ISSN 0288-4534

CODEN: KPPJAD

Powder andParticle Journal

No.40 (2023)

Hosokawa Powder Technology Foundation

KONA Powder and Particle Journal No. 40 (2023)

About this Journal
KONA Powder and Particle Journal is a refereed scientificjournal that publishes articles in the field of powder andparticle science and technology annually since 1983. Thisjournal is distributed free of charge to the members of scientific communities and universities and research libraries
throughout the world by the Hosokawa Powder Technology
Foundation (www.kona.or.jp) established by Mr. Masuo
Hosokawa in 1991.
The Chinese character “ 粉 ” in the cover is pronounced as“KONA” in Japanese, and means "Powder", which was
hand-written by Mr. Eiichi Hosokawa, the founder of the
Hosokawa Micron Corporation (www.hosokawamicron.
co.jp/en/global.html).

About the Cover
The fast increasing demand for lithium-ion and future typesof lithium batteries requires the further development of an
economically and, at the same time, ecologically sustainablesupply and production circle. Starting with the processing of
the primary raw materials and especially secondary, recycledmaterials, over the extraction of the valuable materials, theirrefinement to battery grade intermediates, the (re)synthesisof active and passive materials to the production of the cellcomponents like electrodes and finally the battery cells andsystems. Sustainability can only be achieved if the end-oflife battery systems are used as secondary material source
for the production of new battery cells, through which therecycling of the materials and the re-synthesis or reconditioning of the active materials becomes mandatory. By
this milling, dispersing, classification and sorting processes
get a central role within the circular processing of lithiumion batteries and next battery generations. Review on this
topic is discussed on pp. 50–73.

www.kona.or.jp

Editorial Board
Editor-in-Chief
M. Naito(Osaka Univ., Japan)

Asian / Oceanian Editorial Board
X.S. Cai(Univ. of Shanghai for Sci. & Tech., China)
T. Charinpanitkul(Chulalongkorn Univ., Thailand)
Y.F. Chen(Chinese Academy of Sciences, China)
H. Choi(Changwon National Univ., Korea)
S. Das(Indian Institute of Technology, India)
M. Fuji(Nagoya Institute of Technology, Japan)
E. Iritani(Nagoya Univ., Japan)
H. Kage(Kyushu Institute of Technology, Japan)
A. Kawasaki(Tohoku Univ., Japan)
D.J. Lee(National Taiwan Univ., Taiwan)
J.H. Li(Chinese Academy of Sciences, China)
H. Makino(CRIEPI, Japan)
S. Matsusaka(Kyoto Univ., Japan)
K. Okuyama(Hiroshima Univ., Japan)
Y. Otani(Kanazawa Univ., Japan)
Y. Sakka(NIMS, Japan)
Y.-S. Shen
(Univ. of New South Wales, Australia)
Y. Shirakawa(Doshisha Univ., Japan)
H. Takeuchi(Gifu Pharmaceutical Univ., Japan)
W. Tanthapanichakoon (The Royal Society of Thailand, Thailand)
C.H. Wang(National Univ. of Singapore, Singapore)
S. Watano(Osaka Metropolitan Univ., Japan)
Secretariat
T. Yokoyama(Hosokawa Powder Tech. Foundation, Japan)
L. Cui(Hosokawa Micron Corp., Japan)
European / African Editorial Board
Chairman
G.M.H. Meesters(Delft Univ. of Technology, Netherlands)
G. Biskos
F. Bourgeois
M.-O. Coppens
A. KwadeA. Mainza
M.G. Rasteiro
J.M. Valverde
Secretariat
S. Sander
S. Wilk(The Cyprus Institute, Cyprus)(Univ. of Toulouse, France)(Univ. College London, UK)(TU Braunschweig, Germany)(Univ. of Cape Town, South Africa)(Univ. of Coimbra, Portugal)(Univ. of Seville, Spain)(KONA Europe e.V., Germany)(KONA Europe e.V., Germany)

American Editorial Board
Chairman
B.M. Moudgil(Univ. of Florida., USA)
A.J. Hickey
A. Misra
A.D. Rosato
L.M. Tavares
W.-N. Wang
Q. Zhang
Secretariat
C.C. Huang(Univ. of North Carolina, USA)
(Univ. of Kansas, USA)
(New Jersey Institute of Technology, USA)(UFRJ, Brazil)(Virginia Commonwealth Univ., USA)
(Univ. of Manitoba, Canada)(Hosokawa Micron Intl. Inc., USA)

■ Publication Office

Hosokawa Powder Technology Foundation (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■ Printing Office: Nakanishi Printing Co., Ltd., Japan

Headquarters of Hosokawa Micron Corporation■ Publication Date: 10 January 2023

KONA Powder and Particle Journal No. 40 (2023)

Contents<Editorial>
1

Editor's Preface<Review Papers>
3

Analysis and Modeling of Non-Spherical
Particle Motion in a Gas Flow
Coal model(Spheroid)

Pulverized coal
100

CD

10
1
0.1
0.1

Hiroaki Watanabe and Wei Zhang
14

Smart Powder Processing for Excellent
Advanced Materials and Its Applications

CDprs
CDfrc
CD

1

Re

10

100

Drag model
for rotating spheroid

Coal dispersion Spheroid dispersion
in simulationin experiment

Bonding(with glow discharge)
Dry processing

Bulkmaterial

Deposition to
substrate

Makio Naito, Takahiro Kozawa, Akira Kondo
and C.C. Huang
29

Assembling

Synthesis&
Bondingex.
Attrition-type millSmart powder
processing without
heat assistance

DDS device, catalyst etc.

Nanoparticle
Synthesis

Wet processing

Nanoparticle
synthesis

ex.
High energy
planetary ball millNanoparticle
morphology
control

Nano-porous
film etc.

Assembling

Recycling ofParticle bonding and disassemblingcomposite materials

Interfacial Engineering of Particulate &Surfactant Systems for Enhanced
Performance in Industrial Applications

Jiaqi Dong and Brij M. Moudgil
Comminution and Classification as Important
Process Steps for the Circular Production of
Lithium Batteries

M
R

A

Arno Kwade, Marcel Möller, Jannes Müller,
Jutta Hesselbach, Sabrina Zellmer,
Stefan Doose, Julian Mayer, Peter Michalowski,
Malcolm Powell and Sandra Breitung-Faes

S

Anode

Al

Solvent based elektrolyte with Separator

Cu

50

CathodeE

Ci

KONA Powder and Particle Journal No. 40 (2023)

Recent Research Trend in Powder Process
Technology for High-Performance RareEarth Permanent Magnets

3.0
MA(Sm-Fe-N)

2.5

Coercivity (MA/m)74

Contents

MA(Nd-Fe-B)

2.0

HDDR
(Nd-Fe-B)

1.5 Melt spinning(Nd-Fe-B)

HDDR(Sm-Fe-N)

1.0

Pulverization(Sm-Fe-N)

Overnitriding(Nd-Fe-B)

0.5

Kenta Takagi, Yusuke Hirayama,
Shusuke Okada, Akihide Hosokawa
and Wataru Yamaguchi

Reduction diffusion(Sm-Fe-N)

0.0
0.01

0.1

1

10

Grain size (μm)

94 Morphology Control of Transition Metal
Oxides by Liquid-Phase Process and Their
Material Development

Shu Yin and Takuya Hasegawa
109

Cellulose Nanofibrils Pulverized from
Biomass Resources: Past, Present, andFuture Perspectives◆ Cellulose nanofibril as a hot material in the world!
TEMPO-oxidation
Chemical method

Cellulose nanofibril(CNF: a bundle of CMF)

Micro-grinding(Supermasscolloider®)Physical method~3 nm

Aqueous Counter
Collision (= ACC)
Physicochemical method

0.5 nm

β-D-Glucose
Wood fiberin cell walls

Cellulose
microfibril (CMF)
Cell walls
in tracheid (or vessel)

20~40 µm

Tetsuo Kondo
124

Functionalized Graphitic Carbon Nitrides for
Photocatalytic H2O2 Production: Desired
Properties Leading to Rational Catalyst
DesignZhengyuan Teng, Wenan Cai and Teruhisa Ohnoii

Major cell wallcomponent

Wood

KONA Powder and Particle Journal No. 40 (2023)

149

Contents

Mechanistic Design of Spray-Dried Powders

Mechanistic Formulation Design of SprayDried Powders

Perform formulation andprocess design

Define desired
powder criteria
No

Spray dry samples

Criteria met?
Yes

Next
steps

Mani Ordoubadi, Hui Wang andReinhard Vehring
Performance Testing for Dry Powder Inhaler
Products: Towards Clinical Relevance

90° standard
inlet
Flow throughinhaler

172

Perform necessary
characterizations

Cascade impactor

No IVIVC

Time

Sara E. Maloney, Jeffrey B. Mecham andAnthony J. Hickey
186

Flow throughinhaler

Realistic mouththroat model

Cascade impactor
Time

Progresstowards IVIVC

Environmentally Friendly Green Synthesis ofFine Particles by Dry Mechanical Processes
Toward SDGs: A Review

Tomohiro Iwasaki
197

Recent Advances in the Fabrication andFunctionalization of Nanostructured Carbon
Spheres for Energy Storage Applications

Kiet Le Anh Cao, Ferry Iskandar, Eishi Tanabe
and Takashi Ogi

iii

KONA Powder and Particle Journal No. 40 (2023)

Contents<Original Research Papers>
219 Computational Fluid Dynamic Modelling of
Fully-Suspended Slurry Flows in Horizontal
Pipes with Different Solids Concentrations

Gianandrea Vittorio Messa, Qi Yang, Maria
Graça Rasteiro, Pedro Faia, Vaclav Matoušek,
Rui C. Silva and Fernando Garcia
Evaluation of a Coating Process for SiO2/
TiO2 Composite Particles by Machine
Learning Techniques

60

CTi [%]

236

Taichi Kimura, Riko Iwamoto, Mikio Yoshida,
Tatsuya Takahashi, Shuji Sasabe andYoshiyuki Shirakawa
250

〇
△
□
◇
✕

50

V
rTi
t
S
T

40

30
-3.0

0

-1.5

1.5

3.0

Experimental conditions [-]On the Similarity of Austin Model andKotake–Kanda Model and Implications forTumbling Ball Mill Scale-Up

Si*

Austin’s Scale-Up Methodology(Population Balance Model)
Small-Scale Batch Ball
Mill/ Pilot-Scale
Continuous Ball Mill

AustinModel

Si

SCALE-UP

Large-Scale ContinuousBall Mill

Kotake-Kanda
Model

?

262

A Hybrid PBM-DEM Model of High-Pressure
Grinding Rolls Applied to Iron Ore Pellet
Feed Pressing

Relative compressive force (-)

Ecevit Bilgili
1.0
0.8
0.6
0.4
Pilot scale
Industrial scale

0.2
0.0
-0.55

-0.35

-0.15

0.05

0.25

0.45

Relative axial (y-distance)from center of the roll (-)

Victor A. Rodriguez, Túlio M. Campos,
Gabriel K.P. Barrios, Gilvandro Bueno andLuís Marcelo Tavares

iv

Cumulative passing (%)

100

10

1

0.1
0.001

Feed
ExperimentHybrid model
MTC model
0.01

0.1

Particle size (mm)1

KONA Powder and Particle Journal No. 40 (2023)

Contents<Information Articles>
277 The 54th Symposium on Powder Technology and Special Lecture for the 30th Anniversary of theEstablishment of HPTF

279 The KONA Award 2021

Selected research achievements for the KONA Award 2021 (Awardee: Prof. Dr. Wolfgang Peukert):
Particle based product innovations by understanding and controlling particle interactions.

Selected research achievements for the KONA Award 2021 (Awardee: Prof. Dr. Shuji Matsusaka):
Advanced characterization of fine particles and the development of novel powder handling systems.

281 General Informationv

Online ISSN : 2187-5537
Print ISSN : 0288-4534

Powder andParticle Journal
Impact factor:

CiteScore:

Indexed in Scopus, Web of Science and DOAJ

<Diamond Open Access> FREE for Reading & Publication
1983
1990
2009
2013
2014
2016
2017
2019
2021

First publication
About the journal
3 editorial blocks worldwide
KONA Journal is a peer-reviewed, international,
Registered in Web of Scienceinterdisciplinary and Diamond OA journal that
Registered-published on J-STAGE publishes articles on powder and particle science
Started Advance Publicationand technology.
Introduced CC-BY license
Adopted Editorial Manager®
Registered in DOAJ
Registered on J-STAGE Data

This journal has been publishedannually since 1983 and is distributed
free of charge to researchers, membersofthescientificcommunities,
universities and research libraries
throughout the world, by Hosokawa
PowderTechnology
Foundation
established by Mr. Masuo Hosokawa
in 1991.

•
•
•
•
•
•
•
•
•
•
•
•
•
•
•

Characterization
Computer simulation
Nanotechnology
Material science
Powder process
Particle formation
Dry powders & granules
Aerosols & Colloids & Slurry
Metals & Ceramics & Minerals
Organic-inorganic composites
Biomaterials & Pharmaceuticals
Secondary battery, Fuel cell
Electronic materials
3D printerRecyclingetc.

URL: http://www.kona.or.jp/

HOSOKAWA Powder Technology Foundation (HPTF)

KONA Powder and Particle Journal No. 40 (2023) 1–2/https://doi.org/10.14356/kona.2023017

Editorial

Editor’s Preface
Makio Naito, Editor-in-Chief
Professor, Osaka University, JapanI am very honored to be the Editor-in-Chief of “KONA Powder and Particle Journal” starting June 2022 as the
successor of Emeritus Professor, Kikuo Okuyama. It is my great pleasure to publish the latest issue of the Journal
No. 40 (2023). I have been working on the research and development of powder characterization, powder processing,and their applications for advanced materials for over 40 years, because powder technology is very attractive field for
my research works. As you know, “powder”, an assemblage of small solid particles, exhibits very unique behavior.
For example, depending on the circumstances, a powder can behave like a gas, a liquid, or a solid. Also, because of
its larger specific surface area and smaller volume relative to a bulk material, powder can have very unique properties. This is especially true for “nanoparticles”, which offer a wide range of industrial applications ubiquitous in ourdaily life and create promising materials for scientific and technical innovations in the future. Furthermore, the 2030
Agenda for Sustainable Development (the 2030 Agenda) is a set of international development goals from 2016 to2030, which was adopted by the United Nations Sustainable Development Summit held in September 2015 buildingon the success of Millennium Goals (MDGs). The 2030 Agenda listed “Sustainable Development Goals (SDGs)”
consisting of 17 goals and 169 targets in order to eradicate poverty and realize a sustainable world. Powder technology
is the key to achieve these goals for a sustainable society worldwide.
To contribute to the advancement of powder technology on a global scale, Mr. Masuo Hosokawa, the second
President of Hosokawa Micron Corporation published the first issue of “KONA Powder and Particle Journal” in
1983. Then, he founded Hosokawa Powder Technology Foundation in 1991, and shifted the management issues of
the KONA Journal to it. Besides, Mr. M. Hosokawa proposed the concept of “nanoparticle technology” long before
the United State President Bill Clinton’s National Nanotechnology Initiative in 2000. As a chief editor, Mr. M.
Hosokawa decided to inaugurate the Nanoparticle Technology Handbook to advance the development of powder
technology. Subsequently, the Foundation published the Nanoparticle Technology Handbook in Japanese in 2006,
which was translated into English and published as the first edition of the Nanoparticle Technology Handbook in2007 by Elsevier. Since then, nanoparticle technology has rapidly advanced, and been applied broadly and in many
new applications. Therefore, the Handbook was updated twice in 2012 and 2018, respectively, after Mr. M. Hosokawa
passed away in 2010, Then, the third edition was organized by four editors, including myself (The chief editor), and
Dr. Kohei Hosokawa (the President of Hosokawa Micron Corporation), contributing to the success of the publication.
The KONA Journal has evolved for over 40 years. In 1990, three Editorial Boards (Asian/Oceania, Europe/Africa
and America) were established to manage the Journal worldwide. After the involvement of the Foundation management, the KONA Journal was registered in Web of Science in 2009. Furthermore, it was registered and published on
J-STAGE in 2013. Besides, Advanced Publication started in 2014. DOAJ was registered in 2019. J-STAGE Data wasregistered in 2021. Today, “KONA Powder and Particle Journal” is a peer-reviewed, international open access journal.
It is published on-line annually on the homepage of Hosokawa Powder Technology Foundation.

Copyright © 2023 The Author. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

1

With the great effort of all Editorial Board Members, the impact factor of the Journal in 2021 has reached 3.919
showing the KONA Journal is highly evaluated as an international, academic journal. I would like to express mysincere appreciation to all Board Members for their contributions to the success of the KONA Journal publication.
Aiming at higher quality journal in the future, I would like to emphasize the following issues for the Journal:
Firstly, the KONA Journal should keep aiming to publish mainly review articles on powder science and technology.
As former Editor-in-Chief, Prof. Kikuo Okuyama mentioned at the preface of No. 37 (2020), review articles are especially important to fulfill demands from researchers and engineers who are looking for reliable sources which summarize all the rapidly advancing research to understand the recent research trends, latest information, and future tasks
in the special subjects. Recent technology has rapidly advanced in the diverse areas including information technology
and materials informatics. Powder technology is also rapidly developing by interacting with the new technologies.
Such cutting-edge review articles will give readers the newest information through the KONA Journal.
Secondly, the KONA Journal should continue fostering development of powder science and technology and promoting the synergetic interactions with diverse science and technology communities. Research related to powder andparticles has widely spread from advanced materials including ceramics, polymers, metals and composites, medical
devices and pharmaceuticals to information technology. Research area for sustainable society is also very important.
The KONA Journal should bridge the gaps between powder technology and diverse technology communities, tobecome more attractive journal for readers from various disciplines.
Thirdly, the continuous financial and human support from Hosokawa Powder Technology Foundation is critical toachieve higher quality of the KONA Journal. The 54th Symposium on Powder Technology was held on September 5,2022 at the Imperial Hotel Osaka, in conjunction with the 30th Anniversary event of the Foundation. At the opening
address, Mr. Yoshio Hosokawa, the President of the Foundation and the Chairman of Hosokawa Micron Corporation,
introduced the history of the Foundation, and emphasized that the KONA Journal is now a highly evaluated academic
international journal, contributing to the development of powder science and technology in the world. As the new
Editor-in-Chief, I would like to express my sincere appreciation to the great support of the Foundation.
At last but not the least, taking use of this opportunity, I would like to express my gratitude to all paper reviewers
shown here for the successful publication of the “KONA Powder and Particle Journal” (No. 40), and my sincere
thanks to all those who have contributed to the publication of the journal.
FUJI, Masayoshi
HICKEY, Anthony
KAGE, Hiroyuki
KANO, Junya
KAWASAKI, Akira

Makio Naito
Editor-in-Chief
Professor, Osaka University
October, 2022

2

KWADE, Arno
MAKINO, Hisao
MAZZINGHY, Douglas Batista
MUTTIL, Pavan
SAKAI, Mikio

SAKKA, Yoshio
SANDER, Steffen
SHIRAKAWA, Yoshiyuki
TAKEDA, Shin-ichi
TAVARES, Luis Marcelo

KONA Powder and Particle Journal No. 40 (2023) 3–13/https://doi.org/10.14356/kona.2023009

Review Paper

Analysis and Modeling of Non-Spherical Particle Motion
in a Gas Flow †Hiroaki Watanabe 1* and Wei Zhang 2
1 Department of Advanced Environmental Science and Engineering, Kyushu University, Japan

2 China College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, China

Abstract
This paper describes the reviews of the recent works in analysis, modeling, and simulation of the motion of a nonspherical particle. The motion of the non-spherical particles was analyzed in detail by means of a fully resolved
direct numerical simulation (DNS). From the DNS data, the PDF-based drag coefficient model was proposed andapplied to the particle dispersion simulation in an isotropic turbulent flow to assess the effect of the particle shapeby comparing it with the motion of a spherical particle. Moreover, the model was applied to a large-eddy simulation(LES) of particle dispersion in an axial jet flow and validated by comparing it with the experimental data. Results
showed that the effect of the particle shape was clearly observed in the characteristics of the particle dispersion in
the isotropic turbulent flow by evaluating the deviation from the Poisson distribution (D number) and the radial
distribution function (RDF). It was found that the non-spherical particle’s representative Stokes number becomeslarger as the sphericity increases. Furthermore, it was also revealed that the effects of the particle size distribution
and the shape observed in the experiment was precisely captured by the LES that coincided with the trend found in
the isotropic turbulent flow.
Keywords: non-spherical particle, modeling, numerical simulation, experiment, DNS, LES

1. Introduction

A dispersed gas-particle two-phase flow is frequently
utilized in many industrial applications, such as conveyance and energy conversion systems of solid materials. It isan urgent issue to reduce the environmental impact of thesefacilities for a low-carbon society. To design such highly
efficient devices, it is essential to deeply understand the
behavior of particles in a fluid flow.
A non-spherical particle is frequently seen in the industry as a pulverized solid particle such as coal which may
behave in a different motion from a spherical particle.
A great many efforts have been conventionally made toinvestigate the motion of the non-spherical particle. The
majority of the works were dedicated to a spheroidal particle. Feng J. et al. (1995) performed a three-dimensional
computation of the force and torque acting on an ellipsoid
settling slowly in a viscoelastic fluid. They found that thesigns of the perturbation pressure and velocity around theparticle for inertia were reversed by viscoelasticity and thetorques were also opposite signs. It was also found that theequilibrium tilt angle was a function of the elasticity num†*

Received 1 March 2022; Accepted 15 April 2022
J-STAGE Advance published online 18 June 2022
Corresponding author: Hiroaki Watanabe;
Add: 6-1 Kasuga-koen, Kasuga, Fukuoka 816-8580, Japan
E-mail: whiroaki@tse.kyushu-u.ac.jp
TEL: +81-92-583-7674 FAX: +81-92-592-8447

ber which was the ratio of the Weissenberg number andthe Reynolds number. Broday D. et al. (1998) investigatedthe motion of non-neutrally buoyant prolate spheroidal
particles freely rotating or fixed with orientations in vertical shear flows. They revealed that the spheroids freely
moving translated along periodic trajectories with no net
lateral drift, whereas the particles with fixed orientations
were unstable with the drift velocity growing exponentiallywith time. The conditions for the unstable motion werealso discussed as a function of the particle shape, via its
aspect ratio and its inertia. Mortensen P.H. et al. (2008)
performed a direct numerical simulation (DNS) of prolate
ellipsoidal particles suspended in a turbulent channel flow.
It was found that the ellipsoidal particles tend to align withthe mean flow direction in the near-wall region, while theorientation became isotropic in the core region. In addition,
when the particle inertia increases, the particles were lessoriented in the spanwise direction and more oriented in thewall-normal direction.
Mathematical modeling of the complex motion of thenon-spherical particle has been also examined in many
works. Haider A. and Levenspiel O. (1989) proposed
the explicit equations for the drag coefficient of falling
non-spherical particles by fitting the experimental data of
isometric and disk type solids with classification in termsof the sphericity. In this work, the drag coefficient is afunction of the sphericity and Reynolds number, and the

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

3

Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13

model is applied to an LES of a turbulent axial jet flow andorientation is not considered. Holzer A. and Sommerfeld
validated with the experiment.
M. (2008) also proposed a simple correlation formulafor the drag coefficient of arbitrarily shaped particles byintroducing the crosswise and lengthwise sphericities in
2. Modeling of non-spherical particle motion
addition to the normal sphericity into the drag coefficient
In this section, the motion of a single non-sphericalequation based on a number of the experimental andparticle is numerically investigated by means of a fully
numerical studies. Rosendahl L. (2000) developed theresolved gas-particle two-phase flow DNS. The DNS data
particle shape description based on the minor axis, the asis analyzed to formulate the effects of the non-sphericity
pect ratio, and the superelliptic exponent to account for thein the motion equation. The Basset-Boussinesq-Oseen
shape effect of ellipsoids and cylinders and Zastawny M. et(B.B.O.) equation is expressed as,al. (2012) extended the model to account for the drag force,dv
the lift force, the pitching torque, and the torque caused(1)ρpVp FD  FL  FB  Fprs  Fmass  FBasset ,dtby the rotation of the particles based on the fully resolved
DNS. They should be in useful forms when the orientation
where ρp, Vp, and v are the density, volume, and velocity ofof the particle is considered in a Lagrangian particle trackthe particle, respectively. FD, FL, FB, Fprs, Fmass, and FBasset
ing simulation.
are(1)
the drag force, lift force, buoyancy, pressure gradient,In the modeling of the motion of the non-spherical partivirtual mass, and Basset history term, respectively. In thiscle in the earlier stage, the sphericity is simply used to constudy, because the drag force mainly contributes to thesider the effect of the non-sphericity on the drag coefficientmotion of the particle in the pulverized coal combustion
and the other related parameters to characterize the particle
field, we pay special attention to FD via modeling of the
motion, while it cannot consider the effect of oscillation
drag coefficient CD.
The drag force is regarded as the summation of the conand rotating in a fluid flow. In the later stage, the effecttributions of the pressure and frictions as,of rotation is considered by introducing the orientationsuch as the crosswise and lengthwise sphericities. In the
FD  FDprs  FDfrc .
(2)
CFD framework, conventionally the Reynolds-averaged
And the drag coefficient can be also expressed as,Navier-Stokes (RANS) simulation in which the entire scaleFDof the turbulent eddy is modeled and the time-averaged(2)CD 
 C Dprs  C Dfrc ,1
quantities are evaluated is mainly used as a standard tool to(3)2
ρf u  v A
predict the particle behavior in the industry due to its lower
2
computational cost in spite of its lower accuracy for a longwhere A is the projection area of the particle to the flowtime (e.g Watanabe H. and Otaka M. (2006), Watanabe
direction. As shown in Eqn. (3), CD is also considered as aH. et al. (2015), Hashimoto N. and Watanabe H. (2016)).
sum(3)of CDprs and CDfrc which are influenced by the pressure
However, with the great advance in computer performance,
and the friction, respectively. CD for a spherical particle canmassively parallel large-scale computing by means of abe regarded as a simple function of the Reynolds number
large-eddy simulation (LES) in which the eddies in the
Re and as mentioned earlier, many works propose the dragsubgrid-scale are only modeled and the fully unsteady phecurve equations. Clift R. et al. (1978) summarized some ofnomena can be considered is expected to be a new standard
the most popular expressions, and provided the following
tool (e.g. Kurose R. et al. (2009), Muto M. et al. (2019),
recommendation as,
Ahn S. et al. (2019), Watanabe H. and Kurose R. (2020)).
(4)CD  3 / 16  24 / Re for Re  0.01 ,In such a highly fidelity unsteady simulation framework,
the motion models which can consider the oscillation and24
CD 
1  0.1315Re 0.820.05log Re  for 0.01  Re  20 ,rotation of the particle would be essential.
Re
24
(4)
While the models taking the orientation of the particles(5)
CD 
Re 0.82  0.05 log Re  for 0.01  Re  20 ,
1  0.1315
Re
into account have been proposed in some existing works,24
CD 
1  0.1935Re 0.6305 for 20  Re  260 , (6)as mentioned earlier, another modeling strategy will be re(5)Re
ported hereafter in this paper. In typical industrial applica(5) flow
tions, a tremendous number of particles exist in a fluid
where Re is based on the slip velocity and particle size as,such as a pulverized coal combustion boiler. Here we inρ uv d(6)Re  f,troduce a concept of probability density function (PDF) to(7)μmodel the motion of non-spherical particles. In Section 2,
the motion model of the non-spherical particle is developed
where d is the diameter of the particle and μ is the viscosity
by a fully resolved DNS. In Section 3, the model proposedof the fluid, respectively.
(7)
here is assessed in an isotropic turbulent flow by means ofEqns (1) to (6) are only suitable for a spherical partia DNS with the point mass approximation. In Section 4, thecle. A non-spherical particle is typically assessed by the4Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13

sphericity which is defined as the ratio of the surface areaof a sphere to that of a given particle where the sphere andthe given particle have the same volume as,
π1/3 6Vparticle S sphere.

S particle

2/3

(8)

S particle

According to the examination by Haider A. andLevenspiel O. (1989), CD becomes larger as ϕ decreases.
(8)

2.1 The arbitrary Lagrangian-Eulerian (ALE)
methodTo deeply understand the six-degree of freedom for aparticle motion, the arbitrary Lagrangian-Eulerian (ALE)
method is employed (Hirt C.W. et al. (1974)). The governing equations of the ALE method are expressed as,ρf u  u '   ρf   ρf   u ,
t

(9)

u
1
1
(10)
 u  u '  u  p p  σ  g ,
t
ρf
ρf
(9)
where ρf and u are the fluid density and velocity, respectively, u′ the mesh velocity, p the pressure, σ the surface(10)tensor. The ALE method is equivalent to the Eulerianstressmethod when u′ = 0 and is equivalent to the Lagrangian
method when u′ = u. Here, the particle velocity v is employed as u′ to consider the Lagrangian particle motion.
The slip velocity u – v appears when the particle translates,
rotates and oscillates. This means that the entire computational grid around the particle moves in the ALE method.
The motion of the single particle is calculated by thegoverning equations of a rigid body as,

m

d 2z
F,
dt 2

(11)

d 2α dα  dα 

 I
(12)
M ,
dt 2
dt  dt (11)where m is the particle mass, I the moment of inertia of particle, z the displacement angle, α the rotation angle, F the(12)on the particle, M the torque on the particle, whichforce
are obtained by considering the integration of the fluid
force over the surface and the gravity. Subsequently, we
can calculate the moving and angular velocities of the particle. The presented numerical procedure was implemented
into the unstructured FVM parallel computing solver FFRComb (NuFD/FrontFlowRed extended by Kyushu Univ.,
Kyoto Univ., CRIEPI, and NuFD) (Zhang W. et al., 2018a;
2018b). The presented ALE method has been properly
validated by comparing with the correlation expression of
the drag coefficient curve by Clift R. et al. (1978) and withthe experiment of the falling particles in water by Mordant
N. et al. (2000). See more details of this DNS in Zhang W.
et al. (2018b).
I

2.2 Computational details

To observe the motion characteristics of various particle
shapes, pulverized coal and spheroidal particles are examined. Fig. 1 shows the shapes of the particles. The 3D dataof the coal particle was obtained by the X-ray CT scanner.
The shape of the spheroidal particle was determined by
the equivalent volume. In our preliminary examination,
the spheroid with the equivalent volume shows the closest
behavior of the coal particle rather than the spheroids withthe equivalent surface area and sphericity. Fig. 2 shows thecomputational grid for the coal particle. The computational
domain is discretized with about 12 million cells and isdivided into 1,024 regions for the MPI parallel computing.
It takes approximately 15,000 node-hour for one case.
The grid resolution around the particle is confirmed to beenough to resolve the boundary layer thickness formed on
the surface within the range of the particle Reynolds number Re in this study following the theorem by Schlichting etal. (1955) and the examination by Muto et al. (2012).
The flow velocity was set to 15 m/s, and the particle
was placed in the upward flow at the beginning of thecomputation. The particle was released at t = 0 without theinitial velocity. The major axis of each particle was set in
the vertical direction. The initial Re was estimated as 45.

2.3 Results and discussion

Fig. 3 shows the drag curves for (a) coal particle and (b)spheroid with the equivalent volume. In Fig. 3, CDprs andCDfrc are also displayed as well as CD. It is found that the CD
curve oscillates for the both particles. Because the major
axis is set in the vertical direction, the initial projection areain the fluid direction is small and it is located in unstableequilibrium. With the small disturbance of the pressure or
friction variation on the surface, the particle starts oscillating and rotating. It is found that the contribution of CDfrc is
dominant, while the large oscillation is observed for CDprs.
It is also understood that the trend for the spheroid shows agood agreement with that for the coal particle.
Fig. 4 shows the drag curves with the various initial
angles of the major axis of the spheroid. Here, the two-digit
number after “EV” (equivalent to the volume) in the figure
displays the initial angle from the horizontal axis. In spite(a) Sampled coal(b) Spheroid

Fig. 1 Shapes of (a) sampled pulverized coal particle, and (b) spheroid
with equivalent volume. Reprinted with permission from Ref. (Zhang etal., 2018a). Copyright: (2018) Elsevier B.V.

5

Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13

Fig. 2Computational grid for sampled coal particle. Reprinted with permission from Ref. (Zhang et al., 2018a). Copyright: (2018) Elsevier B.V.

100

CD

10
100

0.1
0.1

CDprs
CDfrc
CD

1

CD

1

10

Re
(a) Sampled coal

CD

CDprs
CDfrc
CD

Re

10

5

1

6

7

100

logC D  logC D min  logC D max  logC D min  10Re(b) Spheroid

100

Fig. 3 Drag curves for (a) sampled pulverized coal particle, and (b)spheroid with equivalent volume. Reprinted with permission from Ref.
(Zhang et al., 2018a). Copyright: (2018) Elsevier B.V.

of the difference in the timing to start the oscillation among
the cases, all the oscillating curves are located withina certain range of the maximum and minimum smooth

6

1

Re

curves. The trend of the oscillation is analyzed in termsof the probability density in Fig. 5. Here, the probability
density function (PDF) of the sine curve is displayed aswell in Fig. 5. It is revealed that the PDF of the sine curve
can precisely reproduce that of the spheroid particle. From
this consideration, the characteristics of the oscillating CD
can be formulated by the following simple expression as,

10

0.1
0.1

4

EV00
EV30
EV60
EV90
MIN

Fig. 4 Drag curves for spheroid with equivalent volume with various
initial angles. Reprinted with permission from Ref. (Zhang et al., 2018a).
Copyright: (2018) Elsevier B.V.

100

1

10

1
0.1

100

9
8

CD

10

sin 2πθ  1
,
2
(13)

where θ is the random number from 0 to 1, and C D max andC(13)D min are determined by the computation of the six-degreeof freedom motion of the target particle and those can beapproximated by the expressions by Haider A. and[31]Haider
A.
andLevenspielO.
(1989)
and
Clift
R.
et
al.
(1978)
as,
[32]


CD

(14)

24
1  A Re B C log Re  for 0.01  Re  10 ,
Re

(14)

Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13
0.03

Spheroid
Sine curve

PDF

0.02

0.01

0

0

0.2

0.4

0.6

0.8

1

(log C D − log C Dmin)/(log C Dmax − log C Dmin)
Fig. 5 PDFs of drag coefficient for spheroid and sine curve. Reprinted
with permission from Ref. (Zhang et al., 2018a). Copyright: (2018)
Elsevier B.V.


CD

24
1  X Re Y  for 10  Re  100 .
Re

(15)

Here, the model parameters, A, B, C, X, and Y are determined by the computation as mentioned earlier.
(15)

3. Dispersion of non-spherical particles in an
isotropic turbulent flow

In this section, the non-spherical particle motion model
developed in the previous section is applied to the EulerianLagrangian two-way coupling DNS with employing theparticle-source-in cell (PSI-CELL) model (Crowe C.T. etal. (1977)) to observe the effects of non-sphericity.

3.1 Governing equations

The governing equations for the fluid flow consist of themass and momentum conservations as,ρf ρf u j

 0,
t
x j

(16)

ρf ui ρf u j uip σ ij
 S ui ,
(17)
t
x j
xi x j(16)
where ui and σij are the fluid velocity and stress tensor,
respectively. S ui is the source term of the interphase mo(17) transfer and evaluated by,mentum
S ui  

1
N m ReC D ui  u p  ,

[33]
ΔV
τ p 24(18)

where up is the particle velocity and ∆V the control volume.
τp is the particle response time. The stokes number St char(18) the behavior of particles in a fluid flow is defined
acteringwith the Kolmogorov time scale τk as,
St 

τp
.
τk

(19)

The particles are treated as mass points and tracked by
the Lagrangian manner. The gravity and lift forces are(19)

Fig. 6 Computational domain and vortex visualization of isotropic
turbulent flow. Reprinted with permission from Ref. (Zhang et al.,2018a). Copyright: (2018) Elsevier B.V.

ignored. The effects of the pressure gradient, virtual mass,
and Basset history term are neglected. The motion equations for the particles are expressed as,

dxp
 up ,

(20)

dt
du p

1 Re

C D ui  u p  ,dt
τ p 24(20)
where xp is the particle position.

(21)

3.2(21)
Computational details

Fig. 6 shows the computational domain displaying the
distribution of the vortices distinguished by the second
invariant of the velocity gradient tensor, Q = 10,000. The
domain is a (2π)3 cm3 cube divided by 1283 grids. Allboundaries are set as periodic boundaries. The isotropic
turbulent field generated in this study has the RMS (rootmean square) of the velocity of 2.7 m/s and the Taylor’s
microscale-based Reynolds number of 60 which is closed
to the condition by Ooi A. et al., (1999). The isotropic
turbulent flow is evaluated by the energy spectrum fieldand St is controlled by varying the particle size. The one
spherical and five different sphericity spheroidal particles
are examined. Table 1 shows the particle properties. All themodel parameters for each particle shape are obtained by
the computation of the six-degree of freedom motion of thetarget particle as described in Section 2. See more detailsof this DNS in Zhang W. et al. (2018b).

3.3 Results and discussion

The characteristics of the particle dispersion is evaluatedby the D number (Fessler J.R. et al. (1994)) which shows
the deviation between the target particles dispersion and7

Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13
5000

Table 1 Cases performed in isotropic turbulent flow.

Sphericity [-]Diameter of equivalent
volume sphere [m]

4500

SV

1.00

4.23 × 10–5

4000

EL96

0.96

4.23 × 10–5

EL90

0.90

4.23 × 10–5

ES90

0.90

3.43 × 10–5

EL85

0.85

4.23 × 10–5

ES85

0.85

2.91 × 10–5

g(r) [-]

Cases

SV
EL96
EL90
EL85

3500
3000
2500
2000

0

1

2

0.6

D [−]

0.4
0.3

0.4

0.5

SV
EL96
EL90
ES90
EL85
ES85

0.2
0.1

0

0.3
0.5

0.6

1

2

0.4

St [−]

4

3

4.1

4

5

Fig. 7 Computational domain and vortex visualization of isotropic
turbulent flow. Reprinted with permission from Ref. (Zhang et al.,2018a). Copyright: (2018) Elsevier B.V.

the Poisson distribution defined as,Dσ  σ Poi
,
λ

(22)

where σ and σPoi are the standard deviations, and λ is themean number of particles in each small box which is set to(22)
analyze D. In this study, 243 boxes are applied to the wholedomain. Fig. 7 shows the variation of D with the various St
for the six cases. It is found that the effect of the sphericity
changes at the point where the peak value of D appears.
In the region of the smaller St from the point of the peak,
D becomes larger with increasing the sphericity. On theother hand, in the larger St region, the trend is opposite. In
other words, as the sphericity increases, the D curve shifts
toward the larger St direction. This means that the spheroid’s apparent (representative) Stokes number St’, which
represents the dispersion behavior becomes larger.
The characteristics of the particle dispersion is also
analyzed by the radial distribution function (RDF) which
describes the characteristics of particle clustering in termsof its length scale. RDF is defined as,

g ri  

N p i / ΔVi,
Np / V(23)

where Np = N(N – 1)/2 is the total number of particle pairs
and V is the volume of the whole domain. Np(i) is the total(23) of particle pairs with a distance between r + ∆r/2
number
i

8

3

4

5

Fig. 8 Sum of g(r) values conditioned by r/η < 12 for each case.
Reprinted with permission from Ref. (Zhang et al., 2018a). Copyright:(2018) Elsevier B.V.

0.5

0St [-]

and ri – ∆r/2. ∆Vi is the volume of the shell located at a
distance ri with the thickness ∆r. Fig. 8 shows the RDF
variation with varying St for the representative four cases.
In Fig. 8, RDF is evaluated as the sum of g(r) conditioned
by r/η < 12, where η is the Kolmogorov length scale. In thecondition to analyze D, r/η can be approximately estimatedas around 12. It is found in Fig. 8 that as St increases, g(r)initially increases and then decreases after appearing thepeak value at St = 1.0 except SV. These trends agree withthe result observed in Fig. 7.

4. Dispersion of non-spherical particles in aturbulent jet flow

In this section, the non-spherical particle motion model
developed in Section 2 is applied to the EulerianLagrangian two-way coupling LES to investigate the particle dispersion behavior in a more practical condition. The
model results are compared with the experimental results.

4.1 Governing equations

The governing equations of the fluid and particle motions are almost the same as described in Section 3, whilethe variables are evaluated with the spatial and Favre filtering in the coarser grid. The subgrid-scale (SGS) terms are
considered by the dynamic Smagorinsky model proposedby Moin P. et al. (1991). The interphase momentum transfer is evaluated by the PSI-CELL method.

4.2 Experimental and computational details

Fig. 9 shows the schematic diagram of the targeted
cold flow experiment. The particles are transported by the
airflow and injected vertically by the coaxial jet burner. The
particle image velocimetry (PIV) method is used to measure particle motion. The laser sheet is applied at different
heights of 0, 20, 30, 60, 90, 120, 150, 180, and 210 mmfrom the burner exit. At each height, 1,000 graphics aretaken and processed to obtain the time-averaged profiles.

Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13

In the experiment, the two cases, pulverized coal, andspherical polymer particles are used to observe the effectof the particle’s shape. The densities of coal and polymer
are almost the same about 1,200 kg/m3. The averaged flow
velocity is set to 7.59 m/s and the particle mass flux is set
to 1.29 × 10–4 kg/s. Fig. 10 shows the particle mass flux ofthe different diameters.
The computational domain is a 360 mm height cylinder
with a diameter of 100 mm. The axial jet burner is locatedat the center of the bottom which has a 6 mm diameter and
60 mm height. The number of cells is 11.5 million. Table 2
shows the four cases performed in this study. As shown in
Fig. 10, the particle size distribution is expressed by the
representative six diameters in the simulation. The model
ExhaustCylindricallensParticlefeederAir
flow

Mass flow
meter

Jet
burner

Laser

Pulse
generator

CameraFig. 9 Schematics of experimental configuration. Reprinted with permission from Ref. (Zhang et al., 2018b). Copyright: (2018) Elsevier B.V.

Mass flow rate [kg/s]

2.0E-04
monodisperse
polydisperse

1.5E-04
1.0E-04
5.0E-05
0.0E+00
0

20

40

60

80

Diameter [mm]

100

120

Fig. 10 Particle mass fluxes of different diameters for monodispersedand polydispersed cases.
Table 2 Cases performed in turbulent axial jet flow.

Cases

Particle shape(model)Diameter [m]

Note

COAL

Non-spherical

Polydispersed

Exp

Spherical

2.8 × 10–5

Exp

SPE_S

Spherical

2.8 × 10–5

CFD

SPE_R

Spherical

Polydispersed

CFD

SPO_S

Spheroid

2.8 × 10–5

CFD

SPO_R

Spheroid

Polydispersed

CFD

POLYMERparameters in Eqns. (14) and (15) for the spheroidal particle with the sphericity of 0.85 are employed to approximate
the non-spherical coal particle behavior.

4.3 Results and discussion

Fig. 11 shows the instantaneous and the time-averaged
distributions of the Mie scattering intensity obtained by theexperiment for (a) pulverized coal COAL and (b) monodispersed polymer POLYMER, and the distributions of theparticle number density by the simulation for (c) monodispersed sphere SPE_S, (d) polydispersed sphere SPE_R,(e) monodispersed spheroid SPO_S, and (f) polydispersed
spheroid SPO_R, respectively. Here, the left-hand side ofeach figure displays the instantaneous distribution, and theright-hand side displays the time-averaged distribution.
With focusing on the experimental result, the region thatshows the high intensity of Mie scattering for COAL appears near the burner nozzle exit, while that for POLYMER
appears at the downstream region from the nozzle exit. For
POLYMER the number density of the particles increases
once due to the interaction between the turbulent eddies andthe particles, while the particles for COAL simply disperse
the downstream. In the higher region, the difference turns
clearer. POLYMER clusters strongly from the middle to thehigher region. In the simulation result, the same concentration downstream from the nozzle exit can be found for themonodispersed cases, SPE_S and SPO_S, while the simple
dispersion is observed for the polydispersed cases, SPE_R
and SPO_R. The trend obtained by the simulation agrees
with that observed in the experiment. It can be consideredthat the difference in the region where the peak value ofthe Mie scattering intensity appears between the two casesin the experiment is mainly attributed to the particle size
distribution. It is also found that the higher number density
region appears downstream for the spheroid cases rather
than that for the sphere cases. This is affected by the difference in the particle shape. In the higher region, while thepolydispersed cases tend to randomly disperse in space, themonodispersed cases cluster and form the branch-shaped
structure.
Fig. 12 shows the instantaneous distribution of theparticles near the burner nozzle exit for the sphere cases,(a) SPE_S and (b) SPE_R. For SPE_R, the number of thesmaller particles which have a short response time is large.
They are entrained well by the vortices and the ring-likestructure is formed at the nozzle exit. For SPE_S, since theparticles are larger enough to be less likely to trace the vortices due to the longer response time than those observed in
SPE_R, the ring-like structure does not appear.
Fig. 13 shows the time-averaged width of the region
where the traveling particles exist (the dispersion width)in the axial direction. The particle dispersion width monotonously increases in the axial direction for all cases.
Between the sphere cases, since the dispersion width for9Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13

High

High

Low

(a) COAL

Low(b) POLMER

High

Low

(c) SPE_S

High

High

Low

(d) SPE_R

High

Low

Low

(e) SPO_S(f) SPO_R

Fig. 11 Mie scattering intensity in experiments (a) COAL and (b) POLYMER, and number density in CFD (c) SPE_S, (d) SPE_R, (e) SPO_S, and(f) SPO_R. Reprinted with permission from Ref. (Zhang et al., 2018b). Copyright: (2018) Elsevier B.V.

SPE_R is larger than that for SPE_S in the entire region,
the particle size distribution significantly affects the dispersion width. On the other hand, between the spheroid cases,
the difference in the dispersion width is not observed. It
is considered that the particle shape affects the dispersion
width.
The characteristic length of the particle clustering isanalyzed by RDF as described in the previous section.

10

Fig. 14 shows the variation of RDF in the characteristic
length scale. It is found that RDF is significantly affectedby the particle size distribution, since the monodispersed
cases and the polydispersed cases respectively show theindividual trend. While RDFs for SPE_S and SPO_S are
almost the same in the upstream region at z = 30 mm,
SPE_R shows a higher value than SPO_R in the shorter
length scale region. In the middle stream at z = 90 mm, theHiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13(a) SPE_S(b) SPE_R

Fig. 12 Instantaneous particle distribution near burner nozzle exit for (a) SPE_S and (b) SPE_R. Reprinted with permission from Ref. (Zhang et al.,2018b). Copyright: (2018) Elsevier B.V.

cussed in the previous section. It is considered that thetrend observed in the turbulent axial jet in this section also
follows this fact.

Width [mm]705. Conclusions
35

0

Fig. 13

SPE_S
SPE_R
SPO_S
SPO_R
0

50

100

150

200

Height [mm]

250

300

Variation of particle dispersion width in axial direction.

difference between SPE_R and SPO_R becomes smaller,
whereas the value for SPO_S becomes slightly higher thanthat for SPE_S. And then, in the downstream region atz = 180 mm, RDF for SPE_R almost agrees with that for
SPO_R, while the difference between SPE_S and SPO_S
becomes marked. From this observation, it is revealed thatthe effect of the particle shape on their dispersion becomesmarked toward the downstream, although the particle
size distribution tends to suppress this trend. This behavior should be characterized by the Stokes number. The
non-sphericity of the particle changes its apparent Stokes
number. In the region where the apparent Stokes number
is smaller than the order of unity, the particle dispersion
is suppressed, and in contrast, the particle dispersion isenhanced by the larger apparent Stokes number, as dis-This paper describes the reviews of the recent works inthe analysis, modeling, and simulation of the motion of anon-spherical particle. The motions of the non-sphericalpulverized coal, sphere, and spheroidal particles wereanalyzed in detail by means of the fully resolved DNS of
the six-degree of freedom particle motion by employing the
ALE method. It was found that the motion of pulverizedcoal particle can be reproduced by the spheroidal particle
with the equivalent volume. From the detailed analysis of
the motion of the spheroidal particle, it was revealed that
the PDF of the sine curve can capture the variation of thedrag coefficient due to the particle oscillation and rotation.
The motion model based on the six-degree of freedom
motion DNS was applied to the particle dispersion simulation in an isotropic turbulent flow by mean of the point
mass approximation DNS. The results showed that theeffect of the particle shape was clearly observed in thecharacteristics of the particle dispersion by evaluating the
D number and RDF. It was found that the non-sphericalparticle’s representative Stokes number becomes larger as
the sphericity increases.
Furthermore, the proposed motion model was applied
to the LES of particle dispersion in an axial jet flow andvalidated by comparing it with the experimental data. It
was revealed that the effects of the particle size distribution

11

Hiroaki Watanabe et al. / KONA Powder and Particle Journal No. 40 (2023) 3–13
100
90
80
70

RDF [-]

Information Center of Particle Technology, Japan. HWis grateful to Dr. Kazuki Tainaka of CRIEPI for his great
support to conduct the PIV measurement in the experiment.

SPE_S
SPE_R
SPO_S
SPO_R

Nomenclature

60
50
40
30
20
10
0

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

Length [mm](a) z = 30 mm
100
90

SPE_S
SPE_R
SPO_S
SPO_R

80

RDF [-]

70
60
50
40
30
20
10
0

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

Length [mm](b) z = 90 mm
100
90

SPE_S
SPE_R
SPO_S
SPO_R

80

RDF [-]

70
60
50
40
30
20
10
0

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9(c) z = 180 mm
Fig. 14 Variation of RDF in characteristic length scale for (a)z = 30 mm, (b) z = 90 mm (c) z = 180 mm. Reprinted with permission
from Ref. (Zhang et al., 2018b). Copyright: (2018) Elsevier B.V.

and the shape observed in the experiment were precisely
captured by the LES that coincided with the trend found in
the isotropic turbulent flow.

This study was partly supported by JSPS KAKENHI
Grant No. 25420173 and 16K06125, and a grant from the12

Direct numerical simulation
Large-eddy simulation
Particle image velocimetry
Reynolds-averaged Navier-Stokes simulation
Subgrid scale
projection area of particle (m2)drag coefficientparticle size (m)D number (-)
force acting on particle (kg m s–2)
gravity (m s–2), radial distribution function
length (m)particle mass (kg)total number of particle pairs (-)
pressure (Pa)distance of particle pairs (m)Reynolds number (-)
Stokes number (-)
time (s)fluid velocity (m s–1)
mesh velocity (m s–1)
volume of domain (m3)particle volume (m3)particle velocity (m s–1)
sphericity (-)
Kolmogorov length scale (m)mean number of particles (-)
gas viscosity (Pa s)
gas density (kg m–3)
particle density (kg m–3)
stress tensor, standard deviation
standard deviation of Poisson distribution
particle response time (s)1Length [mm]Acknowledgements

DNS
LES
PIV
RANS
SGS
A
CD
d
D
F
g
l
m
Np
p
r
Re
St
t
u
u′
V
Vp
υ
ϕ
η
λ
μ
ρf
ρp
σ
σPoi
τp

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Authors’ Short Biographies
Hiroaki Watanabe
Prof. Hiroaki Watanabe received his Ph.D. from Kyoto University in 2008. He worked as a
researcher at Central Research Institute of Electric Power Industry from 1998 to 2014. He joined
Department of Mechanical Engineering at Kyushu University as an Associate Professor in 2014 andbecame a Full Professor of Department of Advanced Environmental Science and Engineering in
2020. His research interest is mathematical modeling and simulation of multiphase turbulent reacting
flows. Modeling of co-combustion of solid carbon fuels with carbon-free gaseous fuels is currently
being examined.

Wei Zhang
Dr. Zhang Wei received his Ph.D. from Kyushu University in 2017 and then worked as a doctoral
researcher at Kyushu University for half a year. He moved to Nagoya University and worked as adesignated Assistant Professor from 2018 to 2020. Now he is a Lecturer working in the College of
Mechanical and Transportation Engineering, China University of Petroleum-Beijing. He mainly
works on the numerical simulation of gas-solid multiphase reacting flows for carbon conversion,
such as the CO2 methanation, and the gasification of carbon fuels including coal, biomass, andpetroleum coke.

13

KONA Powder and Particle Journal No. 40 (2023) 14–28/https://doi.org/10.14356/kona.2023001

Review Paper

Smart Powder Processing for Excellent Advanced Materials
and Its Applications †Makio Naito 1*, Takahiro Kozawa 1, Akira Kondo 1 and C.C. Huang 2
1 Joining and Welding Research Institute, Osaka University, Japan
2 Hosokawa Micron Powder Systems, USA

Abstract
To create advanced materials with minimal energy consumption and environmental impacts, a green and sustainable
powder processing technology is essential. The authors have developed this technique based on powder grinding
technology. In this paper, the authors will explain the recent progress of the smart powder processing, and its
applications. Firstly, particle bonding process, and novel one-pot processing methods to synthesize nanoparticles,
to create nanostructured composite granules and to form nano-porous films on substrates in dry phase will be
discussed. Their applications on the advanced material fabrications contributing to the sustainable economy will also
be explained. Then, the use of grinding technology in wet processing to synthesize nanoparticles and control theirmorphology will be explained. Smart powder processing can be a foundation to move forward material development
technologies and create many more high-quality advanced materials in the future.
Keywords: smart powder processing, particle bonding, lithium-ion battery, fuel cell, thermal insulation material

1. Introduction
The 2030 Agenda for Sustainable Development (the2030 Agenda) is a set of international development
goals from 2016 to 2030, which was adopted by theUnited Nations Sustainable Development Summit held in
September 2015 building on the success of Millennium
Development Goals (MDGs). The 2030 Agenda listed“Sustainable Development Goals (SDGs)” consisting of
17 goals and 169 targets in order to eradicate poverty andrealize a sustainable world. The SDGs are universal goals
applicable, not only to developing countries but also developed countries.
Powder technology directly contributes to achieve thesegoals with a wide range of powder processing technology,
and the development of advanced materials is one of the
key issues to promote the achievements of these goals(Hosokawa et al., 2007; Naito et al., 2010; 2018). Recently,
some novel powder processing techniques have been developed to fabricate the advanced materials. Smart powder
processing is a green and sustainable powder processing
technology, because it creates advanced materials without
consuming a large amount of energy.
†

*

14

Received 28 July 2021; Accepted 22 September 2021
J-STAGE Advance published online 13 November 2021
Corresponding author: Makio Naito;
Add: 11-1, Mihogaoka, Ibaraki, Osaka 567-0047, Japan
E-mail: m-naito@jwri.osaka-u.ac.jp
TEL: +81-6-6879-8660 FAX: +81-6-6879-8680

In this paper, the authors introduce a smart powder processing developed based on powder grinding technology.
Grinding process produces fine particles by giving feedmaterials the mechanical forces strong enough to breakthem. From a different viewpoint, the process is thought tobe a useful method to act an effective force onto the surfaceof fine particles for their bonding. This function can beapplied for many kinds of particle design and structuralcontrol without any heat treatment. Fig. 1 shows the smart
powder processing so far developed by the authors, which
was developed based on the concept of the particle bonding process to make advanced composites (Koishi, 1987;
Yokoyama et al., 1987; Naito et al., 1993; 2009; Naito andAbe, 2006; Naito and Kondo, 2016). The particle bonding
process developed by the authors has many unique features. For examples, it establishes direct bonding between
particles without any heat treatment or binders of any kindin the dry phase through the enhanced particle surface activation induced by mechanical energy and the high intrinsic
surface reactivity of nanoparticles to fabricate desired
composite particles.
As a result, a new one-pot processing method to synthesize nanoparticles without applying extra heat wasachieved using particle bonding technique. Furthermore,both the synthesis of nanoparticles and their bonding withother kind of particles to make nanocomposite granules
could be completed at the same time, which leading to thepossibility of controlling the nano/microstructure of thecomposite particles/granules and resulting in customized

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

Bonding(with glow discharge)
Dry processing
ex.
Attrition-type mill

DDS device, catalyst etc.

Nanoparticle
Synthesis

Assembling
Bulk material

Synthesis&
Bonding
Deposition to
substrate

Smart powder processing
without heat assistance

Wet processing
ex.
High energy
planetary ball millNano-porous
film etc.

Nanoparticle
synthesis

Assembling

Nanoparticle
morphology control
Recycling ofcomposite materials

Particle bonding and disassembling

Fig. 1 Smart powder processing developed based on powder grinding technology.

nano/micro structural new materials by a simple manufacturing process. Besides, as shown in Fig. 1, coating
nanoparticles on the substrate to form mechanically deposited nano-porous film was also demonstrated. At thefirst part of this paper, the dry process of the smart powder
processing and its applications for developing advanced
materials which contribute to saving energy and providing
clean energy are explained (Naito and Kondo, 2016; Naitoet al., 2009).
Based on the powder grinding technology, the authors
recently developed wet mechanical processing method
to synthesize nanoparticles and control their morphology
from raw materials in liquid without any heat assistance asshown in Fig. 1. At the second part of this paper, the wet
process of the smart powder processing will be explained.
In addition, controlling the bonding between different
kinds of materials could also make it possible to disassemble them for the purpose of recycling their elemental
components. It led to the development of novel technology
for recycling composite materials as demonstrated in theliteratures (Naito et al., 2009; 2011; Kondo et al., 2010;2012).

2. Particle bonding process
Grinding process is to use mechanical forces from thegrinding machines to fracture materials into small pieces.
From a different viewpoint, the mechanical forces can also
be applied on the surface of particles to create inter-particle
bonding effects. Table 1 shows the equipment having
been applied for making composite particles (Naito et al.,1993). As seen in the Group I, most kinds of fine grinding

Table 1 Main machines used for particle bonding.

Type/machines
Impaction-type
pin mill, disc millCentrifugal classifying type
Attrition-type mill
I

Ball milltumbling
vibrationplanetary
centrifugal fluidizing
Agitated ball mill
mixing vessel typeJet mill
MortarII

Cylindrical vessel type with rotating disc
Elliptical vessel typewith high-speed elliptical rotor

machines can be used in the particle bonding process.
Group II shows the intensive mixing apparatus used forparticle bonding applications. Several commercial particle
bonding machines have been developed based on theirmechanical principles. For example, the MECHANOFUSION® System has a rotating chamber and a fixed
arm head with a certain gap against the inside wall of
the chamber (Yokoyama et al., 1987; Naito et al., 1993).
During its processing, the powder material is pushed next
to the inside wall of the rotating chamber by the centrifugal
force and is compressed through the gap against the fixed
arm head, wherein receiving various kinds of forces, such

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Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

1
3 ρp d p n  1 
2π ρa d a (1)where dp : core particle diameterda : fine particle diameter(1)ρp : true density of core particles
ρa : true density of fine particles
Eq. (1) is a starting guideline to determine the fine particle fraction. In practice, the fine particle fraction can beselected in a wide range.
As to the bonding mechanism, it depends on the combinations of powder materials and processing conditions. At
the contact surfaces between particles, extremely high local
temperature and strong mechanical stress were observed(Dachille and Roy, 1960). The local temperature between
particles could be ten times higher than the apparent temperature of processing chamber (Nogi et al., 1996). Sucha high local temperature could create unique phenomena
such as micro-welding or chemical interaction between
fine particles and core particles, or among fine particles(Lian et al., 2015).
Apparently, processing conditions such as mechanicalactions from the machine rotation can directly affect theperformance of particle bonding process. For coating acore particle with fine guest particles, the particle bonding process is proposed as shown in Fig. 3 (Naito et al.,1993). The rotation speed of rotor or chamber and theprocessing time determine the mechanical actions and its
repeated number for processing the powder mixture. This
includes two steps: First, the surfaces of fine particles andcore particles are mechanically activated, as a result, fineparticles adhere onto the surfaces of core particles as thesolid line in shown Fig. 3. At this step (Region I in Fig. 3),
the BET specific surface area of the composites gradually
decreases with processing time. However, at the second
step (Region II in Fig. 3), as fine particles deposited on thesurfaces of core particles are gradually compacted, BET
specific surface area starts leveling off with the processing
time, which resulting in lower porosity, smaller pore size,
higher mechanical strength of surface layers and stronger
bonding between surface layers and core particle. Therefore, the apparent density (porosity and pore size) andstrength of surface layers can be controlled by adjusting
the rotation speed and its processing time. However, thesurface layer formed on the core particle can be fractured

Fixing ratio offine particles

as compression, shear, attrition, impact and rolling forces.
The processed powder is then dispersed by a scraper in thechamber. Particle bonding effect is created as the above actions repetitively applied to the powder material during thechamber rotation. In addition, an electric discharge option
is available to generate glow discharge in vacuum between
the fixed arm head and the rotating chamber during theoperation to activate nanoparticles (Naito and Abe, 2006).
Prevailing parameters affecting the particle bonding
process are summarized in the Fig. 2 (Naito and Kondo,
2016), including the properties of powder materials and theprocessing conditions.
The properties of powder materials affect the structure
and bonding mechanism of composite particles. For making core/shell type composite particles, the particle size
ratio between core particles and shell particles should beproperly selected. The mixing ratio is also an importantfactor to form composite particles with a desired structure.
For example, the weight fraction of fine particles n is
calculated by the following equation (Naito et al., 1993)
assuming the fine particles are closely allocated onto thesurface of core particle, and dp ≫ da.

Fine particles
Core
particles

Powder materials
Particlecomposite
process

Processing conditions
・Mechanical Actions(Mechanical stress, Processing time etc.)
・Temperature(Average, Local temperature)
・Atmosphere・Charging procedures of powder materials
etc.

Fig. 2 Factors controlling particle bonding process.
Reproduced with permission from Ref. (Naito and Kondo, 2016). Copyright 2016, Elsevier.

16

Specific surface area

・Particle size (Core particle, Fine particle)
・Particle properties(Shape, Surface,
Mechanical strength etc. )・Mixing ratio
etc

RegionI

RegionII

Processing time
Fig. 3 Particle bonding process for core/fine particles.

Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

after certain processing time if the rotation speed is toohigh as the dotted line shown in Fig. 3, due to the effect of
surface grinding on composite particles (Naito et al., 1993).
Besides, Distinct Element Method (DEM) simulation was
also applied to a high-speed mixer (Endoh et al., 2004),
and to an attrition-type mill (Soda et al., 2012) respectively
to elucidate the particle bonding process. Both papers concluded that the motion of core particles, their contact forces
and frequencies are important factors to control the particle
bonding process.
Temperature and atmosphere during the mechanicalprocessing also affect the performance of particle bonding
process. Like the powder grinding operation, particle bonding process generates a large amount of heat. Temperature
control is essential when processing heat-sensitive materials. Besides, the processing atmosphere is also importantto process powder materials in the dry phase for better
products.
To custom the structure of composite particles, thecharging procedures of powder materials are critical.
Fabricating composite particles with a uniform surface
layer of fine particles can be achieved by adding a small
amount of fine particles repeatedly into the processed
powder at certain time intervals (Naito et al., 1998). As
to the multi-layered composite particle, it can be createdby adding different kinds of fine powders at certain timeintervals. These techniques have been used for many industries including the applications for drug delivery systems(Fukumori et al., 2005).

a

3. Application of particle bonding process foradvanced materials
Particle Bonding Process has a lot of potential to develop
various kinds of advanced materials. In this section, theauthors introduce the following four kinds of advanced materials which are applied for the purpose of energy saving
and clean energy related issues.

3.1 Development of high performance thermal
insulation materials
The fabrication of highly performance thermal insulationmaterials by particle bonding process was reported earlier(Abe et al., 2005; Abe et al., 2008). Interest in thermal
insulation materials has been intensified globally, because
energy saving issues signified the importance of efficient
thermal insulation. In this study, nanoparticle bonding
process was used to make composite fibers coated withporous fumed silica layer in the dry phase. Fig. 4 shows
the proposed dry processing method to fabricate fumed
silica compact by using composite fibers (Abe et al., 2005).
Fumed silica / fiber composites porously coated with silica
nanoparticles were fabricated at the first stage and thencompacted into a board by dry pressing. The composites
were produced by a particle bonding process without collapsing the fiber glass and with nano-scale pores made by
the fumed silica. The proposed method had the advantage
of preventing contacts between fibers in the compacts dueto the existence of coating layer.
Table 2 shows the composition of the thermal insulationb
Fumed silica

Glass fibersc< ~100nm

Fumed silica / fiber
composites

Fiber reinforced porous
Fumed silica compact

Fig. 4 Fabrication method of fibrous fumed silica compacts: (a) mixing of raw materials, (b) particle bonding to coat glass fiber with fumed silica, (c)dry pressing of the composites from (b) to produce bulk body. Reproduced with permission from Ref. (Abe et al., 2005). Copyright 2005, John Wileyand Sons.
Table 2 Composition of the thermal insulation board.

Material

Compounding ratio (mass%)

Average diameter

Fumed silica nano particle(Hydrophilic or hydrophobic)

60

10 nm (Hydrophilic)
12 nm (Hydrophobic)

SiC particle20

3.3 μmGlass fiber

20

10 μm

17

Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

board (Lian et al., 2015). When the hydrophilic fumed
silica nanoparticle is used as starting material, the bonding
between hydrophilic glass fiber and nanoparticles in addition to the bonding between nanoparticles is well formed
due to the dehydration reaction between OH group on theparticle surface. Fig. 5 shows the thermal conductivity of
the hydrophilic fumed silica compact specimens (Lian etal., 2015). The light weight board with 86 % porosity (apparent bulk density: 405 kg/m3) was obtained, and its thermal conductivity was 0.031 W/mK at 600 °C. They werelower than molecular conductivity of still air (0.05 W/mK
at 400 °C) and at the same level as those obtained fromsilica aerogel (Kwon et al., 2000) and fumed silica compacts (Smith and Hust, 1989). These results indicated thatthe obtained compacts had the nano-scale porous structure.
The mechanical strength of the compacts depends on theirapparent density and the amount of glass fibers as determined by the bending strength. In this case, three-point
bending strength was 0.3 MPa. This made it possible tomachine the compacts for various applications.
On the other hand, the surface chemical structure of the
fumed silica also affects the bonding mechanism between
nanoparticles and that between nanoparticles and glass fiber (Lian et al., 2015; 2017). When the hydrophobic fumed
silica was used as starting material as shown in Table 2,
the thermal conductivity of the obtained specimen showed
lower value than that made by hydrophilic fumed silica asshown in Fig. 5. Since the hydrophobic methyl group decomposed around 400 °C, the thermal conductivity of thecompact after 400 °C heat treatment was also measured,and found it was increased by the heat treatment, but still
lower than that of the compact with hydrophilic fumed
silica. Microscopic observation results made clear that thesurface layer thickness of hydrophobic fumed silica on thesurface of glass fiber was thinner than that of hydrophilic
fumed silica due to weaker bond between particles. Therefore, the hydrophobic fumed silica contributed to lower
thermal conductivity of the compact. It means that the raw
powder properties are also critical to achieve higher perfor-

Thermal conductivity[W/(m∙K)]

0.040
0.030

3.2 Development of fuel cell electrodes
Fuel cell is touted to be the power generation of 21st
century because of their high energy efficiency and clean
exhaust. The application of particle bonding process forthe fuel cell development has been focusing on making
composite particles to reduce its production costs andincrease the long-term stability of cells and stacks bylowering its operation temperature without losing power
density. For controlling the microstructure of composite
electrodes, various kinds of composite particles such aslarge core-particles coated with nanoparticles (Fukui et al.,
2004; Hagiwara et al., 2006; Misono et al., 2006) and interdispersed composite mixture consisting of several kindsof nanoparticles (Sato et al., 2009) have been successfully
fabricated using the particle bonding process.
For the solid oxide fuel cell (SOFC), nickel-yttria stabilized zirconia (Ni-YSZ) was widely used SOFC anodematerial due to its excellent electrochemical properties athigh temperatures. Fig. 6 shows NiO-YSZ inter-dispersed
composite particles consisting of NiO and YSZ nanoparticles processed by particle bonding process (Sato et al.,2009). Fig. 6(a) is the SEM micrograph of the composite
particles. Fig. 6(b) is the detailed structure of a composite
particle observed by TEM. NiO and YSZ phases in thecomposite particle were identified by EDX analysis in the
micrograph. The micrograph indicates successful fabrication of the composite particles. NiO and YSZ nanoparticles
were well dispersed and their sizes were in good agreementwith those estimated from the specific surface area of starting particles (NiO: 160 nm and YSZ: 75 nm).
Fig. 7 shows SEM micrographs of the anode made ofcomposite particles before the reduction operation, just

(a)

0.020

(b)

YSZ

YSZ

NiO

Hydrophilic silica added
Hydrophobic silica added
after 400 °C heated of the board (

0.010
0.000

0

200

400

YSZ

NiO

)

600

Temperature [°C]Fig. 5 Thermal conductivity of thermal insulation boards for temperature. Reproduced with permission from Ref. (Lian et al., 2015).
Copyright 2015, Elsevier.

18

mance for the thermal insulation materials. For example,
when fumed alumina nanoparticle and ceramic fiber wereselected as the starting powder materials, the thermal conductivity of the obtained bulk compact could achieve lower
value than 0.065 W/mK at 1,000 °C. It is a very promising
result for thermal insulation materials at very high temperature applications (Tasaka et al., 2020).

500 nm

50 nm

YSZ

NiO

Fig. 6 NiO-YSZ inter-dispersed composite particles: (a) SEM image,(b) TEM image. Reproduced with permission from Ref. (Sato et al.,2009). Copyright 2009, Elsevier.

Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

(a)

(b)

1 μm

(c)

1 μm

1 μm

Fig. 7 Cross-sectional SEM images of the anode: (a) before reduction (b) after reduction (c) after long-term stability test at 700 °C for 920 h. Reproduced with permission from Ref. (Sato et al., 2009). Copyright 2009, Elsevier.

after the reduction operation, and after the long-term stability test at 700 °C for 920 h (Sato et al., 2009). Before
the reduction operation, the grain size of the anode wassmaller than 1 μm without unusually large ones. The NiO
shrunk when it was reduced to Ni and producing porous
structure in the anode. Thus, the uniform porous structure
after reduction as seen in the Fig. 7(b) suggests that Ni
was uniformly distributed around the three-dimensional
YSZ framework in the entire anode. In addition, there wasno significant structural change even after the long-term
stability test. The grain size was kept at about 0.5 μm without large grains, which indicating the insignificant grain
growth of Ni in the anode made of inter-dispersed composite particles under the testing conditions and exhibiting
lower polarization than the anode made of coated composite particles prepared by the authors (Fukui et al., 2004).
Controlling the microstructure of composite particles as thestarting materials for SOFC anode fabrication is the key toimprove its performance.
Particle bonding process is also effective to develop
superior electrode of molten carbide fuel cell (MCFC).
For example, Ni particles uniformly coated with fine CoO
particles were prepared by particle bonding process (Fukui
et al., 2000; 2001). A new cathode structure, where the NiO
core coated with an outer layer of lithiated cobalt and nickel
solid-solution oxide (Li(Co,Ni) oxide), was formed by oxidation and lithiation using the CoO/Ni composite particles.
As a result, the solubility of nickel in this Li(Co,Ni) oxide
layer into carbonate melt decreased to two-thirds of thatof NiO when used as a cathode for MCFCs. Other examples made by the particle bonding process were filamentshaped Ni particle uniformly coated by CoO fine particles,
and that by MgFe2O4 fine particles as starting material,
respectively. The cathode made by the starting composite
particles showed lower solubility of nickel into carbonate
melt (Fukui et al., 2003).
Furthermore, particle bonding process was applied todevelop low-platinum catalyst for polymer electrolyte fuelcell (PEFC) (Munakata et al., 2011). Composite catalyst
consisting of tungsten carbide (WC) and platinum/carbon(Pt/C) was fabricated by bonding them together in thedry phase. Small Pt particles with thin carbon layer wasobserved on the surface of WC particles in the composite
catalyst by TEM analysis. The composite catalyst showeda similar catalytic reactivity to that from pristine Pt/C
catalyst for hydrogen oxidation with about 25 % of Pt
loading (Munakata et al., 2011). It suggests that the particle
bonding process is a promising way to develop low cost
catalysts for PEFC.

3.3 Development of CNT/Al composites
The advanced carbon nanomaterials with extraordinary
mechanical and physical properties, represented by carbon
nanotubes (CNTs) and graphene nanosheets, are regarded
as ideal reinforcements for developing high performance
structural composites. Recently, great efforts have been
done to develop carbon nanotubes reinforced aluminum
matrix composites, due to the urgent need in many areas
such as transportation for advanced light weight structural materials with good stiffness and ductility. For thispurpose, a new powder metallurgy route via combinationof particle bonding process and shift-speed ball milling
process was developed (Chen et al., 2018; 2019). At first,
CNTs were well dispersed and uniformly coated on the surface of 6061 Al particles by particle bonding process. Then,the composite particles were processed by a planetary ballmill at low rotation speed for 6 h to make CNT/6061Al
flake particles, and processed at high rotation speed for 1 h
to make cold welded CNT/6061 Al particles. The obtained
composite particles were cold pressed, consolidated by sintering, and hot extruded into rod samples. As a result, theextruded 1.5 mass% CNT/6061Al composites exhibited
simultaneous enhancement in Young’s modulus, tensile
strength and ductility, compared to the counterpart without
particle bonding process at the first step (Chen et al., 2018).

3.4 Development of all-solid-state Li-ion batteries
and traditional Li-ion batteries
All-solid-state Li batteries (ASS-LIBs) are suitableapplications of 5 V-class cathode materials with high energy densities. However, it was hard to fabricate bulk-typebatteries composed of a mixture of active materials and19Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

Phosphate-type
Glass SELCP (LiCoPO4)

LCP bulk

LCP
bulk
InterfaceSE bulk

Fig. 8 The assembling process of bulk-type all-solid-state Li-ion batteries. Reproduced with permission from Ref. (Kozawa et al., 2019b).
Copyright 2019, Springer Nature.

solid electrolytes because of the difficulty of ensuring a
conductive path and suppressing the formation of reactive
layers at the interface. To solve this problem, the authors
proposed the assembling procedures of the composite
cathode and the ASS-LIBs as shown in Fig. 8 (Kozawa etal., 2019b). The first step was the mechanical treatment of
the composite LiCoPO4 (LCP) granules by particle bonding process. The granule was inter-dispersed composite
particle containing the LCP, phosphate-type glass solid
electrolyte (AN-15), and multi-walled carbon nanotubes(MWCNT) as the conductive material. They were homogeneously dispersed and organized a network structure asshown in Fig. 8. Then, the produced composite granules
were shaped into pellets by pressing. The heat treatment at
low temperature brought the AN-15 in close contact with
the LCP through softening. The ASS-LIB were assembled
by using a Li metal anode and a polymer-solid electrolyte(polymer-SE) film, so that the properties of composite LCP
cathodes could be evaluated.
Fig. 9 shows STEM-EELS measurements performed at
the interface between LCP and AN-15 solid electrolyte of
the cathode pellet. The spectra were collected with a step
size of 2 nm. The interface between LCP bulk and SE bulk
(AN-15) did not reveal any gaps. The EELS data for the
Co-M edge at ~780 eV was collected in the direction fromSE bulk to LCP bulk. It revealed no band of the Co-M
edge was detected in the region of existence of AN-15. It
was concluded that the mechanical and subsequent heat
treatment resulted in a good interface between LCP and the
AN-15 particles without any Co diffusion. As a result, thefirst discharge capacity was a bit lower than that of traditional LCP/Li battery using organic liquid electrolytes, but
the discharge capacities kept higher value up to 20 cycles.
In another case, the inter-dispersed composite granule of
LiCoO2/Li1.3Al0.3Ti1.7(PO4)3 was fabricated by the particle
bonding process to make the cathode for ASS-LIBs withgood results (Nakamura et al., 2016).
Furthermore, the particle bonding process was also
applied to improve the anode performance of traditional

20

SE
bulk

Fig. 9 STEM-EELS measurements performed at the interface between
LCP and AN-15 solid electrolyte (SE) of the cathode pellet. Reproducedwith permission from Ref. (Kozawa et al., 2019b). Copyright 2019,
Springer Nature.

Li-ion batteries. For example, silicon can be used as anodematerial due to its high theoretical specific capacity anda relatively low working voltage. However, silicon experiences the large volume changes up to 300 % associatedwith lithium ion, which leads to poor electrochemical properties caused by loss of the electrical contact between active
materials and other effects. To overcome this problem, a
new structure of the anode was proposed (Lee et al., 2020).
It is the silicon/graphite granular composite with uniform
amorphous carbon coating layer. Besides, the granule
has uniform inner free space to buffer the volume changeof silicon. Actually, the granule was prepared by spraydrying process, and its outer layer was coated with pitch byparticle bonding process. Then, the composite granule washeated at 800 °C to form the amorphous carbon coating
layer. The pouch cell made by using the composite granule
showed good long-term cycle properties with the capacity
retention 71 % over 500 cycles at 1 C rate.

4. One-pot mechanical synthesis of
nanoparticles without heat assistanceMaterial synthesis at low temperature using particle
bonding process began with the attempt to create MgB2
from raw powders (Abe et al., 2003; 2006). Mechanicallyprocessed large magnesium particles and submicron amorphous boron particles could embed boron particles into thesurface of magnesium particle. Also, MgB2 was found at
this embedded region after being annealed at a low temperature under argon atmospheric pressure.
Particle bonding process with low temperature plasma
discharge was also applied to dope TiO2 nanoparticles
with nitrogen without providing any heat (Abe et al.,2006), which usually accomplished (Asahi et al., 2001) at
500~600 °C under NH3 flow. Since high temperature could
cause undesirable grain-size growth of TiO2 nanoparticles,
particle bonding process with low temperature plasma discharge was preferable for this application (Naito and Abe,2006). NH3 (10 %)/Ar plasma was generated at differentMakio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

gas pressures in a particle bonding processing chamber,where an anatase TiO2 powder with a BET specific surface
area of 300 m2/g was processed and uniformly irradiated.
As generated plasma irradiation at 300 Pa, the TiO2 powder
had a specific surface area of 283 m2/g, and, noticeable
absorption in visible light range was observed. In addition,
the powder showed an improvement of the photo-catalyticoxidation activity of CH3CHO under visible light. These
results indicate that the presented plasma-enhanced particle bonding processing is capable of modifying TiO2
nano-powder to improve its photo-reactivity without much
reduction in specific surface area.
Using the features of particle bonding process, new
one-pot processing to synthesize nanoparticles without any
heat support was developed. First, a rapid synthesis of perovskite type lanthanum manganite starting with a mixtureof industrial grade powders was demonstrated (Sato et al.,2006; Ohara et al., 2008a). Traditionally, LaMnO3+δ wassynthesized through the solid-state reaction of component
oxide powders such as La2O3 and Mn3O4 at 1300 °C,
which required many manufacturing steps. Particle size
enlargement during the thermal reaction and limited degreeof chemical homogeneity with some impurities during thepowder processing have been an issue for its producers.
However, with the rapid mechano-chemical synthesis,
one-pot synthesis of LaMnO3+δ from a mixture of industrial
grade La2O3 and Mn3O4 could be accomplished without using any heat. The one-pot processing was applied mechanical forces from the particle bonding process to achieve thesolid state reaction on the powder mixture without usingball media.
Adding to the synthesis of LaMnO3+δ, Table 3 summarizes the examples of nanoparticles synthesized by the
one-pot mechanical method. It shows various kinds ofcomposite oxides have been synthesized by the one-pot
processing. For example, YAG:Ce3+ phosphor is used forwhite light emitting diodes (LEDs) of next generation
lighting. LED is a highly energy-efficient lighting technology, and has the potential to fundamentally change the future of lighting. Widespread use of LED lighting can havegreatest impact on energy savings worldwide. However,
YAG:Ce3+ phosphor has been manufactured by solid-state
reaction at over 1600 °C. To achieve low cost manufacturing of the phosphor with more energy saving process,drastic decrease of the calcination temperature is essential.
To decrease the synthetic temperature of solid-state
reaction, the addition of BaF2 and YF3 was usually used
as fluxes. Therefore, the authors achieved the YAG:Ce3+
nanoparticle synthesis by applying the one-pot mechanicalsynthesis from Y2O3, Al2O3 and CeO2 with BaF2 (Kanai
et al., 2017) and those with YF3 (Kanai et al., 2018). Thissynthesis route is very promising as a novel energy saving
process. The only concern was the flux must be removed
from the product after the synthesis. Now, further research
work to develop one-pot synthesis of YAG:Ce3+ without
any flux materials is on-going.

