
Oblate spheroids (E < 1) whose larger dimension, 2R, issomewhat larger than the sieve aperture can pass if properly oriented. The minimum opening size is determined

Richard Hogg / KONA Powder and Particle Journal No. 32 (2015) 227–235

as shown in Fig. 2.
It can be seen from the geometry of the figure that
X = Y = x + y where x and y are the coordinates of thepoint of tangency of the elliptical particle profile and thesieve opening. Thus,
2  x  y


dA

(15)

The line XY is tangent to the surface of the particle and,for a square opening, has a slope dy/dx of minus one. Differentiating Eqn. 1,
B2 x
x
dy

 2

E 2 
1
dx
R y
y

(16)

i.e.,

y  E 2x(17)

Combining Eqns. 15, 16 and 17,


d A R 2 1  E 2(18)

It is convenient to express the sieve size dA relative to thevolume diameter dv which is given by Eqn. 3 Combination of Eqns. 3 and 14 leads to
dA 

dv
E 1/3

(19)

spheroids sieve size for ellipsoidal particles is, in principal, independent of the longest axis but in practical applications will generally be limited for highly elongatedparticles.
An example of the effect of elongation the sieve/volume
diameter ratio is presented in Fig. 3. It can be seen that
relative to particle volume, sieving gives an overestimateof size for ellipsoids or oblate spheroids and an underestimate for prolate spheroids.

3.2 Microscopy/Image Analysis
Direct measurements of particle dimensions by microscopy are generally performed on particles in a stable configuration on a substrate. Image analysis typicallyprovides a size estimate based on the projected area Aps ofthe image. The corresponding size dps is defined as the diameter of a circle with the same projected area as the particle. Thus,d ps  2

Aps

(21)



For prolate spheroids, the projected area is
Aps π
RB πR 2 E

so thatfor a prolate spheroid (E ≥ 1). Similarly, Eqns. 3 and 18
d ps 2R E E  1give
For oblate spheroids,dv
1  E 2
d A  1/3
(20)E2d  2R
ps

(22)

(23)

(24)

for an oblate spheroid (E ≤ 1).
Expressed relative to the volume diameter using Eqn. 3
Eqn. 20 can also be applied to an ellipsoidal particle
d ps d v E 1/6 ( E  1)
(25)
using the ratio of the intermediate to the smallest of theandthree principal axes in place of E. As in the case of prolated ps

Fig. 2

Limiting sieve size for an oblate spheroid.

Fig. 3dv
E 1/3( E  1)(26)

Effect of elongation on sieve size relative to volume diameter.

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It follows that particle size relative to volume diameterfor spheroids is always overestimated by microscopy forboth oblate and prolate types. As expected, the effect ismost pronounced for flattened (oblate) particles.

3.3 Sedimentation
Particle size estimates by sedimentation are generally
presented in terms of the Stokes diameter dst defined as
the diameter of a sphere that has the same (terminal) freesettling velocity in a fluid (under laminar flow conditions)as the particle. At the terminal velocity, the buoyant
weight of the particle (proportional to particle volume,i.e., dV3) is balanced by the hydrodynamic drag force acting (proportional to the so-called drag diameter, dd). The
Stokes diameter is then defined as

d st 

d v3
dd

(27)

Since, especially under laminar-flow conditions, the fluiddrag results primarily from shear at the particle surface, it
is often assumed (Allen, 1997) that the drag diameter dd
is approximately equal to the surface diameter dS. Thus,d st 

d v3
ds

(28)

Substitution for the volume diameter from Eqn. 3 and forthe surface diameter from Eqns. 10, 11 and 13 leads toaxes of the particle are inclined to the vertical, they possess a horizontal as well as a vertical component of velocity (Happel and Brenner, 1965). The effective Stokes
diameter (based on the vertical component) then depends
on orientation. The values given by Eqn.20 correspond to
the average orientation. The orientation effect is generally
small, with the effective Stokes diameter varying by about±10 % from the average (Happel and Brenner, 1965).
The effect of elongation on sedimentation size relative
to volume and sieve diameters is illustrated in Fig. 4. It
can be seen that sedimentation generally underestimatessize relative to particle volume for both oblate and prolate
spheroids. Except for oblate spheroids with elongation between 0.5 and 1.0, sedimentation methods substantiallyoverestimate size relative to sieve size for prolate and underestimate it for highly flattened (oblate) spheroids.

3.4 Light Scattering
Particle size measurements based on light scattering
estimate size from the projected area of particles in random orientation. For identical non-spherical particles thiscan result in an apparent size distribution due entirely toorientation. The projected area Ap of a spheroidal particle
oriented at an angle θ to its major axis (see Appendix) is
given byA p  πR 2

1/4

 2E 2 
d st  2R 
 f  E 

(29)

Relative to the volume diameter,
1/4

 2E 2/3 d st  d v  f E (30)

For materials with broad size distributions it is common to combine sieving and sedimentation to provide anestimate of the complete distribution. The relationship between sedimentation and sieve sizes can be obtained from
Eqns. 14, 18 and 29. Thus,

1  E 2tan 2 1  E 4tan 2 
1  1  E 4 tan 2  E 4tan 4 

(33)

The corresponding size is the projected area diameter dp
defined as the diameter of a circle with the same area as
the projection of the randomly oriented particle. Thus,
dp  2Apπ(34)

For randomly oriented particles, the probability that agiven particle is oriented at angle θ (0 ≤ θ ≤ π/2) is equal

1/4

d st

2d A  E 2 
1  E 2  2 f 0 E ( E  1)(31)

for oblate spheroids and1/4

 2E 2 d st d A  f p  E 

(E  1)(32)

for prolate.
Under laminar flow conditions, anisotropic particles
such as spheroids settle in random orientation and, if the230

Fig. 4 Effect of elongation on sedimentation size (Stokes diameter, dst) relative to volume, dv and sieve, dA diameters.

Richard Hogg / KONA Powder and Particle Journal No. 32 (2015) 227–235

to 2/π. Thus, for a collection of particles, the number fraction oriented at an angle less than θ isF0  

2
π

0    π 2(35)

For prolate spheroids, the same function expresses thefraction of randomly oriented particles whose projectedarea in a fixed direction is less than the value Ap as defined by Eqn. 33. In the case of oblate spheroids, the function represents the fraction with projected area greaterthan Ap. Thus, for prolate spheroidsFF
F0   E  1
0  Ap 
0 d p (36)

3.4.2 Laser Diffraction/Scattering

Particle sizing procedures such as the Mircrotrac andMalvern systems which are based on diffraction and scattering by assemblies of suspended particles also yield apparent distributions for identical non-spherical particles
due to orientation effects. These systems generally present results as volume distributions F3(dp) The apparent
number distributions described by Eqns.33, 34, 36 and 37
can be transformed into volume fractions using d p3dF3    0/2
 0 d p3d(38)

Instruments based on scattering or obscuration of lightby individual particles passing through a beam generally
present results as a number distribution of projected area
diameter. For randomly oriented identical spheroidal particles an apparent size distribution is generated according
to Eqns. 33, 34, 36 and 37. Examples of such apparent distributions are given in Fig. 5. In each case, the maximum
size is equal to the value of the stable projected area dps
defined by Eqns. 25 and 26.

As before, F3(dp) is equal to F3(θ) for prolate particles and
1–F3(θ) for oblate. Examples of calculated volume distributions obtained by numerical integration of Eqn.38 after
substitution from Eqns.33 and 34 are shown in Fig. 6
Both the number and volume distributions show significant broadening and a general shift to larger apparent
sizes due to the shape effects. In each case the effects are
greatest for highly flattened particles. While the range ofapparent sizes is the same for both number and volume
distributions, the shift to larger apparent sizes is greaterfor the volume case due to the emphasis on the coarser
material. In practice, these apparent distributions will besuperimposed on the actual distribution of sizes.
The effects of elongation on the apparent median size

Fig. 5

Fig. 6

while for oblate,
F0  Ap  F0 d p  
1  F0   E  1(37)3.4.1 Optical Particle Counters

Apparent number size distributions for randomly oriented spheroids. (a) oblate (E < 1), (b) prolate (E > 1).

Apparent volume size distribution for randomly oriented spheroids. (a) oblate, (E < 1), (b) prolate, (E > 1).

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Richard Hogg / KONA Powder and Particle Journal No. 32 (2015) 227–235

for the number and volume distributions relative to volume diameter are compared in Fig. 7. Also included in
the figure are the variations in the apparent median size
relative to the sieve size and the Stokes diameter, calculated using the relationships between volume diameterand sieve size or Stokes diameter as given by Eqns. 19, 20
and 30. It can be seen that in each case the light scattering
procedure overestimates size relative to the other methods
for both oblate and prolate particles. The effects are generally greater for oblate particles with the exception ofsieving for which overestimation is greatest for prolate
particles.

3.4.3 Electrical Sensing Counters

Instruments such as the Coulter counter and Elzone
systems give a direct measurement of particle volume
based on the change in conductivity of an electrolyte due
to displacement by suspended particles. The systems canprovide the number distribution F0(dv) by simple counting
or the volume distribution F3(dv) by accumulation of individual volumes. The relationships between volume diameter as determined by electrical sensing and other size
measures are illustrated in Figs. 3, 4 and 7.

4. Discussion
The analysis presented above provides a simplified
framework for evaluating the effects of shape on particle
size estimates obtained from commonly used experimental procedures. It is interesting to evaluate the analysis
using the results of experimental measurements. Comparisons of particle size data obtained by different procedures are presented in Table 1.
The elongation estimates given in the table were ob-tained from the expected values shown in Figs. 3, 4 and 7.
The results suggest that these quartz particles wereslightly flattened with an elongation of about 0.3. The
quartz data indicate a relatively small deviation from thespherical shape. A more pronounced deviation can be examined using data for kaolin particles presented byDumm (1986). In this study, median sizes of 3.75 μm bylight scattering and 1.12 μm by centrifugal sedimentation
were reported, giving a ratio dp/dst of 3.35, corresponding
from Fig. 7 to an elongation of 0.063.
The volume diameter dv can be estimated fromdv dp
 d p  d st 
 d st  d v 

Estimation using Eqn. 30, gives a value of 0.75 for theratio dst /dv and of dv = 1.50 μm. The average diameter
D = 2R of the kaolin particles can be estimated using Eqn.
3 giving D = 3.77 μm. The particle thickness T is equal toDE giving T = 0.24 μm. These values fall well within theaccepted range for kaolin particles.
Naito et al. (1998) presented a comparison of light scattering and electrical sensing data for silicon carbide whiskers. The relative apparent size was given as 1.41 which,from Fig. 7, corresponds to an elongation of either 0.3 or
10. The latter value is in good agreement with the micrographic data lengths of “several” up to about 20 μm andabout 1 μm diameter reported in the paper.
It was demonstrated in Section 3.3 above that lightscattering measurements on identical, non-spherical particles may result in an apparent size distribution due entirely to random orientation effects. Hogg et al. (2004)

Table 1

Particle size measurements on fine quartz by
various techniques

Methods

Fig. 7232

Variation in apparent median light scattering sizes relative to volume, sieve and Stokes diameters.

(39)

Appt. Size Estimated
Ratio
Elongation

SourceSediment.,
Coulter

0.97

0.44 or 2.3

Dumm,(1986)

Sediment.,
Sieving

0.96

0.4

Cho et al.
(1998)

Light Scat.,
Sediment.

1.52

0.28

Dumm,
(1986)

Light Scat.,
Sieving

1.25

0.28

Austin and Shah,(1983)

Light Scat.,
Sieving

1.27

0.3

Hogg et al.,2004

Light Scat.,
Coulter

1.48

0.26

Dumm,
(1986)

Coulter
Sieving

1.11

0.31

Van Orden et al.
(1979)

Richard Hogg / KONA Powder and Particle Journal No. 32 (2015) 227–235

have presented light scattering results for narrow sieve
fractions of glass beads and crushed quartz. The glass
bead results are shown in Fig. 8. normalized for the various size fractions with respect to the particular lower
sieve size dAL.
It is clear that the size distribution by light scattering issignificantly broader than the expected distribution within
the sieve interval as indicated by the solid line in the figure (based on an assumed uniform distribution in that
range). The broadening of the apparent distribution canprobably be attributed to the somewhat limited resolutionof the scattering procedure (Hogg, 2008).
Sieving errors could lead to the appearance of particles
smaller than the lower sieve size but not the apparently
oversize material seen in the figure. Similarly normalized
results for crushed quartz are presented in Fig. 9. Despite
considerable scatter among the results, it can be seen thatthe relative size shows no systematic variation with theactual (sieve) size, implying that the average particle
shape is largely independent of size; a condition often assumed in the analysis of particle systems (Leschonski,1984). The shift in the light scattering distribution relative
to sieve size as noted previously is presumed to result
from the shape effect. For sizes smaller than the median,the experimental data are in quite close agreement with acalculated, composite distribution (solid line in the figure)taking into account the distribution of sizes within thesieve fraction, assumed to be uniform (dashed line in the
figure). The discrepancy in the coarser sizes is again attributed to limited resolution of the light scattering procedure, possibly compounded by deviations from theassumed smooth spheroidal shape

5. Conclusions

Fig. 8Fig. 9Apparent light scattering size distributions for narrowsieve fractions of glass beads.

The analysis presented above shows that particle shape
can have significant effects on the results of experimental
measurements of size. Using particle volume as a uniquemeasure of size, it has been shown that shape effects canlead to either overestimation or underestimation of size bycommonly used procedures. The spheroid model allows
measured apparent size ratios obtained using different
procedures, e.g., sieving/sedimentation to be used to estimate actual volume diameter and elongation and to predict results from other methods such as light scattering.
Sieving and sedimentation lead to a shift in apparent
size but, for particles of similar shape, do not affect theform of the distribution. Optical (light scattering or in situ
image analysis on suspended particles) and other radiation
scattering procedures, on the other hand, show an apparent distribution of sizes for non-spherical particles of uniform size and shape.
Comparison of experimental apparent size of crushed
quartz particles by several methods gave consistent estimates of elongation, indicating somewhat flattened particles. Comparison of apparent size of kaolin particles bylight scattering and sedimentation were consistent withhighly flattened particles with dimensions in broad agreement with accepted values for kaolin platelets.
Light scattering measurements on narrow sieve fractions of spherical glass beads showed no size shift but
some broadening of the distribution which is attributed tolimited resolution of the procedure. Similar measurements
on irregular crushed quartz particles gave a significant
shift in size and greater broadening of the apparent distributions, consistent with the predicted orientation effects.
The results showed no systematic variation in apparent
size relative to sieve size indicating that the average parti-

Apparent light scattering size distributions for narrowsieve fractions of crushed quartz particles.

233

Richard Hogg / KONA Powder and Particle Journal No. 32 (2015) 227–235

cle shape was independent of size.
The analysis presented here shows that comparisons
between sizing procedures can provide information on
particle shape in addition to size. Rather than the common
practice of presenting size as an equivalent sphere diameter, particles are characterized by volume diameter andelongation.

References
Allen T., Particle Size Measurement, 5th Ed., Chapman andHall, London, 1997.
Austin L.G., Shah I., A method for inter-conversion of Microtrac and sieve size distributions, Powder Technology, 35(1983) 271–278.
Cho H., Yildrim K., Austin L.G., The conversion of Sedigraphsize distributions to equivalent sub-sieve size distributions,
Powder Technology, 95 (1998) 109–117.
Dumm T.F., An evaluation of techniques for characterizing
respirable coal dust, MS Thesis, The Pennsylvania State
University, University Park, PA., 1986.
Happel J., Brenner H., Low Reynolds number hydrodynamics,
Prentice Hall, New Jersey, 1965.
Hogg R., Turek M.L., Kaya E., The role of particle shape in size
analysis and the evaluation of comminution processes, Particle Science and Technology, 22 (2004) 355–366.
Hogg R., Issues in particle size analysis, KONA Powder andParticle Journal, 26 (2008) 81–93.
Leschonski K., Representation and evaluation of particle size
analysis data, Particle Characterization, 1 (1984) 89–95.
Naito M., Hayakawa O., Nakahira K., Mori H., Tsubaki J.,
Effect of particle shape on the particle size distribution
measured with commercial equipment, Powder Technology,
100 (1998) 52–60.
Van Orden D.R., Austin L.G., Hogg R., A Method of Correcting
Coulter Counter Size Distributions , Proceedings International Powder and Bulk Solids Handling and Processing
Conference, May (1979).

x  usin

(A2)y  ucos

(A3)

A point u, z on the profile lies on the surface of the particle and on the circumference of the circle, radius r,
drawn at x in the y-z plane. The radius r can be determined using Eqn. A1 with y = r, i.e.,
x2 

(A4)

r 2 R 2 1  2 
 B 
The vertical height of the particle profile above thepoint x, y is given by2z
r2  y2(A5)Then, from Eqns. A2, A3 and A4,
 u 2sin 2  2 2z2 
R 2 1   u cos 
B2 


(A6)

which can be expressed in the form of the equation for an
ellipse in the u-z plane:
z2
u2
 2 
1
2
R
D

(A7)

with D given by1
sin 2 cos 2


2
D
B2
R2

(A8)

The area of this elliptical profile isAs  πRD(A9)or, after substitution for D and expressing in terms of theelongation E,As 

πR 2 Esin 2  E 2cos 2

(A10)

Appendix: Projected Area of Randomly
Oriented Spheroids
Consider a particle with the general shape of a spheroid
as shown in Fig. A1. The particle profile in the orientation
shown (x-y plane) is described by the equation of an ellipse:
x2
y2
 2 
1
2
B
R

(A1)

The cross-sectional area of a transverse plane SS, drawn
through the origin, at an angle θ to the y-axis can be determined as follows: Referring to Fig. A1, the profile lieson the u-z plane perpendicular to the x-y plane and at anangle θ to the y-z plane. The coordinates of a point on theu-axis are defined by

234

Fig. A1

Longitudinal profile of a prolate spheroid.