5. One-pot mechanical process to synthesize
nanoparticles and their bonding to makenanocomposite granules
The motivation to make nanocomposite granules wasraised by the development of the cathode of lithium-ion
batteries (LIBs). For example, lithium-ion phosphate(LiFePO4) is one of the key cathode materials for use
in large-format LIBs, due to its intrinsic structural andchemical stability that leads to the safety and long cycle
life of the batteries. Nevertheless, it has two main obstacles
inherent to the practical applications i.e. slow kinetics
of lithium-ion diffusion coefficient and poor electronic
conductivity. To improve these undesirable properties, theparticle size reduction to nano-scale dimensions and the
conductive coating on their surfaces become necessary.
However, these two approaches lead to another problem
i.e. decreasing packing density of electrode, which causes
an issue on the energy density of LIBs. One of the breakthrough for this unfavorable problem is to prepare granuleparticle which has the internal pores to encourage the penetration of electrolyte. As a result, the ideal structure of theTable 3 Examples of synthesized nanoparticles and its applications by the one-pot mechanical method.

Synthesized nanoparticles

Applications

BaTiO3 (Kondo et al., 2006; Ohara et al., 2008b)

Dielectric material

LiCoO2 (Kondo et al., 2014)
LiMnPO4 (Yoshida et al., 2013)
LiFePO4 (Kozawa et al., 2014a; 2015)
LiCoPO4 (Matsuoka et al., 2017)
LiNi0.5Mn1.5O4 (Kozawa et al., 2014b; Kozawa and Naito, 2015)

Li-ion battery

La0.8Sr0.2MnO3 (LSM) (Chaichanawong et al., 2006)
La0.6Sr0.4Co0.2Fe0.8O3 (Xi et al., 2015)

Fuel cell (SOFC)

Y2.97Al5O12:Ce3+0.03 (Kanai et al., 2017; 2018)

Phosphor for LED

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Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

1. Particle size reduction

2. Carbon coating

LiFePO4

Reduction of Li + ion

diffusion length

Addition of electron
conductivity

Carbon

LiFePO4: 10−9 S cm−1
Carbon: >102 S cm−1

Liquid electrolyte

3. Granulation structure
Increase of electrode density
Fig. 10 LiFePO4/C composite granule consisting of nano-sized LiFePO4 and carbon.

starting material of LiFePO4 cathode is designed as shownin Fig. 10 (Kozawa et al., 2014a).
To make the structure, conventional process begins
with the synthesis of LiFePO4 by solid-state reaction
which needs calcination step controlling the atmosphereby flowing inert or slightly reductive gases to preventthe oxidation from Fe(II) to Fe (III). Then, the surface
coating on the synthesized LiFePO4 with a carbon, and thesubsequent granulation process is required to prepare theaimed LiFePO4/C composite granule as shown in Fig. 10.
However, an ultimate synthesis method for the structure
is a direct formation from raw materials under ambient
atmosphere.
To achieve this purpose, one-pot mechanical process
was applied. Staring materials, Li2CO3, FeC2O4·2H2O andNH4H2PO4 were selected, and ketjen black was used as
conductive carbon. The additive of carbon was also helpful
to improve the flow abilities of raw powders. The powders
were put into the chamber of attrition-type mill and themechanical synthesis was carried out below 4,500 rpm
for 20 min. The processing of attrition-type mill was done
under ambient atmosphere.
Fig. 11 shows the XRD patterns of the products (Kozawa
et al., 2014a). In spite of the presence or absence of carbon,
LiFePO4 was formed by the mechanical treatment underambient atmosphere. Synthesis of LiFePO4 usually requires inert or reductive gases to prevent the iron oxidation.
Therefore, this process has a great advantage over the conventional LiFePO4 synthesis. The effect of carbon addition
appeared in the formation of by-product phase. In the caseof the mechanical treatment without carbon, impurities
were detected as by-products. The oxidation state of ironin Li3Fe2(PO4)3 is trivalent, so that iron oxidation takes
place during the mechanical treatment without carbon.
On the other hand, by adding the carbon, the formationof trivalent iron compound was suppressed, though trace

22

Without
carbon

LiFePO4

With
carbonPDF No. 01-070-6684

Fig. 11 XRD patterns of LiFePO4 synthesized by one-pot mechanicalprocess. Reproduced with permission from Ref. (Kozawa et al., 2014a).
Copyright 2014, Elsevier.

amount of Li3PO4 was detected. The carbon addition on
the mechanical treatment exerted an important role in theprevention of iron oxidation as well as the improvementof conductivity and powder flow ability. Besides, applying
higher electric power in the mechanical synthesis, a single
phase of LiFePO4 could be obtained in a shorter time.
Fig. 12 shows the microstructure of nanoporous
LiFePO4/C composite granules prepared by the one-pot
mechanical process (Kozawa et al., 2015). As expectedfrom Fig. 10, the granule was composed of synthesized
LiFePO4 nanoparticles, and its inside had nanoporous
structure as shown on the fracture surface in Fig. 12,
Fig. 13 shows the discharge capacity and coulombic efficiency against the cycle number at 0.1 C rate (Kozawa etal., 2015). The discharge capacity gradually increased andreached to 130 mAh/g after 15 cycles. This might be due tothe slow penetration of electrolyte into the granules while
the coulombic efficiency kept constant at 94–96 % during
the cycles. Further design improvement of the granule
including the pore structure should be done to advance theperformance of the cathode.

Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

The one-pot mechanical process was also applied toprepare nanocomposite granules as the starting materials
for the cathode of SOFC (Hosokawa et al., 2014; Xi etal., 2016). In this case, the one-pot mechanical processachieved not only electrocatalyst nanoparticles synthesis
from the raw powder materials but also their bonding withsolid electrolyte nanoparticles to form inter-dispersed composite granules. The purpose of the composite granule wasto create the triple phase boundary (TPB) region as broadas possible while maintaining sufficient porous channels
for gas diffusion in the resultant cathode. Actually, theauthors prepared the following two kinds of composite
granules for this purpose. The first one was the combination of strontium doped lanthanum manganite (LSM) as
electrocatalyst and scandium stabilized zirconia (ScSZ) as
solid electrolyte (Hosokawa et al., 2014). The second one
was the combination of La0.6Sr0.4Co0.2Fe0.8O3(LSCF) as
electrocatalyst and Ce0.9Gd0.1O1.95(GDC) as solid electrolyte (Xi et al., 2016).
In the latter case, commercially available La2O3,
Sr(OH)2, Co3O4 and Fe2O3 with stoichiometric quantities were put into the chamber with one-fifth of the total
amount of GDC nanoparticles, and then, the powders weremechanically treated under ambient atmosphere without
external heating. During the mechanical processing, one-

Fracture surface

Pore
Fig. 12 Microstructure of nanoporous LiFePO4 /C composite granules.
Reproduced with permission from Ref. (Kozawa et al., 2015). Copyright2015, Elsevier.

Fig. 13 Cycle performance of the synthesized LiFePO4/C composite
granules at 0.1 C rate. Reproduced with permission from Ref. (Kozawa
et al., 2015). Copyright 2015, Elsevier.

fifth of the total amount of GDC powder was added every
4 min. The total processing time was 20 min. The XRD
pattern of the composite particles after 20 min. processing
exhibited mainly LSCF and GDC phase, and they formedinter-dispersed nanocomposite granules wherein both elements were uniformly dispersed. The obtained cathodeafter sintering exhibited a finely composed porous structure
consisting of fine grains ranging from nano to submicron
sizes. It indicated that quite low polarization resistance in
the intermediate temperature range. The obtained results
suggest that the one-pot mechanical process is a very promising, simple, and energy-saving approach for producing
high-quality LSCF-GDC composite powders.

6. Mechanically assisted deposition of
nanocomposite film by one-pot processing
To make porous composite films with a large surface
area for chemical sensing and energy storage/conversion,
direct deposition of mechanically activated nanocomposite
particles on the substrates in the dry phase is desirable.
Primarily, the apparatus used in this study (Abe et al.,2012) consisted of a closed vessel and a feather-type rotor.
As an example, the anode of Nickel-yttria stabilized zirconia (Ni-YSZ) cermet was fabricated on YSZ substrate bythis apparatus. NiO/YSZ composite particles were first put
into the chamber and dispersed by the rotor. The dispersed
and mechanically activated particles were then transported
to the substrate and deposited on it. As reported by Akedo(Akedo, 2006), dense ceramic layer consisting of fractured
particles or deformed particles could be successfully
formed on the substrate when the ceramic particles speeded
up to 100–500 m/s. On the contrary, it was demonstrated
that the fine aggregates or clusters could be bonded together at a much lower impact speed of about 30 m/s formaking the porous films. Besides, the rapid film formation
was found with the nanoparticles due to their high specificsurface area.
Fig. 14 shows the effect of the processing time on the
thickness of the deposited film with the NiO-YSZ nanoparticles (120 m2/g) (Abe et al., 2012). Noticeable film formation was found at ~ 10 min after starting the process, whichindicating that the fine aggregates of the nanocomposite
particles were formed and activated in the early stage ofthe processing and started to deposit on the substrate at an
impact speed of about 30 m/s. No unusually large grain
was found on the porous film after the heat treatment at
1200 °C. The fine composite microstructure was a result of
the homogeneously distributed NiO and YSZ nanograinsin the deposited film. The polarization of the porous film as
the anode of SOFC showed good performance as reported
by Abe etc. (Abe et al., 2012).
The deposition process can be also applied to suppress

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the mold growth on the surface of materials (Nomura et al.,2018). Mold growth can trigger a variety of serious humanhealth problem such as allergies and asthma. Designingsurface unfavorable for the adhesion of fungal spores wasconsidered an effective method to prevent fungal growth.
In this study, the effect of hydrophilic surface treatment
on the adhesion of fungal spores onto substrates wasinvestigated using Aspergillus oryzae as a model fungus.
The glass substrate coated with hydrophilic fumed silica(BET equivalent diameter: 7 nm) was prepared using thismethod. As a result, the fungal spores that strongly adheredon the coated glass substrate under atmosphere conditions
were easily removed by lightly hand-washing in water.
In addition, the removal ratio of the fungal spores on thecoated glass was higher than that on plasma-treated glass.
Atomic force microscopy revealed that there was almost noadhesive force between the spores and the glass substratecoated with fumed silica.

NiO-YSZ film thickness /μm

500
Activation

400
300

~6 μm/min
200

100

0

0

10

20

30

40

50

60

Processing time / minFig. 14 Relationship between film thickness and processing time. Reproduced with permission from Ref. (Abe et al., 2012). Copyright 2012,
John Wiley and Sons.

7. Wet mechanical processing to synthesis
nanoparticles and their morphology
control
Wet mechanical processing has been used to synthesize
particles for years. For example, particle synthesis via
solid-solution reaction incorporating mechanical actions
by using a multi-ring media mill was pioneered by Riman’sgroup (Shuk et al., 2001; Suchanek et al., 2002). However,variations of particle synthesized by conventional wet
mechanical approach are still limited, and the particle
preparation with specific morphologies has not been accomplished yet.
The authors applied grinding apparatus used for theparticle bonding to synthesize nanoparticles with aunique structure in the wet phase. At first, the synthesis of unique morphology of lithium titanate hydrate,
Li1.81H0.19Ti2O5·xH2O (LHTO) was done by applying ahigh energy planetary ball mill (Suzuki et al., 2017). This
material is used as a precursor of Li4Ti5O12 (LTO), a typical
LIB anode material. The traditional hydrothermal methods
to synthesize LHTO were constrained by the restrictive
preparation requirements, such as handling moisture-sensitive titanium precursors and high concentration NaOH
solutions. In contrast, the high energy planetary ball millenabled us to achieve the synthesis and morphology control of LHTO only by using low-cost materials (LiOH andTiO2) at room temperature.
Fig. 15 shows the TEM images of the raw TiO2 powder
and the products after planetary ball milling at 150 G up to
10 h (Suzuki et al., 2017). The TiO2 powder used consistedof ultrafine nanoparticles of approximately 5 nm in diameter. After the planetary ball milling, thin sheets of LHTO
were formed, and the widespread LHTO sheets wereformed after 3 h as shown in the figure. Then, the LHTO
sheet was transformed into the nanotube morphology bycurling after 5 and 10 h. Direct formation of LHTO

Fig. 15 TEM images of the raw TiO2 particles and the products after milling at 150 G for 3 h and 10 h. Reproduced with permission from Ref. (Suzuki
et al., 2017). Copyright 2017, Elsevier.

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Makio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

nanotubes was successfully achieved by the wet mechanical method. Then, thermal treatment of the LHTO nanotubes was conducted at 500 °C. As a result, an LTO phasewith a spinel structure was obtained, and the nanotube morphology of LHTO was retained in the LTO product. The
anode properties of the prepared LTO nanotubes exhibiteda first discharge capacity of 160 mAh/g at 0.1 C rate.
To clarify whether the normal or tangential componentof collisions makes a larger contribution on the synthesisof LHTO, the effect of collision direction on a reaction rateof LHTO was studied through the combined analysis of
the experimental results and the simulated ball motion byDistinct Element Method (DEM) (Kozawa et al., 2021b).
As a result, collisions of balls in the normal direction were
found to contribute strongly to the wet mechanochemical
reaction.
The wet mechanical processing was also applied for themorphology-controlled NH4MnPO4·H2O (Kozawa et al.,2019a) and NH4CoPO4·H2O (ACP) (Kozawa et al., 2021a),respectively. For the latter case, ACP platelets were synthesized via the wet mechanical processing of NH4H2PO4
and Co(OH)2 in water using a planetary ball mill. The
formation of ACP was achieved by the dissolutionprecipitation reaction, involving the gradual dissolutionof water-insoluble Co(OH)2 into the NH4H2PO4-dissolved
acidic solution. The use of Co(OH)2 with a brucite-type
structure allowed the ready formation of the layered ACP
phase. Despite the ball milling treatment, ACP particles
with platelet morphologies were produced, and their sizes
and shapes were varied, depending on the milling conditions. The formed ACP platelets were subjected to grinding,consequently affording a decrease in size and subsequentdissolution. However, an increase in supersaturation led to
the recrystallization and crystal growth of the ACP platelets. This dissolution-recrystallization cycle during milling
allows the repeated growth of the ACP platelets. The ACP
powder was converted into LiCoPO4, while maintaining
the shape and size, by a simple solid-state reaction with
Li2CO3. The flake-like LCP cathode exhibited a charge/
discharge plateau at ~4.8 V and a better discharge capacityof 109 mAh/g because of the high specific surface areaand small platelet thickness (Kozawa et al., 2021a). Other
applications of the wet mechanical processing including
the synthesis of Sr3Al2(OH)12 hydrogarnet (Kozawa etal., 2018), and the fabrication of composite cathode ofLiMn2O4 coated by LiMnPO4 (Kozawa et al., 2020) havebeen investigated.

8. Conclusions
Smart powder processing was developed based on
powder grinding technology. It is a very promising technique for achieving green and sustainable manufacturing,as well as developing advanced materials contributing
to establishing sustainable economy. In this paper, theauthors explained the recent progress of the smart powder
processing, and its applications. At first, the authors introduced particle bonding process, novel one-pot process tosynthesize nanoparticles, process to create nanostructured
composite granules, and process to form nano-porous films
on substrates in the dry phase. Then, the authors discussed
the wet smart processing with grinding technology. Smartpowder processing can be a foundation to move forwardmaterial development technologies and create many morehigh-quality advanced materials in the future.

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DOI: 10.4164/sptj.47.768

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Kondo A., Nakamura E., Kozawa T., Abe H., Naito M., YoshidaJ., Nakanishi S., Iba H., One-pot mechanical synthesis of thenanocomposite granule of LiCoO2 nanoparticles, Advanced
Powder Technology, 25 (2014) 1280–1284. DOI: 10.1016/j.
apt.2014.03.005
Kondo A., Ohmura T., Abe H., Kano J., Naito M., Developmentof recycling process for waste FRP mortar pipe, Journalof the Society of Powder Technology, Japan, 49 (2012)
827–831. DOI: 10.4164/sptj.49.827
Kondo A., Sato K., Abe H., Naito M., Shimoda H., Mechanochemical synthesis of barium titanate from nanocrystalline BaCO3
and TiO2, in: Characterization and Control of Interfaces forHigh Quality Advanced Materials II: Ceramic Transactions(eds K. Ewsuk, K. Nogi, R. Waesche, Y. Umakoshi, T.
Hinklin, K. Uematsu, T. Tomsia, H. Abe, H. Kamiya and M.
Naito), 198 (2006) 375–380. DOI: 10.1002/9781118144145.
ch57
Kozawa T., Fukuyama K., Kondo A., Naito M., Wet mechanicalroute to synthesize morphology-controlled NH4MnPO4·H2O
and its conversion reaction into LiMnPO4, ACS Omega, 4(2019a) 5690–5695. DOI: 10.1021/acsomega.9b00026
Kozawa T., Fukuyama K., Kondo A., Naito M., Wet millingsynthesis of NH4CoPO4·H2O platelets: formation reaction,
growth mechanism, and conversion into high-voltage
LiCoPO4 cathode for Li-ion batteries, Materials Research
Bulletin, 135 (2021a) 111149. DOI: 10.1016/j.materresbull.
2020.111149
Kozawa T., Fukuyama K., Kushimoto K., Ishihara S., Kano J.,
Kondo A., Naito M., Effect of ball collision direction on a wetmechanochemical reaction, Scientific Reports, 11 (2021b)
210. DOI: 10.1038/s41598-020-80342-w
Kozawa T., Harata T., Naito M., Fabrication of an LiMn2O4@
LiMnPO4 cathode for improved cycling performance at hightemperatures, Journal of Asian Ceramic Societies, 8 (2020)
309–317. DOI: 10.1080/21870764.2020.1743413
Kozawa T., Kataoka N., Kondo A., Nakamura E., Abe H.,
Naito M., One-step mechanical synthesis of LiFePO4/C
composite granule under ambient atmosphere, Ceramics
International, 40 (2014a) 16127–16131. DOI: 10.1016/j.
ceramint.2014.07.043
Kozawa T., Kataoka N., Kondo A., Nakamura E., Abe H.,
Naito M., Facile preparation of porous LiFePO4/C composite granules by mechanical process, Materials Chemistry and Physics, 155 (2015) 246–251. DOI: 10.1016/j.
matchemphys.2015.02.038
Kozawa T., Kondo A., Fukuyama K., Naito M., Koga H., Shimo
Y., Saito T., Iba H., Inda Y., Oono T., Katoh T., NakajimaK., Bulk-type all-solid-state batteries with mechanically
prepared LiCoPO4 composite cathodes, Journal of Solid StateElectrochemistry, 23 (2019b) 1297–1302. DOI: 10.1007/
s10008-019-04218-4
Kozawa T., Kondo A., Nakamura E., Abe H., Naito M., Koga H.,
Nakanishi S., Iba H., Rapid synthesis of LiNi0.5Mn1.5O4 bymechanical process and post-annealing, Materials Letters,
132 (2014b) 218–220. DOI: 10.1016/j.matlet.2014.06.097
Kozawa T., Naito M., Facile preparation of core@shell andconcentration-gradient spinel particles for Li-ion battery
cathode materials, Science and Technology of Advanced
Materials, 16 (2015) 015006. DOI: 10.1088/14686996/16/1/015006
Kozawa T., Suzuki Y., Naito M., Scalable synthesis of Sr3Al2(OH)12hydrogarnet by wet milling and its thermal decomposition
behavior, Materials Chemistry and Physics, 212 (2018)245–251. DOI: 10.1016/j.matchemphys.2018.03.060
Kwon Y.G., Choi S.Y., Kang E.S., Baek, S.S., Ambient-driedMakio Naito et al. / KONA Powder and Particle Journal No. 40 (2023) 14–28

silica aerogel doped with TiO2 powder for thermal insulation,
Journal of Materials Science, 35 (2000) 6075–6079. DOI:
10.1023/A:1026775632209
Lee D., Kondo A., Lee S., Myeong S., Sun S., Hwang I., SongT., Naito M., Paik U., Controlled swelling behavior andstable cycling of silicon/graphite granular composite
for high energy density in lithium ion batteries, Journalof Power Sources, 457 (2020) 228021. DOI: 10.1016/j.
jpowsour.2020.228021
Lian T.W., Kondo A., Kozawa T., Akoshima M., Abe H., Ohmura
T., Tuan W.H., Naito M., Effect of hydrophobic nano-silica
on the thermal insulation of fibrous silica compacts, Journal of Asian Ceramic Societies, 5 (2017) 118–122. DOI:
10.1016/j.jascer.2017.03.003
Lian T.W., Kondo A., Kozawa T., Ohmura T., Tuan W.H., Naito
M., Effect of fumed silica properties on the thermal insulation
performance of fibrous compact, Ceramics International, 41(2015) 9966–9971. DOI: 10.1016/j.ceramint.2015.04.076
Matsuoka M., Kondo A., Kozawa T., Naito M., Koga H., Saito
T., Iba H., Effect of carbon addition on one-step mechanicalsynthesis of LiCoPO4/C composite granules and their powder
characteristics, Ceramics International, 43 (2017) 938–943.
DOI: 10.1016/j.ceramint.2016.10.022
Misono T., Murata K., Fukui T., Chaichanawong J., Sato K.,
Abe H., Naito M., Ni-SDC cermet anode fabricated from
NiO-SDC composite powder for intermediate temperature
SOFC, Journal of Power Sources, 157 (2006) 754–757. DOI:
10.1016/j.jpowsour.2006.01.074
Munakata H., Tashita T., Kanamura K., Kondo A., Naito M.,
Development of low-platinum catalyst for fuel cells bymechano-chemical method, Journal of the Society of Powder
Technology, Japan, 48 (2011) 364–369. DOI: 10.4164/
sptj.48.364
Naito M., Abe H., Particle bonding technology for composite
materials -microstructure control and its characterization,
in: Characterization and Modeling to Control Sintered
Ceramic Microstructures and Properties: Ceramic Transactions, C. DiAntonio (Ed.), 157 (2006) 69–76. DOI:
10.1002/9781118407080.ch8
Naito M., Abe H., Kondo A., Smart powder processing forenergy and environment, WIT Transactions on Ecology
and the Environment, 154 (2011) 51–59. DOI: 10.2495/
CHEM110061
Naito M., Abe H., Kondo A., Yokoyama T., Huang C.C., Smartpowder processing for advanced materials, KONA Powder
and Particle Journal, 27 (2009) 130–141. DOI: 10.14356/
kona.2009013
Naito M., Hotta T., Asahi S., Tanimoto T., Endoh S., Effect ofprocessing conditions on particle composite process by ahigh-speed elliptical-rotor-type mixer, Kagaku Kogaku Ronbunshu, 24 (1998) 99–103. DOI: 10.1252/kakoronbunshu.
24.99
Naito M., Kondo A., Chapter 8 - Smart Powder Processing
for Green Technologies, in: Singh M., Ohji T., Asthana R.
(Eds.), Green and Sustainable Manufacturing of Advanced
Material, Elsevier, Oxford, 2016, pp.197-221, ISBN: 978-012-411497-5. DOI: 10.1016/B978-0-12-411497-5.00008-4
Naito M., Kondo A. Yokoyama T., Applications of comminution
techniques for the surface modification of powder materials,
ISIJ International, 33 (1993) 915–924. DOI: 10.2355/
isijinternational.33.915
Naito M., Okumiya M., Abe H., Kondo A., Huang C.C., Powder
processing issues for high quality advanced ceramics, KONA
Powder and Particle Journal, 28 (2010) 143–152. DOI:
10.14356/kona.2010013

Naito M., Yokoyama T., Hosokawa K., Nogi K., Nanoparticle
Technology Handbook, Third Edition, Elsevier, 2018, ISBN:
978-0-444-64110-6. DOI: 10.1016/C2017-0-01011-X
Nakamura E., Kondo A., Matsuoka M., Kozawa T., Naito M.,
Koga H., Iba H., Preparation of LiCoO2/Li1.3Al0.3Ti1.7(PO4)3
composite cathode granule for all-solid-state lithium-ion
batteries by simple mechanical method, Advanced.
Powder Technology, 27 (2016) 825–829. DOI: 10.1016/j.
apt.2015.10.013
Nogi K., Naito M., Kondo A., Nakahira A., Niihara K. YokoyamaT., New method for elucidation of temperature at the interface between particles under mechanical stirring, Journalof the Japan Society of Powder and Powder Metallurgy, 43(1996) 396–401. DOI: 10.2497/jjspm.43.396
Nomura T., Minamiura M., Fukamachi K., Yumiyama S.,
Kondo A., Naito M., Adhesion control of fungal spores onsolid surfaces using hydrophilic nanoparticles, Advanced
Powder Technology, 29 (2018) 909–914. DOI: 10.1016/j.
apt.2018.01.007
Ohara S., Abe H., Sato K., Kondo A., Naito M., Effect ofwater content in powder mixture on mechanochemical
reaction of LaMnO3 fine powder, Journal of the European
Ceramic Society, 28 (2008a) 1815–1819. DOI: 10.1016/j.
jeurceramsoc.2007.11.014
Ohara S., Kondo A., Shimoda H., Sato K., Abe H., Naito M.,
Rapid mechanochemical synthesis of fine barium titanate
nanoparticles, Materials Letters, 62 (2008b) 2957–2959.
DOI: 10.1016/j.matlet.2008.01.083
Sato K., Abe H., Misono T., Murata K., Fukui T., Naito M.,
Enhanced electrochemical activity and long-term stabilityof Ni-YSZ anode derived from NiO-YSZ interdispersed
composite particles, Journal of the European CeramicSociety, 29 (2009) 1119–1124. DOI: 10.1016/j.jeurceramsoc.
2008.07.050
Sato K., Chaichanawong J., Abe H., Naito M., Mechanochemical
synthesis of LaMnO3 fine powder assisted with water vapor,
Materials Letters, 60 (2006) 1399–1402. DOI: 10.1016/j.
matlet.2005.11.038
Smith D.R., Hust J.G., Microporous fumed-silica insulation board
as a candidate standard reference material of thermal resistance, U.S., National Institute of Standards and Technology,
NISTIR 89-3901, 1989.
Soda R., Yagi K., Kano J., Saito F., Naito M., DEM simulation of
the powder composite process by a mechanical method, Journal of Smart Processing, 1 (2012) 218–223. DOI: 10.7791/
jspmee.1.218
Shuk P., Suchanek W.L., Hao T., Guliver F., Riman R.E.,
Senna M., TenHuisen K.S., Janas V.F., Mechanochemicalhydrothermal preparation of crystalline hydroxyapatite powders at room temperature, Journal of Materials Research, 16(2001) 1231–1234. DOI: 10.1557/JMR.2001.0170
Suchanek W.L., Shuk P., Byrappa K., Riman R.E., TenHuisen
K.S., Janas V.F., Mechanochemical–hydrothermal synthesis of carbonated apatite powders at room temperature,
Biomaterials, 23 (2002) 699–710. DOI: 10.1016/S01429612(01)00158-2
Suzuki S., Kozawa T., Murakami T., Naito M., Mechanochemicalhydrothermal synthesis of layered lithium titanate hydrate
nanotubes at room temperature and their conversion to
Li4Ti5O12, Materials Research Bulletin, 90 (2017) 218–223.
DOI: 10.1016/j.materresbull.2017.02.011
Tasaka T., Ohmura T., Hagihara S., Kondo A., Naito M., Effect oftemperature history on the thermal and mechanical propertiesof fibrous fumed alumina compacts, Journal of the Societyof Powder Technology, Japan, 57 (2020) 612–618. DOI:

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10.4164/sptj.57.612
Yokoyama T., Urayama K., Naito M., Kato M., Yokoyama T.,
The Angmill mechanofusion system and its applications,
KONA Powder and Particle Journal, 5 (1987) 59–68. DOI:
10.14356/kona.1987011
Yoshida J., Nakanishi S., Iba H., Kondo A., Abe H., Naito
M., One-step mechanical synthesis of the nanocomposite
granule of LiMnPO4 nanoparticles and carbon, Advanced
Powder Technology, 24 (2013) 829–832. DOI: 10.1016/j.

apt.2013.01.010
Xi X., Kondo A., Kozawa T., Naito M., LSCF-GDC composite
particles for solid oxide fuel cells cathodes prepared byfacile mechanical method, Advanced Powder Technology, 27(2016) 646–651. DOI: 10.1016/j.apt.2016.02.022
Xi X., Kondo A., Naito M., Simple mechanical process to synthesize La0.6Sr0.4Co0.2Fe0.8O3 perovskite for solid oxide fuelcells cathode, Materials Letters, 145 (2015) 212–215. DOI:
10.1016/j.matlet.2015.01.116

Authors’ Short Biographies
Makio Naito
Makio Naito is a Professor at the Joining and Welding Research Institute (JWRI), Osaka University in Japan. He served as the President of The Society of Powder Technology, Japan from 2015 to2019. His publications cover a wide range of studies in the fields of advanced materials and novel
powder processing and characterization. He has received several prestigious awards including the
Richard M. Fulrath Award from the American Ceramic Society (ACerS) in 2002 and the KONA
Award in 2019. He has been a Fellow of the ACerS since 2010, and serves on the ACerS Board of
Directors from 2020.

Takahiro Kozawa
Takahiro Kozawa is an Assistant Professor at the Joining and Welding Research Institute (JWRI),
Osaka University in Japan. He received his Ph D. in science from Kochi University in 2012. He was
fellowships (DC1, PD) from the Japan Society for Promotion of Science from 2011 to 2012. He
became a specially designated researcher at JWRI in 2012 and has been in his current position since2014. His research interests include the R&D on environment-friendly powder synthesis methods
and particle design of energy materials such as lithium-ion and solid-state batteries.

Akira Kondo
Akira Kondo received B.S. and M.S. degrees in chemical engineering from Nagoya University,
Japan in 1989 and 1991, respectively. He received Ph D. degree in material science from Osaka University in 1999. He was with Hosokawa Micron Corp. from 1991 to 2005, where he engaged in the
R&D of powder processing technology. He became a specially designated researcher at the Joining
and Welding Research Institute (JWRI), Osaka University in 2005, and is engaged in the R&D of
powder processing for advanced materials.

C.C. Huang
C. C. Huang is the Director of Technology at Hosokawa Micron Powder Systems, an operating
unit of Hosokawa Micron International Inc., a global supplier of systems and equipment related tomaterial sciences and engineering. He holds an M.S. degree in engineering from Illinois Instituteof Technology and a Ph.D. degree in chemical engineering from West Virginia University. He
specializes in powder and nanoparticle processing, powder characterization, powder granulation,and fluidization. He has published over 30 articles and 8 patents, chaired several meetings, and is anactive member in a number of scientific and engineering societies.

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KONA Powder and Particle Journal No. 40 (2023) 29–49/https://doi.org/10.14356/kona.2023002

Review Paper

Interfacial Engineering of Particulate & Surfactant Systems forEnhanced Performance in Industrial Applications †Jiaqi Dong and Brij M. Moudgil *Center for Particulate and Surfactant Systems (CPaSS), Department of Materials Science and Engineering, University of Florida, USA

Abstract
Particulate and surfactant systems are an integral part either in processing or product lines in essentially every
major industry, including Energy and Minerals, Pharmaceutical, Agriculture & Food, Microelectronics, Healthcare,
Cosmetics, Consumer Products, and Analytical Instrumentation & Services. In most applications, product and process
specifications depend on the synergistic or competitive interactions between the particles and reagent schemes. The
primary goal of our research efforts has been to generate the structure-property-performance correlations-basedknowledge and technology platforms for industry to develop more sustainable products and processes. Engineering
the physicochemical/mechanical properties of surfaces, particles, and self-assembling surfactant systems enables
their enhanced performance in industrial applications. Specifically, understanding and control of the nano and atomicscale forces between particles and synthesis of functionalized particles form the foundation for targeted contributions
in biomedical, advanced materials and minerals, sensor, and coating technologies. A synoptic overview of selected
projects is presented in this review. Additional details can be found in the topic-specific references listed at the endof this manuscript.
Keywords: interfacial engineering, functionalized particles, antibacterial coatings, antiscalant additives, corrosion
inhibitors, precision particles

1. Introduction

Over the last few decades, the theme of research at
the Particle Engineering Research Center (PERC) andsubsequently at the Center for Particulate and Surfactant
Systems (CPaSS—NSF Industry/University Cooperative
Research Center) has been focused on developing thestructure-property-performance correlations in the particle
and surfactant systems. In most projects, control of forces
between particles in suspensions and functionalizationof particles form the foundation of targeted advances inseparations, dispersion & flocculation, and coating technologies.

2. Synopsis of selected research topics
2.1 Competitive adsorption

Controlled adsorption of molecules on selected particles or moieties is necessary for achieving the desired
separation of minerals, and also for the release of active
ingredients in drug formulations. Center researchers havedevoted considerable efforts to understand the selectivity
principles and develop guidelines for achieving selective†*

Received 3 August 2021; Accepted 24 November 2021
J-STAGE Advance published online 29 January 2022
Corresponding author: Brij M. Moudgil;
Add: 1180 Center Drive, Gainesville, FL 32603, USA
E-mail: bmoudgil@perc.ufl.edu
TEL: +1-352-846-1194 FAX: +1-352-846-1196

coatings of surfactant/polymers on targeted surfaces. For
example, Engelhard Corporation has commercialized the“site blocking agent” concept developed in our research(Behl et al., 1993; 1996). Researchers in the pharmaceutical industry and microelectronic manufacturing, especiallychemical mechanical polishing (CMP) slurry developers
have also recognized the relevance and significance of ourwork on competitive (selective) adsorption of polymer andsurfactant molecules.
The synthesis of functional particles has been a major
focus of the Center research endeavors. An inorganic crystalline Cryptomelane type hydrous manganese dioxide wassynthesized and successfully evaluated in ion exchange
columns for selective removal of potassium ions fromsodium ions. The synthesis protocol of this material was
designed based on the “ion memory effect” displayed by
specific minerals. Selective uptake by such functional materials is achieved by controlling the spatial arrangement of
the structural ions. The objective of this work was to design
an environmentally-benign water recycling technique inpaper manufacturing (Prakash and Moudgil, 1995).
A novel flotation process based on in-situ generationof gas bubbles on the ink particles as they detach fromthe fibers was developed to deink wastepaper by PERC
researchers (El-Shall et al., 2005).

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

2.2 Dispersion and flocculation—science andtechnology

Efforts to optimize CMP slurries revealed, to our knowledge, for the first time that shear and normal forces between
particles and surfaces must be controlled independently ofeach other to achieve maximum removal rate with minimal
defects (Basim et al., 2003). Additionally, selective adsorption of surfactants on various wafer components formed thebasis of next-generation selective polishing slurries later
commercialized by a university spin-off company. Thesefindings also led to the development of reagent schemes
for controlling the structure of particulate agglomerates(flocs) for the dewatering of suspensions. Additionally,
the development of the theoretical basis and experimental
measurements of mechanical properties of self-assembled
surfactant structures, such as micelles at interfaces, revealed that specific materials could catalyze the structureformation process on surfaces at a lower concentration thanin the bulk (Rabinovieh et al., 2005 and Singh et al., 2001).
Based on this work, new reagent schemes were proposed
for wafer polishing slurries and for crystal habit modification additives.
The synthesis of nanoscale rough surfaces coupled withmodeling the effect of nanoscale roughness and humidity
on interparticle forces have provided new insights into
aggregation and dry dispersion phenomena important todevising the dedusting schemes (Rabinovich et al., 2002).

2.3 Cross-contamination in powder mixtures

Changes in the particulate behavior in a mixture understorage and handling have been an enigma to scientists inthe past. It has been well known that particulate systems
can exhibit different bulk behavior even when the physicaland surface chemical properties such as shape and size, size
distribution, zeta potential, and viscosity of the systems are
similar. In this regard, research on cross-contamination of
powders has shown that even slight contact between two
solid surfaces may be sufficient for atomic/nanoscale material transfer from one surface to the other, thereby reducing
the selectivity of reagent coatings on the two surfaces.
When apatite and dolomite particles were gently mixed,
the transfer of material was too small to be detected byconventional surface analytical tools such as EDA, ESCA,
SEM, TEM, but was sufficient to result in measurable
changes in their zeta potential. It was further determinedthat the cross-contamination in particulate mixtures could
be controlled with chemical additives (Mehta N, 1993).

2.4 Polyhydroxy fullerenes (PHF)—their novel
properties and applications

A new phenomenon, namely the optical ignition of polyhydroxyl fullerenes (PHF), was discovered in our laboratory (Krishna et al., 2010; 2016a). This discovery led to thesynthesis of carbon nanostructures without the use of any

30

metal catalysts (Krishna et al., 2010). It resulted in practical uses of PHF in designing new antimicrobial coatings,
selective ablation of cancerous cells, new herbicides, andplant growth promoters. (Gao et al., 2011; 2016; Krishna etal., 2015; 2016b; Sharma et al., 2013a).

2.5 Effect of kaolin clays on casting rate

In a recently completed project on the engineering ofclays, the characterization of clay samples from different
locations revealed that morphology, crystallinity, and shapeof the clay particles play a major role in the dewatering
behavior and casting rate of ceramic products. (Badr et al.,2021).

2.6 Science and technology of odor control

In an odor control study, porous silica particles impregnated with nano copper deposits were reported to be much
more efficient odor control materials. However, challenges
were encountered by an industry partner when attempts
were made to scale up the process. A systematic characterization of the product formulation steps revealed that
not only the chemical composition of the copper catalyst
but also the morphology of the nano deposits in the pores
played a critical role in achieving the optimal product performance (Singh et al., 2010).

3. Review highlights of selected research
themes

3.1 Particle assisted removal of microbes fromsurfaces

In general, antibiotic-based strategies have limited
efficacy owing to bacterial persistence and resistance toantibiotics. As a result, there has been an alarming growthof multi-drug-resistant pathogens. It is well known that
non-alcohol-based cleansing formulations although may
reduce 99.9 %+ bacteria and yet leave enough bacteria
to cause infection. The need for alternative approaches
has never been greater. The current project aimed at theremoval of microbes rather than killing them.
Proper handwashing using soap, as per CDC guidelines,
is quite effective. Soap molecules can outright kill the
microbes or decrease the force of adhesion between the
microbe and the skin. However, quite often in practice,
due to lack of time, water or soap, improper handwashing
can leave enough microbes on the skin to cause infections.
Choices of more effective antimicrobial soaps are restricted
due to health and safety regulations. Alcohol-based sanitizers are known to kill all microbes, but their repeated use
without the application of skin emollients can lead to anincreased risk of infection due to skin damage. For thesereasons, engineered particles were developed to achieve
higher efficacy of soap-based handwashing practices.
Considering that the microbes are negatively charged,
the proposed hypothesis was investigated using positively

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

charged (cationic) silica particles. Silica particles (isoelectric point around pH of 2.8) are negatively charged
under natural pH conditions (about pH 5.6) and can exhibit
a positive charge when coated with polyethyleneimine(PEI). E. coli and VITRO-SKIN® served as the bacteria
and skin surface, respectively. A brief description of theexperimental procedures is given below. Additional details
are provided in Nandakumar (2018).
Materials and reagents employed in this study andtheir respective sources (in parenthesis) are as follows:
Silica of different particle sizes and Polyethyleneimine—
Mn10,000 (Sigma-Aldrich, USA); Ampicillin sodium salt
and Neutralizer D/E broth (Thermo Fisher Scientific);
Artificial skin substrate -VITRO-SKIN® (IMS Inc. USA);
Escherichia coli—ATCC 25922GFP (ATCC).
In order to remove any organic contaminants from theobtained silica surface, 1 g of the particles were treated with
1 M HCl for 60 min and thoroughly washed before conditioning with 40 mL of 2 wt % polyethylene imine—PEI, at
pH 10.5 for 17 hours in a rotisserie. After recovering the
PEI coated silica particles via centrifugation and washingthem to remove excess PEI, they were lyophilized using a
LABCONCO® freeze drier for storage purposes. Modified
or unmodified silica particle suspensions were prepared in
0.5 % Tween 80 solution unless otherwise noted.
Brookhaven ZetaPlus®, and Coulter® LS13320 were employed for zeta potential and particle size measurements,
respectively. For determining the amount of polymer coating, elemental analysis on a Carlo Erba NA1500 CNHS
elemental analyzer was employed.
As per manufacturers protocol, 1.5 cm × 1.5 cm patchof VITRO-SKIN® was hydrated overnight using glycerol:water (15:85) binary mixture in a humidity chamber. Anton
Paar® Physica Electro Kinetic Analyzer was used to measure
the zeta potential. Surface energy was determined via contact
angle measurements with water, glycerol, ethylene glycol,
and diiodomethane, using the Owens-Wendt model (Owens
and Wendt, 1969).
The surface energy of the E. coli strain (ATCC
25922GFP) was determined using a light scattering technique described elsewhere (Zhang X. et al., 2015). MATS
(Microbial Adhesion to Solvents) assays were used to study
the bacterial cell surface properties, including hydrophobic
components and electron donor groups according to theprotocol established (Bellon-Fontaine et al., 1996; Hamadi
and Latrache, 2008). Bacterial adhesion to solvent wasestimated by Eq. (1) (Bellon-Fontaine et al., 1996), where
A0 is the optical density at 600 nm of the aqueous suspension before mixing, A is the optical density of the aqueous
suspension after mixing with the solvent pair.

A

100
% adherence  1 
A0 


(1)

Interactions of modified and unmodified silica particles

with bacteria were assessed by enumerating remnant bacterial population on the substrate following treatment withparticles. Removal efficacy was calculated using Eq. (2).
log10 bacterial removal= log10 Inoculum on skin
− log10 bacteria remaining on skin(2)

The plate count method was used to estimate the celldensity in the suspension, and the results were reported interms of the total number of bacteria recovered as CFU. A
wide-field fluorescence microscope (Cytation 5) was employed to assess cell membrane damage and cell viability.
In order to determine the extent of bacterial killing by theparticles, tests were conducted by contacting the modified
silica particles with E. coli. The cell viability illustrated inFig. 1 established that the modified particles by themselves
do not kill the bacteria and hence would be suitable forremoval studies.
Test results plotted in Fig. 2 demonstrated that modified
silica particles interact strongly with E. coli cells and can
reduce bacterial concentrations by four log orders in 30
seconds in contrast to a maximum of 1.5 log reduction withunmodified control particles.

3.2 Particle design parameters for the removal of
E. coli from artificial skin

A major hypothesis of this study was that microbial
removal is possible only if the particle-bacterial interactions are strong enough to overcome the bacterial–
substrate adhesion forces. Furthermore, particle size anddensity should be such that they impart enough shear force
to remove the microbe-particle construct from the skinunder handwashing conditions (e.g., fluid velocity, thevolume of water, or time—20 seconds). Additionally, there
need to be enough particles (particle loading, wt %) in the

100 µmFig. 1 Viability of microbes when attached to modified particles
using fluorescence microscopy. Green—viable microbes attached toparticles. Red—inactive microbes (Red intensity enhanced). Reprinted
with permission from Ref. (Nandakumar et al., 2019). Copyright: (2019)
Elsevier.

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system to efficiently cleanse the skin. Accordingly, the roleof adhesion forces (zeta potential), particle size, fluid velocity, and particle loading were systematically evaluated(Nandakumar et al., 2019).
It is observed from Fig. 2 that an additional 2.5 log
bacterial reduction on the skin surface is possible withthe modified particles. It was further determined that theremoval of bacteria dispersed on the skin (e.g., E. coli) waseasier than the aggregated ones, e.g., S. aureus, see Fig. 3(Nandakumar, 2018).
Overall, this approach exhibited the potential of using
modified particles to remove bacteria from surfaces as analternative to kill-based methodologies.

3.3 Microbial disease control in the citrus plants:
controlled release of insect repellants

In the past, engineered particle coatings have been successfully employed to control plant diseases in pear, apple,
and cotton plants. Particle coatings of foliage primarily
compromise the ability of insects to visualize, settle, feed,
move and oviposit. They can also mask the color of theleaves and stems, making it difficult for insects to recognize them from a distance (Grafton-Cardwell et al., 2013;
Glenn, 2012; Puterka et al., 2005; Wenninger et al., 2009).
Sometimes the films themselves can be toxic to insects.

Log removal of E.coli
from skin in 30 s

6

System: E.coli initialcount -107 cells
Hydrodynamics-1500 rpm, 30s/1s pulse
Particles -1wt% in 0.5% Tween 80

54Detection limit

3
2
1
0

SP10

SC10

Control Water control
tween80

Fig. 2 Removal of E. coli using modified and unmodified silica particles. Reprinted with permission from Ref. (Nandakumar et al., 2019).
Copyright: (2019) Elsevier.

Fig. 332

Center researchers were approached to study the potentialof particle coatings for managing the “Citrus Greening”
diseases, which was detected by the Florida citrus growers
back in 2005. “Citrus greening” (also known as the Huanglongbing or HLB) is a bacterial disease transmitted byan insect, the Asian citrus Psyllid (ACP). ACP attacks the
younger flush making them more prone to infection, thusincreasing the vulnerability of citrus trees during flush. The
disease reduces the quality and quantity of the fruit yield,
thereby threatening the economic viability of the Florida
citrus industry. Some of the methods used and/or suggested
to control citrus greening are as follows:● Spraying of insecticides to control the insect attack.
However, repetitive applications are required.
● Quarantine of infected trees, although it controls the
spreading of the disease, the cost is a major drawback.
● Applying particulate (kaolin clay) coatings is effective insuppressing the disease, but rain fastness and exposuredue to leaf growth remain major challenges.
Kaolin-based coatings have been reported to control
the spread of the disease to varying degrees of success.
However, the development of gaps upon the growth ofleaves and washing off the coatings due to rains reduce
their effectiveness for protection from the insects. Interestingly, contrary to conventional thinking, particle coatings
are not only non-phytotoxic, but they improve water use
efficiency. Hydrophobized Kaolin films have been shown
to reduce heat stress by reflecting sunlight from the whitefilm surface, and the porous nature of the films allows
efficient gaseous exchange, thereby maintaining healthy
photosynthesis in the plants (Glenn and Puterka, 2004;
Puterka et al., 2005).
A systematic study was conducted to overcome a major
limitation of the emerging gaps in the coatings due to leaf
growth. Two approaches were proposed to overcome thischallenge (i) developing a coating that slowly released
an insect repellant such as garlic oil and (ii) coating thefoliage with colored clays that masked the olfactory cues,

E. coli—dispersed on skin (left); S. aureus—agglomerated on skin (right).

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

thus keeping the insects away from the citrus trees. These
coatings were to also incorporate additives that enhance therain fastness of the particles to the leaves.

the hydrophobic pores. This study indicated the potential ofcontrolled release of insect repellants as a methodology forcrop protection purposes (Sharma et al., 2013b).

3.4 Encapsulation of garlic oil (insect repellent) in
unmodified and modified clays

3.6 Colored clays as an optical and physicaldeterrent for insect attack of citrus plants

3.5 Release profile of encapsulated garlic oil

Preliminary data of release profile of GO from the clays
is plotted in Fig. 5. The cumulative release of GO after fourdays from Claytone 40 is 13 % of the total encapsulatedgarlic oil (0.4 g/g of clay) as compared to 34 % of 0.2 g
of GO uptaken in 1 g of unmodified MMT, and 84 % GO
alone. It is hypothesized that uptake of GO in the clays is
primarily due to electrostatic attraction between the modified clays and the garlic oil, along with uptake of GO within

0.20
0.15
0.10

Surfactant modified
MMT

0.25

Claytone 40

0.30

Unmodified
MMT

0.35

Unmodified
kaolinGO in clay/ Clay (wt/wt)

0.40

0.05
0.00

Fig. 4 Uptake of GO in unmodified MMT, modified MMT, andunmodified kaolin clays (clay = 1 g; the initial amount of GO = 0.4 g)(error bars with one std dev). Reprinted with permission from Ref.
(Sharma et al., 2013b). Copyright: (2013) The Authors, patented in U.S.

This project aimed to investigate if colored clays withreflectance in the visible range can prevent feeding on
the citrus leaves by the Asian Citrus Psyllid (ACP), especially feeding on the fresh flush (fresh leaves). Coloredkaolin clays were formulated using select dyes, including
FDA-certified colorants, and approved for agricultural use
by the EPA (list §180.920).
Basic dyes were used to prepare the colored kaolin clays(Moudgil et al., 2017; Sharma et al., 2015). Several clays
from different suppliers were evaluated, and only a whiteclay sample was selected for further processing. Clay
particles surfaces were modified using cationic surfactants
and/or polymers, followed by mixing them with the dyes.
This step was essential to prevent the leaching of the dyes
from the clay particle suspensions. Zeta potential of theunmodified clays was determined to be about –31 mV andchanged to +5 mV to +22 mV when treated with the acidic
dyes, and –13 mV to –23 mV upon treatment with thebasic dyes. Zeta potential was measured using a Zeta Plus
Analyzer from Brookhaven instruments.
Ten different colored clays were developed using dyes—
FD&C Blue 1, FD&C Red 40, Basic Blue 54, Crystal
Violet, and FD&C Yellow 6, D&C Violet 2, as shown in
Fig. 6. The optical properties of the colored clays werecharacterized using an integrating sphere (Lambda 800
UV-vis Spectrometer) for total reflectance measurements.
These measurements were carried out by depositing a clay
film on a glass slide.
It was determined that the majority of the colored clay
coatings increased the reflectance in the 400–500 nm range
relative to the uncoated leaves. The colorant strength of the100

Cumulative release of GO (%)

The uptake of garlic oil (GO) was investigated using
modified montmorillonite (MMT), surfactant treated montmorillonite (NMMT), and Claytone® 40 (hydrophobized
montmorillonite). Pristine kaolin clays were employed as
the negative control.
Fig. 4 shows that the highest uptake of GO was achievedin modified MMT clays such as Claytone 40 and NMMT
(0.4 g GO/g clay), followed by unmodified MMT (0.2 g
GO/g clay). The uptake of GO in pristine kaolin clays is almost negligible (0.04 g GO/g clay), possibly due to the lackof intergallery spacing and the hydrophilic nature of thekaolin clays (Narayanan et al., 2012; Sharma et al., 2013b).
Among the montmorillonite clays, the trend is consistent with the intergallery spacings (d001) measured
with the help of an X-ray diffractometer—Claytone
40 > NMMT > MMT. The increase in the intergallery
spacing of Claytone 40 (29 Å) in the presence of GO(34 Å) suggests possible encapsulation of GO in between
the layers. Similar results have also been reported in the literature for the sorption of aromatic compounds in Claytone
40. These set of experiments reinforced our hypothesis thatthe intergallery spacings and the hydrophobic nature of theclays are important for the integration of GO to the clays.

90

(a)

80
70
60
50

(b)

40
30
20

(c)

10
0

0

1

2

Time (days)

3

4

Fig. 5 The release profile of (a) GO alone, (b) GO from unmodified
MMT, and (c) GO from Claytone 40. (Temp 22 °C; error bars with one
std. deviation). Reprinted with permission from Ref. (Sharma et al.,2013b). Copyright: (2013) The Authors, patented in U.S.

33

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

a

b

100
90

% Reflectance

80
70
60
50
40
30
20
300

D&C Violet 2 %R
Bright Red (Basic Red 1 & 2)
FD&C Red 40
Pyla Cert. Green
Unmod. Clay/Ref. Material
400

Basic Blue 54
FD&C Blue 1
FD&C Yellow 5
Pyla Fluor. Green500

Wavelength (nm)

600

700

Fig. 6 (a) Colored Kaolin clays developed with surfactants and dyes. (b) The reflectance spectrum of the colored clays. Reprinted with permission
from Ref. (Moudgil et al., 2017). Copyright: (2017) The Authors, patented in U.S.

3.8 Rain fastness enhancement

Fig. 7 The choice of ACP to go to the tree coated with different colored
clays as compared to Surround® and uncoated leaves. Reprinted withpermission from Ref. (Moudgil et al., 2017). Copyright: (2017) The
Authors, patented in U.S.

coated leaves was found to depend on the thickness of thecoating.

3.7 Effect of the colored coatings on inhibition of
Asian Citrus Psyllid (ACP) insect

The selected colored clay formulations were applied to
the leaves of citrus plants to deter the Asian Citrus Psyllid
Diaphorina citri. One hundred psyllids were released in
the cage, and they had 24 hours to choose a location before counts were made. Fig. 7 shows the results from thetreatment, and the values are the numbers of psyllids oneach tree.
It is observed from Fig. 7 that ACP has a very low
preference for Bright red, followed by FDC red- andviolet-colored trees. FDC Yellow clays seem to attract
more insects than Surround® coated (white) trees (control).
These measurements indicated the potential of colored clay
coatings in managing the ACP attack of the fresh flush.

34

Several formulations were prepared with different adjuvants to determine how well the clay particles adhere to thecitrus foliage. Adjuvants were selected based on their ability to act as stickers, binders, or viscosity modifiers. Someof the adjuvants tested resulted in the desorption of the dyefrom the clays. The colored clay formulations containing
Kollicoat, i.e., a pharmaceutical excipient—polyvinyl acetate dispersed in water and stabilized with povidone andsodium dodecyl sulfate, Polymer adjuvant, i.e., sodium
polyacrylate, and Xanthan gum yielded coatings with enhanced rain fastness properties.
Several colored clay formulations containing different
concentrations of the three adjuvants were sprayed onto thecitrus plants, along with white only and colored clays only
(no adjuvants) as controls. The coatings were evaluatedfor color intensity after 1–2, 4–5, and 7 cm of rainfall (seeFig. 8). Kollicot and polymer additives were determined
to be the best adjuvants for improving both spreading andrain fastness of the colored kaolin clays, withstanding up to
7 cm rain (Moudgil et al., 2017).

3.9 Multimodal contrast agents for cancer
detection and treatment

Methods for early detection of cancer using low-costmeans remain a critical need. This requires tools that canyield high resolution and sensitivity without compromising the safety of patients. Data from complementary
imaging modalities are often evaluated to enable early
and accurate detection. Currently employed techniquessuch as Computed Tomography (CT), Positron Emission
Tomography (PET), Magnetic Resonance Imaging (MRI),and Ultrasound possess different detection limits, spatialtemporal resolution, accuracy, and long-term safety upon
multiple exposures. However, none of these methods, when

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

Initialcoating

1-2 cmAfter exposure to rainfall
4-5 cm7 cm

Controlno adjuvant

Colored clay
no adjuvant

Clay+ Polymer
adjuvant

Clay+ Kollicoat

Clay+ Xanthangum

Fig. 8 Images of citrus plants coated with clay, dyed-clay, dyed clay + polymer adjuvant, dyed clay + Kollicoat and dyed clay + Xanthan gum before
and after exposure to 1–2 cm, 4–5 cm and 7 cm rainfall. Reprinted with permission from Ref. (Moudgil et al., 2017). Copyright: (2017) The Authors,patented in U.S.

used by themself (alone), yield the required structural andfunctional information at affordable costs. Availability ofsafe multimodal—multifunctional contrast agents can promote early detection and treatments. Also, they can enable
deep tissue imaging capabilities with some of the currently
employed detection tools.
Photo Acoustic tomography (PAT) is a noninvasive imaging technique that employs nonionizing radiation (visible to near-infrared region) to contrast biological tissue andcells. Absorption of “pulsed” incident light by the biological tissue generates an acoustic signal (ultrasound) which isprocessed to obtain an image of the object. Furthermore, it
is possible to image deep tissue because of the lower scattering of the ultrasound waves. However, enhancements
in detection are needed for better resolution of the healthyand diseased tissue and cells. Multimodal contrast agentscan enhance low-cost imaging using the capabilities of
PAT. This may allow high-resolution in vivo imaging ofpatients with metallic implants who are, otherwise, unable
to undergo MRI imaging.
Previously reported attempts to develop contrast agents,
although successful but were of limited applicability
because of their relatively larger size (120–150 nm). Goldsilica hybrid material termed gold speckled silica (GSS)
nanoparticles were synthesized using nonionic water-in-oil(W/O) emulsion technique. An outline of the synthesis
protocol is presented below; additional details can be foundin the related publications (Hahn et al., 2011; Sharma et al.,2010).

3.10 Synthesis and evaluation of Gd doped GSS
nanoparticles as Multimodal contrast agentsThe synthesis of the multimodal nanoparticles involved
the following steps, (i) preparing the W/O microemulsion
by mixing Tx-100 (nonionic surfactant), cyclohexane, andn-hexanol as co-surfactant with the appropriate amount ofwater. Adding the predetermined amount of TEOS (tetraethyl orthosilicate) in the beginning to the microemulsion
and equilibrating the mixture for 30 minutes), (ii) initiating
the hydrolysis and polymerization of TEOS by adding
NH4OH. Equilibrating the mixture for 24 hours and adding TSPETE (N-trimethoxysilyl propyl ethylenediamine
triacetic acid trisodium salt) and TEOS for modifying thesilica nanoparticles, followed by stirring overnight, (iii)
adding Gd (III) acetate solution and stirring for another fourhours, (iv) adding HAuCl4, and 1.1 M solution of reducing
agent (hydrazine hydrate) and stirring for ~12 hours, (v)separating the Gd doped GSS nanoparticles from the micro-emulsion by adding ethanol. Centrifuging the solution
to recover nanoparticles of gold, (vi) washing the particles

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Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

thoroughly with ethanol and with water to remove surfactant molecules completely. (vii) redispersing the particles
in nano-pure water for further characterization. As shownin Fig. 9, the gold speckled silica particles (GSS) were
25–60 nm in size, were fluorescent, and exhibited visible
NIR absorbance (Sharma et al., 2010). Gd doped GSS
particles exhibited unique photothermal properties, which
are suitable for PAT imaging and possessing high relaxivity
for MRI purposes (Sharma et al., 2012).
Fig. 10 illustrates the following: a) membrane labeledwith Alexa Fluor® 488 WGA dye; b) nucleus stained with
TO-PRO-3 stain; c) NIR Fluorescence from ICG–SiO2
nanoparticles; d) an overlay of Fluorescence from the
ICG–SiO2 (pseudo-colored–red) showing the labelingand internalization of the breast cancer cells with thedye-doped nanoparticles. e) Pre-injection epifluorescence
image (IVIS, Caliper) of the mouse with 745 nm excitation
and 840 nm emission showing minimum backgroundtissue fluorescence; f) NIR fluorescence signal from immediately after the first injection of Gd-doped ICG–SiO2
nanoparticles showing the increase in signal with minimal
interference from the background fluorescence; g) NIR
fluorescence immediately following the second injectionof Gd-doped ICG nanoparticles. The ROI quantificationshows an increase in signal pre- and post-injections.

Gaps in the gold speckled silica surface permit waterexchange between the Gd in the silica core and the bulk
water, necessary for obtaining an efficient MR contrast.
Simultaneously, the metal-dielectric interface generates anefficient PAT contrast enabling the applicability of theseparticles for diagnostic as well as therapeutic purposes.
Imaging and photothermal ability of the fluorescently
doped GSS nanoparticles were evaluated in vitro by injecting GSS nanoparticles intratumorally into nu/nu mice bearing human breast cancers. The tumor region was irradiatedwith a NIR laser (output 500 mW) for 10 minutes. Morethan 60 % GSS loaded cells underwent necrotic death ascompared to 30 % in control experiments (Sharma et al.,2010). These experiments indicated the suitability of using
sub 50 nm NIR absorbing and luminescent GSS nanoparticles for imaging as well as their in-vitro and in-vivo photothermal ablation properties. Further studies were suggested
to establish the viability of the GSS type nanoparticles
applications for detecting and treating cancerous tissues inpractical biological settings.

3.11 Self-stimulating photocatalytic antimicrobial
coatings

Contact or touch-based infections are more common
than generally recognized. Any contact with common

b)

a)

10 nm

5 nm

20 nm

1000

800

Temperature (℃)

d)

GSS
Water

32

600

400

Si 2s
Si 2p3Au4f

N 1s
Au 4d3Au 4d5C 1s

28

Au 4p3
O 1s

N(E)

O KVV

Cu 2p3c)200

Binding Energy (eV)

24

20
0

0

50 100 150 200 250 300 350 400

Time (s)Fig. 9 Characterization of rhodamine doped gold speckled silica (GSS) nanoparticles (a) representative TEM image of ca. 40 nm rhodamine doped
GSS nanoparticles GSS showing the speckled silica surface; inset Z-contrast digital TEM; (b) higher magnification showing irregular shaped, discontinuously placed, 1–5 nm crystalline nano-gold deposits on silica; (c) representative XPS spectra; (d) ca. 11 °C increase in temperature of pegylated
GSS nanoparticles in water suspension. Reprinted with permission from Ref. (Sharma et al., 2010). Copyright: (2010) Royal Society of Chemistry.

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Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

1.0
14.5

0.8
0.6

14.0

13.5
×106

2.0
1.5
1.0

0.4
0.2
×109

0.5
×109

Fig. 10 In vitro and in vivo functional evaluation of ICG–SiO2 and Gd-doped ICG–SiO2 nanoparticles. NIR fluorescent image of breast cancer
MDA-MB-231 cells. Reprinted with permission from Ref. (Sharma et al., 2012). Copyright: (2012) John Wiley & Sons.

surfaces, including cell phones, keyboards, door handles,
clothes, taps, and mops, can transmit infectious microbes.
Currently available antimicrobial coatings that slowly
release silver or copper ions have a limited lifetime,difficult to employ, and are relatively costly. In contrast,
TiO2 coatings that photo-catalytically mineralize organic
contaminants are self-cleaning and produce no toxic byproducts, and are relatively inexpensive. However, a major
limitation of the TiO2 by itself has been the need for UV
light since, by itself, it exhibits poor photocatalytic properties in visible light.
Among the various techniques investigated for enhancing the photocatalytic property of TiO2 in visible lightinclude mixing TiO2 with organic dyes—by far the least
complicated method. However, degradation of the dyeitself poses a major limitation of this approach. This is
attributed to the generation of the reactive oxygen species(ROS) because of the transfer of light-initiated carriers
to the TiO2 surface and their reaction with the surrounding water/oxygen molecules. Interestingly, polyhydroxy
fullerenes (PHF) behave the same way as organic dyes andare comparatively more efficient in scavenging electrons.
Degradation of the PHF molecules can be tailored by synthesizing the number and placement of hydroxyl groups on
the core fullerene structure.
Basic concept: The observation with the dye-sensitizedsolar cells became the basis of the contaminant-activatedphotocatalysis concept. It was hypothesized that a dyesuch as Mordant Orange or Procian Red, when coated onpristine TiO2, can enhance photocatalysis by absorbing
visible light and transferring the generated electrons
to the TiO2 surface. Similarly, when a bacterium suchas Staphylococcus aureus comes in contact with the
TiO2—dye construct, it should get mineralized. In anotherscenario, if the bacterium (or any other organic entity) is
capable of absorbing the visible light, it should lead to its
own degradation (“self-stimulating photocatalytic degradation”) by the generated ROS species (Krishna et al., 2018).
The organic “contaminant” activated photocatalytic concept, along with measured visible light spectra of various
surfaces, with and without different coatings, is presented
in Fig. 11. Methods, protocols, and tools employed in thisresearch are briefly described below. Additional details areprovided by Krishna et al.

3.12 Coating formulations and testing protocols

The preparation of photocatalytic coating formulations
involved sonicating 10 mg of anatase in 20 mL water at
pH 9.5 for 30 minutes. Rutile and silica coatings, with andwithout PHF, were prepared in the same fashion. Coating
formulations were used for further testing within an hour of
their preparation.

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Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

(a)

(b)

(c)

Fig. 11 Contaminant-activated photocatalysis. (a) schematic of microbial mineralization with PHF as an auxiliary light harvester. (b) Ground-state
absorption spectra for anatase (TiO2), polyhydroxy fullerenes (PHF), and PHF+ TiO2. (c) Pseudo first-order rate coefficients for degradation of Mordant
Orange dye on anatase (TiO2) and anatase + 0.01 (w/w) PHF (TiO2 + 0.01PHF) coatings. Dark control measures the ability of the photocatalytic coatings to degrade dye in the dark. N = 10. Reprinted with permission from Ref. (Krishna et al., 2018). Copyright: (2018) the authors, published by nature,
Scientific Reports.

The efficacy of the photocatalytic coatings was tested
using ceramic tiles. 0.4 mL of coating formulation wasapplied to the tile surface and dried for one hour at 40 °C
in the dark. Subsequently, the second coat of the same ordifferent coating material was applied. Organic dye or S.
aureus suspension was applied to the dried surfaces. In
S. aureus, a suspension (2–3 × 10 CFU/mL) was spread
onto each coated tile surface, yielding a surface loading of6400–9600 CFU/cm. S. aureus-coated tiles were dried in
the dark in a biosafety cabinet for 3 hours.

contaminants, including bacteria and viruses, when incontact with pristine TiO2 (anatase), can further accelerate
the contaminant degradation rate. Considering the wideranging applications of TiO2 in paints, food products,
pharmaceuticals, and polymer composites, possible photocatalytic activity aspects should be considered when
designing such products (Georgieva et al., 2013; Krishna
et al., 2006; Nandakumar et al., 2017; Zhao et al., 2009).

3.13 Two-layer coating strategy

Several industrial processes are adversely impacted by
the crystallization of minerals or the formation of “scale” onthe surface of the processing equipment. Examples include
phosphoric acid manufacturing for fertilizer production,
desalination plants, boilers for steam production, and water
treatment facilities. Scale deposition severely reduces theprocess efficiency and requires frequent cleanings thatcost money. In phosphoric acid manufacturing, multistage
evaporators are frequently used to obtain 52–55 wt % P2O5
phosphoric acid. The concentrated phosphoric acid gets
supersaturated with respect to calcium sulfate and otherscale-forming constituents, which subsequently accumulate on the pipe walls. These deposits reduce the outputof the system and require frequent removal to maintainefficiency (Carr et al., 2014). Even a thin layer of deposits
can create significant resistance to heat transfer.
Over the last several decades, research attempts haveFirst, applying a layer of rutile (or silica) on the surface
to be treated, followed by a thin coating of anatase, wasdevised to protect the base surface from any damage
caused by the reactive oxygen species formed due to photocatalysis. Preliminary experiments employing an MRSA
surrogate (ATCC 25923) showed promising results.

3.14 Testing in a beta facility

Commercial equipment was employed to apply the coatings, with a commercial primer (BioShield NuTiO) usedas the binder between the two coatings. Coated surfaces
were tested for antimicrobial efficacy using the bacterial
count technique (Krishna et al., 2018). The efficacy of thecoatings on various surfaces is illustrated by the resultsplotted in Fig. 12.
It was anticipated that visible light-absorbing organic38

3.15 Inhibition of scale formation by surfactant
and polymeric additives

Bacterial Burden (CFU/ 4 in2)

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

Fig. 12 Beta facility testing. For a given surface, the bars represent
counts (n = 3) as a function of time from 0 to 12 months. W = Wall;
T = Thermostat; L = Locker; K = Knob; D = Soap Dispenser; R = Bathroom Rail; B = Bed Rail; C = Counter. Blue dashed line --the thresholdof microbial counts for benign surfaces, and the yellow dashed line
- - the average microbial counts on copper surfaces in a clinical trial.
Reprinted with permission from Ref. (Krishna et al., 2018). Copyright:(2018) the authors, published by nature, Scientific Reports.

continued to identify the additives that at least might delay the scale formation if not eliminate it outright. In thephosphoric acid production plants, research approaches
have focused on the use of water-soluble inhibitors that
may block the development of the supercritical nuclei or
retard the growth of the calcium sulfate crystals. Several
theories have been proposed for antiscaling properties ofdifferent additives, but the inhibitors that can perform at aplant scale on a consistent basis have been lacking. Overthe last decade, Solvay researchers have developed anddemonstrated an antiscalant technology using Phosflow®
that substantially reduces the fouling issue in the wetprocess phosphoric acid production plants (Carr et al.,2014).
Phosphoric acid is produced by reacting phosphate rock(tricalcium phosphate) with sulfuric acid at high temperatures. This reaction generates calcium sulfate as the
byproduct, some of which can deposit on heat exchanger
walls when the phosphoric acid is concentrated. Calcium
sulfate exists in three forms: calcium sulfate dihydrate,
CaSO4·2H2O (Di-hydrate or DH); calcium sulfate hemihydrate, CaSO4·0.5H2O (also known as bassanite); andcalcium sulfate anhydrous, CaSO4 (anhydrite). Plants generally produce gypsum (calcium sulfate dihydrate) initially,
which over time can be baked into anhydrite (CaSO4)—
which is dense and difficult to remove (Carr et al., 2014). It
has been proposed that in a system supersaturated with DH,
first crystals of hemihydrate (CaSO4·0.5H2O (HH)) precipitate out, which subsequently hydrate into DH. Once the
HH crystals are formed, they start to grow into nanorods.
At low to medium supersaturation ratios, both bassanite
and gypsum crystals seem to form. At high supersaturation
ratios, only gypsum precipitates have been observed (noBassanite). It was further reported that the existence of the

precursor phase (bassanite) could be increased by adding
carboxylic acid (Polat and Sayan, 2017). Changes incrystal morphology were also observed. Inhibition of scale
through the complexation and adsorption of an additive is
not unique to CaSO4. A comprehensive review of calcium
sulfate precipitation is presented in Driessche et al. (2017).
In order to understand the underlying mechanisms of
the Phosflow® (PF) technology, a systematic study was
conducted. Turbidity and induction time measurements
were used to calculate the inhibition efficiency (E), thesurface energy (γ), the nucleation rate (Js), the free energy(ΔGcr), and the critical nucleus size (r). Solubility changes,
SEM, FTIR, and XRD, were employed as investigative
tools in the in-situ study of how additives affect mineralcrystallization on surfaces. It was anticipated that a basic
understanding of the scale formation process would allow
the development of commercially viable other (greener)
additives not only for phosphoric acid plants but also forother systems (Tanquero et al., 2021).
Experimental protocols employed are briefly described
below. Reagent grade chemicals, including phosphoric andsulfuric acids, and calcium hydrogen phosphate monobasic
CaH4(PO4)2–∙2H2O were purchased from the Fisher Scientific Company and were used as received. Polyethyleneimine of different molecular weights (Mn 0.6 k, 1.8 k, 10 k,
and 60 k) was obtained from Sigma Aldrich and Acros
Organics Co.
Predetermined volume and ratio of phosphoric and sulphuric acid solutions were added to a 500-mL beaker andheated to 88 °C. The desired amount of calcium phosphatemonobasic monohydrate, a specific volume of deionizedwater or water containing a polymeric additive, along witha corresponding amount of 32.5 g/g (mass%) sulphuricacid was added to the beaker. The reaction temperature was
kept constant at 88 °C. The turbidity of the resulting reaction product was measured at different times throughout
the reaction using a HACH 2100AN Turbidimeter. Particle
Size measurements were conducted using the Accusizer
780 AD. Recovered solids were imaged using an Olympus
Optical Microscope.
The effect of PhosFlow (PF) addition is illustrated by
Fig. 13 and data given in Tables 1 and 2. PF addition increased the induction time, reduced the surface free energy
for nucleation, and delayed the growth of the gypsum (Carret al., 2014; Tanquero et al., 2021; Zhang L. et al., 2015).
Effects of polymeric additives: Different pathways bywhich polymeric scale inhibitors can affect nucleation andgrowth processes include: (a) chelating the active ionsin the nucleating solutions (Jamialahmadi and MüllerSteinhagen, 2007); (b) adsorption onto nuclei interferingin nucleation and/or crystal growth (He et al., 1994); (c)dispersion of the scale-forming species (Amjad, 1988); and(d) increasing the local viscosity of the medium thereby affecting the kinetics of the nucleation and growth processes

39

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49(Driessche et al., 2017).
Chemical structure, molecular weight, and dosage ofadditives all are known to govern the efficacy of polymeric type inhibitors (Abdel-Aal et al., 2015; Jain et al.,2019; Akyol et al., 2006). Based on the literature review,
three polymeric additives—Poly(acrylic acid) (PAA),
Poly(diallyldimethylammonium chloride) (PDADMAC),
and Polyethyleneimine (PEI) were selected for the present
study. Screening tests revealed that only PEI has the potential of affecting the scale formation in the phosphoric acidsystem. As shown in Fig. 14, PEI effectiveness increased
with an increase in polymer molecular weight, and the optimal concentration was determined to be around 40 ppm.
Higher interfacial energies upon PEI addition andmorphological changes suggested a plausible mechanism
140

NA

PF

involving adsorption of the polymer molecules on specificcrystal faces resulting in inhibition of nuclei growth. Another possibility was stated to be the polymer changing thestability of the precursor molecules, thus prolonging theirinduction period. The exact role of PEI as a scale inhibitor
requires further studies, including monitoring of the structural and morphological changes of the crystals formed,
especially during phase changes. In this regard, techniquessuch as cryo-TEM or small-angle X-ray scattering havebeen successfully employed to probe molecular-scale
mechanisms (Sleutel et al., 2014).

3.16 Surfactant coatings for corrosion inhibition

Corrosion of metal structures, pipelines, boilers, andother industrial equipment is a major challenge in the manufacturing sector. It is estimated by NACE Internationaland other sources that the industrial impact of corrosion

35

100

Induction Time, min

Induction Time, min

120

80
60
40
20
0

1

1.5

2

2.5

Supersaturation Ratio

3

3.5

Fig. 13 Effect of PhosFlow (PF) on the induction time as a functionof supersaturation ratio. Note: the legend NA and PF represent without
additive and with additive PhosFlow, respectively. Reprinted with permission from Ref. (Tanquero et al., 2021). Copyright: (2021) Canadian
Journal of Chemical Engineering.

30
25

NA

0.6k

10k

60k

1.8k

20
15
10
5
0
2.4

2.6

2.8

3

3.2

3.4

Initial Supersaturation Ratio

3.6

3.8

Fig. 14 Effect of PEI molecular weight on the induction time as afunction of supersaturation ratio. Reprinted with permission from Ref.
(Tanquero et al., 2021). Copyright: (2021) Canadian Journal of Chemical
Engineering.

Table 1 Effect of different supersaturation ratio of calcium sulfate on nucleation rate and free energy change for formation of critical nucleus size.

Nucleation rate Js 1028 nuclei/cm3·s

Gibbs free energy ΔG 10–20 kJ/mol

Without PFWith PF

Without PFWith PF 75 ppm

2.466

9.19

22.43

1.19

0.75

2.672

13.35

28.33

1.00

0.63

2.877

17.53

33.60

0.87

0.54

3.083

21.56

38.25

0.76

0.48

Supersaturation ratio

Table 2 Effect of different supersaturation ratio of calcium sulfate on critical nucleus size and the number of molecules per a nucleus with and without PF.

Critical nucleus radius 10–8 cm

Number of molecules per a nucleus

Without PFWith PF

Without PFWith PF (75 ppm)

2.466

4.68

4.01

5

3

2.672

4.30

3.68

4

3

2.877

4.00

3.42

3

2

3.083

3.76

3.21

3

2

Supersaturation ratio

40Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

in the US alone is in excess of $500B per year. Corrosion
inhibition strategies include designing new materials forcorrosion resistance, minimizing contact with corrosive
elements, and/or applying protective coatings. The application of organic corrosion inhibitors is quite common in oil
and water transport pipelines. Surfactant blends constitutemost of the corrosion inhibitors.
Most of the effective inhibitors comprise heteroatoms
such as O, N, S, and π bonds, which enable them to attach
to the metal surface (Bockris et al., 2000). The specificchemical nature of a given corrosion inhibitor (CI) depends
on the environment (nature of ions, pH, temperature, etc.).
Organic corrosion inhibitors, especially surface-active
agents (surfactants), have been reported to be some of
the most effective inhibitors due to their ability to form
self-assembled films at interfaces, as shown in Fig. 15(Fuchs-Godec, 2009; Zhu et al., 2016; 2017). These barrier
layers inhibit the transport of corrosion, causing ionic species to and from the surface.
Despite their advantages and wide usage, the scientificunderstanding of corrosion inhibition using surfactants
at the molecular level is not comprehensive. One of the
major aims of the current research was to characterize thesurfactant films formed on the steel surface to gain a better
understanding of the corrosion inhibition mechanism. A
brief description of the materials, methods and experimental protocols is outlined below. Additional details can befound elsewhere (Rajopadhye, 2018).
The standard practice for laboratory immersion corrosion testing of metals (ASTM G31) was followed to prepare the test samples. AISI1010 carbon steel rod (Obtained
from McMaster-Carr, composition: C 0.13 %, Mn 0.4 %, P
0.04 %, S 0.05 % and balance Fe) was used to fabricate theworking electrode. A circular rod of the cross-section areaof 0.08 cm2 (accurate within ±1 %) was coated in epoxy
resin such that only the circular cross-section of the rod
at the end was exposed to the electrolyte while immersedin the test solution. The exposed end of the steel rod
was polished using 600, 800, 1000, and 1200 grit siliconcarbide abrasive pads to remove the surface oxide layer,
Molecularinteractions ofsurfactant
monomers withmetal surface

Formation of
surfactants
structures at
the interface

Bilayers

Micellesfollowed by fine polishing using 1 μ colloidal silica slurry
and diamond paste. The RMS surface roughness of the polished specimen was determined to be ~25 nm using AFM
surface topography. The steel specimen thus prepared was
degreased by sonicating in ethanol followed by thoroughrinsing using DI water and dried by the jet of nitrogen gas.
The steel surface thus prepared was exposed to the test
solution no later than 30 minutes after the polishing wasdone in order to keep surface oxidation to a minimum level.
The surfactants investigated in this study were purchased from Sigma-Aldrich (research-grade) and wereused as received. All the surfactant solutions were preparedby adding the calculated amounts of surfactants to triple
distilled DI water (Resistivity of 18.1 MΩ.cm) to prepare
the stock solutions. These stock solutions were used toprepare the desired concentration by successive dilutions.
All the electrochemical investigations were performed
in a standard 3-electrode electrochemical cell, MultiportTM(supplied by Gamry Instruments Inc.), connected to theReference3000TM potentiostat (Model number 20215, supplied by Gamry Instruments Inc.) equipped with an inbuilt
lock-in amplifier. The saturated calomel electrode (SCE)
housed inside the Luggin capillary was used as a referenceelectrode, whereas a high-density graphite rod was used
as an auxiliary electrode. 800 mL of test solution wastransferred to the Gamry MultiportTM electrochemical cell
after the solution pH was adjusted. The cell was closed byplacing the glass lid with a Viton O-ring to seal the vessel.
All the tests were performed at 25 ± 1 °C. The temperatureof the test solutions was maintained by circulating water
through the jacket around the electrochemical cell. All
experiments were repeated multiple times to ensure thereproducibility of the data.

3.17 Effect of nature of the surfactant head groupon corrosion inhibition

In an attempt to isolate the effect of the head groupchemical nature, octadecyl trimethyl ammonium bromide(OTAB) was selected as a counterpart to oleic imidazoline(OI), an active ingredient in commercial corrosion inhibitor

Modification of thetransport propertiesof reacting species
causing corrosion

Improved
performance as
corrosion
inhibitors

Diffusional barrier

Fig. 15 Self-assembled surfactant structure as a diffusional barrier to the transport of corrosion causing species. Reprinted with permission from Ref.
(Rajopadhye, 2018). Copyright: (2018) University of Florida.

41

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

blends. They both have 18 carbon atoms in their hydrocarbon chain, albeit different chain structures—OTAB straightchain: OI with a double bond in the hydrocarbon chain.
The inhibition efficiency of the two surfactants was determined to be 78 % vs. 83 % for OI and OTAB, respectively,
indicating that the contributions from the two polar headgroups are relatively well matched.

3.18 Effect of hydrophobic interactions on
corrosion inhibition

The corrosion inhibition contribution of the hydrophobic
effects due to the chain-chain interactions was investigatedby comparing the inhibition performances of different
inhibitors with the same head group chemistry but varying chain lengths. Several homologous members (from10 to 18 CH2 groups) of quaternary ammonium bromide(TAB) were tested at the same bulk concentration for theircorrosion inhibition efficiency. It was determined that thecorrosion rate decreased with an increase in the hydrocarbon chain length. Both corrosion rate and the polarization
resistance are plotted as a function of hydrocarbon chain
length in Fig. 16.
The observed results can be explained based on the degree of surface activity of the surfactant molecules. Higher
surface activity due to higher chain length results in higher
and probably denser surface coverage, thereby providing
more corrosion protection. Therefore, in spite of the same
number of molecules being present in the bulk solution foreach surfactant, better corrosion protection was provided
by OTAB as compared to DTAB since a higher number of
OTAB molecules are expected to be adsorbed at the interface due to their higher surface activity, thereby blocking agreater number of active sites for corrosion.

3.19 Effect of surfactant concentration

Comparison of the corrosion inhibition efficiency as afunction of DTAB and OI concentration in Fig. 17 revealed
that optimal corrosion inhibition occurs at a lower concen%IE =For DTAB

CR0 − CR
x100
CR0

tration than their respective critical micelle concentration(CMC) values. OI was determined to be more effective
than DTAB, possibly due to the stronger electrochemicalinteraction of the imidazoline ring with the steel surface
resulting in more effective corrosion protection.
The initial part with the higher slope in both the plots
represents the region where the corrosion rate is increasingly reduced as more and more active sites are blocked
by the adsorbed inhibitor molecules. This region is fairly
straight-lined, indicating that the corrosion inhibition
is directly proportional to the surface coverage by thesurfactant molecules. The second part, also almost straightlined, albeit with a relatively lower slope, indicates almost
complete coverage of the surface with monolayer and/or
surfactant aggregate structures. The corrosion protectionin this region was attributed to the blocking of the active
corrosion sites as well as the resistance to the transport ofcorrosive species through the surfactant aggregates.
The inflection point in each plot represents a structuralchange in the adsorbed layer from monolayer to multilayer(bilayers or micelles!). Concentration at the inflection

Fig. 16 Corrosion rate (CR) and the polarization resistance (Rp) for
AISI1010 carbon steel exposed to 0.01 M HCl solution in the presenceof the homologous series of TAB inhibitors at 0.01 mM concentration
at 298 K. Reprinted with permission from Ref. (Rajopadhye, 2018).
Copyright: (2018) University of Florida.

%IE =CMC (in bulk)
CAC (at the surface)CR0 − CRx100
CR0

For Oleic Imidazoline

Fig. 17 Comparison of corrosion inhibition efficiency of DTAB and OI as a function of concentration. Reprinted with permission from Ref.
(Rajopadhye, 2018). Copyright: (2018) University of Florida.

42

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

point is termed as the critical aggregate concentration(CAC), the concentration at which the aggregate structures
start forming at the interface. The CAC value for DTAB
(0.05 mM) is about an order of magnitude higher than for
OI (CAC = 0.005 mM). This could be due to the higher
surface activity of OI molecules as compared to DTAB asa result of differences in their hydrocarbon chain lengths.
Interestingly, the CAC values (CACDTAB = 0.07 mM and
CACOI = 0.008 mM) are lower than the bulk CMCsof the two surfactants (CMCDTAB = 15 mM and CMCOI= 0.03 mM), indicating the possibility of the metal surface
catalyzing the self-structuring process resulting in theaggregation taking place on the surface (CAC) at a lower
concentration than in bulk (CMC) (Singh et al., 2001).

3.20 Surfactant adsorption mechanism

The mode of adsorption for the OI-type additives has
been reported to be chemical in nature. In the present study,
adsorption of DTAB and OI was determined to obey theLangmuir adsorption isotherm. The free energy calculations of 9.3 kJ/mol for DTAB and 9.4 kJ/mol for OI are
more indicative of the physical adsorption mechanism. The
literature reports are inconclusive in this regard (Keera etal., 2012; Zhu et al., 2015; 2017). Further investigations
are required to reach a conclusive determination about
the adsorption mechanism of the most effective corrosion
inhibitors.
Overall, the two key contributions to corrosion inhibition – physical (the hydrophobic interactions between the
chains) and electrochemical (the head group interactions
with the surface) were isolated in this study by varying
one component while keeping the other constant. It wasdemonstrated that the physical contributions play anequally important role in determining the corrosion inhibition properties of the surfactant-based inhibitors. The
chain-chain interactions of surfactant molecules that drive
them to the interface determine the number of surfactant
molecules available for adsorption and thus dictate theoverall surface coverage and the corrosion inhibition efficiency.

3.21 Precision gold nanoparticles synthesis

Precision particles of very narrow size distribution,
especially metal (e.g., gold) nanoparticles, are needed foradvanced sensor applications and as reference materials
for toxicology assessments. Additional commercial applications of such particles are limited due to their high cost.
Narrowly sized gold nanoparticles (AuNPs) can cost up to$10,000 per gram AuNPs. One of the major reasons for theirhigh cost is their labor-intensive batch production which issusceptible to batch-to-batch variation. The aim of thisproject was to develop a continuous production techniquethat is scalable for cost-effectively and high-throughput
production of precise metal nanoparticles. AuNPs canbe synthesized using the Turkevich method. The average
particle size, size distribution, and morphology werecharacterized by UV-visible spectroscopy, Dynamic Light
Scattering (DLS), and Transmission Electron Microscopy (TEM). Chemical composition was analyzed with
XPS, FT-IR, and ICP-MS Spectrometry. The precision of
nanoparticles, i.e., size distribution, was quantified using
the polydispersity index, PDI (ISO13321, 1996).
Over the last decades, nanoparticle synthesis using flow
chemistry has attracted more attention because of its automation capability and potential for low-cost productionof high-precision nanomaterials (Nightingale et al., 2014;
Stitt, 2002). Typical flow chemistry involves the mixing ofreactants in a chip-based microfluidic cell under specificconditions (Wagner et al., 2008). However, relatively low
throughput and reactor fouling are some of the major limitations of microfluidic reactors for nanoparticle synthesis.
Moreover, attempts to scale up such reactors often lead tothe loss of precision control of the particle properties suchas the size and size distribution.
In this study, initial efforts to produce narrowly sizedgold nanoparticles using a single-phase flow reactor were
unsuccessful primarily due to reactor fouling. The attempts
to overcome reactor fouling by changing the reactor material, flow rate, and particle synthesis chemistry were notsuccessful. However, these attempts revealed that most
likely fouling was caused by heterogeneous nucleationand the growth of gold on the reactor surface. Foulingin continuous nanoparticle synthesis can be overcome incarefully designed two-phase or biphasic flow systems thatcomprise two immiscible fluids. In the biphasic reactor,
the continuous phase is selected such that it preferentiallywets the reactor surface, and the dispersed phase consistsof reactant solution. In the present study, silicone oil was
selected as the continuous phase since it was expected to
wet the reactor surface, Perfluoro alkoxy, or PFA tubing.
Gold chloride (HAuCl4) and trisodium citrate (Na3C6H5O7)
constituted the two aqueous phase reactants. A schematicof the flow reactor is illustrated in Fig. 18.
As shown in Fig. 19, silicone oil was introduced fromboth sides of the cross-junction (IDEX, ETFE P-723) as
the continuous phase and droplets of aqueous reactants
were generated when the already mixed solution of the tworeactants was merged into the silicone oil. Each aqueous
droplet dispersed in the silicone oil acted as an individual
mini reactor. Additional experimental details can be foundin the publication (Dong et al., 2022). It is important thatthe system conditions of concentrations, flow rate, andtemperature, and pressure are controlled such that theaqueous reagents are completely mixed during their flowin the channel before the mixed reactants are discharged/
dispersed into the non-aqueous phase. It should be notedthat to avoid nucleation before complete mixing of thereactants, the channel temperature is maintained at room

43

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49
Back pressure
regulator

Piston pumps

PRS

Reagent reservoirs

Flow reactor

UV-visible
spectrophotometer

Sample collection

Fig. 18 The layout of the flow reactor system used for gold nanoparticle synthesis. Reprinted with permission from Ref. (Dong et al., 2022). Copyright: (2022) The Authors, published by KONA Powder and Particle Journal.

Aqueous solution ofyellow dye
representing HAuCl4

Laminar flow

Aqueous solutionof blue dyerepresenting NaCt

Silicone oil

Biphasic
segmented flow

Mean Particle Size
by % Intensity (nm)

160
120
100
80
60
40
20
0
Silicone oil

Fig. 19 Experimental setup showing the generation of liquid-liquidbiphasic segmented flow. The yellow and blue dye represent HAuCl4
and NaCt solutions, respectively. Reprinted with permission from Ref.
(Dong et al., 2022). Copyright: (2022) The Authors, published by KONA
Powder and Particle Journal.

Biphasic Trial 1 (± 3.7 nm)
Biphasic Trial 2 (± 4.3 nm)
Biphasic Trial 3 (± 4.5 nm)
Single-phase Trial 1 (± 46.7 nm)
Single-phase Trial 2 (± 44.8 nm)
Single-phase Trial 3 (± 62.8 nm)

140

0

30

60

90

44

150

180

210

Fig. 20 DLS measurement of the mean particle size of the AuNPs
synthesized in biphasic flow reactor. Reprinted with permission from
Ref. (Dong et al., 2022). Copyright: (2022) The Authors, published by
KONA Powder and Particle Journal.

4. Concluding remarks
temperature to slow the precipitation reaction kinetics; andto accelerate the kinetic process, the droplets of the mixed
reactants are heated to 100 °C as soon as they are dispersedin the non-aqueous phase.
No fouling was observed in the biphasic flow reactor. Moreover, as indicated in Figs. 20 & 21, the gold
nanoparticles produced were consistent over several hoursof biphasic reactor operation. This was attributed to theelimination of fouling and the much more efficient mixingof the reactants due to the recirculating flow fields encountered inside of each aqueous segment/droplet dispersed in
the continuous fluid. The precision of the nanoparticles
produced was determined by calculating the polydispersity
index (PDI) values. Polydispersibility index values of thegold nanoparticles (PDI = 0.068) were determined to bevery close to the PDI of the reference material generated inbatch synthesis (PDI = 0.065). Overall, it was determinedthat fouling in the continuous flow reactors can be overcome by avoiding the nucleation reaction in the vicinity of
the reactor surface; otherwise, it could impact the particle
properties and the reaction yield.

120

Time (min)Although there have been considerable advances inparticle synthesis, functionalization, characterization, andperformance assessment methodologies, a number of challenges remain that need to be overcome before large-scale
applications of engineered particulate systems becomea reality. Some of the challenges and opportunities, especially related to the fields of research discussed in thispaper, are outlined below.
In the area of particle assisted microbe adhesion andremoval from household surfaces, skin, bio-implants,
hospital and general use fabrics, etc. in the form of sprays& coatings, controlled release devices, and wipes, there is a
critical need for a better understanding of the mechanismsof bacterial interactions with different types of surfaces
and engineered particulates including polymers, inorganic,
metal, skin, etc. (D’Accolti et al., 2018; Varshney et al.,2021). Qualitative or semi-quantitative information about
the mode of microbial adhesion exists, but there is a lackof quantitative data on the various forces involved in microbial adhesion and removal. Reliable data is needed todevelop robust modeling and simulation schemes. Specifically, in the handwashing applications field, reducing thetime it takes to wash off the microbial contaminants below

Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

Fig. 21 DLS measurements of particle size and size distribution ofgold nanoparticle samples synthesized using liquid-liquid biphasic flow
reactor. The particle size and size distribution remained constant in the
8-hours uninterrupted experiment. Reprinted with permission from Ref.
(Dong et al., 2022). Copyright: (2022) The Authors, published by KONA
Powder and Particle Journal.

a safe threshold is an important goal. Furthermore, developing suitable hand hygiene, body cleansing formulations,and detergency for water-stressed regions of the world isfast becoming an acute necessity.
Large-scale applications of particulate coatings for crop
protection require low-cost functionalized materials suchas clays for foliar applications not only for protection
from insects but also for on-demand delivery of actives
and micronutrients to the plants. Moreover, functionalized
particles must not cause any harmful effects to crop quality
and yield. Multifunctional particle formulations and smart
aggregates for on-demand delivery purposes can help
reduce the dependence on chemicals that might otherwise
result in the generation of antibiotic-resistant microbes. Selective killing of only harmful insects and bacteria, leaving
the healthy microbes and insects intact, remains a prized
long-term goal.
On the particulate systems mediated disinfectant front
using photocatalytic coatings, considerable progress has
been made in identifying suitable doping additives to enhance photocatalysis under visible light. However, the low
rate of disinfection requiring relatively longer disinfection
times remains a major competitive disadvantage for largescale applications. Additionally, nanotoxicity continues
to be a challenge for titania-based disinfection coatings(Jafari et al., 2020; Liao et al., 2020; Rtimi et al., 2019).
Bismuth oxide coatings have been reported to outperform
other photocatalytic materials, including titania. However,long exposure times are required for microbial inactivation(Ratova et al., 2018). For the disinfection of surfaces,
increasing the efficacy of applied coatings to reduce thefrequency of disinfectant applications is a critical need.
Practical application of surfactants for corrosion in-hibition has been a theme of several studies not only toenhance the effectiveness of current formulations but also
to develop greener surfactant coatings (Sliem et al., 2019;
Zhuang et al., 2021). Facilities for testing the efficacy of
formulations under field conditions of high temperature,pressure, salinity, pH, flow conditions and particulatecontamination, etc., are scarce. Suggestions for setting up
a flow loop incorporating above mentioned parameters
should yield the data needed for developing robust predictive models (Zhu et al., 2017).
Biomedical applications of nanoparticles continue tobe a strong driver for advanced applications of engineered
particulate systems for the detection and treatment ofcancer. Several comprehensive review articles have summarized the latest developments in this regard. Despite thepositive outlook, major challenges need to be overcome
before large-scale clinical implementation of such materials becomes a reality. These include toxicity, long term
biodistribution, biodegradability, in addition to protocols
for large scale production of multimodal particles at low
cost (Aghebati-Maleki et al., 2020; Gallo et al., 2013; Hanet al., 2019; Hsu et al., 2020; Luo et al., 2021; Silva et al.,2019; Thanapandiyaraj et al., 2018).
Despite several studies across the globe, the toxicity ofnanoparticles continues to be a major obstacle for theirlarge-scale usage in biomedical, consumer goods, healthcare, and pharmaceutical products. Lack of availability ofwell-characterized reference materials for conducting comprehensive toxicological assessments has been a problem
for industrial and academic researchers alike. In the USA,
the National Institute of Environmental Health Sciences(NIEHS) has funded several groups to generate the required data. Most recently, NIEHS funded a consortium
led by the Center for Nanotechnology and Nanotoxicology,
T.H. Chan School of Public Health, Harvard University
in collaboration with MIT, University of Maine, andthe University of Florida to create a repository of wellcharacterized engineered nanomaterials for toxicologicalstudies. Availability of precise nanoparticles (narrowly
sized and/or with specifically designed functionalities) at
low cost, however, remains a barrier because of batch production of such particles that invariably involves numerous
iterations to generate modest quantities of narrowly sized
nanoparticles thereby driving up their prices. In this regard,
biphasic continuous flow reactors have shown promise
pending their successful scale up for larger-scale production (Dong et al., 2022).
In recent years, AI has become a popular tool forcomplex materials and systems design, simulation, andoptimization. For example, Shams et al. employed AI
methods to estimate and optimize the silver nanoparticle
concentration on the treated silk fabrics for antimicrobial
properties (Shams Nateri et al., 2019). Vinoth and Datta
used the artificial neural network and genetic algorithm to45Jiaqi Dong et al. / KONA Powder and Particle Journal No. 40 (2023) 29–49

design a novel nanoparticle reinforced polymer composite
for replacements in hip joints (Vinoth and Datta, 2020).
Mahmoud et al. implemented the artificial neural network
to simulate and predict the removal efficiency of several
organic contaminates from aqueous systems using ironnanoparticle sorbents (Mahmoud et al., 2018).
AI techniques have also been utilized in various biomedical applications, including image inspection via machinelearning, histopathology examination, making unbiased
medical decisions, molecular and medicine performancemodeling (Chaddad et al., 2021; Jin et al., 2020). AI has
the ability to precisely detect nanoparticle biomarkers orcontrast agents in biological systems from medical imaging
modalities (Zhang et al., 2021). It also has the potential topredict and model the property and response of a hypothetical contrast agent in specific systems. The main limitationof AI applications remains the lack of professional data
curation and quality assurance (Bi et al., 2019). Other
challenges include validating the predictive AI models, understanding the fundamental mechanisms of the AI models,
and reaching a consensus in the research community about
the AI outcomes (Bi et al., 2019).
Greater interactions among the industry and academic
researchers not only would advance the field of particle
and powder technology-mediated processes and products
but also help create a well-trained cadre of sorely needed
young researchers and practitioners in the field. Additionally, meaningful collaborations among physical scientists
and engineers, life scientists, and clinicians are necessary
to develop nanoparticle-based sensors, multimodal contrast agents, and other health monitoring and treatment
devices. Overall, in the last few decades, there have been
tremendous advances in the field, and more exciting new
discoveries are expected in the coming years, especiallywith the advent of AI-mediated particulate systems-related
innovations.

Acknowledgments

The authors convey special thanks to Dr. P. Sharma, Dr.
V. Krishna, Dr. V. Nandakumar, and Dr. J. Tanquero for
their valuable assistance with some of the figures incorporated in this paper. The authors acknowledge the financial
support of the National Science Foundation Center for Particulate and Surfactant Systems (CPaSS) and the industry
members (NSF Award #1362060, NSF Award #1602032).
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors anddo not necessarily reflect the views of the National Science
Foundation or the CPaSS industry members.

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Authors’ Short Biographies
Jiaqi Dong
Jiaqi Dong received his B.S. in Materials Science and Engineering from the Illinois Institute ofTechnology in 2013. He then attended the University of Florida as a graduate student and received
his M.S. in Materials Science and Engineering in 2016. Currently, he is a Ph.D. candidate under thesupervision of Dr. Brij Moudgil and Dr. Bahar Basim at the Center for Particulate and Surfactant
Systems (CPaSS). His current research focus is metallic nanoparticle synthesis and functionalization
using flow chemistry.

Brij M. Moudgil
Dr. Brij M. Moudgil is a Distinguished Professor of Materials Science and Engineering at the
University of Florida. He received his B.E from the Indian Institute of Science, Bangalore, India, andhis M.S. and Eng.Sc.D degrees from Columbia University, New York. His current research interests
are in surfactant and polymer adsorption, dispersion and aggregation of fine particles, adhesion,and removal of microbes from surfaces, synthesis of functionalized nanoparticles, anti-scaling andsurfactant mediated corrosion inhibitors, photocatalytic degradation of hazardous microbes, andnanotoxicity. He has published more than 400 technical papers and has been awarded over 25 patents.
He is a member of the U.S National Academy of Engineering.

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KONA Powder and Particle Journal No. 40 (2023) 50–73/https://doi.org/10.14356/kona.2023006

Review Paper

Comminution and Classification as Important Process Steps for
the Circular Production of Lithium Batteries †Arno Kwade 1,2*, Marcel Möller 1, Jannes Müller 1, Jutta Hesselbach 2, Sabrina Zellmer 2,
Stefan Doose 1, Julian Mayer 1, Peter Michalowski 1, Malcolm Powell 3
and Sandra Breitung-Faes 1
1 Institute for Particle Technology, Technical University of Braunschweig, Germany
2 Fraunhofer Institute for Surface Engineering and Thin Films IST, Germany
3 University of Queensland, Australia

Abstract
Lithium-ion batteries (LIBs) provide the largest source of electrical energy storage today. This paper covers the useof comminution processes and, thus, crushers and mills for particle breakage and dispersing, as well as classifiers forparticle separation within the process chain, from the raw material to the final lithium battery cell and its recycling at
end of life. First of all, the raw materials for the active material production have to be produced either by processing
primary raw materials, or by recycling the spent lithium batteries. The end-of-life battery cells have to be shredded,
the materials separated and then milled in order to achieve the so-called black mass, which provides a secondary
material source with very valuable components. Using these materials for the synthesis of the cathode active
materials, milling has to be applied in different stages. The natural graphite, increasingly used as anode material, has
to be designed in mills and classifiers for achieving targeted properties. Nanosized silicon is produced by nanomillingusing stirred media mills as a primary option. Conductive additives for LIBs, like carbon black, have to be dispersed
in a solvent with machines like planetary mixers, extruders or stirred media mills. In the future, mechanochemical
synthesis of solid electrolytes will especially require additional application of comminution processes.
Keywords: graphite, silicon, battery recycling, active material synthesis, solid state batteries, electrode production

1. Introduction

The most popular energy storage system today is thelithium-ion battery (LIB). LIBs offer superior performancein terms of energy density (Wh/L) and specific energy (Wh/kg), as well as lifetime and cycling stability. Moreover, due
to their mass production, their costs decrease continuously
and, due to increasing application knowledge, the safety is
steadily improving. Regarding LIB cell costs, about 75 %is attributed to the materials used for the cell production(Kwade et al., 2018). A schematic presentation of an electrochemical basic unit consisting of cathode, separator andanode, as well as electrolyte, is shown in Fig. 1 left. Bothelectrodes consist of particles which are bound to eachother and to a current collector by a binder. As active material on the cathode side, particles consisting of lithium transition metal oxides (e.g. NMC, NCA, LMNO) or a lithium
metal phosphate (e.g. LFP) are used together with carbon
†

*
1
2

50

Received 12 June 2021; Accepted 29 September 2021
J-STAGE Advance published online 23 April 2022
Corresponding author: Arno Kwade;
Add: Volkmaroder Str. 5, 38104 Braunschweig, Germany
Add: Bienroder Weg 54 e, 38108 Braunschweig, Germany
E-mail: a.kwade@tu-braunschweig.de
TEL: +49-531-391-9603 FAX: +49-531-9633

based conductive additives like carbon black, carbon nanotubes (CNT) and/or fine graphite particles. On the anodeside, as a rule, natural or synthetic graphite particles with
tailored surface functionalizations are employed together
with small amounts of carbon-based conductive additives.
Recently, the specific capacity of the anodes has been
increased by adding smaller amounts of silicon, which has
about a 10 times higher specific capacity than graphite,
despite possessing severe cycling stability challenges due
to extreme volume changes during cycling.
In addition to the development of improved active
materials, comprehensive research is being conducted on
the development of solid electrolytes for the so-called AllSolid-State Batteries (ASSB) as next generation lithium
battery technology (Janek and Zeier, 2016). As depicted inFig. 1 right, such ASSB cells have a solid electrolyte acting as ionic conductor within the cathode, as well as a separator layer between anode and cathode. Solid electrolytes
can be sulfide, polymer and/or oxide based, among others,
and are produced by mechanochemical processes. Lithium
based anodes can be employed in an ASSB, avoiding, or atleast strongly minimizing, lithium dendrite growth, as wellas significantly increasing the energy density.
Another promising lithium battery cell type is the

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Arno Kwade et al. / KONA Powder and Particle Journal No. 40 (2023) 50–73

CO2-footprint and to become independent of the primary
raw material resources, the buildup and establishmentof a circular economy, especially circular production of
LIB, is a major task for the near future. Within the circular
production, the End-of-Life (EoL) battery systems haveto be dismantled and the components have to be fed to a
recycling process, resulting in secondary raw materials
that are used to produce new battery active materials,
especially cathode active materials. The overall recycling
process can consist of mechanical (especially shredding,milling), classification and sorting, thermal, hydrometallurgical and/or pyrometallurgical process steps (Doose etal., 2021b). According to existing or proposed regulationsin the different regions of the world (e.g. newly proposed
EU directive), batteries must be recycled at high recycling
rates, especially for reuse of the transition metals as Ni, Co
and Cu, with up to 95 % recovery, as well as for Li and theoverall battery system with at least 70 % recovery. A schematic outline of the circular production of lithium batteries,
especially lithium-ion batteries, is shown in Fig. 2.
The primary, and increasingly the secondary, raw materials are used as the basis for the production/synthesisof the cathode and anode active materials. Within the
production of these particulate active materials, millingand classification processes play a major role for tailoring
the particle size distributions and particle shapes, as well as
dispersing calcinated materials. The particle size distributions and particle shapes are important for the subsequent
electrochemical performance and cycling stability of the
LIB cells.
For the production of the anodes and cathodes, thedifferent components (as shown in Fig. 1) have to be welldispersed in a solvent by using mixing and milling devices.
The resulting microstructure of the carbon-based conductive additives is decisive for the future cell performance.

Lithium-Sulfur-Battery (LiS), which has the potential toachieve very high specific capacities, which is especially
important for aerospace applications or application withintrucks (Zhao et al., 2020). Similar to ASSB, these cells
have a lithium-based anode and a cathode based on asulfur-carbon composite material. Both liquid electrolyte
and solid electrolytes can be employed.
Today the different materials have to be produced fromprimary resources mainly by mining and subsequentmineral processing. For the metals lithium, nickel, cobalt,
manganese, aluminum and copper, as well as for graphite,almost 3 million tons in 2025 (with approx. 0.5 million tonsof nickel and graphite) and about 7 million tons will be
required in 2030 (with approx. 1 million tons of nickel andgraphite) overall according to the Electric Vehicle Outlook
2018 of Bloomberg New Energy Finance (Bloomberg
NEF, 2018). Therefore, the conservation of primary material resources, and interdependence on individual primaryresources and raw material producers, is an important task
for the coming years. Moreover, the actual development
challenges are the improvement of the fast-charging behavior and the CO2-footprint of the LIB. In order to reduce theAlLi

Solid electrolyte separator

Cu

All-Solid-State Battery
Anode
CathodeAl

Separator with liquid electrolyte

Cu

Lithium-Ion Battery
CathodeAnode+Fig. 1 Compartment of Lithium-Ion Battery (LIB) (left) and all-solidstate battery (ASSB) (right).

M
R

A

M

Mining raw materials

S

Production active materials

E

Electrode production

C

Cell and system production

A

Application phase

R

Recycling of EoL batteries

S

E

C
Fig. 2

Circular production of LIB.

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After production, the electrodes are assembled together
with the separator to form the battery cell, as described indetail for example in the literature (Kwade et al., 2018).
The cells can be cylindrical, prismatic or pouch types, andare lastly filled with electrolyte, formatted, aged and closed
to be shipped for integration into a battery system for application in electromobility, stationary storage or power tools.
After the application phase, usually lasting more than ten
years, the lithium-ion batteries reach their end of life andhave to be shipped to a dismantling and recycling facility.
Finally, besides the critical discussion and further
presentation of the perspective of milling, dispersing andclassification processes for today’s LIB based on liquid
electrolytes, an outlook of milling and classification processes for the production of ASSB will be given. The
synthesis of solid electrolytes is often achieved throughmechanochemical synthesis within planetary, vibration oragitated ball mills. Moreover, mills are often employed
for the preparation of the composite materials for thecathode. Another possible application is the production ofcomposite materials for LiS battery cells, which have highpotential for usage in aviation, due to their low weight inrelation to their storage capacity.

2. Raw material supply

Today, the raw materials for the cathode active material
production, mainly ores with transition metals, and forthe anode active material production, i.e. natural graphiteand synthetic graphite, as well as silicon, are extracted
from primary natural resources. However, the electromobility and energy turnaround can only be successful if thematerials used for the production of the energy storage
devices underly a circular economy: i.e. closed material
cycles. Therefore, besides primary resources, the use of
EoL batteries as secondary material source is growing inimportance. Within the processing of both primary andsecondary raw materials, comminution, classification andsorting processes play a major role.

2.1 Processing of primary raw materials

Mineral processing is the link between mining andchemical beneficiation of valuable elements, like lithium,for battery applications. The valuable minerals upon which
our industrialized society depends have been formed andor laid down in host rock, from which they need to be
extracted. The minerals are embedded and dispersed inmany forms and structures, presenting a wide range ofrequirements for their recovery from different ore bodies.
Most valuable metals occur naturally in the form of theiroxides, sulfides, silicates, carbonates or similar. Extractive
processing usually involves the conversion of the mineral
into the valuable metal, or for lithium to a soluble component like LiCO3 (Amarante et al., 1999; Talens Peiró etal., 2013; Lee, 2015). Mineral processing is responsible52

for concentrating the raw mined rock, which generally
contains the valuable minerals in concentrations from a few
percent down to parts per million, to a concentrate stream
with 10 % to 40 % valuables that is sent for refining to theend metal product. In order to concentrate the minerals,
they have to be mechanically separated from the hostrock to be exposed to a recovery process. The exposure
should be sufficient to differentiate particles that containthe valuable minerals from those that are barren, or havean extremely low grade of valuables. The recovery process
can be physical (density, magnetic susceptibility), basedon surface chemistry (flotation), or exposure to a lixiviant
used to dissolve the minerals (leaching), each requiring a
different degree of mineral exposure. The size to which therock needs to be reduced depends upon the mineral association and recovery process. The process of rock breakage
for liberation of the minerals is known as comminution.
Predictions that mineral processing would become lessimportant in favor of hydro- and pyrometallurgy are only
true for a few special cases (Moskvitch, 2014). For mostores, including lithium containing pegmatites, mechanicalbreakage and concentration creates reactive surfaces suited
to hydrometallurgical recovery of the valuable components(Baláž, 2003). For lithium ores, concentration is followedby either calcination or roasting, and then a leaching
step. Both are often followed by a carbonate precipitation(Barbosa et al., 2014; Amer, 2008; Sitando and Crouse,
2012; Yan et al., 2012).
In order to reduce the particle size by about a thousandfold, a number of progressive breakage stages are required.
Blasting of in-situ rock, followed by primary crushing,provides a minus 200 mm feed of rock. This can be stagecrushed to minus 12 mm for grinding in tumbling ball millsthat use steel grinding media, and produce products in therange of 80 % passing (P80) 60 μm up to 80 % passing
1 mm. Large semi-autogenous grinding (SAG) mills that
contain a mixture of rock and steel balls as grinding media
are common for large ore bodies of low grade. SAG mills
are fed direct from the primary crusher, have up to 24 MW
power draw, and process 1500 to 3500 tons per hour (tph)of ore. They are generally followed by one to three ballmills. For a required grind size finer than 60 μm, stirred
mills are generally used as the final grinding stage. These
provide a lower energy use for fine grinding, and the higher
energy density reduces the footprint of the equipment(Palaniandy et al., 2015; Taylor et al., 2020).
Most commonly, hydrocyclones are used to classify the
product by size prior to the recovery process. However,these are intrinsically inefficient and allow coarse oversize
to be passed on, and fine final product to return to the milland be over-ground. Additionally, the milling process is
non-selective, resulting in overgrinding of material as
is passing along a mill. More extensive descriptions of
the comminution process can be found in works such as

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those of Wills’ Mineral Processing Technology (Wills andNapier-Munn, 2006).
Comminution is exceedingly energy-intensive, using
2–3 % of the electrical energy consumed worldwide
energy and around 40 % of energy in mining (Ballantyne
and Powell, 2014). The energy requirement rises almost
exponentially with finer grinding, but linearly with the production of fine product (Ballantyne et al., 2015), emphasizing the need to improve processing at the fine end (minus
500 μm), or reduce the need to produce material below thissize. Due to this energy intensity, the need to drive down
the energy cost is a significant factor in reducing the environmental cost of producing metals. This has led to many
ongoing process improvements designed to reduce energy
consumption.
Alternate circuits that include high pressure grindingrolls (HPGR) (Schönert, 1988) are becoming more common for hard-rock applications, as they offer a reduction
in energy use, but they still need to be fed by conventional
crushers and require ball mills to produce the final grindsize, compromising the overall energy savings. Selective
breakage of minerals along the grain boundaries has long
been a pathway pursued to reduce energy and improve recoveries. It has been met with limited success thus far, suchas in the application of microwave pretreatment (Kingman,2006), or electrical fragmentation (Parker et al., 2015), but
remains an important area of research.
In light of the high energy use and low efficiency of
breakage and selection of minerals, a major research thrustis captured in the work of Powell (2019): “The objective ofindustrial comminution processes is to conduct the minimum degree of breakage required to permit recovery of thevaluable minerals or satisfy product quality needs.”
There is a compromise between energy use and adequate
liberation of the minerals to enable their recovery. Too little
breakage results in minerals remaining trapped (unliberated) and not recovered or too coarse for the recovery process to select. Too much breakage results in loss throughfines (slimes) that cannot be selected. The objective of
breaking the rock into a narrow size range, well suited
to recovery, is severely compromised by the relatively
crude nature of the crushing, milling and classificationequipment used for bulk processing of ores. Fine screensare providing greatly improved classification comparedto hydro cyclones, but are currently only suited to lower
throughput operations (less than 500 tph). However, theyshow greater promise in light of finding that the change inclassification can improve the liberation characteristics ofthe ore, increasing recoveries while reducing energy needs(Frausto et al., 2021).
Powell has proposed flexible circuit design to improvethe balance of energy applied to the rock, and enable early
selection and progressive rejection of waste material.
Considerable research and process development is being

applied to coarser recovery processes, such as coarse
flotation, that allow coarser product sizes to be recovered.
Another approach to improving recoveries is by increasing
the surface reactivity through mechanochemical treatment—the addition of chemical reagents combined withmechanical grinding (Dessemond et al., 2019).
Overall, the extraction of raw materials will remain
essential to the production of batteries for many decades
to come, as demand far outstrips available resource in thecircular economy. Due to the high energy and environmental footprint of current production processes, improving
the efficiency of this production is an important aspect ofsustainable energy production.

2.2 Secondary raw materials from recycling

In order to achieve an economically and ecologicallysustainable battery production, the recycling of the circulated lithium-ion batteries is of tremendous importance(Peters et al., 2017; Kwade and Diekmann, 2018; Zhao etal., 2020). According to Xu et al. (2020), the circulating
batteries in consumer products, EV application and otheruse cases will increase many times by 2030. To cover the
raw material demand in the targeted sustainable productionof LIB, recycling represents a central and elementarybridge between LIB at end-of-life or production scrap, andthe production of new cells (Habib et al., 2020).

2.2.1 Recycling impact on battery value chain

The key element to reduce the carbon footprint,
minimize raw material dependencies and increase sustainability of batteries, is the recycling of the recyclables
within the battery to reuse them in a new production cycle(Velázquez-Martínez et al., 2019).
Approaches to achieve this goal are described in theliterature in wide variety, and are already successfully
implemented at the research and, in some cases, the industrial level (Brückner et al., 2020; Velázquez-Martínez
et al., 2019; Zeng et al., 2014; Arshad et al., 2020; Kwade
and Diekmann, 2018). However, the ambitious targets ofthe EU or the CEID consortium (Kwade et al., 2020) withmaterial recycling rates of up to 95 % of e.g. nickel (Ni),aluminum (Al), cobalt (Co), and 70 % of lithium, have
not yet been achieved. Compared with other countries andregions of the world, the EU sets very high target recycling
quotas with the proposed Battery Directive, which are also
pursued equally by China. Producers are obliged to collectthe batteries brought into circulation and return them tothe material cycles. Other Asian countries aim to achieve
similar goals (South Korean RoHS/ELV/WEEE Act, 2007
and Japan’s End-of-Life Vehicle Recycling Law).
With the help of the process steps for the LIB recycling,depending on the combination as depicted in Fig. 3,
most of the valuable materials can be recovered and used
for further battery applications or, if more meaningful,53

Arno Kwade et al. / KONA Powder and Particle Journal No. 40 (2023) 50–73
Housing,
Tabs, etc. HeatCu

(A)
Deactivation(B)

Dismantling
Mechanical Processing(C)(with electrolyte recovery)

Pyrometallurgy(D)

Hydrometallurgy(E)

Electrical Housing,energyTabs, etc.

Pyrolysis

Cu, Al Co, Ni

Li

Co, Ni, Mn

Fig. 3 Overview of potential recycling process chains for lithium ion batteries. Reprinted from Ref. (Doose et al., 2021b) under the terms of the
CC-BY 4.0 license. Copyright: (2021) The Authors, published by MDPI.

high-value application alternatives. With regard to the desired raw material-independent production sites of LIB, theprocess of recovery, after initial raw material supply to the
producers, allows to both increase sustainability and environmental protection by decreasing the need for primarymaterial from extraction sites, as well as to minimize theequipment required for production of the valuable metalsalts for active material synthesis (Bonsu, 2020; Mathieux
et al., 2017).

2.2.2 Potential LIB recycling processes

Today, LIBs with liquid electrolytes dominate the mainmarket application (BEV, PHEV, portable devices andstationary energy storage). Regardless of the type of application, LIBs contain nickel, cobalt, manganese, aluminum,
copper, graphite, polymeric and/or iron components, andin new technologies, silicon (Diekmann et al., 2017). From
this point of view, it is clear that processes for recycling
LIB’s are of enormous importance.
In principle, the processes of recycling LIB have a recurring scheme, which can be described independently fromthe chosen processing route. The first step is to deactivate
the battery cells by discharging and short-circuiting them,by treating them with saline solution or pyrolyzing them atmore than 200 °C (Georgi-Maschler et al., 2012; Hanischet al., 2015a). Subsequently, four different process types
can be used and combined in different ways: mechanical, thermal, pyrometallurgical and hydrometallurgicalprocesses. Fig. 3 shows possible different process routes
that can be applied. The individual processes allow only
very limited possibilities to recover the materials, which
is why a combination is essential. Pure pyrometallurgicalprocesses (see Fig. 3(A)), as with direct smelting of LIB

54

battery modules, are already industrially established, butallow only high recovery rates for transition metals suchas nickel, cobalt and copper, whereas lithium in particularcauses difficulties and is found especially within the resulting slag. To enable recovery of lithium and manganesealong the way, the route has to be further linked to hydrometallurgical and undoubtedly mechanical processes aswell. Other components such as graphite, organic solventsand polymers are thermally consumed as an energy source.
For this reason, overall a relatively low total recovery rate
can be assumed (Brückner et al., 2020).
Direct pyrometallurgical recycling can also be precededby a mechanical crushing and sorting step, with subsequentpyrometallurgical treatment of the black mass or shredded
battery material (see (C) and (D) in Fig. 3). The proportionof mechanical processing is a variable factor here. This
pretreatment increases the recovery rate, as material suchas polymers and aluminum can also be recovered.
Another route starts with the pyrolysis of the entire
battery system to achieve safe handling in further process
steps (see (B) in Fig. 3). By this route, all volatile components are removed, including a high portion of the fluorine
containing components, which requires a complex gas
cleaning. Afterwards, the parts of the battery system thatare not burned are dismantled, and the cells are further
mechanically processed using a shredder and separation
devices, such as zig-zag-classifiers (more details see descriptions for route (B) in Chapter 2.2.3).

2.2.3 Mechanical-hydrometallurgical processing

A complex process route of LIB recycling is a combination of mechanical and hydrometallurgical steps,
without an intermediate pyrometallurgical process, but

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Discharge

Disassembly

Crushing

DryingMixing 1st Classification 2nd Crushing

Sieving

2nd Classification

Fig. 4 Exemplary process diagram for the LithoRec recycling process for LIB. Reprinted with permission from Ref. (Kwade and Diekmann, 2018).
Copyright: (2018) Springer Nature.

with low temperature treatment or, eventually, a pyrolysis
process step after shredding (Brückner et al., 2020;
Georgi-Maschler et al., 2012; Kwade and Diekmann, 2018;
Velázquez-Martínez et al., 2019). Upstream of mechanicalprocessing, is a step to deactivate the modules and battery
cells, which can be achieved by electrical deep discharge
to 0 V and short circuiting, saline solutions, or shock freezing (Georgi-Maschler et al., 2012; Hanisch et al., 2015b;
Shaw-Stewart et al., 2019). The discharged battery modules or cells are then crushed, the shredded product usuallytreated thermally, and the components are separated into
the individual fractions in a sequence of different sortingand classification processes. The obtained black mass canbe processed by hydrometallurgical processes to produce
metal salts or graphite.
Two established, and largely mechanical processes for
LIB recycling are the so-called LithoRec and Accurec
processes. The LithoRec process was developed within theframework of public funding in Germany and subsequently
commercialized within the company Duesenfeld GmbH,
Wendeburg, Germany. The developed process chain, according Fig. 3(E) and Fig. 4, combines deep mechanicalprocess steps with subsequent hydrometallurgy (Hanisch etal., 2015b; Diekmann et al., 2017; Kwade and Diekmann,2018). In a first process step, the battery packs are electrically deep discharged and short-circuited. Material that has
already been removed, such as the casing, battery management system and cables, can be forwarded for conventional
recycling. The battery modules or cells are shredded in a
4-roller shredder or a rotor shear, whereby product sizesof 10 to 20 mm have been shown to be advantageous forthe subsequent process steps according to Hanisch etal. (2015b); Kwade and Diekmann (2018). Due to thepresence of electrolyte components (Ethylene carbonate(EC), Ethyl methyl carbonate (EMC), Dimethyl carbonate(DMC)), which pose in example a risk of flammability, it
is recommended to carry out dry crushing in a nitrogen
atmosphere (Kwade and Diekmann, 2018; Diekmann et al.,2020; Doose et al., 2021a). The comminution is followed
by a drying step to remove electrolyte components. At
temperatures of 100–140 °C, the highly volatile components DMC and EMC can be removed and recoveredfrom the comminuted material (Stehmann et al., 2018).
Alternatively, this can also be carried out in a vacuum
drying process at reduced temperature, or at evaluatedtemperatures of more than 200 °C under inert or vacuum
atmosphere (pyrolysis). The aim is to remove as much of

Fig. 5 Change of the cumulated distribution of fragments of a battery
module after different process steps. Reprinted from Ref. (Diekmann etal., 2017) under the terms of the CC-BY 4.0 license. Copyright: (2017)
The Authors, published by Electrochemical Society.

the carbonates as possible, but to avoid the formation ofreactive hydrogen fluoride (HF) from the conductive salt
LiPF6 and PVDF binder. An optional mechanical mixing
tool, used within the drying process, can also increase thedetachment of active material from the conductor films, aswell as the uniformity of thermal treatment of the material
to be dried. This step is followed by mechanical separationof the present materials by way of a combination of air
sifting by zig-zag-classifier, shredding and further sieving
steps. In the first sifting step, components of the moduleand cell casings are removed from the shredded material.
Subsequently, the remaining electrode foils, separator foil,polymeric components and black mass are shredded in a
cutting mill to a maximum size of 10 mm. In addition, thisprocess further promotes the detachment of black massfrom the current collector foils. In a downstream air-sieving
step, with sieve widths of 500 μm, the black mass isseparated from the remaining fraction. The influence ofadditional 2nd comminution and separation of the components can be seen in Fig. 5. As the process depth increases,
the percentage of fine material increases, which is mainly
due to the increasing amount of black mass detached fromthe electrode foils. The black mass obtained can then be
fed to hydrometallurgical processing for the recovery of
Co, Ni, Mn and Li. In addition, the remaining fraction isseparated from separator foil in a further sifting process
and decoated aluminum and copper foil as well as plastic
components can be forwarded for further commercial
recycling (Diekmann et al., 2017; Kwade and Diekmann,55

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2018). The combination of these mechanical processes canresult in more than a 90 % recovery of the black mass.
Another process already commercially established isthe so-called Accurec recycling (Accurec GmbH, Krefeld,
Germany) process for LIB, according to Fig. 3(B). The
process combines thermal (pyrolysis, pyrometallurgical),mechanical and hydrometallurgical processes to recovervaluable materials from LIBs. The process begins with theseparation of battery cells and casing materials, as well
as BMS and cables. These are subsequently fed into theconventional recycling processes. In the next process step,
the dismantled battery cells are deactivated by means of athermal vacuum step. In this batchwise process step, thecells are safely deactivated under vacuum with up to 600 C,
in order to remove electrolyte components and volatilehydrocarbons (Brückner et al., 2020; Georgi-Maschler etal., 2012). The pyrolysis material obtained is subsequently
fed into a milling process in order to separate the valuable
materials from each other and make them accessible formechanical separation processes. The processed material
is separated in several separation steps with the help ofvibrating sieves, magnetic separators and sifters. The exact
process sequence and parameters are not described in detailin literature. With this process chain, the electrode active
materials can be separated from iron, nickel, copper andaluminum components and processed separately in thefollowing processes. The electrode active materials aresubsequently pyrometallurgically processed at 800 °C under reducing conditions (e.g. regarding contained graphitefrom the anode) to form an alloy that may contain nickel,
copper, cobalt and iron. The excess slag may contain lithium. Both fractions can subsequently be hydrometallurgically processed for high-value production, and transferred
to a new application.
In addition to these two recycling process chains, there
are further processes based essentially on mechanicalprocessing that have already been developed, or are indevelopment. For example, the Battery Resources “Closed
Loop” process (Gratz et al., 2014), the OnTo Technology,
and others that have already been established and whosefunction has been demonstrated.

2.2.4 Mechanical-pyrometallurgical processing

Commercially, the pyrometallurgical process route forrecycling of LIBs has been established and implemented
for the longest. In this process chain, the battery modules
or cells can be processed directly by the pyrometallurgicalhigh temperature treatment (see (A) in Fig. 3), or subjected
to mechanical processing beforehand (see (C) and (D) in
Fig. 3). With regard to an increased overall recycling rate, it
has been shown to be advantageous to carry out mechanicalprocessing beforehand (Brückner et al., 2020). Mechanicalprocessing, from removal of the casing, to comminutionof the modules and cells, and separation of the valuable

56

materials into individual components, can be carried out
analogously to the process steps used in mechanical-hydrometallurgical processing routes. The obtained black masscan be fed to pyrometallurgical processing. Of course,whole cells can also be given for further processing after
removal of the casing. Pyrometallurgical processes include
high temperature processes, such as melting and roastingof the materials to obtain battery slag, alloys and kiln dust.
Black mass processing in pyrometallurgy begins with aheating phase in the range of 150–500 °C, during whichelectrolyte, binder and other remaining polymeric constituents are removed. In the next process phase, under reducing
conditions, temperatures of up to 1450 °C are reached (bydecomposing graphite in the black mass, coke, NH4Cl,
CaCl2 and/or NaHSO4), and the products obtained are battery slag with Li-containing components LiO2 and Li2O3
and alloys. Ni, Co and Cu can be individually removed
from the process and recovered, due to their different
melting points. However, the other valuable materials (Li,
Mn and Al) are contained in the kiln slag and/or the dust,
and can only be recovered at high cost. The alloy, as well as
the slag, and dust obtained are subsequently be processedhydrometallurgically, usually after milling.
As an industrial example, the process of Umicore
ValéasTM (Bruxelles, Belgium) can be cited. The process
is focused on the recovery of the metals Co and Ni from
LIB and NiMH batteries, and is a combination of mechanical, pyro- and hydrometallurgical processes (VelázquezMartínez et al., 2019). Compared to hydrometallurgicalprocessing the pyrometallurgical processing of LIBs isvery insensitive against different battery types, changing
feed material and impurities. However, a subsequenthydrometallurgical treatment of the pyrometallurgicalproducts obtained is necessary after pyrometallurgical processing, since intermediates that have no further industrial
application are primarily produced (Brückner et al., 2020).

3. Active material production

To enable high growth in battery cell production, theavailability of battery grade primary and secondary raw
materials in form of metal salts, such as carbonates,
sulphates and hydroxides (as basis for the cathode active
materials), and graphites and silicon (as basis for the anodeactive materials) are essential.

3.1 Cathode active material

Common cathode active materials (CAM) are lithium
metal oxides (e.g. LiNi1–x–yMnxCoyO2 and LiNi1–x–yCoxAlyO2,
known as NMC and NCA, respectively) or lithium iron
phosphates (e.g. LiFePO4, so-called LFP). The mean size,
size distribution and morphology of CAM particles have aremarkable influence on the final structure, as well as on
the resulting electrochemical performance of the lithiumion battery (Ju and Kang, 2008; Sinha, 2009; Du et al.,Arno Kwade et al. / KONA Powder and Particle Journal No. 40 (2023) 50–73

2010). In fact, lower CAM particle sizes have shown tobring advantages, such as improved charge and discharge
rates due to an increased electrolyte-CAM contact area.
However, there are also some disadvantages, such as theincrease of undesired side reactions due to a larger specificsurface area. As a consequence, the ongoing formation anddevelopment of the solid electrolyte interface can occur,
leading to self-discharge of the cell and thus, unwanted
safety issues (Sinha, 2009). More specifically, targeted
particle design has been addressed and directed throughdifferent techniques, the most common being milling andclassification processes at various stages of CAM production.
The synthesis of CAM can be performed via different
techniques. For NMC materials, for example, precipitation
reactions forming hydroxides (Cao H. et al., 2005) or
carbonates (Guo et al., 2011; Zhang Y. et al., 2006), solgel syntheses (Peng et al., 1998), hydrothermal syntheses(Burukhin, 2002), syntheses via spray drying, combustion
reactions (Dahbi et al., 2012) and solid-state reactions (Ganet al., 2005) have already been investigated, achieving
different product qualities. In addition, there are further
methods for coating (Lu et al., 2013), doping (Naghash andLee, 2001) and modification (Hashem et al., 2011) of thesurfaces in order to enhance properties such as specific capacity, thermal stability or ionic conductivity. An overview
of the process chain for the production of CAM, and thefinal application of milling and classification processes for
precise control of particle size, is presented in Fig. 6.
Liu N. et al. (2014a) used a ball mill at the beginning of
the synthesis route in order to pretreat a LiFePO4-precursor
suspension (300 rpm with zirconia grinding balls of 1 mm
diameter). This led to higher homogeneity and narrower
size distribution in the subsequent spray drying synthesis
process, which was combined with a carbon-thermal
reduction to finally produce LiFePO4/C (carbon coating).
They also demonstrated that smaller particles were cor-

Precursor Synthesis

Fig. 6

Lithiation

Particle Refinement

Couette-Taylor
Flow Reactor

techniqueorco-precipitation

reactant

CSTR

related with a higher electrochemical performance. The
initial discharge capacities of samples with particle sizesof about 12 μm reached about 158 mA h g–1, whereas thoseparticles with sizes of 1.5 μm achieved 167.6 mA h g–1.
They assumed that there was a decrease in the surface resistance, along with an improvement in the surface electronic
conductivity, because of more homogeneous and complete
carbon coating. Also, in this line of research work, Li H.
et al. (2021) investigated the influence of ball milling after
producing the precursor material by a co-precipitation
process. After calcinating, the cathode material tailoredby milling (Li1.2Ni0.13Co0.13Mn0.54O2) showed improved
electrochemical properties.
Furthermore, ball milling is used to perform solid statesynthesis by mixing and grinding all precursor materials
at the same time (Cambaz et al., 2016; Kang et al., 2008;
Yudha et al., 2019; Zhang Y. et al., 2006). A detailed understanding of the material manufacturing process can befound in the published studies of Kang et al. (2008) andZhao et al. (2016), who varied the milling time in order toproduce LiFePO4 and LiFePO4/C respectively. Kang et al.
demonstrated that small crystallites are achieved throughlonger milling periods. Based on their results, they proposed that CAM particles with very low sizes may hinder
the insertion and extraction of Li+ ions, and thus decrease
the electrochemical performance. Nevertheless, by finding
the optimum parameters of milling, along with optimized
heat-treatment parameters, they were able to produce
LiFePO4, which demonstrated a stable cycle performanceand a discharge capacity of 160 mA g–1 at 0.1 C. Zhao et al.
showed similar results, and additionally discovered that theeffect is more significant at low temperatures (Zhao et al.,2016). This is caused by a hindrance of the lithium ion diffusivity, and an increment in the charge transfer resistanceat the interfaces between electrode and electrolyte.
Furthermore, it has been demonstrated that calcinating
the particles leads to an increase in their sizes, comparedprecursor solution +precipitation agent +chelating agent

dry mixing

calcination

metal-aggregates +Li-salt

stirring

or ball milling

cathode active material

Process chain for the production of cathode active materials by co-precipitation, mixing, calcination and further conditioning.

57

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to the precursor material used, which results in a decreased
specific capacity (Martha et al., 2009). Taking all thesefacts into consideration, controlling the final particle size
distribution via a subsequent milling and classification processes is the key motivation of many studies (Ibarra-Palos
et al., 2005; Kim et al., 2011; Stein IV et al., 2016). Millingparameters (e.g. process time, grinding media size andmilling speed) have a direct impact on the size and morphology of the processed particles, a topic which has also
been tackled by some researchers. In combination withcomminution as a downstream process after synthesis,
different classification processes are also used to achieve
the target particle size. In particular, Stein IV et al. (2016)
described the relationship between crystallite size andprocess parameters. Similar to the work of Kang et al.
(2008), the smallest crystallite size did not lead to the best
electrochemical performance, which proved to be significantly dependent on local structures within the electrode.
These interdependencies between the grinding process andstructural properties of active materials were thoroughly
investigated by Pan et al. (2019). They varied the process
times and identified relevant relationships with regard
to the processing of NMC 442 cathode materials. Basedon this study, it can be concluded that a reduction of theparticle size can also be accompanied by structural changes
in the form of a disordering of the layered structure, whichthen results in higher coulombic inefficiencies and capacity
fading during cycling.

3.2 Anode active material

Graphite (natural and synthetic) is the most widely used
active material on the anode side for lithium-ion batteries
because of its good reversibility, stable voltage profile andgood processing capabilities (Asenbauer et al., 2020). The
high reversibility is due to the layered structure of graphite,in which the lithium ions can intercalate during charging
and deintercalate during discharging of the battery (Yazami

and Touzain, 1983). The intercalation process takes placestep-wise, which is reflected as plateaus in the voltage
profile. At the fully lithiated state, the theoretical specificcapacity of graphite totals 372 mAh g–1 (Asenbauer et al.,2020). Despite its good reversibility and high specific capacity compared to the CAM, the anode is still in the focus
of interest to increase the energy density (Wh/L) and performance of the cell. The two major research topics on theanode side are the increase of the specific capacity by using
new active materials, like silicon or tin, and the reductionof the initial capacity loss from the formation of the Solid
Electrolyte Interface (SEI). The SEI is formed in the firstcharge step as a consequence of the instability of the electrolyte in the voltage window of the anode, and consumes
lithium, thus, reducing the amount of active lithium in thecell and the capacity (An et al., 2016; Ng et al., 2009). Thisinitial loss of lithium is directly correlated to the specificsurface area on the anode side, which should therefore be
minimized (An et al., 2016; Wang H. et al., 1999).
In order to decrease the specific surface area, two different processes are applied: surface coating and mechanical spheronization of graphite particles. By coating thegraphite particles with an additional layer, the nano- andmicron-sized pores can be closed, and hence the specificsurface area is decreased (Yoon et al., 2001). This process
is only reasonable for blocking smaller pores, otherwise thecoating thickness would become too large, with negative
impacts on the ion transport and energy density (Asenbauer
et al., 2020; Casino et al., 2020). In order to change theparticle morphology, a mechanical spheronization can beused. An overview of the mechanical treatment of anodeactive materials is shown in Fig. 7.

3.3 Spheronization of graphite

Normally, graphite exhibits a flake-like structure, consisting of multiple stacked layers of graphene with a large
thickness in the horizontal direction (basal plane) andSpheronizedgraphiteA) Graphite
Classifier millB) Silicon

Si
Si Si

Nano siliconStirred media millSiliconSi
Si Si

Carbon coated
Silicon graphite
compositeC)
Graphite
Fluidized bed

Oven

Fig. 7 Overview of possible treatments of anode active materials; A) Mechanical spheronization of graphite, B) Nano-grinding of silicon, C) Preparation of silicon graphite composites. Adapted with permission from Ref. (Müller et al., 2021). Copyright: (2021) Elsevier B.V.

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low thickness in the vertical direction (Lämmerer andFlachberger, 2017). By spheronization using a series ofimpact milling and classification process steps, the graphite
morphology is modified to a more spherical shape, often
referred to as “potato”-like shape (Asenbauer et al., 2020;
Lämmerer and Flachberger, 2017).
This resulting structure, with a reduced specific surface
area, leads to various benefits compared to the pristinegraphite material, e.g. higher tap-density (Asenbauer et al.,2020; Kwon et al., 2021; Lämmerer and Flachberger, 2017),
lower electrode tortuosity (Ebner et al., 2014; Müller et al.,2018) and increased energy density (Müller et al., 2018).
Furthermore, the winding of the graphene layers during
spheronization leads to more superficially localized edge
planes, enabling new active sites at the surface for lithium
ion intercalation. This facilitates faster ion transport and,thus, a higher rate capability and specific capacity for the
spheronized graphite particles (Lämmerer and Flachberger,
2017; Natarajan et al., 2001; Wu et al., 2011). In addition to
the reduction of specific surface area, spheronization also
diminishes and narrows the particle size distribution, both
enabling a better cell performance and preventing lithium
plating (Bläubaum et al., 2020).
However, with increasing stressing time within the impact mills, more and more basal planes are destroyed andthe highly reactive edge planes created at the surface areprone to higher SEI growth and the exfoliation of graphiteby the electrolyte and, therefore, diminishes the capacity
again (Wang H. et al., 1999). Moreover, it was found thatthe open porosity in the structure (accessible for the electrolyte) increases, but the closed porosity (not accessible
for the electrolyte) decreases with increasing stressing
time (Mundszinger et al., 2017). Consequently, the processparameters and stressing times have to be carefully selected
for optimized particle design and cell performance. Moreover, the spheronization is often combined with subsequentcarbon-coating, to further decrease the specific surface area
and contact of the superficial edge planes with the electrolyte (Lämmerer and Flachberger, 2017).
The preparation of these spherical graphite particles isoften carried out via impact stressing of the graphite particles in fluidized bed mills, jet mills or classifier mills. In
industrial applications, a cascade of several dry-operated
classifier mills, as shown in Fig. 7 A), is mostly used.
Within these classifier mills, a disc with grinding tools ina rectangular shape, or shaped as rounded blocks, rotatesaround a central classifier wheel. The grinding tools canbe adjusted towards the breakage or deformation of themilling feed. For the purpose of spheronization, a roundedshape is preferred so that the particles are only plastically
deformed, or the edges broken off. The particle fragments
are removed by the integrated classifier, and the spheronizedproduct remains in the process chamber. In the standard
type of operation, the spheronization is carried out in acascade of up to 20 mills. This process is, however, ineffective, because of its high operation cost, difficult scalability
and low flexibility for change of product (Northern Graphite). The final yield amounts for only 30–50 %, meaningthat more than half of the graphite used initially exits theprocess as fine powder, which cannot be used as active
material anymore (Northern Graphite) (Steinrötter, 2011).
As previously discussed small particles exhibit a highspecific surface area which enables on the one hand higher
charging and discharging rates without lithium plating
but on the other hand increases the irreversible capacity
loss due to strong SEI growth (Bläubaum et al., 2020;
Utsunomiya et al., 2011) and necessitates the usage of morebinder leading to a decrease of energy density on electrodelevel due to larger inactive material content (Landesfeind
et al., 2018). Furthermore, it was found that narrow particle
size distributions in the single-digit micrometer range(x10 ≈ 4 μm, x50 ≈ 6 μm, x90 ≈ 9 μm) result in the best
cell performance, among others highest specific capacity,
cycling stability and C-rate capability (Bläubaum et al.,2020). Therefore, both mean particle size and width ofparticle size distribution are important factors for achieving
a maximum possible cell performance.
Therefore, new classifier mills (e.g. FACULTY® S
series from Hosokawa (HMC, 2021) and GyRhoy® from
Netzsch) have been developed recently, which are able toreplace these mill cascades and achieve the same throughput by reduced energy consumption and higher productyields. In contrast to the mill cascades, these mills operate
in a batch or semi-batch mode.
Obrovac et al. (2020) developed a new simple method
for spheronizing graphite particles by using ZrO2 as hostparticles in a dry micro-granulation. The graphite flakes
roll up around the ZrO2 particles and form an additional
layer with increasing thickness over time. At one point,
this graphite layer becomes instable and flakes off as“potato”-shaped particles, which are then further rounded.
Several other processes have been developed for the
spheronization of graphite on laboratory scale, demonstrating superior characteristics of the structures obtained(Kwon et al., 2021; Lai et al., 2012; Wang H. et al., 1999).

3.4 Silicon: Next generation anode material

Silicon is a key material for many different application
fields, because of its outstanding physical properties. It is
the second most abundant element in the earth’s crust, andeasily accessible. In the past ten years, the rising popularityof lithium-ion batteries (LIBs) led to growing interest in
the use of silicon as an anode material, as the theoreticalspecific capacity of silicon (3580–4200 mAh/g) is roughly
ten times higher than that of graphite (372 mAh/g) (Ashuri
et al., 2016). During the charge/discharge cycles, silicon
undergoes significant volume changes (up to 300 %).
This leads to breakage of the silicon particles, resulting in

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continuous exposure of the active silicon surface and, thus,
Lithium consumption through surface reactions. Moreover,the electrode structure becomes damaged, and results inloss of connection between particles and current collector,
and destruction of the SEI. In the end, a silicon based
battery shows poor cycling stability (Hu et al., 2014; Sunet al., 2016a).
In order to overcome the challenge of volume expansion and particle cracking, the failure mechanisms ofsilicon-based electrodes need to be understood in orderto create a fully functioning silicon-based anode. For thispurpose, the mechanisms during cycling of the battery
must be investigated (Szczech and Jin, 2011). Sun et al.
(2016a) discovered that pulverization of silicon particles
and electrochemical passivation of some silicon particles
lead to failure of the battery cell during lithiation. Liu andHuang (2011) investigated the charge and discharge behavior of batteries using silicon possessing varying sizes
and structures. They could show that a critical particle
diameter of 150 nm exists, at which no cracking during
volume expansion occurs. This critical particle diameter ismentioned and confirmed in other publications (Liu X.H.
et al., 2012; Sun et al., 2016b; Szczech and Jin, 2011).
This also leads to the design of different nano structures,such as silicon based nanowires (Zhang C. et al., 2013a),nanotubes (Wu et al., 2012), nanosheets and nanoporous Si
particles. Even though nano-sized silicon is able to maintain its structure without cracking, the problems from thelarge volume expansion (unstable formation of the SEI andcontact loss of particles) still remains, although to a lesser
extent. For that reason, silicon composite materials are targeted as promising solutions. Here, a composite material,
often graphite, is used as framework for nano-sized siliconparticles. The goal is to allow the silicon to expand inside
the composite, while the electrode structure is maintainedby the composite framework. By looking at the literature,
the approach of customized composites is the most promising electrode design, both in terms of the electrochemical
performance, as well as the mechanical integrity of silicon-based battery cells (see Chapter 3.5).
An important challenge is the production of nano-sized
silicon, which can either be achieved via bottom-up or
top-down processes. Bottom-up methods usually dependon chemical approaches, and often require long reaction
times, expensive chemicals, complex process control andup-scaling difficulties (Hou et al., 2015; Nilssen et al.,2020). In top-down methods, coarser materials are broken
down into smaller fragments by comminution. The choiceof the right comminution machine highly depends on thesize of the starting material.
Silicon is a rather hard and brittle material, rendering itsuitable for grinding tasks. Leblanc et al. (2015) describe acomminution process route from silicon ingots, with sizesof a couple of centimeters down to the nanometer range.

60

First, different silicon feedstocks, such as quartz or gravel(SiO2), were treated thermally in a furnace. By using acarbonaceous material, the silicon feedstock was converted
to molten MG-silicon ingots. Subsequently, the ingots werecrushed and ground in several dry grinding steps comprising of a jaw crusher, a roll mill and a jet mill. After thesemilling steps, the obtained silicon powder was in the range
of a few micrometers (Leblanc et al., 2015). However,silicon in this size range is often already available fromthe semiconductor and photovoltaic industry, which cuts
silicon wafers for their applications and produces pure
micron-sized silicon dust as byproduct.
The subsequent nano-comminution is carried out in asolvent, in order to prevent agglomeration of the particles.
Most commonly, stirred media mills are used for this task
(Fig.7 B)), and in the lab scale planetary ball mills may
be employed. This type of mill was intensively investigated by researchers over the past decades (Breitung-Faes
and Kwade, 2008; 2014; Knieke et al., 2010). Hou et al.
(2015) applied a stirred media milling process for silicon,
and investigated the structural changes and their impact
on battery performance. They report that the bead milling
strategy improved the battery performance because ofamorphization of crystal structure, the formation of anoxidized surface (SiO2) and smaller particle size compared
to micrometer-sized silicon. Early investigations of phasetransition in silicon during bead milling were conducted byGaffet and Harmelin (1990). A major challenge in nanocomminution is the stabilization of the ground particles.
Here, the choice of an appropriate solvent is essential.
Reindl et al. investigated the impact of dispersing silicon
nanoparticles in different organic solvents, such as 1butanol or toluene (Reindl et al., 2007; 2008). They foundthat the silicon particles have a pronounced tendency toagglomerate in organic solvents. They could stabilize thesilicon nanoparticles by using fish oil as a steric stabilizing
agent (Reindl et al., 2008). Nöske et al. (2019) applied astirred media mill for the nano-comminution of siliconin ethanol. They performed an electrostatic stabilization,by preparing a sodium hydroxide solution in ethanol. The
analysis of particle size and zeta-potential revealed thatat pH*-values greater than 7.5 the silicon nanoparticles
can be stabilized, and do not agglomerate. As a result,they suggest a two-step grinding process and electrostatic
stabilization at pH* = 7.5. The results of their approachcontaining a pre- and fine grinding step can be seen in
Fig. 8. Considering both grinding steps, the final product,
particles with a size of x50,3 = 75 nm, is obtained after a
specific energy input of 106 MJ kg–1 (Nöske et al., 2019).
The impact of electrostatic stabilization was also reported by Švrček et al. (2005). They claim that nano-sized
silicon particles in ethanol could be stabilized by adding
ammonia. Additionally, Nilssen and Kleiv (2020) investigated the impact of stabilization on the efficiency of siliconArno Kwade et al. / KONA Powder and Particle Journal No. 40 (2023) 50–73

Fig. 8 Primary particle size vs. specific energy input for the two-step
grinding of silicon proposed by Nöske et al. (2019). Reprinted from
Ref. (Nöske et al., 2019) under the terms of the CC-BY 4.0 license.
Copyright: (2019) The Authors, published by Electrochemical Society.

nano-comminution in stirred media mills. They could showthat the lack of sufficient stabilization leads to an increasein the viscosity of the slurry. However, they also reportthat the non-stabilized slurry did not have a significant
impact on the grinding efficiency. This was attributed tothe pseudoplastic rheology of the slurry and high shear
rates in the grinding chamber. Moreover, they investigated
the importance of optimal stress energy on the grindingof silicon by varying the stirrer tip speed and size of thegrinding media. Their parameter combinations suggest thatat the lowest specific energy input, an optimum exists ata point of low stress energy (Nilssen et al., 2020; Nilssen
and Kleiv, 2020). From a production point of view, thismeans a longer overall grinding or production time, butless product contamination caused by grinding media wear.
The impact of wear from silicon grinding on battery performance is still being investigated. In general, the choice ofappropriate grinding parameters is a compromise between
productivity and product purity (Nilssen et al., 2020).

3.5 Silicon composites

Even though silicon is the most promising anode material for the future, silicon has only been applied in small
amounts (typically <5 wt %) in commercial cells (Choi and
Aurbach, 2016). As previously discussed, this is mainly
due to the challenges arising from the large volume changeof silicon during lithiation (up to 300 %), whereas graphite
shows only minor volume expansion of 10 %. Therefore,
the combination of silicon and graphite has attracted muchattention. While graphite with its high electrical conductivity, good cycling characteristics and low volume expansion
provides a stable matrix, even small amounts of silicon cansignificantly enhance the capacity. This can be illustrated
by the fact that by using 10 wt % silicon, the specific capacity of the anode can be nearly doubled.

In current commercial cell manufacturing, small silicon
contents are simply blended with the graphite active material during electrode production (Choi and Aurbach, 2016).
However, when using larger silicon contents, the resulting
cell performance is poor and requires an optimized particle
design. Therefore, multiple composite structures for siliconand silicon graphite composites have been developed,
and been proven to be beneficial for capacity retention.
These structures can be divided into three main approaches(Franco Gonzalez et al., 2017; He et al., 2021; Jin et al.,2017): nanostructures, porous and other 3D-structures and
Si/SiOx composites.
Two different composite structures comprising silicon
nanoparticles are possible: a core-shell structure with alayer of silicon nanoparticles on graphite, or the encapsulation of the nanoparticles (Li M. et al., 2014; Müller et al.,2021; Sui et al., 2018). Müller et al. demonstrated a good
performance, for which silicon nanoparticles are prepared
beforehand by grinding in a stirred media mill, and thencoated onto coarser graphite particles by scalable fluidized
bed granulation (Si@Gr). After subsequent pitch-coating(Si@Gr/C), the composite demonstrated good cyclingstability and rate performance (Müller et al., 2021). The
manufacturing process of these composites is shown in
Fig. 7 C).
Additionally, silicon nanoparticles can be encapsulated
in hollow carbon spheres. The silicon particles can expandin the highly conductive carbon matrix without any volume
change of the composite particle itself. This characteristic
is advantageous, because it prevents electrical isolationeven after pulverization and allows for a stable SEI layerand superior cycling characteristics (Liu Y. et al., 2014b).

4. Electrode slurry production

The production of electrodes initially requires the classical mechanical process of dispersive mixing to obtain acoatable slurry (Kwade et al., 2018; Liu H. et al., 2021;
Wenzel et al., 2015). In this context dispersive mixing
means that beside a homogenization of the material mixture (i.e. a distributive mixing) material components are
reduced in their size and changed in their structure by mechanical stresses and, thus, comminution of these material
components. Here the fine carbon black (CB) agglomerates
and aggregates have to be reduced in size by breaking off
solid bridges and van der Waals based adhesion between
the CB primary particles. Typically, after a dry homogenization, and an optional dry intensive, i.e. dispersive
mixing, the powder components (active materials (AM),conductive agents like carbon black (CB) and polymericbinders) are mixed with a solvent and homogeneously
distributed in it. During the dispersive mixing, the main objective is to fragment the carbon black agglomerates, and tocomminute CB aggregates to defined sizes and structures,without damaging the other components (Bockholt et al.,61

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2016b; Wenzel et al., 2015). Particularly in combination
with polymer binders, the size and distribution of CB
defines the formation of the microstructural network in theslurry, and later the electrode (Gaikwad and Arias, 2017).
This microstructure determines the resulting electrodeproperties and the cell performance (Bockholt et al., 2016b;
Zhang C. et al., 2013a), which turns the process steps ofdispersive mixing into a central and property-defining partof the knowledge-based production of high quality LIB
electrodes. The type of conductive additives and binder
systems used will vary depending on the electrode type
(Zhang Q. et al., 2013b). For example, polyvinylidene fluoride (PVDF) binders in combination with different typesof CB and conductive graphite (CG) are used in cathodeslurries. According to recent literature, cathode slurries aremostly N-2-methylpyrrolidone-(NMP)-based, while anodeslurries are processed with water, using a binder systemof carboxymethyl cellulose (CMC) and styrene butadiene
rubber (SBR) (Gordon et al., 2020; Kwade et al., 2018).

4.1 State of technology

Due to the highlighted significance of size and distribution of conductive additives for the microstructural formation, the characterization of conductive additives before,
during, and after mixing is of particular importance. Thus,a methodology via light diffraction and scattering for determining the size of CB particles from electrode slurries,
including a sample preparation routine, was developed
and tested (Dreger et al., 2017). In addition, other studies
identify the condition and effects of CB via electrical,
viscometric, imaging, or ultrasound scattering techniques.
During dispersion, the formulation strategy and the processparameters determine the particle size and distribution(Wenzel et al., 2014). Thus, numerous publications focus
on the relationship of processing and electrode slurry
properties. For example, the importance, and subsequentconsequences, of formulation strategies were shown. Here,
optimal electrochemical rate performance was achieved
due to ideal electrode porosities by utilizing moderate
dispersing intensities for dry and binder type formulations(Haselrieder et al., 2014). In order to set the CB size, anadditional dry mixing step prior to dispersion in a (high)intensity mixer can be applied: prolonged exposure of
the components leads to a lower density of the coating
due to increased deagglomeration of CB, and ultimately
to increased adhesive strengths and improved discharge
capacities and cycle stabilities (Bockholt et al., 2016a).
These effects generally result in lower porosities and higher
cohesiveness of the electrode coating. Additionally, CB attaches to the AM particle surfaces, establishing short-range
electrical contacts all around the NMC particles (Bauer etal., 2015; Bockholt et al., 2016a). These CB-coated AM
particles can also be achieved by dispersion processes
without prior dry mixing. For example, kneading processes

62

in planetary mixers are suitable for high-intensity stressingof slurries at high solids contents (~80 wt % and above),favoring very small sizes of CB in the range of 100 nm,
which are particularly important for the formation ofshort-range conductive paths (Mayer et al., 2020). Processcontrol in batch mixers concerning the dispersing intensity is also proving to be very important for optimizing
electrodes, specifically with thicker layers for high-energy
applications (Kremer et al., 2020).
For the dispersive mixing, the specific energy is a key
evaluation value. It can be determined by the power inputof the machine, minus the no-load power consumption,summed over time, in relation to the mass of the slurry.
To highlight the role of the specific energy input, Fig. 9shows exemplary results for CB deagglomeration with adissolver (Dispermat AE03, VMA Getzmann) and a planetary mixer (PMH10, NETZSCH) by relating the decreasing
volumetric median of the typically bimodal particle size
distribution of CB, determined as described by Dreger
et al. (2017), to the specific energy. The influence of thedispersing machines is directly evident: In the planetary
mixer, the mixing tool velocities affect the final particle
sizes of CB that can be achieved. In the dissolver, however,the particle size is exclusively dependent on the specificenergy. Additionally, the dissolver shows a higher energy
efficiency, but is less suitable for scaling to industrial standards due to its poorer distributive mixing properties.
Based on such experimental results, model-like descriptions of the dispersion of LIB slurries can be created purely
empirically (Grießl et al., 2021), or by means of classicalrelationships of particle stressing in slurries (approaches
according to Kolmogorov (1958); Krekel (1966); Kwade(2003)). Dispersion models enable knowledge-based scaling of the process machines, and converting discontinuous

Fig. 9 Dependence of CB size on specific energy of the dispersion
process for a planetary mixer (dashed lines) and dissolver (full line).
Symbols indicate the progressive dispersion time (15, 30, 45 and 60 min)for each test series from left to right.

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into continuous processes, which will become increasingly
important in the future.

4.2 Future perspective

In order to increase the economic efficiency of the dispersion process and, thus, enable large-scale production,even at labor expensive locations, the conversion of typicalbatch mixers used industrially into continuous mixing
processes is advised (e.g. using twin-screw extruders(Schunemann et al., 2016)). Accordingly, the applicationof extruders for electrode production is increasingly moving into the focus of research and process development.
For instance, a laboratory-scale dissolver process wascompared to a discontinuous kneading and a continuous
extrusion process. The continuous production of slurries
by the low-solvent extrusion processes exhibit good electrochemical performance, showing good deagglomeration
and almost complete coverage of active material particles
by CB (Dreger et al., 2015). Further investigations showed
the decisive influence of the solids content on the extrusion-based production of cathode slurries. Higher solids
contents reduce sedimentation effects and binder migration.
In addition, they define the fragmentation of the conductive
CB and, thus, the resulting properties of manufactured
cathodes (Haarmann et al., 2020). Other works show theuse, or combination, of batch and continuous processes.
The powder components are prepared for a subsequent extrusion-based coating process by means of intensive mixers
and kneaders, whereby the actual dispersion takes place
before the extrusion process (Seeba et al., 2020). Beside
extrusion, the dispersing in continuously operated stirred
media mills was also reported from industry.
On the material side, due to their advantageous properties, fiber-like conductive additives, such as carbon nanotubes (CNT) and vapor-grown carbon fibers (VGCF), areincreasingly used in research and industrial applications as
a supplement, or a complete replacement, for conductive
CB (Landi et al., 2009; Park et al., 2018; Qi et al., 2015).
CNTs are only needed in small quantities, but are in somecases more expensive than CB (Bauer et al., 2015). LIB
cathodes with CNTs show low initial Coulomb efficiencies and large voltage hysteresis, but exceedingly highelectrical conductivity (Bauer et al., 2015). This advantage
results from the ability to connect several active material
particles within the electrode structure due to its lengthand also obtain an improved cycle stability and C-rate
performance (Huang et al., 2010; Zhang Q. et al., 2013b).
For the dispersing of the fiber-like conductive additives,
high-pressure homogenizers are a well-suited alternative
to conventional equipment. In terms of cathode active
materials, adaptations will be necessary in the future for the
more nickel-rich layered oxides (e.g. NCM811), which areboth sensitive to moisture and mechanically more unstable(Heck et al., 2020).