Richard Hogg / KONA Powder and Particle Journal No. 32 (2015) 227–235

An observer viewing the particle at an angle θ to the horizontal axis (x) sees an area defined by a plane connecting
tangents to the particle surface in the viewing direction.
As indicated in Fig. A1, this plane is at angle φ to thetransverse (y) axis of the particle. The slope of the tangent
line is given bytan  

dy
dx

(A11)

Differentiation of Eqn. A1 leads to
R 2xtan  2
B y

(A12)

It can be seen from Fig. A1 thattan 

xy(A13)

so that

tan

R2


tan
B2

1
tan
E2

(A14)

(A15)

where, from Fig. A1,(A16)

   

for a prolate spheroid. The cosine can be expressed ascos 

1
1  tan 2tan
 tan   


(A17)

tan  tan1  tan tan(A18)Then, substituting for φ using Eqn. A14,tan 

E 2  1 tan1  E 2tan 2

(A19)

Substitution into Eqn. A17 gives

1  E 2tan 2
1  1  E 4 tan 2  E 4tan 4

cos  (A20)

A parallel analysis for an oblate spheroid leads to theidentical result: Eqn. A20.
The area As can also be expressed in terms of the angle
θ using Eqn. A14 and the identities:
sin 2 

The projected area Ap of the particle is the area of plane
SS projected onto the plane PP perpendicular to the tangents. Thus,

Ap  Ascos

andtan 2
1  tan 2

(A21)

1
1  tan 2

(A22)

and

cos 2 

Then, from Eqns. A10, A14, A15, A20, A21 and A22,
Ap  πR 2

1  E 2tan 2 1  E 4tan 2 
1  1  E 4 tan 2  E 4tan 4

(A23)

Eqn. A23 describes the dependence of the projected areaof a spheroid of elongation E on its orientation (angle θ)
relative to its elliptical axis.

Author’s short biography
Richard Hogg
Richard Hogg is Professor Emeritus of Mineral Processing and GeoEnvironmental
Engineering at the Pennsylvania State University. He received a B.Sc. from the University of Leeds and the M.S. and PhD degrees from the University of California at Berkeley. A member of the National Academy of Engineering since 2012, Dr Hogg's researchinterests include fine particle processing, particle characterization, and colloid and surface chemistry.

235

KONA Powder and Particle Journal No. 32 (2015) 236–252/Doi:10.14356/kona.2015016

Original Research Paper

Discrete Element Method (DEM) for Industrial Applications:
Comments on Calibration and Validation
for the Modelling of Cylindrical Pellets †Michele Marigo * and Edmund Hugh Stitt
1

Johnson Matthey Technology Centre, UK

Abstract
As a consequence of increasing computer power and more readily useable commercial and open source codes,
Discrete Element Method (DEM) is becoming widely used across a range of applications to simulate increasingly
complex processes. This is exacerbating the challenge of setting up simulations for industrial applications. The
literature on input parameter selection is divided. A number of papers report methods for their direct
measurement. Others, by contrast, propose a “calibration” approach where the particle properties are derived as
adjustable parameters by quantitative comparison of experimental and simulation results. This paper reports on
the calibration and validation of the input parameters for the specific case of cylindrical tablets represented byconjoined spheres. The initial steps are to not only assign and optimise the DEM input parameters but also
optimise the shape representation; what degree of linearity of the edges and angularity of the corners are required
to accurately reflect the cylindrical shape. The model was used to simulate two configurations of a rotating drum:
an “attrition tester” with a single longitudinal baffle and an un-baffled drum. The results were compared
qualitatively and quantitatively with experimental data. While the qualitative comparison was good in most cases,
detailed quantitative comparison fared less well, with some significant errors. This study highlights some of the
key issues for a wider-spread of industrial applications for DEM.
Keywords: parameters, particle mixing, granular blending, DEM, cylindrical pellets

1. Introduction: DEM as a possible tool forindustrial applications
In recent years, advances in computing speed and power
as well as improvements in programming have opened theway to model complex granular flow using the Discrete
Element Method (DEM), originally presented in (Cundall
and Strack, 1979). The particulate system is modelled as
an assembly of singular discrete and interacting particles.
It has been used to investigate a number of complex particulate systems, as a result of considerable scientific advances in the development of models for capturing theinformation and detailed interactions at the particle level.
The DEM technique provides much needed insight into
the mechanisms governing particle flow. The discrete nature of the outputs for such numerical simulations can enhance fundamental understanding of the granular motion†

Received 30 June 2014; Accepted 29 September 2014
J-STAGE online 28 February 2015
1
P.O. Box 1, Belasis Avenue, Billingham, Cleveland, TS23 1LB,
United Kingdom* Corresponding author: Michele Marigo;
E-mail: Michele.Marigo@matthey.com
TEL: +44-1642-522245 FAX: +44-1642-522245

236

and can then help to improve the design and operation ofsystems involving particulate material (Cleary, 2000). A
key application of DEM is the possibility to model equipment of complex geometry and the complex kinematics forthese geometries. The developers of commercial DEM
packages, such as EDEM (DEM-Solutions, 2014) or open
source codes such as LIGGGHTS (CFDEM Project, 2014)
have placed emphasis on integration of DEM with computer aided design CAD packages.
The value of DEM hitherto is demonstrated by the broad
variety of applications reported in the literature and thewide range of industries where it has been applied, suchas chemicals, pharmaceuticals, ceramics, metal, food andagricultural. Many DEM simulations have been publishedin the literature modelling diverse granular processes
such as comminution (Djordjevic et al., 2003; Mori et al.,
2004; Gudin et al., 2006; Powell et al., 2008; Tavares andCarvalho, 2009), granulation (Gantt and Gatzke, 2005;
Hassanpour et al., 2009; Nakamura et al., 2009 ), flow ina hopper (Cleary and Sawley, 2003; Anand et al., 2008;
Ketterhagen et al.,2009; Ketterhagen and Hancock, 2010),
die filling for tableting (Wu, 2008; Guo et al. 2010),
fracture of agglomerates (Kafui et al., 1994; Thornton etal.,1996; Ning et al., 1997; Liu et al., 2010), packing of©2015 Hosokawa Powder Technology Foundation

Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

particles (Matuttis et al., 2000; Zhang et al., 2001; Dutt etal., 2005; Lochmann et al, 2006; Fu et al, 2006; Aste etal., 2007), bulk compression of particles (Foo et al., 2004;
Hassanpour and Ghadiri, 2004; Samimi et al., 2005;
Markauskas and Kacianauskas, 2006; Mehrotra et al.,2009), flow in screw extruders and conveyers (Moysey
and Thompson, 2005; Owen and Cleary, 2009), vibratory
screening, filling of dragline bucket, conveyor belt design,
earth-mover bulldozer plate design (Cleary, 2000, 2010;
Zhang et al., 2008). Another significant application for
DEM simulations has been to study the mixing of granular materials, which is still poorly understood and is almost certainly carried out in non-optimal fashion in mostapplications (Roberts, 1998). Granular mixing in blenders
has also been extensively studied to compare mixer designs or look at the effect of operating variables on mixing, segregation and flow patterns (Muguruma et al.,
1997; Arratia et al., 2006; Chaudhuri et al., 2006; Yang etal., 2008, Xu et al., 2010; Marigo et al., 2013; Alizadeh etal., 2014).

1.1 Barriers to the use of DEM for industrial
applications
Due to computing considerations there are some limitations in the current state of the art for DEM software forindustrial applications. In particular there are restrictions
on the number, size and shape of particles that can realistically be modelled. These limitations will be overcomein due course by code development and parallel computing using high performance clusters. The main constraint
and difficulty for the application of DEM in an industrial
context will be related to the determination (or calibration), in a reasonable time scale, of the input parameters
necessary for the simulations. These limitations lead to
uncertain accuracy of the DEM modelling and often
DEM simulations results can be considered only qualitative, or at best semi-quantitative instead of being quantitative; indicative rather than predictive.
The representation of real particle shapes is still one of
the key issues along with the size and number of particles
that can be modelled. The majority of DEM simulations
consider only a small number of particles with diametersin the order of mm and a maximum number of particles in
the order few thousands to achieve a reasonable simulation time (Lim, 2002; Lemieux et al., 2008). The simple
spherical shape and the low number of particles that DEM
can readily handle at the moment is generally not comparable to real industrial systems which typically consist of
millions of small, irregular particles often with a particle
size distribution. Therefore, modelling capabilities aremainly limited by high computational requirements that
results from the explicit time integration scheme based onsequential calculations over the desired modelled time pe-riod, with a low time-step. For example, the simulation of
one million non-spherical particles in a complex 3D geometry is not reasonably performed by a single processor
and this has been addressed by parallelisation of computers using high performance cluster (HPC) or graphic processing unit (GPU) (Radeke et al., 2010; Stoltz et al.,2013). Consider however powder blending in a real industrial mixing operations some of the segregation phenomena that occur over a long period of time: in the order of
second or minutes. However, typical times that can currently be simulated in DEM are usually much shorter: inthe order of fractions of a second.
Another major challenge for DEM simulations is tosimulate non-spherical particles in an efficient manner.
The effect of particle shape can be important and it can bea dominant factor in many cases. In dynamic systems, forexample mixing operations or hopper discharge, differences in particle shapes can have a significant effect sinceparticle shape contributes to one of the mechanisms thatcan promote segregation (Cleary and Sawley, 2002; Li etal., 2004; Roskilly et al., 2010; Metzger et al., 2011). The
unconfined yield strength, a factor that influences thebulk flow properties of a powder, is also affected by particle shape. The particle shape controls the number of contacts per adjacent particle and the direction of thosecontacts relative to a line passing through the centre of
two adjacent particles (Johanson, 2009). Particle shape, inparticular circularity, affects the packing characteristics
in terms of void fraction and bed height (Miyajima et al.,2001).
Different methods of modelling complex shapes havebeen presented in literature, for example complex particles can be represented in DEM codes as clusters of inter-­penetrating or non-penetrating spheres, polyhedrons,
ellipses, ellipsoids or polygons. Clusters of inter-penetrating
or non-penetrating spherical particles with different or
constant diameters are rigidly connected to form an approximation of any desired shape by using the multisphere (MS) method (Favier et al., 1999; Kruggel-Emdenet al., 2008; Kodam et al., 2010; Bharadwaj et al., 2010).
The development of non-spherical shapes to represent thereal particle can be complicated since more sophisticatedparticle contact models should be developed rather thanthe usual simple sphere contact model. The detection of
the contact and subsequent calculation of the forces andtorques for edge-edge, edge-corner, corner-corner contacts
can be complicated and computationally expensive incase of polygons and polyhedrons (Favier et al., 1999).
However, by using the multi-sphere method it is possible toensure computational efficiency for contact detection andforce calculation and many DEM packages implement suchmethods (Kodam et al., 2010). A drawback of this method
is that to precisely represent a complex shape requires
clustering of many spherical particles which increases

237Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

computational memory requirements proportionately.
Probably the most important issue in the application of
DEM for real industrial applications is, however, thespecification of the material or particle properties required as input parameters by DEM; whether they areevaluated by measurement or by “calibration”. This isparticularly true in the case of real, non-ideal particles,
especially when they are of complex shape and small(sub-millimetre). The simulation input parameters are infact often not measured and the values are sometimes assumed without proper justification since there is no robust
method for their determination (Asaf et al., 2007). Whilst
DEM is being increasingly used, experimental quantitative validations of the simulation results are rarely reported (Chung and Ooi, 2008). It is often not clear how
the input parameters are selected or when they are calibrated or measured and the final simulation it is often notvalidated. Thinking about predictivity of the resulting
model, in order to interpret the simulation results confidently correct input parameters should be selected in addition to validation of the simulation results against
experimental data (Dong and Moys, 2003).
Two main approaches for the definition of the DEM input parameters can be found in the literature. The first approach is widely employed and involves the determinationof DEM simulation parameters by comparing numericalresults with some bulk experiments and “calibrating”(more realistically described as fitting) the input parameters to achieve a combination thereof such that the simulations and experiments give comparable macroscopic
results. For example, an automated optimisation algorithm
to select the ideal set of parameters that best match a DEM
simulations with experiments in the shortest time frame
possible has been developed by DEM-Solutions Ltd fortheir EDEM software. The idea is to conduct a laboratory
scale experiment that recreates the typical flow regime
and bulk behaviour seen in an idealised application andrun a series of simulations varying the combination of input parameters that allow a global fitting between DEM
simulations and experiments (Johnstone, 2010). This wasa sand-pile test where the angle of repose for the experimental pile was matched to the one generated by DEM
simulations as functions of various combinations of input
parameters. Since the only output that is checked is theangle and the input parameters can have multiple combinations many sets of these would give the same macroscopic output (Curry et al., 2010). A similar DEM
parametric approach has been carried out for the operation of a Freeman powder rheometer to understand how
the measured force and torque values are affected by key
particle properties such as particle size and shape (Asaf etal., 2007). Numerical simulations based on the Hertz
Mindlin contact model were compared with experimental
results whereby it was shown that particle shape and ma-

238terial parameters such as friction coefficients (sliding androlling) were important factors determining the force andthe torque on the rheometer blade. It was observed interestingly that the DEM fitted input parameters for glass
beads were different to those reported in the literature. It
was also noted that good agreement with experimental results could be obtained for various combinations of particle shape, sliding and rolling friction coefficients.
These two examples suggest that since the parameters
are identified using macroscopic comparison, the parameters for the contact laws seem to be poorly linked to measurable physical parameters of the powder material anddifferent combinations of input parameters can lead tosimilar end results (Pizette et al., 2010). They emphasisethat parameter estimation via a fitting route where thenumber of parameters exceeds the number of experimental
variables is a fundamentally ill-posed problem and thus
inevitably has no unique solution. These observations infer that given the general approach to determination ofthese input parameters they do not necessarily have aphysical meaning but rather are simply treated as adjustable parameters, irrespective of the fact that the model iscomposed to give them a distinct physical meaning.
The second approach involves the development of systematic methodology for the direct experimental determination of the required input parameters. Some of thenecessary input parameters are easily measurable whileothers are not, and this strongly depends, for example on
the size and shape of the particles. The influence of particle
size, shape, solid density and porosity is of course important and their determination is relatively straightforward.
For example for the particle shapes, particle descriptors
can be generated from digital image segmentation technique and implemented as particle shape parameters togenerate corresponding irregular shaped DEM particles(Williams et al., 2014). Conversely, measurement of single-­
particle properties such as the coefficient of restitution,
friction, Young’s modulus of elasticity and Poisson’s
ratio are possible with available tests but are not always
practical (Grima and Wypych, 2011). Considering theother particle contact properties, attempts are presented
in the literature, but mainly applied to particles withdiameters in the order of mm. The determination of DEM
input parameters has been reported for pharmaceutical
tablet processing operations. Friction was measured using
a rheometer used as “a Pin-on-disk” where the measurement of the friction coefficient between particles andaganst a rotating disk was measured for relative large tablets (Suzzi et al., 2012). Rolling friction was measured forlarge pharmaceutical tablets and several common materials, such as glass beads and steel ball bearings using aslope with a given height and by measuring the distancetravelled by the particle (Ketterhagen et al., 2010). The
coefficient of restitution has been measured extensively inMichele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

the literature using high-speed video, this is calculated as
the ratio between the final velocity after the impact andthe initial velocity before the impact for spherical particles. For non-spherical particles however the impact will
result in a more complex trajectory involving particle rotation and the description of such impact is thus significantly more complicated. The three-dimensional motionof irregular shaped particles to determine the resulting
coefficient of restitution, using polyethylene pellets,
showed the wide spread for the measured coefficient ofrestitution (Hastie, 2013). Elastic and elastic-plastic macroscopic mechanical properties for mm size zeolite granules were used to calibrate the mechanical properties ofthe modelled granule in DEM using the force-displacement
behaviour measured by the diametrical compression test
(Müller and Tomas, 2014). Overall, it is clear that obtaining the input parameters for the real materials is a challenge, especially if the particle size is small and often
even without considering other important attributes ofeach particle (e.g. surface asperities, particle shape, stiffness, hardness and size). It is not clear that this is a realistic
objective for heterogeneous materials with irregularities
in particle shape and size, non-uniform surface asperities,
inconsistency in moisture content and non-linearity offriction (Suzzi et al., 2012).
A further complication is that particles are all single
entities within a population. Commonly, only averagematerial values are used in DEM simulations as input parameters. Particles can have a size distribution and some(most?) of the input parameters in truth are a function of agiven particle’s diameter within that size distribution.
Hence there are some questions that need to be addressed:
how or should this aspect be taken into account? Is it viable to use an average value for different discrete particles?