5. Outlook—Solid electrolytes
5.1 Mechanochemical synthesis

In order to enable scalable production of solid-state batteries, solid electrolytes are required in sufficient quantities
and with high and consistent quality. Although planetaryball mills, vibrating ball mills, and stirred media mills
should all be suitable for mechanochemical syntheses ofsolid electrolytes, so far planetary ball mills have almost
always been used on a laboratory scale (Schlem et al.,2021b).
The reasons might be the simple experimental setup,
as well as the wide-spread use. As their ability for largescale synthesis is quite poor, a transfer to continuous ball/bead mill types (e.g. stirred media mills) seems desirable.
Most publications on mechanochemical routes consider
the effect of processing parameters and conditions for theconversion time, instead of energy input. As this makes it
difficult to distinguish between effects of energy dissipation
and activation thresholds, and simply faster mechanicalprocessing, further investigations with focus on process
technology are still required.
Among solid electrolytes, thiophosphates and sulfides
represent the material system considered the most promising class of materials, with very high Li ionic conductivities
up to 10–2 S/cm and beyond (Kato et al., 2016). They areusually produced by classical solid-state syntheses via dry
process routes (Dietrich et al., 2017; Kraft et al., 2018), or
via solvent-based processes (Ghidiu et al., 2019). To ensuresufficient ionic conductivity, σion, and electrochemical stability (e.g. against high-voltage cathode materials), the absence of possible impurities is especially crucial. The lattercan result from solvents used in the synthesis process andnecessitate thorough washing (Kudu et al., 2018). Whileclassical solid-state synthesis in sealed quartz ampoules
does not seem suitable for scalable production (Kudu etal., 2018), mechanochemical synthesis within mills is apromising approach. Compared to the alternative methods,
the products have predominantly amorphous structures,
due to the kinetically controlled reactions, but can often be
crystallized by a subsequent annealing step (Gautam et al.,2019). Sulfide electrolytes can be prepared as pure Li-P-S
phases from starting materials Li2S and P2S5 with different
stoichiometries (Dietrich et al., 2017; Tatsumisago et al.,2002), or in combination with metals (e.g. Si or Ge), or
halides (Cl, Br, I) (Zhang Z. et al., 2018). Compared toclassical solid-state syntheses, mechanochemical methods
allow the synthesis of additional metastable compounds
and local structures (Stöffler et al., 2019). Of particularimportance are the argyrodites, Li6PS5Cl and Li6PS5Br, as
well as their variants with iso- and aliovalent substitutions,in whose synthesis an annealing step follows a ball-milling
process (Kraft et al., 2018; Rao and Adams, 2011). A significantly poorer σion is exhibited by Li6PS5I (Hanghofer etal., 2019), although σion here can be increased by two orders

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of magnitude by subsequent “soft” ball-milling of 120 min
at 400 rpm (Brinek et al., 2020). This is attributed to thetransition from micro- to nanocrystallinity. However, it isby no means the case that mechanochemical stress leads toan improvement of the material in every case: as shown byDewald et al. (2021), ball-milling of Li6PS5Cl synthesized
via classical solid-state reaction leads to a decrease in σion.
Among solid electrolytes for sodium batteries, Na3PS4
is frequently synthesized mechanochemically (Nguyen
et al., 2019; Takeuchi et al., 2018). Nguyen et al. (2019)
thoroughly studied the influence of milling media size, discrotation speed, and process time in a planetary ball mill.
They found that the same σion can be obtained by different
synthesis conditions. The mechanochemically prepared
compounds mostly have higher σion, which is attributed todifferent achievable crystal structures, as well as disorderin the structure of Na+ (Krauskopf et al., 2018). Mechanochemical methods are also well suited for introducing defects by doping, achieving σion as high as 41 mS/cm (Fuchs
et al., 2020; Hayashi et al., 2019). The relationship between
the structure influenced by the mechanochemical processes—micro-, macrostrain, and defects—and ion transport is studied in detail by Famprikis et al. (2020).
Another class of solid electrolytes that can be produced
via mechanochemical reactions are halides (Asano etal., 2018; Li X. et al., 2019). They provide high electrochemical stability against cathode active materials, aswell as mechanical deformability at room temperature(Li X. et al., 2020). Schlem et al. (2020) showed thatfor Li3ErCl6 and Li3YCl6, cation disorder induced byball-milling leads to an increase in σion. In contrast, for
Li3YBr6 the σion can be increased by subsequent annealing(Schlem et al., 2021a). Recently, it was demonstrated thata halide superionic conductor without rare earth metals,
namely Li2.25Zr0.75Fe0.25Cl6, with a Li+ conductivity up to≈1 mS cm–1, could be prepared in a planetary ball mill
(Kwak et al., 2021).
In order to combine the advantages of different typesof solid electrolytes (i.e. the high σion at room temperatureof inorganic electrolytes and the good processability ofpolymer electrolytes) so-called hybrid electrolytes areproduced. For example, the reaction of 70Li2S·30P2S5(mol%) and different –OH group terminated oligomers ina planetary ball mill can lead to the formation of P–O–C
bonds. To combine an oxide electrolyte with polymer,
LLZO was mixed with an acetonitrile solution of PEO and
LiClO4, and subsequently uniformly homogenized in a low
energy ball mill (Dixit et al., 2019). The slurry obtained
was subsequently coated via coextrusion, demonstrating ascalable manufacturing process.

5.2 Conditioning

For use in composite cathodes or separators, in addition
to the (electro)chemical properties, the particle size of64

the solid electrolyte is crucial (Kerman et al., 2017). For
example, a particle size smaller than 1 μm allows a more
uniform distribution in the cathode and, thus, a bettercontacting of the active material (Calpa et al., 2019). Dixit
et al. (2020) conditioned the microstructure and interface
between the separator and the metallic lithium anode using
iodine doping, as well as ball-milling and annealing. They
milled an LPS/0.5LiI mixture together with heptane for
12 h, at a rather low rotation speed of 200 rpm, to reduce
the particle size.
In addition, mechanochemical methods can be used toobtain desirable cathode composite microstructures, which
are crucial for the performance of all solid-state batteries(Nagao et al., 2012). A ball-milled cathode composite
for Li-S solid-state batteries consisting of Li6PS5Cl andsulfur-carbon composite outperformed a hand-ground one
with respect to initial capacity, as well as capacity retention(Ohno et al., 2019). This is attributed to the grinding of theactive material, leading to intimate contact in the composite, which helps to mitigate volume changes during cycling.
An alternative to the application of ball mills is dry-coatingof the active material particles with the solid electrolyte
by a dry blending device (Nara Machinery Co., Ltd.) toachieve a high contact area, as well as well-percolated ion
transport pathways (Nakamura et al., 2020).
Graebe et al. (2017) reported the use of a centrifugal
mixer for the preparation of a composite material for asolvent-free manufacture of polyethylene oxide (PEO)
based solid state electrodes. This dry mixing and mechanofusion process step is the key challenging step, as it
ensures a free-flowing powder, necessary to produce adense solid-state cathode or solid-state separator. This isaccomplished by coating the PEO secondary particles with
SiO2 or C65 to prevent further agglomeration.

6. Conclusion and outlook

The increasing demand for lithium-ion-batteries andfuture types of lithium batteries, especially for the ramp-up
of electromobility worldwide, requires the further development of an economically and, at the same time, ecologically sustainable supply and production chain, starting withthe extraction and processing of the primary and secondary
raw materials, over their refinement to battery grade intermediates, to active and passive materials in the productionof the cell components, like electrodes and finally battery
cells. However, sustainability can only be reached if theend-of-life battery systems are used as secondary material
source for the production of new battery cells, throughwhich the recycling of the materials and the re-synthesisor re-conditioning of the active materials, especially forthe cathode, becomes mandatory. Accordingly, regulations
will be issued by the governments, like the EU, to ensurevery high recovery rates for the entire battery system andespecially for the valuable metals. Latest by this demand

Arno Kwade et al. / KONA Powder and Particle Journal No. 40 (2023) 50–73

milling, dispersing and classification processes get acentral role within the circular processing of lithium-ion
batteries and next battery generations.
However, as shown in the review, comminution andclassification is not only very central for the primaryand secondary raw material processing, but also for the
production of the cathode and anode active materials and,in the future, the mechanochemical synthesis of the solid
electrolytes. The dry and wet mixing and dispersion of
the electrode material mixture requires processes in which
the particles are stressed, so that besides different typesof mixers, extruders and wet operated mills are also used
for the wet dispersion of components, especially in thecase of nano-sized components. The mill and classifier
manufacturers, the plant designers and builders, as wellas the research institutions, should embrace these opportunities and develop new machinery and new processes tosignificantly increase the sustainability of circular battery
cell production. In the near future, great attention should
lie on energy efficient recycling processes, which ensure
a high recycling rate of nearly 100 % and high component
purity, as well as processes for dry electrode processing,where the dry pretreatment of the material mixture is a
crucial process step. Moreover, the tailoring of the particle
size distributions and particle morphologies, especially for
new material classes like monocrystalline cathode active
material and solid electrolytes, is an important task for theparticle technology community.

Acknowledgements

Many thanks to Michael Grube, Moritz Hofer and Clara
Sangrós for their input to this review in the field of active
materials and the production of sulphidic solid electrolytes
as well as Heather Cavers for an overall review of thespelling.

Nomenclature
Al
AM
ASSB
BEV
BMS
Br
CAM
CB
CEID
CG
CMC
CNT
CO2
DMC
EC
ELV

Aluminum
Active materialAll-solid-state battery
Battery electric vehicle
Battery management system
Bromine
Cathode active material
Carbon black
Circular Economy Initiative Germany
Conductive graphite
Carboxymethyl cellulose
Carbon nano tubes
Carbon dioxide
Dimethyl carbonate
Ethylene carbonate
End of life vehicle

EMC
Em,slurry
EoL
EU
Gr
HF
HPGR
LFP
LMNO
Li
LIB
LiFePO3
LIS
LLZO
LPS
Mio.
NCA
NiMH
NMC
NMP
PEO
PHEV
PVDF
SAG
SBR
SEI
Si@Gr
Si@GrC
tph
VGCF
wt
x50,3
σion

Ethyl methyl carbonate
Specific energy related to slurry mass
End of life
European Union
Graphite
Hydrogen fluoride
High pressure grinding rolls
Lithium iron phosphate
Lithium manganese nickel oxide
Lithium
Lithium ion batteries
Lithium iron phosphate
Lithium sulfur battery
Lithium lanthanum zirconium oxide
Li3PS4 (sulfidic solid electrolyte)
MillionLithium nickel cobalt aluminum oxide
Nickel metal hydrate
Lithium nickel manganese cobalt oxide(LiNi1–y–zMnyCozO2)
N-2-methyl-pyrrolidone
Polyethylene oxide
Plug-in hybrid electric vehicle
Polyvinylidene fluoride
Semi-autogenous grinding
Styrene-butadiene rubber
Solid electrolyte interface
Graphite with Si-nanoparticles
Graphite with Si-nanoparticles and carbon coating
tons per hour
Vapor grown carbon fiber
weightVolumetric median particle size
ionic conductivity

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Authors’ Short Biographies
Arno Kwade
Arno Kwade worked 9 years as a process engineer in leading positions in industry after finishinghis doctorate in 1996. Since 2005 he is leading as Professor the Institute for Particle Technology at
TU Braunschweig. His research focus lies on developing process-structure-property relationships
for processes in which particles are mechanically stressed and formulated, from milling and mechanochemical synthesis over mixing to production and calendaring of electrodes for lithium-ion andall-solid-state batteries. Today he is Chairman of the Battery LabFactory Braunschweig (BLB) andthe Center of Pharmaceutical Engineering (PVZ).

Marcel Möller
Marcel Möller received his master‘s degree in biochemical and chemical engineering at the TU
Braunschweig in 2019. After a year of industrial experience at Volkswagen AG as engineer for battery slurry production, he joined the Institute for Particle Technology. In his research, he targets highquality and low-cost production of nano-sized silicon via wet-grinding. For that purpose, he develops
different formulating strategies as well as suited process conditions and evaluates the impact on the
production of the silicon nanoparticles.

Jannes Müller
Jannes Müller got his master’s degree at the Technical University Braunschweig in 2018 and is
working as a PhD student in the working group Battery Process Engineering at the Institute for Particle Technology since then. His research focuses on the scalable preparation of nano-silicon graphite
composites as anode material for lithium-ion batteries. In his recent publication he demonstrated the
preparation via fluidized bed granulation with subsequent carbon coating enabling high performancesilicon materials.

Jutta Hesselbach
Jutta Hesselbach studied mechanical engineering at the TU Braunschweig, where she proceeded
her studies in the field of process engineering as a research associate at the Institute for Particle Technology. Within this field, she completed her doctoral thesis in July 2020 at the TU Braunschweig.
Now she works at the Fraunhofer Institute for Surface Engineering and Thin Films IST within thedepartment Process and Production Engineering for Sustainable Energy Storage Systems, where she
was promoted to Head of the Group “Material and Process Development”.

Sabrina Zellmer
Sabrina Zellmer studied mechanical engineering at the TU Braunschweig, where she proceeded
her studies in the field of nanomaterials as a research associate at the Institute for Particle Technology. She completed her doctoral studies in the field of particle functionalization and spray drying.
During her work at the iPAT, she broadened her scientific expertise by working in the BLB, becoming
the head of the junior research group “Solid-State Battery Materials and Electrodes”. Since January
2019 Sabrina Zellmer is Head of Department Process and Production Engineering for Sustainable
Energy Storage Systems at the Fraunhofer IST.

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Arno Kwade et al. / KONA Powder and Particle Journal No. 40 (2023) 50–73

Authors’ Short Biographies
Stefan Doose
Stefan Doose received his master degree in biotechnology with a focus on process engineeringfrom TU Braunschweig in 2017. Subsequently, he started his work as a research assistant (PhD
student) in the Battery Process Engineering research group at the Institute of Particle Technologyof Technische Universtität Braunschweig in 2017 with a focus on mechanical safety studies withaccompanying online gas analysis as well as mechanical-thermal recycling processes of lithium-ion
batteries.

Julian Mayer
Julian Mayer received his Bachelor of Engineering from the University of Applied Sciences
Munich in 2013 and subsequently completed his Master’s degree from the Technical University of
Braunschweig in 2016. Since then, he has been researching the continuous and discontinuous dispersion of lithium-ion battery slurries, with focus on the carbon black fragmentation at the Institute forParticle Technology. Since 2020, he has been deputy head of the Battery Process Engineering group.

Peter Michalowski
Peter Michalowski studied physics at the University Jena and graduated in the field of superconductivity. Subsequently, he worked on his doctoral thesis project on cathodes for lithium ion batteries
at the University Oldenburg and received his title in 2017. He joined the Institute for Particle Technology at the TU Braunschweig in 2018 and became leader of the junior research group “Solid-State
Battery Materials and Electrodes” in 2019. Since the start of 2021 he has been the head of the battery
processing division of the iPAT and part of the head office of the Battery LabFactory Braunschweig.

Malcom Powell
Malcom Powell has applied fundamental comminution research to design and process improvement on over 60 mines worldwide during 35 years at Mintek, UCT, and then as Professor of comminution at the JKMRC in Australia. Malcolm collaborates extensively, with close compatriots on
5 continents forming the Global Comminution Collaborative (GCC)—providing an expert researchand consulting. Malcolm’s research vision is of integrated total process simulation as a tool for
innovation—linking geology, mining, energy and size reduction, gangue rejection and recovery into
flexible process design and process optimization.

Sandra Breitung-Faes
Sandra Breitung-Faes studied process engineering at TU Clausthal and received her doctoral
grade in 2009 at the Institute for Particle Technology at the TU Braunschweig with the topic of wet
stirred media milling of ceramic nano particles. Afterwards she had the position of a team leader for
grinding, a group leader for grinding and dispersing and is now, since 2018 the division manager for
powder and slurry processes at the Institute for Particle Technology of TU Braunschweig.

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KONA Powder and Particle Journal No. 40 (2023) 74–93/https://doi.org/10.14356/kona.2023007

Review Paper

Recent Research Trend in Powder Process Technology for
High-Performance Rare-Earth Permanent Magnets †Kenta Takagi *, Yusuke Hirayama, Shusuke Okada, Akihide Hosokawa and Wataru Yamaguchi
Magnetic Powder Metallurgy Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Japan

Abstract
Higher performance is constantly required in rare earth permanent magnets, which are an indispensable componentof the motors of electric vehicles. When producing sintered magnets, advanced structural control is necessary in the
powder metallurgy process in order to achieve high performance. Especially in recent years, it has become important
to develop processes for Sm-Fe-N magnets and metastable phase magnets as next-generation magnets to replace
the Nd-Fe-B magnets. Because the crystal grain refinement of sintered magnets is most effective for improving
coercivity, production methods for raw powders have evolved from the traditional pulverization to chemical synthesis
approaches, and as a result, a submicron-sized Sm-Fe-N powder with huge coercivity has been developed. State-ofthe-art physical synthesis methods have also been applied successfully to the synthesis of nanopowders. Since controlof the grain boundary is very effective in Nd-Fe-B magnets, this approach has also been evolved to Sm-Fe-N magnets
by nano coating. On the other hand, since technologies for crystalline orientation control and high-density sintering
are indispensable for improvement of remanence, new low-thermal load consolidation techniques such as spark
plasma sintering are being developed for Sm-Fe-N magnets and metastable phase magnets in order to overcome theinherent low thermal stability of these materials.
Keywords: rare earth permanent magnets, powder metallurgy processes, grain refinement, crystalline orientation,
grain boundary control

1. Introduction

Rare earth permanent magnets are a typical material
produced by powder metallurgy technology, and most ofthese products are now used in electric motors. Permanent
magnet synchronous motors (PMSMs) using rare earthmagnets are much more efficient than inductive motors,
and were the driving force behind the industrializationof hybrid and electric vehicles in the late 1990s. Morerecently, higher efficiency has been required in motors in
response to the growing need for global CO2 reduction,and new high-performance permanent magnets are being
developed from this viewpoint. Among rare earth magnets,
materials based mainly on Nd-Fe-B, Sm-Co and Sm-Fe-N
have been industrialized, but most magnets used in motors
are Nd-Fe-B magnets. As a practical problem, the prices ofthe specific rare earth and heavy rare earth elements that are
the raw materials for these rare earth magnets, such as the
Nd and Dy used in Nd-Fe-B magnets, are easily affectedby the international situation due to their strongly uneven
†

*

74

Received 16 January 2022; Accepted 29 March 2022
J-STAGE Advance published online 21 May 2022
Corresponding author: Kenta Takagi;
Add: 2266-98 Anagahora, Shimo-Shidami, Moriyama-ku, Nagoya,
Aichi 463-8560, Japan
E-mail: k-takagi@aist.go.jp
TEL: +81-52-736-7561 FAX: +81-52-736-7406

regional distribution (Seo Y. and Morimoto S., 2014). In
particular, since the “rare earth shock” of 2010, difficult
development aimed at improving magnetic characteristics
while considering resource problems has been urgently
needed.
Various magnetic properties must be considered whendeveloping permanent magnet materials for motors, but
the most important are the maximum energy that can beextracted from the magnet (BH)max and coercivity, whichindicates the resistance to the opposite magnetic field.
Although (BH)max is determined by various magnetic properties such as remanence, coercivity and rectangularity, themost influential factor is remanence. In other words, thefirst consideration when developing a high-performancemagnet is improvement of coercivity and remanence.
The potential for improving coercivity depends on theanisotropic magnetic field, which is an intrinsic propertyof each magnetic compound, but coercivity very strongly
depends on the crystal grain size and grain boundary
structure. For example, the anisotropic magnetic field of
Nd2Fe14B is about 1/3 that of Sm2Fe17N3, but the coercivityof the Nd2Fe14B bulk magnet, which is achieved by using
advanced microstructural control technology, exceeds thatof Sm2Fe17N3. Many studies so far have shown that thebasic guidelines for developing high coercivity magnets
are selection of a magnetic material with a high anisotropic

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

magnetic field, refinement of its microstructure and appropriate control of its grain boundary structure.
On the other hand, the guideline for high remanence isachieving remanence closer to saturation magnetization,
which is an intrinsic property of the compound. The easy
axis and hard axis of magnetization in a magnetic crystal are mutually orthogonal, and high magnetization isachieved in the direction of the easy axis. This means that ahigh crystal orientation is necessary in a magnet with a highremanence. Moreover, since the occupied volume ratio of
the magnetic phase in a sintered magnet is also important,pores and non-magnetic phase should be reduced to theabsolute minimum.
As described above, in the development of highperformance rare earth magnets, various factors such as
grain refinement, grain boundary control, crystal orientation and high density must be achieved simultaneously.
For this reason, most modern rare earth magnet materials
are produced by powder metallurgy technology, which enables advanced microstructural control. However, various
restrictions arise in the production process because the rareearth powders which are the constituents of the magnets
are more easily oxidized than aluminum and magnesium,and thus, various restrictions arise in the process of passing
through powder with an extremely large specific surface
area. In order to solve this complex problem, various
advanced powder processes from powder preparation to
consolidation have been developed in an integrated manner. This paper describes the trends in the developmentof powder processes for improving coercivity and remanence, focusing on Nd-based and Sm-based magnets. The
development and expectations of the powder process formetastable phase magnets and nanocomposite magnets,
which are considered to be next-generation magnets, arealso described.
Fig. 1 compares the various powder processing tech3.0
MA(Sm-Fe-N)

Coercivity (MA/m)

2.5

MA(Nd-Fe-B)

2.0

Reduction diffusion(Sm-Fe-N)
HDDR(Nd-Fe-B)

1.5 Melt spinning(Nd-Fe-B)

HDDR(Sm-Fe-N)

1.0
0.5
0.0
0.01

Pulverization(Sm-Fe-N)

Overnitriding(Nd-Fe-B)

0.1

1Grain size (μm)

10Fig. 1 Comparison of various preparation methods of rare earthmagnet powder. The blue and orange areas represent isotropic andanisotropic powders, respectively.

niques in term of coercivity. The traditional mechanicalpulverization, mechanical alloying and melt spinning
methods were applied in powder preparation with the aimof refining the crystal size to improve coercivity. Grain
refinement made it possible to achieve high coercivity inthe powders produced by these methods, and coercivity ashigh as 1.7 MA/m has been achieved in Nd-Fe-B magnets.
Therefore, for example, Nd-Fe-B and Sm-Fe-N based
powders produced by melt spinning were industrialized
at an early stage. However, since these powders have apolycrystalline structure with random crystal orientations, it is difficult to orient the crystals when they areused as bulk magnets, which means they cannot be useddirectly in high-remanence magnets. The hydrogenationdisproportionation-desorption-recombination(HDDR)
process and the reduction-diffusion (R-D) process havebeen developed in recent years to overcome this problem.
The HDDR process is capable of producing polycrystalline powders with a crystalline orientation, and the R-Dprocess make it possible to obtain very fine single-crystal
powders. These respective processes realize anisotropic
powders with high coercivity of more than 1.5 MA/mand huge coercivity of up to 4.7 MA/m, respectively.
Recently, advances in mechanical pulverization methods
such as ball milling and jet milling have made it possible toproduce submicron-sized powders, and physical synthesis
approaches such as the thermal plasma method have been
applied successfully to nanopowders.
In addition, as mentioned above, the development ofhigh coercivity magnets by grain boundary control insintered compact is being actively carried out with NdFe-B magnets. Forming Dy-Fe-B with a high anisotropic
magnetic field at the grain boundaries of Nd-Fe-B magnets
greatly improves coercivity. Initially, the grain boundary
phase was formed by using a mixed powder (two-alloy
method), but the development of the grain boundary diffusion method has made it possible to obtain high coercivity
magnets. In the Sm-Fe-N magnet, high coercivity of up
to 4.7 MA/m has been obtained by adding Zn to the grainboundaries, and more recently, sintered magnets with highcoercivity and high remanence have been discovered by
combining low oxygen powder metallurgy with powder
coating.
Various powder processes have been also examined to
improve remanence. Although the current-generation NdFe-B magnets can be densified by liquid phase sintering
under atmospheric pressure, since Sm-Fe-N magnets
are thermally decomposable, high temperature sinteringcannot be applied. Low thermal load sintering techniquessuch as spark plasma sintering (SPS) have been studied, but
remanence is still limited to a low level due to insufficientdensification. As a new approach, high strain rate consolidation techniques such as the shear compression method
and the powder rolling method are being studied. For

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Nd-Fe-B magnets, a special processing method called the
hot-deformation method was developed to simultaneously
achieve a high crystal orientation and grain refinement,that is, both high remanence and high coercivity at thesame time. The strongest Nd-Fe-B bulk magnet to date wasproduced by this process.
As described above, novel powder processes are constantly being developed for high-performance rare earthmagnets. The next chapter introduces the individual
powder processes and powder metallurgy processes forhigh-performance magnets in more detail.

2. Powder processes for enhanced coercivity
2.1 Production technologies for fine powder
2.1.1 Pulverization

The traditional approach to grain refinement in powder
metallurgy is pulverization of the material into a fine powder. Currently, the gas flow-type pulverization method (jetmilling) is often used for fine pulverization of Nd-Fe-B
magnet materials. Unlike ball mills, a crushing medium is
not used in jet milling, so impurities can be reduced. As an
additional advantage, although the particle surface of therare earth alloy powder is extremely active, deterioration ofmagnetic properties due to oxidation of the particle surface
can be suppressed if the oxygen concentration of the pulverizing gas is sufficiently reduced. In general production
processes for Nd-Fe-B magnets, the powder is refined to asize of several μm by high throughput counter-type jet milling before the sintering process. In recent years, Sagawa et
al. have shown that swirling-type jet milling using helium
gas, which can generate a high-speed gas flow, can refine
the powder down to a size of 1 μm (Sepehri-Amin H. et al.,2011). Nakamura et al. succeeded in producing a Nd-Fe-B
magnetic powder with a size of 0.33 μm by helium-gas jet
milling using HDDR powder as the raw powder, as show
in Fig. 2 (Nakamura M. et al., 2013). By combining theultrafine powders produced by this type of process withgrain boundary control technology, Nd-Fe-B sinteredmagnets with coercivity of 1.59 MA/m have been obtained(Sugimoto S. et al., 2015).
Improvement of coercivity by fine pulverization has
also been attempted with other alloys. Takagi et al. appliedswirling-type jet milling to a Sm-Fe-N alloy and demonstrated that it can be pulverized to 1 μm (Takagi K. et al.,2020). However, the coercivity of the sintered magnets
derived from this fine powder was only about 0.8 MA/m,which is much smaller than the coercivity expected fromits huge anisotropic magnetic field. This is considered to
be is closely related to the mechanical damage introduced
during jet milling. That is, crystals which contain defects
such as dislocations have a lower anisotropic magnetic
field than perfect crystals and tend to become nucleationsites for the reverse magnetic domain. As a result, coercivity is significantly reduced by surface damage in the
powder. It may be noted that the coercivity of the Nd-Fe-B
powder prepared by the pulverization method is actually
very small, but since the Nd-Fe-B sintered magnet canbe consolidated by liquid phase sintering, the surface
damage can be recovered, resulting in high coercivity inthe sintered magnet. However, only solid-phase sinteringin the low temperature range is possible with the Sm-Fe-N
magnet, as this material is a thermally-decomposable alloy,
so the coercivity of the powder is inherited unchanged in
the sintered magnet. Hosokawa et al. conducted a detailed
TEM investigation of the Sm2Fe17N3 fine powder producedby jet milling. As shown in Fig. 3, that study revealed thatnumerous dislocations and nanocrystallization occur on
the particle surface, and these types of damage increase as
pulverization progresses (Hosokawa A. et al., 2021a). In
fact, it was found that the decrease in the crystallinity ofthe particle surface leads not only to the above-mentioned
decrease in coercivity, but also to a decrease in remanence.
Although the reason for this decrease in remanence has not
yet been clarified, this suggests that some solution, such as

Fig. 2 BSE image of (a) hydrogen decrepitated HDDR alloy, and SE images of jet-milled powders of (b) HDDR and (c) SC alloys. The median
diameter (d50) of the jet-milled powder of the HDDR alloy in (b) is about 0.33 μm. Reprinted with permission from Ref. (Nakamura M. et al., 2013).
Copyright: (2013) AIP Publishing LLC.

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Fig. 3 (a) TEM BF image of a jet-milled powder particle, and (b) and (c) corresponding SADPs. (d) TEM BF image of a UFP and its corresponding
SADP. Reprinted with permission from Ref. (Hosokawa A. et al., 2021a). Copyright: (2021) Elsevier B.V.

heat treatment, is required in order to recover the damage.
Besides jet milling, it has often been reported that further
pulverization is possible by ball milling using a surfactant.
In traditional ball milling, crushing and rewelding are
performed repeatedly, so particle size reduction becomessaturated, but addition of a surfactant to the solvent suppresses rewelding, allowing pulverization to progress. Asa pioneering study, Chakka et al. pulverized Sm-Co and
Nd-Fe-B to sizes of less than 30 nm by surfactant-assistedball milling (Chakka V.M. et al., 2006). Akdogan et al. andYue et al. have also produced SmCo5 nanopowder with acoercivity of 1.4 MA/m and Nd-Fe-B nanopowder with
0.3 MA/m by surfactant-assisted ball milling (Akdogan
N.G. et al., 2009; Yue M. et al., 2012). However, as in
the case of jet milling, the high coercivity expected fromthe particle size has not been obtained so far, and this isthought to be the result of mechanical damage induced by
the milling process.

2.1.2 Chemical synthesis

As mentioned in the previous section, milling methods
have the problem of causing damage to the particle surface
and thus are not an appropriate approach for magnetic
alloys such as Sm-Fe-N which are not suitable for liquid
phase sintering. Moreover, milling cannot be considered an
ideal method for producing powders for bonded magnets
due to the issue of reduced coercivity. Therefore, chemical
powder synthesis processes which enable direct productionof high coercivity fine powders have been developed recently. The most successful chemical synthesis method for
rare-earth magnet fine powders is the reduction-diffusion(R-D) method, which was originally developed by Robert
E. Cech of General Electric in 1969 to produce SmCo5
magnetic powder (Cech R.E., 1973; 1974). In the R-D
method, rare earth magnet alloy powders are produced byheating a mixture of rare earth oxide powders, transition
metal powders and Ca in an inert atmosphere. During heating, the rare earth metals that are reduced by Ca diffuse into

the transition metal particles, resulting in an alloy powder.
Therefore, the particle size of the alloy powder is greatly
affected by the particle size of the raw transition metal
powder. Conventionally, a fine alloy powder is produced
by grinding the R-D powder to the order of tens of microns
after synthesis from coarse transition metal and rare earthoxide raw powders. Recent research has demonstrated thatmicron-sized fine alloy powders can be directly synthesized
by the R-D process from fine raw powders synthesized bya wet chemical method, and it has been reported that thepowders synthesized by the R-D process exhibit higher coercivity than milled powders because the R-D powders arefree of surface damage (Tada S. et al., 2012). Particularly,
the last ten years of research have successfully shown that
submicron-sized alloy powder can be synthesized by usingof nano-sized oxide raw powders prepared by the pyrolysis
or sol-gel method, and by using techniques to preventnecking and particle growth during Ca reduction (Ma Z.-H.
et al., 2018; Zhang H. et al., 2011a). As mentioned above,
the R-D method has high potential for producing fine rareearth magnetic powders and has been energetically studied,especially for Sm-Co and Sm-Fe-N alloys.
SmCo5 has a large anisotropy field and has been reported
to exhibit high coercivity. Fig. 4 summarizes the particle
size dependency of coercivity for SmCo5 fine powders.
(Dong Y. et al., 2019; Li C. et al., 2018; Ma Z.-H. et al.,2018; Ma Z. et al., 2019a; Ma Z. et al., 2019c; Ma Z.
et al., 2015; Shen B. et al., 2019; Tang H. et al., 2020a;
2021; Tang H. et al., 2020c; Wu Q. et al., 2020b; Zhang
H. et al., 2011a). Coercivity improves as the average particle diameter becomes finer, but reaches its maximum atabout 400 nm and then decreases in the smaller diameter
range. This decrease is considered to be due to the more
pronounced effect of particle surface oxidation as theparticle size decreases, i.e., as the specific surface area
increases. The highest coercive force at room temperature was 5.8 MA/m, as reported by Dong et al. (Dong Y.
et al., 2019), when a hydroxide powder synthesized by77

Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93
7

5
4
3
2

1

10

100

Particle size (nm)1000

Fig. 4 Particle size dependency of coercivity of SmCo5 powder (Dong
Y. et al., 2019; Li C. et al., 2018; Ma Z.-H. et al., 2017; Ma Z. et al., 2019a;
Ma Z. et al., 2019b; Ma Z. et al., 2015; Shen B. et al., 2019; Tang H. et al.,2020a; Tang H. et al., 2021; Tang H. et al., 2020b; Wu Q. et al., 2020a;
Zhang H. et al., 2011b).

coprecipitation of a SmCl3, CoCl2 and CaCl2 aqueous solution was mixed with KCl and Ca and subjected to a R-Dprocess at a relatively low temperature of 1 133 K. In theirstudy, the CaO coating, which was derived from CaCl2
and dilution of Ca by KCl, prevented necking and growth
between particles, resulting in an ultrafine powder with anarrow particle size distribution.
Of the rare earth magnetic powders produced by the
R-D method, the Sm2Fe17N3 powder is the only raw material powder which is produced commercially for bonded
magnets. Fig. 5 shows the relationship between the particle
size and coercivity of the Sm2Fe17N3 powder (Hirayama Y.
et al., 2016; Okada S. et al., 2019; Okada S. et al., 2017;
Okada S. and Takagi K., 2019; Dong Y. et al., 2019; Tada
S. et al., 2012; Takagi K. et al., 2021). Okada et al. revealed that Sm2Fe17N3 fine powder synthesized by the R-D
method exhibited high coercivity even at the submicron
size, unlike powders prepared by milling. In particular,
they reported that Sm2Fe17N3 with a giant coercive forceof 2.5 MA/m and an average particle diameter of about
400 nm can be produced by improving various processing
steps (Okada S. et al., 2019; Okada S. et al., 2017; Okada
S. and Takagi K., 2019; Takagi K. et al., 2021). They employed a synthesis process in which Sm-Fe oxide powders
synthesized by coprecipitation were subjected to hydrogen
reduction, reduction-diffusion and nitridation. Using a
different approach, Dong et al. applied a particle growth
suppression technique, which was used to achieve thepreviously described synthesis of SmCo5 fine powder withhuge coercivity, to the synthesis of Sm2Fe17N3 fine powder,and reported Sm2Fe17N3 ultrafine powder with a particle
size of about 100 nm. However, the coercive force wasonly about 1.2 MA/m (Dong Y. et al., 2019).

78

2.0

1.5

1.0

1
0

Hirayama (2016)
Okada (2017)
Okada (2019)
Tada (2012)
Takagi (2021)
Dong (2019)

2.5

Coercivity (MA/m)

6

Coercivity (MA/m)

3.0

Tang (2020b)
Ma (2015)
Ma (2017)
Tang (2021)
Shen (2019)
Dong (2019)
Ma (2019a)
Li (2018)
Wu (2020)
Ma (2019b)
Zhang (2011)
Tang (2020a)

0.5

10

100

Particle size (nm)1000

Fig. 5 Particle size dependency of coercivity of Sm 2Fe17N3 powder(Hirayama Y. et al., 2016; Okada S. et al., 2019; Okada S. et al., 2017;
Dong Y. et al., 2019; Tada S. et al., 2012; Takagi K. et al., 2021).

Although there have been reports on the synthesis of NdFe-B alloys, coercivity was not outstanding, being limited
to around 0.5 MA/m (Jeong J.H. et al., 2016; Swaminathan
V. et al., 2013). Since Nd-Fe-B is a ternary system, adjustment of the composition seems to be difficult, especially incontrolling the amount of B (Guo Y. et al., 2019). Moreover,because Nd-Fe-B is more easily oxidized than Sm-basedmaterials, decreased coercivity due to surface oxidation isconsidered to have a serious influence. As for ThMn12-type
alloys, which are expected to be a new type of magnet,
Gabay et al. reported (Sm,Zr)1(Fe,Co,Ti)12 powder witha high coercivity exceeding 1 MA/m by the R-D method
using raw powders prepared by a mechanochemical process (Gabay A.M. and Hadjipanayis G.C., 2021). Although
TbCu7-type Sm-Fe-N is expected to be a next-generation
magnet with high saturation magnetization, there had been
no reports on production of this material by the R-D routebecause it is a metastable phase. However, Okada et al.
recently developed a low-temperature R-D method using
molten salts such as LiCl as a solvent for Ca, which enables
a R-D reaction below the melting point of Ca, and succeeded in synthesizing a TbCu7-type Sm-Fe single-crystal
fine powder at an R-D reaction temperature of about 873 K(Okada S. and Takagi K., 2021).

2.1.3 Physical synthesis

Physical synthesis approaches for rare earth magnetic
powders include, for example, vapor phase methods suchas an arc discharge method and a high frequency induced
thermal plasma method, as well as the cluster beam deposition method. Vapor phase methods are able to synthesize
ultrafine particles much smaller than those prepared by
the breakdown methods described in Section 2.1.1, andthese ultrafine particles are also extremely pure, as they

Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

are free of the particle surface contamination which is adrawback of the chemical synthesis processes described in
Section 2.1.2. Many reports on nanoparticle synthesis ofrare earth-transition magnetic alloys using the gas phasemethod have been based on the cluster beam deposition
method, in which the alloy is deposited on a substrate(Sellmyer D.J. et al., 2015). In particular, many reports
have examined the synthesis of Sm-Co nanoparticles.
However, while the obtained particle size was smaller than
50 nm, maximum coercivity was only about 0.6 MA/m atroom temperature (Abbas N. et al., 2019; Balamurugan B.
et al., 2012; Balasubramanian B. et al., 2011; He S. et al.,2013; Landi G.T. and Santos A.D., 2010; Stoyanov S. etal., 2003). Here, the high coercivity expected in nanoparticles is not obtained due to the difficulty of synthesizing
nanoparticles with high crystallinity and avoiding surface
oxidation due to the large surface area. Recently, Hirayama
et al. energetically investigated the synthesis of these metal
nanoparticles by a thermal plasma process, especially
focusing on easily oxidizable metals such as Al and rareearth alloys. They newly modified a conventional thermal
plasma method so that the nanoparticles can be synthesizedand handled under an extremely low oxidizing atmosphere,
as indicated in Fig. 6. Their research demonstrated that Al
nanoparticles obtained by this method form necking under
low temperature heating because surface oxidation is minimized (Hirayama Y. et al., 2018; Kim D. et al., 2021), andFe-Co alloy nanoparticles with reduced surface oxidation
exhibited the same saturation magnetization as the conventional bulk alloy without a hydrogen reduction treatment(Hirayama Y. and Takagi K., 2019). In the synthesis ofrare earth-transition metal nanoparticles, Hirayama et al.
showed the possibility of synthesizing a TbCu7-type metastable phase in a Nd-Fe system by this process by taking
advantage of its high quenching rate (Hirayama Y. et al.,2021). Synthesis of core-shell particles with a Sm-Co shell/
Co core structure, which could not be obtained until now,
has also been reported (Park K. et al., 2021), and is a very

noteworthy new process for raw material powders for thecreation of novel permanent magnets in the future.

2.2 Grain refinement technologies
2.2.1 Rapid solidification

The previous section introduced attempts to develop
single crystalline powder with the finest possible particle
size for production of high coercivity fine-grained sinteredmagnets. On the other hand, the production methods forpolycrystalline powders composed of ultrafine grains accompanied by rapid solidification have also been studied.
The most common method is single roll melt spinning, andin fact, magnetic powder production by this method has
already been commercialized. Fig. 7 shows a schematic
diagram of a melt spinning device and an image of thetypical nanostructure (Mishra R.K., 1986). In the melt
spinning method, a molten metal is directly poured into
a rotating cold copper roll to achieve an extremely rapid
quenching effect. Traditionally, this method was used in thedevelopment of amorphous and nanocrystalline materials.
In the 1980s, it was reported that high coercivity could beobtained by alloying Fe with Tb, Pr, La, etc., and later,
similar coercivity enhancement in Re-Fe alloys was also
reported. General Motors (GM) was apparently searchingfor Re-Fe-based hard magnetic materials at the time,and Croat et al. at GM reported that Nd-Fe-B produced
an excellent magnetic powder with coercivity of about
1.2 MA/m (Croat J.J., 1982).
The flake powder obtained by melt spinning has theadvantage that extremely high coercivity due to the effectof grain refinement can be achieved because fine grains ofseveral 10 nm to several 100 nm can be obtained without
pulverization. However, since the orientation of each crystal grain is random, remanence and (BH)max are inferior tothose of anisotropic magnets. In spite of this weakness, thispowder has been applied practically in bonded magnets, inwhich the powder is kneaded with resin and molded, andmelt-spun powder is also an indispensable raw material for

Low cooling rate

[nucleation][condensation]
Alloy droplet

Ar gasanisotropic particle

ultrafineparticle
single crystal

Oxgen level < 0.5 ppm(0

100 nmStable (Th2Zn17)

100 nmMetastable (TbCu7)

01

)

)

+

001 010

ZA:[100]

10[Glove Box]

PowderfeederInductioncoil(0[evaporation]

High cooling rate

2 nm

Fig. 6 Experimental set-up of low-oxygen induction thermal plasma process. The cooling rate can be controlled by introducing extra Ar gas at the
tail of the plasma, which results in differences in the formed phase. Reprinted with permission from Ref. (Hirayama Y. et al., 2021). Copyright: (2021)
Elsevier B.V.

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Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

(a)

(b)

Raw Ingredient
Moltenmetal
Cu roll
Rapidly solidified
ribbonsFig. 7 (a) Schematic illustration of melt spinning apparatus and (b) typical microstructure of as-spun ribbons. Reprinted with permission from Ref.
(Mishra R.K., 1986). Copyright: (1986) Elsevier B.V.

hot-deformation magnets, which have attracted attention
recently, as will be explained in detail in Section 3.1.
The rapid solidification method also has the advantageof obtaining a non-equilibrium phase. For example, in
the TbCu7 type Sm-Fe-N alloy, it is possible to obtain asupersaturated Fe composition by melt spinning, and a highsaturation magnetization of 1.7 T, which exceeds the valuesof Sm2Fe17N3 and Nd2Fe14B, can be obtained (Katter M.
et al., 1991; Sakurada S. et al., 1996). This TbCu7 type
Sm-Fe-N melt-spun powder was partially commercialized
as a bonded magnet (Morii K. and Hasegawa F., 2008). As
another example of melt-spun powders, Toshiba commercialized a 240 kJ/m3 class sintered magnet using a Sm-CoFe-based magnet with a TbCu7 structure (Horiuchi Y. et al.,2015; Horiuchi Y. et al., 2014), and this magnet is alreadyused in the main motors of railway vehicles.
The single roll melt spinning method is also used forresearch on nanocomposite magnets, which are candidates
for next-generation magnet materials. Nanocomposite
magnets, also known as exchange spring magnets, are a
new type of magnet. The original concept was first proposedby Kneller and Hawig (Kneller E.F. and Hawig R., 1991).
When the hard magnetic phase and the soft magnetic phaseare composited at the nano scale, the soft magnetic phaseis expected to behave as if it were a hard magnetic phase
due to exchange coupling at the interface. Prior to thiswork, Coehoorn of Philips in fact had performed an experimental work and reported a magnet similar to this concept
in which nano-sized α-Fe was dispersed in the Nd2Fe14B
matrix by melt-spinning Nd-Fe-B powder with a Fe-richcomposition (Coehoorn R. et al., 1989). Later, Skomski
and Coey predicted that the combination of Sm2Fe17N3
and FeCo would yield ultra-strong nanocomposite magnets
with (BH)max = 1 000 kJ/m3 (Skomski R. and Coey J.M.D.,
1993). Following those pioneering works, nanocomposite
magnets using melt-spun powder were actively investigated (e.g. (Hirosawa S. and Kanekiyo H., 1996; Hirosawa
S. et al., 2003; Manaf A. et al., 1993a; Manaf A. et al.,
1992; 1993b)), and Hitachi Metals later developed a new

80

process in which amorphous powder obtained by melt
spinning is annealed to obtain a hard/soft nanocomposite
structure. This class of powder was commercialized under
the name Sprax®. Nevertheless, the values of (BH)max forthe nanocomposite magnets reported to date have neverexceeded that of the single-phase Nd2Fe14B magnet, except
for a thin film reported by Cui et al. (Cui W.B. et al., 2015).
The work by Zhang et al. (Zhang J. et al., 2005) is anotherinteresting experiment with thin films, wherein the (BH)max
obtained in the SmCo/Fe composite is superior to thatof the SmCo hard phase. As indicated by these thin-film
experiments, it is definitely important to align the crystal
orientations in order to achieve the combined advantagesof both the hard phase and the soft phase. Although this
precise microstructural control is still difficult by methods other than the thin-film technique, thin films cannot
be commercialized as permanent magnets. Thus, it isessential to develop new manufacturing processes thatwill potentially enable mass production of permanent magnets in a bulk form. Our group is currently energeticallyconducting research targeting anisotropic nanocomposite
magnets by combining the melt spinning method, hot deformation method and addition of low melting point alloys(Hosokawa A. and Takagi K., 2018b; 2019; Hosokawa A.
et al., 2019).

2.2.2 Solid synthesis

Another approach for synthesizing a powder consistingof ultrafine crystal grains is mechanical alloying (MA). In
many studies on Nd-Fe-B-based, Sm-Fe-N-based and SmCo-based alloys, rare earth magnetic powders with crystal
grain sizes of several 10 nm to several 100 nm have beensynthesized for MA. For example, the mechanical-alloyed
Nd-Fe-B powder produced by Zhang et al. had a highcoercivity of 1.7 MA/m, which is comparable to the value
obtained by the melt spinning method (Zhang Z.-D. et al.,1998), and Teresiak et al. demonstrated that Sm2Fe17NX
powders produced by MA exhibited a huge coercivity of
2.8 MA/m (Teresiak A. et al., 1999). However, the MA

Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

process has not been industrialized, as its low productivity
makes it unsuitable for mass production.
MA has been used in many studies to explore metastablecompounds such as TbCu7 type and ThMn12 type alloys
because it is possible to synthesize a metastable phase bysolid-phase synthesis without melting, and grain refinement
is also possible. In particular, MA was actively utilized inresearch on ThMn12 type alloys, leading to clarificationof the magnetic properties of ThMn12 type Nd-Fe-M-N
alloys and Sm-Fe-M alloys (M: Ti, V, Mo) (Jin Z.-q. etal., 1997; Schultz L. et al., 1990; Shengen Z. et al., 2001).
The ThMn12 type compound has recently been discovered
to have magnetic properties superior to those of existing
compounds, and has attracted renewed attention (Hirayama
Y. et al., 2015; 2017). Nevertheless, MA is rarely used incurrent development of magnet materials because only
polycrystalline powder can be obtained and as mentioned
above, it is not suitable for mass production.

2.2.3 Gas reaction

As an epoch-making method for producing fine polycrystalline magnetic powders, approaches utilizing the reaction
between solids and gases have attracted strong interest.
Because rare earth transition metal alloys react with hydrogen and nitrogen to form unique microstructures, researchon producing crystallographically-oriented polycrystalline
powder by using gas reactions has been widely conducted. The hydrogenation-disproportionation-desorptionrecombination method (HDDR) makes it possible toproduce a polycrystalline powder consisting of submicron-sized crystal grains from single crystal powder by using the reaction between a rare earth transition metal alloy
phase and hydrogen gas. For example, when an Nd-Fe-B
alloy is heated in a H2 atmosphere, the alloy is temporarily
decomposed into an NdH2 phase, Fe phase, Fe2B phase,
etc. (decomposition). Subsequently, when dehydrogenationis performed by heating in a reduced pressure environment,
the phase returns to the Nd-Fe-B phase (recombination). In
this process, the Nd-Fe-B phase forms fine crystal grains
with sizes controlled in the range from 0.2 to 0.6 μm, depending on the treatment temperature. In particular, unlike
rapid solidification and MA, HDDR treatment under appropriate conditions is capable of producing crystallographically-oriented grains as shown in Fig. 8 (Takizawa R. et al.,2017). That is, although this alloy is a polycrystal, particles
close to a single crystal can be produced, which means
the powder can be used as a raw material for anisotropic
magnets. The latest theory concerning the mechanism of
microstructure refinement and crystal orientation is summarized in the literature by Hirokawa et al. (Horikawa T.
et al., 2021). The maximum coercivity of the reported HDDR-processed Nd-Fe-B powder is 1.3 MA/m (Morimoto
K. et al., 2003), and coercivity is improved to 1.6 MA/mby diffusing Cu at the grain boundaries in the subsequentFig. 8 Typical microstructure of HDDR-treated Nd-Fe-B powder. (a)TEM-BF image and (b) inverse pole-figure map obtained by TEM-PED.
Reprinted with permission from Ref. (Takizawa R. et al., 2017). Copyright: (2017) Elsevier B.V.

process (Sepehri-Amin H. et al., 2010).
HDDR can also be used to produce fine-grained SmFe-N and Sm-Co powders with high coercivity. The internal
crystal grain size of HDDR-processed SmCo5 powder has
been refined to less than 100 nm, which achieved a huge
coercivity exceeding 3 MA/m (Bulyk I.I. and Panasyuk
V.V., 2012). While high coercivity of 1.4 MA/m has also
been obtained with HDDR-processed Sm2Fe17N3 (Arlot R.
et al., 1999), crystal orientation has not been realized in anyof these alloy systems, and HDDR is currently effective
only for Nd-Fe-B alloys. On the other hand, HDDR canalso be used to synthesize fine-grained powders of TbCu7
type and ThMn12 type alloys (Dirba I. et al., 2019; Takagi
K. et al., 2018), suggesting that it may be a very promisingprocess, provided crystal orientation can be realized inalloys other than Nd-Fe-B in the future.
For Sm2Fe17N3, a unique grain refinement method
employing a nitrogen gas reaction is possible (Saito K. etal., 1994). When nitriding Sm2Fe17 alloy powder, various
Sm2Fe17Nx compound powders with different nitrogencontent x can be obtained by tuning the nitriding conditions. While it is known that nitriding with a mixed gas ofammonia and hydrogen can overnitride the compound to a
maximum nitrogen content of about x = 6, it is also known
that the highest saturation magnetization and highest anisotropy are obtained when x = 3. Thus, optimally-nitrided
Sm2Fe17N3 having a small diameter (typically, up to 3 μm)
is used in permanent magnet applications.
Overnitriding Sm2Fe17Nx to the region of x > 3 induces
the interesting phenomenon of nanocrystalline cell formation. According to classical TEM results, this microstructure is composed of very fine nanocrystalline cells (30 to
100 nm) and amorphous cell walls dividing the individual
cells. Several researchers have reported that this techniquemakes is possible to obtain reasonably high coercivity even81Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

with coarse powders without reducing the particle size
(Imaoka A. et al., 1998; Imaoka N. et al., 1995; Saito K.
et al., 1994). In particular, in overnitridation treatment of
Mn-added Sm2Fe17Nx, excess N and Mn were concentrated
on the amorphous cell walls, resulting in the formation of
domain walls. A maximum coercivity of about 0.7 MA/m
was reportedly obtained by this approach. A group at Osaka
University later reported that addition of Ti, V and Cr in
addition to Mn had the same effect of increasing coercivity
as Mn. A group at Sumitomo Metal Mining reported that ahigh-performance powder with (BH)max = 175 kJ/m3 was
obtained with an Sm2(FeMn)17Nx system by combining
crushing treatment after overnitriding, and this powder was
actually commercialized. Although the coercivity of the
Sm2Fe17N3 powder produced by the pulverization method
was drastically reduced when exposed to a high temperature environment of about 200 °C, it is noteworthy that theovernitrided coarse powder exhibited excellent thermal
stability (Iseki T. et al., 2003; Ito M. et al., 2003; Ito M. etal., 2002; Majima K. et al., 1997; Ohmori K. et al., 2006).
The coercivity mechanism of optimally-nitrided
Sm2Fe17N3 is normally classified as the nucleation-type,but in contrast, an analysis of the initial magnetization
curves of the overnitrided Sm2Fe17Nx revealed that its
coercivity mechanism is pinning-type (Imaoka et al.,1995). This viewpoint was later supported by the Lorenz
microscopy, which directly captured the domain wall being
pinned at the Mn- and N-enriched amorphous cell wallsin the overnitrided Sm2(FeMn)17Nx, as indicated in Fig. 9(Kim J.J. et al., 2007; Yasuhara A. et al., 2005; Yasuhara A.
et al., 2004).
As demonstrated above, while overnitriding method is
an excellent approach for obtaining high coercivity magnetic powders, they have the disadvantage that residual
magnetization decreases when the nitrogen content reaches
x > 5, where coercivity increases. This decrease in remanence was originally thought to be caused by spontaneousrotation of the nanocrystalline cells after cell formationby overnitriding. Therefore, our group initially attempted
to develop a new process that would make it possible tocontrol the cell rotation during nitridation. We began byinvestigating what actually occurs in Sm2Fe17NX during
overnitridation treatment. An interesting finding during
this investigation was that the formation of this nanocell
structure can be observed with a recent high-performance
SEM which makes it possible to observe a very large fieldof view (Hosokawa A. and Takagi K., 2017; Hosokawa A.
and Takagi K., 2018a). Coupling SEM and TEM enables
multi-scale observation of the Sm2Fe17NX, and it has beenfound that a large number of plate-shaped amorphousbands are generated and then form a ladder-like structure,
as shown in Fig. 10. These amorphous bands eventuallyform a cell-like structure inside an initially singlecrystalline coarse particle. Since this investigation also
confirmed that the size of the cells decreases as the(local) nitrogen content increases, the degradation ofresidual magnetization is thought to be due simply tothe increased amount of low magnetization amorphous
regions (Hosokawa A. and Takagi K., 2017; Hosokawa A.
and Takagi K., 2018a). This means that it is not necessary
to control the rotation of the crystalline cells, and new,completely different guidelines are needed to improve thisprocess.

2.3 Grain boundary engineering
2.3.1 Nd-Fe-B magnets

The only drawback of Nd-Fe-B magnets is their heat
resistance. When used in an onboard vehicle motor, theoperating temperature is as high as 150 to 200 °C, andcoercivity is significantly reduced by heating. An effective
solution to this problem, which will make it possible towithstand use in the high temperature automotive environment, is to increase the absolute value of coercivity
or, alternatively, to reduce the temperature dependency ofcoercivity (temperature coefficient). Since no significant
improvement in the temperature dependency of coercivity
has been realized in Nd-Fe-B magnets, neodymium magnets which can withstand high-temperature use have beendeveloped by increasing the absolute value of coercivity.
In order to increase coercivity, appropriate control in/around the grain boundary phase is required. A grainboundary diffusion method for this type of control has beenFig. 9 (a) and (b) Domain wall pinning at amorphous boundaries captured by Lorenz microscopy and (c) schematic diagram of the pinning mechanism. Reprinted with permission from Ref. (Yasuhara A. et al., 2005). Copyright: (2005) Elsevier B.V.

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Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

Local nitrogen content

High

TEM

SEM

Small

Schematics

C-axisSm2Fe17N3(Single crystalline)
Amorphous phaseFig. 10 Typical microstructural evolution during formation of cell structures. Reconstructed with permission from Ref. (Hosokawa A. and Takagi
K., 2017). Copyright: (2017) Acta Materialia Inc. Published by Elsevier Ltd.

developed based on a report by Park et al. (Park K.T. et al.,2000). Since Nd-Fe-B magnets have a new creation-type
coercivity mechanism, suppressing the formation of reverse magnetic domains on the surface of crystal particles
is extremely effective for improving coercivity. Therefore,a (Nd-Dy/Tb)2Fe14B phase having a higher anisotropic
magnetic field than the main Nd2Fe14B phase is formed
only in the grain boundary by diffusion of Dy and Tb, resulting in a significant increase in coercivity. In particular,
after preparing the sintered compact by the conventional
process, metallic Dy and Tb or their oxide/fluoride areapplied to the surface of the sintered compact and diffused
at 800 to 900 °C. Because only the grain boundary phasebecomes a liquid, diffusion of Dy and Tb is limited to thegrain boundary.
Another technique for increasing coercivity is the lowtemperature eutectic alloy diffusion method, which can be
mentioned as a method for increasing coercivity by controlling the grain boundary phase. In this method, magnetic
decoupling between particles is induced by diffusing a low
melting point eutectic alloy into the grain boundaries toincrease the thickness of the grain boundary phase, which
greatly improves coercivity. Many reports on this technology have appeared in connection with HDDR powders
with fine crystal grains and heat-sensitive processed magnets, and significant improvement of coercivity has beenachieved. In particular, this method is very important forthe development of high coercivity magnets which do not
use the heavy rare earth elements Tb and Dy.

2.3.2 Sm-Fe-N magnets

The grain boundary diffusion method described in theprevious section cannot be applied to Sm-Fe-N magnets
because Sm-Fe-N is thermally decomposed below the diffusion treatment temperature of 800 to 900 °C. Therefore,
different approaches are applied to grain boundary control
of Sm-Fe-N magnets.
The oldest attempt of grain boundary control in thehistory of Sm-Fe-N development is the “metal-bonded
magnet” proposed by Otani et al. (Otani Y. et al., 1991).
Since Sm-Fe-N has a low decomposition temperature(Coey J.M.D. et al., 1991; Katter M. et al., 1992; Zhang
D.T. et al., 2007), conventional sintering methods involving high temperature heating cannot be applied. Therefore,
use of low melting point metals such as Sn, Bi, Zn, or Al
as a binder was proposed to obtain a bulk magnet at a sufficiently low temperature (Huang M.Q. et al., 1991; Otani
Y. et al., 1991). However, preparation of a metal-bonded
magnet with a Zn bond led to the discovery Zn bonding significantly increases coercivity. As a result, much researchhas been devoted to Zn-bonded magnets as a technology
for improving coercivity, but some critical problems were83

Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

discovered in the course of development. Specifically, although coercivity increases monotonically with the amountof Zn added, more than 20 wt% of Zn must be added toachieve coercivity on the order of 1 MA/m, as indicated inTable 1 (Ito S. et al., 2004; Kuhrt C. et al., 1992; Otani Y.
et al., 1991; Prabhu D. et al., 2012). However, the magnetic
Sm-Fe-N phase is inevitably diluted by addition of Zn,
resulting in a decrease in remanence, and it was also foundthat heavy Zn addition significantly deteriorates the squareness of the demagnetization curve (Kuhrt C. et al., 1992;
Müller K.H. et al., 1996). Therefore, even if coercivity
increases, (BH)max decreases.
The mechanism of coercivity improvement by Zn was
initially attributed to soft magnetic α-Fe crystallites on theparticle surface, which remain as an impurity in the raw
material preparation process (Kuhrt C. et al., 1992). In

addition, the non-magnetic Γ-FeZn reaction phase spreads
to the grain boundaries and was believed to block themagnetic coupling between Sm-Fe-N grains (Wendhausen
P.A.P. et al., 1993). Based on a study utilizing microstructural observation, Hiraga et al. explained that Zn reacts with
the Sm-Fe-N main phase into the Sm-(Fe, Zn)-N phase on
the surface of the grains, which smooths the grain surfaces,
and the Sm-(Fe, Zn)-N phase isolates the by-product soft
magnetic α-FeZn from the main phase, thereby suppressing
the formation of reverse magnetic domains (Hiraga K. etal., 1993). However, certain problems remain unsolved:
In particular, this interpretation relies on the assumptionthat the Sm-(Fe, Zn)-N phase is non-magnetic, and it does
not explain why squareness is significantly deteriorated.
Recently, Matsuura et al. conducted more detailed microstructure observations (Matsuura M. et al., 2020) (Fig. 11),

Table 1 Magnetic parameters of sintered samples for various heat treatment conditions. Reprinted with permission from Ref. (Prabhu D. et al., 2012).
Copyright: (2012;) Elsevier B.V.

Coercivity and remanence for various heat-treatment conditions
Zn conc.
As-sintered
425 °C/1 h
435 °C/1 h(wt %)Br (T)
μ0Hc (T)Br (T)
μ0Hc (T)μ0Hc (T)Br (T)

μ0Hc (T)Br (T)

10

0.56

0.67

1.11

0.67

1.39

0.68

1.67

0.69

15

0.42

0.56

1.24

0.62

2.75

0.62

2.33

0.62

20

0.65

0.53

1.54

0.58

1.78

0.59

2.13

0.44

450 °C/1 h

Magnetic properties of the initial SmFeN powder: μ0Hc = 1.2 T and μ0Ms = 1.25 T.

Fig. 11 Schematic diagram of microstructural changes in Sm-Fe-N (SFN) bulk magnets during annealing. Reprinted with permission from Ref.
(Matsuura M. et al., 2020). Copyright: (2020) Elsevier B.V.

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Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

and concluded that the non-magnetic Γ-FeZn phase surrounds the soft-magnetic α-FeZn crystallites and breaks themagnetic coupling between the grains.
While the mechanism of improved coercivity by Zn is
still unclear, the amount of Zn added should be reduced tosuppress the harmful effects of Zn. In order to maximize
the effect of Zn with limited addition, it will be necessary
to form a thin layer of Zn at the grain boundaries, and thislayer should be as thin and uniform as possible. Various
thin-film and coating techniques such as vacuum deposition (Fukunaga H. et al., 1996; Makita K. and Hirosawa
S., 1997) and photolysis of organic metal complexes (Arlot
R. et al., 1997; Izumi H. et al., 1996; Machida K.-i. et al.,1995) have been applied to Sm-Fe-N powders for this purpose. Initial attempts showed meaningful improvements inthermal stability and oxidation resistance, but the improvement in coercivity was modest and coercivity remained
inferior to the levels achieved by conventional processes.
The problems of these thin-film approaches are considered
to be two-fold: (1) Since the original purpose was metalbonding, the research target was biased toward low melting
point metals. However, now that other low-temperature
consolidation techniques are available, the scope of research should be extended to materials with higher melting
points (Imaoka N. et al., 2008; Saito T. et al., 2017). (2)The direct effect on the Sm-Fe-N main phase could not be
investigated because the added metals were not in directcontact with the magnetic phase, but were in contact with
non-magnetic surface oxides. Thus, a process for forming a
coating on a surface without an oxide film can be expected
to have a higher coercivity improvement effect with a small
amount of Zn addition.
Recent efforts to implement these two approaches
simultaneously include a combination of powder coating

Fig. 12 Effects of sputter coating of 20 nonmagnetic metals on surface
oxide-free Sm 2Fe17N3 powders on coercivity. The powders were heat
treated at 500 °C for 2 min after coating. Reprinted with permission
from Ref. (Yamaguchi W. and Takagi K., 2020). Copyright: (2020)
Elsevier B.V.

technology with the low oxygen powder metallurgy
process. Sm-Fe-N fine powder without a surface oxide
film is produced by pulverization in a low oxygen atmosphere, followed by sputter-coating with continuous stirring (Yamaguchi W. et al., 2020). This is a highly versatile
powder coating technique which is capable of forming a
direct metal-metal interface, and it can also form a thin anduniform film even on a material with a high melting point.
Yamaguchi et al. used this technique to coat Sm-Fe-N
powder with 20 kinds of non-magnetic metal elements,and found that, in addition to Zn, addition of metals suchas Al, Sn and Ce is also effective for improving coercivity
as indicated in Fig. 12 (Yamaguchi W. and Takagi K.,
2020). Matsuura et al. also succeeded in producing a Znadded Sm-Fe-N anisotropic sintered magnet with (BH)max= 179 kJ/m3 by a similar coating technique (Matsuura M.
et al., 2018).

3. Powder technologies for enhancement of
remanence
3.1 Hot deformation techniques

To produce a sintered magnet with high residual magnetization, it is important to produce a green compact inwhich the easy axis of magnetization of the crystal grains isaligned in one direction and to achieve the highest possible
densification of the compact by sintering. In addition, as
described above, it is also essential to construct a fine grain
structure for high coercivity. Since all these simultaneous
improvements have become saturated in the conventional
powder metallurgy process consisting of magnetic-aligned
compaction and atmospheric sintering, the hot deformation
process has attracted attention as a new process.
Lee reported that an anisotropic magnet with the easy
axis oriented in the press direction was obtained when asintered compact produced from Nd-Fe-B melt-spun powder was upset at an elevated temperature (Lee R.W., 1985).
Mishra and Lee observed hot-deformed anisotropic magnets by TEM and found that the grains were significantly
flattened in shape and all the crystal grains were oriented
in the c-axis // press direction (Mishra R.K. and Lee R.W.,
1986; Sepehri-Amin H. et al., 2013) as shown in Fig. 13.
Since that time, intensive research has been carried out, andmagnets with (BH)max exceeding 350 kJ/m3 have been reported (Mishra R.K., 1987; 1989; Mishra R.K. et al., 1988;
Mishra R.K. et al., 1990; Mishra R.K. et al., 1993). The
formation of this oriented microstructure is not the result ofa deformation texture mechanism, but a unique mechanism
involving liquid phase formation of an Nd-rich phase at
the grain boundaries in the hot working temperature range(Grunberger W. et al., 1997). When the grain boundary
phase is liquified at the hot deformation temperature, diffusion between the main phase and the liquid phase becomes
extremely active. At this time, high stress is generated in
the direction of uniaxial compressive stress, that is, in the85Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93

b

a

(a)

(b)

C-axis

500 nm
100 nmFig. 13 (a) Typical microstructure of highly textured nanocrystalline
structure after die-upsetting and (b) schematic diagram. Reprinted withpermission from Ref. (Sepehri-Amin H. et al., 2013). Copyright: (2013)
Acta Materialia Inc. Published by Elsevier Ltd.

vertical direction of the crystal grains, while stress is low inthe lateral direction. “Dissolution” is promoted at this highstress location, the substance moves toward the low-stresslocation through the liquid phase, “reprecipitation” occurs,
and as a result, the above-mentioned oriented microstructure is formed (Pharr G.M. and Ashby M.F., 1983; Raj R.
and Chyung C.K., 1981; Wakai F., 1994). What is important here is that when a liquid phase grain boundary exists
between Nd2Fe14B main phases with a highly anisotropic
crystal structure, the rate of dissolution-reprecipitation
changes greatly depending on the crystal orientation. In the
Nd-Fe-B phase, it is generally thought that only specificcrystal grains having an azimuth relationship of stress axis// c axis grow preferentially in the ab axis direction, and an
oriented structure is obtained.
Daido Steel succeeded in commercializing hot-deformed
magnets (Akiya T. et al., 2014a; b; Liu J. et al., 2014).
Another advantage of hot-deformed magnets is that theytend to have improved thermal resistance since the thermal
stability of coercivity increases as the crystal grain size
decreases. In this connection, studies on hot-worked magnets of the ThMn12 type using Sm as the main raw material
have been reported recently (Schonhobel A.M. et al., 2020;
Schonhobel A.M. et al., 2019).

3.2 Low-thermal load consolidation

As mentioned in 2-3-2, Nd-Fe-B alloys and Sm-Co
alloys can be sintered by high temperature heating, but SmFe-N alloys are thermally decomposed at about 620 °C,
making it difficult to produce bulk magnets by sintering.
In addition, non-equilibrium alloys such as the TbCu7
type and ThMn12 type also transform to a stable phaseat high temperatures, so consolidation in a low thermalload environment is considered necessary. Therefore,various low thermal load sintering techniques have been
examined for these alloys. Spark plasma sintering (SPS)
is a hot-pressing process with rapid heating, and has been
applied to Sm2Fe17N3 alloys and metastable alloys such as
TbCu7 type Sm-Fe-Ne alloys (Saito T., 2008; Zhang D.T.

86

40 µm

Fig. 14 Cross-sectional SEM images of (a) Sm 2Fe17N3 anisotropic
sintered magnet and (b) TbCu7 type Sm-Fe-N isotropic sintered magnet
produced by high-pressure current sintering. Reprinted with permission
from Ref. (Takagi K. et al., 2012c; Takagi K. et al., 2020). Copyright:(2012; 2020) Elsevier B.V.

et al., 2007). Although these materials cannot be heated tohigh temperatures, these studies have demonstrated thatsintered magnets with a relative density of more than 90 %can be obtained without degrading the magnetic propertiesof the powder by sintering at a high pressure exceeding
1000 MPa as shown in Fig. 14 (Nakayama H. et al., 2012;
Saito T., 2008; Takagi K. et al., 2012a; Takagi K. et al.,2012b; Takagi K. et al., 2020). In particular, Matsuura
et al. and Takagi et al. used a low oxygen powder metallurgy process combined with SPS to perform sintering
without decreasing coercivity due to surface oxidation,
and achieved (BH)max exceeding 190 kJ/m3, which is thehighest value reported for a Sm2Fe17N3 sintered magnet(Matsuura M. et al., 2018; Takagi K. et al., 2020).
Prototype bulk magnets using non-heat forming processes such as shear compression (Saito T. et al., 2005;
Saito T. and Kitazima H., 2011; Saito T. and Sato H., 2006;
Saito T. et al., 2006) and shock consolidation (Mashimo T.
et al., 2000; Mashimo T. et al., 1999) have been reported,
and recently, the authors also conducted research that
refocuses on powder rolling (Hosokawa A. et al., 2021b).
A method called high-pressure torsion processing has also
attracted attention in recent years (Edalati K. and Horita Z.,
2016; Valiev R.Z. et al., 2000). A schematic illustration ofhigh-pressure torsion is shown in Fig. 15. Since permanent
magnets are generally brittle, it is not possible to apply
huge strain by shear deformation by ordinary methods.
However, high-pressure torsion makes it possible to apply
huge shear strain without breaking brittle materials thanks
to hydrostatic stress (Hosokawa A. et al., 2014a; Hosokawa
A. et al., 2014b). Recently, many applications of this technique to permanent magnet materials have been reported(Gorshenkov M.V. et al., 2020; Popov A.G. et al., 2006;
Shchetinin I.V. et al., 2020; Straumal B.B. et al., 2015). It
has also been reported that Nd-Fe-B powder can be consolidated without heating by applying a huge strain exceeding
10 in shear strain, and as a result, the internal structureof the magnet can be improved as shown in Fig. 15(b)(Hosokawa A. and Takagi K., 2018b; Hosokawa A. et al.,

Kenta Takagi et al. / KONA Powder and Particle Journal No. 40 (2023) 74–93aDisc sample(green body)b

Porosity decreases
towards outside
Cross section
500 nmShear

Pore
Center (strain = 0)
Porosity 47%

Outer (strain = 7)
Porosity 10%

Fig. 15 Schematic illustration of high-pressure torsion process. (a) The deformation mode at the perimeter region is essentially simple shear.
(b) Example of densification by high-pressure torsion captured by SEM. Porosity decreases with the distance from the center.

2019). Although mass production cannot be expected by
the high-pressure torsion route, research in this direction
is needed in order to obtain clues for envisioning a newprocess that utilizes shear processing without relying on
high-temperature heating.

4. Concluding remarks

In order to improve the efficiency of electric motors,
continuous improvement of the performance of the rareearth magnets which are their core component is essential.
Research on conventional Nd-Fe-B magnets has provided
design guidelines for control of the internal microstructureof sintered magnets, such as grain refinement, nanoscale
grain boundary control and crystal orientation, to improve the performance of rare earth permanent magnets,
and an advanced powder metallurgy process which canefficiently achieve these types of control is being developed. Especially in recent years, process development for
Sm-Fe-N magnets and metastable phase magnets as nextgeneration magnets has become important. In order toimprove coercivity, it is necessary to refine the crystal grainsize, and for that purpose, refinement of the raw powder isbeing studied. Research on pulverization is evolving fromtraditional pulverization approaches such as jet milling tochemical/physical synthesis methods such as the reduction
diffusion (R-D) method and the thermal plasma method,
and the submicron- and nano-sized Sm-Fe-N powder obtained by these methods have achieved huge coercivity of
2.5 MA/m. Methods for synthesizing nanopolycrystalline
powders using gas reactions such as the HDDR method
and the overnitriding method have also been examined,
and crystal-oriented ultrafine polycrystalline powders havebeen synthesized successfully. In Nd-Fe-B magnets, it
has been found that, in addition to grain refinement, grainboundary phase control is also very effective in improving
coercivity. Recently, grain boundary control of Sm-Fe-N
sintered magnets by sputter coating on the powder surface
has been investigated, and a new grain boundary phase has
been proposed. To improve remanence, uniaxial orientation
technology for crystal grains and high-density sintering

technology are indispensable. Especially for Sm-Fe-N
magnets and metastable phase magnets, which have low
thermal stability, new low thermal load consolidation
techniques such as rapid hot pressing and large-strain
consolidation are being developed. However, for practicaluse of next-generation magnets, it will be necessary to develop a powder consolidation technique that enables higher
densification.

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Authors’ Short Biographies
Kenta Takagi
Kenta Takagi is a senior researcher at the National Institute of Advanced Industrial Science andtechnology. He obtained his bachelor’s degree and master’s degree in Materials Science from Tohoku
University in 1995 and 1997, respectively. Later he obtained his Doctor of Engineering from Tohoku
University in 2003. His latest research interests revolve around permanent magnets. In particular, heis motivated in studying on powder processing for low-sinterable and metastable magnetic alloys.

Yusuke Hirayama
Yusuke Hirayama is a senior researcher at the National Institute of Advanced Industrial Scienceand technology. He obtained his Doctor of Engineering from Osaka University in 2012. His latest
research is focused on magnetic materials. He is now working on the development of the physicalprocess of metal nanoparticle.

Shusuke Okada
Shusuke Okada is a senior researcher at the National Institute of Advanced Industrial Scienceand technology. He obtained his Doctor of Engineering from Osaka University in 2013. His latest
research is synthesis of magnetic material powder by wet-chemical process.

Akihide Hosokawa
Akihide Hosokawa is a senior research scientist at the National Institute of Advanced Industrial
Science and technology. He obtained his Ph.D. degree 2010 at McMaster University (Hamilton, ON,
Canada). His research interest is to develop new generation ultrastrong permanent magnets throughdevelopment of novel microstructural control techniques.

Wataru Yamaguchi
Wataru Yamaguchi is a senior research scientist at the National Institute of Advanced Industrial
Science and Technology. He received his doctorate from the University of Tokyo in 1997. He iscurrently working on methods to improve the performance of a group of rare-earth magnets throughgrain boundary engineering.

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Review Paper

Morphology Control of Transition Metal Oxides by
Liquid-Phase Process and Their Material Development †Shu Yin 1,2* and Takuya Hasegawa 1
1 Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, Japan
2 Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Japan

Abstract
It is well known that the functionality of inorganic materials strongly depends on the chemical composition,
morphology, particle size, crystal facet, etc., which are strongly influenced by the synthesis process. The precise
control of the synthesis process is expected to lead to the discovery of new functionality and improvement of thefunctionality of materials. For example, in a high-temperature solid-phase reaction, it is difficult to control themorphology of nanocrystals. On the other hand, synthesizing functional materials using solution processes, such ashydrothermal and solvothermal reactions, makes it possible to control the morphology and particle size precisely.
Usually, the solution process is strongly related to the dissolution reprecipitation mechanism. Therefore, the material
composition can be strictly controlled and is suitable for forming fine particles with high crystallinity. In this review
paper, the role of the solvent in the solution process, its effect on particle size and morphology of the transition metaloxide, and the related functional improvement will be focused. Furthermore, the direct formation of functional thinfilms by the solution process and the morphology control by non-oxide materials by the topotactic reaction will also
be introduced.
Keywords: morphological control, liquid phase synthesis, transition metal oxides, thin films, IR light response

1. Introduction

It is known that the functionality of inorganic materials
strongly depends on the chemical composition of the materials, particle morphology and size, specific surface area,
crystal structure, crystal facet, etc. In order to realize functional control of inorganic materials, precise control of thesynthesis process is important. Until now, while there aremany materials synthesis processes, liquid phase reactionuses liquid solvent as a reaction medium and carries out thereaction in a sealed pressure reaction vessel (autoclave) iscalled a hydrothermal or solvothermal reaction. Not only
the temperature but also the pressure control of the reaction
system, together with the use of additives, the particle
morphology and size of the product, crystal structure, crystal plane, crystallinity, etc. can be efficiently controlled.
Therefore, it has been accepted that the hydrothermal or
solvothermal processes show great potential for improving
the functionality of various materials, and offer many applications such as non-toxic pigments, photocatalysts, ultraviolet rays, and infrared shielding materials etc. (Cao etal., 2022; Guo et al., 2010; Hermawan et al., 2021; Taufik et
Received 29 June 2022; Accepted 25 July 2022
J-STAGE Advance published online 10 September 2022
* Corresponding author: Shu Yin;
1,2 Add: 2-1-1, Katahira, Aoba-ku, Sendai 980-8577, Japan
E-mail: yin.shu.b5@tohoku.ac.jp
TEL: +81-22-217-5597 FAX: +81-22-217-5597†

94al., 2020; Xue and Yin, 2022; Yin, 2015; Yin and Asakura,2019). The liquid phase reactions proceed under mild
conditions compared to high-temperature methods such as
solid-phase reactions, which are environmentally friendly.
They are expected to be effective for the industrial synthesis of materials. Usually, electric furnace heating is used
for traditional hydrothermal and solvothermal reactions.
Using a microwave irradiation heating method becomes
a complex chemical reaction field, resulting in a quickly
synthesizing process for nano / micro functional materials,
generating a metastable crystal phase, suppressing crystal growth, etc. (Liu et al., 2012). In addition, for oxidematerials, elements replaced by cations and anions in thematerial will significantly change their physical and chemical properties, and it is expected that new functionality ofthe material will be discovered, and the functionality ofmaterials will be dramatically improved (Cao et al., 2022;
Kageyama et al., 2018; Zhao et al., 2017). It is possible tosynthesize oxides and nitrides with different morphologies
and sizes via a topotactic method, using the precursors
with controllable morphology and particle size synthesizedfrom the solution process. It is expected that some novel
material functionality could be found because of theirunique morphologies (Hermawan et al., 2018b; 2020; Yinet al., 2012). Furthermore, the liquid phase method could
directly prepare powder materials and functional thin films(Yin and Sato, 2005). In this review paper, some examples

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Shu Yin et al. / KONA Powder and Particle Journal No. 40 (2023) 94–108

will be introduced in detail from the viewpoint of particle
morphology control and functionality control.

2. Composition, morphology, and functional
control by a solution reaction process
2.1 Hydrothermal and solvothermal reactions

A chemical reaction that occurs in the presence of hot
water at a temperature above 100 °C and high pressure ofmore than 1 atm is called a hydrothermal reaction. It is said
that it was initially developed by geology-related scientists
in the former Soviet Union to study the mechanism ofmineral formation, and to imitate the high-temperature,high-pressure reaction environment similar to that ofunderground magma. Since the 1980s, it has been used
as a new method for synthesizing inorganic functional
materials, and has made remarkable progress. Dependingon the type of chemical reaction, the hydrothermal process
is divided into five categories: hydrothermal oxidation,hydrothermal synthesis, hydrothermal precipitation, hydrothermal decomposition, and hydrothermal crystallization(Somiya, 1984). In recent years, hydrothermal reactions
have come to be used not only for inorganic materials but
also for organic synthesis. Instead of a water solvent, various organic solvents can be used as reaction solvents forthe mentioned liquid-phase reactions. Among them, whenliquid ammonia is used as a solvent, it is called amonothermal (Hertrampf et al., 2018). When using glycol or ionicliquid as a solvent, it was called glycothermal (Zhu et al.,2020) or ionothermal (Jaleel et al., 2022). A liquid phasereaction using various solvents containing water is universally referred to as a solvothermal reaction. Compared tohigh-temperature methods such as solid-phase reactions,many solvothermal reactions can synthesize materials ina relatively low-temperature range of around 200 °C anda low-pressure range with little energy consumption. In
addition, some post-processing processes such as calcination or pulverization can be omitted after the solvothermal
treatment. So, it is an environmentally friendly synthesis
method and could be classified as a soft chemical process(Byrappa and Yoshimura, 2012).
Many solvents in solvothermal reactions have change-

able physical and chemical properties due to temperatureand pressure changes, resulting in different reaction environments by simple condition changing (such as temperature, pressure, pH, additives, etc.). In general, solvothermal
reactions produce particles by a dissolution-reprecipitation
mechanism, in which the existence of water molecules
significantly affects the crystalline growth of the products(Sato et al., 1998).
Fig. 1 shows the dependence of the dissolution reprecipitation mechanism in the liquid phase reaction and theparticle size dependence of oxides material synthesized inwater or organic solvents. The water solvent has a high dielectric constant of 78.3, resulting in an environment witha low supersaturation and the particles tend to grow larger.
In contrast, in organic solvents such as ethanol (EtOH), thedielectric constant is low as 23.8, the dissolution abilityof the solvent is limited, and the supersaturation degree ishigh. In this case, instead of particle growth, nuclei are easy
to generate, and many fine particles tend to be produced
simultaneously, leading to the formation of fine particles
with less agglomeration. When water is used as a solvent,the particle size grows relatively large, and hard agglomeration can be observed because of hydrogen bonding inthe solution. On the other hand, when the organic solvent
methanol or ethanol is used, less agglomeration could beobserved. As the proportion of water in the water-ethanol
mixture solvent increases, a tendency can be observedthat the particle size of the oxide to be produced tends toincrease. This behavior is strongly related to the existenceof water, which can be reasonably explained by the dissolution-reprecipitation mechanism (Sato et al., 1998).

2.2 Composition, crystal phase control, andfunctionality of titanium dioxide

Semiconductor titanium oxide photocatalytic materials
can effectively utilize solar energy and realize environmental purification, self-cleaning, etc. Generally, oxide semiconductor photocatalysts have a large band gap and can
only be excited by ultraviolet rays. Therefore, it is essential
to provide a visible light response from the viewpoint of
improving the efficiency of solar energy. In order to realize

Fig. 1 (A) Dissolution reprecipitation mechanism in liquid phase reaction system, (B) Water-methanol ratio and particle size dependence of produced
oxide (inserted TEM photographs: oxides produced by (a) solvothermal reaction, and (b) hydrothermal reaction).

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Fig. 2 (A) TEM images, (B) Raman spectra and (C) photocatalytic deNOx photocatalytic activity of TiO2-xNy powders with different phase compositions selectively synthesized by solvothermal reaction at 190 °C for 2 h in (a) TiCl3–HMT aqueous solution at pH 1, (b) TiCl3–HMT aqueous solution
at pH 9, (c) TiCl3–HMT methanol solution at pH 9; ((a) anatase (□); (b) rutile (▼); (c) brookite (●); (d) P-25, standard photocatalyst Degussa P-25)).
[Part of the figure is reproduced from Ref. (Yin et al., 2005) with permission from the RSC]

the visible light activity of titanium oxide-based photocatalysts, anion-doping is an effective way (Asahi et al., 2001;
Kageyama et al., 2018; Zhao et al., 2017), and so far, RF(high frequency) magnetron sputtering, ion implantation,
partial oxidation of non-oxide titanium compounds are utilized as the methods for doping anions on titanium oxide.
Usually, it requires high-temperature treatment, expensive
precursors synthesized in a high-temperature atmosphere,
large-scale equipment, etc., and it cannot necessarily besaid that they are environmentally friendly. The synthesisof anion-doped oxides by solvothermal reactions can berealized in a solution around 200 °C. Various crystal phases
can be selectively synthesized, and no post-treatment isrequired, and anion dopants or dopants such as nitrogen,sulfur, and fluorine can be realized in one step (Cao et al.,2022; Yin et al., 2005; Yin and Asakura, 2019). In a typicalsynthesis process, a titanium compound (TiCl3) is introduced in urea or hexamethylenetetramine (HMT) aqueous
solution, followed by hydrothermal/solvothermal treatment
at 190 °C. By controlling the reaction temperature, solvent
and pH, it is possible to selectively synthesize three representative nitrogen-doped polymorphs of titanium oxide,i.e., anatase, rutile, and brookite phases as a single phase.
All the nitrogen-doped titanium oxides possessed excellent light absorption and excellent NOx removal (deNOx)
photocatalytic activity under ultraviolet and visible lightirradiation (see Fig. 2) (Yin et al., 2005).
In general, titanium oxide is widely used as a photocatalyst and white pigment. Adding various transition metalelements is usually utilized to improve the functionality

96of the photocatalyst and realize the coloring of inorganic
white pigments. However, in many cases, crystalline
defects are produced due to metal ion doping, hindering
photocatalytic activity improvement. In addition, due tothe toxicity of transition metal ions, safety risks are concerned when they are used as raw materials for cosmetics,
etc. Applying mixed ions doping makes it possible tosynthesize colorful titanium oxide without using transition
metals except for titanium (Cao et al., 2022). It is known
that titania possesses yellow color when nitrogen is doped
at the oxygen site of the titania and shows gray color when
the boron element is doped. When nitrogen and boron are
co-doped, the band gap is further reduced, and absorption
in visible light becomes remarkable. At that time, various
colors can be realized by changing their crystal phase. The
presence of slight differences in the levels of the original
valence bands of anatase, rutile, and brookite results in theformation of the colorful titanium oxide, such as red, cyan,
and yellow-green can be realized. This result indicates thattitanium oxide can be used as a colorful inorganic pigment,
which is non-toxic without any other transition metal addition (Cao et al., 2022).

2.3 Morphology and functional control of zinc
oxide

Zinc oxide possesses a hexagonal structure and haspiezoelectric properties. Z.L. Wang et al. reported themorphology control of zinc oxide by a high-temperature
sublimation method and various unique morphologies
such as nanowires, nano-plates, nano-combs, nano-rings,

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nano-springs, etc. can be synthesized (Wang, 2005; Wangand Song, 2006). In particular, the concept of nanogenerators using piezoelectric ZnO nanowires significantly
impacted the world. On the other hand, in the high-temperature sublimation method, since the generated morphology differs depending on the temperature of the substrate in
the electric furnace, it is difficult to ensure complete uniformity of the morphology because the substrate temperaturechanges a little with their position in the furnace. Therefore, it is challenging to realize the scale-up of the products.
On the other hand, the solution process can cover the
disadvantages of the high-temperature sublimation method
and is also suitable for product scale-up. When slowly crystallized in a low-concentration solution, zinc oxide tends
to grow according to the c-axis direction. While, when theion concentration increases, nucleus formation accelerates
and tends to become fine particles. Therefore, it is usuallychallenging to synthesize spherical microparticles with low
anisotropy. In the solution process, various factors such as
solvent, precursor type and concentration, reaction temperature and time, and various additives significantly affectthe zinc oxide crystal growth and particle morphology.
When various alcohol solvents were utilized, zinc oxidewith different microstructures, such as symbiotic nanorods,
radial symbiotic nano-screws, and incomplete crystals, wasobtained. It seems to be because solvents with different
dielectric constants have different solubilities on zincions, affecting the precipitation rate in the dissolutionreprecipitation process. Fig. 3 shows the SEM photographs of various representative particle morphologies
that successfully synthesized the solution process under

different synthesis conditions. The ZnO tends to produce
rod-like particles by growing in the c-axis direction(Fig. 3(a)(b)(f)(j) etc.) (Long et al., 2009a; Yin and Sato,2005). When ethylene glycol was used, the morphology
was significantly different from that obtained in ordinary
alcohols. A mono dispersion rod-like structure with a low
aspect ratio is successfully obtained. In all cases, the sizeof the particles was relatively uniform. By controlling theamount of ethylene glycol additive, it was possible to get
the rod-like morphology with a precisely controlled aspectratio. Furthermore, using different precursors makes it
possible to realize a significant change in morphology.
For example, when zinc chloride is used, plate-like zincoxide can be synthesized (Fig. 3-(c)). It is because zincoxide particles (Fig. 3-(c,d)) exhibiting plate-like or nutlike morphologies are generated through layered structure
precursors such as simoncholeite, by taking in chloride
ions and acetate ions into the products. The plate-likeprecursor can be decomposed by heat treatment while theirmorphology remains maintained, and converted to oxidewith a similar morphology by the topotactic reaction (Longet al., 2009b). Spherical particles are ordinarily difficult tosynthesize with high yields (Long et al., 2010a; 2010b). As
an additive, the triethanolamine could be utilized to synthesize uniform spherical microparticles (Fig. 3(e)). Also,
the as-prepared zinc oxide particles are further annealedin hexamethylenediamine (HMT) solution for more than
72 h. In that case, the microstructure of the particle surface
is significantly changed by the dissolution reprecipitation
reaction, resulting in the formation of developed uniquesurface morphologies, such as screw-shaped, hollow nut,

Fig. 3 SEM images of the zinc oxide particles with various morphologies synthesized by solution reaction process. (a) 0.01 M ZnNO3–HMT (95 °C,
3 h), (b) 0.05 M ZnNO3–50 %EG–HMT (95 °C, 12 h), (c) 0.05 M ZnCl2–50 %EG–HMT (95 °C, 12 h), (d) 0.05 M Zn(CH3COO)2–50 %EG–HMT(95 °C, 12 h), (e) 0.025 M ZnNO3–Diethanolamine–HMT (95 °C, 3 h), (f) post heat-treatment (a) in HMT solution for 72 h, (g) post heat-treatmentof (d) in HMT solution for 72 h, (h) post heat-treatment of (e) in HMT solution for 72 h (i) 0.025 M ZnNO3–50 %EG–HMT (95 °C, 3 h), (j) 0.001 M
ZnNO3–75 %EG–HMT (95 °C, 24 h), (k) 0.001 M ZnNO3–Urea (95 °C, 3 h), (l) microwave-hydrothermal reaction at 190 °C for 10 min, with monoethylamine additive. [Part of the figure is reproduced from Ref. (Long et al., 2010a) with permission from Springer Nature]97

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Fig. 4 Photoluminescence spectra of ZnO crystals with different
morphologies.

rose-shaped, with a large specific surface area (Fig. 3(f–h))(Long et al., 2010a; 2010b). In addition, when urea wasused as a precipitating agent, the effect of CO2 gas produced by hydrolysis reaction resulted in the formation ofunique flower-shaped particles (Fig. 3(k)).
In the microwave-assisted hydrothermal and solvothermal process, instead of the electric furnace heating, irradiating a microwave with a frequency of 2.45 GHz equivalent
to that of a microwave oven is utilized. The products can beobtained in a short period of heating. In the reaction field towhich 2.45 GHz microwave irradiation is applied, a large
amount of crystal nuclei are produced rapidly due to thefast temperature rise affecting the reaction product generation behavior. Also, it is possible to synthesize metastable
crystal phase and unique morphologies of products that are
difficult to be obtained by normal hydrothermal reaction(Liu et al., 2012). As shown in Fig. 3(i), zinc oxide synthesized by microwave-assisted hydrothermal reaction has apetal-like morphology with uniform particle size.
The morphology and microstructure of zinc oxide particles change significantly in the solution. It depends on thesolubility of zinc ions in the solvent, the rate of reprecipitation, and differences in ion adsorption ability on the surfaceof zinc oxide crystals. Therefore, some physicochemical
properties related to their particle morphology are expected. For example, all the ZnO can be excited by 325 nm
light irradiation, and a peak of near-ultraviolet emission of
390 nm can be observed. It was also observed that different
particle morphology results in different luminescence
properties with a clear morphological dependence (LongT. et al., 2010a; 2010b), and the emission intensity shows
the sequence of bud-like > rod-like > flower-like > nut-like(see Fig. 4) (Long et al., 2010b).

2.4 Homogeneous particle morphology andfunctional control of cerium oxide

Similar to the plate-like zinc oxide synthesis process, pre-98

cursors’ topotactic morphological transcription techniques
can be used to control the morphology of other inorganic
compounds. For example, in the case of cerium oxide,
spherical-like, rod-like, and plate-like cerium compound
precursors can be successfully synthesized by applying aroom-temperature aging process before a normal homogeneous precipitation reaction at 70 °C. Then, the precursors
are decomposed to produce cerium oxide by heat treatment.
As a result, the topotactic reaction can successfully synthesize a spherical, rod-like, plate-like cerium oxide. Under
typical synthetic conditions, a 0.008 M aqueous solutionof cerium (III) nitrate is mixed with a 0.5 M aqueous urea
solution and aged at room temperature (25 °C) for 0 h,
72 h, 144 hours, followed by heating the aged solution at
70 °C for 2 hours. The cerium carbonate-based precursors
with different morphology, spherical-like, rod-shaped, andplate-like, were obtained under different room temperatureaging times. The size of the seed crystal generated at theinitial stage changed depending on the room temperatureaging time, thereby changing the shape of the grown crystal at the evaluated temperature process. Finally, similarmorphologies of cerium oxides could be obtained by thefollowing heat treatment at 400 °C (Yin et al., 2010).
It has also been found that cerium carbonate precursors
having a larger plate-like size can be synthesized at room
temperature by a simple solution process (Yin et al.,2012). In a typical thesis process, 5 mL of 1.0 M trivalentcerium nitrate solution was slowly added into a 45 mL
0.33 M–0.45 M sodium hydrogen carbonate/sodium carbonate mixed solution. Then, the precipitate slurry is kept
in a constant temperature water bath of 25 °C for 24 hours,
followed by filtration and washing, then drying at 60 °C
overnight to obtain a single crystal of plate-like cerium
carbonate octahydrate. After calcination at 400 °C for 1 h,
the plate-like cerium oxide particles could be synthesizedwith similar plate-like morphology to that of the cerium
carbonate single crystal. Fig. 5 shows the SEM, TEM
images, crystal structure, and morphologies changing
process of the plate-like cerium carbonate octahydrate
precursor. Since cerium carbonate octahydrate precursor
has a layered structure, plate-like morphology is easy to
be formed. In the early stages of the reaction, nucleation is
first carried out, then the crystal growth proceeds to create
the plate-like morphology, and the plate-like particles gradually become larger. At about 30 min of reaction time, anapparent plate-like crystal is formed. As the time is further
extended, tiny crystals coexisting dissolve progressively,
and the cerium ions gather on the surface of the large crystals and gradually become larger, because of the Ostwald
ripening mechanism. The spiral growth of crystals was also
observed in some cases. The as-prepared plate-like cerium
oxide particles possess high gloss derived from their large
plate-like particle size and also show good slipperinessand suppressed oxidation catalytic activity, indicating its

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Fig. 5 Typical (a) SEM and (b) TEM image (insert SED pattern) and (c) the layered structure of Ce2(CO3)3·8H 2O projected on the a–b and a–c planesof the plate-like cerium carbonate hydrate single crystal synthesized in 0.1 M Ce(NO3)3 and 0.3 M NaHCO3 mixed solution. (d) Mechanism of crystalline formation and crystalline growth. [Reproduced from Ref. (Yin et al., 2012) with permission from the RSC]

potential applications as new functional cosmetics(Minamidate et al., 2010; Yin et al., 2012).

2.5 Novel water molecule control releasesolvothermal process (WCRSP) for thesynthesis of uniform nanomaterials

Usually, a simple solvent such as water or alcohol is usedin the conventional solvothermal reaction. In this case,particle growth and agglomeration are easy to occur. By
controlling the dielectric constant of the solvent and introducing some additives, it is possible to control the numberof crystal nuclei and the crystal growth rate of particle
formation, which is effective for synthesizing fine particles
with good dispersibility. Also, the particle morphology
can be precisely controlled by using a surface surfactant
as an additive. Since the slight amount of water in thesolvent significantly affects crystal growth, further precise
control of the synthesis process is required to synthesize
monodispersed nano-sized uniform particles. Here, a novel
solvothermal process, denoted “Water Controlled-Release
Solvothermal Process (WCRSP)” is designed to synthesize
the particle with homogeneous morphologies.
The WCRSP uses a mixture of alcohol and carboxylic
acid as a reaction solvent and skillfully utilizes the waterproduced by the esterification reaction between alcohol andcarboxylic acid at high temperatures. The released watermolecules amount in the reaction solvent can be precisely
controlled by changing the ratio of the starting alcoholand carboxylic acid solvents. The released water amountsignificantly affects the crystal growth of the products. In
principle, changing the composition of solvents will resultin different water amounts in the reaction system, finallyaffecting the particle size and morphology of the products.
Therefore, the WCRSP process can be utilized for controlling particle size and morphology because the watermolecular is slowly released, and the reaction environment
can be kept in a constant state, resulting in the formation ofhomogeneous particles size and morphology (Guo et al.,2010; 2011d; 2013a).
Fig. 6 shows the principle and reaction mechanism of
the WCRSP process. There are three key points in the
WCRSP process. First, water is not the starting solvent in
the reaction, but carboxylic acids and alcohols are used.
At high temperatures, the esterification reactions proceed,
and water molecules are produced and released in thereaction system; Second, the number of water molecules
can be previously controlled by controlling the solvent
composition; Third, the subsequent reaction should be ahydrolysis reaction. In some cases, the release of watermolecules is also possible by etherification between various alcohol molecules. Still, in the case of esterification
using carboxylic acids and alcohols, the control of watermolecules is efficient. When the reaction temperature rises,
the reaction between or among the liquid solvents suchas ethanol (C2H5OH), n-butanol (C4H9OH), and acetic
acid (CH3COOH) is carried out. The self-dehydration of
ethanol, n-butanol and the cross-dehydration between different organic molecules are carried out. Also, the reaction99

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with acetic acid produces ethyl acetate (CH3COOC2H5) or
n-butyl acetate (CH3COOC4H9), at the same time releasinga certain amount of water molecular (Gao et al., 2019).
Therefore, an increase in the reaction temperature benefits
the formation of ether.
In the case of titanium oxide formation, tetra-nbutyl titanate (Ti(OC4H9)4) or titanium tetraisopropoxide(Ti(OC3H7)4) are utilized as a titanium source, acetic acid
(CH3COOH) and various alcohols are used as a solvent.
The mixture is sealed in a 100 ml Teflon-lined stainlessautoclave and reacted under a hydrothermal reaction at
200–240 °C for 24 h. The typical chemical reactions areshown in Eqns. (1) & (2):R1–COOH + R2–OH → R1–C–O–R2 + H2O
Ti(OC4H9)10 + 2H2O → TiO2 + 4C4H9OH
Nb2(OC2H5)10 + 5 H2O → Nb2O5 + 10C2H5OH

(1)
(2)
(3)

The esterification reaction of carboxylic acids andalcohols produces esters and water molecules. The watermolecule produced by Eqn. (1) is utilized in the hydrolysis reaction with tetra-n-butyl titanate of Eqn. (2), andfinally, titanium oxide is synthesized. The number of watermolecules produced can be controlled by controlling theratio of carboxylic acids and alcohols. Furthermore, using
various alkoxides makes precise composition control ofthe products possible. For example, the Nb-doped titaniumoxide (NTO) can be synthesized by introducing the desired
amount of a niobium alkoxide (Nb2(OC2H5)10) into the titanium alkoxide to realize the composition control, as shownin the reaction of Eqn. (3) (Asakura et al., 2018).
Fig. 7 shows the TEM images of the 15 % Nb-doped
TiO2 particles (NTO) synthesized in the solvents with various ethanol: acetic acid (x:y) ratios. The produced and released water amount increases with the acetic acid amountincrement in the solvent. As mentioned in Fig. 1, during thedissolution-reprecipitation process, water as a solvent witha large dielectric constant prefers to enhance crystalline
growth, resulting in large particle size. As a result, lessFig. 6 Principle and reaction mechanism of the WCRSP for TiO2 formation in ethanol and acetic acid solution (TsOH (p-toluenesulfonic acid) is anacid catalyst that acts as a reaction-promoting catalyst, and the reaction can proceed without using it.) [Reproduced from Ref. (Gao et al., 2019) withpermission from the IOP].

Fig. 7 TEM image of 15 % Nb-doped titanium oxide particles (NTO (x:y), x:ethanol (ml); y:acetic acid (ml)) synthesized by WCRSP in an acetic
acid-ethanol mixed solvent. (a) NTO(60:0), (b) NTO(55:5), (c) NTO(50:10), (d) NTO(40:20), (e) NTO(35:25), (f) NTO(30:30). [Reproduced from Ref.
(Asakura et al., 2018) with permission from the IOP]

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acetic acid in the WCRSP produces less water, leading to
the formation of the tiniest particles, as shown in Fig. 7(a).
When the ethanol: acetic acid ratio reaches 30:30 (i.e., 1:1),
the most significant amount of water is produced, leading
to the formation of the large particles, as shown in Fig. 7(f).
Another example is the mixed-valence state tungsten
bronze structure MxWO3 (x = 0.33, M = Cs, Na, K, etc.),
in which alkali metal ions are doped into the W site tungsten. The mixed-valence state tungsten base compound
possesses high free electron density. It is expected to be
applied to various new functional materials such as infrared shielding materials, transparent conductive materials,
etc. Conventionally, MxWO3 is synthesized by solid-state
reaction, and a high temperature of above 1000 °C is required. Usually, high reaction temperature results in a large
product particle size. Also, morphological control is quite
challenging. (Takeda and Adachi, 2007). Similar to that ofNb–TiO2, the influence of the solvent is also obvious forthe synthesis of M0.33WO3. Fig. 8 shows the SEM imagesof the MxWO3 (M = Cs, K, Na) obtained by the solution
process. The Cs ion has a large ionic radius and is difficult
to dope with high concentration. The maximum Cs doping
amount is about 1/3, forming a tunnel structure with ahexagonal phase (x = 0.33). In a traditional hydrothermal
synthesis process, a single phase of Cs0.33WO3 can be
successfully produced; however, the particle growth isremarkable, and the particle size reaches large than 500 nm(Fig. 8-(a)) (Liu et al., 2010).
WCl6 + 3H2O → WO3 + 6HCl
(4)
WO3 + xCsOH + x/4 C2H5OH→ CsxWO3 + x/4 CH3COOH(5)On the other hand, by using the WCRSP process underthe esterification reaction, the number of released watermolecules can be precisely controlled. As a result, thesingle phase of MxWO3 with rod-like uniform morphology

can be successfully obtained. Eqn. (4) shows that tungsten
oxide is first generated, tungsten oxide further reacts with
CsOH, the presence of alcohol also serves as a reducing
agent. Finally, a part of tungsten is reduced, and themixed-valence state CsxWO3 is generated (Eqn. (5)). Because of the continuously released small amount of waterin the reaction system, the particle growth due to the dissolution reprecipitation is suppressed. As a result, the particle
size can remain small, producing Cs0.33WO3 nanorods witha length of 90 nm and a diameter of 10 nm (Fig. 8(b)) (Guoet al., 2010; 2011d), which is about 1/5 or less of the sizeof that prepared by normal hydrothermal process (500 nm)(Liu et al., 2010). The tungsten bronze structure CsxWO3
has a hexagonal phase structure shown in Fig. 8(c). In thecases of smaller ions such as sodium and potassium being
utilized instead of cesium, the nanowire morphology (Guoet al., 2010; 2011a; 2011c; 2011d) with long length andtetragonal structure will be produced (see Fig. 8(d–f)). The
small particle size of the mixed-valence state possesses
many benefits such as excellent IR shielding properties,
less light scattering resulting in high transparency, andease of being introduced in cells for bio-applications. The
related results will be introduced in the following.
Similarly, in the WCRSP process utilizing alcohols andcarboxylic acids as solvents, it is possible to synthesize andcontrol the morphology of various other oxide particles.
Fig. 9 shows various successful synthesized examples withunique particle morphologies. By using the WCRSP process, TiO2 spherical particles with different sizes (Guo etal., 2013a), unique octahedron attached (Ta,N)-TiO2 fibers,
SnO spherical/hollow particles, plate-like, Moyatto balllike, and tailed-shaped W18O49 particles, etc. are successfully synthesized by using different precursors (Guo et al.,2012a; 2012d; Yin et al., 2018). All the particles possess
uniform and unique morphology. Therefore, the WCRSP
method can be used as a general-purpose synthesis way. In

Fig. 8 (a) SEM photograph of M0.33WO3 by traditional solvothermal reaction, (b) TEM image of monodisperse Cs0.33WO3 nanorods synthesized by
WCRSP process using ethanol and 10 % acetic acid as reaction solvents (90 nm in length, 5–10 nm in diameter); (c) Hexagonal crystal structure of
Cs0.33WO3 (d) SEM image of NaxWO3, (e) TEM image of K xWO3 nanowire; (f) crystal structure of tetragonal M xWO3, (M = K, Na). [Reproduced from
Ref. (Guo et al., 2011a; 2011d, Liu et al., 2010) with permission from the RSC, Elsevier, ACS]101

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Fig. 9 Uniform oxide particles with controllable morphology synthesized by WCRSP. (a) TiO2 spherical particles, (b) Fibrous (Ta, N) –TiO2
with the octahedral structure on the surface together with high specificsurface area; (c) SnO spherical particles; (d) SnO hollow particles; (e)Plate-like WO3 particles, (f) Moyatto ball-like W18O49 particles (using
W(CO)6 as a precursor), (g) W18O49 (using W(EtO)6 as a precursor).
(h) W18O49 (using WCl6 as a precursor). [Part of photos are reproduced
from Ref. (Guo C. et al., 2012a; 2012d; 2013a; Yin S. et al., 2018) withpermission from the RSC, ACS]

addition to ethanol, a series of higher alcohols such as butanol, ethylene glycol, and higher carboxylic acids such as
acetic acid, butanoic acid, hexanoic acid, and octanoic acidis available as a reaction solvent and expected to be applied
for the synthesis of various kinds of uniform inorganic
functional particles with unique morphologies.
Fig. 10 shows the schematic illustrations of the simulated experiment and temperature increment during the
halogen lamp irradiation, together with the transmittanceand reflectance spectra of CsxWO3 nanorods and ITO glass.
Again, the CsxWO3 film shows a better temperature depress
effect than those of ITO. The mixed-valence state tungsten
bronze compounds MxWO3 (M = Cs, Na, K, etc.) havehigh free electron density. The free electrons move backand forth between W5+ and W6+, resulting in an absorptionof light corresponding to the vibration of electrons, leading
to infrared light absorption. The tungsten bronze MxWO3
is comparable with tin-doped indium (ITO) transparent
conductive oxide film. The MxWO3 can cover a morecomprehensive infrared shielding range, realize excellent
IR light-shielding properties over a broad infrared region
than ITO conductive thin films, and show great potential asa smart window.
In order to improve visible light transparency andfunctionality of thin films, it is effective to reduce lightscattering by using nanosize particles. The MxWO3 particles synthesized by the WCRSP show a tiny size of about
20 nm, and light scattering is suppressed when a thin film
is formed, resulting in high transparency. Furthermore,
the resistance of the pressed powder reached 0.03–0.96(Ω·cm), suggesting its potential as a conductive material(Guo et al., 2012c). In addition, the nanowire morphology

Fig. 10 (a–c) Schematic illustrations of simulated experiment and (d) temperature increment during the halogen lamp irradiation for 1 h. Sealed
boxes were covered by quartz glass, CsxWO3 coated quartz glass, and ITO glass (10 Ω/sq.). (e) Transmittance and reflectance (R-a, R-b, R-c) spectraof Cs xWO3 nanorods and ITO glass (5 Ω/sq. and 10 Ω/sq.), The background shows the relative energy wavelength distribution of solar spectrum on thesea level. [Reproduced from Ref. (Guo et al., 2011b; 2011d) with permission from the RSC]

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of MxWO3 (M = K, Na) shown in Fig. 8(c,d) possesses
a low interparticle resistance so that it can realize better
conductivity than nanoparticles (Guo et al., 2011c; 2012c).
Some other tungsten bronze materials, such as (NH4)xWO3
and W18O19 with a reduced tungsten valence state, also
consist of mixed-valence state of W5+ and W6+. These
compounds also contain free electrons and excellent infrared light-shielding properties (Guo et al., 2012b; 2012d;
2013b).
Fig. 11 shows the correlation between the particle sizeof W18O19 and the infrared shielding property. The W18O49
is also one of the mixed-valence state tungsten-based materials with a monoclinic crystal structure. As the particle
size decreases, it can be confirmed that the infrared lightshielding property has been improved. It is thought tobe due to the polaron effect on the surface of the W18O49
particles. In the W18O49, W5+ and W6+ are reversed dueto the electrons’ movement. It leads to the surface charge

revision of tungsten ions next to each other. As a result,free electrons cause plasmon oscillations on the surfaceof tungsten in the mixed-valence state, shielding lightwith energies lower than the infrared wavelength without
passing through. The larger the surface of the particles,
the stronger the polaron effect is considered to appear(Guo et al., 2012d). On the other hand, since WO3 is not amixed-valence state substance, it cannot cover the light in
the infrared region and has no infrared shielding property.
In addition, uniform Cs0.33WO3 nanoparticles can successfully be introduced into cancer cells by hydrophilic
surface treatment using a PEG-silane coupling agent.
The effect of the temperature rise under irradiation with a
980 nm infrared laser (0.07 Wcm–2) is confirmed, suggesting that Cs0.33WO3 nanoparticles may be utilized as a new
hyperthermia treatment material for cancer cells (Guo etal., 2013b). Fig. 12 shows the temperature increment effectunder IR light irradiation, with and without hydrophilic

Fig. 11 (A) SEM image of spherical W18O49 with different particle sizes; (B) Transmission spectrum (infrared shielding characteristics) of W18O49
samples; (C) Infrared light shielding mechanism of W5+-W6+ mixed-valence state tungsten-based compounds. [Reproduced from Ref. (Guo et al.,2012d) with permission from the ACS]

Fig. 12 (a)Temperature increment thermographic images and (b)temperature distribution profile with/without Cs0.33WO3 nanorods ((c) TEM image)dispersed in simulated culture solution PBS, under infrared light irradiation for 2–12 min. (d) Efficient temperature increment by controlling theconcentration of nanoparticles and irradiation time. [Reproduced from Ref. (Guo et al., 2013b) with permission from RSC]103

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Cs0.33WO3 nanoparticles introduced in the pseudo-culture
medium PBS. An efficient temperature increment effect canbe observed by controlling the concentration of Cs0.33WO3
nanoparticles and the IR light irradiation time. The cancer
cells take in excess nutrition, and blood vessels are often
incomplete. It is difficult to transport decomposed productsin blood; therefore, lactic acid accumulates and shows theproperty of being vulnerable to temperature. As a result, thecancer cells are more susceptible to heat than normal cells.
IR light possesses a deep reach distance in the humanbody and causes minor damage to normal cell tissues.
By incorporating Cs0.33WO3 nanoparticles into cancer
cells and exposing them to infrared light irradiation, it ispossible to selectively kill cancer cells that can reach atemperature range above 42 °C under IR light irradiation.
In order to efficiently introduce the nanoparticles thatabsorb infrared rays into the cancer cell, it is necessary
to produce a uniform morphology and small particle size,
so the WCRSP process shows its effectiveness and great
potential for practice bio-applications.

2.6 Direct formation of functional thin films bysolution reaction

Some vapor synthesis methods, such as vapor deposition, CVD, sputtering, etc., are usually used for thin-film
formation. In the above cases, external energy such as
high vacuum, high frequency, and a high electric field is
required for these synthesis processes. On the other hand,
as a method of forming a thin film starting from a powdermaterial, pressure molding method, doctor blade method,
spin coating method, or using the powders sintering bodyor powder casting body as a target for sputtering arewidely utilized. Furthermore, as a novel thin-film synthesis
method, the direct film formation of oxides is also possibleusing a solution process. Here, an example of the direct
formation of a zinc oxide particle film is introduced.
In Section 2.3, it was introduced that the zinc oxide
particles with various morphologies can be synthesized in
the aqueous zinc nitrate solution with the addition of a specific concentration of hexamethylenetetramine (C6H12N4,
HMT). A glass substrate was introduced into the reaction
system during the synthesis process. Not only the zinc oxide particles are produced in the solution, but a uniform thinfilm could also be precipitated on the surface of the glass
substrate directly. The mentioned liquid-phase reaction can
synthesize a relatively durable oxide particle film. Furthermore, the nanostructure of the zinc oxide particle filmchanged significantly by a long-term aging process treatment, similar to that of the formation process of rose-likeparticles. Fig. 13 shows the hydrophilicity evaluation results and surface morphology of particle films synthesized
by the solution process. The contact angle of water drop on
the surface of the zinc oxide single crystal (001) plane was
93°. However, the zinc oxide particle film with nanorod
morphology shows a contact angle of 165°, indicating a
super-water repellency of the surface. In contrast, the ZnO
particle thin film with developed nano-screw morphology
shows a complete opposite property. The water drop on the
ZnO surface shows a contact angle of 0°, showing superhydrophilicity. Interestingly, the same material of ZnO
with quite different morphologies can achieve the opposite
super hydrophilicity and superhydrophobicity (Long et al.,2010b; Yin and Sato, 2005). The unique microstructure ofzinc oxide thin films is thought to depend on the solubility

Fig. 13 (Upper) Top view CCD image of water drop on various surfaces. (a) ZnO single crystal (001) surface; (b) superhydrophobic surface of ZnO
film with nanorod superstructure; (c) super hydrophilic surface of ZnO film with the nano-screw superstructure; (Middle) Side view CCD images(Evaluation of contact angle / hydrophilicity); (Bottom) SEM and TEM images of (b) nanorods and (c) nano-screws. [Reproduced from Ref. (Yin andSato, 2005) with permission from the RSC]

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Fig. 14 (Left) Formation process and SEM image of TiO2-xNy/ZnO oriented tube structure composite thin film; (Right) Excellent transparency andconductivity of ZnO film consisting of hexagonal nanorods oriented on the substrate.

of zinc ions in the solvent, the dissolution-reprecipitation
rate, differences in ion adsorption capacity at different zincoxide crystal surfaces, etc.
This zinc oxide morpho-regulatory method can also be
used to realize new functionality by combining it withother functional materials. Combining high photocatalytic
active nitrogen-doped titanium oxide with developed
nanostructured zinc oxide particle thin film is an exciting
combination. The thin film of TiO2-xNy on a glass substrateis prepared by a spin coating method at first, and then thefilm is set in a Zn(NO3)2–HMT solution to precipitate a
ZnO complex film at 95 °C. In another case, orientatedZnO nanorods are grown on the TiO2-xNy surface, thenleached by alkali solution to produce a complex tubular
ZnO structure (Fig. 14 (left)). The complex structure of
ZnO or tubular ZnO possesses excellent adsorption properties, which can accelerate the photocatalytic reaction on
TiO2-xNy. The complex structures of the ZnO / tubular ZnO
thin films also have a fantastic light-limiting effect. The irradiated light can be reflected inside the complex structure
or tubular structure to achieve multiple irradiations on the
TiO2-xNy photocatalytic surface (Huang et al., 2012; Yin etal., 2008).
In addition, in order to get a high-quality and dense ZnO
thin film, another seed crystalline method is utilized. At
first, ZnO nanoparticles less than 10 nm are prepared andcoated in advance on the surface of a glass substrate bya spin coating method. Then, the as-prepared thin film is
introduced in a Zn(NO3)2–HMT solution to precipitate a
ZnO complex film at 95 °C. The nano-sized crystals on thesubstrate act as seeds during the following precipitation
process. It is found that zinc oxide-oriented dense film thatgrew uniformly in the c-axis direction can be synthesized
perfectly (Fig. 14 (right)). The hexagonal columnar rods
are arranged in the same direction, making it possible tobe a dense ZnO thin film in which only the {002} surfaceof zinc oxide is exposed. Because of the highly orientatedcrystal growth with high density and less porosity, suchhigh transparency of 81.2 % and excellent conductivityof 9.68×10–2 Ω·cm are successfully realized, indicating

its potential applications on transparent oxide conductive
thin film.

2.7 Morphological control and functional
improvement of non-oxide by topotactic
reactionTransition metal nitrides and oxynitrides have been focused on their novel functionality, and their importance has
been recognized. By replacing the oxygen site of oxides
with an anion having a different electronegativity, the material’s electronic structure will be significantly changed,
and the discovery of some novel physical and chemical
properties is expected (Kageyama et al., 2018). Usually, thesynthesis of transition metal nitride and oxynitride requires
a high temperature, and its morphology control is difficult.
As mentioned above, the solution process has the merits ofparticle size and morphological controllability so that theoxide could be utilized for the synthesis of the morphology
controllable nitrides and oxynitrides. At first, hydrothermal or solvothermal reactions synthesize morphologicallycontrolled metal oxides or hydroxide precursors. Then,these precursors are utilized to synthesize the non-oxidematerials with similar particle morphologies and sizes by
topotactic reactions under relatively low and mild reaction
conditions. This method provides a possibility to synthesize
nitrides and oxynitrides with controllable morphologies.
For example, under hydrothermal conditions, aluminumhydroxide γ-AlOOH (boehmite) with a nest-like or plate
shape morphologies can be synthesized by controlling thereaction temperature, pH, and adding a surfactant. By using
these aluminum hydroxides as the precursor, it is possible
to synthesize aluminum nitride with different morphologies
such as plates, fibers, nanosize, etc., by treating the mentioned precursors in ammonia gas. However, if the nitridation temperature is too high, the morphologies of the oxide
precursor may collapse. During the nitridation treatment,a small amount of hydrazine addition can accelerate thenitridation reaction and reduce the nitridation temperatureand time (Hermawan et al., 2020).
Fig. 15 shows the SEM photographs of boehmite and105

Shu Yin et al. / KONA Powder and Particle Journal No. 40 (2023) 94–108

Fig. 15 (a) Topotactic morphology conversion from AlOOH (left column) to AlN (right column); (b) sintering behavior of AlN particles with different morphology; (c) Hydrogen gas response properties of morphologically controlled AlN. [Reproduced from Ref. (Hermawan et al., 2018b; 2020)
with permission from the Springer & Taylor]nitrides with various morphologies. The sintering performance and hydrogen gas sensing properties of nitrides withvarious morphologies are also characterized. It is suggested
that such topotactic reaction could realize the morphology
control of non-oxides materials. Furthermore, the morphological dependence of functionality of the nitrides isobserved. As shown in Fig. 15(b), the sintering propertiesof nitrides with different particle morphologies are significantly changed. However, the plate-like particles areeasily arranged in the same direction and could be sinteredat relatively low temperatures. Furthermore, aluminum
nitride having a plate-like morphology, showed a superiorhydrogen gas sensing functionality compared to aluminum
nitride with other morphologies (Hermawan et al., 2018b).
Also, in the case of α-GaOOH precursor, the same topotactic reaction enables the synthesis of nitrides (GaN) withthe same morphology of oxides (β-Ga2O3). Therefore, it isfound that the GaN possesses better hydrogen gas sensing
sensitivity than that of β-Ga2O3 with the same morphology(Hermawan et al., 2018a).

3. Future outlook

The morphology control of various transition metaloxide materials and their functionality development are
reviewed. This paper focuses on the synthesis process andthe relationship between morphologies and their functionalities. Hydrothermal and solvothermal processes are environmentally friendly processes that provide the possibility

106

for morphologies and particle size control. Water molecules
are essential in promoting the dissolution—reprecipitation
process and significantly affect the particle morphologies
and size. The utilization of esterification reactions between
various alcohols and carboxylic acids (WCRSP process)allows uniform morphologies and controllable particle
size. Furthermore, the effectiveness of morphological control using the topotactic reaction is confirmed. The oxides
and non-oxides with unique morphologies show many
applications, such as photocatalysts, ultraviolet/heat ray
shielding, cancer cell hyperthermia treatment, hydrophilic/hydrophobic thin films, and gas sensor, etc. Different novel
functionality can be realized by morphological control,material design, and the optimization of their synthesis
processes, and further research and progress are expectedin the future.

Acknowledgements

This scientific work was partly financed from the JSPS
Grant-in-Aid for Scientific Research on Innovative Areas“Mixed anion” (No. 16H06439, Grant-in-Aid for Scientific
Research (20H00297).

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Authors’ Short Biographies
Shu YinDr. Shu Yin received a Ph.D. in applied chemistry from Tohoku University in 1999. After that, hebecame a research assistant, lecturer, Associate Professor, and then a Full Professor at the Institute ofMultidisciplinary Research for Advanced Materials (IMRAM), Tohoku University. Now he is alsoa Professor at the Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. His
research interests include morphological control, photocatalysts, UV-IR shielding, smart window,
gas sensor, hydrothermal/solvothermal process, etc. He had won some scientific awards such as the69th CerSJ Awards for Academic Achievements in Ceramic Science and Technology, and the APTDistinguished Paper Award etc.

Takuya Hasegawa
Dr. Takuya Hasegawa received a Ph.D. degree in engineering from Niigata University in 2016.
He joined at Kochi University, Japan as an Assistant Professor in 2016, and then transferred to theInstitute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University as
Assistant Professor in 2019. His research topics are the development, including the synthesis, structural characterization and discovery of functionalities, of new photo-functional inorganic materials.

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Review Paper

Cellulose Nanofibrils Pulverized from Biomass Resources:
Past, Present, and Future Perspectives †Tetsuo Kondo
1 Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Japan

2 Enviro-Sustainable Materials Science Lab, Institute of Agriculture, Tokyo University of Agriculture and Technology, Japan

Abstract
Advances in nanotechnology have changed conventional concepts in materials science. This has also strongly
influenced natural biomass products with hierarchically built-up structures. In general, hierarchical structures in biobased materials are built up by molecular self-assembly, followed by nanoassembly to form higher-level structures.
Key to each step is the formation of interactions at each individual scale. Nature usually achieves such fabrication
through a bottom-up process. However, fabrication can also be achieved through a top-down process, with various
such downsizing methods now in development. This review article aims to describe trends in nanofiber technology
among downsizing processes applied to cellulose as a representative biomass, ranging from fundamentals to recent
techniques. The advantages of our recently developed technique, nanopulverization by aqueous counter collision,
are also discussed. This method successfully decomposes interactions selectively without damaging the molecular
structure, finally liberating components of various sizes into water to provide a transparent and homogeneouscomponent–water system. As nanocellulose research is a broad area involving various fields, the cited references arelimited to the scope of the author’s knowledge.
Keywords: cellulose nanofibril, cellulose nanocrystal, nanocellulose, aqueous counter collision (ACC), TEMPOoxidized cellulose nanofibril, amphiphilicity, Janus-type nanofibril surface

1. Introduction

Nanotechnology, which is based on clarification of thesurface structure of substances on the nanoscale and theirinterface interactions, has changed conventional concepts
in materials science. Nanotechnology has also been extended to living organisms, known as nanobiotechnology.
Furthermore, nanometer-scale interactions between macromolecules and organisms have the potential to create new
structures (Drexler, 1992; Taton, 2003).
Regarding nanofiber technology in the field of textiles,
the US government was quick to recognize the innovative
effects of nanofibrils and consider them a strategicallyimportant technology. Japan has fallen behind in nanofibril
development, information technology (IT), and humangenome decoding owing to the absence of a national strategy. However, Japan shows great potential regarding fiber
technology owing to its long history of cultivating fiber andtextile industries. In the 21st century, evaluating the characteristics of practical fibers and controlling their structure
at the nanoscale have become important. This can lead†
1
2

Received 29 August 2021; Accepted 6 December 2021
J-STAGE Advance published online 16 February 2022
Add: West 5th, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
Current Add: 3-5-8 Saiwai-cho, Fuchu-shi, Tokyo 183-8509, Japan
E-mail: tekondo@agr.kyushu-u.ac.jp; tuat-tekondo@go.tuat.ac.jp
TEL/FAX: +81-42-367-5588

to light high-strength structural materials for optical andelectronic devices, drug discovery, regenerative medicine,and environmental purification.
This review article focuses on cellulose as a representative natural fiber. A brief history of cellulose is provided,
and then nanocellulose manufacturing methods are outlined, covering the nature of cellulose nanofibers anddifficulties in their manufacture. Future perspectives on
nanofibrils are also described.
Herein, the term “nanocellulose” represents nanosizedcellulose materials with a fiber width of 100 nm or less,which are generally categorized as cellulose nanofibrils(CNFs) with high-aspect ratios (≥100) and cellulose nanocrystals (CNCs) with low-aspect ratios (<100).

2. History of cellulose and nanocellulose
2.1 Brief history of cellulose materials

Cellulose, the most abundant natural fiber, is a β-1,4glucan-linked carbohydrate polymer discovered in 1838
by French chemist Anselme Payen (1795–1871). After
graduating from École Polytechnique, Payen became
manager of a borax-refining factory in the suburbs ofParis and developed new methods for refining sugar andrefining starch and alcohol from potatoes. In 1835, Payen
became a professor of applied chemistry at École Centrale
Paris, where he separated the components of wood by

Copyright © 2023 The Author. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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treating various woods with nitric acid. He discoveredthat a fibrous substance could be obtained, with elemental
analysis revealing a chemical formula of C6H10O5, that
he named “cellulose” in an 1838 publication in Comptes
Rendus (Payen, 1838). Today, the Anselme Payen Award isan annual prize awarded by the Cellulose and Renewable
Materials Division of the American Chemical Society(ACS) to researchers that have made a major contribution
to cellulose-related research.
Although cellulose has been known for around 180
years, its nanofibril has only recently become considered asa new biomaterial. As shown in Fig. 1, wood cell walls arecomposed of fiber bundles of cellulose at the microscale,
which are constructed from a minimum aggregate (elementary fibril at the nanoscale) to form a higher order microfibril. Cellulose forms a complex hierarchical structure fromthe nanoscale to macroscale through lamination, resulting
in the formation of a layered structure of lamellae as cell
walls.
With advances in nanotechnology, cellulose nanofibrils
or nanocrystals (known collectively as nanocellulose) withnanometer-scale widths have been developed. Furthermore,
owing to its sustainability and wide-ranging applications,the use of nanocellulose will contribute to the conservationand maintenance of the global environment. In this context,
technology for the preparation of nanocellulose has rapidly
developed in the last 10–15 years, as described below.

2.2 Advantages of nanocellulose

Nanocellulose obtained by various preparation processes
has exhibited specific gravity that is one-fifth that of steel
and specific strength (1–3 GPa; Saito et al., 2013) morethan five times that of steel. Nanocellulose also showsthermal expansion deformation that is only approximately

one-fiftieth that of glass from –200 °C to 200 °C, and is ahigh-performance material with a specific surface area of≥250 m2/g (Klemm et al., 2018). The advantages of nanocellulose are listed in Table 1.

2.3 Global research trends in nanocellulose

A proposal submitted by Canada for deliberation of theinternational standardization of nanocellulose has been
accepted by the International Organization for Standardization (ISO/TS21346). In this proposal, the main target
is cellulose nanocrystals, which are formed by treating
cellulose microfibers with sulfuric acid.
Historically, Nickerson and Habrle (1947) first showedthat crystalline cellulose of a certain size (degree of polymerization, approx. 200–250) can be obtained by refluxing
with cellulose with 2.5 N sulfuric acid. Mukherjee andWoods (1953) confirmed that this material was nanosized
crystalline cellulose (cellulose nanocrystals) by transmission electron microscopy. In Canada, Marchessault
et al. (1959) reported that the dispersed water suspension
became a gel-like substance at a concentration of ≥13 wt%,
indicating a nematic-order sequence, while Revol et al.
(1992) confirmed the formation of a chiral nematic structure during evaporation of this dispersion. This historicalresearch is closely related to the aforementioned Canadian
ISO proposal.
Research on nanocellulose in the USA started late compared with Canada, Scandinavia, and Japan. However, in
recent years, collaboration among industry, academia, andgovernment has been promoted in the USA, with universities and research institutes providing samples without
restrictions such as intellectual property rights. In addition
to accelerated open innovation, the USA also plays a central role in the ISO.

0.5 nm~3 nm
Wood fiberin cell walls

β-D-Glucose

Cellulose
microfibril
Cell wall with cells

20~40 µm

Fig. 1110

Wood

Major cell wallcomponent

Hierarchical structure of natural cellulose containing nanocellulose.

Tetsuo Kondo / KONA Powder and Particle Journal No. 40 (2023) 109–123
Table 1 Common advantages in cellulose nanofibrils (CNFs) produced by various procedures.

[Nanocellulose having width less than 50 nm: sustainable, light weight, high strength]-Plants are sustainable and abundant resources up to 1 trillion [t] = 6 x (oil deposits)-Plant cell walls comprise CNF as a basic skeletal component-CNF is a high-performance green nanofiber as the essential of a wood
-CNFs, a microcrystal, are built up of cellulose molecules having extended polymer chain
Properties

Reference Value

Fiber size

Length: > 1 μm
Width : < 20 nm

Density≅ 1.6 g/cm3

Strength (7–8 × steel)

Elastic modulus: 140 Gpa
Tensile strength : 3 Gpa

Liner thermal expansion (≅ Quartz glass)

0.1 ppm/K (longitude)

Thermal stability in elastic modulus–200 °C – +200 °C

Highly thermal conductivity (≅ Glass)

0.8 W/mKIn Europe, the EU has conducted large-scale nanocellulose
projects in recent years, centered in laboratories in Finlandand Sweden (Finland National Technology Center (VTT)and Sweden Wallenberg Wood Science Center (WWSC)),
which are now working toward practical use.
In Japan, companies such as Nisshinbo Holdings Inc.,
Daicel Co., and Asahi Kasei Co. have proposed patents on
the miniaturization of natural cellulose fibers using a stonemill-type grinder. To our knowledge, Taniguchi was thefirst to prepare nanofibrils with a width of 20–90 nm, whichis considered the beginning of nanocellulose research(Taniguchi and Okamura, 1998). When a suspension undergoes ultrafine defibrillation between two rotating desks
with a shear force applied in a direction perpendicular tothe long axis of the fiber, the fiber width ranges from a few
nanometers to several tens of nanometers. Subsequently,
various methods for nanocellulose production have been
proposed, with Japan contributing to diverse methods
worldwide. The Japan Revitalization Strategy, revised inJune 2014, stated that the use of nanocellulose materials
should be promoted. Subsequently, the Nanocellulose
Forum centered on industry, government, and academia
under an all-Japan system was founded. The Nanocellulose
Forum was recently succeeded by Nanocellulose Japan(NCJ), a private-sector organization launched in 2020.
Accordingly, the production of cellulose nanofibrils andcellulose nanocrystals has been facilitated, and researchand development sites are now considering how to use
such nanosized natural materials in society. The practicalapplication of nanocellulose is rapidly gaining momentum.

3. Nanocellulose production—Correlation
between nanocellulose manufacturingmethod and chemical characteristics—
3.1 General aspects

In the last two decades, various technologies have beendeveloped for isolating cellulose nanofibrils derived frombiomass raw materials (Kondo, 2008; Thomas et al., 2020;
Isogai, 2021; Heise et al., 2021). Three typical methods
proposed in Japan early on are compared in Fig. 2. The firstis a chemical method, TEMPO (2,2,6,6-tetramethylpiperidine-1-oxy radical) catalytic oxidation, that has been applied to natural cellulose (Fig. 2, right) (Saito and Isogai,2004). The second is nanofibril preparation by a stone milltype grinder, which is a chemical-free process that does notchemically modify the cellulose surface (Fig. 2, center)(Yano and Nakahara, 2004; Nakagaito and Yano, 2005a;
Uetani and Yano, 2011). Furthermore, we have proposed
an aqueous counter collision (ACC) method involving thecollision of opposing high-pressure water jet streams of
aqueous suspension samples. ACC can be applied not only
to cellulose materials, but also to the nanominiaturizationof biomass (Fig. 2, left) (Kondo et al., 2005; 2014a; Koseet al., 2011c).
Notably, nanocellulose exhibits different characteristics
depending on the production method used. As shownin Figs. 2 and 3, the nanocellulose surface obtained by
TEMPO oxidation and the grinder method exhibit highhydrophilicity similar to that of conventional natural cellulose fibers. In contrast, the nanocellulose surface obtainedby the ACC method is more hydrophobic and amphiphilic(Kondo and Kasai, 2015; Tsuboi et al., 2014; Tsuji et al.,2021).

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Physicochemical method

Physical method

Aqueous Counter
Collision (= ACC)

(Supermasscolloider®)Micro-grinding

Chemical method
TEMPO-oxidation

Carboxyl groups at C-6 position
introduced every 2nd glucose unit

Elastic-plastic wave by ACC

Fine friction grinding
with high energy

Electrostatic repulsion
on the fiber surface(with 10-15 nm in width)

Nanofibrils

Nanofibrils including a spider
web-like network

Surface-modified fine nanofibrils
highly-dispersed in water

Amphiphilic surface

Hydrophilic surface

Hydrophilic surface

propagated through raw materials(hydrophilic/hydrophobic sites)
Miscible in hydrophobic polymers

without chemical modification

Fig. 2(hydroxyl groups)

Surface modification:

Derivatization of OH-groups

Surface modification:

Exchanging of counter ionsComparison of the three main cellulose nanofiber manufacturing methods proposed at an early stage in Japan.

3.2 Typical production methods for cellulose
nanofibrils
3.2.1 Production of cellulose nanofibrils by chemicaltreatment

Chemical processes for the preparation of cellulose
nanofibrils mostly involve initial loosening of the crystalline domains and interfibril interactions in cellulose
raw materials before facile mechanical separation into
nanofibrils.

3.2.1.1 Cellulose nanocrystals (CNCs) prepared bysulfuric acid treatment

Generally, CNCs are rod-like cellulose fibers thathave been hydrolyzed to remove noncrystalline fibers bytreatment with sulfuric acid (952 g/L) at 30 °C or 40 °C
for 24 h, as described in Section 2.3. The degree of polymerization is almost constant, at approximately 200–250(Nickerson and Habrle, 1947). CNCs are also known as
cellulose nanowhiskers. In 1959, Marchessault et al. reported that acid-treated cellulose and chitin microcrystal(microfibril) gels at concentrations of >13 wt% exhibitedbirefringence, which was derived from the nematic ordersequence, while Revol et al. (1992) reported that a chiralnematic structure was formed by a CNC dispersion throughevaporation. CNCs are considered to be stably dispersed inwater owing to electrostatic repulsion between the sulfuric
acid groups partially introduced on the fiber surfaces.
Based on this study, the self-assembly of cellulose (Araki etal., 1998; 1999) or grafted CNC (Araki et al., 2001; Heux etal., 2000; Gousse et al., 2002; Zhou et al., 2005; Lönnberg

112(carboxylate groups)

et al., 2006) dispersions has been developed (Trache etal., 2017). Furthermore, the use of cellulose nanowhiskers
or nanofibers with a high axial ratio (aspect ratio) as areinforcing agent for composite materials has received attention (Oksman and Sain, 2006; also see the recent review
by Shojaeiarani et al., 2021).

3.2.1.2 Cellulose nanofibrils prepared by TEMPO
catalytic oxidation (TOCNFs)

Shibata and Isogai (2003) found that TEMPO (2,2,6,6tetramethylpiperidin-1-oxyradical) catalytic oxidation selectively oxidized the primary hydroxyl group at the C6
position of regenerated cellulose (viscose rayon) to an
aldehyde under mild conditions, as shown in Fig. 3A.
This reaction occurs in an aqueous medium at pH 10–11
and consumes sodium hypochlorite, but the catalytic
amounts of TEMPO and sodium bromide can be recycled.
Furthermore, Saito and Isogai (2004) showed that, when
this TEMPO-catalyzed oxidation was applied to natural
cellulose, specific surface modification occurred, introducing carboxyl groups and aldehyde groups at a high density
only on the surface of crystalline cellulose microfibrils.
When TEMPO-oxidized natural cellulose was subjected
to simple mechanical treatment in water using a mixer orsimilar apparatus, a highly viscous transparent dispersion
could be obtained by increasing the amount of carboxylgroups introduced. Transmission electron micrography
showed that a transparent dispersion of isolated cellulose
single microfibrils with a width of approx. 3 nm was
obtained by mechanical treatment of TEMPO-oxidized

Tetsuo Kondo / KONA Powder and Particle Journal No. 40 (2023) 109–123(A) TEMPO Oxidation

TEMPO-oxidized
microfibril

Wood Cellulose
microfibril

C6
TEMPO-oxidation at pH 10,
under normal conditions
with a reaction time <90 min

3 nm

Glucose units
on fiber surfaces(B) ACC Process

Carboxyl groups at C-6 position
introduced every 2nd glucose unit
on the fiber surface

ACC-cleavage fromreducing ends

Fig. 3 Schematic diagrams of (A) chemical characteristics of nanocellulose obtained by TEMPO oxidation (Courtesy of Prof. Isogai at Universityof Tokyo) and (B) the ACC process, which is likely to expose hydrophobic van der Waals (200) faces normally hidden inside the native crystalline
cellulose fibers to the surface.

wood cellulose (Saito et al., 2006). They also indicated that
TEMPO-catalyzed oxidation does not oxidize the inside of
crystalline microfibrils, meaning that C6 hydroxyl groups
on the surface are selectively oxidized to the corresponding
sodium carboxylate salt, which causes charge repulsion
between the nanofibrils.

3.2.1.3 Nanoelements prepared by enzymatichydrolysis

Using an enzyme derived from Trichoderma, Hayashi
et al. attempted to hydrolyze microbial cellulose crystalline nanofibrils with a width of 40–60 nm secreted from
Gluconacetobacter bacteria and natural cellulose fibers
derived from seaweeds, which have both crystalline formsof cellulose (Iα and Iβ). Crystalline phase Iα has beenreported to be more susceptible to enzymatic degradation(Hayashi et al., 1997a, b). Furthermore, the residue of ashort elements (width, 10 nm; length, 350 nm) obtained byhydrolysis with an enzyme of the same Trichoderma origin
was found to mainly comprise highly crystalline Iβ phaseby wide-angle X-ray diffraction, electron diffraction, and
FTIR measurements (Hayashi et al., 2005). This material
has potential applications as an additive, especially in foodand cosmetics.

3.2.1.4 CNFs prepared by mechanochemicaltreatment

Mechanochemistry using planetary ball milling (Endoet al., 1999; Takai-Yamashita et al., 2021) is a sustainable
method for the synthesis of functionalized cellulose nanofi-

bers (CNFs). In this process, changes in the microstructureof a CNF aqueous sol under planetary ball milling were
investigated in terms of its rheological behavior, crystallinity, and diameter distribution. A decreased thixotropy
hysteresis loop observed in the ball mill-treated CNFs
indicated a weaker interaction among the fibers, but with athree-dimensional structure retained.

3.2.2 Production of cellulose nanofibrils (CNFs) bymechanical treatment

Mechanical processes for the preparation of cellulose
nanofibrils mostly involve shear stress, where pulp isdispersed in water, pre-crushed using a Supermasscolloider® grinder (Masuko Sangyo Co. Ltd., Saitama, Japan),and further micro-/nano-fibrillated using a high-pressure
homogenizer (M110P Microfluidizer, Microfluidics Corp.,
Newton, MA) (Yano and Nakahara, 2004; Nakagaito and
Yano, 2005a; Uetani and Yano, 2011). Under typical treatment conditions, the pulp fibers are first processed underhigh shear in a Masuko Supermasscollider grinder for threepasses, and then further fluidized using a microfluidizer
equipped with 200- and 100-μm chambers at 2000 psi for
10–12 passes (Huan et al., 2017).
As a result, cellulose nanofibrils with a spider web-likenetwork can be obtained. Additional compounding anddehydrating with a phenol resin, oxidized starch, and polylactic acid in a mold, followed by heat-pressing, allows ahigh-strength composite molded product to be obtained.
Instead of the cellulose nanofiber network, when the bacterial nanocellulose pellicle (gel-like membrane produced

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Effect

ACC

Constant Load
Creep

StaticDynamic

ImpactHigh-speed
Impactconstant strain

resonance

Elasticplastic
wave

Transmissionof impact wave

10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1

100 101 102 103 104 105 106 107

Strain rate [s-1]Fig. 4 Classification of energy loading effects vs strain rate onto materials (Yu and Qiu, 2018). Typically, ACC process provides both interfacialpeeling and a thermodynamic effect onto samples. Adapted with permission from Ref. (Yu and Qiu, 2018). Copyright: 2018 Tsinghua University.

by Gluconacetobacter bacteria) composed of a threedimensional nanofibril network was subjected to heatpressing treatment, the resulting high-strength composite
material had a Young’s modulus of approx. 30 GPa, which
was 1.5 times higher than that of the material obtainedusing wood cellulose nanofibrils (Nakagaito et al., 2005b).
This difference was attributed to the dense network inthe bacterial cellulose pellicle, which was uniform at ascale of a few tens of nanometers (Yano et al., 2005). To
our knowledge, this result was the starting point for subsequent research on resin nanocomposites with high tensile
strength using nanocellulose. Nanocellulose being lightweight, more than five times stronger than steel, and having
a low linear expansion (one-fiftieth that of glass) could leadto a reduction in weight of car bodies to encourage bettergas mileage, especially through its use in automobile parts.
This matches the current global direction of realizing a
low-carbon society. Recently, Kitagawa and Yano proposed
the “Kyoto Process” for integrated production of cellulose
nanofiber materials (Kitagawa and Yano, 2012; Igarashi
et al., 2018). Such research on developing resin materials
with excellent strength per unit weight is being conducted
extensively worldwide. Numerous studies on nanocomposites containing nanocellulose have been published to date.
For coverage of literature in this area beyond the scope of
this review, please see other recent reviews (Oksman et al.,2016; Klemm et al., 2018; Shojaeiarani et al., 2021).

3.2.3 Production of cellulose nanofibrils byphysicochemical treatment

The key phenomenon in physicochemical processes forthe preparation of CNFs is selective interfacial cleavage
among weak intermolecular interactions by a propagating
elastic–plastic wave (as shown in Figs. 3B and 4), which
is attributed to shockwaves generated by impact dynamics

114

depending on the strain rate (s–1) through impact energy
transmission (Yu and Qiu, 2018). Namely, loading as totalmechanical energy / areas induces impact energy transmission to cause an effect in materials that relates to strain rate.
When heterogeneous interface of the material is sufferedby such concentrated loading energy, interfacial peelingwithin it is likely to occur.
Our aqueous counter collision (ACC) method for thenanopulverization of bio-based materials is a typicalphysicochemical process without any chemical modification (Kondo et al., 2005; 2014a; Kose 2011c). The ACC
method relies on the impingement of two high-speed jetsof aqueous suspension of raw materials expelled througha pair of opposing nozzles. Typically, the ejection pressureof 200 MPa generates a supersonic flow of dual water jets
at Mach 2. Schematic diagram is shown in Figs. 3B and 4.
The calculated kinetic energy generated by impingementof the water jets in the ACC method is up to 14.3 kJ/mol atan ejection pressure of 200 MPa (Kondo et al., 2014a). The
relationship between water ejection pressure and calculated
kinetic energy of water molecules in the ACC process
is shown in Table 2, together with a summary of typicalbonding energies. Regardless of dissipative energy loss
during the ACC process, the kinetic energy, which can beconverted into elastic–plastic waves, remains higher thanthe van der Waals forces and weak hydrogen bonds at anejection pressure of 200 MPa (Kondo et al., 2014a). The
width and size distribution of the nano-pulverized samples
are dependent on the ACC ejection pressure and numberof repeat cycles/passes, respectively. The propagation ofshock waves resulting from ACC occurs through elastic
crystalline domains to cleave the hydrophobic van der
Waals (200) planes normally hidden inside native crystalline cellulose fibers (Cousins and Brown, 1995) withthese hydrophobic planes eventually being exposed on theTetsuo Kondo / KONA Powder and Particle Journal No. 40 (2023) 109–123
Table 2 Energy barrier capable of being overcome by ACC.

(A) Calculated potential energy of a water molecule in ACC
process
Ejecting pressure /MPa

Kinetic energy /kJ mol–1

50

3.6

100

7.2

150

10.8

200

14.3

(B) Hydrogen bonding energies
O−H···O Hydrogenbonded compound–∆E(kJ mol–1 Hydrogen bonds)
Dimer (gas)LiquidCH3COOH

29.3

24.3

H2O

21.0

14.2

CH3OH

16.8–18.9

19.7

C2H5OH

16.8

17

C3H7OH

25(C) Various bonding energies
Type of bond

Bonding energy /kJ mol–1

H–OH (covalent bond)499

H–H (covalent bond)436

Ion–ion250

Medium hydrogen bond

21–62

Weak hydrogen bond

4.2 × 10–1–4.2London dispersion force2

Dipole–dipole

0.6–2

surface (Li and Renneckar, 2011) (see Fig. 3B). The resulting ACC–CNFs have a Janus-type amphiphilic surface
composed of both hydrophobic and hydrophilic planes in
an aqueous system (Tsuji et al., 2021). Kose et al. (2011c)
reported that ACC provides a single CNF from bacterial
nanocellulose (BNC) pellicles, denoted ACC–BNC herein,
which are nanofibers engaged in a 3D network secreted
by Gluconacetobacter bacteria, that exhibited amphiphilic
properties (Kose et al., 2011a). In BNC, which is alreadyhighly crystalline, shock waves are simply propagatedthrough the elastic crystalline domains from the initialstage, resulting in the direct cleavage of certain interfaces.
Later, Kondo et al. (2014a) reported ACC pulverization
steps for microcrystalline cellulose (MCC) containing 30 %noncrystalline domains derived from wood-based cellulose
fiber (pulp). Regarding the nanopulverization behavior of
MCC in the ACC method, initially, a shear stress is mainly
generated through the ACC flow system to pulverize the

MCC microfibers into fibers with widths of a few hundred
nanometers. Highly crystalline domains remain in thecellulosic samples after the effects of shear stress. A drastic
decrease in fiber width then occurs, presumably due tothe cleavage of weak hydrogen bonds between crystalline
domains of CNFs. Accordingly, ACC nanopulverization isconsidered to be an effective synergistic process that generates shear stress and propagating shock waves in various
biomass resources (Kose and Kondo, 2011b; Kondo et al.,2014b; Jiang et al., 2016; Tsujita and Kondo, 2019; Ye etal., 2021).
Additionally, Kawano and Kondo (2014) reported thatcarbon materials without dissociative functional groups
treated using the ACC method exhibited negative zeta
potentials and dispersed in an aqueous system, whichmight be due to water molecules present on the surface.
Therefore, the resulting ACC–CNFs might disperse in an
aqueous system, even if hydrophobic planes are exposedon the CNF surfaces.
Recently, the ACC method afforded CNFs with different
surface properties from various cellulosic raw materials
comprising different hierarchical structures depending on
the species present, as described later (Tsuji et al., 2021).
Nanocellulose is usually considered to comprise symmetrical nanofibrils, nanorods, or fibrillated nanofibrils,
regardless of asymmetric effects caused by the presenceof one reducing end. Asymmetrical nanocellulose would
have considerably extended utility in new applications. We
have produced an asymmetric cellulose nanofibril with ashape that changes from a thick nanobarrel at one end to athinner cylinder with a few flourishes towards the reducing
end. The “cellulose nanoanemone” fibrils, named after sea
anemones, were fabricated by the ACC of bacterial cellulose pellicles cultured under dissolved oxygen conditions(see Fig. 5) (Utsunomiya et al. 2022; Nagashima et al.,2015). The cellulose Iα crystalline phase located on thefiber surface was transformed into cellulose Iβ crystalline
phase owing to propagation of the collision energy of highspeed water jets in this treatment (Kose et al., 2011c).

4. Characteristic advantages of the ACC
process

4.1 Amphiphilic Janus-type surface properties of
ACC–CNFs

ACC–CNFs are likely to exhibit specific phenomena
owing to their two anisotropic faces in aqueous media(Tsuji et al., 2021). Kose et al. (2011a) reported that the
wettability of solid substrates, regardless of whether thesurface was hydrophilic or hydrophobic, can be switchedby spray-coating with ACC–CNFs. Tsuboi et al. (2014)
reported that ACC–CNFs can be used as emulsifiers and/or
emulsion stabilizers without a surfactant. The properties ofthe obtained emulsions differed depending on the starting
raw materials used (Yokota et al., 2019). Recently, Kondo

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et al. reported that the preferential adsorption of ACC–
CNFs to hydrophobic polymer surfaces results in hydrophilic properties (Kondo et al., 2017; Ishikawa et al., 2021).
As mentioned above, ACC–CNFs are assumed to exhibit
two distinct physicochemical properties (amphiphilicity)
due to being composed of hydrophobic planes and theinitial hydrophilic planes in aqueous systems. Therefore,a novel concept regarding the physicochemical surface
properties of ACC–CNFs, namely, amphiphilic Janus-type
fibers, is introduced, in which two anisotropic faces arepresent along the entire fiber axis that exhibit different
physicochemical properties on the surface. An analogous
concept, Janus particles, has been studied extensively.
Janus particles, named after the two-faced ancient Roman
god, have two anisotropic faces with different physicochemical properties owing to a half-and-half surface strucStem

Non-fibrillated
ca. 100 nm in widthca. 11 μm in length
Non-reducing end site
Cellulose Iα like structure

BranchFibrillated
ca. 10 nm in widthca. 7 μm in length
Reducing end site
Cellulose Iβ

2 μm

BranchStem

Sea anemone

Fig. 5 Images of a typical asymmetric “cellulose nanoanemone”. The
TEM and schematic images (bottom of figure) define the “stem” and“branch” of the cellulose nanoanemone.

ture (Walther and Müller, 2008; Yabu et al., 2013; Jiang etal., 2010). However, such unique surface characteristics
have yet to be reported in fibers. Therefore, the surface
characteristics of ACC–CNFs are defined differently totypical Janus particles.
Amphiphilic Janus-type fibers are described as fibers
with two types of surface bearing hydrophilic and hydrophobic faces that are individually present along the entire
single fiber axis in aqueous systems (Kose et al., 2011a;
Tsuboi et al., 2014; Yokota et al., 2019). Accordingly,
amphiphilic Janus-type fibers can also be described as
Janus-type faces along a single fiber axis, which is different
to typical Janus particles with a half-and-half structure.
Recently, the surface characteristics of single ACC–CNFs
have been visualized at the nanoscale using fluorescencemicroscopy (FM) observation with fluorescence probes(Tagawa et al., 2021). Fluorescent-probe carbohydratebinding modules (CBMs), Congo red (CR), and Calcofluor
white (CFW) have been employed as reagents for thequalitative and quantitative evaluation of surface characteristics in ACC–CNFs, focusing on their hydrophobic andhydrophilic planes (Tsuji et al., 2021). CBMs are includedin cellulase as a binding domain that anchors selectively
to the hydrophobic phase of cellulose (Linder and Teeri,1996), CR adsorbs onto cellulose crystalline surfaces
as a monolayer (Hubbe et al., 2012; Nge et al., 2013;
Mazeau and Wyszomirski, 2012), and CFW has been used
to detect plant hydrophilic cellulose structures (Hughes
and McCully, 1975) and CNFs (Peretz et al., 2019). The
three-dimensional structure of CBM, and chemical structures of direct dyes CR and CFW, are shown in Fig. 6.
According to the fluorescence microscopy images, CNFs
stained with CFW were approximately 10 times wider
than those observed by field emission scanning electron
microscopy (Tagawa et al., 2021). Therefore, the surface(A) Carbohydrate-binding modules fused to green fluorescence protein(CBM-GFP)

90°⇒ van der Waals (200) (= Hydrophobic) planes(B) Congo red (CR)

SO3Na
N

NH2

N

SO3Na

H
N
NH2

N

⇒ Hydrophobic planes

OH

H
N

N
N

OH

Fig. 6116

N(C) Calcofluor White (CFW)

N
N

SO3Na
OH

N
N
H

OH

N

SO3Na

N
N

N
H

⇒ Hydrophilic planes

Site-specific fluorescence probes for CNF surfaces.

Tetsuo Kondo / KONA Powder and Particle Journal No. 40 (2023) 109–123

characteristics of CNFs can be visualized by confocal
laser microscopy (CLMS) using site-specific fluorescenceprobes and a highly sensitive fluorescence detector.
The localization of hydrophobic and hydrophilic planeson the ACC–BNC surface was visualized by staining withtypical site-selective fluorescent probes CFW, CBM fusedwith green fluorescent protein (CBM–GFP), and CR.
ACC–BNC is a suitable model material for investigating
ACC–CNFs because BNC is a pure cellulose sample.
Nanofibrils cannot be observed by optical microscopy owing to resolution limitations. However, fluorescence-stained
nanofibrils were visible by CLSM, as shown in Fig. 7,
owing to the spread of fluorescence emissions with similar
shapes. The width magnification achieved by fluorescenceemission was approximately 13 times that achievedby transmission electron microscopy (TEM). The same
fiber was observed separately by fluorescence staining,
using CFW for hydrophilic surfaces or CBM–GFP forhydrophobic surfaces (Figs. 7a and 7b). Each fluorescentemission of CFW and CBM–GFP was distinguishedin most fibers. When images (a) and (b) in Fig. 7 weremerged, the hydrophobic areas (b) were fixed empty areas
surrounded by hydrophilic areas (a) in the merged image(c) of Fig. 7. Therefore, ACC–BNCs can be concluded tohave hydrophobic and hydrophilic faces on a single ACC–
BNC surface. This result reflects the concept that, in ACC
nanopulverization, shockwaves propagate through highly
crystalline domains, samples are chopped into individual
nanofibrils, and the resulting hydrophobic faces might besimilar to exposed van der Waals (200) planes from thenative cellulose crystals (Li and Renneckar, 2011).
In the visualization described above, the ACC–BNC
surface had an amphiphilic Janus-type fiber structure withhydrophobic and hydrophilic faces in aqueous systems, and(a) CFW(b) CBM-GFP

CR was found to be a substitute for CBM in hydrophobic
face evaluation. Therefore, CR can be used to quantitatively evaluate the hydrophobic face, partly because a
CBM–GFP calibration curve by weight is difficult to prepare and some CNFs might aggregate in the acetate buffer.
The hydrophobic and hydrophilic faces of ACC–BNC were
determined by quantitative measurement of the amountsof CR and CFW adsorbed on a Langmuir monolayer. The
maximum monolayer adsorption amounts of CR and CFW
on ACC–CNFs were calculated from the Langmuir isotherms, as shown in Table 3. CR and CFW were adsorbedon all ACC–CNFs. Fig. 8 shows the relative occupied area
(OA) ratio of CR to CFW on ACC–CNFs as a functionof CR occupancy for the surface area of ACC–CNFs.
The results indicated that the degree of hydrophobicityof the ACC–CNFs was in the order of BNC > BBKP >HBKP ≈ MCC. Therefore, the surface hydrophobicity of
ACC–CNFs depends on their origin (Tsuji et al., 2021).
Considering the recent damage of bamboo as an unnecessary resource, despite this natural material containing
various active ingredients, the above results are of interest
and potential importance regarding the development of afunctional biomass nanofibrous material.

4.2 Specific adsorption phenomena caused by two
anisotropic faces in amphiphilic Janus-type
ACC-CNFs
4.2.1 ACC-assisted oil-in-water Pickering emulsions

The characteristic surface of ACC–CNFs can be expected to expand common understanding of nanocelluloses. First, the amphiphilicity is promising for applications
as emulsifiers and stabilizers. Oil-in-water Pickering
emulsions with long-term stability are readily prepared byultrasonic mixing of aqueous ACC–CNF dispersions with(c) Merged＊ All scale bars:
2 µm
CBM-GFP
image【Schematic diagram】
CFW(Hydrophilic face)

ACC-BNC

Focusedpoint
CBM-GFP(Hydrophobic faces)

Fig. 7

Hydrophilic and hydrophobic faces on the ACC–BNC surfaces stained separately with CFW and CBM–GFP.

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Table 3 Characteristic values for individual ACC–CNFs from different origins.

(A) Maximum monolayer adsorption amounts of dyes onto CNFs based on the Langmuir adsorption isotherms and zeta potentials of
individual CNFs.
Derivation

CR adsorption (mg/g CNF)

CFW adsorption (mg/g CNF)

Zeta potential (mV)BNC

7.5 ± 0.987.0 ± 8.5–18.8 ± 2.1

BBKP

7.1 ± 0.6

131.5 ± 10.2

–36.7 ± 2.1

HBKP

4.7 ± 0.6

103.7 ± 18.3

–44.7 ± 1.2

MCC

4.1 ± 0.8

119.1 ± 7.6–29.3 ± 2.1(B) Average height, width, and SSA values for ACC–CNFs based on AFM observation of the samples. Adapted with permission from
Ref. (Tsuji et al., 2021) Copyright 2021 American Chemical Society.
Height (nm)Width (nm) (Data from TEM)

SSA (m2/g)

BNC

12.1 ± 5.5

50.5 ± 15.9 (34 ± 13)

127.9

BBKP

4.8 ± 2.8

25.2 ± 7.1 (20.7 ± 8.3)

307.8

HBKP

4.2 ± 2.9

31.8 ± 11.8 (22.3 ± 12.4)

338.5

MCC

4.4 ± 1.9

22.4 ± 6.1 (15 ± 6)

337.4

Relative ratio of OACR/CFW(×1/100)

Derivation

8

Surface hydrophobicity

6
4
2
R2 = 0.9509
0

0

2

4

6

OACR/SACNF (%)

8

10

Surface hydrophobicity: BNC > BBKP > HBKP ≈ MCC
Fig. 8 Relative occupied area (OA) ratio of CR to CFW on ACC–CNFs
as a function of CR occupancy for the surface area of ACC–CNFs, indicating surface hydrophobicity depending on the origin.
Note: BNC: Bacterial nanocellulose pellicle; BBKP: Bamboo-derived
bleached kraft pulp; HBKP: Hardwood-derived bleached kraft pulp;
MCC: Microcrystalline cellulose (from wood kraft pulp). Longitudinal
axis corresponds to relative ratio of OA of CR to CFW. Horizontal axis
corresponds to OA of CR per surface area of CNFs based on AFM data.

nonpolar solvents. The emulsification abilities of ACC–
CNFs for stable Pickering emulsions have been proven tobe significantly greater than those of CNFs prepared byhigh-pressure homogenization or other chemical preparation methods. This is attributed to ACC exposing inherently hydrophobic surface planes of CNFs. Furthermore,
the emulsification and stability of the Pickering emulsions
were sensitive to solvent properties, such as permittivity,
density and viscosity (Yokota et al., 2019).
In a stable emulsion prepared from a water dispersion of
0.1 wt% ACC–CNFs derived from bamboo pulp, hollow
particles, similar to colloidosomes (Thompson et al., 2015),

118

were formed from the bamboo-based emulsion droplets bycarefully washing the excess nanocellulose with water andthen freeze-drying, as shown in Fig. 9 (left). The sphericalparticles were covered with a stable thin layer composed of
ACC–CNFs and were completely hollow inside (Fig. 9bleft), while fluffy fibrous objects (Yokota et al., 2019;
Tsuboi et al., 2014) between spherical particles were negligible. By removing the unanchored CNF, the thin layer
around droplets could be observed in higher magnification
images. This thin layer contained pores approximately
10–500 nm in size, as shown in Figs. 9c and d (left). Thissuggested that the obtained emulsion droplets were fabricated and stabilized by adsorption of ACC–CNFs onto theoil surface to form an oil-in-water emulsion. Therefore,
colloidosome microcapsules were formed in this Pickering
emulsion without requiring further stabilization treatment.
To control the surface properties of ACC–CNFs, theirsurfaces at the oil–water interfaces of a Pickering emulsion can be selectively acetylated (Ishida et al., 2021). To
achieve this, ACC–CNFs adsorbed on the surfaces of oil
droplets containing the reaction reagents undergo localizedinterfacial reactions. Such acetylation reactions occur
while maintaining the crystallinity and fibrous morphologyof the original CNFs. The surfaces of films cast from theseacetylated ACC–CNFs had unique self-aggregation properties (Ishida et al., 2021) that contrasted markedly withthose of films cast from acetylated ACC–CNFs prepared inhomogenous dispersions (Yokota et al., 2021).

4.2.2 Adsorption of Janus-type amphiphilic ACC–
CNFs onto microparticles of semi-crystalline
polymers

As a further development of the above research, ACC–

Tetsuo Kondo / KONA Powder and Particle Journal No. 40 (2023) 109–123

Emulsifiers and/or
emulsion stabilizers

ACC-CNFs

CNFs Water

cross-section

3D

PP

Oil

Emulsion
phase
Oil-in-Water (O/W) type
Stable Pickering emulsion

Note: SEM images of bamboo nanocellulose hollow
particles with (a) spherical and (b) cracked shapes;(c) fracture surface of hollow particle and (d) magnified
image of skin area, indicating nanofibril network
structures.

100 µm

100 µmSurface covering of PP particles
Note: CLSM images of i-PP particles coatedwith ACC-CNFs of various origins stainedwith CFW.

Fig. 9 Specific adsorption properties of ACC–CNFs: Pickering emulsion formation (left) and adsorption onto i-polypropylene (i-PP: right). Left:adapted with permission from Ref. (Yokota et al., 2019). Copyright 2019 Elsevier; adapted with permission from Ref. (Tsuboi et al., 014). Copyright2014 De Gruyter. Right: adapted with permission from Ref. (Ishikawa et al., 2021) Copyright 2021 American Chemical Society.