1.2 Considerations of Computational Time
Generally the total computational time for a given simulation is a combination of multiple factors:• Number of particles in the system: more data points to
be calculated;• Shape of the particles: usually “complex” shapes aredescribed by conjoining particles which cause an increase in the number of particles within the systemand therefore time;• Size of the particles: smaller particles require asmaller time-step;• Choice of material properties: material properties influence the simulation time-step;• Total desired simulated time.
In the DEM simulation the time-step, ΔTstep, is definedas the time between each iteration. A simulation is stable
only if the timestep employed is lower than a criticaltimestep ΔTcritical, which is generally defined as a fractionof the natural frequency of an equivalent mass-spring
system. For the commercial EDEM software the criticaltime step is defined as the critical Rayleigh timestep, TR
as follows:Tstep  Tcritical TR

πR



G
0.1631v  0.8766(1)

Where TR is function of the particles properties such as
radius and density, R is the particle radius, ρ the particle
density, G the shear modulus and ν the Poisson’s ratio.
A number of techniques (tricks?) exist for reducing thecomputational cost DEM simulations. One of this is “particle scaling”, also referred to in the literature as particle‘coarse graining’ or ‘parcel-based, by artificially increasing the particle size (Radl et al., 2011; Shigeto et al., 2011;
Hilton and Cleary, 2012). The shear modulus, G, also hasa major influence in the critical time step. It is common
practice in the literature to arbitrarily reduce the value ofthe shear modulus as this dramatically increases the critical time-step, thus reducing the simulation time. However, it has been shown that this approach could lead toerrors on the simulation results depending on the actual
system that is modelled (Lommen et al., 2014). Using anangle of repose test and a penetration test it was shownthat by reducing the shear modulus below to 107 Pa or
keeping the average normal overlaps below 0.3 % of theparticle radius these parameters would not alter the simulation results. On the other hand, it was shown using abulk compression test that properties such as the bulk
stiffness and bulk restitution changed as a result of thestiffness reduction. This example showed that DEM users
should always be cautious and verify the impact of varying the input parameters case by case and possibly verify
the approach through some real validations.
In the context of industrial applications for DEM it ismaybe more prudent to consider is the actual “totalmodelling time” rather than only the simulation time
where the former includes the time required to evaluate
the DEM input parameters, the geometry and simulation
set-up time, the time for the simulation itself plus the data
post-processing and “validation time”. To provide justification and economic value to many typical industrial applications, the total modelling time to set up, solve andanalyse a DEM model has to be relatively quick and fasterthan actually carrying out experiments, unless it is justifiable by the fact that computer simulations can give insightof information at the particle level that would be difficult
or impossible to obtain by experiments.

2. Scope of the present investigation
In this paper we assess the validity and time require-239

Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

ment of the calibration approach to model parameter selection, and test the quality of the model by evaluating theaccuracy of the predictions for different scenarios against
experimental data. Specifically, a shape optimisation forcylindrical α-alumina pellets and a subsequent parameter
calibration using the “sand-pile” test were performed. The
model was then used to simulate two different geometries
of a rotating drum and the results compared to experimental data.
The work in this paper was done using the commercial
DEM code (EDEM Version 2.4.3) using a E5645 2.40GHz
DUAL-QUAD PC machine with 12GB of RAM using a 2
core licence.
The handling of particulate material in rotating drums
is a fundamentally important unit operation in many industries such as pharmaceutical, chemical, minerals, foodand agricultural industries, as well as many others. The
aim of this work is to model the motion of 3.12 × 2.96 mm
alumina cylindrical pellets within two rotating drum systems: a conventional cylindrical rotating drum and a baffled attrition test rig.
As discussed above, the first major step was to determine the input parameters for the simulations and additionally, in the case of a complex shape, what is the best
combination of multiple spheres to represent the right pellet geometry in order to capture the its effect during thetablet motion. The approach that has been applied in thiswork is represented in Fig. 1.
The intent was thus to establish an adequate representation of pellet shape (using the multi-sphere representation), then “calibrate” the DEM input parameters andsubsequently validate the model—all using a different experimental method:• Shape: cylinder filling, where it is presumed the packing density would be strongly influenced by the shape(although it is noted that packing density can be correctly predicted while the bed structure is incorrect(Caulkin et al., 2009));• Input parameters: from discharge of a hopper (using
approximately the same number of pellets as above) as

Fig. 1 Approach for the DEM model: shape calibration, input
parameters calibration and model validation.

240

an experiment that features both static and dynamic
aspect: going from static to dynamic to static again as
the pile is formed;• Validations:– Attrition test: shape and parameters from previous
steps with similar number of pellets but a dynamic
system involving pellet impacts;– Rotating drum: higher number of particles and overa range of rotational velocities.
Each of these activities and their results will now be
described along with comments on the time required tocomplete the task.

3. Model derivation
3.1 Pellet shape representation
The ultimate aim of the present study was to model the
motion of cylindrical alumina pellets (3.12 × 2.96 mm andpellets density of about 1800 kg/m3 within two rotating
drum systems: a baffled rotating drum and a plain (i.e. noflights) rotating drum. The first aspect of building themodel was to assess and optimise the representation of
the cylinders using the multi-sphere approach. This was
carried out by comparing the model results to experiments for loading the pellets into a horizontally and vertically aligned cylinder. This approach has previously beenused for the validation of a semi-stochastic particle loading simulation software (Xu et al., 2008).
Considering the Hertz-Mindlin contact model employed in the EDEM software, if only gravity is considered as an external force then the particle characteristics
and contact input parameters that must be defined are:
• Particle shape• Particle size
• Particle density• Particle shear modulus and Poisson’s ratio• Coefficients of friction: static and rolling, between
particle-particle and particle-wall material• Coefficients of restitution: particle-particle and particle-­wall material
Pellet shape representations were generated using themultiple sphere approach; the cylindrical pellet template
was generated in CAD and approximated with conjoined
spheres. For the specific purpose of establishing the best
representation of pellet shape a single set of DEM input
parameters was arbitrarily assumed. These values arelisted in Table 1, which were used in all simulations relating to the optimisation of the pellet shape representation.
Note that the shear modulus is set to a low value of2 × 106 in order to shorten simulation times. The only pellet properties that were actually known were the pellets
size and density whereas all the other parameters wereMichele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 2Lower packing density (fill level) is obtained if a poor pellet shape representation. Left side: Perspex
drum (D × L: 240 × 90 mm) filled with 20 % fill level cylindrical pellets. Right side: DEM results using
22,000 pellets.

Table 1

Initial set of DEM input parameters

Parameter

Value
3

Particle (p) density, ρp [kg/m ]1800

Particle shear modulus, G [Pa]2 × 106

Particle Poisson’s ratio, ν p

0.25
3Vessel (v) density, ρv [kg/m ]1000

Vessel Poisson’s ratio, ν v

3 × 109

Particle-particle static friction, μs-pp

0.25

Particle-particle rolling friction, μr-pp

0.65Particle-vessel static friction, μs-pv

0.6Particle-vessel rolling friction, μr-pv

0.65Particle-particle restitution, ε pp

0.01

Particle-vessel restitution, ε pv

0.85

chosen according to the authors’ prior DEM experience.
An example of the effect poor particle representation
on packing of cylindrical pellets into a rotating drum isgiven in Fig. 2. The experiment was carried out using ahorizontal Perspex drum (D × L: 240 × 90 mm) which
was filled to 20 % volume fill level. Using the known pellet density and the weight of material this corresponded toabout 22,000 pellets in the DEM. Two pellet shapes,
Shapes 1 & 2 were generated and the pellets properties
such as mass, volume, moments of inertia were calculatedusing the grey cylindrical template reported in Fig. 2.
Due to the poor representation of the real shape theoverall bulk or packing density is evidently wrong (dottedline in Fig. 3). This would have a significant effect on theoverall flow and behaviour in a dynamic system. The
main reason is probably excessive rounding of the sharp
corners, which it can easily be envisaged will result incloser packing of the pellets and thus a higher packing
density—as witnessed by the results. Evidently a geometrically more correct representation of the pellet shape isrequired.
Representing complicated shapes by using a simple
sphere can thus lead to errors. Glued spheres clusters do
not duplicate the overall non-spherical particle shape exactly, especially the sharp corners. Although algorithms
have been developed to approximate particle shapes using
glued spheres (Radshaw and O’Sullivan, 2004; Wang etal., 2006), the fine details of the overall non-sphericalparticle surface remain irregular even with a large number of constituent spheres.
This inaccuracy can be improved by using for example
a higher number of spheres with smaller or mixed diameters to achieve a better representation of the square sharp
corners and flat edges, Table 2.
By more accurately defining the particle details better
precision and a more accurate representation of the packing would be achieved. However this considerably increases the computational time since in the multi-sphere(MS) a higher number of particles would be considered
into the simulation.
Experiments for comparison were run by loading pellets into a vertical cylindrical Perspex container (diameter
52 mm). Three different fill levels were used: 200, 250

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Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 3

Cylindrical pellet packing into a 52 mm tube using refined pellet shape representation by increasing conjoined spheres number and decrease sizes.

Table 2

Shapes refinement using multi-sphere MS

Typen° (radius-mm)

Shape 1

12 (0.765)

Shape 224 (0.765)

Shape 324 (0.765)
18 (0.35)

Shape 448 (0.765)
34 (0.35)

and 300 grams corresponding respectively to 5212, 6515
and 7818 pellets. Equivalent DEM simulations were run
for the different shape representations, each with an increasingly complex multi-sphere assembly.
The qualitative output from this is shown in Fig. 3.
Again, it is shown that an inadequate particle shape representation can lead to clear errors and in this case artefacts
in terms of the packing structure of pellets. The pellet
shape is exported from the DEM software using a purecylindrical basis (centroid and orientation angles).
The poor representation of the particles corners leads
to apparent overlaps due to the rounded corners and topellets penetrating the cylindrical container wall.
The simulation and experimental results are compared
quantitatively in Fig. 4. Shape 4 appears to provide a reasonably good approximation for the packing behaviour of
the real pellets in terms of bed height since the DEM results fall within the experimental error, whereas the othertwo shapes were statistically different. Shape 4 also shows
little evidence of significant overlap and intersection of
the cylindrical wall.

242

Fig. 4 Plot comparison bed height measured experimentallyand DEM simulation results for shape 2, 3 and 4 (experimental results = mean ± SD, n = 3).

However, to run just 2 seconds with about 8,000 pellets
the time was just over 45 hours on a 2-core machine, 9.5
hours using 8 cores. This does not bode well for future
simulations over longer times for actual applications also
involving many pellets.

3.2 DEM input parameters calibration
To determine the DEM input parameters for the cylindrical pellets the typical “calibration” strategy was used
to determine their values, as discussed in the introduction, using the “sand-pile” test as the exemplar. For theexperiments, see Fig. 5, a Perspex funnel was filled with
300 grams of the pellets and the pellets were dropped on
to flat Perspex surface. The angle and the height of thepile were compared to the results of DEM simulations
carried out using the “Shape 4” representation from theMichele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 5

Parameter calibration: experimental and simulation
set-up plus representative results.

Table 3 Comparison simulation time for each simulationfor 2 seconds simulations until tablet settlement
300 grams(7818 pellets)

Shape 2

Shape 3

Shape 42 cores license

3.5 h

12.6 h45.1 h8 cores licence

9.43 hpreceding section.
It is expected that varying the various DEM input parameter values would result to a different final results forthe discharge of pellets from the container in terms ofshape, angle and height of the pile. The successive parameter sets used in simulations for the calibration of the
shape 4 pellets are reported in Table 4. This of course
considers only a subset of the whole parameter set. The
artificially low value for the shear modulus has been
maintained and the literature value for Poisson’s ratio has
also been accepted. Initially the coefficients of rolling friction were set to zero as in previous work regarding tablet
motion in a pan coater it was assumed zero for shaped particles as the rolling resistance was expected to be accounted
for by the actual shape representation (Ketterhagen, 2011).
The results from the simulations with these successive
parameter sets are shown in Fig. 6 and the quantitative
outputs from each simulation for pile height are comparedwith the experimental value in Fig. 7.
For the early runs the pile could not actually be formed
due to excessive rolling of the pellets.
The coefficient of rolling friction actually has a significant impact on the results for this particular particle flow

Table 4

List of DEM input parameters varied between
simulations for material calibration (“guessing”)

Run

εpp

μspp

μsrpp

εpw

μspw

μrpw

1

0.4

0.625

0

0.4

0.625

0

2

0.5

0.625

0

0.45

0.625

0

3

0.55

0.625

0

0.5

0.625

0

4

0.65

0.625

0

0.55

0.625

0.05

5

0.85

0.625

0

0.75

0.625

0.05

6

0.4

0.625

0

0.4

0.625

0.05

7

0.4

0.625

0

0.4

0.625

0.02

8

0.35

0.625

0.05

0.35

0.625

0.05

9

0.35

0.625

0.05

0.35

0.625

0.05

10

0.4

0.625

0.065

0.4

0.625

0.025

11

0.4

0.625

0.065

0.4

0.625

0.04

12

0.4

0.7

0.07

0.4

0.7

0.05

problem as the cylindrical pellets can just roll on the sideof the cylindrical shape. It was necessary to introduce a
non-zero value for both the particle-particle and particle-­
surface rolling coefficients. The other parameters wererelatively unchanged from their assumed initial values,
with only a modest increase in both static friction coefficients.
In total 12 simulation runs were required to achieve aparameter set that yielded a satisfactory representation of
the “pellet-pile”. It can be noted that for the last 3 sets ofparameters (Simulation 10, 11 and 12) the pile heights
were all within the experimental measurement error. Thisimplies that any of the last 3 sets could be assumed as“calibrated” set of input parameters. For the subsequentvalidation in the next sections set 12 was arbitrarily used.
A more extensive systematic statistical and theoreticalevaluation should be carried out in order to assess the relationship between the DEM input parameters and themodel outputs for a number of several granular flow scenarios. However, this reinforces some of the time scale
limitation for industrial applications of DEM.
Each simulation was run for 4 seconds, sufficient time
for the pellets to settle into a static state. Each of thesesimulations required 60 hours on 2 cores, (estimated at 13hours on 8 cores), thus a total of 720 machine hours plus
the time to set up the simulations and do the results postprocessing. This is very clearly a very time-intensive step.
The timestep used in this simulation was 30 % of the critical timestep given by Eqs.1, and it corresponded to 1e-5
seconds.
The total required (elapsed) time to achieve this will, ofcourse, not fall in linear fashion. The time to set up thesimulations and to process the results is likely to be more

243

Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 6

DEM results for the pellet-pile for the different input parameter sets given in Table 4.

4.1 Baffled rotating drum: attrition tester

Fig. 7Height for the pile obtained from DEM simulations
compared to the one obtained experimentally.

or less constant and not a function of the power of thecomputer used to do the simulations. In reality parameterisation of the model has taken several weeks, and this is asignificant barrier to the wider application of DEM to industrial problems.