CNFs tend to preferentially adsorb onto hydrophobic
isotactic polypropylene (i-PP) microparticles (see Fig. 9,
right) and linear low-density polyethylene (LLDPE),
which demonstrates their characteristic amphiphilicity.
This is achieved by simply mixing the components together
in aqueous media (Ishikawa et al., 2021). The surfacecovered products were confirmed by visualization using CLMS. Furthermore, thermodynamics measurements
obtained by differential scanning calorimetry (DSC)
showed that the melting points of i-PP/ACC–CNFs
and LLDPE/ACC–CNFs were lower than those of theuntreated polymers, indicating interactions between the
ACC–CNFs and polymer surfaces. Therefore, coating thepolymer particles with the ACC–CNFs, as confirmed by
CLMS, reduced their melting points. This demonstrated thepossibility of a novel fusion between synthetic polymericmaterials and biomass nano-objects (Kondo et al., 2021).
Prior to the CLSM observations, the i-PP particles
were mixed with Janus-type amphiphilic ACC–CNFs in
an aqueous medium, fully dried, and stained with CFW.
As described earlier, nanoscale fibers can be observed by
CLSM, even below the level of microscopic resolution,owing to scattering of the fluorescence emission.
Fig. 9 (right) shows a dark field fluorescent image ofa particle coated with the ACC–CNFs. The bright spots
indicate fluorescence due to stained ACC–CNFs on the surface of i-PP particles, showing that the i-PP spheres werecompletely covered by the ACC–CNFs. These images also
indicated that, during the process of washing away excess
CFW, the CNFs remained attached to the i-PP particle
surface, demonstrating that interfacial interactions existed
between the i-PP and ACC–CNF surfaces after adsorption
had occurred by shaking in water.
Particles of other polymers, namely, high-density
polyethylene (HDPE), linear low-density polyethylene(LLDPE), and polyethylene terephthalate (PET), werealso treated with an aqueous suspension of ACC–CNFs
using the same method as for i-PP. The CLSM images
indicated that more ACC–CNFs were present on the surface of LLDPE microparticles than on those of HDPE andPET particles (Ishikawa et al., 2021). Accounting for the
self-fluorescence for PET showed that ACC–CNFs were
not necessarily adsorbed on the PET surface. Furthermore,
the high-magnification CLSM images suggested that i-PP
and LLDPE particles were more efficiently coated with
ACC–CNFs than HDPE and PET particles.
Among the materials tested, i-PP particles had thegreatest affinity for ACC–CNFs. When the i-PP/ACC–
CNFs were heated from 20 to 200 °C at 10 °C/min, they
exhibited a small but typical endothermic shoulder peak at
142.6 °C after fusion commenced at approximately 113 °C
and before the melting peak at approximately 162 °C.
This indicated that coating with ACC–CNFs reduced thei-PP melting point in some areas (presumably surface
areas coated with ACC–CNFs) and therefore the reduction
probably occurred on the surface (skin side) of the microparticles. In agreement with the CLMS data, DSC analysis
showed that ACC–CNFs coated the i-PP particles as amonolayer.
The effect of the origin of ACC–CNFs on their affinity
for i-PP particles was also examined. ACC–CNFs from
BBKPs and ACC–BNC derived from bacterial cellulose
pellicles exhibited strong interfacial interactions with i-PP,
while relatively hydrophilic ACC–MCC derived frommicrocrystalline wood cellulose had a lower affinity. A
better understanding of the interfacial interactions between
various polymer particles and ACC–CNFs will lead to thenovel fusion of bio-based materials and synthetic polymer
materials at the nanoscale (Kondo and Kasai, 2015; Kondo
et al., 2017; Ishikawa et al., 2021).

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Tetsuo Kondo / KONA Powder and Particle Journal No. 40 (2023) 109–123

5. Conclusions and future perspectives

Nanocellulose is a lightweight material, more than fivetimes stronger than steel, and with a low linear expansionof one-fiftieth that of glass. Accordingly, Yano (2016) has
proposed using this material to reduce car body weight,
especially through its use in automobile parts. Therefore,
a stream of resin materials with excellent strength per unit
weight is undergoing vigorous development.
Our research group at Kyushu University, in collaboration with Chuetsu Pulp & Paper Co., Ltd., developed
the Janus-type amphipathic ACC–CNFs described herein,
which are more hydrophobic than other nanocellulosematerials, and also proposed a novel fusion of bio-basedmaterials and synthetic polymer materials at the nanoscale.
Therefore, this section will discuss future perspectives andtrends for the preparation of nanocomposites containing
CNFs.
Various additives have been widely used to improve theproduction performance of tailor-made polymeric products. These include plasticizers, flame retardants, impact
modifiers, antioxidants, antimicrobials, and UV stabilizers(Hahladakis et al., 2018). The global additives market expanded steadily to over 50.0 billion USD by the end of thelast decade, with the total weight of additives anticipated
to reach 17 megatons by 2020 (Marturano et al., 2017). As
additives are often more expensive than resins, two different production designs are considered to counterbalance
usage and cost, namely, “high-price and low-loading” or“low-price and high-loading”. Inorganic materials, suchas calcium carbonate, silica, glass fiber, and talc, which
are relatively inexpensive, have usually been used in highvolumes as fillers to improve the mechanical properties ofpolyolefins.
In recent years, nanoscale fillers, such as nanocarbon
particles, various inorganic/organic nanoclays, and metaloxide nanoparticles, have been used at low loading levels(Winey and Vaia, 2007). Wood flour has been successfully
mixed with various polyolefins to form wood–plastic
composites for use as organic fillers (Clemons, 2002).
Furthermore, in the last two decades, nanocelluloses (NCs)(Klemm et al., 2018; Nechyporchuk et al., 2016), including
cellulose nanofibrils (CNFs), microfibrillated cellulose(MFC), and cellulose nanocrystals (CNCs), which are
believed to be predominantly hydrophilic, have been extensively studied for use as fillers (Kargarzadeh et al., 2017).
This results from their large surface areas of >50 m2/g
(Kose et al., 2011c; Klemm et al., 2018), high mechanicalstrength (Saito et al., 2013), and low thermal expansion in
the direction of the fiber axis (Nishino et al., 2004).
As described herein, NCs can be manufactured using
the following three methods, each of which has a different
effect on the surface characteristics, dispersibility, andcompatibility of the NCs regarding mixing with commodity polymers: (i) Mechanical processes, mostly involving

120

shear stress; (ii) chemical processes that loosen crystalline
domains and interfibril interactions; and (iii) physicochemical processes that selectively cleave weak intermolecular interactions by propagating generated shockwaves.
Methods (i) and (ii) provide polar hydrophilic nanofibrils.
Therefore, when such hydrophilic CNFs are added as fillers
to polyolefins, their surfaces must either be chemicallymodified to render them hydrophobic, or they must be usedin conjunction with a compatibilizer (Oksman et al., 2016;
Nodera et al., 2016). Alternatively, chemically treated
raw cellulose materials can be nano-pulverized and compounded with the polymer matrix in a parallel or sequential
manner (Igarashi et al., 2018).
In contrast, aqueous counter collision (ACC) is aphysicochemical process involving the impact-induced
nanopulverization of bio-based materials using a pair ofhigh-speed jets containing microsized target materials
in aqueous media. The CNFs produced by ACC exhibit
unique characteristics owing to the pulverization mechanism, as described herein.
Many plasticizers, which are used for solvation in matrix
polymers, reduce intermolecular forces and decrease therelaxation temperature. Therefore, an evaluation of the affinity between matrix polymers and secondary ingredientsis important for understanding their mutual contribution to
the properties of the material, and the design of novel andsustainable multicomponent polymeric products. Regarding the use of nanocellulose in nanocomposites, such a
study is still required.
Furthermore, applications to electronic devices (Kasuga
et al., 2021) and other fields (Apelgren et al., 2019; Haiet al., 2020; Halim et al., 2019; Kim et al., 2021) appear
feasible for practical use. From a future perspective, theexpansion of nanocellulose research worldwide means that
describing all potential application fields for nanocellulose
is currently almost impossible.

Acknowledgments

The author thanks Dr. Simon Partridge from Edanz forediting a draft of this manuscript.

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Author’s Short Biography
Tetsuo Kondo
Tetsuo Kondo is the Full Professor of both laboratories of Biomacromolecular Materials Lab,
and Biomaterial Design Lab at Graduate School of Bioresource and Bioenvironmental Sciences,
Kyushu University. Recently, he has joined Tokyo University of Agriculture and Technology as anendowed chair of Enviro-Sustainable Materials Science Lab. He graduated BSc, MS and Ph.D. form
The University of Tokyo. He obtained his second Ph.D of Engineering from Kyoto University. For
more than 35 years, Dr. Kondo has been contributing extensively to the interdisciplinary interfaces
between chemistry and biology of cellulose, based on cellulose physical chemistry through basic andapplied research using various cellulose from different origins.

123KONA Powder and Particle Journal No. 40 (2023) 124–148/https://doi.org/10.14356/kona.2023004

Review Paper

Functionalized Graphitic Carbon Nitrides for Photocatalytic
H2O2 Production: Desired Properties Leading to Rational
Catalyst Design †Zhengyuan Teng, Wenan Cai and Teruhisa Ohno *Department of Applied Chemistry, Faculty of Engineering, Kyushu Institute of Technology, Japan

Abstract
Photocatalytic H2O2 production based on graphitic carbon nitride (g-C3N4) materials has been attracting increasing
attention. However, it is difficult to reveal the inner relationships among the structure, properties and performance ofa g-C3N4-based photocatalyst by simply summarizing preparation methods, properties and performances in previous
works. In this review, the three most important issues for improving H2O2 generation based on the band diagram andphysicochemical properties of pristine g-C3N4 are proposed. Improvement of the charge separation, promotion of thelight absorption and introduction of active sites for 2e– oxygen reduction reaction to suppress side reactions are themost three attractive strategies for enhancing the activities. Following discussion of these strategies, representative
functionalization methods are summarized on the basis of the most desired properties for improving the photocatalytic
activities for H2O2 production. Other influence factors for improving H2O2 production such as addition of electron
donors and adjustment of pH value of the solution are also discussed. Future challenges for photocatalytic H2O2 basedon g-C3N4 materials are also summarized to provide future directions in this field.
Keywords: carbon nitride, photocatalysis, functionalization, H2O2

1. Introduction

H2O2 has been recognized as one of the 100 most important chemicals worldwide (Jones C.W., 1999; Pesterfield L.,2009). The reactive oxygen content in H2O2 (47.1 % w/w)
is the highest among all chemicals. No toxic by-products
are produced from H2O2, making H2O2 a highly efficientand environmental-friendly oxidant. H2O2 has been widely
applied for disinfection and sterilization (McDonnell G.,
2014; Teng Z. et al., 2019), organic synthesis (Zhan W.
et al., 2018) and wastewater treatment (Zhang M.-h. etal., 2019). A one-compartment fuel cell using H2O2 inwhich H2O2 is used as both an oxidant and a reductant
has recently been developed (Mousavi Shaegh S.A. et al.,2012). Studies on this fuel cell showed that the theoretical
output potential is 1.09 V, which is comparable to that ofa conventional hydrogen (H2) fuel cell (1.23 V) (Mousavi
Shaegh S.A. et al., 2012; Kim D. et al., 2015; Yamada Y.
et al., 2013). Compared to H2, H2O2 showed a promisingprospect as a water-soluble solar fuel (H2O2, 3.0 MJL–1, in
an aqueous solution higher than the intensity of compressed
H2 gas, 2.8 MJL–1, 35 MPa) (Kim D. et al., 2015). Unlike†

Received 22 December 2021; Accepted 6 February 2022
J-STAGE Advance published online 2 April 2022
Add: 1-1 Sensuicho, Tobata Ward, Kitakyushu, Fukuoka 804-8550,
Japan
E-mail: tohno@che.kyutech.ac.jp
TEL: +81-093-884-3318 FAX: +81-72-867-1658

124hydrogen, H2O2 is fully soluble in water and is easily transportable, making it an ideal energy carrier alternative to
H2. The anthraquinone (AQ) method for H2O2 productionis the main process used in industry (more than 95 % of
total production, Fig. 1) (Campos-Martin J.M. et al., 2006).
The AQ process includes the following four steps: (i) inan organic solvent, hydrogenation of anthraquinone (AQ)
is firstly catalyzed on an Ni/Pd catalyst; (ii) hydrogenatedanthraquinone (HAQ) is oxidized with the aid of a catalyst;(iii) H2O2 is extracted by pure water, and HAQ is used toreproduce AQ; and iv) the as-prepared H2O2 is purified
and concentrated for following commercial use. These
hydrogenation and oxidation reactions consume a lot ofenergy. Additionally, the AQ process is not environmentalfriendly because large amounts of wastes are generated(Campos-Martin J.M. et al., 2006).
Efforts have been made to develop new methods for
H2O2 production under more moderated reaction conditions
with less waste production. (Yi Y. et al., 2016). Direct synthesis from H2 and O2 in the presence of a Pd catalyst or abimetallic Au–Pd catalyst is a possible alternative route for
H2O2 production based on the reaction of H2 + O2 = H2O2.
Typical reaction conditions mainly include H2/O2 mixture
gas catalyzed by noble metal catalysts (Xia C. et al., 2019;
Freakley S.J. et al., 2016). The ratio of the H2/O2 mixture
needs to be precisely controlled in the process to avoid therisk of explosions (Xia C. et al., 2019; Freakley S.J. et al.,Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148

OH
O

R

R

OH
O2

O

O

H2 Pd/Ni catalyst
OH

HydrogenationR+ H2O2R

O

OH

Regeneration

ExtractionO

Remains· Wastewater· Exhaust gas· Solid waste

R

O

H2O2

Practical applications

Distillation
Transportation

Fig. 1

Schematic of the anthraquinone oxidation (AQ) process.

2016). Recently, much attention has been given to the possibility of efficient H2O2 production by a photocatalytic reaction process, which would be a significant breakthroughin H2O2 chemistry. A photocatalytic approach would enable
the use of explosive H2 gas to be avoided and it would only
need light, O2 and water, with no pollutant generated in theoverall process. Much interest has therefore been shown inphotocatalytic strategies for H2O2 production.
In past one decade, many kinds of semiconductors
including metal oxides, metal sulfides, metal organic compounds and organic semiconductors have been preparedfor photocatalytic H2O2 production (Hou H. et al., 2020).
Among these photocatalysts, graphite-like carbon nitride(g-C3N4), also named polymeric carbon nitride (PCN), asa metal-free polymer semiconductor (n-type), possesses
a stacked two-dimensional (2D) structure of tri-s-triazine
connected via tertiary amines (Wang X. et al., 2009).
Because of its unique electrical, optical, structural andphysicochemical properties, g-C3N4 has been recognized
as a new class of cost-efficient multifunctional materials forelectronic, catalytic and energy applications (Banerjee T. etal., 2021). Shiraishi and co-workers first found in 2014 thatpristine g-C3N4 showed some photocatalytic activity for
H2O2 production with the addition of ethanol as an electron
donor (Shiraishi Y. et al., 2014b). Since then, more than 80
studies on photocatalytic H2O2 production based on g-C3N4materials have been published. Several reviews summarizing some of the recent progress in photocatalytic H2O2
production have also been published (Hou H. et al., 2020;
Zhang G. et al., 2020). Most of those reviews focused on
how the various protocols for preparation of functionalized
g-C3N4 influenced the photocatalytic activity (Hou H. etal., 2020; Zhang G. et al., 2020). However, it is difficult toreveal the inner relationships among the structure, properties and performance of a g-C3N4-based photocatalyst
by simply summarizing preparation methods, properties
and performances (Zhang G. et al., 2020; Torres-Pinto A.
et al., 2019). Generally, the structure of a photocatalyst
defines its properties, and the properties of the photocatalyst determine its performance. Thus, it is necessary to
determine the desired properties of a g-C3N4-based photocatalyst for H2O2 production. Determination of the desired
properties would lead to a comprehensive understandingof the photocatalyst from rational structure design to good
performance.
In this review, we firstly show the principles of photocatalytic H2O2 production. Then the three most important
issues for improving H2O2 generation based on the band
diagram and physicochemical properties of pristine g-C3N4
are proposed. By combining the fundamental principles
with preparation strategies and possible influencing factors
in liquid-phase photocatalytic systems, future challenges
and possible directions for H2O2 production using functionalized g-C3N4 catalysts are presented in this review
with the hope that they will inspire design and preparation
strategies for not only carbon nitrides but also a variety ofphotocatalysts with good performance.

2. Principles of photocatalytic H2O2
generation

Since solar energy is renewable and sustainable, photocatalysis using power systems has received much attention
in the past few years and it has been applied to various
fields including water splitting for H2 and O2 production125Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148(Wang X. et al., 2009; Wang Q. and Domen K., 2020), CO2
reduction (Liu R. et al., 2020), nitrogen fixation (Zhang
G. et al., 2020), degradation of pollutants (Koe W.S. etal., 2020) and disinfections (Teng Z. et al., 2019), organic
synthesis and H2O2 production (Hou H. et al., 2020).
Photocatalytic power systems are cost-efficient and easy
to operate compared with photo-electrochemical systems
and photovoltaic-electrochemical systems (Hisatomi T. and
Domen K., 2019). There are three typical steps in a photocatalytic process (Fig. 2) (Nosaka Y. and Nosaka A., 2016).
Sufficient light absorption, efficient charge separation, andsurface reactions are crucial properties for typical efficientphotocatalysts.
Photocatalytic production of H2O2 follows the photocatalytic reaction principles. H2O2 can be generated througheither oxygen reduction reaction (ORR) or water oxidation
reaction (WOR) (Fan W. et al., 2020) as shown by a schematic diagram of photocatalytic H2O2 production in Fig. 3.
The light-driven 2e– WOR pathway (Eqn. (3), Table 1) isdifficult to be achieved because of the uphill thermodynamics (1.76 V vs. a reversible hydrogen electrode, simplified
as RHE, Table 1), i.e., the decomposition of as-produced
H2O2 will occur at this potential (1.76 V) because H2O2

Ox

Red
Ox

hv

Migration

Excitation

Migration

Volumerecombination

Surfacerecombination

Fig. 2

Red

Surfacerecombination

Photoexcitation and charge decay pathway.

a

Ox
CB

is an excellent hole scavenger (Shi X. et al., 2017). In thecase of the ORR pathway, H2O2 can be produced by either
a 2e– direct oxygen reduction (O2 → H2O2) route or asequential 1e– indirect reduction (O2 →•O2– → H2O2). For
h+ in consumption, the most effective reaction route is tooxidize H2O into O2 and H+ via a 4e– pathway (Eqn. (1)).
Eqn. (7) to Eqn. (10) in Table 1 show production of H2O2
via the two-step 1e– reduction pathway. First, superoxide
radicals (•O2–) are formed (Eqn. (7)) and further produce
HO2• radicals with the reaction of H+ (Eqn. (8)). Then, theHO2• radicals could produce HO2– anions by undergoinganother 1e– reduction (Eqn. (9)). Finally, HO2– reacts with
H+ to generate H2O2. Eqn. (2) shows the one-step 2e– direct reduction for H2O2 production. In this process, O2
directly reacts with two protons to form an H2O2 productvia two-electron photoreduction. It should be noted thatthe back reaction of WOR via a 4e– pathway is the ORR
process via a 4e– pathway (Eqn. (1)), which could also be acompetitive reaction for H2O2 production via 2e– ORR. The
most ideal situation would be for photocatalytic productionof H2O2 to be generated from H2O and O2 via 4e– WOR and
2e– ORR, which is an uphill reaction (ΔG0 = 117 kJ mol–1).

3. Desired physicochemical propertiesof polymetric carbon nitride forphotocatalytic H2O2 production

Since Wang and his co-workers first discovered thephotocatalytic activities of g-C3N4 (Wang X. et al., 2009),
g-C3N4-based particulate photocatalysts have attracted
much interest. The band gap of g-C3N4 (Carbon nitrides produced by thermal treatment at 500~550 °C with nitrogenrich precursors have various names as polymeric carbon
nitride (PCN), graphitic carbon nitride (GCN), and melon)
is ~2.7 eV, corresponding to an optical wavelength of~460 nm, which makes it a promising visible-lightresponse photocatalyst with an appropriate band structure(Fig. 4) (Ong W.-J. et al., 2016; Hou H. et al., 2020).
Additionally, the conjugated heptazine ring in the PCN
matrix can provide active sites for 2e– ORR (Shiraishi Y.

b

Red

CB
2H++O2+2eH2O2(0.695 V vs. SHE)

2H2O-2eVB

H2O2(1.76 V vs. SHE)

Red

VB
Ox

Fig. 3 Schematic diagram for photocatalytic H 2O2 production. (a) Photocatalytic H 2O2 production via water oxidation reaction. (b) Photocatalytic
H 2O2 production via oxygen reduction. CB is the abbreviation for conduction band. VB is the abbreviation for valence band.

126

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148
Table 1 Standard electrode potentials for aqueous ORR half-reactions (Gao J. and Liu B., 2020). Reprinted with permission from Ref. (Gao J. and Liu B.,
2020). Copyright: (2020) American Chemical Society.

Eqn. No.

Half-reactions

E0 (V vs. SHE*)

E0 (V vs. RHE**)(1)

O2(g) + 4H+ + 4e– ↔ 2H2O

1.229

1.229

(2)

O2(g) + 2H+ + 2e– ↔ H2O2

0.695

0.695

(3)

H2O2 + 2H+ + 2e– ↔ 2H2O

1.763

1.763

(4)

O2(g) + 2H2O + 4e– ↔ 4OH–

0.401

1.229

(5)

O2 + H2O + 2e– ↔ HO2– + OH––0.0650.764(6)

HO2– + H2O + 2e– ↔ 3OH–

0.867

1.696

(7)

O2(g) + e– ↔ O2–

–0.33

–0.33

(8)

O2(g) + H+ + e– ↔ HO2•

–0.05

–0.05

(9)

HO2• + e– ↔ HO2–

0.76

0.76

(10)

HO2• + H+ + e– ↔ H2O2

1.44

1.44

(11)

H2O2 + H+ + e– ↔ H2O + •OH

0.793

0.793

(12)

•OH + e– ↔ OH–

1.89

2.72

Redox Potential/V (pH=7)*SHE: Standard hydrogen electrode.
**RHE: Reversible hydrogen electrode.

-1.5
-1.0

CBM = -1.3
e-

0.0

1.0
1.5
2.0

O2 + e- + H+ = HO2 (aq)

2e-

-0.5

0.5

Possible Redox process during
H2O2 production (pH=7).

1e-

hv
h+

VBM = 1.4

2

O2 + e- = O2-

1

O2 + 2e- + 2H+ = H2O2

2

4h+

1
2h+

2H2O + 4h+ = O2 + 4H+
2H2O + 2h+ = H2O2 + 2H+

For electrons
consumption

For holes
consumption

Fig. 4 Band diagrams of g-C3N4 and summary of potentials for redox reactions during H 2O2 production by using O2 and/or H 2O. CBM is the abbreviation for conduction band minimum and VBM is the abbreviation for valence band maximum.

et al., 2014a; Shiraishi Y. et al., 2014b), which is similar
to the catalytic properties of graphene for 2e– ORR (Kim
H.W. et al., 2018; Lu Z. et al., 2018). As such, g-C3N4 has
emerged as a star material in the field of photocatalytic
H2O2 production.
However, the activity of g-C3N4 for photocatalytic H2O2
production is still restricted by low efficiency because ofsome drawbacks of pristine g-C3N4. Firstly, a large concentration of defects is inevitably introduced into g-C3N4
during thermal polymerization (Kim D. et al., 2015; Ong
W.-J. et al., 2016). This means that the inner-plane structureof g-C3N4 is not completely composed of covalent bonds,
i.e., the interactions are hydrogen bonds between melons([C6N9H3]n) (Fig. 5) (Kessler F.K. et al., 2017). As a result,the charge separation and charge migration are significantly hindered, resulting in a high charge recombination
rate of PCN. Secondly, the large band gap (2.7 eV) andsmall absorption of visible light (Kubelka-Munk absorbance usually less than 2, λ = 420 nm) result in insufficientvisible-light harvesting. Thirdly, although the side-on
adsorption of molecular oxygen on the π-conjugatedheptazine ring enables g-C3N4 (Shiraishi Y. et al., 2014b)
to have some selectivity for 2e– ORR, it is difficult for the
stepwise 1e– to 1e– ORR reaction to be prevented since
signals of superoxide radicals can still be detected during
the photocatalytic reactions with electron spin resonanceduring the photocatalytic reactions (Li S. et al., 2016). In
this case, reaction sites with higher selectivity for 2e– ORR
have to be constructed for improving the overall activity.
To overcome these drawbacks, many protocols including
tuning morphologies, defect engineering, loading cocatalysts, copolymerization of semiconductors, doping
elements and hybridization have been developed. Eachof these functionalization strategies overcome one or two

127

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148aMelamine(C3H6N6)

500-580 °C

Cyanamide(CH2N2)550 °C

Dicyanamide(CH2N2)550 °C

ThermalPolymerization

Urea(CH4N2O)

520-550 °C
Graphitic carbon nitride(g-C3N4)

460-650 °C

Thiourea(CH4N2S)bCyanamideDicyanamide

Melamine

Melem

Polymeric g-C3N4
Fig. 5 (a) Schematic illustration of the synthesis process of g-C3N4 by thermal polymerization of different precursors such as melamine, cyanamide,dicyanamide, urea, and thiourea. The black, blue, white, red, and yellow balls denote C, N, H, O, and S atoms, respectively. (b) Reaction pathway forthe development of g-C3N4 using cyanamide as the precursor. Reprinted with permission from Ref. (Ong W.-J. et al., 2016). Copyright: (2016) American
Chemical Society.

drawbacks by changing the following properties of pristine
PCN: (i) suppressing charge recombination, (ii) narrowing
the bandgap or/and promoting light absorbance, and (iii)
introducing active sites for selective 2e– ORR (Fig. 6).
Based on the improved physicochemical properties, functionalization strategies for improvement in the activity forphotocatalytic H2O2 production can also be divided into
three parts, which provide a comprehensive understanding
from material preparation to physicochemical properties,
thus providing the insight into the relationships among
structure, properties and performance.

3.1 Suppressing charge recombination
3.1.1 Hybridization
3.1.1.1 Hybridization with carbon-based materials

Carbonaceous nanomaterials are promising modifiers forphotocatalysts because of their sp2-hybridized electronic
structures (Shiraishi Y. et al., 2014a; Kim H.W. et al., 2018;
Lu Z. et al., 2018; Yang S. et al., 2018). These materials
usually work as electron transfer materials and photosensitizers to broaden the adsorption edge and improve thecharge migration (Yang S. et al., 2018; Thakur S. et al.,2017; Zeng X. et al., 2017).
Carbon nanotubes (CNTs) with a π-conjugative structure
are capable of accepting, transporting and storing electrons

128

for g-C3N4 (Tasis D. et al., 2006). An amination reaction
process was developed by Zhao and co-workers to introduce CNTs into nanosheets of g-C3N4, in which g-C3N4
was covalently combined with CNTs (Fig. 7a, b) (Zhao S.
et al., 2018a). The CNTs covalently combined with g-C3N4
promoted charge migration by shifting the CB level toenhance the single-electron reduction of O2 to ·O2–, thus
leading to enhanced ORR activity (Zhao S. et al., 2018a).
As shown in Fig. 7c, the photocurrent of g-C3N4/CNTs was
significantly increased compared with that pristine PCN.
Shiraishi and co-workers also used reduced graphene oxide(rGO) for hybridization with a g-C3N4/perylene diimide(PDI) catalyst for further activity improvement (Fig. 7d,e). The improved activity may due to the ultra-thin twodimensional nature of rGO (g-C3N4/PDI/rGO0.05) withhigh charge carrier mobility (Kofuji Y. et al., 2016a). As
shown in Fig. 7f, the photocurrent of g-C3N4/PDI/rGO0.05
is significantly larger than that of g-C3N4/PDI. However, nocurrent response is found by using a rGO electrode, indicating a synergic effect of g-C3N4/PDI and rGO. It should
be noted that the formation of tight junctions between the
g-C3N4-based photocatalyst and carbon-based material isextremely important for the formation of electron transfer
pathways since the photocurrent intensity of the physicalmixture of rGO and g-C3N4/PDI is 20 % smaller thanZhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148

Fig. 6aSolutions for overcoming the disadvantages of pristine g-C3N4 by manipulating physicochemical properties via 2e – ORR.

b

c

0.4

Light OffLight On

g-C3N4
g-C3N4-CNTs

J (10-6 A cm-2)

0.3

g-C3N4

0.2

0.1

0.0

d

110

g-C3N4-CNTs

e

H2O2

g-C 3

010
000

O2

rGO

1,4-endoperoxide

O260Light off

H2 O

002

0

f 0.8 Light on

I

D
N 4/P

Current density (μA cm-2)

CNTs-COCl

120
Time (s)g-C3N4/PDI/rGO0.05

g-C3N4/PDI

0.6

180

g-C3N4/PDI + rGO0.05

0.4

rGO

0.2
0

-0.2

0

100

200
Time (s)300

Fig. 7 Hybridization with a carbon-based material for improved charge separation. (a) TEM images of g-C3N4 -CNTs. (b) Process for preparationof g-C3N4 -CNTs. (c) Photocurrent response of g-C3N4 -CNTs or g-C3N4 -loaded electrodes under light at a bias of 0.25 V vs. SCE (λ ≥ 400 nm). (d)TEM image of g-C3N4/PDI/rGO0.05 and its SAED pattern (inverse contrast) with indexed graphite pattern. (e) Schematic illustration of the proposedmechanism for photocatalytic H 2O2 production on g-C3N4/PDI/rGO. (f) Photocurrent responses of g-C3N4/PDI/rGO0.05 and other g-C3N4 -based catalysts measured on FTO in 0.1 M Na2SO4 solution under visible light (λ > 420 nm) at a bias of 0.5 V (vs Ag/AgCl). SCE is an abbreviation for saturated
calomel electrode, SAED is an abbreviation for selected area electron diffraction, and FTO is an abbreviation for fluorine-doped tin oxide. Reprinted
with permission from Ref. (Zhao S. et al., 2018a; Kofuji Y. et al., 2016a). Copyright: (2018) Elsevier and (2016) American Chemical Society.

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a
PCN

Reduction

Metal oxides
Oxidationb-2

E(V/NHE, pH=0)
H2O2 Production

-1
0
1
2
3

·O2- Oxidation
Fig. 8 Schematic diagram of (a) semiconductor heterojunctions and (b) diagram of band positions based on experimental results for common metalbased photocatalysts. NHE is an abbreviation for normal hydrogen electrode.

the photocurrent density of g-C3N4/PDI (Kofuji Y. et al.,2016a).

3.1.1.2 Hybridization with metal and metalcompounds

Fabrication of metal compounds to form a Schottky
barrier between metal-based semiconductors and PCN
material has been proved to be an efficient approach forenhancing the photocatalytic efficiency for several reactions (Ong W.-J. et al., 2016; Teixeira I.F. et al., 2018). The
nature of the formation of Schottky barriers in PCN/metalcompounds is due to the differences between the chargepotentials of metal compounds and PCN under light irradiation (Zhang Z. and Yates J.T., 2012). The electrons tend
to migrate to semiconductors that have more positive potentials, while holes tend to migrate to those with negative
potentials (Nosaka Y. and Nosaka A., 2016). Although theoverall redox potential of the photocatalyst is decreased,
the charge separation of charge carriers is significantly
improved, thus leading to a significantly improved charge
separation (Fig. 8a). However, there are very few reportson the promotion of photocatalytic efficiency for H2O2 production by construction of heterojunctions based on PCN/
metal compounds. There are two possible reasons. On the
one hand, the potential of the conduction band minimum(0.9~1.3 V vs. SHE) of PCN (Fig. 4) is more negative
than that of common metal compounds. In this case, thephotoelectrons tend to accumulate on the metal oxides.
Thus, the potential of the metal compounds determines130

the reduction potential of the heterojunction materials. As
shown in Fig. 8b, the photocatalytic reduction potential of
TiO2, Cu2O, CdS and ZnS can hardly facilitate 2e– ORR
because of the kinetically favored 1e– ORR to form •O2–,
thus resulting in a stepwise ORR to generate H2O2. For
other metal compound semiconductors, 1e– ORR can beprevented because of the positively shifted CBM. However, the potential differences between the redox potentials
of O2, 2H+/H2O2 and CBM of Fe2O3, CuWO4, SnO2, WO3,
etc. are quite small, which may lead to a poor thermodynamic driving force for 2e– ORR. On the other hand,formed H2O2 can be decomposed by metal compounds bydisproportionation or photoreaction (Li X. et al., 2001).
Therefore, the formation of heterojunctions by contact of
PCN with one metal compound may be an unacceptablestrategy unless a unique electron transfer pathway between
PCN and metal compounds is formed, such as in Z-scheme
catalysts, so that reduction will occur on the surface ofPCN (Wang S. et al., 2020a).
Loading active noble metal nanoparticles (NPs) is oneof the effective approaches for boosting the photocatalytic
activity of a photocatalyst for H2O2 production (Hirakawa
H. et al., 2016; Tsukamoto D. et al., 2012). Zuo and coworkers observed a similar improvement in the activity ofan Au/C3N4 photocatalyst for H2O2 production (Zuo G. etal., 2019). In that study, the effects of different noble-metal
cocatalysts on g-C3N4 for H2O2 production were also
compared, and the results showed that g-C3N4 loaded with
Au nanoparticles (NPs) had the highest activity for H2O2

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production (Zuo G. et al., 2019). The enhancement of
H2O2 production was attributed to the efficient separationof photogenerated electrons and holes between Au NPs
and g-C3N4 (Zuo G. et al., 2019). The function of Au NPs
on PCN for charge separation is similar to that of carbon
materials. The Au NPs can improve the carrier mobilityof photoelectrons so that the charge recombination can besuppressed.

a significant improvement after the copolymerization ofaromatic diimides. It should be noted that the introductionof more C=O groups significantly improved the photocurrent (Fig. 9b) (Kofuji Y. et al., 2017). The C=O groups inthese aromatic diimides may accumulate negative charges
and then increase the thickness of the space-charge region,
which might improve the separation efficiency of charge
carriers (Koe W.S. et al., 2020) and disinfections (Teng Z. etal., 2019). Based on this phenomenal result from the properties of C=O groups, Ohno and co-workers prepared C=O
functionalized PCN by copolymerization of 2,5,8-triaminotri-s-triazine (melem) and barbituric acid (BA) (PCNBA,
Fig. 9c) (Teng Z. et al., 2020). In the photoluminescence(PL) spectra (Fig. 9d), there was a significant decrease inintensity together with a notable increase in photocurrent
intensity (Teng Z. et al., 2020). The copolymerization of
BA significantly suppressed recombination and accelerated
the charge separation.
In the case of incorporation with semiconductors, Shiraishi
and co-workers combined PCN/PDI with boron nitride(BN) to separate holes since BN has a relatively negative
VBM (Kofuji Y. et al., 2018). In the system of g-C3N4-PDIBN0.2-rGO, the photocurrent increased about 20 % (Kofuji
Y. et al., 2018). It is notable that the oxygen evolution of
g-C3N4-PDI-BN0.2-rGO was increased by more than 15 %compared with that of g-C3N4-PDI-rGO (Kofuji Y. et al.,2016a), further confirming that the charge separation introduced by BN separated photogenerated holes, leading to

3.1.1.3 Hybridization with metal-freesemiconductors and molecules

The advantages of cost efficiency, mechanical flexibility
and easy operation makes organic semiconductors promising alternatives for inorganic materials to improve thecharge separation (Chung D.D.L., 2019). Triazine (Jiang
X. et al., 2015) and aromatic diimides have been used as
modifiers for g-C3N4 based photocatalysts (Shiraishi Y. etal., 2014a; Kofuji Y. et al., 2016a; Kofuji Y. et al., 2018;
Kofuji Y. et al., 2017; Kofuji Y. et al., 2016b). Shiraishi
and co-workers incorporated several aromatic diimides(Shiraishi Y. et al., 2014a; Kofuji Y. et al., 2018; Kofuji Y.
et al., 2016b; Kofuji Y. et al., 2017) into the g-C3N4 matrix
by thermal condensation (Fig. 9a). All of the g-C3N4/PDI,
g-C3N4/biphenyl diimide (BDI) and g-C3N4/mellitic triimide (MTI) catalysts successfully produced H2O2 (millimolar level) in pure water with O2 (Shiraishi Y. et al., 2014a;
Kofuji Y. et al., 2016a; Kofuji Y. et al., 2018; Kofuji Y. etal., 2017; Kofuji Y. et al., 2016b). The photocurrent showed

b

O

O

O

O

N

N
O

O

+

N

N

NH2
N

N

N

N

H2N

N

N

N

O

N

N

O
O

NH2

g-C3N4/PDIX

Light onLight off

g-C3N4/MTI4g
g-C3N4/PDI51

3
2
1
0

0

100

d
Intensity (a.u.)

c

N

N

N

N

N

N

N

N

N

O

N

N

Current density (μA cm-2)

a

f

200
Time (s)300

f :PCNBA0
e:PCNBA0.01
d:PCNBA0.05
c:PCNBA0.1
b:PCNBA0.2
a:PCNBA0.5a
400 450 500 550 600 650 700 750
Wavelength (nm)Fig. 9 Hybridization with electron-deficient organic semiconductors for improved charge separation. (a) Scheme of the synthesis of g-C3N4 hybridized by aromatic diimide using g-C3N4/PDI x as an example. (b) Photocurrent responses of g-C3N4/MTI49 and g-C3N4/PDI51 in 0.1 M Na2SO4 (bias:
0.5 V vs Ag/AgCl). (c) Process for polymerization of PCNBA with barbituric acid and melem as precursors. (d) Photoluminescence spectra of PCN
and PCNBA samples. Reprinted with permission from Ref. (Kofuji Y. et al., 2017; Teng Z. et al., 2020). Copyright: (2017) American Chemical Societyand (2020) Elsevier.

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3.1.2 Doping
3.1.2.1 Metal and non-metal doping

Optical, electronic and other physical properties of
g-C3N4 materials could be manipulated by doping strategies that introduce additional elements and impurities into
the framework (Liu X. et al., 2020). In the case of charge
separation, one of the most efficient strategies is to incorporate alkaline metal ions. Alkaline metal ions were used inseveral works to functionalize PCN, leading to a promoted
charge separation for organic synthesis (Qiu C. et al., 2018)(Fig. 10a). Hydrogen evolution reduction (HER) (Sun S. etal., 2019), carbon dioxide reduction (COR) (Wang S. et al.,2020b), and nitrogen fixation, (Li X. et al., 2018a) can all
be facilitated by incorporation of alkaline metal ions into
PCN. Moon and co-workers incorporated potassium (K),CN
Intensity (105 CPS)

PCN
Intensity (a.u.)

KPCN

KBr
20
30
40
2 theta (degree)

10

50

d
H2O2 concentration (mmol)c

6

KPD-CN-342

λex = 400 nm

KPD-CN-7.5

3

30
CN

25
AQY (%)

ba

phosphorus (P), and oxygen (O) elements into a g-C3N4
matrix by thermal calcination of melamine with potassium
phosphate (KPD, K2HPO4) (Moon G.-h. et al., 2017). The
charge separation of KPD was significantly improved because of the significantly decreased PL intensity (Fig. 10b).
The hector-elements in the g-C3N4 matrix increased thecarrier lifetime to a picosecond range. Consequently, thegeneration rate of H2O2 by K-P-O-doped g-C3N4 wasnotably promoted compared to that by pure g-C3N4 (Moon
G.-h. et al., 2017) (Fig. 10c). Halogen (Br or Cl)-doped
g-C3N4 nanorods could also accelerate H2O2 production(Zhang C. et al., 2018). The charge carrier transfer in thematrix of g-C3N4 was promoted by the introduction ofheteroatoms (Zhang C. et al., 2018). Kim et al. studied theco-doping of metal (K) and nonmetal ions (P and F) into
the g-C3N4 framework (Kim S. et al., 2018). The co-doped
g-C3N4 also significantly promoted charge separation. Under visible light irradiation, the co-doped g-C3N4 exhibited
higher activity for photocatalytic production of H2O2 thanthat of bare g-C3N4 (Fig. 10d).
Zhang et al. used density functional theory (DFT)
calculation to reveal how the incorporated alkaline ionschange the charge distribution over different atoms (Zhang

KPD-CN-7.5

20

2

15
10

1

5
0

e

450

500
550
600
Wavelength (nm)

0

650

320

370
420
470
Wavelength (nm)520

Turnover frequency (h-1)

an improved charge separation. Perylene imides (PIs) weremodified on g-C3N4 nanosheets (Yang L. et al., 2017) tofabricate a Z-scheme configuration for promotion of charge
separation. As a result, more electrons from the CB of theg-C3N4 part efficiently generated H2O2, while the holesof g-C3N4/PI oxidized OH– to OH (Yang L. et al., 2017),
which also subsequently reacted to produce H2O2.

0

f

CN
CN + KPF
KPF_CN
KP_CN
KF_CN
PF_CN
K_CN
P_CN
F_CN

1.5

1.0

0.5

0.0

0

1

2

3
Time (h)45Fig. 10 Metal and/or non-metal doping for improving the charge separation. (a) XRD patterns of pristine PCN and K+-incorporated PCN. (b) PLemission spectra (λ = 400 nm) of bare carbon nitride (CN), KPD-CN-3, and KPD-CN-7.5. (c) Apparent quantum yield (AQY, left axis) and turnover
frequency (right axis) of bare CN and those of KPD-CN-7.5 for H2O2 production with monochromatic light irradiation. (d) Time profiles of photocatalytic H 2O2 generation using CN samples modified with various dopants and a control sample of a physical mixture of CN and KPF6 (CN + KPF). The
experimental conditions were 0.5 g L –1 photocatalyst, 10 vol% ethanol, pH 3, continuously purged with O2 and polychromatic light through a long passfilter (k > 420 nm). Charge distributions of (e) K-GCN and (f) KCl-GCN obtained from density functional theory (DFT) calculations. Reprinted withpermission from Ref. (Qiu C. et al., 2018; Moon G.-h. et al., 2017; Kim S. et al., 2018; Zhang P. et al., 2019). Copyright: (2018) The Authors (Qiu C. etal.), published by John Wiley & Sons, (2017) American Chemical Society, (2018) Elsevier and (2019) Springer Nature Ltd.

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P. et al., 2019). The introduction of incorporated potassium
species resulted in an accumulation on the first PCN layer.
With the further assistance of Cl atoms, the electron distribution between the layers was more balanced (Fig. 10eand 10f), thus leading to a significantly improved interlayer
charge transfer (Zhang P. et al., 2019).
Non-metal doping of PCN also enables manipulation of
the electronic configuration of PCN with significantly improved charge separation (Zhang P. et al., 2020). Wei et al.
developed an oxygen-enriched g-C3N4 (OCN) photocatalyst that achieved enhanced H2O2 production (Wei Z. et al.,2018). Compared to g-C3N4, the OCN samples achieved
significantly enhanced no-sacrificial H2O2 production efficiency. Electrochemical impedance spectroscopy (EIS)and photoluminescence (PL) spectra all proved that charge
separation efficiency was significantly promoted after the
O element had been introduced into the PCN matrix.

3.1.2.2 Doping with polyoxometalates and theirderivatives

Polyoxometalates (POMs) are composed of cations
and polyanion clusters with high structural diversity (Han
X.-B. et al., 2015). Under light irradiation, a charge transferfrom O2– to Mn+ (n = 5, 6) usually occurred in these catalysts and lead to the formation of a hole center (O–) andtrapped electron center (M(n–1)+) pair. POMs in the PCN
matrix can serve as electron donor-acceptor pairs, which
could significantly promote the charge separation or/and
serve as co-catalysts (Han X.-B. et al., 2015). Zhao andcoworkers synthesized a series of POMs clusters-modified
g-C3N4 samples for efficient photocatalytic H2O2 evolution
by the thermal condemnation method (Zhao S. et al., 2017;
Zhao S. and Zhao X., 2019; Zhao S. et al., 2018b). The
amount of H2O2 formed by g-C3N4/PW11 reached 3.5 μmol
within 60 min, while the catalytic performance of pureg-C3N4 was only 1.3 μmol (Zhao S. et al., 2017). That
research group also used other POM clusters, including[SiW11O39]8– (SiW11), (NH4)3PW12O40 (NH4-PW12) and(NH4)8Co2W12O42 (NH4-Co2W12), for covalent combination with g-C3N4 (Zhao S. and Zhao X., 2019). All of theclusters significantly improved the charge separation ofpristine PCN. Action spectra measurements revealed thatthe band excitation results in the generation of H2O2 inboth g-C3N4 and g-C3N4-CoWO systems (Han X.-B. et al.,2015; Zhao S. et al., 2017; Zhao S. and Zhao X., 2019),
indicating that the function of POM incorporation may beas the same as that of ion doping since POM clusters are all
well dispersed in the PCN matrix. The inner-panel chargetransfer and the charge transfer between the layers may
be significantly promoted by the introduction of highly
dispersed POM clusters.

3.1.3 Defect engineering

Recently, it was found that defects in semiconductors

enhance photocatalytic activity if they are precisely controlled (Pei Z. et al., 2013; Xiong J. et al., 2018; Meng A.
et al., 2020). Introduction of carbon vacancies (Li S. et al.,2016; Goclon J. and Winkler K., 2018) and introduction of
nitrogen vacancies are usually defect engineering used in
a g-C3N4 photocatalyst (Li X. et al., 2018b; Qu X. et al.,2018; Shi L. et al., 2018; Zhu Z. et al., 2018). These strategies can change the electronic configuration and reactant
molecules to promote H2O2 production. For example, Liet al. synthesized g-C3N4 functionalized carbon vacancies(Cv-g-C3N4) through thermal annealing in an oxygendeficient environment (Ar flow) (Li S. et al., 2016). This
Cv-g-C3N4 was also functionalized with a large number ofamino groups with the formation of carbon vacancies (Li S.
et al., 2016). Under visible light irradiation (Kessler F.K. etal., 2017), the H2O2 yield of Cv-g-C3N4 was much higher
than that of pure g-C3N4 (Li S. et al., 2016). The improved
photocatalytic activity of Cv-g-C3N4 for H2O2 production
can be ascribed to the promoted charge separation andalteration of the H2O2 production pathway from a stepwise
1e– to 1e– pathway to a 2e– pathway.
Nitrogen vacancies in g-C3N4 can also boost the photocatalytic activity for H2O2 production (Zhu Z. et al., 2018).
Li et al. (Li X. et al., 2018b) and Qu et al. (Qu X. et al.,2018) showed that nitrogen vacancies can be introducedin the matrix of g-C3N4 by dielectric barrier discharge(DBD) plasma treatment. Their results showed that H2O2
evolution can be improved by up to 10 times comparedwith that produced by pristine PCN (Li X. et al., 2018b).
In another related study, Shi et al. created a holey defective
C3N4 photocatalyst with nitrogen vacancies via a hydrazine
photoreduction method. The transfer of charge carriers
from the bulk to the surface was significantly improved,
as confirmed by PL measurements (Shi L. et al., 2018).
The introduced nitrogen vacancies narrowed the bandgap
and the formation of defect states within the bandgap
together with a notably suppressed electron-hole recombination. Therefore, the holey defective g-C3N4 photocatalyst
showed much higher photocatalytic activity for visible
light-driven H2O2 production than that of pristine g-C3N4.
In summary, the functionalization strategies of doping
and defect engineering for improving the charge separation
for PCN-based materials used for the 2e– ORR process are
similar to the functionalization strategies for other reactions
such as HER, COR, and ORR. However, the hybridizationof PCN-based materials, especially hybridization withelectron-deficient semiconductors, provides a PCN-based
photocatalyst with unique electronic properties for improving the charge separation. We believe that copolymerization strategies to introduce other electron-deficient groups
with higher charge separation efficiency might enable thegeneration of π-conjugated and π-stacked donor–acceptor
couples (Shiraishi Y. et al., 2019) in the PCN matrix, leading to a higher efficiency for photocatalytic H2O2 synthesis.

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3.2 Improving light harvesting

Although pristine PCN can use visible light for a photocatalytic reaction, the large band gap (2.7 eV) results ininsufficient visible light harvesting for further efficiency
improvement (Ong W.-J. et al., 2016). Thus, improving
the light absorbance of a g-C3N4-based photocatalyst andnarrowing the band gap are two practical strategies for
H2O2 production.
Copolymerization of PCN with other organic molecules/
semiconductors, such as triazine, barbituric acid and aromatic diimides, not only improves the light absorbanceof the photocatalyst but also narrows the bandgap. As
mentioned in Section 3.1.1.3, Shiraishi and co-workers
prepared a series of PCN samples hybridized with atomic
diimides with a significantly narrowed band gap from
2.7 eV of pristine PCN to 2.4 eV of PCN/PDI (Shiraishi Y.
et al., 2014a; Kofuji Y. et al., 2017; Kofuji Y. et al., 2016a).
The light absorbance of PCN samples hybridized witharomatic diimides improved to about ~20 % at λ = 400 nm,
indicating improved light absorption. Additionally, Ohno
and co-workers co-polymerized melem with barbituric acidto expand the light absorption edge from 445 nm (2.76 eV)of pristine PCN to ~550 nm (2.18 eV) of PCNBA0.5(Fig. 11a) (PCNBA is an abbreviation for polymetric carbon nitride doped by barbituric acid, 0.5 means calcinationof 3 g melamine and 0.5 g barbituric acid) (Teng Z. et al.,2020). The absorbance of PCNBA0.2 at 420 nm was alsobIntensity (a.u.)

PCNBA PCNBA PCNBA
0.01
0.05
0.1

PCNBA PCNBA
0.2
0.5

c

400

d

4
2
0
300

KPF_CN_20
KPF_CN_15
KPF_CN_10
KPF_CN_5
KPF_CN_3
CN
KPF6

400
500
Wavelength (nm)600

Absorbance (K.M.)

Absorbance (K.M.)

8
6

f

a:PCNBA0
b:PCNBA0.01
c:PCNBA0.05
d:PCNBA0.1
e:PCNBA0.2
f:PCNBA0.5a600
Wavelength (nm)

800

4

100
80

3
GCN

60

2

40

AKCN
1
0
350

AQY (%)aincreased by 200 % compared with that of pristine PCN(Fig. 11b) (Teng Z. et al., 2020).
Doping of alkaline metal ions can also expand the lightabsorption to a small extent (0.1 eV~0.3 eV) combinedwith significantly light improved absorbance (50 %). Alkaline metal doping and non-metal doping were also proved
to be efficient strategies for improving light absorbance(Moon G.-h. et al., 2017; Kim S. et al., 2018; Zhang P.
et al., 2020). Choi and co-workers found that the lightabsorption ability of CN samples was gradually promoted
by increasing the amount of KPF6, even though KPF6
alone did not exhibit any visible-light absorption (Moon
G.-h. et al., 2017) (Fig. 11c). A very recent work using K
and S-co-doped PCN with functionalization of the –OH
group (AKMT) also showed significant improvement inboth light absorption and narrowing of the bandgap (Zhang
P. et al., 2020). The light absorption edge expanded from~450 nm of pristine PCN to ~520 nm of AKMT (Zhang P.
et al., 2020) (Fig. 11d). With the addition of a sacrificial
reagent, AKMT showed apparent quantum yields (AQYs)
of 76 % and 40 % at 480 nm and 500 nm for H2O2 production, respectively. By comparison, pristine PCN exhibited
negligible activities at the same wavelengths.
Defect engineering can also slightly improve the lightabsorption of photocatalysts, especially for semiconductorbased materials (Li S. et al., 2016) and Zhu et al. (Zhu Z.
et al., 2018) reported that the UV-vis absorption spectra of

20
400

450
500
550
Wavelength (nm)6000Fig. 11 Typical strategies for improving light harvesting. (a) Photographs show the corresponding powders of PCNBA samples. (b) UV-vis spectraof PCN and PCNBA samples: absorbance spectra. (c) Diffuse reflectance UV-vis spectra (DR-UVS) of modified CN samples prepared with various
KPF6 contents. (d) UV-vis spectra and apparent quantum yield (AQY) of H2O2 production as a function of irradiation wavelength with 10 % EtOH(v/v) under visible light irradiation (λ ≥ 420 nm) and T = 25 °C. Reprinted with permission from Ref. (Teng Z. et al., 2020; Zhang P. 2019). Copyright:(2019) Springer Nature Ltd. and (2020) Elsevier.

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g-C3N4 and carbon vacancy-functionalized g-C3N4 showeda red shift of absorption edges compared with that of pristine ones. Nitrogen vacancies also significantly improvelight absorption (Li X. et al., 2018b; Qu X. et al., 2018; Shi
L. et al., 2018). Ye and co-workers compared the opticalproperties of bulk PCN (BCN) and those of N-defected
CN samples (DCN) (Shi L. et al., 2018). The absorption
edges of the samples showed red shifts of ~40 nm compared with that of pristine g-C3N4 samples without thermal
treatment (Shi L. et al., 2018). They also revealed that theelectrons could be accommodated by forming defect states
in BCN, which greatly promoted light harvesting (Shi
L. et al., 2018). Compared with the copolymerization ofan electron-deficient semiconductor and metal/non-metal
doping, the influence of defect engineering is relatively
small because defect states are usually located just below
the CBM. In this case, the extent of narrowing of the bandgap by introducing defects is limited. Copolymerizationof electron-deficient semiconductors seems to be the mostpromising functionalization strategy for both improving
light absorbance and expanding the absorption edge.

largest SSA (228 m2 g–1) showed significantly decreased
activity and selectivity for H2O2 formation (Fig. 12b)
(Shiraishi Y. et al., 2015). Mesoporous g-C3N4 with thelargest surface area inherently contains the largest numberof primary amine moieties (Shiraishi Y. et al., 2015). The
H2O2 selectivity was significantly suppressed since these
defects can also serve as active sites for 4e– reduction ofO2 (Fig. 12c) (Shiraishi Y. et al., 2015). These defects
also further decomposed the formed H2O2, leading to significantly suppressed activity. Results of density function
theory (DFT) calculations confirmed (i) a large distributionof lowest unoccupied molecular orbital (LUMO) electrons
onto the primary amine moieties and (ii) a decrease in
H2O2 selectivity with an increase in the amount of primaryamine moieties, strongly suggesting that the primary amine
moieties on the surfaces of mesopores behave as the active
sites for four-electron reduction (Fig. 12d–f) (Shiraishi Y.
et al., 2015). However, the specific types of those defects
were not described in their reports. Specification of thetypes and fine structures of defects is important to further
improvement by defect engineering strategies.

3.3 Introduction of active sites for selective 2e–ORR

3.3.2 Copolymerization with electron-deficient
semiconductors

Introduction of active sites with specific physicochemical properties can significantly improve both selectivity
and activities (Bo Y. et al., 2020). To give a systematic
review for this topic, three dominant strategies for introducing active sites for photocatalytic H2O2 production arepresents as follows.

3.3.1 Increasing the surface area

PCN prepared by the thermal polymerization method
is usually quite small (<15 m2 g–1) (Teng Z. et al., 2017).
As previously mentioned, the conjugated heptazine ringin the PCN matrix can provide active sites for 2e– ORR(Shiraishi Y. et al., 2014a). A plausible strategy for increasing the number of active sites for 2e– ORR is to increase
the surface area (Mohamed N.A. et al., 2019; Ou H. et al.,2017; Liao G. et al., 2019). However, there is still some
controversy about whether the overall 2e– ORR rate can be
improved by increasing the specific surface area of PCN.
Shiraishi and co-workers investigated the photocatalytic
production of H2O2 with the existence of O2 and Et-OH.
A series of mesoporous g-C3N4 catalysts having different
specific surface areas (SSA) were prepared by a hard template (silica nanoparticles with different diameters) method(Fig. 12a) (Shiraishi Y. et al., 2015). In these systems,
photogenerated holes oxidize Et-OH and the conduction
band electrons localized at the melem unit reduce O2 toform H2O2. This g-C3N4 catalysts exhibit significant higher
SSA of 56 and 160 m2 g–1 compared with that of pristineg-C3N4 (10 m2 g–1) and higher activity for H2O2 production (Shiraishi Y. et al., 2015). However, g-C3N4 with the

Copolymerization with electron-deficient semiconductors such as PDI, BDI and MTI has also been proved to be
one of the most efficient strategies for promoting the selectivity of a photocatalyst (Shiraishi Y. et al., 2014a; Kofuji
Y. et al., 2017; Kofuji Y. et al., 2016a). Calculations basedon the density functional theory (DFT) were investigated toclarify the influence of the PDI unit on the electronic structure of the g-C3N4 framework (Shiraishi Y. et al., 2014a).
Time-dependent results showed that the main transitions
are S0→S1 (highest occupied molecular orbitals, HOMO
→LUMO and HOMO→LUMO+2). The iso-surfaces
showed that electron distributions of the melem-PDI model(main transitions HOMO→LUMO+2) are located mainly
at the melem units with partial distribution to the PDI units(Shiraishi Y. et al., 2014a). The high electron affinity of the
PDI units in g-C3N4/PDI may significantly improve thepolarization of the whole frameworks.
Being specific, the electrons on HOMO are concentrated
at the N2 and N6 atom positions of the melem unit, andthose on LUMO+2 are located at the C1 and N4 atoms(Shiraishi Y. et al., 2014a). The Raman spectra of g-C3N4/
PDI and g-C3N4 confirmed that the side-on adsorption of
O2 was significantly promoted on g-C3N4/PDI, indicatinga crucial role of copolymerization of electron-deficient
semiconductors.

3.3.3 Doping/Heteroatom incorporation

Incorporation of metal species or no-metal species in
the PCN matrix can both improve the charge separationand introduce active sites for specific reactions (Hu S.

135

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148bc

H2O2 formed / μmol

100

1.6

X = 56
X = 160

X = 22850

j-1 / cm2 mA-1

a

GCN(10)n = 2.07
Jk = 0.795

1.4

2e- process

1.2

X = 104e- process

GCN(228)n = 2.72
Jk = 1.19

1
0

d

0

6

12

18

e

24

0.02

0.04
ω-1/2 / rpm-1/2

0.06

f

LUMO
LUMO

LUMO
ELUMO = -1.03 eV

ELUMO = -1.82 eV
ELUMO = -1.74 eV

4.98 eV
4.3 eVHOMO
EHOMO = -6.01 eV

4.25 eVHOMO

HOMO
EHOMO = -6.12 eV
EHOMO = -5.99 eV

Fig. 12 Influence of surface defects on H 2O2 production by using a PCN-based material. (a) Different N atoms of PCN. (b) Time-dependent change
in the amounts of H 2O2 during photoreaction on the respective GCN(x) catalysts. (c) Electron transfer numbers during the photocatalytic H 2O2 production. Interfacial plots of the main orbitals for (d) single, (e) double, and (f) triple melem-conjugated models, calculated by the function of B3LYP at
the 6-31G (d) level. Reprinted with permission from Ref. (Shiraishi Y. et al., 2015). Copyright: (2015) American Chemical Society.

et al., 2018; Zhang Z. et al., 2022). By the templateassisted method, Hu and co-workers fabricated hollow
copper-doped g-C3N4 microspheres. The g-C3N4 withcopper incorporation showed a much higher H2O2 production ability (~4.8 mmol·L–1) than that of pristine g-C3N4(0.45 mmol·L–1). DFT simulations showed that the Cu(I)-Nsites work as reactive sites to activate molecular O2 (Hu S.
et al., 2018). The OCN proposed by Zhu and co-workers
was proven to show a high selectivity for 2e– ORR for
photocatalytic H2O2 production, which is attributed to
the functionalized C-O-C groups on OCNs (Wei Z. et al.,2018). Choi and co-workers revealed by DFT calculations
that the K incorporation could result in improved adsorption for O2 molecules (Zhang P. et al., 2019) (Fig. 13a,b). They also investigated whether electrons on S-doped
g-C3N4 can be donated to the antibonding π* orbital ofadsorbed *O2 (with –0.40 |e|) for forming efficient ORR
sites, as well as AQ doping as cocatalysts (Fig. 13c). The
strong electron pushing effect between carbon and oxygen
facilitates subsequent protonation in ORR kinetics (Chu
C. et al., 2020). Both the activity and selectivity of ORR

136

can be changed by manipulating the properties of metallic
sites (Kulkarni A. et al., 2018). As elaborated in several
electrochemical catalysts, the end-on O2 adsorption usuallylead to highly selective 2e– ORR. It is difficult to preventthe splitting of O-O bond breaking on surface of metal
particles since end-on O2 molecular adsorption and side-on
O2 molecular adsorption occur on metal particles (Choi
C.H. et al., 2014; Chu C. et al., 2019). Benefiting from thedesirable features of a single atom catalyst (SAC) (Sun H.
et al., 2021), the adsorption of O2 molecules on atomicallyisolated sites is often end-on type, which could reduce thepossibility of O-O bond splitting (Kulkarni A. et al., 2018;
Wang A. et al., 2018). For example, SACs with Pt2+ (ShenR. et al., 2019) and Co-N4 (Gao J. et al., 2020; Jung E. etal., 2020) centers could electrochemically reduce O2 to
H2O2 via a 2e– ORR pathway with ultrahigh selectivity(>96 %). However, it is difficult for Pt2+ and Co-N4 sites tobe coupled in the photocatalytic system due to their highcharge recombination characteristics, which originate fromthe intermediate band formed by the half-filled d electrons(Teng Z. et al., 2021a). Very recently, Ohno and co-workers

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148ac

Co single atom
on C3N4 plane(H2O oxidation center)

Top

Anthraquinone
on C3N4 edge
(O2 reduction center)

Side

b

Co1/AQ/C3N4
Fig. 13 Typical strategies for introducing active sites for efficient 2e – ORR. (a) An enlarged top view of KCl-OH-GCN, and (b) an enlarged top viewof pristine GCN. The yellow and blue colors represent electron accumulation and depletion at an isosurface value of 0.002 Å e –3. The brown, gray,and violet colors represent carbon, nitrogen, and potassuim, respectively. (c) Spatial separation of Co single atom (as an oxidation center) and AQ (asa reduction center) cocatalysts by anchoring them in the center (i.e., pyridinic N) and on the edge (i.e., primary/secondary amine N) of 2D ultrathin
C3N4. Reprinted with permission from Ref. (Zhang P. et al., 2019; Chu C. et al., 2020). Copyright: (2019) Springer Nature Ltd. and (2020) The Authors(Chu C. et al.), published by National Academy of Science.

developed an Sb single atom photocatalyst (Sb-SAPC) for
non-sacrificial photocatalytic H2O2 synthesis in a water andoxygen mixture under visible light irradiation (Teng Z. etal., 2021a). The Sb-SAPC was prepared by a wet chemical
method using NaSbF6 and melamine as precursors. As
shown in the image acquired from high-angle annulardark field scanning TEM (HAADF-STEM) measurement,
the bright spots with high density are uniformly dispersedin the entire carbon nitride matrix (Fig. 14a). Electronenergy loss spectroscopy (EELS) (Fig. 14b) measurement revealed bright spots corresponding to Sb atoms, inwhich the oxidation state of Sb is regulated to +3 with a4d105s2 electron configuration (Fig. 14c, d). The results ofexperimental and theoretical investigations indicated thatthe adsorption of O2 on isolated Sb atomic sites is end-on
type, which promotes the formation of Sb-μ-peroxide (SbOOH), leading to an efficient 2e– ORR pathway for H2O2
production (Fig. 14e). The 2e– ORR efficiency of Sb species improved by almost one magnitude in the presence of
2-propanol (Fig. 14f). The apparent charge recombinationof Sb-SAPC was significantly suppressed by introducing Sb
species into the PCN matrix (Fig. 14g, h). With highly concentrated holes, this catalyst also achieved non-sacrificial
H2O2 production using water as an electronic donor.

3.3.4 Cocatalyst loading

Cocatalyst loading is a traditional strategy for introducing active sites for specific reactions (Nosaka Y. andNosaka A, 2016). For instance, pristine PCN shows quite
low efficiency in photocatalytic hydrogen production, andloading of platinum species on PCN usually significantly
improves the HER performance (Wang Q. and Domen K.,
2020). The reason for loading a cocatalyst to boost the photocatalytic reactions is usually the decreased overpotentialof the reactions on the co-catalyst surface compared withthat on pristine photocatalysts (Hirakawa H. et al., 2016;
Tsukamoto D. et al., 2012; Peng Y. et al., 2017). AQ species
can enhance the selectivity of O2 reduction to H2O2 basedon the mechanism of the current industrial H2O2 production
process (Kim H.-i. et al., 2018; Chu C. et al., 2020). Kim
and co-workers prepared several AQ-modified PCN samples with significantly improved selectivity for 2e– ORR(Chu C. et al., 2020). Loading an AQ cocatalyst onto ultrathin g-C3N4 significantly improved the selectivity of H2O2
production, combined with a 1.9-fold enhancement of theamount of H2O2 production (Chu C. et al., 2020). Several
works in which Au nanoparticles were loaded onto the
C3N4 matrix all showed significantly improved 2e– ORR
efficiency (Chang X. et al., 2018; Zuo G. et al., 2019). This137

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148c

0.4%
0.1-0.2 nm 0.2-0.25 nm

1.0

0.5

Count (105)
70
60
50
40

450
Energy loss (eV)

Data

Sb

N

Fit

C

H

2
4
Radial distance (Å)

652Reaction for 1h with
10% v/v 2-propanol30

a: Pristine PCN
b: PCN_Na15
c: Sb-SAPC15

0

4.5

-50

Pristine PCN
PCN_Na15
Sb-SAPC15-40a

470 nm20
10

Wavelength (cm-1)

h445 nm

990

896

a

g
Pristine PCN
PCN_Na15
Sb-SAPC15

990

713

-8
0

650

855

Side-on adsorption

Z’’ (104 ohm)

H2O2 concentration (mg L-1)

f

250

30600

Sb-OOH

0
-4

Sb

713
30520

Energy (eV)NSb-N shell = 3.3

Intensity (a.u.)0│χ(R)│(Å-4)

4N

3.7 eV

30500
30550
Photon energy (eV)

d8

C

e

30460

0
30450

b3

End-on adsorption

Intensity (a.u.)•

IsolatedsingleSb3+ sites

First derivative (a.u.)

99.6%

e

Sb-SAPC15
Sb2O5
Sb foil

1.5

Normalized χμ(E)

a

-30
-20
-10

7.575

c
400

450

500
550
Wavelength (nm)

600

650

0
0

5

10

15
20
Z’ (104 ohm)

25

30

35

Fig. 14 Single atomic sites for efficient ORR. (a) High-magnification HAADF-STEM image of Sb-SAPC15. The insert shows size distribution of thebright spots. (b) Electron energy-loss spectroscopy (EELS) spectrum of Sb-SAPC15. (c) Sb-K edge X-ray absorption near edge structure (XANES) of
Sb foil, Sb2O5 and Sb-SAPC15. (d) Fitting of the extended X-ray absorption fine structure (EXAFS) data for Sb-SAPC15 based on the model obtained
from DFT optimization. Inserted figures: optimized molecular models based on DFT for EXAFS fitting. (e) Experimental Raman spectra recorded
during the photoreaction in a 2-propanol aqueous solution with saturated oxygen. Spectra a, b, c and d: PCN, Sb-SAPC1, Sb-SAPC5 and Sb-SAPC15
in 10 % (v/v) 2-propanol aqueous solutions. Spectrum e: Sb-SAPC15 in pure water. (f) Comparison of H 2O2 production in 10 % (v/v) 2-propanolaqueous solutions catalyzed by pristine PCN, PCN_Na15 and Sb-SAPC15. (g) Photoluminescence spectra of PCN, PCN_Na15 and Sb-SAPC15 at an
excitation wavelength of 380 nm (h) Electrochemical impedance spectroscopy (EIS) spectra (Nyquist plots) of pristine PCN and Sb-SAPC15 in thefrequency range from 100 kHz to 0.01 Hz at 0.6 V (vs. Ag/AgCl) under visible light irradiation. Reprinted with permission from Ref. (Teng Z. et al.,2021b). Copyright: (2021) Springer Nature Ltd.

phenomenon may be attributed to the cocatalytic effects of
the Au surface on the photocatalytic 2e– ORR, which has
already been proved by investigating other photocatalysts
and photocathodes (Hirakawa H. et al., 2016).
In summary, there are two fundamental strategies for
introducing active sites for the improvement of 2e– ORR
of a PCN-based photocatalyst. One is to manipulate theelectronic configurations of the functionalized PCN catalyst so that the selectivity and activity of the π-conjugatedheptazine ring for boosting H2O2 production can be significantly enhanced. Similar strategies have also been used insome other carbon-based materials for promoting selective
H2O2 production. On the contrary, the nature of melem

138

sites is side-on adsorption of oxygen (Shiraishi Y. et al.,2015), which might result in unexpected splitting of O-O
bonds, thus leading to the possible 4e– ORR process and alimited selectivity for 2e– ORR (Siahrostami S. et al., 2013;
Verdaguer-Casadevall A. et al., 2014). The other strategy is
to introduce active sites beyond the π-conjugated heptazine
ring for selective 2e– ORR. Up to now, only a few cocatalysts have been developed for selective ORR (Section
3.3.4). Further development of 2e– ORR sites beyond the
π-conjugated heptazine rings may overcome the disadvantage of side-on O2 adsorption and shed light on further
improvement for H2O2 production.

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148

4. Other factors for boosting photocatalytic
efficiency for H2O2 production

tainable process for photocatalytic H2O2 synthesis requires
the use of earth-abundant water as an electron donor,
i.e., water oxidation reaction instead of alcohol oxidation
should occur for hole consumption.
There have been several studies in which mechanisms forphotoreduction reactions were systematically investigatedby using g-C3N4 (Wang Y. et al., 2019). Since the functionsof electron donors were clarified in those works, we will
focus on the crucial properties and functionalization strategies for achieving H2O2 production with water and oxygen(Fig. 15a). Typically, water oxidation via a 4e– pathway isusually recognized as an oxygen evolution reaction (OER)
that is usually needed to overcome the large overpotential.
VBMs of photocatalysts for OER usually have quite positive potentials (VBMTiO2-rutile ~2.9 V vs. SHE, VBMBiVO4~2.53 V) (Fan W. et al., 2020; Hirakawa H. et al., 2016).
As shown in Fig. 15b, pristine PCN is unable to generate
H2O2 in the presence of water and O2 because the valenceband maximum (VBM) lies at approximately 1.4 V, whichshows an insufficient thermodynamic driving force forOER (ca. 0.8 V versus RHE, pH 7) (Shiraishi Y. et al.,2014a). To overcome this obstacle, band engineering andthe use of a cocatalyst are two effective strategies.
The bandgap position of PCN can be easily manipulated

4.1 Adding electron donors or not

Sacrificial reagents are commonly used to consume
the photogenerated electrons or holes so that half of thereaction can be significantly boosted (Schneider J. and
Bahnemann D.W., 2013). In the case of g-C3N4-based
photocatalysts, most researchers used electron donors sothat reduction reactions could be maximized in a certain
photocatalytic system since an electron donor prevents
the photogenerated electrons from recombining withholes (Wang Y. et al., 2019; Wang M. et al., 2017). g-C3N4
has shown promising prospects for catalyzing several
photochemical reduction reactions with the existence ofelectron donors. The reason may be as follows: the CBMof pristine PCN (–1.3 V) is quite negative, thus resulting insignificantly larger redox potentials of hydrogen evolution
reaction (HER), nitrogen reduction reaction (NRR), carbon
dioxide reduction reaction (CRR) and 2e– ORR (0.695 V
vs. SHE) (Wang Y. et al., 2019). Combined with the meritsof the side-on O2 adsorption on melem units (forming 1-4
endoperoxide species) as acceptable 2e– reduction sites,
PCN satisfied the thermodynamic potential for 2e– ORR
with the existence of alcohol. However, a green and sus-

b

a

c

1.169

1.582

O2,2H+/H2O2
O2,4H+/2H2O

1
*

pH=0

VBM=1.4
pH=14

d

e

hv

0.0

-0.2

2.63 eV

0.997

0.584
pH=7

-1.23 V

1.40 V
g-C3N4

Melem4-0.76 V

2.68 eV

2.59 eV

1.86 V
g-C3N4/PDI51

VB

1.83 V
g-C3N4/BDI50

O2/ H2O2
0.08 VO2/ H2O
0.62 V
H2O2/ H2O
1.17 V

Et-OH

Enhancedcharge separation
Enhanced
Oxidation potential

14

6
4
2
500

600

Wavelength (nm)0700g-C3N4/BDI51
g-C3N4
0

12

24
t/h

36

48

no
Et-OH

no
Et-OH

g

16

8

400

20

18

10

g-C3N4/BDI50

40

20

12

-0.4
O2

100

0

Uv-vis spectrum of PCNBA0.2
PCNBA0.2 with ethanol
PCNBA0.2 with ethanol
PCNBA0.2 with ethanol

1/2 O2 + 2H

CH3CHO

Melem4
BA2

CB

vs. Ag/AgCl(pH = 6.6)

f
H 2O

Or
Melem4BA1-0.82 V

g-C3N4/BDI50

H2O2 concentration (mmol)

0

1.995

AQY (%)-1

2

Energy (hatree)

H2O2 concentration (mmol)

120
CBM=-1.3

Absorbance (a.u.)

Potential ( V vs RHE)

-2

16
12
8
4
0

no
Et-OH

no
Et-OH

PCNBA PCNBA PCNK
0.2Co5
0.2
10

PCNNa
10

Fig. 15 Typical strategies for preparation of a g-C3N4 -based catalyst for photocatalytic H 2O2 production with water and O2. (a) Real hydrogen electrode potentials of 2e – ORR and 4e – WOR compared with the band diagram of PCN. (b) Electronic band structures of pristine PCN and g-C3N4/BDIx.
(c) Changes in the amounts of H2O2 formed during photoreaction with respective catalysts. For the reaction in (c), 100 mg of catalyst was used. The
irradiance at 420–500 nm is 27.3 W m –2. Blue points show the results of photoreaction with water containing ca. 100 μmol of H 2O2. (d) Calculated
HOMO and LUMO of Melem_4 model (representing PCN), Melem_4BA1 (representing PCNBA with a low motif concentration) and Melem_4BA2(representing PCNBA with a high motif concentration). (e) Schematic diagram of PCNBA xCox for accelerating water oxidation. (f) Absorption spectrum of PCNBA0.2 and action spectra for H 2O2 formation on the respective catalysts. (g) Time course of O2 evolution measured under Ar pressureof 0.6-kPa and 300-W xenon lamp irradiation with 0.5 g L –1 of the catalyst, 1 g L –1 La2O3, and 20 mM AgNO3 in 100 mL of water. Reprinted withpermission from Ref. (Kofuji Y., et al., 2016b; Teng Z. et al., 2020). Copyright: (2016) American Chemical Society and (2020) Elsevier.

139

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148

by functionalization. Shiraishi and co-workers successfully
manipulate the band positions of a PCN-based photocatalyst by copolymerized melamine with electron-deficientaromatic diimide (Shiraishi Y. et al., 2014a; Shiraishi
Y. et al., 2014b; Kofuji Y. et al., 2016a; Kofuji Y. et al.,2017; Kofuji Y. et al., 2016b). The valence band of the
as-prepared co-polymerized PCN is about 1.8~2.4 V (vs.
NHE). Taking g-C3N4/PDI as an example, the activity
results revealed a significant role of band engineering.
Pristine g-C3N4 (VBM = 1.40 V vs. NHE) barely produces
H2O2, and g-C3N4/BDI51 (VBM = 1.86 V vs. NHE) produces 31 μmol of H2O2 after photocatalytic reaction for
48 h (Kofuji Y. et al., 2016b) (Fig. 15c). g-C3N4/BDI50(VBM = 1.83 V vs. NHE) shows a higher performance(41 μmol) (Kofuji Y. et al., 2016b). With the additionof electron acceptors, O2 can be detected by GC, whichfurther confirmed that water oxidation to generate oxygen
consumed the generated holes during the photocatalytic
reactions (Kofuji Y. et al., 2016b). Introduction of boron
nitride (BN) can further improve the activity of the g-C3N4/
PDI-rGO photocatalyst because of the enhanced charge
separation efficiency (Kofuji Y. et al., 2018). Following
those works, Ohno and co-workers further revealed byanalysis of the density of states that the copolymerizationof electron-deficient units results in the introduction of O
2p states (Teng Z. et al., 2020). The introduced O 2p statescompose the valence band with the states of N 2p and C 2p,
resulting in a more positive potential than that of pristine
PCN for which the VB is only composed of the states of N
2p and C 2p (Fig. 15d) (Teng Z. et al., 2020). Thus, the OER
activity is significantly promoted (Fig. 15e). Additionally,
preparation of POM-incorporated PCN samples with ideal
electronic configurations and band positions also played
similar roles (shifting the VBM to a more oxidative potential) for boosting the OER, which successfully achieved
the H2O2 production via ORR and WOR (Zhao S. et al.,2018b; Zhao S. et al., 2017; Zhao S. and Zhao X., 2019).
Another strategy for improving the OER activity of PCN
is loading a cocatalyst for OERs. Ohno and co-workers
loaded Na2CoP2O7 on the band-engineered PCNBA0.2
by a simple ball milling method (Fig. 15e) (Teng Z. et al.,2020). The time-dependent H2O2 production and action
spectra confirmed the cocatalytic effects of Na2CoP2O7(Teng Z. et al., 2020). The action spectra showed that its
bandgap excitation produces H2O2 (Teng Z. et al., 2020).
The apparent quantum yield (AQY) of PCNBA withloading of 5 % Na2CoP2O7 (PCNBA0.2Co5 %) at 420 nm(8.0 %) was two-times higher than that of PCNBA0.2(4.0 %) (Fig. 15f) (Teng Z. et al., 2020). Additionally, no
H2O2 was produced at the light-irradiation wavelengths
between 550 nm and 650 nm, indicating that Na2CoP2O7
works as a cocatalyst and does not work as a photocatalyst
for production of H2O2 (Teng Z. et al., 2020). More recently, Chu and co-workers prepared single-atom dispersed

Co as a co-catalyst for OER (Chu C. et al., 2020). X-ray absorption fine-structure spectroscopy (FT-EXAFS) showedthat the oxidation state of the Co single atoms coordinatedby P atoms is close to +2 (Chu C. et al., 2020). The OER
activity of g-C3N4 was significantly increased (8.4-fold)by the introduction of Co single atomic sites (Fig. 15g).
The chemical states of P-coordinated Co atoms are close
to +2, indicating that the unique electronic configurationof Co(II)-P may provide active sites for OER during solar
H2O2 synthesis (Chu C. et al., 2020). It is notable that the
VBM of this single-Co dispersed PCN (~1.4 V vs. SHE)
(Chu C. et al., 2020), quite close to that of PCN, is significantly more positive than that of metal oxides (>2.3 V vs.
SHE). Thus, the Co(II)-P single atomic sites may significantly boost the kinetic process of water oxidation. Veryrecently, the Sb-SAPC proposed by Ohno and co-workers
also showed good activity based on non-sacrificial photocatalytic H2O2 activities. Based on the results detailed
characterizations and analyses, the following reaction
mechanism (Fig. 16) of Sb-SAPC for photocatalytic H2O2
production is proposed. Firstly, efficient charge separation
occurs on Sb-SAPC under visible light irradiation, resulting in the generation of photoexcited electrons and holes
for ORR and WOR, respectively. Then water molecules areoxidized to evolve O2 by photogenerated holes localized
at the N atoms near the single Sb atoms. Simultaneously,
O2 dissolved in water and that generated from the WORboth participate in the ORR process to produce H2O2. It
is notable that the efficient charge separation, ideal single
atomic sites for end-on type O2 adsorption and close spatial
distribution of active sites boost both the 2e– ORR and 4e–
WOR for efficient H2O2 production.
In summary, accelerating the photocatalytic oxygen
evolution reaction by using a PCN-based photocatalyst
is the most crucial challenge for achieving photocatalytic
H2O2 production with water and O2. Band engineering andloading cocatalyst for OER may respectively improve theoxidation potential and promote the kinetics for 4e– OER.