4. Model application and model validation
The previous results have established a “calibrated”
DEM model: “Shape 4” and input parameter set 12. Results from DEM simulations will now be compared to twopellet flow situations:● A rotating drum with a single baffle: in fact an attrition tester for catalyst particles (ASTM D 4058, 2011);● A conventional rotating drum with no baffles.
The basis for comparison of the experimental results
was a simple qualitative and macroscopic comparison ofparticle distributions in the first case. For the latter, however, a far more quantitative approach was taken and thecomparison based on:● Upper and lower dynamic angles of repose● Particle imaging velocimetry (PIV)244

The attrition tester is described in an ASME procedure(ASTM D 4058, 2011), for the attrition testing of catalyst
particles. It consists of a horizontal rotating drum with
D = 254 mm, L = 152 mm and a single baffle with 51 mm
height extending the full length of the drum. For the experiments 115 grams of pellets were used and the drum
was rotated at 40 and 60 rpm. A high speed video camera(Photron SA4 FASTCAM) was used to capture images ofthe particle flow through one of the end plates using aframe rate of 1,000 Hz.
For the simulations 3000 pellets (an equivalent to thepellets used in the experiment) were used and the simulation was run for 10 sec. The Pellets were created and allowed to settle from 0–3 sec before the rotation wasstated. The rotation was then modelled for 7 seconds. At
40 rpm this corresponds to 4.7 rotations while at 60 rpm
this corresponds to 7 rotations. Snapshots from the DEM
results after the total 10 second simulations were compared qualitatively with the experimental images in Fig. 8
and 9 for 40 rpm and for 60 rpm respectively.
Both sets of results showed a good qualitative comparison between the general behaviour of the pellets in termsof their locations and the spread of the pellet cluster at all
baffle plate angles. Qualitatively we appear to have, therefore, a good and accurate model. However, it is important
to note some interesting little discrepancies between experiments and simulations. For example, in Fig. 8 when
the baffle is in the vertical position (frame 7), the tablets
take off the baffle earlier than the simulations. Instinctively, it could be attributed to the selection for the particle-­wall interaction in parameters, perhaps not being highenough. However, this is not valid in Fig. 9, where for thesame baffle position the tablets seemed to detach correctlyfrom the baffle. It is also worth noting that in Fig. 8,
frame 5, the high speed image shows more tumbling thanthe DEM results, this might be attributed to particle-­particle interaction parameters. Therefore, it could be saidMichele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 8Comparison pellets flow for attrition test with 3000 pellets between high speed camera and DEM at
40 rpm. 8 baffle positions (represented in yellow).

that this approach of inferring the set of parameters fromthe calibration tests employed is not suitable to obtain an
optimal calibration. However, it is also important to notethat most of the DEM literature would consider this qualitative comparison good enough and it would claim thatthe model has been validated arguing that a model will
never be perfect but good enough to give a useful result.
For the record, each of the simulations required 12hours machine time on a 2 core computer (estimated at 2.5hours for 8 cores). In this case the data post-processing isonly qualitative and therefore relatively quick—as indeed
is the processing of the experimental results. This, ofcourse, is not the case if a quantitative approach to validation is taken.

4.2 Rotating drum
The rotating drum was made of Perspex and of diameter D = 240 mm and length L = 90 mm with flat endplates
and no baffles or flights. It was operated at a 20 % volumetric fill level at three speeds: 20, 35 and 50 rpm. High
speed videography was used to capture images to allow
evaluation of the upper and lower angles of repose (Fig.

10). The digital video images were also used to evaluate
Eulerian particle velocity maps at the end plate using a
Particle Imaging Velocimetry (PIV) approach to image
analysis described later.
For the simulations, 22,000 pellets were introduced into
the drum, representing also a 20 % fill level. They wereallowed to settle for 2 seconds and rotation was thenstarted. The rotation was modelled for 3 seconds corresponding to 2.3 and 5.8 rotations at 20 and 50 rpm respectively. Snapshots for comparison with the experiments
were taken at the end of the total 5 seconds simulation,from which the dynamic angles of repose were evaluated,
Fig. 11. Each of the simulations required 220 hours machine time on a 2 core computer (estimated at 46 hours
for 8 cores).
Fig. 12 shows the evolution of the average pellet speed
though the 3 seconds of simulation time for both simulated drum speeds. This shows that a steady state has ostensibly been achieved after 1.3 and 2 seconds (20 and
50 rpm respectively) and thus the 3 seconds simulation
time seems to be adequate.
Fig. 13 shows the global quantitative comparison between the experiments and the simulations for the dy-

245Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 9Comparison pellets flow for attrition test with 3000 pellets between high speed camera and DEM at
60 rpm. 8 baffle positions (represented in yellow).

namic angles of repose and the bed height.
Although general trends for the angles as function of
the speed are in good agreement, some discrepancies areapparent. The bed height predicted by the DEM simulations is much higher than the experimental values as is
the upper dynamic angle of repose (β). These discrepancies could be related, for example to the value for thestatic friction value between particle and wall which isprobably inadequately captured by the “sandpile” test
used for calibration or perhaps the shape representation
due to the rounded corners. It could be argued that theparameter calibration should be repeated; but this surely
defeats the stated object of the calibration exercise!
It has recently been emphasised that validation of complex models, such as DEM, against a single objectivefunction may be neither adequate nor sufficient (Stitt etal., 2013). Therefore, the model here is compared to detailed particle velocity fields in addition to the global results above. For the DEM results, the velocity is calculatedby dividing the side view in a grid 0.5 × 0.5 mm (muchsmaller than the pellets sizes) and by averaging the velocity for the pellets going through each element of the grid

246

Fig. 10

Angles of repose, upper and lower, for the Perspex rotating drum 20 % filled.

Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 11 Angles of repose, upper and lower, from DEM simulation using 22,000 pellets represented by Shape 4 andInput parameters by simulation 12.

Fig.   13 Comparison angles of repose, upper and lower andbed height from DEM simulation and high speed camera. These values where taken after 3 seconds of drum
rotation.
Fig.   12

Average pellets bed velocity when the rotation isstarted. Showing the bed reaching a steady state quite
quickly.

over 1 second (when the bed average velocity is constantin Fig. 12) to generate an Eularian velocity field that canbe compared to the Particle Imaging Velocimetry (PIV)
results.
PIV is a well-established and widely used technique forvelocity imaging of both gases and liquids (Raffel et al.,2007) that can also be applied to particulate flows. It measures whole velocity fields by taking two images shortly
after each other and calculating the distance individual
particles travelled within this time, tracking individual
particles using a cross correlation technique. From the
known time difference and the measured displacement
the velocity is calculated to generate a Eularian velocity
map. More extensive descriptions of the procedure are
given in the literature.
Successive digital images at a frame rate of 1,000 Hz
for the rotating drum were recorded using the high speed
camera were analysed using the PIV software provided by(LaVision, 2014).
Fig. 14a–d, show the comparative velocity fields evaluated from the simulations and the experimental PIV for
both drum rotating speed: 20 and 50 rpm. As discussed
previously, the main pellet bed features, trends in bed

shape and height, are reasonably well captured by the
DEM simulations. Moreover, the velocity fields are well
captured near the drum wall and the central part of thepellet bed. For both drum speeds the lowest pellet velocities are correctly captured within the central core part ofthe pellet bed. However, the top bed velocity is muchhigher from the DEM. In Fig. 14e the distribution for thevelocity field is given to show the general good agreement
again in terms of general trends but poor validation in
capturing the top bed pellet velocity.
It is interesting to note that comparisons of PEPT and
DEM also indicate that DEM systematically over-predicts
velocities by approximately 10 % in free flowing systems(Hassanpour et al., 2011; Marigo et al., 2013). The discrepancies in this case do, however, seem to be larger
than this. To what extent this depends on the selection of
the input parameters (perhaps choice of static/rolling friction between pellets material and drum wall?) is of course
not clear. What is clear is that the model calibration carried out using the sand-pile test does not appear adequate
for its application to a rotating drum.

4.3 Discussion
Despite the lengthy and careful approach to model
parameterisation, in line with literature methods, thederived model shows significant discrepancies when247Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

Fig. 14a–d) Comparison for the velocity field between DEM simulations and PIV experiments. e) Velocity distribution. The pellets velocity on the top of the bed is over predicted by DEM.

compared to experimental validation data. While it ispossible to hypothesise on the sources of these errors, ascribing the blame to one or two given parameters, due tothe nature of the model parameterisation it is not simple
to assess the origins of these errors.
On a broader front, this raises questions over the efficiency and accuracy of the “calibration” approach to parameter estimation. In this study the “sand-pile” type test
was used—as it has been in the literature. Two measured
parameters were used to optimise the values of 6 adjustable fitted parameters. This is an inherently ill-posed
problem and as such multiple combinations of parametervalues will provide an equally good fit. So, while a good
representation of the “sand-pile” test was obtained in themodel calibration, there is no way of assuring that the fitted parameter values are correct without detailed statistical evaluation of the quality of fit (e.g. 95 % confidence
intervals). Is the “sand-pile” test a reasonable basis for parameter evaluation for a rotating drum model application,
as also suggested in the literature? The response to thisquestion has, for the moment, to be equivocal. What isclear is that significant improvements are required inmethods to assign the values of the model parameters(which all have clear physical definitions) in DEM applications in order to achieve a quantitatively correct model.

5. Conclusions
DEM is still in its infancy when compared to other248

modelling tools such as Finite Element Analysis (FEA)and Computational Fluid Dynamics (CFD). However, thedevelopment of DEM and its increasing applications arefollowing a similar pattern to those previously seen with
CFD. Many limitations and difficulties regarding the application of DEM for real industrial scale applications exist.
With advances in computational power and programming, DEM is becoming more and more accessible toacademia and industry alike. At present, on commerciallyavailable desktop computers, simulations of up to a million particles can be performed. On large high performance computing clusters, the trajectories of millions ofparticles can be computed.
The use of this modelling technique is accordingly expanding and it is being exploited in many applications.
Despite this, as extensively reported and demonstrated,
there are still some limitations that need to be addressed
to use DEM as a possible fully predictive rather than indicative tool for modelling real scale industrial systems.
Some of these issues are highlighted as follows:• DEM can be used to describe granular problems considering each particle as a discrete discontinuous element within the system. DEM simulations are widelyreported for large-scale particles. Limitations on theparticle size are mainly due to computational limitations as a result of the higher number of particles to beconsidered resulting in a drastic increase of necessary
calculations. In addition the computational effort increases dramatically with the decrease of particle sizedue to the smaller time-step, which results in an in-Michele Marigo et al. / KONA Powder and Particle Journal No. 32 (2015) 236–252

creased number of necessary iterations.
• Considering the limitation with the size dependency
of some problems there is still a question on the formulation of the local constitutive contact laws to account for some of the important inter-particle effects
that could arise in case of small-scale particles.
• There are some limitations regarding the representation of particles with simplified spherical discrete elements. Different approaches using representation ofcomplex shapes are being developed, however there is
still a need of developing and validating relationships
between the local constitutive laws depending on the
shapes of the discrete elements. By using a multiple
sphere approach there is a limitation on computational
effort due to the increased complexity due to the large
number of spheres considered in the simulations.
• Clear limitations regarding the determination of the
choice of the input parameters for the simulations incases of different materials still make DEM a tool for
simplified systems which user consider “model” particles such as for example glass bead. A methodology
for gathering the input parameters has not yet beenclearly defined especially in the case of small particles.
This paper has investigated the particle shape representation for a system involving cylindrical pellets using tube
loading and the model calibration for DEM input parameters using the “sand-pile test. The resulting model has
then been subjected to a qualitative and quantitative validation against experimental data for two rotating drum
systems; one with a single baffle, one unbaffled. The
lengthy procedure from getting the input parameters to
the validation DEM application has proven quite challenging for these real industrial (albeit rather small scale)applications.
The results indicate that the use of calibration is notwholly successful in allowing quantitative prediction of
the applications. It is, however, not possible to interrogate
the root causes of the errors in the final application simulations. The paper also highlights the high demands on
computer simulation time and human resource necessary
to complete this exercise. There is a need to improve inboth of these areas if DEM is to gain more traction as adesign rather than a research tool.
The implication is that the current methods of model
parameterisation are not adequate. This begs the question
as to whether model calibration using a dissimilar system
to the final application is appropriate. It has been highlighted that the parameterisation by “calibration” is inherently ill posed. This means that in terms of the fitting
exercise there will inevitably be questions over the parameter confidence intervals and cross correlation between some of their values. There are few reported
attempts to understand the quality of the fit other than bymeans of simple residuals.

As a DEM modelling community, there is an over-riding
need to better establish protocols for the evaluation andestimation of the key particle properties—the input parameters. This paper is intended to highlight and exemplify
the shortfalls with current methodologies and to encourage further work in this unattractive but yet absolutely
vital area.

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Author’s short biography
Michele Marigo
Michele Marigo is a Principal Scientist at Johnson Matthey’s Technology Centre. He
obtained an undergraduate degree with Master’s in Mechanical Engineering from University of Padua, Italy and a doctorate in Chemical Engineering EngD from Universityof Birmingham, UK. Michele’s expertise include materials science, particle engineering,
discrete element modelling (DEM) and finite element modelling (FEM).

Hugh Stitt
Hugh Stitt is a Scientific Consultant in Johnson Matthey’s Technology Centre. He
holds BEng and PhD from the University of Bradford, UK. He is a Visiting Professor at
the University of Birmingham and at Queen’s University Belfast. He is a Fellow of theInstitution of Chemical Engineers and a Fellow of the Royal Academy of Engineering.
He has 25 years of industrial research experience across a variety of themes related tocatalytic reaction engineering and catalyst manufacture with an emphasis on both experimental techniques and modelling with over 100 refereed publications.

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KONA Powder and Particle Journal No. 32 (2015) 253–263/Doi:10.14356/kona.2015017

Original Research Paper

Direct Laser Forming of Titanium Alloy Powders
for Medical and Aerospace Applications †Hideshi Miura
1

Department of Mechanical Engineering, Kyushu University, Japan

Abstract
Titanium and its alloys have been widely used for various industrial and medical applications because of their
excellent characteristics of low density, high corrosion resistance and high biocompatibility. However, it is noteasy to produce the more complicated shape and precise components with low cost because of their poor
workability. Therefore, Direct Laser Forming (DLF) which is one of Additive Manufacturing (AM) techniques isdesired to be a suitable and advanced processing technique for fabricating Ti alloy components. In this paper,
DLF technique has been introduced to fabricate Ti-6Al-7Nb alloy parts for medical applications and Ti-6Al-4V
alloy parts for aerospace and automobile by evaluating various properties.
Keywords: additive manufacturing, direct leaser forming, Ti alloy powders, mechanical properties, microstructure

1. Introduction
Additive manufacturing (AM) technologies have finally
hit the mainstream. Since 25 years of development as“rapid prototyping” techniques. AM techniques are a collection of manufacturing processes which join materials
to make physical 3D objects directly form virtual 3D
computer data. These processes typically build up parts
layer by layer, as opposed to subtractive manufacturingmethodologies which create 3D geometry by removing
material in a sequential manner. In the technical community, an international consensus has coalesced around theuse of “additive manufacturing” whereas in the popularpress the technologies are known as “3D printing.” Every
existing commercial AM machine works in a similar way.
First a 3D CAD file is sliced into a stack of 2-dimensional
planar layers. These layers are built by the AM machineand stacked one after the other to build up the part.
Today, there are seven different approaches to AM, anddozens of variants of these approaches. As most of theseapproaches were first patented in the late 80’s and early
90’s, in many cases the fundamental process patents have
expired or are expiring soon—thus opening up the marketplace for significant competition in a way that was impossible over the past 20 years, due to intellectual
property exclusivity1). ASTM International released the†1

Received 8 October 2014; Accepted 27 October 2014
J-STAGE online 28 February 2015
744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
E-mail: miura@mech.kyushu-u.ac.jp
TEL/FAX: +81-92-802-3207©2015 Hosokawa Powder Technology Foundation

standard terminology in 2012 that classified AM technologies into seven broad categories. Below are quick summaries of the different types of 3D printing according to
website of U.S. Department of Energy2).
• Material extrusion: The largest installed base of AM
techniques is based upon material extrusion. Material
extrusion machines work by forcing material through a
nozzle in a controlled manner to build up a structure. The
build material is usually a polymer filament which is extruded through a heated nozzle—an automated version of
the hot-glue-gun used for arts & crafts. After a layer ofmaterial is deposited by the nozzle onto a platform, theplatform either moves down or the nozzle moves up; andthen a new layer of material is deposited. In instanceswhere two nozzles are installed in a machine, one of thenozzles is typically used to deposit a water-soluble support material. Three or more nozzles are sometimes usedin machines designed for tissue engineering research, sothat scaffolds and other biologically-compatible materials
can be deposited in specific regions of the implant.
• Material jetting: Just like a standard desktop printer,material jetting deposits material through an inkjet printerhead. The process typically uses a plastic that requires
light to harden it (called a photopolymer) but it can also
print waxes and other materials. While material jetting
can produce accurate parts and incorporate multiple
materials through the use of additional inkjet printernozzles, the machines are relatively expensive and build
times can be slow.
• Binder jetting: In binder jetting, a thin layer of powder (this can be anything from plastics or glass to metals
or sand) is rolled across the build platform. Then the253Hideshi Miura / KONA Powder and Particle Journal No. 32 (2015) 253–263

printer head sprays a binding solution (similar to a glue)
to fuse the powder together only in the places specified in
the digital file. The process repeats until the object is finished printing, and the excess powder that supported theobject during the build is removed and saved for later use.
Binder jetting can be used to create relatively large parts,
but it can be expensive, especially for large systems.
• Powder bed fusion: Powder bed fusion is similar tobinder jetting, except the layers of powder are fused together (either melted or sintered—a process that uses heator pressure to form a solid mass of material without melting it) using a heat source, such as a laser or electron
beam. While powder bed processes can produce highquality, strong polymer and solid metal parts, the raw material choices for this type of AM are limited.
• Directed energy deposition: Directed energy deposition can come in many forms, but they all follow a basic
process. Wire or powder material is deposited into thinlayers and melted using a high-energy source, such as alaser. Directed energy deposition systems are commonlyused to repair existing parts and build very large parts,
but with this technology, these parts often require more
extensive post processing.
• Sheet lamination: Sheet lamination systems bondthin sheets of material (typically paper or metals) together
using adhesives, low-temperature heat sources or otherforms of energy to produce a 3D object. Sheet lamination
systems allow manufacturers to print with materials thatare sensitive to heat, such as paper and electronics, andthey offer the lowest material costs of any additive process. But the process can be slightly less accurate thansome other types of AM systems.
• Vat photopolymerization: Photopolymerization—
the oldest type of 3D printer—uses a liquid resin that is
cured using special lights to create a 3D object. Depending on the type of printer, it either uses a laser or a projector to trigger a chemical reaction and harden thin layers of
the resin. These processes can build very accurate parts
with fine detail, but the material choices are limited andthe machines can be expensive.
In these seven approaches, Powder Bed Fusion by using
laser or electron beam and Directed Energy Deposition
are most useful for fabricating the metal components. Especially, Direct Laser Forming (DLF), namely Selective
Laser Melting (SLM) or Direct Metal Laser Forming(DMLF), is one of the new AM techniques that emergedin the late 1980s and 1990s3). During the DLF technique,a product is formed by selectively melting successive layers of powder by the interaction of a laser beam. Upon irradiation, the powder material is heated and, if sufficient
power is applied, melts and forms a liquid pool. Afterwards, the molten pool solidifies and cools down quickly,
and the consolidated material starts to form the product.
This process is repeated until the product is completed.

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The non-irradiated material remains in the building cylinder and is used to support the subsequent layers. After theprocess, the unused powder is sieved and can be reused.
For some high reactive material such as Ti alloys, the process needs to be conducted under an inert argon or vacuum atmosphere.
A schematic illustration of typical DLF technique isdepicted in Fig. 1. Compared to conventional manufacturing techniques, DLF offers a wide range of advantages,
namely a lower time-to-market, a near-net-shape production without the need of expensive moulds, a high material utilization rate, direct production based on a CAD
model, and a high level of flexibility. Moreover, due to theadditive and layer-wise production, the DLF technique iscapable of producing more complex geometrical featuresthat cannot be obtained using conventional production
routes. Some previous researches have concentrated entirely on the influence of the process parameters on the
product properties such as the surface roughness and relative density4–8), or have investigated the obtained mechanical properties9–11) and the feasibility of the DLF techniquefor applications in, for example, the biomedical and aero
nautical industries12–14).
In this paper, DLF technique is employed to form thecomplex shaped compacts using two types of titanium
alloys: Ti-6Al-4V and Ti-6Al-7Nb. Ti-6Al-4V is the mostwidely used titanium alloy. It features excellent mechanical properties, such as high strength, low density and outstanding corrosion resistance. Therefore, Ti-6Al-4V has
led to a wide and diversified range of successful applications which demand high levels of reliable performance insurgery and medicine, aerospace, automotive, chemicalplant and other major industries. Especially, Ti-6Al-4V
alloy offers the best all-round performance for a variety
of weight reduction applications in aerospace, automotive
and marine equipment15,16). The other titanium alloy is
Ti-6Al-7Nb, which was developed as a biomedical replacement for Ti-6Al-4V alloy because Ti-6Al-4V contains va-

Fig. 1A schematic illustration of DLF process.

Hideshi Miura / KONA Powder and Particle Journal No. 32 (2015) 253–263

nadium (an element that has demonstrated cytotoxic
outcomes when it is isolated). Ti-6Al-7Nb is a dedicatedhigh strength titanium alloy with excellent biocompatibility for surgical implants. Used for replacement of hip
joints, it has been in clinical use since early 198617–19).
However, titanium and titanium alloys suffer from serious
disadvantages of poor machining properties. It takes
much time and cost to form Ti alloys as desired shapes of
products.
Therefore, in this paper, net-shaping technology, DLF
technique is applied to Ti alloys for efficient processing;also it is progressed in vacuum atmosphere to prevent theoxidation of Ti alloys. This research was aimed at challenging the three dimensional titanium alloy compacts
with more complex shapes, which are often used inbio-medical, aerospace and other field.

2. The honeycomb structure for medical
application
In this section, the honeycomb structure with array ofhexagons is taken for an example in order to investigate
the effect of parameters of DLF technique on the featuresin vertical plane, which perpendiculars to the scanningplane. The gas-atomized Ti-6Al-7Nb alloy powder (meanparticle size: 26 μm) was employed to fabricate the honeycomb structure with dimension of 6.5 × 6.5 × 6.5 mm.
The sizes of the hexagon edge in scanning plane were designed as 600 μm. Fig. 2 shows the schematic diagram of
the honeycomb compact.
The specimens were manufactured by an ytterbium fiber laser with 300 W maximum laser power, 50 μm beam
diameter and the continuous wave. To prevent the oxidation of the specimens, the scanning cab was evacuated by
the vacuum pump at first, and was filled by pure argon
gas. As a result, the scanning was performed at 600 Pa ofFig. 2argon pressures.
In order to investigate the effect of characteristics oflaser beam on the morphology and microstructure of
Ti-6Al-7Nb alloy, two types of laser scanning process (Aand B) were proposed in this section. The experimental
parameters are listed in Table 1. For process-A, the highpower of laser beam of 280 W was employed and thebeam scanned the powder layer only one time. Instead,the lower power of laser beam of 20 W was employed inprocess-B, and a pre-sintering (one time) and the repeating scanning (20 times) was employed to achieve thesmooth scanning-track.
When the proportional shaped specimens were fabricated by DLF using CAD data, the morphology of surface
was discussed based on the images of Scanning Electron
Micrographs (SEM). The measurement for density, compression test and tensile test were carried out as well toevaluate the mechanical properties of specimens.
The precision in vertical plane is one of the importantfactors to describe the morphology of a formed component. Fig. 3 shows the SEM images of formed parts by theprocess-A and process-B, which parameters are showedin Table 1. Both the cross-sectional views in XY plane
and in XZ plane are investigated to present the characteristics in vertical plane. Obviously, it was found that ahigher density are obtained by process-A. The voids appear in the view of XZ plane, even the number of scanning
repetition is 20 times. The reason can be explained that
Table 1

Processing Parameters of DLF Technique
Process-A

Process-B

Laser Power (W)

280

20

Scan Speed (mm/s)

80

80

Number of Scanning1

20

Powder Layer Thickness (μm)

80

100

Schematic graph and sizes of (a) a honeycomb compact and (b) a hexagon cell in honeycomb compact.

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Hideshi Miura / KONA Powder and Particle Journal No. 32 (2015) 253–263

Fig. 3

Images of cross-sectional views in XY plane and XZ plane for the formed specimens by process-A andprocess-B in Table 1.

the energy of 20 W of laser beam is not sufficient for thefull melting of Ti-6Al-7Nb alloy powder. Therefore thehigher power is helpful to increase the relative density of
the formed specimens.
Some tests were carried out in order to investigate theproperties of the honeycomb structures. Fig. 4 shows thetesting results of density and some mechanical properties
such as compressive strength and Young’s modulus of thereal human bone and specimens which are fabricated following the process-A and process-B as shown in Table 1.
In Fig. 4(a), the density of specimens and human bone are
exhibited and compared. Both the values of the specimens
by DLF technique are higher than those of human bone.
In Fig. 4(b) the compressive strength of specimens by
DLF technique are compared with the strength of humanbone (21–116 MPa)20). The compressive strength is oneof the important parameters which present the capabilityof artificial material for implanting. The specimen byprocess-A shows highest value, and instead the specimen
by process-B shows the similar value with real bone. Furthermore, the elastic modulus along X, Y axis is shown in
Fig. 4(c) and compared with the reported value of real human chancellors bone (1.2–4.6 GPa)20). For the purpose of
medical implanting, the Young’s modulus should matchthe value of human bone very well, at least is in this same
grade. From the results in Fig. 4(c), porous structure byprocess-B shows a corresponding value with the real
bone.

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Artificial bones and implants should have not only thesame mechanical properties as real bone but also the biocompatibility and high growth potential for bone cells. To
improve the osseointegration, honeycomb structure is apossible simple and effective structure. Porous micro
structure helps bone cells to grow into, so that it can beused as scaffold. It is reported that the bone cells are cultured well if the size of the unit cell structure is about
300 μm21), which is similar scale as a micro structure ofcancellous bone. The hole diameter of the lower rightsample in Fig. 5 is close to this dimension.
Mouse osteoblast cell line, MC3T3-E1 cell, was used
for culturing experiment on the present titanium alloy
compacts. Honeycomb structures with hole diameter
about 700, 500 and 300 μm were prepared to culture theosteoblasts. The structures were settled in plastic dishes
containing α-MEM (α-minimum essential medium) supplemented with 10 % FBS (fetal bovine serum). The cells
were applied on them at 200 cells/mm2 and cultured at
37 °C in a CO2 incubator. Fig. 6 shows the SEM imagesof cell-cultured samples after 14 days and 28 days. The
cells appear to the upper surface of honeycombs, and theextracellular matrix (ECM) is also observed. In the finest
structure with 300 μm holes, ECM formed chord-likestructure inside the holes, which could promote proliferation of osteoblasts. Fig. 7 shows the magnified view aroundthe hole after 21 days culture. More ECM is observed in
the smaller pore structures than in the larger one.

Hideshi Miura / KONA Powder and Particle Journal No. 32 (2015) 253–263

Fig. 4

Testing results for specimens by process-A, process-B
and the referenced values of human bone: (a) density
measurement, (b) compressive strength and (c) Young’s
modulus.

3. Improvement of mechanical properties foraerospace application
So many researches of this process (DMLF) with Ti
alloys were reported 22–25). Main issue is to improve therelative density and accuracy of final products by DMLF
process. That is, improvement of adhesion property between layers and the roughness of surface, for that, optimization of laser parameters, laser scanning strategy andre-melting of surface by laser have been studied and reported 26). In this study, we focused on the metal powder
feeding, especially feeding layer height (FLH). The reason is; in the same laser scanning condition, the property

of final product is strongly affected by the amount ofmelted metal pool mainly decided by feeding layer height.
In this point, the effect of feeding layer height on the final
product should be preliminary investigated before laserparameter optimization. Therefore, this study presents theeffect of FLH on the mechanical properties and surface
roughness of laser formed Ti alloy parts
The material used for this study is Ti-6Al-4V alloy
powder (Osaka Titanium technologies, TILOP64). This
powder, produced by a gas atomization process, is spherical with a particle size under 45 μm. Mean particle size is34.4 μm. All specimens were manufactured on the inhouse developed DMLF machine by our research group.
This DMLF installation is equipped with a ytterbium
fiber laser (IPG YLR-300 SM), which produces a laser
beam with a wavelength of 1070 nm and can reach a maximum power of 300 W in continuous mode. For the laser
used in this research, the spot diameter is about 50 μm.
The forming chamber is first evacuated and then filledwith an inert argon atmosphere to reduce the oxidation
during laser forming. At first, to investigate the effect oflaser parameters on the surface roughness, single layer
test was performed. Rectangular single layer (5 × 5 mm)
was irradiated in various laser parameters and optimized
laser condition was decided. Secondly, multi-layer specimens were prepared under the optimized laser condition.
The forming conditions for single- and multi-layer specimens are shown in Table 2. The surface morphology androughness was estimated by laser microscope (OLYMPUS
OLS 4000). Multi-layer specimens (bar-type tensile specimen) were prepared in various FLH from 80 μm to250 μm. Relative density of the tensile specimens wasmeasured by image analyzer. Five pictures of cross-section
for each specimen were used for image analyzing and theaverage was evaluated. The etched cross-section was observed in optical microscope. Oxygen content was estimated by oxygen analyzer (LECO TC-500SP). Tensile
strength were also assessed for each specimen.
Fig. 8 shows the representative surface morphologies
of single layer specimens with forming conditions. Scan
direction is from left to right on the picture. When low laser and scan rate, melted powder forms ball shape andthey are independently scattered on the substrate (Fig.
8a). If scan rate increases, balls start to be connected toeach other and form connected larger ball shape (Fig. 8g).
If laser power increases, these start to wet on the substrate(Fig. 8b), finally flat surface appears on the substrate withimproved surface roughness (Fig. 8c) because offull-melting of powder.
Fig. 9 shows the green-scale mapping of average surface
roughness (Sa). Dark green means high value and brightgreen is low value. In the condition of low laser power andlow scan rate that results independent balls on substrate,surface roughness is 50–58 μm. As the increase of laser

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Fig. 5Honeycomb structures of Ti-6Al-7Nb fabricated by the present laser-forming process. Length below
each photo shows the designed edge length of hexagonal walls, and the scan speed and number of the irradiation times are shown in the left of the images.

Fig. 6

SEM micrographs of honeycomb structures, in which osteoblasts were cultured for 14 days or 28 days.

Fig. 7SEM image of osteoblasts on the honeycomb structures
after 21-day culture. (a)700 μm hole diameter, (b)300 μm).

power, surface roughness increases due to the connection
between balls and partial wetting on substrate, however,
if flat surface appears in high laser energy density (highlaser power and low scan rate), surface roughness im-

258

proves as 12 μm. Fig. 10 shows the surface roughness ofmulti-layer specimens in various FLH. Surface roughnessof multi-layer specimens increased in compare with thatof single layer specimens, however in the case of 100 μm
FLH, the increasing is suppressed under 100 μm which isalmost one third of other high FLH values.
Fig. 11 shows the relative density of multi-layer specimens fabricated in various FLH. The spcimens of 100 μm
FLH which has low surface roughness shows almost fulldensity, 99.8 %. The others show low density and highdeviation. From these points, to improve relative density
and surface roughness, small FLH is very effective in
DMLF process. In Fig. 12, tensile strength in term of various FLH was shown. In 100 μm FLH, high tensile
strength 1083 MPa that is higher than ASTM value wasobtained. In high FLH over 150 μm, in spite of low den-

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Table 2

Process conditions for single and multi-layer specimens
Laser power, W

Laser power, W

Single layer

40–200

25–240Multi layer 1

120

50

Multi Layer 2

260

80

Fig. 8Fig. 9Scan pitch, μm

Feeding layer height, μm
33.4

100

100–250
80

Surface morphologies of single layer specimens by various laser parameters.

The green-scale mapping of single layer surface roughness (Sa, μm) measured by laser microscope; dark green
indicates high value and bright green indicates low value.

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Fig. 10 Surface roughness in different FLH.

Fig. 11

Tensile strength of multi-layer specimens fabricatedby various FLH (dashed line shows the value of
ASTM standard, 950 MPa).

Fig. 13

Cross-section of multi-layer specimens fabricated invarious FLH; (a) 100 μm, (b) 150 μm, (c) 200 μm, (d)250 μm.

Relative density of multi-layer specimens fabricatedby different FLH.

sity, quite high tensile strength was obtained.
Fig. 13 shows the cross sectional micrographs of tensile
specimens. Laser scan direction is perpendicular to the
pictures and scanning order is from right to left at 100 μm
scan pitch. Low density specimens are explained by thecreation of large pores. They are diagonally aligned in thefront view (Fig. 13b, c and d). These pore pattern was
also observed in Refs27). It was found that the slope angleof pore alignment is dependent on the hatch spacing: thehigher the spacing, the higher the slope angle. When thehatch spacing equals the melt pool width 28). Fig. 14 shows
the cross section of tensile specimen manufactured in
200 μm FLH. The upper image is surface layer showing
rough surface and the bottom image shows inside of specimen. Surface of specimen has wave pattern formed byinhomogeneous melting and solidification of powder. The
angle of the wave coincides with the angle of inside diagonal pore. This means that the valley space of wave pattern remains as pore because the powder existing there ispulled into the melted pool and solidifies on the top areaof wave. That is, high FLH causes rough surface having
wave pattern and this makes the unique pores structure,
therefore to control and improve the surface roughness

260

Fig. 12and density, small FLH which results flat surface isstrongly necessary.
In microstructure, acicular martensitic phase due torapid cooling and α + β phase were observed in all specimens (Fig. 15). In 100 μm FLH, martensitic phaseshowed epitaxial growth in direction to layering direction29). Fig. 16 shows oxygen content of tensile specimens
in different FLH. Oxygen content slightly increased compared to raw material. However, this is not a concerning
value for the decreasing of mechanical property30).
Fig. 17 shows the tensile test results of as fabricatedHideshi Miura / KONA Powder and Particle Journal No. 32 (2015) 253–263

Fig. 14

The relationship between surface roughness and diagonally aligned internal pores.