4.2 pH value

It is difficult for the light-driven 2e– WOR pathway tobe achieved because of the uphill thermodynamics (1.76 V
vs. NHE), i.e., the as-synthesized H2O2 will decompose at
this highly oxidative potential because H2O2 is an excellent
hole scavenger (Fuku K. and Sayama K., 2016). Therefore, we will focus on the 2e– ORR pathway. Substantial
efforts have been devoted to the 2e– ORR concept with theaim of achieving high-yield production of H2O2 in basic,
neutral, and acidic media (Gao J. and Liu B., 2020; Kim
H.W. et al., 2018; Lu Z. et al., 2018; Iglesias D. et al.,2018). The acid dissociation constant of H2O2 (H2O2H HO 2
↔ H+ + HO2–, K a , pKa = 11.7) suggests
H 2O 2that the main form of H2O2 would be HO2– in an alkaline

(11)
140

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148

hv
Ch
a
r
for ge s
e - a epnd + arathion

2eprocess(H2O2)

1eprocess(·OOH)+(e +H +)4eprocess(H2O)+(e +H +
)

+4h +

+4H+

+(e -+

H +)+(e -+H +)Fig. 16 Mechanism of photocatalytic H 2O2 production. (White, gray, blue, red and magenta spheres indicate hydrogen, carbon, nitrogen, oxygen andSb atoms, respectively.) After shining visible light, the photogenerated electrons are localized at the Sb sites (with a blue glow), and the photogenerated
holes are localized at the N atoms at the melem units (with a red glow). Subsequently, the dissolved O2 molecules are adsorbed (orange arrows) onto the
Sb sites and then become reduced (blue arrows) via a 2e – transfer pathway through the formation of an electron μ-peroxide as the intermediate. Simultaneously, water molecules are oxidized (pink arrows) to generate O2 by the highly concentrated holes on the melem units. Reprinted with permission
from Ref. (Teng Z. et al., 2021b). Copyright: (2021) Springer Nature Ltd.

water solution (pH >11.7). Alkaline H2O2 is widely usedin the pulp bleaching industry. However, the production of
H2O2 in basic media has the following two limitations: (1)
H2O2 is less stable and can self-decompose in an alkalinecondition (especially at pH >9), and it therefore needs to be
consumed in a short time (Gao J. and Liu B., 2020; Qiang
Z. et al., 2002) and (2) H2O2 is more widely used in naturalor acidic media with stronger oxidation ability than in an
alkaline solution. For instance, Fenton’s reagent, which
is widely used in effluent treatment and organic synthesis,
has a favorable pH range of 2.5–3.5 (Gao J. and Liu B.,
2020; Kuo W.G. et al., 1992). Therefore, improving H2O2
production in acidic or neutral media by photocatalysts
has attracted much attention for industrial applications.
Over the past few years, although various g-C3N4 catalysts
have been developed for H2O2 production via the 2e– ORR
process (Shiraishi Y. et al., 2014b), there have been very
few works in which the optimized pH conditions were
investigated.
Kim and co-workers found that photocatalytic activity for H2O2 production was higher at lower pH values(Fig. 17a) with the existence of an electron donor. Thisis probably due to proton conduction within the CN
framework and is much more facilitated in KPF_CN. The
proton conduction of KPF_CN was greatly enhanced when

PF6– anions was introduced into the structure of the g-C3N4
framework (Kim S. et al., 2018). The higher charge carrier
density in KPF_CN might be favorable for facilitating
proton conduction. The higher proton conductivity within
the KPF_CN framework promotes the production of H2O2,
particularly when the reaction between O2 and protons
occurs within the interlayer space (Kim S. et al., 2018).
Ohno and co-workers also investigated the optimal pH
conditions by measuring the amount of H2O2 produced by
the photocatalytic reaction using PCNBA0.2 (Fig. 17b)
(Teng Z. et al., 2020). When ethanol was introduced into
the system, the amount of produced H2O2 remained almost
constant when the pH value was increased from 1 to 9 andthen decreased drastically (Teng Z. et al., 2020). Whenthe pH value was lower than 7, the concentration of protons had almost no effect on the pH value (Teng Z. et al.,2020). This phenomenon revealed that protonation plays
a less essential role in the PCNBA system, indicating thatincorporation of BA units further improved the proton conductivity compared with that of KPF_CN. They also foundthat the lowest [H+] is 10–9 mol L–1 for O2 reduction to H2O2
production. When no sacrificial agent was added to the system, the amount of H2O2 rapidly increased before the pH
value reached 7 and then gradually decreased, indicatingthat a low concentration of OH– also restricts the generation

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Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148

H2O2 concentration (mmoL)a

H2O2 concentration (mg/L)

CN
KPF_CN

0.8
0.6
0.4
0.2
0.0

b

5. Summary and perspectives

1.0

0

2

4

6

8

10

3.0
2.5
2.0
1.5
1.0
0.5
0

2

4

6
8
pH value

10

12

14

Fig. 17 Influence of pH values during H 2O2 production by using a
PCN-based photocatalyst. (a) H 2O2 production in an irradiated suspension of bare CN and KPF_CN_15 at different pH values. (b) Optimization of the pH condition for H2O2 production when PCNBA0.2 was used
as a photocatalyst. Reprinted with permission from (Kim S. et al., 2018)
and (Teng Z. et al., 2020). Copyright: (2018, 2020) Elsevier.

of O2 from water (Wang Q. and Domen K., 2020). Therefore, the optimal pH condition for H2O2 production fromO2 and water using PCNBA is 7-9. The production of H2O2
significantly decreased in both of the works when pH waslarger than 9, being consistent with the fact that H2O2 selfdecomposes in alkaline conditions. Although there havebeen only few works in which the influence of pH on theamount of H2O2 generated was investigated, it was shownthat the pH value did play an essential role during thephotosynthesis process. Additionally, the pH value of theinterfaces between materials and reaction solution might
be even more important compared with the pH value of thewhole solution. The rational design of Helmholtz layer by
the manipulation of the surface properties are extremely
important for improving the H2O2 production (Zeng X. etal., 2020). We believe that systematic investigations of theeffects of pH on the amount of H2O2, surface properties andelectronic configurations should be carried out to reveal themechanisms of ORR and WOR during the photosynthesisof H2O2.

142

Photocatalytic H2O2 production by using a PCN-basedmaterial has shown a promising prospective in environmental and energy-related realms. We have summarized
the principles for photocatalytic H2O2 production, especially for the photocatalytic H2O2 production using g-C3N4.
Advantages and disadvantages for H2O2 production and themost favorable properties of PCN for photocatalytic H2O2
production were briefly summarized. Based on the intrinsic
properties of pristine g-C3N4, the most urgent issue forovercoming the drawbacks was focused on. Notably, current PCN-based photocatalysts still suffer from relatively
low H2O2 yield. Due to the low activity, g-C3N4-based photocatalysts are far from the requirements for industrial applications. Improving the charge separation, promoting thelight absorption and introducing active sites for 2e– ORR to
suppress the side reaction are three attractive strategies forboosting the activities. Following discussion of these strategies, the most representative functionalization method
was summarized on the basis of the most desired properties
for improving the photocatalytic activities for H2O2 production. Other factors for improving H2O2 production suchas addition of electron donors and pH value of the solution
were also discussed.
There are several outlooks and suggestions for further
improvement of photocatalysts. First of all, further improvement in the charge separation of the PCN is necessary. New species of elements or organic semiconductors
for incorporation are highly recommended. Secondly, theabsorption edge of the functionalized PCN can hardly be
extended to >550 nm with a reasonable AQY for H2O2 production (Teng Z. et al., 2020). Further narrowing of band
gap is therefore still of great importance. It is notable that thenarrowing of the bandgap could reduce the thermodynamic
driving force, leading to weakened capability for reduction
reactions and oxidation reactions. Thus, reducing the overpotential for 2e– ORR and oxidation reactions on the PCN
surface is of great importance. Exploring new reaction
sites with higher performance beyond the heptazine ringsin the PCN matrix for 2e– ORR efficiency is also of great
importance for further improvement of photocatalytic activity and selectivity for H2O2 production. Recently, singleatom catalysts (SACs) have shown excellent selectivity for
H2O2 production with Pt2+ (Shen R. et al., 2019; Gao J. etal., 2020; Jung E. et al., 2020) via a 2e– ORR pathway. In
the case of PCN, anchoring single-atom catalysts that havelarge amounts of nitrogen atoms with lone electron pairs iseasy to perform. However, Pt2+ and Co-N4 sites can hardly
be utilized for photocatalytic systems due to poor charge
separation, which origin form the half-filled d electrons of
Pt2+ and Co-N4 may result in a high bulk-recombination
rate of PCN. The preparation of photocatalysts having
atomically dispersed atoms with a d0 or d10 electronic
configuration usually introduce intermediate bands with

Zhengyuan Teng et al. / KONA Powder and Particle Journal No. 40 (2023) 124–148

energetic levels that are slightly more negative than that of
CBM, which can promote electron mobility, thus leading
to a boosted performance (Inoue Y., 2009). It should be
mentioned that measurement of H2O2 production was
carried out in most studies with the addition of an organic
sacrificial electron donor to consume the photogenerated
holes and inhibit the recombination of photogenerated
carries, which is energy-consuming and cost-inefficient.
Despite of band engineering and cocatalyst loading, theconstruction of an all-solid-state Z-scheme photocatalytic
system is another recommended strategy for achieving
non-sacrificial H2O2 production with water and oxygen(Xu Q. et al., 2018). Additionally, it is necessary to develop
approaches other than optimization of pH conditions toprevent decomposition of the generated H2O2. The stabilityof organic semiconductors during photocatalytic H2O2
production should also be investigated (Liu L. et al., 2021).
Last not but least, attention should be given to precise
determination of the amount of H2O2 generated because
the species in the solution can influence the accuracy for
determination (Wei Y. et al., 2021).
To optimize the design of functionalized PCN with maximized photocatalytic H2O2 production, a comprehensive
understanding of the photocatalytic mechanism for H2O2
production is necessary. Both experimental and theoreticalinvestigations should be simultaneously carried out. In
situ measurements by Kelvin probe microscopy (Zhu J.
et al., 2015) and X-ray absorption fine structure analysis(Yang H. et al., 2018) could be used to study the dynamic
behavior of photogenerated carriers on the surface and thechanges of oxidized states in a photocatalyst (Hou H. et al.,2020). In situ Raman spectroscopy may reveal the processfrom the adsorption of O2 to dissociation of H2O2, thus
leading to a more precise understanding of the mechanism.
In the case of fundamental calculations and simulations forinvestigation of the mechanism, ground state properties
such as density of states, adsorption energy, and the structural and electronic properties have been widely used for
explanation of physicochemical properties for the designof an ideal photocatalyst for H2O2 production (Dong J.-C.
et al., 2020). However, investigation of the excitation properties of PCN materials has not been sufficient to obtain afundamental understanding of the excitation behavior ofa photocatalyst. We suggest that excitation properties ofthe functionalized PCN, such as transition densities basedon time-dependent DFT calculations (Laurent A.D. et al.,2013; Teng Z. et al., 2021b; Che H. et al., 2021), should
be investigated in future studies in order to establish a satisfactory blueprint for designing a PCN catalyst for H2O2
production. Last but not least, since H2O2 production is agreen oxidant for many chemical reactions, the development of highly value-added chemicals based on the photocatalytic systems is needed. Using the in-situ generated
H2O2 for synthesis of chemicals could also be achieved on

the basis of previous work using D2O to produce deuterated
chemicals (Zhang B. et al., 2021). We believe that better
design strategies and guidelines for not only PCN-basedmaterials but also other efficient photocatalysts can beestablished for H2O2 formation if the above-mentioned
properties can be achieved.

Acknowledgements

The authors acknowledge the financial support of
Mitsubishi Chemical Corporation, JSPS Grant-in-Aid for
Scientific Research (B, No. 20H02847), and Grant-in-Aid
for JSPS Fellows (DC2, 20J13064).

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Authors’ Short Biographies
Zhenyuan Teng
Dr. Zhenyuan Teng is currently a JSPS Research Fellow (PD) in the Research Initiative forSupra-Materials (RISM), Interdisciplinary Cluster for Cutting Edge Research, Shinshu University.
He obtained his B.S. degree in 2016 and then acquired the Master degree (2018) from Yangzhou
University. He obtained his Doctoral degree from Kyushu Institute of Technology in 2021 under
the supervision of Prof. Teruhisa Ohno. His research interest focuses on the design and synthesis ofsingle-atom organic-inorganic photocatalytic systems for energetic and environmental applications.

Wenan Cai
Wenan Cai received his B.S. in Beijing Institute of Petrochemical Technology in 2016. Currently,
he is a Ph.D. student under the supervision of Prof. Ohno Teruhisa in Kyushu Institute of Technology.
His research interest is the development and design of long-wavelength response photocatalysts for
H2O2 production.

Teruhisa Ohno
Prof. Teruhisa Ohno is currently the dean of faculty of engineering in Kyushu Institute of Technology. He obtained his Doctoral degree in Kyushu University in 1988. He worked as an Associate
Professor in Kyushu University from 1992 to 1994 and in Osaka University from 1994–2003. Thenhe moved to Kyushu Institute of Technology as a Full Professor. From 2016 to 2021, He published
57 SCI papers. Field-Weighted Citation Impact: 2.04, Citation Count: 1,108, h-index: 48, h5-index:
15. A number of papers have published on influential periodical, such as Applied Catalysis B:
Environmental, ACS catalysis and Nature Catalysis. From 1983 to now, published about 202 SCI
papers, these papers were totally quoted more than 11,763 times and h-index is 49 (Scopus Data). His
research interests are: 1. Development of visible light sensitive photocatalysts; 2. Nanoscale surface
structure control of TiO2 photocatalysts; 3. Development of photocatalyst and photoelectrode system
for CO2 reduction; 4. Visible light responsive photocatalyst and photoelectrode for H2O2 production.

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KONA Powder and Particle Journal No. 40 (2023) 149–171/https://doi.org/10.14356/kona.2023012

Review Paper

Mechanistic Formulation Design of Spray-Dried Powders †Mani Ordoubadi *, Hui Wang and Reinhard Vehring
Department of Mechanical Engineering, University of Alberta, Canada

Abstract
Spray drying is gaining traction in the pharmaceutical industry as one of the processing methods of choice for the
manufacture of solid dosage forms intended for pulmonary, oral, and parenteral delivery. This process is particularly
advantageous because of its ability to produce engineered particles with improved efficacy and stability by combining
active pharmaceutical ingredients or biologics with appropriate excipients. Moreover, due to its high throughput,
continuous operation, and ability to produce thermostable solid powders, spray drying can be a manufacturingmethod of choice in the production of drugs and other formulations, including vaccines, for global distribution.
Formulation design based on a mechanistic understanding of the different phenomena that occur during the spraydrying of powders is complicated and can therefore make the use of available particle formation models difficult forthe practitioner. This review aims to provide step-by-step guidance accompanied by critical background information
for the successful formulation design of spray-dried microparticles. These include discussion of the tools needed toestimate the surface concentration of each solute during droplet drying, their times and modes of solidification, andthe amount of glass stabilizers and shell formers required to produce stable and dispersible powders.
Keywords: spray drying, powders, formulation design, particle formation, pharmaceutical particles, pulmonary
delivery

1. Introduction

Among the different industrial methods employed in the
manufacture of pharmaceutical powders and solid dosage
forms, spray drying has attracted considerable interest asa fast, scalable, and continuous process (Baumann et al.,2021; Carrigy and Vehring, 2019). By readily allowing aformulator to control or predict many of the physical properties of particles such as size distribution, density, solidstate, and radial spread of different components inside theparticle, spray drying offers significant advantages overmilling, the current industrial processing method of choicefor the large-scale production of microparticles (Alhajj etal., 2021). The importance of glass-stabilized spray-driedproducts has also gained increasing appreciation due to thegrowing demand for thermostable platforms containing
vaccines or other biologics in the battle against global
pandemics and infectious diseases (Capua and Giaquinto,
2021; Gomez and Vehring, 2022; Kristensen et al., 2016).
Moreover, spray-dried powders containing low-watersoluble therapeutics show improved bioavailability due totheir usually achieved amorphous state as compared with†*

Received 6 May 2022; Accepted 31 May 2022
J-STAGE Advance published online 9 July 2022
Corresponding author: Mani Ordoubadi;
Add: 10-203 Donadeo Innovation Centre for Engineering, 9211
116th Street NW, Edmonton, Alberta T6G 1H9, Canada
E-mail: ordoubad@ualberta.ca
TEL: +1-780-492-3446 FAX: +1-780-492-2200

the crystalline state of the raw material, especially when
formulated in the form of amorphous solid dispersions(Davis and Walker, 2018; Huang et al., 2016; Ma andWilliams, 2019).
In addition to the aforementioned pharmaceutical applications, spray drying was originally invented for and is stillwidely employed in the food industry to produce powder
products such as dried milk, instant coffee, and encapsulated food ingredients (Woo, 2019). Such powders haveincreased shelf life, maintain flavor, and expedite global
food distribution (Mohammed et al., 2020).
The process of spray drying, as shown schematically in
Fig. 1, begins with the atomization of a liquid feed solution
or suspension into a fine spray in a drying chamber, where
it is then subjected to a large quantity of a hot drying gas
made up of either nitrogen or dry air, depending on the solvent. The liquid spray is consequently rapidly evaporated,leaving behind solid particles that are then separated fromthe gas flow, most commonly through cyclonic separation(Pinto et al., 2021). Through evaporative cooling, theatomized droplets maintain a relatively low temperature
for most of their drying history, making this processideal for the production of temperature-sensitive powders(Ordoubadi et al., 2019).
Through a mechanistic understanding of the diverse
phenomena that occur during spray drying, a particle engineer can design process and formulation parameters tomeet a set of predefined production objectives and goals.

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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Collection

Pump

Atomizing gas
Drying gas

Tin

FilterDry air or
nitrogen supply

ExhaustFeedsolution

Tout
RHout

Fig. 1 A typical open-cycle spray dryer showing the key stages ofatomization, drying, and collection.

Spray drying process conditions, including inlet and outlet
temperatures, drying gas flow rate, feed flow rate, andatomizing pressure, predominantly influence the size distribution of the particles, the outlet conditions of the dryer,
and the production yield (Carrigy and Vehring, 2019),
while spray drying formulation parameters, including total
feed concentrations, choice of excipients, and feed composition, influence properties such as the radial distributionof each material inside the dried particles, their physicochemical properties, and their general morphology (Alhajj
et al., 2021). Hence, careful design of both the process andformulation parameters can significantly reduce the initialresearch and development stage of product development,
thereby decreasing production time, costs, and associatedrisks (Boraey and Vehring, 2014; Vehring, 2008; Vehring
et al., 2007).
Many studies have succeeded in developing particle
formation models (Boraey and Vehring, 2014; Ivey andVehring, 2010; Ordoubadi et al., 2019; 2021a; 2021b) andprocess models (Carrigy and Vehring, 2019) that can beused during the formulation design of spray-dried powders.
These models are frequently not easily accessible as theiruse requires knowledge of different branches of scienceand engineering, including mechanical, chemical, andmaterials engineering. In this study, we aim to reduce thesechallenges by reviewing the different stages of the designof engineered spray-dried particles and providing sufficient
background information to make this process accessible toa broader group of scientists and researchers.

2. Formulation and process design

Like most design problems, the design of spray-driedmicroparticles is an iterative process. During each iteration, the formulation and process parameters are selectedbased on particle formation models or previous experience.
This includes the estimation of the surface concentrationof each component and the specific moment during the150

evaporation process at which they solidify. Using these
data, the next logical step in the formulation design ischoosing the total solids concentration and feed fractionsof the active pharmaceutical ingredients (APIs), biologics,
and excipients to meet the design objectives based on otherfactors such as the required drug loading, producibility, stability considerations, and regulatory matters. For example,
the feed fractions of a shell-forming excipient and a glass
stabilizer can be selected so that the former solidifies first
and the latter increases the glass transition temperature
above the required limit. Afterward, sample batches are
spray dried and characterized to assess their performance.
In case of unsatisfactory properties, the formulation andprocess parameters are modified until the requirements are
satisfied. The different stages of such a proposed design
process are shown in Fig. 2.

2.1 The choice of the solvent system

One of the first problems a formulator faces in the product development of spray-dried pharmaceutical powders
is the question of which solvent system to use. Depending
on solubility constraints, the answer can be as straightforward as plain water or as complicated as a multi-solvent
system with correct solvent ratios. For the spray drying of
biologics and APIs with adequate aqueous solubility, water
is the solvent of choice because it is safe, inexpensive, andrequires no capture of the evaporated water vapor at theexhaust (Davis and Walker, 2018; Selvamuthukumaran,2019). However, more than 60 % of newly discovered
chemical entities and 40 % of currently marketed drugs
exhibit poor water solubility (Davis and Walker, 2018;
Paredes et al., 2021). Hence, it is inevitable that a particle
engineer will face the task of formulating a spray-dried
platform for a hydrophobic active, especially when striving for significant enhancement in bioavailability after
spray drying into an amorphous powder (Ojarinta et al.,2017; Salama, 2020). For such compounds, an organic
solvent or a mixture of cosolvents can be selected after athorough solubility screening. Solubility limitations are ofsignificance only when the API is intended to be dissolvedin the feed liquid, as opposed to applications in which one
or more of the components are emulsified or dispersed, inwhich case there should be negligible solubility of the suspended material to avoid instability in the feedstock during
manufacturing (Gomez et al., 2021a; Maniyar and Kokare,2019; Wang H. et al., 2022). A handful of organic solvents
have been considered as solvents or cosolvents for feedpreparation, with only a limited number of these deemedsafe for inhaled product development. The solvents mostencountered in the literature are water, acetone, ethanol,
methanol, ethyl acetate, dichloromethane, and tetrahydrofuran (Shepard et al., 2020; Vasconcelos et al., 2016).
Formulation design becomes most complicated when
the solubility of the active ingredient is not high enough

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Start

Define final particle
criteria such as
Thermal properties
Aerosol performance
Size distribution
Intended route of delivery
Drug loading
Manufacturability

o
o
o
o
o
o

Spray dry sample batches
including a lead and at least
one backup formulation

Characterize
physicochemical properties
includingo Morphology
o Solid-stateo Aerosol performance andpowder flowabilityo Powder bulk density
o Thermal properties

Fig. 2

If required, choose
excipients such aso Glass stabilizers
o Shell formers
o Bufferso Carriers and bulking
agentsPerform
solubility
screening andchoose solvent
systemPerform formulation design
with the help of particle
formation models to determineo Total feed concentrationo Mass fractions of allcomponents

Determine the processparameters such aso Drying gas temperature andflow rateo Feed flow rateo Atomizer pressure

No

No

Are
physicochemical
properties
acceptable?

Yes

Is thepowderstable?

Perform stability
tests if required

Yes

Next steps
including preclinical studies
and scale-up
The process of formulation design of spray-dried powders.

in a single solvent system and is instead higher at specificfractions in a multi-solvent system, or when the compound
has a high solubility in a specific solvent but is not stableor dispersible on its own and therefore requires to be spraydried in conjunction with appropriate excipients that aresoluble in another solvent, for example, budesonide, with
its higher solubility in ethanol, and leucine, with its greatersolubility in water (Boraey et al., 2013). For such cases, as
shown schematically in Fig. 3, specific alterations can bemade to the formulation or process design; for example,
a cosolvent system can be used during feed preparation(Alhajj et al., 2021; Boraey et al., 2013; Mishra et al.,2018), or a 3-fluid atomizer can be incorporated (Focaroli
et al., 2020; Kauppinen et al., 2018; Shetty et al., 2020;
Sunderland et al., 2015).
For cosolvent systems, solubility data can be used inconjunction with advanced particle formation models tochoose the optimal solvent ratios. It has previously beenshown that, due to non-ideal vapor-liquid equilibria,
multi-solvent droplets might have an azeotrope-like state
during evaporation at which no change in the solvent
composition can occur (Bader et al., 2013; Ordoubadi etal., 2019). For example, it was found that a microdroplet
composed of 24 % w/w water and 76 % w/w ethanol evaporating in dry air at different temperatures, maintains a constant evaporation rate and droplet composition (Ordoubadi
et al., 2019). When the ethanol content is increased (above
76 % w/w) during evaporation, the droplets will lose their

Solute A soluble in solvent 𝛼𝛼

Solute B soluble in solvent β

A + B in 𝛼𝛼𝛼+ β

A in 𝛼𝛼B in βDispersion gas

Dispersion gas

2-fluid nozzle

3-fluid nozzle

Fig. 3 The use of a 2-fluid nozzle for a cosolvent system and a 3-fluid
nozzle to handle issues of solubility incompatibility between different
components and solvents.

water content first (ethanol enrichment), but when theethanol content is reduced (below 76 % w/w) they will lose
their ethanol content first (water enrichment). A formulator
should be aware of such reversals in the drying behavior
because, for example, they might cause the sensitive active
to precipitate first on the surface of the droplet instead of
the intended shell former.

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2.2 The choice of excipients

Upon spray drying, most APIs and biologics are dried
into an amorphous solid (Baumann et al., 2021; Chang et al.,2020; Chiou and Langrish, 2007). Amorphous spray-dried
powders can be hygroscopic (Li L. et al., 2016; Momin etal., 2018), show poor long-term stability due to the possibility of recrystallization (Singh and Van den Mooter, 2016),
and exhibit low glass transition temperatures (Červinka
and Fulem, 2021). They can also be cohesive, resulting inpoor powder and aerosol performance (Alhajj et al., 2021;
Jüptner and Scherließ, 2019). Furthermore, some of theseadverse properties can exacerbate the others, as in the caseof plasticization due to moisture uptake. To overcome these
problems, biologically inert compounds or excipients canbe formulated with active ingredients to act as carriers,
bulking agents, buffers, dispersibility enhancers, and glass
stabilizers (Lechuga-Ballesteros et al., 2019; Weers andMiller, 2015). Two such excipients frequently used for
pulmonary applications and having the most impact on theparticle formation process, i.e. glass stabilizers and shellformers, are discussed in detail next.

2.2.1 Glass stabilizers

Amorphous materials are in a metastable thermodynamic state and may eventually crystallize due to theirmicroscopic molecular mobility (Kawakami, 2019; Singhand Van den Mooter, 2016). For spray-dried powders,
the rate of this transition depends on the residual solvent
content, ambient humidity, and storage temperature (Chiou
and Langrish, 2007). It has previously been shown thatstoring pharmaceutical glasses at about 50 °C below theirglass transition temperature prevents significant molecularmobility during their lifetime (Hancock et al., 1995). Thisapproach has been used to design and assess spray-dried
platforms containing different pharmaceutical ingredientsand biological entities (Chan and Chang, 2021; Hoe et al.,2014; Shepard et al., 2021). Accordingly, for a powder
product to be room-temperature-stable (assuming a storage
temperature of 25 °C), its glass transition temperature, Tg,
needs to be more than 75 °C. The climate of the product’s
intended market should also be considered. For example,
for global health applications such as vaccine development
for the developing and least developed countries more
stringent conditions need to be accounted for (WHOExpert Committee on Biological Standardization, 2016).
Hence, during formulation design, the particle engineer
can aim for a predicted glass transition temperature so that
Tg > Tst + 50 K, where Tst is the storage temperature. If theglass transition temperature of the amorphous drug is lessthan this value, then a glass stabilizer is required in theformulation to improve the thermostability of the product.
A number of saccharides and polymers such as trehalose,sucrose, pullulan, and inulin are some of the glass formers
that have been studied for the stabilization of spray-dried

152

products (Carrigy et al., 2019c; Gomez and Vehring, 2022;
Weers and Miller, 2015).
The prediction of the glass transition temperature iscomplicated since spray-dried powders may contain residual solvent that can act as a plasticizer (Patel et al., 2015;
Shepard et al., 2020). The amount of the residual solvent
itself is a function of the solvent-uptake properties of thepowder, the outlet vapor activity (relative humidity in thecase of aqueous systems), and the outlet temperature of thedryer (Carrigy and Vehring, 2019). Hence, to determine therequired mass fraction of the glass-forming excipient, Yexp,
the amount of residual solvent can be neglected initiallyand the Fox equation (Fox, 1956) can be rearranged to give1
1Tst  50 K Tg,act
1  Yexp 11Tg,exp Tg,act(1)

where, Tst, Tg,act, and Tg,exp, all in Kelvin, are the long-term
storage temperature, active glass transition temperature,and(1)the stabilizing excipient glass transition temperature,
respectively. Eqn. (1) can only be used when Tg,exp > Tg,act
and Tg,exp > Tst + 50 K. For example, if trehalose is to
be used as a glass-forming excipient with a dry Tg,exp of
390 K (~117 °C) (Drake et al., 2018) to stabilize an activewith a Tg,act of 323.15 K (50 °C), more than 42 % w/w
of the formulation should be composed of trehalose forthe product to be stable at a storage temperature, Tst, of
298.15 K (25 °C). For storage at a temperature of 313.15 K(40 °C), more than 65 % of the system should be composedof trehalose, and for storage at a temperature of 343.15 K(70 °C), the product cannot be stabilized with trehalose atall.
In addition to their anti-plasticizing effect, someglass-forming excipients can be used to stabilize sensitive
biologics such as bacteriophages, vaccines, and proteinsin spray-dried platforms (Carrigy et al., 2019a; Gomez etal., 2021a). The reasons for this protective property are
believed to be twofold: first, the hydrogen bonds formed
between water and protein molecules are replaced withnew bonds formed during drying between the hydroxyl
groups of the stabilizers and the proteins, and second, theimmobilization of the proteins in a glassy matrix significantly reduces the risk of different forms of denaturation(Hinrichs et al., 2001; Mensink et al., 2017). The required
mass fraction of the glass former is believed to dependon the stabilizer and the biological entity, but a minimum
mass ratio of 1:1 was shown to result in acceptable physicalstability in several studies (Massant et al., 2020; Shepard
et al., 2021).

2.2.2 Dispersibility enhancers and shell formers

As mentioned before, and as shown in Fig. 4, spraydried particles (in particular, those made of small-molecule

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a

1 μm

c

b

d

Fig. 4 Micrographs of spray-dried trehalose and different shell formers: (a) trehalose; (b) trehalose and 20 % w/w L-leucine; (c) trehalose and 5 %w/w trileucine; (d) trehalose and 3 % w/w Eudragit. (Reproduced from Bin Karim et al., 2021 with the permission of Elsevier.)

compounds) are usually amorphous with smooth surfaces
and high surface energies (Alhajj et al., 2021). When combined with the small size of the particles, especially thoseintended for pulmonary delivery, these properties result incohesiveness that causes poor powder flowability and undesirable aerosol properties (Jüptner and Scherließ, 2019).
To diminish these problems, another class of excipients,
usually called shell formers or dispersibility enhancers, areincluded in the spray-dried formulations. The shell formers
are intended to precipitate first on the surface of the driedparticles and improve the aerosol properties of the powders
via two mechanisms: first, they increase surface rugosityand hence decrease the contact area with the surrounding
particles or external surfaces (Baldelli and Vehring, 2016);
second, they decrease surface energies (Jong et al., 2016;
Zhou et al., 2015). Both of these mechanisms reduce thecohesive (interparticle) and adhesive (extra particle) forces.
Because it forms solid shells around the interior core of theparticles, this kind of excipient might also protect the valuable actives or biologics from different surface-mediateddegradation mechanisms (Carrigy et al., 2019b; Gomez etal., 2021b) or in some cases provide short-term protection
against moisture-induced interparticle fusion (Li L. et al.,2016; Wang Z. et al., 2021b).
For maximized encapsulation, all shell formers needto precipitate or solidify before other components on thesurface of the droplets during evaporation. Certain properties, such as low crystalline or amorphous solubility, large
molecular size, and high surface activity can cause earlyshell formation. For instance, materials with low solubili-ties can solidify first during drying and tend to accumulate
on the surface of the droplet until a solid shell is formed,
while large molecules enrich the surface and reach theirprecipitation limit faster than smaller compounds. For
example, some of the polysaccharides and other polymericcompounds, such as pullulan, Eudragit, and maltodextrin,
produce a shell early during the evaporation process andcan act as a shell former by increasing the surface rugosity(Bin Karim et al., 2021; Carrigy et al., 2019d; Zhang etal., 2018). Surface activity is another important attributethat can cause surface accumulation of excipients. Theinterfacial adsorption of surface-active molecules decreases the surface energy of the dried particles becauseof the outward orientation of their hydrophobic tails (Jonget al., 2016; Vartiainen et al., 2016). Furthermore, somesurface-active shell formers, like trileucine (Carrigy et al.,2019b; Lechuga-Ballesteros et al., 2008; Ordoubadi et al.,2021a), undergo early phase-separation near the droplet
surface resulting in highly rugose particles with decreased
contact area (Wang H. et al., 2019). Some of the most effective shell formers have two or more of these properties.
For example, both leucine and trileucine are surface-active
and have low solubilities resulting in surface adsorption
as well as early solidification during droplet evaporation(Lechuga-Ballesteros et al., 2008; Ordoubadi et al., 2021a;
2021b).

2.3 Process parameters2.3.1 Feedstock preparation

Spray drying is essentially a method of desiccating liquid

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feedstocks into a dry powder form composed of microparticles. A typical spray drying process involves feedstock
preparation, atomization of the feedstock to form droplets,
desiccation of the atomized droplets in a drying chamber,and separation of the dried particles from the drying gas(Cal and Sollohub, 2010). Carefully designed formulations
intended for spray drying need to be presented in a liquid
form before they can undergo subsequent atomization anddrying processes. The feedstock preparation process for
actives or excipients that are soluble in the selected solvent
system is relatively straightforward and usually involves
mixing the solid and liquid phases at appropriate conditions
with additional agitation if needed. Despite the simplicity
of preparation for solution-based feedstocks, spray drying
from a homogeneous solution can have many variations
and, if properly designed, can produce particles with multifunctional structures, such as layered, hollow, dimpled,porous, etc. (Vehring, 2008). A more in-depth discussion on
this topic will be provided in the particle formation section.
Because a large proportion of all new drug candidates
entering the development pipeline face various problems
due to their poor aqueous solubility (Nagarwal et al.,2011), suspensions have become a popular dosage form of
medication in drug delivery applications (Rabinow, 2004).
For the same reason, the preparation of a stabilized suspension or emulsion feedstock is the most common modification to the simple solution-based spray drying process(Chaubal and Popescu, 2008; Dollo et al., 2003; Kumar
et al., 2014; Soottitantawat et al., 2003). These two-phasesystems are sometimes also employed to transfer someof their pre-existing structural properties onto the final
dry particles. Two typical examples are the hybrid large
porous nanoparticle aggregates reported by Tsapis et al.
(Lintingre et al., 2016; Tsapis et al., 2002) and the porous
PulmoSphereTM lipid particles summarized by Weers et al.
(Weers and Tarara, 2014). Careful selection of surfactants
assisted by a powered mixing process such as high shear
mixing or high-pressure homogenization is usually needed
to produce stable suspension feedstocks for the productionof consistent products throughout the spray drying process(Chaubal and Popescu, 2008; Dollo et al., 2003; Singh and
Van den Mooter, 2016; Verma et al., 2011).
More complex multi-phase systems with more than two
dispersed phases such as feedstocks with multiple suspended solid materials, feedstocks with multiple emulsions,and emulsion-based feedstocks with additional suspended
solids have been explored to meet more specific formulation design targets. For instance, feedstocks consisting of
calcium salts dispersed in an aqueous Eudragit polymericsuspension were spray dried to produce microencapsulatedparticles for a controlled gastrointestinal release profile(Oneda and Ré, 2003). A complex dispersion of polymericnanocapsules containing an oil core was spray dried withcolloidal silica to form nanoparticle-coated microparticles

154(Tewa-Tagne et al., 2006; 2007). Orange oil was encapsulated in the inner compartment of another double emulsion
by spray drying for better protection of the inner material(Edris and Bergnståhl, 2001). Micronized drug particles
such as budesonide, ciprofloxacin, and amphotericin B
were suspended within an oil (perflubron)-in-water emulsion feedstock and spray dried to produce drug-loaded
composite particles for inhalation (Weers et al., 2019).
Many of the studies referenced here employed multistep feedstock preparation processes in which precursors
were manufactured prior to the preparation of the final
feedstock, an approach that is being increasingly used incomplex formulations (Vehring, 2008).

2.3.2 Atomization, drying, and collection

Atomization of feedstocks into liquid sprays significantly increases the specific surface areas of the liquid
phase that are exposed to the drying gas, thereby effectively
facilitating rapid heat transfer from the heated drying gas tothe atomized droplets and mass transfer of volatile solventsto the gas phase (Cal and Sollohub, 2010). Various atomizer designs based on different liquid disintegration mechanisms have been used to break up liquid feedstocks for theformation of dried particles with the desired physicochemical and morphological attributes. The most commonly usedatomizer types can be generally categorized into rotary
atomizers, hydraulic nozzles, pneumatic nozzles, and ultrasonic nozzles (Cal and Sollohub, 2010; Santos et al., 2018).
Among these, pneumatic nozzles, also known as multifluid nozzles, are the most popular atomizer for pharmaceutical applications (Cal and Sollohub, 2010). The
most common configuration of such devices is based ona two-fluid nozzle atomizer in which the liquid feedstock
is supplied to the nozzle, met by compressed atomizing
gas, and atomized due to the high frictional forces between
the two mixing fluids. Pneumatic atomizers are known
for their ease of operation, good control over resulting
particle size, applicability to different types of feedstocks,
and wide range of throughput, all of which characteristics
make them suitable for various applications from lab-scale
research to industrial mass production (Santos et al., 2018).
In terms of size coverage, which is a critical specification
for atomizers, pneumatic nozzles can usually produce
droplets in the 5–200 μm diameter range. The exact droplet
size distribution for each atomizer can be affected by multiple parameters including the atomizer design, feedstock
properties, and liquid and gas supply rate, etc. (Santos etal., 2018) and therefore needs to be calibrated for a better
prediction of the final particle properties (Hoe et al., 2014).
As a rapid process of energy and mass exchange between
the atomized droplets and the surrounding drying gas in acontrolled environment, the drying process is where theliquid formulation eventually solidifies into solid particles.
Many critical parameters are involved in this process

Mani Ordoubadi et al. / KONA Powder and Particle Journal No. 40 (2023) 149–171(Baumann et al., 2021). Temperature and vapor activity
inside the drying chamber are two of the main parametersthat need to be carefully controlled and monitored: thetemperature affects the particle formation process and theresultant particle morphology and structure, while vapor
activities can have a major impact on the long-term physicochemical stability of the final powder product (Carrigy
and Vehring, 2019). Thermodynamic modeling of the drying process has been used to predict the outlet temperatureand relative humidity for different inlet conditions (Carrigy
and Vehring, 2019; Ivey and Vehring, 2010); the concepts
underlying this approach can be equally applied to different
systems for an expedited formulation and spray drying
process development (Carrigy et al., 2018; Wang H. et al.,2020).
A particle separation procedure needs to be implemented
to collect dried particles seeded in the drying gas at theend of the drying process. The device most commonly
employed for such purposes, the cyclone separator, uses
centrifugal forces to collect particles from the gas flow
supplied into a cylindrical chamber tangentially (Baumann
et al., 2021). Fine particles smaller than the cut-off aerodynamic diameter of a specific cyclone operated at thecorresponding gas flow rate will be expelled from the cyclone in an inner counterflow vortex to the exhaust, whilelarger particles will be collected (Santos et al., 2018). Bag
filters based on fabric filtration technologies are anotherparticle separation method used extensively in spray drying
processes and feature high collection efficiency even for
submicron particles, especially when a series of filters with
gradually downsized pores are used (Cal and Sollohub,2010). Other techniques such as scrubbers and electrostatic
precipitators are also used for particle collection in various
spray drying applications (Dobrowolski et al., 2018).
The initial studies and research on new formulations are
frequently performed on small-scale or laboratory-scale
spray dryers, which, compared to pilot-scale and production-scale dryers, generally have lower production rates
and yields due to their smaller drying chambers and smaller
production batches (Poozesh and Bilgili, 2019). The transition to larger dryers requires an informed methodology
development that involves process scale-up, relating to thethermodynamic aspects, and formulation scale-up, relating
to the particle formation mechanisms (Gil et al., 2010).

3. Particle formation theory

3.1 Generation of solid particles during solvent
evaporation

The primary size distribution of spray-dried particles is
an important parameter that should be considered carefully
during process and formulation design in accordance with
the intended application of the powder. For example, fororal lung delivery it is customary to spray dry particles
with aerodynamic diameters in the range of one to five

micrometers (Dabbagh et al., 2018; El-Gendy et al., 2011),
whereas larger particles are acceptable for nasal (Calmet etal., 2019; Kiaee et al., 2018) and other routes of delivery(Ferreira et al., 2020; LeClair et al., 2018). Assuming they
are roughly spherical in shape, the mass median aerodynamic diameter (MMAD) of the spray-dried particles canbe approximated by Vehring (2008)
MMAD  MMD 0

6

ρp
ρ*

3

CF
ρ*

(2)

Here MMD0, a process parameter, is the mass median diameter of the atomized droplets (equal to the initial droplet(2)
diameter,d0, for monodisperse sprays), which is a functionof atomization parameters such as the atomizing pressure
and the liquid feed flow rate for a twin-fluid atomizer (Hoeet al., 2014; Ivey et al., 2018); ρp and ρ* are the density ofthe solid particles and the reference density of 1 mg/mL,
respectively; and CF is the total feed concentration, a formulation parameter. It is evident from Eqn. (2) that the size
distribution of the dried particles is strongly influenced by
the atomized droplet distribution, with weaker sensitivity
to the other variables, such as the commonly unknown
particle density for which only a rough estimate is required
due to the sixth root dependence. This relatively simple but
practical equation does not describe the internal distribution of different components and does not consider theirinstants of solidification, for which the diffusion equations
inside the evaporating droplets need to be solved (Gac,2022).
Upon atomization into the drying chamber, the droplets
may cool down or heat up depending on the initial feedtemperature and the wet-bulb temperature of the solvent
system (Chen X.D., 2008), with possible collision and coalescence of the droplets (Boel et al., 2020). After the brief
initial transient stage, the temperature and evaporation rateof each droplet are stabilized and the constant-rate drying
starts based on the d2-law (Baumann et al., 2021; Vehring etal., 2007). The recession of the droplet surface due to rapid
evaporation causes the concentration of solutes to rise near
the surface. This induced concentration gradient causes
a radially inward movement of the solutes in accordancewith Fick’s law of diffusion. The constant-rate evaporation
continues until the start of solidification, when the solvent
can no longer evaporate unhindered. At this point, the temperature inside the droplet rises rapidly based on the local
temperature and vapor activity inside the drying chamber,and solidification is completed with further loss of thesolvent. The actual shape and morphology of the final driedproduct are related mostly to the rheological and physicalproperties of the solids, the process conditions, and thesolvent system, which, in some cases, can cause inflation,
deflation, or rupture of the particles (Baumann et al., 2021;
Ordoubadi et al., 2021b; Vehring, 2008; Walton, 2000).
The mass transfer equations governing the diffusion

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3.2 Diffusion inside evaporating droplets

As previously explained, the rapid evaporation of thesolvents causes surface accumulation of solutes which inturn results in solute diffusion from the surface towards
the center (Boraey and Vehring, 2014; Nandiyanto andOkuyama, 2011). Because of the small size of the droplets,
it can be assumed that during the constant-rate evaporation
stage the droplets are already moving with minimal relative
velocity with respect to the drying medium, and the effectsof shear stresses on the internal convective circulation canbe neglected. Therefore, the mass transfer equations canbe solved in a spherically symmetric manner in the radial
direction only (Ordoubadi et al., 2019). Based on this assumption, the governing mass transfer equation for solute i
inside an evaporating droplet is as follows:C i
4D   2Ci
2 Ci  κR Ci
 2i 2td  RR R  2d 2 R

(3)where Ci is the temporal concentration of the solute at thedimensionless radial coordinate R = 2r⁄d, r is the radial co(3) here, and d is the time-dependent droplet diameter,ordinate
while Di and κ are the diffusion coefficient of the soluteand the evaporation rate of the droplet, respectively. The
evaporation rate is a measure of the rate of the decrease of
the droplet surface area or

dd 2
κ

(4)

dt
The parabolic partial differential equation for the massdiffusion (Eqn. (3)) can be solved numerically through(4)
integrationin time using the initial condition Ci = C0,i andboundary conditions ∂Ci ⁄ ∂R = 0 at R = 0 and Di∂Ci ⁄ ∂R –(κ / 8)Ci = 0 at R = 1 (Ordoubadi et al., 2019). Here, C0,i isthe initial concentration of the solute i in the feed solution.
The solution of Eqn. (3) for each solute gives the concen-

156

tration of that component for each instance and at eachradial location that can be used to predict their moments ofsolidification.
The behavior of different components under different
drying conditions was previously quantified by the Péclet
number as defined below (Vehring et al., 2007):
Pei 

(5)

κ
8Di

This dimensionless number illustrates the relative significance of the recession of the droplet surface due to(5)
evaporationand the diffusion of the solutes. A very large
Péclet number (Pei≫1) means that the evaporation rateis so rapid that the solute molecules do not have enoughtime to diffuse towards the center and the result will be avery sharp concentration gradient near the surface, with theinterior concentration values being close to the initial mean
concentration for most of the evaporation period. A small
Péclet number (Pei≪1) denotes the fact that the evaporation is slow and the solute molecules can freely diffuse
away from the interface, resulting in a rather constant radial
distribution, approximately equal to the bulk concentration,that increases with time due to the decrease of the droplet
volume. For a moderate Péclet number (0.1≲Pei≲20) there
will be a smooth transition from a maximum concentrationon the surface to a minimum at the center of the droplet.
These three different conditions are shown schematicallyin Fig. 5. This discussion of the internal diffusion basedon Eqn. (3) is true for non-surface-active components inmolecular form. In the case of surface-active materials,
Eqn. (3) needs to be modified to account for surface adsorption (Ordoubadi et al., 2021a; 2021c).
Spray drying at high Péclet numbers usually results inhollow particles such as those of polysaccharides, proteins,and other macromolecules (Bin Karim et al., 2021; Carrigy
et al., 2019c; Hu et al., 2017; Shepard et al., 2021), while
spray drying at low Péclet numbers usually results in solid
spherical particles, such as spray-dried trehalose (Mah etal., 2019; Ordoubadi et al., 2019) and lactose particles (WuL. et al., 2014). Besides the Péclet number, the momentand mechanism of phase separation and solidification also
play an important role in the final morphology of the dried
Concentrationof solutes can only be solved straightforwardly for theconstant-rate portion of the droplet drying. The mass transfer in the initial transient stage requires a full numericalsimulation (Ordoubadi et al., 2019), while the modelingof the hindered evaporation of water due to the semi-solid
crust on the surface during the later stages of solidificationis generally impractical and also requires computational
analysis (Mezhericher et al., 2008; 2010). It can be assumed that the initial transient stage is only a small fractionof the total drying time because of the very small size ofthe atomized droplets and the high heat diffusivity of thesolution. Furthermore, during the formulation design, themain objective is to uncover the component that solidifies
first, not the exact kinetics of the subsequent crystallization
or glass formation. Therefore, the solution of the masstransfer equations during the constant-rate evaporation
period is essential during formulation design and will be
discussed next.

Pei ≫ 10.1 ≲ Pei ≲ 20

Pei ≪ 1

Fig. 5 The effect of the Péclet number on the internal solute distribution during droplet evaporation at a specific time.

Mani Ordoubadi et al. / KONA Powder and Particle Journal No. 40 (2023) 149–171

Evaporation Rate (µm2/ms)

particles, as will be discussed later.
tain relationships have been suggested as a way to estimate
During the formulation design of multicomponent
the transient surface enrichment of solutes, Es,i(t), duringsystems, especially amorphous solid dispersions and glass
droplet evaporation as shown below (Boraey and Vehring,stabilized systems, special attention should be paid to the2014):
Péclet numbers of individual solutes, since a very large
C t
Es,i t  s,idifference in their interior distribution might result in
C m,i t
separate solidification of each component as opposed toIf Pei  0.5 :co-amorphization into a uniform glassy matrix (Carrigy et E
ss,ial., 2019c; Li N. et al., 2020; Ousset et al., 2018).
If 0.5  Pei  25 :As mentioned before, regardless of the Péclet number,(7) E  exp n 
Ess,i  1each solute will have a maximum concentration near the

ss,i
1

exp n1t / t d droplet surface. Hence, it has been customary to provide
1  exp n1
1  exp n1solutions to the diffusion equations in the form of the surIf 25  Pei  200 :face enrichment, Es,i, defined as the surface concentration,
 E75,i  exp n2   E75,i  1 exp n 4t / 3t 
2d
Cs,i, divided by the mean concentration of that solute inside1  exp n2 
1  exp n2 
the droplet, Cm,i. Generally, the surface and mean concentrations change with time, while the surface enrichment
Here n1 = 15Pei–0.7, n2 = 0.95, E75,i = 0.858Ess,i, and t d  d 02 / κasymptotically converges to a constant steady-state value, t d  d 02 / κ is the droplet drying time based on the constant(7)Ess,i, during the constant-rate drying period. This asympevaporation
assumption, while d0 is the initial droplet
totic value can be approximated for different Péclet number
diameter and κ is the evaporation rate defined in Eqn.
[21]
regimes as (Boraey and Vehring, 2014)[21] (4) and provided for some of the solvents in Fig. 7. The
relationships in Eqn. (7) can be used for Péclet numbers
CEss,i  s,iranging from zero to two hundred, which span a broad
C m,iarray of conditions for spray drying applications ranging
If Pei  20 :(6)from small molecules to large suspended nanodroplets.

1  Pei / 5  Pei2 / 100  Pei3 / 4000
For example, the Péclet numbers of trehalose dissolved inIf Pei  20 :water (an example of a small-molecule excipient or active)
Pei / 3  0.363
and an aqueous nanoemulsion with an average droplet
It has previously been shown that for Péclet numbers
size of 100 nm (an example of a very large aggregate orlarger than 0.5 the time to reach steady-state surface en(6)richment
might be a significant portion of the total droplet
140
drying time, td (Boraey and Vehring, 2014). Therefore, for
water
120
acetonethese conditions (Pei > 0.5) the transient evolution of theethanol100
surface enrichment needs to be approximated to accurately
methanolDCM
predict the diffusion-controlled particle formation during
80
THF
spray drying. Through correlation to numerical data, cer60
1.0

0.6
Pe = 0.2, ESS = 1.04
Pe = 1, ESS = 1.21
Pe = 10, ESS = 3.75
Pe = 50, ESS = 17.0
Pe = 150, ESS = 50.4

0.4
0.2
0.0
0.0

20
0
50

0.2

0.4

t/td

0.6

0.8

1.0

Fig. 6 The time evolution of surface enrichment, Es(t), normalized by
the steady-state surface enrichment, Ess (Eqn. (6)), versus the normalized time for different Péclet numbers, obtained from the correlations
given in Eqn. (7). Here, t d  d 02 / κ is the droplet drying time.

《31》

Droplet Temperature (°C)

ES(t)/ESS

0.8

40

0

50

0

50

100

150

200

250

100

150

200

250

40
30
20
10
0
-10
-20
-30

Drying Temperature (°C)
Fig. 7 The evaporation rates and droplet temperatures of several solvents frequently used in pharmaceutical spray drying at different drying
temperatures. The values were calculated using a full numerical model.

157

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biologic) both drying at a temperature of 100 °C (in atypical range of drying temperatures for the spray dryingof pharmaceutical particles) are 1.25 and 153, respectively.
These numbers were calculated using Eqn. (5); the numerical values for the evaporation rate and diffusion coefficients
will be discussed in the next two subsections.
The evolution of surface enrichment with time for arange of Péclet numbers is shown in Fig. 6. It is apparent
from these profiles that for larger Péclet numbers corresponding to the spray drying of larger solutes or at higher
temperatures, the transient behavior of surface enrichment
is more significant than for smaller Péclet numbers.
For such conditions, the use of the correlations given in
Eqn. (7) is the most accessible and practical method forapproximating the surface concentration of each solute.
After the calculation of surface enrichment with the helpof Eqn. (7), the actual surface concentration of each soluteat each time, Cs,i(t), can be obtained from

Cs,i t  Es,i t C m,i t

(8)in which Cm,i(t) is the mean concentration of solute i insidethe droplet, which can be obtained from a mass balance(8)equation compared to the initial state of the atomized
droplet as follows:
t 

Cm,
C0,i 1  i t td 

3/2(9)Here, C0,i is the initial feed concentration of solute i and,as mentioned before, t d  d 02 / κ is the drying time of the(9) assuming a constant evaporation rate.
dropletDuring the actual formulation design, the surface concentration of all excipients[21] and actives can be calculated
for 0 ≤ t ≤ td and compared to their respective solidificationthresholds to determine which component is expected toprecipitate first, as will be discussed in later sections.

3.3 Evaporation rates

As seen in Eqn. (3), the evaporation rate of the droplets
is one of the determining factors of particle formation andsolidification. For single-solvent systems, the evaporation
rate can be assumed to be constant throughout the drying
stage, neglecting the initial stabilization of the droplet
temperature and the final decrease due to solute enrichment(Boel et al., 2020). On the other hand, solvent compositionand evaporation rate vary with time for multi-solvent
systems due to the different volatility of the solvents andany non-ideal effects (Ordoubadi et al., 2019). For thisreason, the evaporation rates of multi-solvent systems
should be predicted using full numerical methods and algorithms as explained elsewhere (Ordoubadi et al., 2019).
In this subsection, the evaporation rates and the droplet
temperatures of some of the solvents most frequently usedin the spray drying of pharmaceuticals and biologics arepresented. To this end, a numerical model, verified and158

validated previously (Gregson et al., 2019; Ordoubadi etal., 2019), was used to calculate the evaporation behaviorsof microdroplets composed of water, acetone, ethanol,
methanol, dichloromethane (DCM), and tetrahydrofuran(THF). The evaporation rates, defined as –dd2/dt, and thedroplet temperatures of these solvents drying at different
drying temperatures and zero vapor activity (dry condition)
are shown in Fig. 7. The equilibrium droplet temperature,
which is close to the wet-bulb temperature, is important indetermining the temperature-dependent properties of thesolutes, such as the diffusivity coefficient and the solubility.
This temperature is also of significance in choosing the optimal process conditions so as not to subject temperaturesensitive biologics to conditions outside of their tolerance.
The drying temperature can be conservatively approximated as the inlet temperature of the spray dryer, eventhough the actual temperatures and vapor activities in thevicinity of a spray plume inside a spray dryer vary significantly and are generally unknown for all the possible
processing conditions (Longest et al., 2020). It can be seenfrom Fig. 7 that the equilibrium temperatures of thesesolvents drying at temperatures as hot as 200 °C barely
reach 40 °C, pointing to the inherent advantage of thespray drying process in protecting sensitive molecules andbiological entities from elevated temperatures due to theevaporative cooling effect.

3.4 The diffusion coefficients

In the absence of any experimental data for the diffusion
coefficients of the solutes in the solvent of interest, which
is usually the case for newly discovered drugs or biologics,
the Stokes-Einstein equation can be used as an approximation, as shown below (Bird et al., 2006; Zmpitas and Gross,2021),Di, j k BTd
3πμ j d i

(10)where Di,j is the diffusion coefficient of solute i in solvent j at infinite dilution, kB≅1.38 × 10–23 m2∙kg∙s–2∙K–1
is (10)the Boltzmann constant, Td is the droplet temperaturein Kelvin, which can be obtained from Fig. 7, μj is the
dynamic viscosity of the solvent at the droplet temperature
in Pa∙s, presented in Fig. 8, and di is the hydrodynamic
spherical diameter of the molecule or particle of interest inm obtained from 3 6Vi / π . Here, Vi is the molecular volume
in m3 that can be obtained via different methods (Bird et al.,2006; Edward, 1970; Kooijman, 2002; Zmpitas and Gross,2021), for example,[22] by using the Van der Waals volumes
obtained from the atomic and group incremental volumes(Blokhina et al., 2017; Bondi, 1964; Edward, 1970). The
hydrodynamic diameters of some of the excipients andAPIs used in the formulation of pharmaceutical solid
particles were calculated based on their Van der Waals
volumes (Edward, 1970) and are presented in Table 1. The

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Dynamic Viscosity (Pa.s)

0.004

water
acetoneethanolmethanol
DCM
THF

0.003

0.002

0.001

0.000
-40

-20

20

0

Temperature (°C)

4060Fig. 8 The dynamic viscosity values of several solvents frequently
used in pharmaceutical spray drying at different droplet temperatures(Yaws, 2010).

molecular diameters in Eqn. (10) is not reasonable when
the solutes are not in molecular solution form. For example,
when the solutes or actives are present in the form of liposomes, micelles, emulsions, nanoparticles, or nanocrystals,
the approximate size of these larger aggregates should be
used to estimate their diffusion and surface enrichment
inside the evaporating droplets.
The use of atomic and group contribution methods indetermining the size of macromolecules, such as proteinsand polymers, is not straightforward. For such compounds,
the use of available correlations and relationships of molecular volume versus molar mass is recommended. For
example, the following correlation was obtained via nanoelectrospray gas-phase electrophoretic mobility molecularanalysis for proteins (Weiss et al., 2018):
0.343

d prt  1.61M prt 
Table 1 The hydrodynamic diameters of some small-molecule excipients and actives typically used in the formulation of spray-dried particles
as obtained from their approximate Van der Waals volumes.

Molecule

Hydrodynamic Diameter(nm)

Lactose/Trehalose/Sucrose

0.82

Mannitol

0.68

Raffinose

0.93

Leucine0.64

Trileucine

0.89

Glycine

0.51

DSPC

1.17

DPPC

1.14

Salmeterol

0.93

Fluticasone

0.90

Beclometasone

0.97

Salbutamol

0.77

Formoterol

0.85

Budesonide

0.92

Vilanterol

0.94

Etravirine

0.86molecular volumes of these compounds were determinedbased on their neutral and anhydrous forms.
The Stokes-Einstein equation should be used with theknowledge that it gives accurate results only for dilute
solutions and for solute molecules that are relatively
spherical and much larger than the solvent molecules. The
concentration-dependency of the diffusion coefficient becomes significant only near the end of droplet evaporationand hence can be neglected for practical uses of particle
formation theory. It should be noted that the use of the(11)Here, dprt is the molecular diameter of a protein in nm and
Mprt is the molar mass in kDa.
(11)

3.5 The onset of solidification or crystallization

The solution of the mass transfer equations inside anevaporating droplet results in the concentration of eachcomponent at different times at each radial coordinate, but
as soon as one of the components starts to solidify, either
due to crystallization or amorphous phase separation, theconcentrations are suspected to change rapidly. This isbecause the evaporation rate is expected to decrease uponshell formation (Boel et al., 2020) and the local sites of solidification can act as source points that result in concentration profiles with sharp gradients (Douglass and Harrowell,2018; Fukui and Maeda, 1998) unlike those predicted by
Eqn. (3). Based on these complications, one straightforward method of predicting the surface composition of thespray-dried particles is to determine which component
solidifies first and assume that this solute will have thehighest amount of surface coverage on the final particles.
To this end, the time evolution of surface concentrationsof all solutes can be compared to a critical concentrationfor each material. Once this concentration is reached thesolidification is expected to commence for that solute. The
compound that reaches its critical concentration earlier
than the other solutes is then expected to be the dominantcomponent on the surface of the particles. The exact prediction of the actual surface compositions is complicatedand requires knowledge of the phase diagram of the system
including all of the solutes and solvents, which is difficult
to obtain for newly discovered drugs and biologics.
The estimation of the solidification thresholds depends
on the ability of the material to form a crystal or glass
during drying; hence, APIs and excipients can be characterized by their tendency to crystallize during spraydrying. The crystallization tendency is related to many factors, including glass transition and melting temperatures,

159

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3.5.1 Crystallizing compounds

For crystallizing compounds, crystalline solubility has
frequently been regarded as the concentration after which
nucleation and crystal growth commence (Vehring, 2008).
However, based on the classical nucleation theory, a certain
level of supersaturation is required for any system to undergo crystal growth due to the nucleation barrier (Karthika
et al., 2016). When the concentrations are increased above
this minimum supersaturated value, the time of nucleation
decreases until crystallization can be assumed to be instantaneous (He G. et al., 2006). Considering the very highrate of the evaporation of droplets during spray drying, thiscritical supersaturated concentration, rather than the solubility, can be deemed a threshold of nucleation and crystal
growth in the formulation design of spray-dried particles(Ordoubadi et al., 2021b; Z. Wang Z. et al., 2021a).
By measuring the nucleation times of a variety of materials dissolved in several evaporating microdroplets at
different conditions, He et al. proposed a semi-analyticalmethod of calculating the critical supersaturation ratio at
which nucleation would be instantaneous (He G. et al.,2006). Such methods still require the measurement or approximation of certain properties that are not readily available, such as the water activity coefficient at saturation.
Hence, the actual crystalline solubility can still be used as afirst approximation in the particle formation models in theabsence of these data.

3.5.2 Glass formers

Most of the newly discovered drugs and biologics do not
crystallize during spray drying but instead phase separate
into glasses or amorphous materials (Baumann et al., 2021;
Wang B. et al., 2021). The precipitation of a material into
an amorphous state rather than a crystalline form takes
place when the metastable region (between the crystalline
solubility and spinodal curves) is traversed fast enough not
to permit nucleation and growth (Zallen, 1998). Shownschematically in Fig. 9, this phenomenon starts immediately after atomization from the initial solute concentration,
C0, at the droplet temperature, Td, and moves isothermally

ve

e

Cu
rv

id
(Sp Coino exisda tan
l)
ce

iqu
uid

-L

C0

Liq

Metastable Region

Solute Concentration

Fig. 9 Phase diagram of a compound and the isothermal route it takes
from atomization to glass formation.

into the supersaturated and unstable regions due to evaporation. Note that the initial temperature stabilization of thedroplet is ignored in this figure. Upon reaching the spinodal
point, precipitation commences based on glass-liquid phaseseparation (solute-rich glass and solvent-rich liquid) or
liquid-liquid phase separation (solute-rich gel and solventrich liquid) depending on the plasticization behavior of
the system (Douglass and Harrowell, 2019). At this point,
the droplet temperature increases due to the decrease inevaporation rate, and eventually the solid particle forms,
making a mixture with the remaining residual solvent(Charlesworth and Marshall, 1960).
The presence of multiple glass-forming compounds
in the formulation introduces complexities that require
multi-phase analysis to model the co-amorphization of allcomponents in a manner similar to the studies conducted
for the preparation of amorphous solid dispersions (Daviset al., 2017; Ziaee et al., 2017). For simplicity, during theearly stages of formulation design of such systems, it canbe assumed that each solute precipitates in the solvent system independently, with the effects of the other components
being neglected.
Particle engineers and formulators can use the conceptof amorphous solubility for each glass former in the system
to determine which component precipitates first. Comparedto crystalline solubility, the amorphous solubility, especially of small molecules, is more difficult to determine
experimentally because of the precipitation of the excess
dissolved material as crystals (Douglass and Harrowell,
2018). Based on thermodynamic considerations, the amorphous solubility of a material, Ca, at temperature T can beobtained from its crystalline solubility, Cc, using Eqn. (12)(Almeida E Sousa et al., 2015; Ilevbare and Taylor, 2013),
ΔGa-c Ca  Cc  exp  I a 2   exp  RT 

(12)

where ∆Ga-c is the difference in free energy of crystalline

(12)
160

ur
ce C
istan lity)xeobi
lid C Solud-So stalline
i
r
u
q
fo
Li (Cryon n
ati eatio
r
tu l
sa uc
er s N ble)pu u al S eo ail
ca tan Avit
nif
Cr nsta (II

Td Temperaturesolubility, the solvent system used, processing conditions,molecular size, and crystal structure (Kawakami, 2019);
hence, the prediction of the final solid phase of spray-driedmaterial is complicated. In the absence of any literature
data, the ability of a material to crystallize during spraydrying can be measured via powder X-ray diffraction or
Raman spectroscopy on an initial spray-dried batch of theexcipient or active of interest during the early stages offormulation development.
The critical concentrations that can be used as a threshold to compare with the instantaneous solute concentrations depend on the predicted mechanism of solidification
or dissolution as will be explained below.

Mani Ordoubadi et al. / KONA Powder and Particle Journal No. 40 (2023) 149–171

and amorphous states, I(a2) is related to the activity of theamorphous state saturated with the solvent, and R is theuniversal gas constant. The free energy difference can beobtained from (Almeida E Sousa et al., 2015; Hoffman,1958),
ΔGa-c Δ f H  Tm  T   T
Tm2

(13)

in which ∆f H is the enthalpy of melting and Tm is themelting temperature of the crystal, both of which are ob(13)tainable from differential scanning calorimetry (DSC). The
activity term, I(a2), can be obtained from the integration of
the water sorption isotherm of the amorphous solid, which
can itself be obtained from dynamic vapor sorption (DVS)
measurements as explained in the literature (Murdande etal., 2010).
Alternatively, the instant of phase separation of glass
formers in evaporating microdroplets can be directly measured via single-particle measurements, as was previously
done for trehalose (Ordoubadi et al., 2021b).

3.5.3 Surface-active compounds

Some excipients and biopharmaceutical actives areamphiphilic, meaning that they possess a hydrophobic
tail as well as a hydrophilic head group, making them asurface-active agent, or simply a surfactant, with a tendency to be adsorbed on air-water interfaces (Rosen andKunjappu, 2012). This adsorption on the interface is observed macroscopically via the decrease in surface tensionof the solution. During spray drying and droplet shrinkage,the internal diffusion of surface-active materials is different from that shown in Eqn. (3) for non-surface-active
solutes. The exact mechanisms of particle formation anddiffusion of surface-active molecules have been discussed
previously (Ordoubadi et al., 2021a). In short, immediately
after atomization, the surface of the droplets acts as a sink
for such molecules. These molecules are then adsorbed
into a monolayer on the interface causing a drop in local
free concentration underneath the surface. This decrease insolution concentration causes an outward radial flux of thesurface-active molecules towards the interface. The surface
adsorption continues until the monolayer becomes saturated with these molecules, after which further evaporation
might cause the adsorbed molecules to dissolve back into
the solution to maintain stability.
For surface-active excipients or APIs, the time to forma saturated monolayer on the surface can be counted
a design criterion and compared to the precipitationthresholds of other components. This is because a surfaceactive dispersibility enhancer needs to make a saturated
monolayer on the surface before the precipitation of othercomponents so that the final dried particle will contain afully packed hydrophobic shell. This shell can potentiallydecrease the surface energy of the particles and possibly

result in wrinkled surfaces, which would in turn improveaerosol properties (Mangal et al., 2015; 2019; Wang H.
et al., 2019). It should be noted that besides making themonolayer, the surface-active molecules will eventuallyeither crystallize or phase separate into a glass based on theprevious discussions. For example, trileucine is expected
to make a monolayer first and then solidify into an amorphous material at a later stage during droplet evaporation(Ordoubadi et al., 2021a).
The surface adsorption of surface-active compounds
is kinetically controlled by two sequential phenomena:
1) diffusion of the molecules towards the surface, and 2)
adsorption and reconfiguration of the molecules into themonomer (Eastoe and Dalton, 2000; Jayasundera et al.,2009). Hence, the calculation of the instant of monolayer
formation on the surface of a droplet is related to the diffusion coefficient of the surface-active compound, its maximum surface excess concentration, Γmax, and its size andcomplexity. The maximum surface excess concentration,with the dimension of kg/m2 or mol/m2, is the maximum
amount of surfactant molecules that can become adsorbed
on an interface and is a measure of surface activity (Rosen
and Kunjappu, 2012). This property can be obtained fromstatic surface tension measurements at different soluteconcentrations. Such a measurement, usually done using
Wilhelmy plate, du Nouy ring, or pendent drop techniques
results in a correlation between the static surface tension,σ, and the solute bulk concentration, Cb (Ebnesajjad, 2011).
By fitting an appropriate empirical isotherm to this data, themaximum surface excess concentration of each solute canbe determined. For example, Γmax in conjunction with theLangmuir adsorption constant, KL, are the two fit parameters in the Szyszkowski surface equation of state as seen
below (Eastoe and Dalton, 2000):σ  σ 0  nRTΓ max ln 1  K LCb (14)

Here, n is equal to 1 for non-ionic or zwitterionic solutes
and is equal to 2 for one-by-one ionic solutes, while R is
(14)
the Universal gas constant, T is the solution temperature in
Kelvin, σ is the surface tension in N/m, and σ0 is the static
surface tension of the solvent in the absence of any solutes.
The maximum surface tension obtained directly from fitting Eqn. (14) to surface tension measurements has a unitof mol/m2 that can be simply converted to other units suchas mg/m2 using the molar mass of the solute molecules. A
typical surface tension measurement with a Szyszkowski
fit to determine the maximum surface excess concentrationis shown in Fig. 10. The critical micelle concentration(CMC) shown in this figure is a property of surfactant
solutions and is the concentration above which no further
decrease in surface tension is possible due to the formationof micelles.
As an example, and based on available tensiometry
data at 25 °C (Gliński et al., 2000; Lechuga-Ballesteros et161

Mani Ordoubadi et al. / KONA Powder and Particle Journal No. 40 (2023) 149–171

σ0Surface tension

σ

=

σ0

−

nR

TΓ

m

ax l

n(

1

+

K

LC

)

Critical micelleconcentration (CMC)

Surfactant Concentration
Fig. 10 A characteristic surface tension-versus-concentration measurement and a fit based on the Szyszkowski equation of state to determine the maximum surface excess, Γmax.

al., 2008), the maximum surface excess concentrations oftrileucine and leucine were approximated to be 0.99 and0.15 mg/m2, respectively. These numbers partially explain
the better shell-forming capabilities of trileucine comparedto leucine (Ordoubadi et al., 2021a; 2021b).
Assuming that the incorporation of the surface-active
molecules into the monolayer occurs instantaneously(diffusion-controlled process), the time required to saturate
a semi-infinite surface, tΓ,i, was calculated to be (He Y. etal., 2015; Ward and Tordai, 1946)t Γ,i 2πΓ max,i
4DiCb,2 i(15)

2where Γ max,i, Di, and Cb,i are the maximum surface excessconcentration of the surface-active solute i, its diffusion(15)
coefficient, and its bulk concentration, respectively. Thisrough [23]estimate for the time to monolayer formation wasused previously to explain the particle formation of spraydried formulations containing surface-active components(Adler and Lee, 1999; Kawakami et al., 2010; Landström
et al., 2000) and can be used in the formulation design ofsuch systems. The limitations of using this equation are thatit does not account for the resistance to surface adsorption
due to the already adsorbed molecules, nor does it take into
consideration the delay in surface adsorption due to thereorientation of the molecules. Furthermore, as explained
previously (Ordoubadi et al., 2021a), this simplified
method cannot model the increase in solute bulk concentration during droplet evaporation or the possible depletion of
the molecules for very small droplets. Regardless of theseshortcomings, the use of Eqn. (15) is simple and practicalfor most formulation design applications.

3.5.4 Nanostructures

Research into the inclusion of nanostructures in spraydried solid powders has grown in the last couple of decades(Johnson et al., 2020; Malamatari et al., 2020). This kind162

of formulation is possible through the preparation of a feednanoemulsion or nanosuspension by dispersing liposomes(Maniyar and Kokare, 2019), nanodroplets (Gomez et al.,2019), nanoparticles (Tsapis et al., 2002; 2005), and otherforms of immiscible nanostructures (Malamatari et al.,2020; Saallah and Lenggoro, 2018; Wang Z. et al., 2022).
For such structures, the instant of close packing on thesurface becomes the critical point during the drying stage
since this is the point at which a shell can begin to form
as these nanostructures come into close contact with eachother. What happens after this point depends mostly on
the interfacial forces between these nanostructures: theshell either collapses or maintains its rigidity and forms ahollow sphere (Ahumada-Lazo and Chen, 2022; Bahadur
et al., 2011; Minoshima et al., 2001). The time of thisclose packing is important during the formulation design,
regardless of what ensues afterward. As an example, ashell-forming excipient acts best if it makes a shell before
the nanodroplets containing a biological entity do. To thisend, the critical time of close packing for nanostructures
can be calculated assuming a critical volume fraction of~0.6 (Ahumada-Lazo and Chen, 2022; Minoshima et al.,2001). This value can be converted to mass fraction toresult in a critical surface value for shell formation, Ync, as

Ync 

3ρliq
2 ρn  3ρliq(16)

where ρliq and ρn are the density values of the liquid feed
and nanostructures, respectively. The critical concentra(16)tion,
Cnc, in mg/mL, can then be obtained from1

Y
1  Ync C nc  Ync  nc ρliq 
 ρn(17)Here the densities should have the same units as the concentration. The surface concentration of the nanostructures(17) from Eqn. (7) can then be compared to this criticalobtainedvalue to obtain their time of shell formation, while theiraverage diameter can be used in Eqn. (10) to find theirdiffusion coefficient.

4. The characterization techniques

As mentioned previously, the sample spray-dried powders need to be characterized to be assessed against certain
predefined criteria. Depending on the specific formulation,these assessments might include both physical and chemical characterizations such as size distribution, morphology,solid phase, bulk density, and composition (Barona et al.,2021; Frank et al., 2022; Mangal et al., 2019; Nicholas
et al., 2020). Some of the physical characterization techniques will be reviewed here.

4.1 Morphology characterization

Frequently, the morphology of the spray-dried powders is the first factor to be studied. By looking at theMani Ordoubadi et al. / KONA Powder and Particle Journal No. 40 (2023) 149–171

SEM

HIM/FIB

Fig. 11 Scanning electron microscopy (SEM) and helium ion microscopy (HIM) micrographs of leucine particles collected from a monodisperse
droplet chain instrument (Ordoubadi et al., 2019). The particle at the bottom was also cut using focused ion beam (FIB) milling to study the interiormorphology. All micrographs are reproduced with the permission of Springer Publishing.

micrographs of the particles, for example, one candetermine qualitatively if the product is dispersible or
flowable based on the surface rugosity; one can also detect
individual particles as opposed to an agglomerate of fusedparticles. The instruments of choice for such observations
are scanning electron microscopes (SEMs), which provide
a surface topology by scanning the sample with an electron
beam. The interaction of the electron beam with the sample
results in the scattering and emission of electrons that arethen collected on different detectors to result in a micrograph (Leonard et al., 2012). Ion microscopy can also be
used in cases where much higher resolutions are required,coating of the samples needs to be avoided, or single particles are to be cut using focused ion beam milling to study
the internal morphology (Heng et al., 2007; Ordoubadi etal., 2019). Sample micrographs of some batches of driedparticles obtained from these methods are shown in Fig. 11.
The rugosity of the spray-dried powders can also bequantified through the measurement of the specific surface
area via Brunauer–Emmett–Teller (BET) analysis relative
to the geometric specific surface area, as was shown previously (Wang Z. et al., 2022).

4.2 Surface composition characterization

The surface composition of spray-dried engineered
particles is critical to the validity of the formulation design.

As an example, a shell former needs to contribute to mostof the composition on the surface of spray-dried particles
to have maximum benefits, while the valuable payload,
whether biologics or APIs, should typically have minimalpresence on the surface.
For surface characterization, two kinds of instruments
are frequently used for spray-dried microparticles: timeof-flight secondary ion mass spectrometry (ToF-SIMS) and
X-ray photoelectron spectroscopy (XPS). Fundamentally,
ToF-SIMS detects ion fragments, while XPS is an elemental analysis. Both methods require relatively complicatedpost-processing procedures for quantification (Mangal etal., 2019; Nicholas et al., 2020; Wu X. et al., 2010); however, compared to XPS analysis, ToF-SIMS has a better
lateral resolution and lower analysis depths (~1–3 nm for
ToF-SIMS compared to ~5–10 nm for XPS) (Alhajj et al.,2021; Nicholas et al., 2020).
Energy-dispersive X-ray spectroscopy (EDX) has also
been used to map the surface elemental compositions of
microparticles (Eedara et al., 2016). This elemental method
has a typical penetration depth of up to 1 μm, which makes
the surface characterization of microparticles more difficult(Alhajj et al., 2021).

4.3 Solid-state characterization

Besides morphology, the solid phase of spray-dried

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powders is one of their most important characteristics
requiring in-depth study. This is because solid phase properties have substantial effects on the long-term stabilityof such powders and can also point to instability duringstorage (Edueng et al., 2019). The methodology mostfrequently used to assess these properties for spray-dried
powders is powder X-ray diffraction (PXRD), which gives
crystallinity information for the bulk powder (Bianco etal., 2012; Lu et al., 2019; Shetty et al., 2022). Microcrystals in the samples cause diffraction of an incident X-ray,translating into peaks in the obtained diffractograms thatcan be used to determine the crystal structure of the known
material inside the powder (Brittain, 2003). The quantification of the fractions of different polymorphs is not easily
achievable using PXRD, and the diffractograms are frequently used in a qualitative capacity to detect the presenceof any crystalline peaks for usually amorphous spray-dried
powders (Li L. et al., 2016; Wu L. et al., 2014). However,the detection of crystallinity of a component in the powder
might not be possible at all if it has a low fraction in the formulation, as the crystalline peaks can be shadowed behindthe stronger amorphous background (Khanal et al., 2022).
Bulk Raman spectroscopy is another analytical tool
widely used to assess both the composition and the solid
phase of powders that also allows the characterizationof individual components through deconvolution of theRaman spectra (Frank et al., 2022; Wang H. et al., 2014).
Through careful method development, the fractions ofdifferent phases of each component, either crystalline oramorphous, can be quantified with very high sensitivity(Feng et al., 2011; Wang H. et al., 2017).
Other spectroscopic techniques have also been used
for the characterization of powders, including solid-state
nuclear magnetic resonance (ssNMR) and Fouriertransform infrared spectroscopy (FTIR), both of which
produce results complementary to the previously discussed
methods (Chang et al., 2020; Chen Y. et al., 2021; French
et al., 2004; Suihko et al., 2005).

4.4 Characterization of thermal properties

Thermal properties, such as the wet and dry glass transition temperatures, moisture content, and moisture uptake,are important characteristics of powders that need to bestudied to determine the long-term stability of the product.
The glass transition temperature, melting point, decomposition point, and other thermal properties of spray-dried
powders, all of which directly influence their physicalstability, are usually measured via differential scanningcalorimetry (DSC) (Clas et al., 1999; Leyva-Porras et al.,2019). Thermogravimetric analysis (TGA) provides complementary information that also includes the quantity ofresidual solvents and moisture levels (Eedara et al., 2018;
Yu et al., 2017).
Moisture absorption is another important property that

164

quantifies the ability of the dry powder to withstand humid
conditions and is frequently measured by dynamic vapor
sorption (DVS), which can also be used to indirectly measure the amorphous content of the sample (Sheokand et al.,2014).
The water content of spray-dried powders influences
their stability and flowability and needs to be measured
during the product characterization, as high water content
can decrease the glass transition temperature through plasticization of the amorphous content of the powder (Shepard
et al., 2020; Shetty et al., 2018). Karl Fischer titration isusually employed to measure the water content present
in the powders either when it remains as residual solvent
or is absorbed during storage and handling (Carrigy andVehring, 2019).