Fig. 16

Oxygen contents of multi-layer specimens.

Fig. 17 Tensile strength and elongation of DLF specimens; (a)as fabricated, and (b) as heat-treated.

Fig. 15

Acicular martensitic phase of multi-layer specimen by
100 μm FLH.

and as heat-treated compacts. After heat treatment, theelongation increased, however, it is not reached to 14 % of
wrought materials. Fatigue strength of direct laser formed
compacts was evaluated using tensile test specimen. Tension and tension fatigue testing was adopted with stressratio R of 0.1 and test rate as 30 Hz. Fig. 18 shows the
S-N curve of heat treated laser formed compacts. The
compacts show around 300 MPa of fatigue strength evennear full density. Normally, wrought materials show
higher than 500 MPa, thus, the fatigue strength of directlaser formed compacts are need to improve.

4. Conclusion
At first, the following conclusions were obtained, as aresult of examining various characteristics of the multilayered and honeycomb structured compacts in order tofabricate the medical devices with the titanium alloy pow-

Fig. 18

S-N curve of DLF specimens.

ders, in the multi-layered compacts of Ti-6Al-7Nb alloy
powders. Their tensile strength was 580 MPa. On theother hand, in the honeycomb structured compacts theirstrength was depended on the size of hole. In the suitablesize of hole (300 μm) for bone in growth, their compressive strength was around 400 MPa which was still high ascompared to that of human bone (170 MPa). The honeycomb structures showed good mechanical compatibility
with real bone, and had superb biocompatibility. Osteoblasts were cultured on the present honeycomb structuresfor 28 days. As a result, osteoblasts proliferated most on
the structure with 300 μm holes.
Also, single layer and multi-layer specimens were fabricated by DMLF process to investigate the effect of feed-261

Hideshi Miura / KONA Powder and Particle Journal No. 32 (2015) 253–263

ing layer height (FLH) on the relative density and surface
roughness of final products. From single layer experiment,
when the scan rate increases, ball shapes which is formedby melted powders were connected with others and it become larger due to the partial melting of metal powder. In
high laser power, metal powder melts fully and wets on
the substrate (previous layer), finally flat surface appears.
From multi layer experiment, small FLH was strongly effective for improving the relative density and mechanicalproperties. Moreover, surface roughness could be improved by small FLH because of full powder melting. In
this study, 70 μm in Rz, 99.8 % in density and 1080 MPa
in tensile strength were obtained by introducing small
feeding layer height. Finally, by optimum fabricating parameter, 1130 MPa of tensile strength, 9 % of elongation
and around 300 MPa fatigue strength were obtained.

Acknowledgement
The author sincerely thank to same lab. Staff, Dr. H.G.
Kang, Dr. F. Tsumori and Dr. T. Osada for their valuable
contribution to this study, and also to Dr. Kurata in Dep.
Mechanical Eng. of Kyushu Univ. for his kind helping in
culturing experiment.

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Author’s short biography
Hideshi Miura
Hideshi Miura is Professor of Mechanical Engineering at Kyushu University in Japanand head of Kyushu University Education & Research Center of Manufacturing. He received his BS, MS, and PhD degrees from Kyushu University. He is also a Former President of Japan Society of Powder & Powder Metallurgy (JSPM) and held the 2012 P/M
World Congress at Yokohama in Japan as Vice–chairman. He has truly pioneered the development of metal injection molding process and direct leaser forming for various metals in Japan. He published about 300 articles, 1 book (Translated the Powder Metallurgy
Science written by R. M. German), 12 patents, and 20 edited books. He received morethan 20 Awards from JSPM, Japan Society of Mechanical Engineering (JSME), Japan
Institute of Metals and Materials (JIM), Iron and Steel Institute of Japan (ISIJ) etc.

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Original Research Paper

Pinning Effect of In-Situ TiCp and TiBw on the Grain Size andRoom Temperature Strength of (TiC + TiB)/Ti Composites †Lei Jia 1*, Biao Chen 2, Shu-feng Li 1, Hisashi Imai 2 and Katsuyoshi Kondoh 2
1
2

School of Materials Science and Engineering, Xi`an University of Technology, China
Joining and Welding Research Institute, Osaka University, Japan

Abstract(TiCp + TiBw)/Ti composite was firstly prepared by spark plasma sintering (SPS) and hot extrusion from Ti-B4C
system, and then isothermally heat treated at 400–800 °C for 24 h to study the pinning effect of the in situ formed
reinforcements. Microstructure and phase composition was investigated by SEM, XRD and TEM, variation of thegrain size of Ti matrix was studied by EBSD analysis, and room temperature strength was also measured after
different heat treatments. Results show that, comparing with pure Ti materials, both the grain size of Ti matrix
and the room temperature strength of the composite almost keep stable after heat treatments, suggesting thepinning effect of in situ formed TiCp and TiBw is effective to suppress the growth of Ti matrix grains during heat
treatments.
Keywords: powder metallurgy, Ti matrix composites, mechanical properties, microstructure, in situ reaction

1. Introduction
Titanium matrix composites (TMCs) were considered
to be the most potential candidates for automotive andspaceflight applications due to their excellent properties,
such as high specific strength, high specific modulus,
good chemical stability and heat resistance (Feng andZhou, 2004; Liu and Huang, 2013). Up to now, B4C ceramic powder became one of the most popular additives
for Ti matrix, where an in-situ reaction took place and resulted in two kinds of ideal ceramic reinforcements, viz.
particle-like TiC and whisker-like TiB (hereafter referredas TiCp and TiBw), during the subsequent high temperature consolidation process (Zhou and Qin, 2011; Ni andGeng, 2008a).
As a structural materials used under a moderate temperature, the stability of the strength is an important factor to evaluate the reliability of Ti materials. Although
there are many factors that influence the strength ofTMCs, such as the grain size, O and N contents, as wellas the amounts and morphologies of the reinforcements(Li and Sun, 2013; Zhang and Chen, 2010; Sun and Li,
2012; Koo and Park, 2012), the strengthening efficiency is
†
1
2

*

Received 30 September 2014; Accepted 10 November 2014
J-STAGE online 28 February 2015
NO.5 South Jinhua Road, Xi`an 710048, China
2-1 Yamada-oka, Suita, Osaka 565-0871, Japan
Corresponding author: Lei Jia;
E-mail: xautjialei@hotmail.com
TEL: +86-29-82312505 FAX: +86-29-82312493

264

accomplished based on the strength of matrix (Meyers
and Chawla, 2009), which is always decided by the grain
size in the case of pure Ti matrix.
In many researches, small particles, such as precipitations, carbon nanotubes, organic or amorphous particles,
have been demonstrated to be effective to stabilize thegrain size, and thus maintain the strength of materials(Xue and Zeng, 2012; Vanherpe and Moelans, 2010). For(TiC + TiB)/Ti composites, there are two kinds of reinforcements with different morphologies. However, it is regretful that there are few reports about the pinning effectof the in-situ formed reinforcements on the grain growth
and the resulting mechanical properties of the composites.

2. Experimental details
In this work, (TiC + TiB)/Ti composite was preparedby in-situ reaction from commercial pure Ti (TC450,~25 μm) and B4C powders (~0.5 μm). Ti and 1.61 wt%
B4C powders were firstly mixed and then consolidated
and reacted by spark plasma sintering (SPS-1030S, SPS
Syntex) at 1000 °C for 60 min under a vacuum < 6 Pa.
Graphite moulds were used and the pressure was 30 MPa.
Then, the SPS billet with a diameter of 42.0 mm was hot
extruded to a rod with a diameter of 7.0 mm by a hydraulic press machine (SHP-200-450, Shibayama). Before hot
extrusion, the billet was pre-heated at 1000 °C for 3 min
and the extrusion speed was 3.0 mm/min. Subsequently,
the extruded rods were cut and isothermally heat treated©2015 Hosokawa Powder Technology Foundation

Lei Jia et al. / KONA Powder and Particle Journal No. 32 (2015) 264–269

Fig. 1

Microstructure characterizations on as-extruded (TiC + TiB)/Ti composites. (a) SEM image, (b) XRD
patterns, (c) and (d) BF and SAED patterns.

at 400, 500, 600, 700 and 800 °C for 24 h in a vacuum< 10 Pa, respectively. Finally, the heat treated rods weremachined to tensile test specimens with a diameter of
3.0 mm and gauge length of 15 mm. For the purpose ofcomparison, pure Ti samples were also prepared and posttreated by the same way as (TiC + TiB)/Ti composite.
Room tensile tests were carried out on a universal testing machine (Autograph AG-X 50 kN; Shimadzu) with across-head speed of 0.6 mm/min. The strain was recordedby using a CCD camera associated with the machine.
Phase compositions were identified by X-ray diffraction(XRD-6100, shimadzu). Microstructures were investigated by field-emission scanning electronic microscopy(JXA-8530F, JEOL). Grain size of the Ti matrix and pureTi was measured by electron backscatter diffraction(EBSD) technology with a TSL (TSL Digiview IV,
EDAX) instrument. Oxygen and nitrogen contents were
investigated by an inserted gas fusion nitrogen-oxygen
determination instrument (EMGA-830, Horiba). Both thetensile strength and the O/N contents measurements wererepeated three times to get an average value and to checkthe reliability of the results.

3. Results and discussion
3.1 Microstructure and phase composition of as
extruded composites
Fig. 1 shows microstructure charactering results of thecomposites prepared by SPS and hot extrusion from TiB4C powder system. It can be found from the SEM image(Fig. 1a) that, the as-extruded composite consists of matrix, particle-like phase and whisker-like phase. XRD pattern (Fig. 1b) shows these three phases are α-Ti, TiC and
TiB. Furthermore, the SAED patterns show the matrix is
α-Ti, whisker-like phase is TiB and particle-like phase isTiC. Such a result also agrees well with other publishedpapers (Ni and Geng, 2008b; Radhakrishna Bahat andSubramanyam, 2002, Jia and Li, 2014; Geng and Ni,
2008).

3.2 Microstructure evolutions by isothermal heat
treatments
Fig. 2 shows the microstructures of (TiC + TiB)/Ti
composite and pure Ti materials after isothermal heat
treatments at different temperatures for 24 h. Combining
Fig. 1a with Fig. 2 a1–c1, it can be found that the micro-

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Fig. 2

Microstructure of (a1-c1) (TiC + TiB)/Ti composite and (a2-c2) pure Ti materials after isothermal heat
treatments at (a) 400 °C, (b) 600 °C and (c) 800 °C for 24 h, respectively.

structures of (TiC + TiB)/Ti composite do not change
after heat treatments, especially the amount andmorphologies TiCp and TiBw. On the other hand, the grain
size of pure Ti materials increases obviously after isothermal heat treatments. However, it is regretful that the grain
size of Ti matrix in (TiC + TiB)/Ti composite cannot befound from the metallographic microstructures.
In order to clarify the pinning effect of TiCp and TiBw
on the growth of Ti matrix grains, EBSD analysis was
carried out, and the results are shown in Fig. 3. It is clear
that, with the increase of isothermal heat treated temperature, the size of Ti grains in pure Ti materials increases
obviously, but that in the (TiC + TiB)/Ti composite does266

not change significantly. Such a result shows without
doubt that, the in-situ formed TiCp and TiBw have important pinning effect on the growth of Ti matrix in the caseof high temperature exposure.
It is noticeable that, the size of Ti grains in both of pureTi and (TiC + TiB)/Ti composite increases first and thendecreases subsequently. Such a phenomenon can be attributed to the recrystallization of the extruded materials
in the following heat treatments, which can be also confirmed from the EBSD images, especially that of pure Ti
materials (Victoria-Hernandez and Yi, 2014). Because of
the larger deformation energy stored in the (TiC + TiB)/
Ti composite, the recrystallization temperature forLei Jia et al. / KONA Powder and Particle Journal No. 32 (2015) 264–269

Fig. 3Size of Ti grains in pure Ti and (TiC + TiB)/Ti composites after isothermal heat treatments at different temperatures for 24 h.

(TiC + TiB)/Ti composite is much lower than that of pure
Ti materials.

3.3 Variations of room temperature strength
Fig. 4 shows the room temperature typical nominal S-S
curves of (TiC + TiB)/Ti composite and pure Ti materials
after isothermal heat treatments at different temperaturesfor 24 h. It can be easily found that, the tensile strength ofpure Ti decreases continuously with the increase of isothermal heat treatment temperature from 400 to 700 °C,
and then increases when the treating temperature increases to 800 °C (Fig. 4a). However, the strength of(TiC + TiB)/Ti composite after isothermal heat treatments
almost keep a constant, except a small decrease after heat
treating at 400 °C for 24 h.
From the viewpoint of strengthening mechanism, thestrength of (TiC + TiB)/Ti composite at room temperature
can be written as (Li and Sun, 2013):
σ TMCs = σ Ti + Δσsol + Δσrefine + Δσreinf

Fig. 4

Typical nominal S-S curves of (a) pure Ti and (b)(TiC + TiB)/Ti composite after isothermal heat treatments at different temperature for 24 h and then tested
at room temperature.

Fig. 5

O and N contents of pure Ti and (TiC + TiB)/Ti composites after isothermal heat treatments at different
temperatures for 24 h.

(1)

where, σ TMCs and σ Ti are the strength of (TiC + TiB)/Ti
composite and pure Ti, Δσsol, Δσrefine, and Δσreinf are thestrength increments caused by O/N solution, grain refinement and in-situ formed reinforcements TiCp and TiBw.
According to the results of microstructure characterizations, isothermal heat treatments do not change theamount and morphologies of in-situ formed reinforcements TiCp and TiBw. On the other hand, the results of
EBSD analysis show the variation of grain size of Ti matrix. Therefore, in order to clarify the influence of pinningeffect on the room temperature strength of (TiC + TiB)/Ti
composite, it is necessary to investigate the change of O
and N contents.
Fig. 5 shows the O and N contents of (TiC + TiB)/Ti

composite after heat treatments at different temperaturesfor 24 h. For the purpose of comparison, the O and N contents of pure Ti materials have also been measured. It canbe found that, isothermal heat treatments do not change
the O or N content significantly for both (TiCp + TiBw)/Ti
composite and pure Ti materials. Therefore, it can be confirmed that, the room temperature strength are mainly influenced by the variations of the grain size of Ti matrix.

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Combining the EBSD results (Fig. 3) and nominal S-S
curves of both pure Ti and (TiC + TiB)/Ti composite (Fig.
4), it can be found that, the changing trend of tensile
strength agrees well with that of the grain size of Ti matrix. Such a phenomenon indicates without doubt that, thepinning effect of in-situ formed TiCp and TiBw is effective
to avoid the growth of Ti matrix, and then keeps the stability of the room temperature tensile strength of(TiC + TiB)/Ti composite after isothermal heat treatments.

4. Conclusions
In this paper, the pinning effect of in-situ formed TiCp
and TiBw on the growth of Ti matrix grains and theresulting room temperature strength of (TiC + TiB)/Ti
composite were investigated systemically. The main
conclusions can be drawn as follows:
1. Particle-like TiC and whisker-like TiB phases can be
fabricated successfully by in-situ reaction from the TiB4C powder system.
2. The amount and morphologies of TiCp and TiBw arestable during isothermal heat treatments, and their pining
effect can suppress the growth of Ti matrix grains.
3. Compared with pure Ti materials, the room temperature tensile strength is stable even after isothermal heat
treatment at 800 °C for 24 h, suggesting a good pinningeffect of in-situ formed TiCp and TiBw.

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Lei Jia et al. / KONA Powder and Particle Journal No. 32 (2015) 264–269

Author’s short biography
Lei Jia
Lei Jia received his PH.D from Xi`an University of Technology in 2012, and thenworked as a lecturer in School of Materials Science and Engineering, Xi`an University ofTechnology, China. From Jun., 2013 to Jul., 2014, he worked as a post-doctoral researcher
in Joining and Welding Research Institute (JWRI), Osaka University, Japan. His researchinterests include preparation of nonferrous alloys and nonferrous metal matrix composites
with excellent combination properties as well as their phase transformation behavior.
Biao Chen
Biao Chen is a doctor candidate for Mechanical Engineering, Osaka University, Japan.
He received his M.D. from Xi’an Jiaotong University, China, in 2012 with a topic on thesynthesis and characterization of multi-functional coatings. He is now pursuing his Ph.D.
in Osaka University focusing on developing high-performance carbon nanotube reinforced aluminum matrix composites. His research interests include carbon nanotubes,
graphene, metal matrix composites, nanocomposites, and nanophase strengthening.
Shufeng Li
Shufeng Li received his Ph.D degree from Nihon University in Tokyo, Japan in 2009
where he studied the sintering behavior and mechanical properties of hydroxyapatite ceramics by powder metallurgy. After his Ph.D., he joined Department of Composite Materials Processing in Joining and Welding Institute (JWRI) at Osaka University, as a
postdoc to investigate the copper, titanium metal matrix composites by powder metallurgy. In 2013, He joined Xi’an University of Technology, China. His research interests
include sintering properties, boundary reaction behavior, microstructure and mechanicalproperties of ceramic-metal matrix composites.
Hisashi Imai
Hisashi Imai is a lecturer in Joining and Welding Research Institute (JWRI), Osaka
University, Japan. He received his PH.D from School of Engineering, Toyama Prefectural University in 2007, Japan. After which he joined JWRI and finally was appointed
as lecture since 2014. His research interests include powder metallurgy, plastic processing and solid-phase bonding.

Katsuyoshi Kondoh
Katsuyoshi Kondoh is an adviser to Osaka University trustees and a professor of Joining
and Welding Research Institute (JWRI), Osaka University. He received both B.S. and M.S.
degrees and PhD from Osaka University. His major research field is powder metallurgymaterials and processing, in particular non-ferrous materials and their composites such as
magnesium, titanium, aluminum and copper. He also studies on high-performance carbon
nanotubes reinforced metal matrix composites and CNTs coating process for mechanicalmaterials and biomaterials. He published more than 200 papers and has about 120 patents.

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KONA Powder and Particle Journal No. 32 (2015) 270–278/Doi:10.14356/kona.2015026

Original Research Paper

Preparation of Composite Powder and Properties by Surface
Modification of Inorganic Pigments for Papermaking †Jun Hyung Cho 1 and Yong Won Lee 1,2*
1
2

Department of Paper Science & Engineeering, Kangwon National University, Korea
Hankuk Paper R&D Center, Korea

Abstract
In this study, functional composite-pigment was prepared by a dry impact-blending method, using the inorganic
materials used for coating of titanium dioxide, GCC, clay, and talc. After considering the particle size of inorganic
pigment, the selected core powder and fine powder were mixed at a suitable mixing ratio. When powder was
electrified, their fine particles could adhere by static electricity on the larger ones termed ‘core particles’, and thenform an ordered mixture. During the surface modification of pigments, the energy caused by fine powder
adhering to core particles was measured, and utilized in examining the efficiency of the change of particle size
surface modification. Afterward, these were susceptible to being composited by impact force, resulting from very
high-speed air circulation. After putting these surface modified pigments in water, their static electricity
properties were measured by particle charge detector and Zeta potential tester. From these results, it was revealed
that the elemental powder properties of papermaking inorganic pigment could be improved.
Keywords: electrostatic properties, surface modification, papermaking pigments

1. Introduction
Particle coating or surface modification technology has
been used for the synthesis of composite materials withdesired end-use properties in many industries, including
pharmaceuticals, food, cosmetics, ceramics, electronics
and special chemicals. At present, most commercial powder coatings are done by wet coating methods such as solgel processes, wet chemical deposition, spray coating, dip
coating, spinning disc coaters and a variety of fluidized
bed coaters. (Cho and Min., 2000; Cho et al., 2001b) Wet
particle coating is used primarily to form a barrier or film
between the large particle and its environment. The coating material usually consists of a solute dissolved in an
organic solvent or an aqueous suspension of the solute.
The organic solvent is usually highly volatile (VOC).
VOCs have been implicated as a major precursor in the
production of photochemical smog, which causes atmospheric haze, eye irritation and respiratory problems andeven some VOCs are carcinogenic. These environmental
drawbacks of wet coating methods have forced research†
1
2*

Received 31 July 2014; Accepted 10 November 2014
J-STAGE online 28 February 2015
1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do, 200-701 Korea
504 Teheranno Kangnam-ku, Seoul, Korea
Corresponding author: Yong Won Lee;
E-mail: iocard@naver.com
TEL/FAX: +82-33-262-4861

270

ers in the powder technology field to find alternative methods for coating of powders.
Dry particle coating is a relatively new and alternative
approach to wet coating methods and it has drawn attention of many researchers (Pfeffer et al., 2001).
Mechanical forces (mechanical impact, shearing etc.)
are used in order to attach submicron-sized small particles
onto relatively larger micron-sized particles, without using
any solvents, binders, or even water. Since the size of thesmall particles is minute, Van der Waals interactions arestrong enough to keep them firmly attached to the large
particles. Depending on the choice of equipment, operating conditions, and particle properties, either a discrete orcontinuous coating of small particles can be obtained.
However, the domestic technological level is a real condition that is receiving basic study at some university universities and research institutes at present. Recently, thetrend that demands multi-functional powder of high added
value is continuously increasing in our industrial society.
So, to invent effective future industry, intensive researchand development for vanguard multi-functional material
development that can break through the limit of existing
powder properties is being conducted (Kim and Lee,2001).
This research seeks to apply inorganic pigment for paper coating, to confirm the possibility that has been raisedin several studies about the field of paper application of asurface modification technology, and evaluate the effi-©2015 Hosokawa Powder Technology Foundation

Jun Hyung Cho et al. / KONA Powder and Particle Journal No. 32 (2015) 270–278

ciency of such a surface modification technology. Pigmentthat occupies most of the component parts of coatingcolor causes great effect on the properties of the physicaland optical properties of the coated paper, and the printability. This pigment influences the electronic propertiesof particles size and shape in the coating color, according
to smectic formation and combination form.
Usually, titanium dioxide particle, whose refractive index is high at the surface of inorganic composite particles,
has been used for the surface modification of the white
pigment itself, and to increase the scattering degree. The
particle sizes of inorganic pigments for paper applicationsof clay, talc, GCC, and titanium dioxide were measured,then selected core particles and fine particles were mixed
at a desirable mixing ratio. When electrified, the fine particles could adhere to the larger ones, due to static electricity. These powders were composited by impact force,
resulting from very high-speed air circulation. During thesurface modifying of pigments, the energy caused by thecore powder’s adherence to fine powder was measured,and the surface modifying efficiency according to thechange of particle size was also investigated (Zhao andYang, 2003).
Finally, design and basic data are presented for the invention of composition particles that can give various
abilities, achieving the objective of this research.

2. Materials and Experiments
2.1 Background on dry particle coating
Dry particle coating to change the surface properties orfunctionality of powders is a very important process formany industries. Typical applications include the modification of flowability, wettability, solubility, dispersibility
etc. for particle properties. In dry particle coating processes, materials with relatively large particle size (coreparticles, 1–500 um) are mechanically coated with small
particles (fine particles, 0.1–50 um), in order to create newfunctionality, or to improve their initial characteristics(Honda et al., 1987, 1989, 1992; Honda, 1992). Since thesize of the small particles is minute, Van der Waals interactions are strong enough to keep them firmly attached tothe core particles. Thus, either a discrete or continuous
coating of fine particles can be achieved, depending on avariety of operating conditions, including processing
time, rotation speed, weight fraction of fine to large particles, and particle properties. Multiple layering is possible,
when using different coating materials, and processing
them one after another (Hersey, 1975, 1977). In a typicaldry particle coating process, core particles are mechanically mixed with fine cohesive powder. At the beginningof the operation, the fine aggregates adhere to the core

particles in their immediate vicinity. When a core particle
carrying fines adheres, its surface collides with a noncoated particle, and it transfers part of its fine particles tothe latter. By friction and collision between the particles,
the agglomerates of fine particles are gradually dispersed
onto the surface of the carriers, which results in an increase in the coated surface area (Tatami et al., 2010).
Afterward, the mechano-fusion system greatly modifies the surface texture, by giving a high level of mechanical energy to the particles. Local melting and a partial or
total penetration of the fine particle component into thebody of the larger particles take place (Cho et al., 2001b).
The dispersion of fine particles actually occurs from theearlier stages of the process. The dispersion rate and thedegree to which the agglomerates are broken up depend
strongly on the mechanical energy input, and therefore,on the type of mixer used.

2.2 Order mixture
The subject of dry particle coating is closely related tothe subject of dry mixing of powders. Theoretically, a binary mixture process should mix two different species ofpowders, so that any sample taken from the mixture would
contain the same proportion of the two powders (Kim andLee, 2001). This is very hard to achieve in practice, when
the powders are either cohesive, or they are very different
in particle size. When powders are cohesive, they naturally form agglomerates, and mixing of these powders requires preliminary breaking up of the agglomerates.
When the powders are very different in particle size, there
is an increased tendency for segregation as the particle
size becomes larger. However, when the particles to bemixed are very different in particle size, the smaller particles tend to adhere on the surface of the core particles(Lee and Seul, 2002). The adhesion force between thesmaller particle and the larger particle is greater than theweight of the smaller particle, so the detachment of small
particles from the surface of larger particles is difficult(Honda et al., 1988, 1991).
The advantage of an ordered mixture is that it provides
a much better degree of homogeneity than random mixing, as long as the particle size distribution of the larger
particles is not too wide. So, in terms of subsequent segregation, ordered mixtures are more stable than random
mixtures. It was also shown by early researchers that having a very wide size distribution of the larger size particles
may lead to “ordered unit segregation”, because of the nature of the poly-disperse core particles (Yeung and Hersey,1979). Fig. 1 shows the concept of ordered mixture.

2.3 Surface modification system
The surface modification systems, which make it possi-

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Fig. 1

Table 1

Core particle

Fine particle

PowderSpecies

Shape

Chemicalcomponents

Particle
Size (um)Specific
Gravity (–)

GCCa

No. 1 GCC (Omya)
No. 2 GCC (Omya)

Amorphous

CaCO3b

1
2

2.93

clay

No. 1 clay (Engel Hard)
No. 2 clay (Engel Hard)

Plate

Al2SiO3

2
4

2.60

talc

Coating talc (Hanseng)

Amorphous

MgSiO26

2.80

titanium
dioxideP-25 (Degusa)

Rounded

TiO2

0.025

3.80

Ground Calcium Carbonate, b Chemical components

ble to combine fine particles in dry condition, use physical methods that are classified into powder mixer andsurface modification system. The fine particle and thecore particle are fed into the powder mixer, where the twoparticles are mixed and dispersed to form an “Ordered(interactive) Mixture”. (Ouabbas et al., 2009; Yip andHersey, 1977a, 1977b, 1977c) The order mixture isweighed, before being fed into the surface modification
system, the main unit of the system. The surface modification system comprises a high-speed rotor, stator, andcycle tube. The processed material fed to the surface
modification system is dispersed, and is repeatedly subject to mechanical effects of mainly impact force, compression, friction and shearing force, as well as theparticles mutual effect, to uniformly embed film or spherein a short time (Fig. 2). (Yoshihara and Pieper, 1999)

2.4 Surface modification of the materials
This experiment used 5 kinds of inorganic pigment thatare usually used for making paper by particle: two GCC,
No. 1 clay, No. 2 clay and talc, which are used for surface
modification of inorganic pigment. Table 1 displays theuse of titanium dioxide (Anatase 80 %, Rutile 20 %) byfine particle, and the basic properties of matter of thesepigments. (Pertti, 1985)

272

Mechanism of surface modification.

Properties of materials

DivisionaFig. 2Model flow of surface modification process.

2.5 Surface modification of pigments
The added quantity for surface modification was calculated by retrieving the ratio between the specific gravity,
and the particles size of core particles and fine particles
used in the experiments. In particular, the mixing ratio
between the mass and volume ratio of repeated experiments measured the efficiency of surface modification(Table 2). The required additional quantity calculated
through the equation, is spouted to the surface modification
system (NHS-0 model), for surface modification by titanium dioxide for inorganic pigments. (Kangwantrakool etal., 2001)
The surface modification operating conditions used in
the experiments were pre-treatment by powder mixer(1000 rpm, 1 min), and then modification by surface modification system (9,000 rmp, 4 min).

2.6 Measurement of adsorption energy
10 g of core particle GCC, clay, talc and fine particle titanium dioxide dispersed powder were added to each device of the electrostatic charge measuring system (TR-8651,
SANKYO POWTECH). Frictional force and rotational
force were added to rotate the rotor for 1 minute at
1200 rpm. After moving to the Faraday well using the suction force of the vacuum pump, each pigment was then

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Table 2

Core particle and TiO2’s compound ratio
Mass ratioCore Particle Fine Particle

No. 1 GCC: titanium dioxide

92.4

7.6

No. 2 GCC: titanium dioxide

96.2

3.8

No. 1 clay: titanium dioxide

95.7

4.3

No. 2 clay: titanium dioxide

97.8

2.2

talc: titanium dioxide

99.1

0.9

Volume ratioCore Particle Fine Particle

No. 1 GCC: titanium dioxide

94.1

5.9

No. 2 GCC: titanium dioxide

97.0

3.0

No. 1 clay: titanium dioxide

97.0

3.0

No. 2 clay: titanium dioxide

98.5

1.5

talc: titanium dioxide

99.3

0.7

measured by charge amount electrometer and quantity
charged side container. At this time, the impact energy adsorption when the fine particle was modified further to thecore particle surface was measured by using the Coulomb
force and the Van der Waals charge (Ohashi et al., 1995).

2.7 Measurement of Zeta potential
A 1 % mixed solution was prepared when the surface
modified papermaking pigment was charged surface
modified pigment in distilled water, to examine whether
the change in the electrical properties of the pigment
was surface modification. After mixing for 5 minuteswith a stirrer, the Zeta potential change of each pH of themixed solution was measured by Zeta potential meter(ZETASIZER 3000HS, MALVERN).

2.8 Measurement of charge density
The charge density was measured by charge density meter (PCD 03 pH-S, HERCULES) to make a 1 % solution
mixed with distilled water to a surface modified pigment.

3. Results and Discussions
3.1 Microstructure
The variations in respect of the titanium dioxide on thesurface of inorganic pigments used as core particle via
surface modifications were measured by FE-SEM (S4300, HITACHI). As Figs. 3–5 show, the surface modification of treated pigment was accomplished better thanthat of primary sample, and titanium dioxide was overall
well distributed (Yeung and Hersey, 1979).

In particular, the plate-like particle shape of the clay
and talc surface of the surface-modified titanium dioxide
was evenly distributed, and the surface modification effect was confirmed to be excellent. The basic model of thesurface modification in the surface modification system
has a spherical shape particle, but it appears that the modification effect is excellent, even in the case of the clay
and talc as a plate (Mujumdar et al., 2004). The surface
modification was performed in terms of mass ratio andvolume ratio separately. Equivalent surface modification
effect on the particle inorganic pigment for the paper
surface-modified by titanium dioxide was found underscanning electron microscopy (FE-SEM) in the comparisonof the measurement such as mass ratio and volume ratio.
Both of them showed uniform surface modification.
And therefore all experiments were carried out by using
the mass ratio.

3.2 Brightness and scattering index
Figs. 6 and 7 shows the degree of brightness and scattering Index of GCC, clay, talc, and titanium dioxide mixed
with the sample, and the mass ratio of the sample surface
modification treatment. The surface was modified in all
samples, even if the brightness of the scattering index sample was larger than that of the simple enhanced mixing.
Separate modification of each sample showed the increased scattering degree compared to the modification of
mixed one because the number of times being refracted
increases and the refraction angle is different when lightgoes through the pigments whose fine particles on the core
particles have been modified (Cho et al., 2001a; Willets etal., 1958).

3.3 Adsorption energy
Table 3 shows the charge amount of the pigment in each,in order to measure the energy at which the fine particle isadsorbed to the core particle. There is a tendency of theparticle charge to increase, as the particle size of the core
particle is increased. In the case of supplying frictional
force and rotational force to the core particle surface, itappeared that the amount of charge value increases, as theparticle diameter increases (Yang et al., 2005). Fig. 8
shows a graph of the measurements of the charges of the
Van der Waals force and the law of Coulomb force calculations. The core particle and fine particle adsorption energy of the particles were found to increase, as the ratio
between the particles diameters increased. This is becauseof receiving a larger impact force of Van der Waals forces
than the Coulomb, when friction occurred by mixing thetwo particles. The adsorption energy is large, and is better
than the surface modification, which was confirmed
through previous research (Ishizaka et al., 1992).

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Fig. 6274

Fig. 3

FE-SEM images of GCC as shown in the (a) GCC pigment and (b) modified with titanium dioxide.

Fig. 4

FE-SEM images of clay as shown in the (a) clay pigment and (b) modified with titanium dioxide.

Fig. 5

FE-SEM images of talc as shown in the (a) talc pigment and (b) modified with titanium dioxide.

Brightness contrast by Chromameter CT-300 (Mixing& modification).

Fig. 7

Scattering index by Elrepho 3300 (Mixing & modification).

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Table 3Fig. 8Charge of a particle calculated from mean particle size, density and specific charge

Division

Particle Size (um)Density (–)

Specific charge (Cg–1)

Value of –Q or q (C)

GCC

4

1.38

–2.86 × 10 –7

9.13 × 10 –19

clay

2

1.73

–1.85 × 10 –7

2.13 × 10 –20

talc

8

1.14

–8.79 × 10 –7

7.14 × 10 –16

titanium dioxide

0.025

2.05

+2.75 × 10 –7

8.33 × 10 –21

Calculation results of the total energy of the monolayer
particle coated system. (a) Curve GCC modified withtitanium dioxide (b) Curve clay modified with titanium
dioxide (c) Curve talc modified with titanium dioxide.

3.4 Zeta potential
The zeta potential was measured, to confirm the change
in electrical properties in the solution of the surface modified pigment. In Fig. 9, the graph shows the Zeta potential of inorganic pigments. The zeta potential of titanium
dioxide measured higher than clay and talc at isoelectric
point 7. It is known that the relative aggregation propensity is strong, cohesive and pulp fiber is improved in clay
and talc, with isoelectric point lower than titanium dioxide. The surface-modified talc and clay increased theisoelectric point of the Zeta potential. These results indicate that talc and clay particles reformed with titanium
dioxide on the surface showed the characteristics oftitanium dioxide particles. (Lee and Jeong, 2000) It appears that when preparing the coating pigment, the viscosity and fluidity of the pigment can be controlled, andwhen used in the filler, the change in quietness of electrical properties modulates the aggregation propensity of
paper dust.

3.5 Charge density
Fig. 10 shows the particle charge density of the surface-­

Fig. 9Measurement of Zeta potential.
(a) Non-modified paper pigments(b) clay modified with titanium dioxide(c) talc modified with titanium dioxide

modified pigment (PCD). It could be seen that pigments
have a charge amount, by supplying a rotational force tothe frictional force resisting the dry particles, in the form
that was affected by the electrical properties in aqueousform.
The charge density of GCC is the highest in the pig-275

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Fig. 10 PCD of mixing & modification pigments.

ments. It is assumed that higher charge density can be accomplished as the contact area and the number of times
being fractionated between the same particle increase
when fractional and rotational forces apply to the spherical shape particle.

4. Conclusions
The inorganic pigments for paper manufacturing weremixed with titanium dioxide at certain proportions, andthe following conclusions were drawn through consideration of the results after modifying the surface of the
mixture using the surface modification system
There were no differences between the mixing ratios
on a mass basis or on a volume basis of the Core Particles
to Fine Particles as confirmed through scanning electron
microscopy (SEM) of the surface-modified organic pigment, and the same effect of surface modification wasshown.
The adsorption energy needed between the particles in
the pretreatment step of the surface modification of inorganic pigment for paper manufacturing increased as theratio of the particle size of the Core particles to Fine particles became larger. In addition, when measuring theadsorption energy, the particle size was observed to havea great influence on the Van der Waals force and the
Coulomb’s force acting between particles. When surface
modification with titanium dioxide was performed on
clay, the whiteness showed more excellent results than thesample which was simply mixed. These results show theeffect of the surface modification with titanium dioxide
for which the whiteness is relatively higher than clay;
however, it was considered that during the process of
surface modification, the whiteness of the sample withsurface-modification was further improved because ofhigher uniformity of the particle distribution and reduction in particle size through the spheroidization treatmentof the particles in the irregular hexagonal form. In addition,
the whiteness of the GCC and talc which were used to276

modify the surface with titanium dioxide was also improved,
as in the surface modification of the clay. The reason is
because the optical properties of the modified pigment
are shown through the fine particles, as they are modified
on the surface of the core particles.
When the Zeta potential was measured in order to determine the change in electrostatic properties, the cohesion tendency according to pH was reduced, and a change
in the isoelectric point was shown in all samples which
underwent surface modification with titanium dioxide. In
the case of PCD measurement, the measured values of thesurface-modified samples were higher than the simply
mixed samples, due to increase of the quantity of electriccharge generated at the time of the collision and friction
between the core particles and the fine particles during
the surface modification.
When the surface of the inorganic pigment for paper
manufacturing was modified with titanium dioxide, theeffects of the surface modification could be confirmed asa change in optical and electrostatic properties. It is considered that these results will provide a basis to compensate for and improve the problems in the process which
can occur at the time of manufacturing the coating color
for the coating on the surface of paper, as well as allow
the addition of other inorganic pigments on the basis ofthese results in the field.

Acknowledgements
This study is supported by Kangwon National University.

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particle by dry coating, Powder Technology, 190 (2009)
200–209.
Pertti A., Talc as a coating pigment in lightweight coatedpapers, TAPPI Journal, 11(1985) 68–235.
Pfeffer R., Dave R.N., Dongguang W., Ramlakhan M., Synthesis of engineered particulates with tailored properties using
dry particle coating, Powder Technology, 117 (2001) 40–67.
Tatami J., Nakano H., Wakihara T., Komeya K., Developmentof Advanced Ceramics by Powder Composite Process,
KONA Powder and Particle Journal, 28 (2010) 227–240.
Willets W.R, Bingham R.T, Maechetti F.R., Paper Coating Pigments, TAPPI Monograph, 20 (1958) 211–231.
Yang J., Sliva A., Banerjee A., Dave R.N., Pfeffer R., Dry particle coating for improving the of flowability of cohesive
powders, Powder Technology, 158 (2005) 21–33.
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Yip C.W., Hersey J.A., Ordered random mixing choice systemmixer, Drug Development and Industrial Pharmacy, 3(1977a) 429–438.
Yip C.W., Hersey J.A., Perfect powder mixtures, Powder Technology, 16 (1977b) 189–192.
Yip C.W., Hersey J.A., Segregation ordered powder mixtures,
Powder Technology, 16 (1977c) 149–150.
Yoshihara I., Pieper W., Hybridization technology for surface
modification of powders without binders, Swiss Pharma, 6(1999) 21.
Zhao J., Yang X., Photocatalytic oxidation for indoor air purification, literature review, Building and Environment, 38(2003) 38–49.

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Jun Hyung Cho et al. / KONA Powder and Particle Journal No. 32 (2015) 270–278

Author’s short biography
Jun Hyung Cho
Jun-Hyung Cho is a professor in the Department of Paper Science & Engineering at
Kangwon National University in Republic of Korea. He received his Master Degree inEngineering and Ph.D. degree in Chemical Engineering from Nagoya University in Japan
from 1984 to 1989.
In 1996, he went for a one year visiting scholar at Wisconsin University in UnitedState and he took another year of journey as a visiting scholar at North East Forest University in China in 2012. His research and consulting interests are in the area of pigment
for Papermaking and Environment-friendly Material.

Yong Won Lee
Yong-Won Lee obtained the B.Eng. and M.Eng., degrees in Paper science & Engineeringfrom Kangwon National University in Republic of Korea. He is presently a Ph.D student
and is working at Han-kuk Paper Company R&D team. He has studied to develop the
new functional pigments with environmentally-friendliness for paper-making industries.
Recently, functional printing paper of the development pigments usign surface modification method for special papermaking.

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Symposium Reports
Hosokawa European Powder Technology Symposium
As one of special events to celebrate the 20th anniversary of Hosokawa Powder Technology Foundation, theHosokawa European Powder Technology Symposium was held at the conference hall of Hosokawa Alpine AG in
Augsburg, Germany on Monday, September 29, 2014. It was the first symposium held by Hosokawa Foundation
outside Japan aiming at the globalization of the Foundation. It organizes the Symposium on Powder Technology inJapan since 1992 taking over from Hosokawa Micron Corporation. The first Symposium on Powder Technology washeld already in 1968 as a special event on the occasion of completion of the new building of Hosokawa Micromeritics Laboratory (current, Powder Technology Research Institute) in Hirakata operation of Hosokawa Micron Corporation. The Hosokawa European Powder Technology Symposium was attended by more than 60 participants fromvarious countries in Europe and Japan including Mr. Miyata, the vice-president of the Foundation and the president(current chairman) of Hosokawa Micron Corporation.
The Symposium was started by Prof. J. Hidaka, General Manager of the Foundation and first the welcome address
was given by Mr. Y. Hosokawa, the president of the Foundation. In this speech, he emphasized the importance of theinformation exchange among the researchers and engineers on the worldwide basis. Following this, four lecturers
made presentation from the viewpoints of material design and powder processing in relation with the main subjectof “Trends in Powder Technology for New Functional Materials” shown in the program below. At the end of this
symposium, Mr. Krieg, the president of Hosokawa Alpine AG, presented the closing remarks to the audience of thesymposium on behalf of the sponsors of this Symposium.
The history and activities of the Foundation were introduced in the preprints and the poster presented in the reception hall, where the registration was made and drinks were served during the coffee break. Many discussions
were conducted in this hall as well as the conference room.

Hosokawa European Powder Technology Symposium
Theme: Trends in Powder Technology for New Functional Materials
Welcome address
Session 1

Mr. Y. Hosokawa (President of Hosokawa Powder Technology)

Chairperson: Dr. B. Furchner (Hosokawa Alpine AG)• Lecture 1“Overview and Prospects of Powder Technology”
Prof. W. Peukert (Erlangen University)• Lecture 2“Advancements, Challenges and Trends in Size Reduction, Pharmaceutical Applications”
Dr. M. Juhnke (Novartis AG)
Session 2

Chairperson: Dr. T. Yokoyama (Hosokawa Micron Corp.)• Lecture 3“Design of Powder Processing Equipment by Computer Simulation”
Prof. J. Kano (Tohoku University)• Lecture 4“Computerization of Powder Processing Systems”
Dr. S. Sander and Mr. G. Kiederle (Hosokawa Alpine AG)
Closing remarks

Mr. P. Krieg (President of Hosokawa Alpine AG)©2015 Hosokawa Powder Technology Foundation

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Registration at the conference hall

Audience of the Symposium

The 48th Symposium on Powder Technology
The 48th Symposium on Powder Technology was held at Tokyo Marriott Hotel, Japan on Thursday, September 4,
2014. It was organized by Hosokawa Powder Technology Foundation with the sponsorship of Hosokawa Micron
Corporation. More than 150 people from the industries and universities attended this symposium. The theme of thesymposium this year was “Advancement of Powder Technology Contributing to Manufacture of Various Materials”.
There were six lectures with questions and answers followed by a get-acquainted party for further free discussions.
The contents of the symposium were as follows.

The 48th Symposium on Powder Technology
Theme: Advancement of Powder Technology Contributing to Manufacture of Various Materials
Opening address
Session 1

Prof. emeritus Minoru Takahashi (Nagoya Institute of Technology)

Chaired by Prof. emeritus Kikuo Okuyama (Hiroshima University)• Lecture 1“Fabrication of Highly-Structure-Controlled Ceramics by Advanced Powder Processing”
Dr. Yoshio Sakka (National Institute for Materials Science)• Lecture 2“Additive Manufacturing Method of Metal Powder Combining Laser Fusing and Milling Technique—Thermal Conductivity of Metal Powder—”Prof. Takashi Ueda (Nagoya University)

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Session 2

Chaired by Prof. Makio Naito (Osaka University)• Lecture 3“Electrode Structure and Ceramics Powders”
Prof. Yoshio Ukyo (Kyoto University)• Lecture 4“Ceramics Fillers in Dielectric Composites for High-Frequency Applications”
Dr. Yusuke Imai (National Institute of Advanced Industrial Science and Technology)
Session 3

Chaired by Prof. Hirofumi Takeuchi (Gifu Pharmaceutical University)• Lecture 5“Recent Trends of Drug Formation Developments in Anticipation of Micro or Nano Particle Technology”
Dr. Tadatugu Tanino (Shionogi & Co., Ltd.)• Lecture 6“Recent Trends of Powder Technology to Contribute to Various Manufacturing Processes—
Pharmaceutical Market—”
Mr. Sakae Mukaigawara (Hosokawa Micron Corporation)
Closing address

Mr. Yoshio Hosokawa (President of Hosokawa Powder Technology Foundation)

Symposium on Powder Technology

Get-acquainted Party

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The 21st KONA Award
Dr. Yoshio Sakka, Managing Director of the Materials Processing Unit at National Institute for Materials Science(NIMS), was selected as the winner of the 21st KONA Award, which is sponsored by Hosokawa Powder Technology Foundation and given to the scientist(s) or group(s) who have achieved distinguished research works in the fieldof particle science and technology.
He received his B.E. in 1978, M.E in 1980, and Ph.D. in 1983 from Kyushu University. He joined the National
Research Institute for Metals (presently NIMS) in April 1983. He became a Group Leader in April 1997, led theFine Particle Processing Group from April 2002, and was Managing Director of the Nano Ceramic Center from
April 2005 to March 2010, before taking up his present position. In 1991–92, he spent a year at the University ofWashington in Seattle, USA.
He has been a member of the board of the Ceramic Society of Japan (May 2007–May 2011 and May 2013–May
2014), the Society of Inorganic Materials, Japan (May 2007-present), and Japan Society of Powder & Powder Metallurgy (May 2011-present). He currently serves as an editor of Science and Technology of Advanced Materials (International), Journal of Materials Science Society of Japan, Journal of Inorganic Materials, Japan, and Journal of
the Japan Society of Powder & Powder Metallurgy.
Dr. Sakka has made outstanding contributions to science and technology of ceramic processing. He has successfully developed many types of nanoparticles, such as metal, ceramic, mixed, and composite particles, by a DCplasma method developed at NIMS. He has also developed new wet-chemical methods, such as the nano-explosion
method, and a new sol-gel processing method by which several types of ceramic mono-dispersed particles havebeen prepared.
In the processing of ceramics by sintering, well-dispersed fine powders are desirable not only to reduce the sintering temperature but also to obtain dense and fine-grained microstructures. By employing colloidal processing,
Dr. Sakka’s group produced world’s first superplastic alumina, which can be elongated to over 550 %. Since thisachievement they have produced several types of high-strain-rate superplastic ceramics. In addition, Dr. Sakka’sgroup succeeded in developing a new method for producing textured ceramics, in which the colloidal processing,such as by slip casting and electrophoretic deposition (EPD), is conducted under a strong magnetic field (c.a. 10 T).
This method has several advantages and can be applied to many noncubic ceramics. They have fabricated many
types of textured ceramics, such as α-Al2O3, TiO2, ZnO, HAP, AlN, SiC, β-Si3N4, MAX phase ceramics, Zr(Hf)B2,
and B4C and showed the unique dependence of their properties on the crystalline plane. β-Si3N4 with high thermal
conductivity of approximately 180 Wm–1 K–1 and the nacre-like structured Nb4AlC3 with over 1000 MPa bending
strength and 15 MPa.m1/2 fracture toughness are typical examples.
Furthermore, Dr. Sakka’s group developed novel sintering techniques, especially those based on the Spark
Plasma Sintering (SPS). They used a combined modeling and experimental approach to clarify the relationship between the SPS parameters and the final properties.
Highly transparent alumina, high-hardness B4C,
diamond particles dispersed in pure WC, and superplastic ceramics of spinel and zirconia systems
are typical examples of ceramics fabricated by SPS.
So far, the number of published original papers
in these research fields is above 600 and that of reviewed papers, book chapters and related articles is
totally 110.
On March 11th, 2014, Mr. Yoshio Hosokawa,
President of the Foundation, handed the 21st KONA
Award to Dr. Sakka at the presentation ceremony
held at Hosokawa Micron Corporation in Hirakata.

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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 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 (mailto: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 and 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. Distribution of KONA is madeby each Secretariat.
©2015 Hosokawa Powder Technology Foundation

Free electronic publication of KONA is available at http://
www.kona.or.jp
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.
•	Original papers submitted for publication become immediately
the property of the Hosokawa Powder Technology Foundation
and remain so unless withdrawn by the author prior toacceptance for publication or unless released by the Editor.
•	The original papers in KONA should not be reproduced nor
published in any form without the written permission of the
Hosokawa Foundation.

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