5. Summary and conclusions

Spray drying can address many of the current and futurechallenges in the preparation of solid dosage forms and is
the ideal manufacturing process for thermostable global
health products. The mechanistic formulation design ofspray-dried engineered microparticles is complex, withmost of the available particle formation models requiring
prior knowledge of the fundamental sciences behind them.
Necessary information was provided to make the use of
these models more straightforward and also to assist in theselection of appropriate excipients. Using such predictive
tools during the early stages of product development cansignificantly reduce the number of experimental iterations
and thus save time and effort. Several challenges remainthat require in-depth studies and cannot be predicted
via simplified theoretical analysis, such as the exact coprecipitation kinetics of glass mixtures or crystalline
components, the final morphology of the particles, orthe long-term stability of the product at different storage
conditions. These emphasize the need for complementary
experimentation.

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Authors’ Short Biographies
Mani Ordoubadi
Dr. Ordoubadi is a postdoctoral fellow in the Department of Mechanical Engineering at the University of Alberta. He holds a PhD from the University of Alberta focused on developing predictive
tools to assist in the formulation design of spray-dried inhalable microparticles to reduce costs andrisks during the early stages of product development of emerging therapeutics in solid dosage form.
He is also experienced in using numerical simulations and computational fluid dynamics related toheat and mass transfer, multiphase flows, and aerosol mechanics.

Hui Wang
Dr. Wang obtained his BSc in Materials Science and Engineering from Southeast University
in Nanjing, China. During his MSc and PhD studies with the Particle Engineering Group at the
University of Alberta, he focused on designing and applying advanced characterization techniques,
including cascade impaction, Raman spectroscopy, and shadowgraphic imaging, for the testing andoptimization of inhalation formulations for pressurized metered dose inhalers (pMDIs) and dry powder inhalers (DPIs). He has rich experience in design, production, testing, and pulmonary deliveryof medicinal microparticles. He is currently a research scientist in the group, working on various
projects related to respiratory drug delivery. His research interests include inhalation formulation
development, pharmaceutical particle engineering, and microparticle encapsulation of biologics.

Reinhard Vehring
Dr. Vehring is a Professor in the Mechanical Engineering Department at the University of Alberta
and holds the George Ford Chair in Materials Engineering. He graduated with a diploma in Mechanical Engineering from the Gerhard Mercator University in Duisburg, Germany, and received adoctorate from the University of Bochum in the field of molecular spectroscopy on microparticles.
Dr. Vehring has held positions in academia and industry advancing aerosol science and particle
technology for more than 28 years. Before returning to academia, he worked on pulmonary deliveryof peptides, proteins, and small molecules at Nektar Therapeutics and was part of the team developing Exubera, the first inhalable insulin. Subsequently, he developed solid dosage forms for virus
vaccines, monoclonal antibodies, and oncology therapeutics at Medimmune, and supported FluMist,
the first nasally administered live attenuated influenza vaccine. Dr. Vehring was the lead inventor forthe co-suspension formulation technology which is used by AstraZeneca to develop metered-dose
inhaler-based therapeutics for respiratory diseases. At the University of Alberta, Dr. Vehring directs
the Particle Engineering facility focusing on advanced micro and nanoparticle design and analysis.

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Review Paper

Performance Testing for Dry Powder Inhaler Products:
Towards Clinical Relevance †Sara E. Maloney, Jeffrey B. Mecham and Anthony J. Hickey *RTI International, Research Triangle Park, USA

Abstract
It is well established that the critical performance metrics for aerosol products are aerodynamic particle size
distribution (APSD) and delivered dose uniformity (DDU). In broad terms, these performance characteristics dictate
the efficiency and reproducibility with which an aerosol is administered clinically. However, these properties alone
do not support in-vitro, in-vivo correlations. There have been numerous publications attempting to more directly
link product performance testing to physiological relevance or further to draw direct correlations of relevance tobioequivalence testing for the development of generic products. While these novel methods have been employed in
product development activity, their suitability for compendial testing has yet to be established. This paper explores thepotential to establish biologically relevant compendial standards for dry powder inhaler products while maintaining
accuracy and reproducibility of data collected to support the quality and performance of the product.
Keywords: dry powder inhaler, therapeutic aerosol, aerodynamic particle size distribution, cascade impaction, in
vitro/in vivo correlation, bioequivalence

1. Introduction

Modern pharmaceutical aerosol products have been
available for the treatment of pulmonary diseases, such asasthma, chronic obstructive pulmonary disease (COPD),
and cystic fibrosis (CF), since the middle of the last century(Cheng Y.S., 2014). Attention was given to the control of
the quality of these products by the manufacturing industry
and government regulators. The importance of accurately and reproducibly controlling the dose of the active
pharmaceutical ingredient (API) and key properties that
influence therapeutic effect, notably aerodynamic particle
size distribution (APSD), was seen as the key to assuring
the desired therapeutic outcomes while minimizing any
adverse effects. To demonstrate the quality of the product,
which underpins its safety and efficacy, various tools wererequired for sampling. These tools, when combined with
instruments in analytical chemistry, could support the
specifications to which process controls could be tuned toassure the quality of these products.
There are several major categories of pharmaceuticalaerosol products, namely pressurized metered dose inhalers(MDIs), dry powder inhalers (DPIs), nebulizers, and soft†*

Received 29 April 2022; Accepted 9 June 2022
J-STAGE Advance published online 3 September 2022
Corresponding author: Anthony J. Hickey;
Add: 3040 Cornwallis Road, Research Triangle Park, NC 27709,
USA
E-mail: ahickey@rti.org
TEL: +1 (919) 541-6771

172

mist inhalers (Cheng Y.S., 2014), which consist of an aerosol formulation and device. Each product presents uniquerequirements for establishing quality and performance
specifications (Uddin S. et al., 2016). The development anduse of DPIs over the last three decades was largely driven
by the global phase-out of chlorofluorocarbon propellants
in medical products due to their deleterious effects on theozone layer (Wu X. et al., 2010) and by the need for analternative dosage form and route of administration forthe products of biotechnology. Dry powder inhalers offer
many advantages, such as their propellant-free dispersal
mechanisms, product stability, portability, and ease of use.
The characterization of quality and performance of DPIs
will therefore be the focus of this review.

1.1 Development tools

Development tools for solid state products include thosethat measure the physicochemical properties of the materials incorporated in the formulation, establishing the quality
foundation. For DPI formulations, analysis of these physicochemical properties via analytical methods is imperative,
as the dispersion of powders, and therefore efficiency of
the DPI product, is highly influenced by such properties.
These properties, such as particle size, crystallinity, surface
rugosity, shape factor, moisture content, and chemicalcomposition, all contribute to interparticulate forces (e.g.,
electrostatic, capillary, and van der Waals forces), whichcorrelate to particle flow and dispersion behavior (Hickey
A.J., 2018a). Beyond physicochemical properties, theCopyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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performance of the product must be established in the final
form, which includes formulation, metering system, anddevice (Uddin S. et al., 2016).

1.2 Regulatory considerations

To establish the quality and performance of inhaled
drug products, a variety of considerations are required.
The drug formulation must be controlled according to
ICH Q1 and Q2 which govern the components (Q1) andtheir quantitative proportion (Q2) in the preparation. The
device is accompanied by a drug master file establishingspecifications and controls on its manufacture. Criticalquality attributes of DPI formulations as specified by theUnited States Food and Drug Administration (US FDA) include assay, impurities and degradants, leachables, foreign
particulate matter, moisture content, net content, microbial
load, and device characteristics (US-FDA, 2018). The
drug product (formulation and device) is also subject toperformance testing to evaluate the APSD and delivered
dose uniformity (DDU) as described in compendia (UnitedStates Pharmacopeia [USP], European Pharmacopeia [EP],and Japanese Pharmacopoeia [JP]) and regulatory guidance
documents, such as those promulgated by the United States
FDA (Kuribayashi R. et al., 2017; 2019).

1.3 Dry powder inhaler products

It is generally accepted that particles with an aerodynamic size of 1–5 µm can enter the lungs (Adams W.P.
et al., 2007; Alagusundaram M. et al., 2010; Hickey A.J.,
2018a). However, the way that the particles are prepared affect their performance characteristics (Hickey A.J., 2018a).
High energy processes used to prepare micron-sized particles, such as jet milling, lead to the formation of particles
with high specific surface areas (i.e., surface area withrespect to mass) and surface energies. These attributes generally result in poor flow, fluidization, and deaggregation(Alagusundaram M. et al., 2010; Chaurasiya B. and Zhao
Y.-Y., 2021; Hickey A.J., 2018a). Formulation strategies,
such as those described below, aim to mitigate these issues.

1.3.1 Lactose blends

Drug products intended for the treatment of asthma and
COPD are primarily lactose blends. These products include
α-lactose monohydrate, a monoclinic crystalline substance,
as a large carrier particle onto which respirable drug particles are attached at very low concentrations. The drug
is prepared in respirable sizes, often by jet milling, andis predominantly crystalline. The drug is then uniformly
distributed in the lactose powder using a tertiary blending
process (Hickey A.J., 2018a). The large carrier particles
occupy the high energy sites of the drug, reducing drug
cohesiveness and improving disaggregation and dispersal(Hickey A.J., 2018b; Wu X. et al., 2010). When the formulation is released from the device by patient inhalation, thedrug detaches from the lactose and enters the lungs. The
lactose (typically 50–150 µm) is too large to be inhaled(Hickey A.J., 2016). This process of detachment occurs
through shear, turbulence, and impacts with the walls of themouthpiece of the device (Hickey A.J., 2018a; b). Of note,
while lactose is most common, alternative carrier molecules, such as mannitol, glucose, trehalose, erythritol, andsorbitol have all been proposed as large carrier particles toserve a similar purpose (Rahimpour Y. et al., 2014; Wu X.
et al., 2010).

1.3.2 Spray dried formulations

Spray dried formulations of drug, accomplished byatomizing a liquid drug-containing solution or suspension
into a hot drying medium, have been used for high dose
dry powder formulations (Hickey A.J., 2018a). Notably,
tobramycin, a drug used to treat Pseudomonas aeruginosa
infection, is delivered at a dose of 112 mg (VanDevanter
D.R. and Geller D.E., 2011), a dose not easily achievablethrough lactose blending. The spray drying process allows
for manipulation of the drug into particles of pure or nearpure drug content that are amorphous in nature with lower
densities than a crystalline solid particle (Hickey A.J.,
2018b; Wu X. et al., 2010). It is also possible to prepare
hollow particles or particles with very low density and highroughness (Gradon L. and Sosnowski T.R., 2014). These
attributes confer lower interparticulate forces than thoseobserved with milled particles, and the powder requires
much less energy input from the patient for high efficiency
dispersion.

1.3.3 Other formulations

Jet milling/lactose blending and spray drying represent
the most common techniques for developing DPI formulations. However, a variety of alternative methods havebeen considered for the preparation of dry powder formulations. Particles prepared through thin film formation
occur through dropwise deposition of the drug solution orsuspension onto a frozen surface to form a thin film, whichis then subject to lyophilization for solvent removal. The
result is a porous, brittle, interconnected matrix that can bedispersed to small particles upon aerosolization (Hufnagel
S. et al., 2022). This process has been used to prepare
various aerosol particles containing both small and large
molecules, including tacrolimus, lactate dehydrogenase,and lysozyme (Engstrom J.D. et al., 2008; Sahakijpijarn
S. et al., 2020). Controlled aggregation of carrier-freemicroparticles following jet milling, such that dispersion
is efficient and reproducible, has been utilized to prepare
clofazimine particles (Brunaugh A.D. et al., 2017) andibuprofen particles (Yazdi A.K. and Smyth H.D.C., 2016)and has also been employed in the Mometasone Furoate
Twisthaler product (Yang T.T. et al., 2001). Heat-sensitive
biological formulations can be prepared using either spray173

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freeze drying or supercritical fluid drying in methods thatavoid high critical temperatures. Both methods are more
complicated and expensive and are typically only used
when necessary for bioactive molecules (Chaurasiya B.
and Zhao Y.-Y., 2021; Gradon L. and Sosnowski T.R.,
2014; Wu X. et al., 2010).

2. Data generation

Characterization of dry powder aerosol formations
includes aspects of both quality and performance. Quality
evaluation, including composition and physicochemical
characteristics, is performed on the dry powder formulation to monitor properties that are known to contributeto aerosol dispersion efficiency, stability, and potency. In
combination with the device and metering system, performance characterizations are then investigated to monitor
the aerodynamic properties of the combined product.

2.1 Qualitative and quantitative composition

To establish quality measures of the drug formulation, it
is important to note the nature of the drug and any additives
that are employed. In the first instance, this relates to theform in which each of the components is supplied withmeasures of purity (Hickey A.J., 2018b). Once the natureof the components has been established, the amounts
employed must be defined and specifications set to assure accuracy and reproducibility of the drug dose. The
quantity of each drug should be reported both in terms ofconcentration (amount of ingredient per unit formulation)and net content in each blister or capsule (US-FDA, 2018).
Analytical instrumentation and techniques, such as X-ray
microanalysis, X-ray photoelectron spectroscopy (XPS),
and inverse gas chromatography (IGC) can be utilized todetermine composition and evaluate homogeneity of thecomposition throughout the formulation (Hickey A.J.,
2018b; Hickey A.J. et al., 2007; Wu X. et al., 2010). The
US FDA defines composition metrics as Q1, or qualitative
composition, and Q2, or quantitative composition. These
metrics are used when defining bioequivalence for thecontents of a drug formulation, where Q1 would informwhether two formulations contained the same active andinactive ingredients, and Q2 would inform whether thoseingredients were in the same concentration for each formulation (Hickey A.J., 2018b).

2.2 Physicochemical characteristics

Since the drug and any additives for a DPI formulation
will be in the solid state, the structure and morphology
must be defined to allow for replication of the formulation
by adopting adequate controls. The typical properties thatmust be measured include morphology and particle size,
crystallinity, polymorphism, and moisture content (Dunbar
C.A. et al., 1998). Dispersion of powders is highly influenced by the size distribution of the particles as well as

174

their shape, surface rugosity, hardness, and porosity due tothe role of these properties on interparticulate interactions,as noted above (Hickey A.J., 2018a). Particle morphology
and geometric size is often visualized using scanning electron microscopy (SEM), where size is manually measured
from images and gives rise to a size distribution in termsof number of particles. In contrast, laser diffraction is a
population-based method that is typically representativeof the true geometric particle size of regularly shaped
particles, and the size distribution can be reported as afunction of particle volume (Hickey A.J., 2018b). Surface
geometries can be visualized with both SEM and atomic
force microscopy (AFM), where AFM can also provide
information on specific particle interactions between drug
and carrier particles or between two drug particles (Hickey
A.J. et al., 2007; Wu X. et al., 2010). Surface roughness,such as that associated with corrugated particles, is often
reported to increase surface dispersibility due to decreased
van der Waals forces (Chaurasiya B. and Zhao Y.-Y., 2021;
Wu X. et al., 2010). However, there is also a drawback
to surface irregularities, as they can promote interlocking
between particles (Wu X. et al., 2010). The effect of theseproperties must be evaluated to determine an optimal surface roughness and shape.
Bulk crystallinity properties are often evaluated using
X-ray powder diffraction analysis (XRPD). The resulting
diffractogram shows a series of peaks, corresponding tocrystalline structural features; the absence of such featuresindicates that the material is amorphous (Hickey A.J.,
2018b). Crystalline solids exhibit long-range molecularorder, whereas amorphous solids show no long-range
molecular order. Polymorphism refers to the ability ofa crystalline material to exhibit more than one crystal
system. To quantify the extent to which polymorphism ispresent, thermal analysis using differential scanning calorimetry (DSC) can be employed, as polymorphs exhibit
different melting points (Hickey A.J., 2018b). Due totheir lack of order, amorphous solids often exhibit greatersolubility, molecular mobility, and bioavailability, as well
as faster degradation kinetics (Wu X. et al., 2010). As aconsequence, amorphous materials readily take up small
amounts of water vapor. This moisture can induce solidstate phase transitions, chemical degradation, and physicalinstability due to capillary forces and can alter the bulk
density of the particles, surface charge, and aerodynamic
properties (Chaurasiya B. and Zhao Y.-Y., 2021; Hickey
A.J., 2016; Wu X. et al., 2010). As a result, it is necessary tomonitor moisture content using either Karl Fischer titrimetric analysis or thermogravimetric analysis (TGA) (Hickey
A.J., 2018b). Further, the FDA recommends evaluating theeffect of storage on moisture content, including storage at
25 °C/60 % relative humidity (RH) and 30 °C/65 % RH
(Lyapustina S., 2018; US-FDA, 2018). Lack of change inmoisture content is considered a measure of stability.

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2.3 Performance

Once the foundational quality of the formulation has
been established, the performance of the drug should beassured. However, the product must be assembled (formulation and device) and testing performed to establish
performance uniformity, as it cannot be assumed. Current
compendial standards include evaluating performance via
monitoring the aerodynamic particle size distribution of theresulting aerosol and delivered dose uniformity. However,additional methodologies have been proposed to increase
physiological relevance and thus clinical translation.

2.3.1 Aerodynamic particle size distribution

The aerodynamic particle size distribution (APSD) is theproperty that defines the performance of inhaled products.
As lung deposition is a function of the APSD, it is clearthat the proportion of the distribution in the desired size
range dictates the dose delivered, and thereby, safe andefficacious treatment (Hickey A.J., 2018b). Generally, it
is proposed that large particles (>5 µm) are deposited via
inertial impaction in the oropharynx and large airways.
Smaller particles (2–5 µm) are likely deposited in the
bronchioles by gravitational sedimentation, and the smallest particles (< 2 µm) are deposited by diffusion in theterminal bronchioles and alveolar region (Chaurasiya B.
and Zhao Y.-Y., 2021; Hickey A.J., 2016; Lee S.L. et al.,2009). By determining the APSD, particle deposition in thelungs can be speculated. However, it is important to notethat this is not a direct indicator of lung deposition, as themethodologies employed to determine APSD utilize a uniform flow rate rather than varying time-flow profiles suchas those exhibited in vivo (Mitchell J. et al., 2007; Mitchell
J.P. and Roberts D.L., 2013).
The APSD is determined by sampling the aerosol generated by the product using a calibrated cascade impactor.
Particles released from an inhaler device are subjected tochanges in flow direction under laminar conditions, where
the inertia of small particles causes them to stay in the flow
stream upon directional change and particles of a greater
size impact on the surface. Several stages are sequentiallyarranged in a cascade impactor, with each stage collecting
particles of a progressively smaller size (Mitchell J. etal., 2007). The US FDA accepted methods for APSD via
cascade impaction include the 8-stage Anderson cascade
impactor operated at 28.3 L/min, the 5-stage Marple-Miller
impactor operated at 60 L/min, and the 7-stage next generation impactor (NGI) with pre-separator in place operated
at 60 or 100 L/min (Frohlich E., 2019; US-FDA, 2018).
The cutoff diameters for each stage of the three listed
methods are presented in Table 1. The length of collection
should be adjusted based on the flow rate to allow for thecollection of 4 L of air (US-FDA, 2018). Of note, the flow
utilized for analysis via NGI affects the stage diameter
cutoffs, and this must be considered (Weers J. and Clark

Table 1 Stage cutoff diameters for common cascade impactor types. a

Stage Cutoff Diameter (D50) (µm)
Stage

Anderson
CascadeImpactor b

Marple-Miller
Impactor c

Next
Generation
Impactor cStage 0———Stage 1

9.0——

Stage 2

5.8

10.0

8.06

Stage 3

4.7

5.0

4.46

Stage 4

3.3

2.5

2.82

Stage 5

2.1

1.25

1.66

Stage 6

1.1

—

0.94

Stage 7

0.7

—

0.55

Filter

0.4

0.625

0.34

a Stage cutoff diameters are reported according to USP General

Chapter <601>. b Cutoff diameters are valid at a flow rateof 28.3 L/min. c Cutoff diameters are valid at a flow rate of
60 L/min.

A., 2017). Once the aerosol has been sampled, data can bedepicted as mass collected on each stage according to thecutoff diameter for that stage from calibration. Importantly,
the APSD can be presented as a mass distribution, where
mass relates directly to the dose of the drug and is themost relevant metric, rather than a number distribution(from SEM) or volume distribution (from laser diffraction)(Hickey A.J., 2018b).

2.3.2 Delivered dose uniformity

A separate technique is utilized to monitor delivered
dose uniformity (DDU) to minimize analytical errors resulting from dividing the dose into recovered fractions andsumming measurements, as would be done with cascade
impaction. Contrarily, DDU is established by samplingaerosol from the drug product into a Teflon tube with a filter
under vacuum (Hickey A.J., 2018b). The delivered dose, or
emitted dose, refers to the proportion of the nominal dosethat leaves the mouthpiece of the inhaler (Hickey A.J.,
2018b). This sampling technique will allow the amountdelivered to be measured, as well as the amount remaining
in the device or metering system (e.g., capsule, blister).
Importantly, as the resistance varies highly between inhaler
devices, the compendial standard specifies a 4 kPa pressure
drop rather than a specific flow rate. Further, the time ofcollection should be set to not exceed a 2-L collected volume at a constant flow rate (Hickey A.J., 2018b; US-FDA,2018).

2.3.3 Aerosol delivery rate

Dry powder inhalers are unique among pulmonary drug

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delivery devices in that the aerosol is administered on theinspiratory flow of the patient. Consequently, the releaseof the drug from the device depends on the response of
the formulation and device to the energy imparted by theinspiratory flow. The temporal response resulting fromthis sequence of events gives rise to the aerosol being
delivered at a particular rate with respect to the inspiratory
flow, which in turn, can affect the site of deposition in thelungs. Multiple studies have compared parameters suchas aerodynamic diameter and emitted dose as a functionof flow rate. For example, Louey M.D. et al (2006) compared the response of powder dispersion released from astandardized entrainment tube with a flow rate of 28.3 and60 L/min. The greater flow rate (i.e., 60 L/min) produced
increased delivered doses and fine particle fractions (i.e.,
percentage of particles < 5 µm) and decreased mass median aerodynamic diameters (MMAD) (Louey M.D. et al.,2006). Similarly, Coates M.S. et al. (2005) discovered that
powder dispersion and throat deposition increased withincreasing air flow. Recently, 25 healthy volunteers werechosen to use an inhaler to provide data for the development of idealized inspiratory waveforms. Using a numerical statistical model, Kugler Sz. et al. (2019) determinedthat peak inhalation flow and total volume inhaled werethe main factors that affect the efficiency of deposition. In
another study, flow rates of 30, 40, 60, and 90 L/min wereA

Photodiode (940 nm)

compared across multiple dry powder inhalers (Buttini F.
et al., 2016). Here, the differences found due to flow rate
were suggested to be inhaler dependent. NEXThaler® and
Diskus® inhaler devices were relatively unaffected by flow
rate, whereas the Turbohaler® demonstrated a large decrease in emitted dose when the device was operated at 30
or 40 L/min (Buttini F. et al., 2016). The specific resistanceof an inhaler is known to control the inspiratory flow rateand dispersion (Clark A.R. and Hollingworth A.M., 1993),
which likely causes these discrepancies between different
inhaler models.
Due to its important influence in APSD and lung deposition, the aerosol delivery rate can be studied using light
obscuration methods that are designed specifically forthis purpose or can employ existing methods such as laser
diffraction. Ziffels S. et al. (2015) reported a methodology
for monitoring aerosol release through a cascade impaction
inlet via light obscuration. Briefly, the inlet of the cascade
impactor was equipped with a clear tube, a photodiode(940 nm), and a phototransistor (Fig. 1). While air flowed
through the tube, reductions in voltage between the diodeand transistor resulted from obscuration by particles(Fig. 1A–D). It was determined that the aerosol transit timethrough the tube, amplitude of obscuration, and area under
the response curve were all influenced by shear conditions(i.e., 1.41 and 4.34 N/m2) and carrier (Fig. 1E–F). These

B

Photodiode (940 nm)

Phototransistor

Phototransistor

C

Photodiode (940 nm)

D

Photodiode (940 nm)

Obscuration (V)Phototransistor

Phototransistor

E

F

Time (s)Fig. 1 Schematic of obscuration of the phototransistor by the powder upon (A) initial generation, (B) entry to, (C) peak powder density in, and (D)departure from the optical sensing volume. Obscuration of the photodetector by albuterol sulfate delivered with lactose carrier with shear conditions of(E) 1.41 N/m 2 and (F) 4.34 N/m 2. Figure adapted with permission from Ref. (Ziffels S. et al., 2015). Copyright: (2015) Elsevier.

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same variables (i.e., shear conditions and carrier) did notaffect the MMAD (Ziffels S. et al., 2015). By only evaluating metrics of APSD and DDU, the role of shear conditions
and carrier would be lost, even though they may exhibit
an effect in vivo. In a different approach, de Boer A.H. etal. (2002) presented the use of laser diffraction to monitor
aerosol dispersion. When the apparatus was combined witha pre-separator to remove large carrier materials, dry powder inhaler formulations were able to be analyzed rapidly
with high accuracy and reproducibility. Importantly, it ispossible using this method to follow the size distribution in
the aerosol cloud as a function of time, facilitating aerosoldelivery rate measurements (de Boer A.H. et al., 2002). As
the rate of delivery is likely to affect the lung depositiondose as well as the site of deposition, these methodologies
allow for in vitro evaluation of aerosol delivery rate.

2.3.4 Physiologically relevant measures

Interest has increased in linking traditional quality
measures to predictions of lung deposition as it applies tobioequivalence. Initial observations found that the fine particle fraction of drug aerosol below 3 µm correlated to lungdeposition as monitored by gamma scintigraphy (Newman
S.P. and Chan H.-K., 2008). This method has since been superseded by sampling through anatomically correct inlets
to the cascade impactor and/or physiologically accurate
inspiratory flow cycles representing healthy and diseased
lung function.
The USP sampling inlet for a cascade impactor, as is
described in USP General Chapter <601>, was the firststandardized inlet and allowed for meaningful comparisons between labs. However, the USP sampling inlet is aright-angled tube that does not accurately mimic airway geometry (Kaviratna A. et al., 2019). Several approaches havebeen used to develop realistic mouth-throat models as asampling inlet to a cascade impactor, including geometries
based on cadaver casts or CT/MRI data or idealized geometries using critical airway dimensions (Newman S.P. andChan H.-K., 2020). Zhang Y. et al. (2007) reported a comparison on mouth-throat deposition from a DPI between the
USP sampling inlet, an idealized mouth-throat model, anda highly idealized mouth-throat model. Following analysis,
it was discovered that the USP inlet had the lowest mouththroat deposition (57.3 ± 4.5 %), showing that both theidealized and highly idealized models, whose depositions(67.8 ± 2.2 % and 69.3 ± 1.1 %, respectively) were muchcloser to the reported in vivo deposition (65.8 ± 10.1 %),improved accuracy of predicting in vivo deposition fromin vitro analyses (Zhang Y. et al., 2007). From this study,
it was realized that additional factors must be consideredwhen developing and testing mouth-throat models, namely
age, peak inspiratory flow, and disease state.
A study reported by Below A. et al. (2013) considered
the role of age on mouth-throat geometry. An idealized

pediatric upper airway model, approximating age 4–5, wasused to evaluate DPI performance comparing three inspiratory flow rates (28–75 L/min). High quantities of drug,
up to 80 %, were deposited in the airway model, facilitating potential pulmonary doses of 29 % and 8–12 % forEasyhaler® and Novolizer® devices, respectively (Below
A. et al., 2013). With a USP sampling inlet, it is likely thatthe potential pulmonary dose would be overestimated.
Dolovich M.B. et al. (2019) evaluated three mouth-throat
models specifically designed from CT scans of three COPD
patients using constant inspiratory flow rates of 30, 60,
90, and 120 L/min. In this study, anatomical differences
between the three patient models were found to be a major
source of variability in lung deposition. The lung dose foreach model was relatively consistent across all flow rates
with the exception of a slight decrease in delivery at thehigher inspiratory flow rates (Dolovich M.B. et al., 2019).
These reports highlight the influence of mouth-throat geometry on predicting lung deposition, describing the needfor more realistic sampling inlets than the current USP
standard. However, an additional factor not considered inthese evaluations includes the reality that the inspiratory
flow is not a constant value but instead increases and thendecreases in rate for each breath.
This dynamic nature of the inspiratory flow is likely
to affect lung deposition, necessitating the incorporationof time-dependent breathing profiles to current in vitro
analyses (Dorosz A. et al., 2020). In combining the rolesof anatomy and breathing profile, Olsson B. et al. (2013)
evaluated three throat models, including high, medium,and low filtering efficiency, and three inspiratory profiles.
The three profiles were chosen to represent the 5th, 50th,and 95th percentiles from 74 inspiratory flow patterns fromhealthy adults (Fig. 2B). For APSD measurements, themouth-throat model was utilized as a sampling inlet to acascade impactor, which was complete with a mixing inlet
connecting to both a pressurized air source (to maintain aconstant flow across the cascade impactor) and a breath
profile generator (to simulate an inspiratory flow cycle forthe inhaler) (Fig. 2A). Three DPIs were investigated, andit was determined that the mouth-throat geometry had asignificant effect for all devices, with smaller throat models facilitating less drug penetrating the model. Further,
the flow profile was found to significantly affect two outof three DPIs, where the amount of drug penetrating themodel, an estimate of potential lung deposition, was lower
for weaker flow profiles (Olsson B. et al., 2013). In anothercomprehensive analysis, Wei X. et al. (2018) comparedeight mouth-throat models, including small, medium, andlarge Virginia Commonwealth University (VCU) models,
small, medium, and large Oropharyngeal Consortium(OPC) models, a medium adult Alberta Idealized Throat,and a USP sampling inlet, as displayed in Fig. 3. In addition to the many models, weak, medium, and strong

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ComputerServomotor
Mouth-throat
model

Cylinder and piston

Inhaler
40 L/min

60 L/min20 L/min

Mixing inlet
NGI impactor

Pressurized air

60 L/min

B 100
Flow Rate (L/min)A

VacuumStrong80

Medium

60

Weak

40
20
0

0

1

2

3

Time (s)4

5

6

Fig. 2 (A) Schematic demonstrating mixing inlet, breath profile generator, and impactor setup. Piston movement allows for flow through inhaler as
set using the breath profile generator but holds flow in impactor constant. (B) Approximate flow profiles representing weak, medium, and strong flow.
Figure adapted with permission from Ref. (Olsson B. et al., 2013). Copyright (2013) Mary Ann Liebert Inc.

A

OPC-L

OPC-M

OPC-S

VCU-L

VCU-M VCU-S

AIT

USP

B

OPC-L

OPC-M

OPC-S

VCU-L

VCU-M

VCU-S

AIT

USP

Fig. 3 Realistic mouth-throat models developed for inhaler in vitro testing: OPC large (OPC-L), OPC medium (OPC-M), OPC small (OPC-S), VCU
large (VCU-L), VCU medium (VCU-M), VCU small (VCU-S), Alberta Idealized Throat (AIT), and USP sampling inlet. (A) side view; (B) internal
geometry. Figure adapted with permission from Ref. (Wei X. et al., 2018). Copyright (2018) Mary Ann Liebert Inc.

inspiratory flow profiles were included using a breath
simulator. Both the model geometry and flow condition had
significant effects on mouth-throat deposition; however,
the effect of flow condition was stronger than that of thegeometry. Consistent with previous reports, the USP throat
retained the least amount of drug (Wei X. et al., 2018). In
all described studies, it is noted that the mouth-throat geometry and inspiratory flow cycle affect mouth-throat andlung depositions. To enhance correlation between in vitro
analyses and in vivo outcomes, the incorporation of thesevariables into in vitro analyses is essential.

3. Data analysis

Following the collection of data through the methods
presented above, further analysis must be performed to extract meaningful conclusions. Further, analytical methods
for comparing these results between different formulations
and devices, such as in the case of preparing generic versions of brand-name drugs, must be implemented. These
necessities give rise to statistical descriptors for under-

178

standing the data and profile comparison techniques, suchas the chi-square comparison and multivariate statisticalanalysis techniques, as described below.

3.1 Statistical descriptors

Historically, cascade impactor data was described interms of population statistics. Each particle deposits according to its aerodynamic diameter, which is defined as aunit density sphere with the same terminal settling velocity
as the real particle according to Stokes’ Law. This aerodynamic diameter encompasses particle attributes including
shape, density, and physical size (Hickey A.J., 2004). However, particles exist in populations, powders, that can be
described in terms of a central tendency of the distribution
and its breadth. Many distributions conform to a log-normal
mathematical function and consequently can be describedby a median according to the mass deposited on each stage,
thereby giving rise to a MMAD. The MMAD also represents a degree of deaggregation, where smaller MMAD
values (i.e., decreased median aerodynamic diameter)

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represent greater particle deaggregation (Louey M.D. et al.,2006). As the MMAD is calculated using the logarithm ofparticle size in a geometric rather than arithmetic function,
the geometric standard deviation (GSD) represents thevariance of the distribution as a unitless dimension. The
relationship between GSD and MMAD is as follows:
GSD =

MMAD
d 84
=
=
MMAD
d16

d 84
d16

(1)

Where d16 represents the aerodynamic diameter one standard deviation below the median (at the 16th percentile) andd84 represents the aerodynamic diameter one standard devith
ation(1) above the median (at the 84 percentile) (Chaurasiya
B. and Zhao Y.-Y., 2021; Finlay W.H. and Darquenne C.,
2020; Hickey A.J., 2004). Aerosols with a GSD greaterthan 1.15 are considered to be polydisperse, where larger
values indicate greater levels of heterogeneity in the size
distribution (Chaurasiya B. and Zhao Y.-Y., 2021; PleasantsR.A. and Hess D.R., 2018). Most therapeutic aerosols
exhibit GSDs in the range of 2–3 (Pleasants R.A. and Hess
D.R., 2018).
Oftentimes, conversion of the APSD to something that
may indicate potential lung deposition is beneficial. In
these cases, the fine particle dose (FPD) and fine particle
fraction (FPF) are defined. While exact cutoffs vary, theFPD is generally referred to as the total dose of dry powder,in terms of mass, that is below 3–5 µm (Chaurasiya B. andZhao Y.-Y., 2021; Dunbar C.A. et al., 1998). The FPF normalizes the FPD to the total emitted dose (Pleasants R.A.
and Hess D.R., 2018). Further, this metric represents a degree of drug deaggregation, where a higher FPF indicatesgreater deaggregation (Louey M.D. et al., 2006).

3.2 Profile comparisons

While statistical descriptors as described above areuseful for data interpretation, the US FDA advises that it
is inadequate to characterize the APSD only in terms of
MMAD, GSD, and FPD/FPF (US-FDA, 2018), especiallywhen trying to compare profiles for bioequivalence determinations. As such, multiple techniques in comparing
APSD profiles have been investigated. The major goals of a
profile comparison test, as specified by the Product Quality
Research Institute (PQRI) and Orally Inhaled and Nasal
Drug Products Technical Committee (OINDP-TC) include:(1) the test is sensitive to differences at each impactor
deposition site; (2) the test is based on a single metric thatincorporates all differences at all sites to minimize thenumber of in vitro tests that must be performed; (3) the test
is applicable to all inhalation products; and, (4) the test is
independent of impactor type (Adams W.P. et al., 2007).
With these goals in mind, multiple approaches have beenpresented.

3.2.1 Stage-by-stage and stage-grouping comparison

The most straightforward method of profile comparisonsinvolves comparing the mass of drug deposited on eachstage individually or as groups of stages (EMEA, 2009;
Taki M. et al., 2011). This methodology is based on thepremise that by comparing only MMAD, GSD, and FPD/
FPF, changes to the distribution in the 1–5 µm range may
be overlooked in vitro but could affect the drug deposition site in the lungs. The European Medicines Agency(EMEA) specified that two products must be within 15 %of each other at each deposition stage, or at 4 justified
grouped stages, when tested at all flow rates to be considered equivalent (EMEA, 2009). While this method is
sensitive to differences in mass at each impactor stage, itdoes not provide a single metric for testing equivalence anddoes not allow for comparisons between different impactor
configurations or types.

3.2.2 Chi-square comparison

The chi-square ratio test does not ascribe meaning to
the data under consideration but does allow comparison of
two data sets which historically were considered the test
and reference profile. The chi-square statistic, calculated
as the sum of the squared differences in deposition ateach impaction site between the two profiles, scaled by
the average deposition on that site, provides a measure of
distance between two profiles (Adams W.P. et al., 2007).
The developed singular test metric is a comparison of thechi-square statistics characterizing both products (Adams
W.P. et al., 2007). However, both products must be tested
the same way with the same impactor configuration. This
test allows statistical inferences to be drawn but requires
subjective input to assure that meaning ascribed to theparticle size distribution (i.e., two profiles are the same), iscorrect by observation.
The PQRI working group conducted an assessmentin which they separated the impactor sized mass fromthe fine particle fraction and applied chi-square analysis.
Subsequently, experts in the field were asked to observe
the profile being compared to confirm that the chi-squareratio test was predicting similarity as would be defined bysimply comparing the data. The goal of this assessment
was to determine a critical value of the chi-square ratiothat would separate identical profiles from those that are
different (Adams W.P. et al., 2007). Unfortunately, it was
discovered that choosing a singular value to separate equivalent from different profile pairs was difficult (Christopher
D. et al., 2007b). Proposed critical values included 7.66
and 2.75. While 2.75 provided better discriminatory power,
it resulted in less consistency when compared to the judgment of experts in the working group. While a promising
method, no guidelines have been set at this time for evaluating using this test (Christopher D. et al., 2007a).

179

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Full APSD evaluations from a cascade impactor require time and resources that may not be necessary forformulation and device optimization as well as routine
quality control analyses. While these evaluations need tobe accurate and precise, it is highly desired that throughput
be increased compared to full cascade impactor and data
analyses (Tougas T.P. et al., 2011). Two concepts have been
introduced to expedite data acquisition and subsequent
analysis: the abbreviated impactor measurement (AIM)concept and the efficient data analysis (EDA) concept.
The AIM concept involves altering the configuration of
the cascade impactor to streamline analysis. Rather thanprovide each stage’s measurement, unnecessary stages

180

A

B
Mass
Particle Diameter (µm)

C

Particle Diameter (µm)

D
Mass

3.3 Efficient data analysis

are removed depending on the goal of the analysis. For
example, a configuration for quality control (AIM-QC) tomonitor the MMAD includes a sampling inlet and initialstage, an intermediate stage near the MMAD, and a final
filter. The intermediate stage as a boundary should split thepowder in half to a small particle mass (SPM) and large
particle mass (LPM). If the SPM and LPM portions do noteach contain 50 % of the impactor-sized powder mass, it
can be determined that the MMAD has shifted (Tougas T.P.
et al., 2011). A different configuration has been proposedfor predicting human respiratory tract deposition (AIMpHRT), containing two intermediate stages in addition toa sampling inlet and final filter. With this method, the firststage sets a boundary at approximately 5 µm, where themass deposited after that stage is referred to as the fine particle mass, or FPM. The second stage is set at a boundary of
approximately 1 µm, where the mass of particles deposited
after that stage denotes the extra-fine particle mass (EPM)(Tougas T.P. et al., 2011). Quantification of the EPM may
be desired as this portion may be related to systemic absorption or may be exhaled before deposition can occur,
especially if one’s breath is not held following inhalation(Tougas T.P. et al., 2011). However, at a minimum for the
AIM-pHRT configuration, it is necessary to distinguish theFPM, which describes the mass of particles with the potential to carry drug into the respiratory tract, from the coarse
particle mass, which will likely not penetrate past theoropharyngeal region (Mitchell J.P. and Tougas T.P., 2013).
Efficient data analysis represents a way to simplify AIMdata as well as full-resolution cascade impaction data. Two
EDA metrics are defined: (1) the large and small particle
mass (LPM + SPM), and (2) their ratio (LPM/SPM)(Tougas et al., 2011). These metrics signify the amplitudeof the mass distribution profile and the position of the

Mass

Cascade impaction data is complicated by the fact that
deposition on each stage is accompanied by variability
from each replicate, while the distribution itself represents
information on the variability of particle sizes, carrying
the consequences for the behavior of the aerosol when
inhaled. Multivariate analysis techniques, such as principal
component analysis (PCA), allow the simplification of data
while retaining as much information as possible. PCA is anorthogonal linear transformation that transforms the data
to a new coordinate system, where the largest variance isaccounted for on the first coordinate, the second greatest
variance is accounted for on the second coordinate, and soon (Christopher J.D. et al., 2013). This process reduces thenumber of variables that must be considered by developing
principal components through a purely statistical model.
The threshold for equivalence using this method is related
to a specified confidence interval (Christopher J.D. et al.,2013).
Another method based on multivariate analysis involves
orthogonal partial least squares analysis (OPLS). While
PCA is an unsupervised statistical method and is generally
used as a pattern recognition tool prior to OPLS, OPLS itself is a supervised method (Shi S. and Hickey A.J., 2009).
Importantly, OPLS is not a statistical test but is an analytical
tool. The resulting OPLS score plot provides a visual representation of the comparison between a test and reference
profile. The larger the visual difference, the further they areseparated based on principal components. However, this isa qualitative test; to derive a semi-quantitative parameter,
R2 can be employed. Two identical profiles would give an
R2 value of zero whereas two completely different profiles
would give an R2 value of one (Shi S. and Hickey A.J.,
2009). The defined equivalence parameter as reported by
Shi S. and Hickey A.J. (2009) is termed Eq, where one subtracted by the R2 value gives this measure of equivalence.
While not a purely statistical method, this proposed metricallows for semi-quantitative analysis when comparing two
APSD profiles.

Mass

3.2.3 Multivariate analysis

Particle Diameter (µm)

Particle Diameter (µm)

Fig. 4 Changes in aerodynamic particle size distribution. (A) Nominal
APSD, (B) shift in area under the curve (C) shift in central tendency(i.e., mean), and (D) shift in both mean and area. Figure adapted withpermission from Ref. (Tougas T.P. et al, 2009). Copyright (2009)
Springer Nature.

Sara E. Maloney et al. / KONA Powder and Particle Journal No. 40 (2023) 172–185

profile on the size scale and can detect changes in position
or total area independently of each other (Fig. 4) (Tougas
T.P. et al., 2009). When EDA is combined with an AIM
approach, the time per measurement is drastically reduced,
increasing throughput, and sensitivity to APSD shifts isenhanced (Mitchell J.P. and Tougas T.P., 2013). A generalrecommendation proposed by Mitchell J.P. and Tougas T.P.
(2013) includes full resolution cascade impaction for initial
screening and developing, AIM-QC for quality control
measures on a smaller list of candidates; and AIM-pHRT
with an anatomically correct sampling inlet for in vitro
equivalence comparisons.

4. Data interpretation

The implications of data obtained from in vitro testingfor safety and efficacy in vivo is an important potential
application, but so far, a true in vitro/in vivo correlation has
been difficult to establish. The following sections describe
some considerations that have been given to this topic andillustrate the significance that success in this endeavorwould have for pharmaceutical product development.

4.1 Inhaled biopharmaceutical classification
systemThe gastro-intestinal biopharmaceutical classification
system (giBCS), which defines the desirability of developing a drug for oral delivery based on its solubility andpermeability, has been a working guide for drug discovery
and delivery since the 1990s (Hastedt J.E. et al., 2016).
The approach is usually depicted as a quadrant schematicin which drugs that are highly soluble and permeable are
logical candidates for further development, drugs that have
low solubility and high permeability, or vice versa, may
be considered candidates for formulation development toimprove their properties, and those with low solubility andpermeability are considered poor candidates for further
development (Hickey A.J., 2018b). Using these criteria,
candidate selection can be addressed early in the discovery
process where many analogues are available for consideration.
In 2015, a workshop cosponsored by the American
Association of Pharmaceutical Sciences (AAPS), US FDA,
and USP was held to address the possibility of developing
an inhaled biopharmaceutical classification system (iBCS)(Bäckman P. et al., 2022; Hastedt J.E. et al., 2016; 2022).
It is important to recognize that the majority of inhaledpharmaceutical agents are intended for localized action.
Consequently, permeability as defined for the giBCS may
not be relevant. In addition, unlike the gastro-intestinal
tract that is essentially a tube through which substancespass by entry and exit, the lungs are a closed container
in which clearance mechanisms are required to remove
any deposited substances. These differences suggest thatalternate or additional metrics must be adopted whendeveloping an iBCS. Considerations for developing an
iBCS include lung physiology, regional aerosol deposition,clearance mechanisms, particle dissolution, permeability,
and absorption (Bäckman P. et al., 2022; Frohlich E., 2019;
Hastedt J.E. et al., 2016; 2022). From this workshop, it
was determined that there is an opportunity in creating thismodel, and additional work on this topic is forthcoming.

4.2 Bioequivalence and in vitro/in vivo correlation

The goal of predicting in vivo deposition from in vitro
data has been an objective of pharmaceutical scientists
for decades. With the significant increase in new products
over the last 20 years and the desire to bring forward generic versions, this objective has taken on urgency. Whenperforming a comparison for bioequivalence, the goal is todemonstrate equivalent performance (i.e., not better, notworse) (Lyapustina S., 2018). However, the complexityof aerosol dosage forms makes them difficult to mimic
and raises many questions about what defines equivalence(Adams W.P. et al., 2010; Apiou-Sbirlea G. et al., 2013). In
oral formulations, the main way to analyze bioequivalenceis through serum concentration over time; however, withinhaled formulations, the lung is the target and systemic
absorption is typically not desired. One metric that has
been suggested for inhaled drug bioequivalence studies
is to look at the concentration of drug in exhaled breathcondensate (Khoubnasabjafari M. et al., 2019). Though
no direct guidance has been placed forward as of yet for
inhaled therapeutics bioequivalence comparisons (ApiouSbirlea G. et al., 2013), profile comparisons based on
cascade impactor data have garnered the most interest, as
described above. However, regulatory statures for comparing inhaled drugs for bioequivalence both in vitro and invivo are urgently needed.
From the foregoing discussion, it can be seen that invitro methods have evolved from standards that would establish therapeutic aerosol quality in terms of accuracy andreproducibility of performance to physiologically relevant
methods that would produce data predictive of lung deposition and disposition. Current compendial in vitro standards
have not consistently correlated with pharmacokinetics andefficacy in vivo (Hickey A.J., 2018b). However, if physiologically relevant advances can be made to these standards,
then there is the possibility that in vivo studies can be limited to the first instance of drug testing and perhaps, in the
future, eliminated as a requirement for regulatory approval.
For dry powder inhalers, physiologically relevant measures of APSD and DDU would seem to be the first step
towards in vitro/in vivo correlation (IVIVC). The goalof IVIVC is to construct a model that can be used to
predict in vivo outcomes, such as lung deposition andperipheral-to-central deposition ratio, from in vitro cascade impactor data (Chow M.Y.T. et al., 2021). In vivo,
peripheral-to-central deposition ratios have been shown to181

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qualitatively deviate between healthy patients and thosewith asthma or COPD, where patients with lung diseases
exhibit slightly less drug deposition in the lung periphery. It
was also proposed that differences in peripheral-to-central
deposition ratios in vivo may be due to intersubject variability caused by differences in airway dimensions, which
occurred to a stronger extent than variability resulting fromflow rate or delivery modality (Clark A.R., 2012). As such,
additional parameters incorporated within cascade impaction analysis, such as physiological throat models and representative inspiratory flow cycles of healthy and diseased
individuals, may prove beneficial in such correlations. As
noted above, since the point at which the aerosol is dispersed on the inspiratory flow may also affect deposition, ameasure of the aerosol delivery rate should be considered
as an additional in vitro analysis for the prediction of invivo outcomes.

5. Conclusions

Pharmaceutical inhalation aerosols are widely used totreat diseases, notably asthma, COPD, infectious diseases,
and rare diseases, such as cystic fibrosis. Dry powder
inhaler products represent a significant proportion of themarket. To assure safety and efficacy of these products,
their quality and performance must be assured in terms of
the accuracy and reproducibility of drug delivery. Since
the effective dose depends on efficient aerosol generationand deposition in the lungs, properties that guarantee theseoutcomes must be measured. Standardized methods havebeen available for decades to define properties that establish quality, but recent efforts have focused on rendering
these quality measures relevant to in vivo deposition andlink to safety and efficacy. As these methods emerge, therole of compendial methods and regulatory guidance may
progress towards in vivo relevance. Ultimately, this may
support considerations of an inhaled biopharmaceuticalclassification system, which can be extrapolated to bioequivalence testing in a manner similar to drugs deliveredby the gastro-intestinal route of administration.

Acknowledgements

The authors would like to thank the RTI Fellow Program
for their financial support.

Nomenclature
AAPS
AFM
AIM
API
APSD
CF
COPD
CT

182

American Association of Pharmaceutical Sciences
Atomic force microscopy
Abbreviated impactor measurement
Active pharmaceutical ingredient
Aerodynamic particle size distribution
Cystic fibrosis
Chronic obstructive pulmonary disease
Computed tomography

DDU
DPI
DSC
EDA
EP
EPM
Eq
FPD
FPF
FPM
giBCS
GSD
iBCS
ICH
IGC
JP
LPM
MDI
MMAD
MRI
NGI
OINDP
OPC
OPLS
PCA
PQRI
RH
SEM
SPM
TGA
US FDA
USP
VCU
XPS
XRPD

Delivered dose uniformity
Dry powder inhaler
Differential scanning calorimetry
Efficient data analysis
European Pharmacopeia
Extra-fine particle mass (µg, mg)
Equivalence parameter
Fine particle dose (µg, mg)
Fine particle fraction (%)
Fine particle mass (µg, mg)
Gastro-intestinal biopharmaceutical classification
systemGeometric standard deviation
Inhaled biopharmaceutical classification system
International Conference on Harmonization
Inverse gas chromatography
Japanese Pharmacopeia
Large particle mass (µg, mg)
Metered dose inhaler
Mass median aerodynamic diameter (µm)
Magnetic resonance imaging
Next generation impactor
Orally inhaled and nasal drug products
Oropharyngeal Consortium
Orthogonal partial least squares analysis
Principal component analysis
Product Quality Research InstituteRelative humidity (%)
Scanning electron microscopy
Small particle mass (µg, mg)
Thermogravimetric analysis
United States Food and Drug Administration
United States Pharmacopeia
Virginia Commonwealth University
X-ray photoelectron spectroscopy
X-ray powder diffraction

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Authors’ Short Biographies
Sara E. Maloney
Dr. Sara Maloney is currently a postdoctoral fellow at RTI International in Research Triangle
Park, NC. She obtained her B.S. in chemistry and forensic chemistry from Towson University and
her Ph.D. in analytical chemistry from the University of North Carolina at Chapel Hill under the
direction of Dr. Mark H. Schoenfisch. Her current research interests include therapeutic aerosol
formulation, characterization, and delivery as well as development of antibacterial biomaterials forlung infections.

Jeffrey B. Mecham
Dr. Mecham is a Research Chemist in RTI International’s Center for Engineered systems, in theTechnology Advancement and Commercialization Division. In this capacity, he is responsible forthe design and synthesis of monomers, polymers, and nanoparticles (at the micro- and nano-scale)with targeted performance capabilities for a wide array of applications. His team are contributors to
the Center’s successful development of particle-based controlled release technologies for triggered
release of therapeutics and chemistries for environmental remediation. The development of suchmaterials over the last several years at RTI has resulted in IP that has been licensed by interested parties for further development and subsequent commercialization of particles with controlled releaseproperties.

Anthony J. Hickey
Dr. Hickey is a Distinguished Fellow at RTI International, Professor Emeritus of Pharmacoengineering and Molecular Pharmaceutics, and Adjunct Professor of Biomedical Engineering at the
University of North Carolina at Chapel Hill. He obtained Ph.D. and D.Sc. degrees in pharmaceuticalsciences from Aston University, Birmingham, UK. He is a Fellow of the American Association for
the Advancement of Science, the American Association of Pharmaceutical Scientists, the Royal
Society of Biology, and the Royal Society of Medicine, and member of the Pharmaceutical Dosage
Forms Expert Committee (DFEC) and Aerosols Sub-Committee of the DFEC of the United States
Pharmacopeia. Dr. Hickey is a highly published (over 300 papers) and cited author having edited or
authored 16 books on pharmaceutical aerosols, particulates, manufacturing and complex systems.
He holds over 25 patents and conducts multidisciplinary research programs in the field of pulmonary
drug and vaccine delivery for the treatment and prevention of a variety of diseases.

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Review Paper

Environmentally Friendly Green Synthesis of Fine Particles by
Dry Mechanical Processes Toward SDGs: A Review†
Tomohiro Iwasaki
Department of Chemical Engineering, Osaka Metropolitan University, Japan

Abstract
The United Nations Sustainable Development Goals (SDGs) have attracted much attention due to increasing interestin global social, energy and environmental problems. The immediate contribution to the achievement of the SDGs
is required in all individual and industrial activities. The industries dealing with fine particles are also no exception
to the demand and have actively worked on the contribution. Conventionally, fine particles have been industriallysynthesized in gas, liquid and solid phases using top-down and bottom-up approaches. Among them, dry synthesis
processes are promising from a viewpoint of the SDGs due to the solvent-free and simplicity, which can lead toenergy saving, efficient resource utilization, waste management, etc. This review describes dry mechanical processes
for synthesis of fine particles, particularly focusing on grinding and mechanochemical treatment.
Keywords: SDGs, particle synthesis, dry grinding,mechanochemically assisted method

1. Introduction

1.1 Spread of SDGs

The United Nations Sustainable Development Goals(SDGs) consisting of 17 goals with 169 targets require
to solve serious global problems relating to, for example,
health, education, safety and human rights as well as
resource, energy and environment. The SDGs have been
widely spread through various media and many people
recognize the importance. At present, contributions to
the achievement of the SDGs cannot be avoided in all
human activities ranging from individual to global level. In
particular, business activities considering the SDGs are becoming more active according to corporate image-building(branding) strategy which can affect the sales of products
due to high consumer recognition. Under such circumstances, we frequently see and hear the SDGs everywhere.

1.2 SDGs in powder processing

mechanicalactivation,mechanochemical

effect,cesses by applying various advanced technologies (Fig. 1).
Examples of possible future contribution of powder processes are listed in Table 1. In Fig. 1 and Table 1, 9 goals
which are expected to be achieved in the near future are
selected as an example. Some powder processes and products considering the SDGs have been reviewed: e.g., food
(Bartolucci et al., 2020), pharmaceutical (Assali and Zaid,
2022), agricultural material (Swoboda et al., 2022), catalyst(Nawaz et al., 2021), antibacterial material (Bhardwaj etal., 2021; Parmar et al., 2022), environmental improvement material (Nnadozie and Ajibade, 2020; Rehman etal., 2021). The use of fine particles in the processes shows
many merits such as uniformity and reactivity; however,some serious demerits such as safety, labor environment
and air pollution are latent. Although attempts for removing
the demerits have been conducted, there are still few cases
emphasizing clearly the contribution to the achievement of

Powder handling and processing, such as storage,
conveying, mixing, reaction, crystallization, grinding,compaction, granulation, classification, separation, andsurface modification, are also required to work diligently
on the contribution to the achievement of the SDGs without
exception. To do so, conventional powder processes may
be modified to eco- and environment-friendly (green) pro-†

Received 29 June 2022; Accepted 22 July 2022
J-STAGE Advance published online 3 September 2022
Add: 1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan
E-mail: tomohiro.iwasaki@omu.ac.jp
TEL: +81-72-254-9307 FAX: +81-72-254-9911

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Fig. 1

Contribution of powder processes to SDGs.

Copyright © 2023 The Author. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Tomohiro Iwasaki / KONA Powder and Particle Journal No. 40 (2023) 186–196
Table 1 Examples of possible future contribution of powder processes.

Goal (Target)

Example2(2.1, 2.4, 2.c)

Improvement of productivity, processability and functionality of foods
Stabilization of foods for long-term storage
Development of functional fertilizers withhigh performance3(3.8, 3.9)

Development of functional pharmaceuticals with high performance
Improvement of productivity and stabilityof pharmaceuticals
Environmental purification

6(6.3, 6.a)Purification and conservation of water
environment
Treatment of waste and polluted water
Recycling and reuse of water

7(7.2, 7.3, 7.a)Generation of clean energy (hydrogenproduction)
Energy saving

8
(8.3)

Improvement working environment

9(9.4, 9.5, 9.b)

Effective use of resources

Reduction of working hours
Innovation in powder processing technologies
Development of processes for synthesis offunctional particles

12(12.2, 12.4, 12.5)

Effective use of fossil fuels by energy
savingReduction and utilization of wastes

14(14.1, 14.3)

Removal of marine pollutants

15(15.3)

Restraint of expansion of cultivated acreage by improvement of food production
efficiency

Improvement of ocean acidification byimmobilization of atmospheric CO2

the SDGs; the technology development in this field seems
to fall behind. Therefore, various improvements can bemade in the powder processes. In particular, energy saving
and/or process simplifying should be immediately realized
in all processes, which can lead to the reduction of process
costs and environmental impacts.

1.3 Contributions in fine particle synthesis
1.3.1 Bottom-up approach

In general, fine particles can be synthesized by various
wet and dry processes using bottom-up (build up) and topdown (break down) approaches. In bottom-up methods,
monomers form via assembly of atoms and molecules andthen particles are obtained by growing them. The formation
process is described by the LaMer diagram. The particle
synthesis processes are operated in gas, liquid and solid

phases, and the particle formation progresses according
to almost the same mechanism regardless of the typeof phases. However, the growth rate of particles greatly
varies in the phases depending strongly on the diffusion
velocity of atoms and molecules. When the growth rate ishigher, the productivity of particles also becomes larger; incontrast, the control of particle size distribution is difficult.
Usually, the bottom-up processes using gas (Kerminen
et al., 2018; Khitab et al., 2018) and liquid (Jadoun et al.,2021; Khitab et al., 2018; Mastalska-Popławska et al.,2020; Shaikh et al., 2021; Wichaita et al., 2019) phases
are employed. However, liquid-phase methods have someweak points: e.g., additional post-treatments, such as
solid-liquid separation, liquid waste treatment, drying anddisintegration of aggregates, which leads to the increasein the energy consumption and process cost. Accordingly,
gas-phase methods without any post liquid treatments,
such as physical vapor deposition (PVD) and chemical
vapor deposition (CVD) with heating by chemical flame,
plasma, laser and high frequency induction, may be slightly
advantageous, although they are low versatility and needhigh temperatures. In any case, bottom-up processes seemto be unsuitable to the contribution to meeting the SDGs.
However, as technologies of measurement and analysis
progress, dry mechanochemical processes using solid-state
reactions have been recognized as a useful bottom-up
method.

1.3.2 Top-down approachOn the other hand, in top-down methods, the raw feed isbroken (ground) into small pieces by applying the mechanical energy. Powder grinding has been traditionally used inhome and industries due to the simple operation. Althoughtechnical problems in grinding, such as the mechanicalwork (energy) for obtaining fine particles with a desired
size, the prediction and control of the size distribution of
products, and the analysis of grinding kinetics, have beenstudied, they are still not completely solved. For contributing to the SDGs, such problems must be solved by using
new technologies (e.g., machine learning).
The grinding processes are classified roughly into dry
and wet methods and various types of machines are used
for dry and wet grinding. The particle formation mechanism is complicated regardless of the methods because
the particle breaking is caused by various kinds of loads
induced by compression, tension, shear, bending, friction,and so on. However, the grinding processes have somemerits which can lead to the contribution to meeting theSDGs: e.g., simple process, high throughput, low cost,etc. The particle breaking occurs according to two mechanisms: volume grinding and surface grinding. For producing fine particles, the latter is effective. In dry grinding
processes, newly formed fine particles tend to aggregate
each other during grinding. When the particle size reaches

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Fig. 2

Overview of connections between dry processing and SDGs.

a few micrometers, the grinding can often be in an apparent
equilibrium state. Further size reduction often requires wet
grinding, which can provide nanometer-sized particles.
For the production of dry powder, however, dry grinding
processes do not need additional post-treatments, such as
solid-liquid separation and drying, and are conventionallyrecognized as a green process with low environmental
impact. Therefore, dry grinding is contributable to theachievement of the SDGs (Fig. 2).

1.3.3 Dry fine particle synthesis

For bulk synthesis of fine particles under dry conditions,
not only dry grinding as a top-down method but also drymechanochemical process as a bottom-up method are aneffective choice. In both cases, the improvement of energy
consumption and productivity is a very important problem
and the efficiencies must be sufficiently increased. In addition, the functionalization and/or enhancement of properties should be made through the processes. These progress
may contribute to many SDGs.
There are still few papers describing dry particle synthesis with an emphasis on the contribution to meeting the
SDGs. This article reviews recent studies on dry mechanical processing for particle formation, which pay attention
to SGDs and are published mainly in the last decade. In
particular, dry grinding and mechanochemical processes
are focused.

2. Dry grinding process

2.1 Relationship between dry grinding and SDGs

Grinding (including comminution and pulverization)of particulate materials under dry conditions have been
conventionally used in many industrial fields for various
purposes such as enhancement of the reactivity due toincrease in the specific surface area and improvement of
the processability and handling. Conventional dry grinding
processes with older technologies can often consume large
energy and cause the reduction of the productivity and the188

discharge of large amounts of waste. Technological innovation in the dry grinding can solve the serious problems.
Therefore, recent dry grinding processes substantially contribute to meeting SDGs, in particular, waste management.
Soni et al. (2022) mentioned a role of grinding process insolid waste management for effective utilization of mineral
resources waste as building construction materials in India.
Many researchers have also devoted to the reduction ofenergy consumption in the grinding processes. For increasing the energy efficiency leading to achievement of SDGs
7, 9, 12 and 13, García et al. (2021) analyzed in detail the
results of the Bond grindability test to elucidate the influence of particle size distribution of feed and ground products on the Bond work index using an experimental design
approach. As a result, the mesh size of a sieve used toclose the grinding circuit and the 80 % passing size of feed
were found to be the important parameters via the ANOVA
test. As a contribution to SDGs relating to food problems,Raheem et al. (2022) refer to the grinding of corn, which isone of the most important crops in the world and is used for
not only human food and animal feed but also bioethanol
production, for the added value enhancement.

2.2 Possible contribution of dry grinding to SDGs

Dry grinding processes have also been employed
traditionally in mining industries (e.g., beneficiation).
Recently, for example, Chelgani et al. (2019) investigated
the grinding effects on the energy consumption in the mineral flotation separation. They found that the dry grinding
mechanically activated the particle surfaces and increased
the floatability, which can reduce the energy consumption
during flotation separation. In various food industries,
dry grinding processes have been widely used, and somereview articles relating to SDGs have been published: e.g.,superfine grinding (Gao et al., 2020), fruit and vegetablepowders (Karam et al., 2016), moisture content effect (Junget al., 2018). It is expected that technological progress on
dry grinding in food industrial fields can contribute directly
to meeting Goal 2: Zero Hunger.
For the achievement of SDGs, the improvement ofenergy consumption in the dry grinding processes issignificantly important. Many researches relating to theanalysis of energy consumption discuss the energy efficiency using the specific grinding energy and/or electric
power consumption (Altun et al., 2015; Altun et al.,2019; Blanc et al., 2020; Katircioglu-Bayel et al., 2020;
Ngamnikom and Songsermpong, 2011; Ogonowski et al.,2018; Tamura et al., 2017; 2020). In particular, Prziwara,
Kwade and co-workers (Prziwara et al., 2018a; 2018b;
2018c; 2019; 2020a) have actively studied in detail theeffects of grinding aids on the grinding efficiency in dry
fine grinding processes. They carefully analyzed the obtained results focusing on the energy consumption and thesurface energy of particles, and give us useful guidelines

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Fig. 4 Effect of changing ball diameter and number of balls on
variation in specific dissipated energy with particle breakage stage.
Reprinted with permission from Ref. (Hirosawa and Iwasaki, 2021).
Copyright: (2021) Elsevier.
Fig. 3 Snapshots of behaviors of particles and balls mimicking various
stages of particle breakage at different values of ball-to-particle mass
ratio (BPR) (ball diameter = 10 mm). Reprinted with permission from
Ref. (Hirosawa and Iwasaki, 2021). Copyright: (2021) Elsevier.

for the selection of grinding aids. For example, they used
the relative fineness increase, defined as the ratio of thespecific surface area of product obtained with a grinding
aid to that obtained without any grinding aid, as an index of
the progress of grinding (Prziwara and Kwade, 2020b). The
fineness increase was correlated with the energy consumption, which expressed quantitatively the difference of thegrinding aid effects between amine-based and glycol-based
additives. Therefore, their research results can contribute to
reduction of the grinding energy consumption.
The work index (Chipakwe et al., 2020) and the impact
energy of media calculated using the simulation of the
behavior (Burmeister et al., 2018) have sometimes beenutilized as the numerical index. Hirosawa and Iwasaki(2021) numerically analyzed the dissipated energy of particle in a planetary mill using the discrete element method(DEM) simulation of the behavior of the particles and balls
to study the dependence of effective grinding energy on theconditions and the progress of grinding (i.e., particle size
reduction) (Fig. 3). They found that using an appropriate
size and number of balls in accordance with the stages ofgrinding is effective for precisely controlling the dissipatedenergy of particles even when the particle size decreased as
the progress of grinding (Fig. 4). Therefore, the numericalsimulation can give us the information relating to optimization of dry grinding processes.
In addition to above, some unique studies which can potentially contribute to meeting SDGs have been reported:e.g., analysis of grinding processes (Bu et al., 2019; 2020;
Krauze et al., 2021; Nava et al., 2020; Prziwara et al.,2020a; Toraman, 2013), characterization and utilizationof ground products (Arsoy et al., 2017; Choi et al., 2016;
Gomez et al., 2019; Guzzo et al., 2019; 2020; Letelier et al.,2019; Liu et al., 2015; Makuza et al., 2021; Okazaki et al.,2022; Peltoniemi et al., 2020; Yang et al., 2021). However,
the grinding mechanism considerably changes dependingon the type of grinding machine, which makes it difficult
to evaluate the energy efficiency using a unified standard.
The standardization of the evaluation method is required
for further contributions.

3. Dry mechanochemical process
3.1 Outline

Generally, in dry grinding of particulate materials, theraw feed receives the mechanical energy induced by shear,compression, impact and friction, and then is ground into
finer particles. At this moment, new particle surfaces form,of which the structure differs from that of the inside. By
applying further the mechanical energy to the particles, thechemical bonding state of atoms microscopically changes(e.g., partial breaking), which can lead to increase in the
disturbance of crystal lattice (lattice defect and latticemismatch). Simultaneously, the plastic deformation energy
can be accumulated in the particles. Such unstable state of
the particles leads to a chemically activated state, which isso-called mechanical activation. This can result in various
physicochemical changes, such as increase in the surface
energy of particles, formation of radicals, adhesion andaggregation of particles, solid-phase reaction. In addition,
this may cause chemical interaction between the particles
and surrounding materials, i.e., mechanochemical reaction(Masuda et al., 2006). Mechanochemical effects are confirmed in many materials such as inorganic and organic
materials and metals and can be practically applied to
not only material synthesis but also material separation,

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resource processing and recycling. For example, the mechanochemical synthesis of porous materials was reviewed
by Szczęśniak et al. (2020). The mechanical activation has
been utilized for the reduction of reaction temperature ofbiochar (Tayibi et al., 2021) and the stabilization of heavy
metals in fly ash for extraction control, i.e., reduction of
the metal toxicity (Yuan et al., 2021). Mechanochemicaltreatments of natural and artificial resources can enable
the recovery of valuables under mild conditions and thesimultaneous grinding can act effectively as a pretreatmentof the subsequent material processing.

3.2 Relationship between dry mechanochemicaltreatment and SDGs

Mechanochemical treatments have conventionally beenused as an effective and powerful tool mainly for synthesisof materials in anticipation of local high-temperature andhigh-pressure environments, induced activation of reactants, rapid reaction, etc. In particular, dry (solvent-free)mechanochemical processes have been tried to apply tovarious inorganic and/or organic systems due to its simplicity and room-temperature operation. Many remarkable
results of dry mechanochemical synthesis have been
produced so far. Furthermore, a lot of articles reviewing
various dry mechanochemical processes have also been
published. Recently, Dong et al. (2022) review recent
findings and potential applications of mechanochemical
processes in the fields of environmental governance andenergy storage, such as treatments of toxic compounds andrecovery of valuables from energy storage materials. Andréel al. (2022) introduce the mechanochemical applications
growing in Portugal, such as crystalline compounds ofpharmaceuticals and bio-inspired metal-organic frameworks, ionic liquids, and liquid crystals. Kumar et al.
(2020) present useful scientific knowledge on value-added
recycling of biomass wastes through the mechanochemical
synthesis of biochar-based materials via various ball milling treatments, which can contribute to the realization ofcarbon neutral society.
Dry mechanochemical syntheses of functional materials
with high performance considering SDGs have been reported in recent years. For example, Danielis et al. (2021)
synthesized the methane oxidation catalyst for natural gas
fueled vehicles by a dry mechanochemical process using aplanetary ball mill. The mechanochemical treatment may
result in the formation of the particular structure suitable tohigh catalytic activity due to promotion of the surface oxygen mobility. The mechanochemically prepared catalyst
showed excellent catalytic performance for simultaneous
NOx reduction and CH4 oxidation (Fig. 5). Sharma et al.
(2022) used a software tool for evaluating greenness ofsome mechanochemical processes with different millingmechanisms for synthesis of nitrofurantoin from a standpoint of the 12 Principles of Green Chemistry (Anastas and

190

Fig. 5 Synthetic reactor results with full gas mixture at 823 K for the
2-modes aged Pd/CeO2 catalyst sample (solid line, full symbols) andthe 4-modes aged commercial Pd-based three-way catalyst (dotted line,
open symbols). Reprinted with permission from Ref. (Danielis et al.,2021). Copyright: (2021) American Chemical Society.

Fig. 6 Comparisons of greenness of batch vs continuous mechanochemical processes for the preparation of nitrofurantoin. Reprinted withpermission from Ref. (Sharma et al., 2022). Copyright: (2022) American
Chemical Society.

Warner, 1998). They show interesting results that a shaker
mill and a twin-screw extrusion exhibit increased energy
efficiency, improved resources use, and reduced humanand environmental hazards compared to other commercial
processes, which was determined based on the evaluation
scores (Fig. 6). The difference in scores is mainly due tothe difference in time of synthesis and in the product yield.
This method may be useful for the development of greener
mechanochemical processes.

3.3 Possible contribution of dry mechanochemicaltreatment to SDGs

Zhang et al. (2022) developed the mechanochemicalsolid-state cross-coupling of amides by nitrogen–carbon
bond activation (Fig. 7), which is conducted under solventless conditions without external heating for short reaction
time. The merit of the method is avoidance of potentiallyharmful solvents, which can lead to minimizing wastes
and reducing environmental impacts. This mechanochemical cross-coupling can be applied to the synthesis ofTomohiro Iwasaki / KONA Powder and Particle Journal No. 40 (2023) 186–196

Fig. 7 General manifold of amide bond activation (A) and the solid-state mechanochemical N–C(O) activation (B). Reprinted with permission from
Ref. (Zhang et al., 2022). Copyright: (2022) John Wiley and Sons.

Fig. 8 Application to the synthesis of pharmaceutical intermediates. Reprinted with permission from Ref. (Zhang et al., 2022). Copyright: (2022)
John Wiley and Sons.

pharmaceutical intermediates (Fig. 8). The contributionof the method to various chemical processes may be quite
large. Hirosawa et al. (2019) analyzed the rate of the mechanochemical reaction to form ZnFe2O4 via high-energy
ball milling of a mixture of ZnO and γ-Fe2O3 based on
the kinetic theories for thermal chemical reactions using

experimental and numerical approaches. The reaction rate
constant was found to be correlated with the energy factor
and frequency factor, similarly to the Arrhenius equation
for thermal chemical reactions (Fig. 9). Their methodof analyzing the rate of mechanochemical reactions cancontribute to not only precise control of the reaction rate

191

Tomohiro Iwasaki / KONA Powder and Particle Journal No. 40 (2023) 186–196

Fig. 9 Relationship between the reaction rate constant and the reciprocal of a parameter representing the kinetic energy of particles. Reprinted
with permission from Ref. (Hirosawa et al., 2019). Copyright: (2019)
Elsevier.

Fig. 10 Mechanochemically assisted synthesis of hematite nanoparticles developed by Onizuka and Iwasaki (2022). Reprinted with permission from Ref. (Onizuka and Iwasaki, 2022). Copyright: (2022) Elsevier.

but also the effective utilization of energy, the process optimization based on the mechanical energy, etc.
Many researchers have actively challenged to drymechanochemical synthesis of various high-performance
particles from inorganic (Goga et al., 2021; Hlova et al.,2014; Muh’d et al., 2015; Pal et al., 2018; Wu et al., 2021),
organic (Oh et al., 2020; Otsuka and Goto, 2022; Zhang etal., 2018) and organic–inorganic (El Ajjouri et al., 2019;
Martínez-Sarti et al., 2020; Tanaka et al., 2013) materials,
which will probably lead to the achievement of SDGs in future. For example, the development of functional fertilizers
by dry mechanochemical syntheses (Alrbaihat et al., 2021;
Barčauskaite et al., 2020; Borges et al., 2021; Tan et al.,2021; Zheng et al., 2021) can contribute widely to SDGs
such as the goals 2, 8, 9, 12 and 15.

3.4 Mechanochemically assisted process forparticle synthesis

Mechanochemical treatments have often been used toassist the progress of chemical reactions. Štrukil and Sajko(2017) developed a novel mechanochemical approachfor solid-state visible light photocatalysis. The method isvery simple; the photochemical reaction proceeds during
milling at room temperature using a visible (blue)-lightirradiated vibration mill with a glass jar. They demonstrated that both the light irradiation and the mechanochemical treatment are required to effectively promote thesolid-state photocatalytic reaction. The method contains
some potential applications in the growing field of photoredox catalysis and supramolecular chemistry. Onizuka andIwasaki (2022) developed a novel solvent-free process forsynthesizing hematite nanoparticles under environmentallyfriendly conditions. This method contains the mechanochemical treatment of ferric nitrate nonahydrate as theiron source and urea as the precipitant, which releases the192

Fig. 11 Crystallite sizes of hematite nanoparticles obtained withdifferent amounts of urea at different heating temperatures. Reprinted
with permission from Ref. (Onizuka and Iwasaki, 2022). Copyright:(2022) Elsevier.

crystal water from the iron source to be used for the ureahydrolysis (Fig. 10). In addition, the mechanochemical
effects promote the hematite formation during heating evenat low temperatures. The particle size was changed by theamount of urea and the heating temperature (Fig. 11). The
low-temperature solvent-free method using urea hydrolysis
may also be applicable to the environmentally friendly
synthesis of other metal oxide nanoparticles. As an effective technique, dry mechanochemical pretreatments for
activating the precursor of chemical reaction by thermal
treatments, such as heating (Çolak et al., 2017; Iwasaki
et al., 2016; Suetsugu, 2014), calcination (Ashiri, 2016;
Bulina et al., 2017; Hashimoto et al., 2020; Kosova andSemykina, 2019; Lejda et al., 2020; Liu et al., 2020; Qiu

Tomohiro Iwasaki / KONA Powder and Particle Journal No. 40 (2023) 186–196

et al., 2021) and hydrothermal treatment (Kanie et al.,2018; Nada et al., 2019; Osuga et al., 2022; Zhang et al.,2020), have often been employed in the synthesis of various functional nanoparticles for weakening the thermal
treatment conditions.

4. Conclusion

Focusing on grinding and mechanochemical processes,
this review presents the possibility of dry mechanical processes to the contribution of the achievement of the SDGs.
In particular, from a viewpoint of the reduction of energy
consumption and waste, dry mechanochemical processes
may be suitable to an on-demand production of small quantity and large variety of high-performance fine particles in
the consuming area. This may also lead to the contribution
to the transition to a decarbonized society. For further
enhancing the functionality and performance, the synthesis of nanosized particles cannot be avoided. However,
nanoparticles have some concerns in health and environment. Thus, the nanoparticles should be used considering
carefully the balance of valuables of nanoparticles and its
contribution to the SDGs. Toward the realization of a sustainable society, various problems relating to fine particles,
such as bioaccumulation of microplastics and nanoparticles
discharged to environments, must be urgently solved. In
addition, the automated system and labor saving is requiredfor improvement of the work environment. Dry particle
synthesis processes can play an important key role for theachievement of the SDGs. The author believes that the drymechanical powder processes have the great potential tocontribute directly or indirectly to all of the SDGs.

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Author’s Short Biography
Tomohiro Iwasaki
Tomohiro Iwasaki is a Professor of Chemical Engineering at Osaka Metropolitan University inJapan. He received his master’s degree in Chemical Engineering from Osaka Prefecture University.
He also received his Ph.D. degree in Chemical Engineering from Osaka Prefecture University. His
research interests include mechanochemical processes, organic–inorganic hybrid materials (organoclays, layered organosilicates) synthesized via intercalation, ferrite nanoparticles for magnetic
hyperthermia treatments, and discrete element simulation of ball mills.

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KONA Powder and Particle Journal No. 40 (2023) 197–218/https://doi.org/10.14356/kona.2023016

Review Paper

Recent Advances in the Fabrication and Functionalizationof Nanostructured Carbon Spheres for Energy Storage
Applications †Kiet Le Anh Cao 1, Ferry Iskandar 2,3, Eishi Tanabe 4 and Takashi Ogi 1*
1 Chemical Engineering Program, Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering,

Hiroshima University, Japan

2 Department of Physics, Institute of Technology Bandung, Indonesia

3 Collaboration Research Center for Advanced Energy Materials, National Research and Innovation Agency - Institut Teknologi Bandung,Indonesia

4 Western Region Industrial Research Center, Hiroshima Prefectural Technology Research Institute, Japan

Abstract
The development of energy storage devices providing high energy and power densities and long-term stability willplay an important role in the future utilization of sustainable energy sources. Numerous efforts have been devoted toachieving these requirements, especially the design of advanced electrode materials. For this reason, there is growinginterest in the innovation of new carbon-based materials with enhanced electrochemical performance. Nanostructured
carbon spheres (CSs) have attracted significant attention due to their prominent properties, such as high surface area,
excellent electrical conductivity, tunable porosity, and surface functionality. This review offers a comprehensive
overview into the recent advances of nanostructured CSs within the last five years, focusing on synthetic strategies
for producing carbon particles with precisely controlled morphologies and interior structures, as well as the potential
applications of these particles as high-performance electrode materials in rechargeable batteries and supercapacitors.
The challenges and perspectives on future research directions are highlighted, focusing on the controlled synthesis
and functionalization of nanostructured CSs with tunable structures and properties that are well-suited to practicalapplications. This review is intended to serve as a helpful resource to researchers involved in the fabrication of new
CS materials and the development of methods to control their structure and morphology.
Keywords: carbon spheres, nanostructured particles, energy storage, batteries, supercapacitors

1. Introduction

The greenhouse effect is widely recognized as one of themain factors responsible for global climate change, which
has begun to affect our daily life and has become a significant concern affecting the existence of all living species(Zhang et al., 2022; Tanno and Makino, 2018). Therefore,
the development of renewable and green energy technologies for a sustainable future, which could mitigate the most
severe impacts of climate change, is a highly desirable goal.
Until now, numerous extensive research efforts are focusedon the design of low-cost and environment-friendly energy
storage devices with high electrochemical performance
characteristics, including rechargeable batteries and supercapacitors (Long et al., 2017; Zhang M. et al., 2018). The†*

Received 20 June 2022; Accepted 7 July 2022
J-STAGE Advance published online 23 September 2022
Corresponding author: Takashi Ogi;
Add: 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527,
Japan
E-mail: ogit@hiroshima-u.ac.jp
TEL: +81-82-424-3765

electrode materials in these electrochemical energy storage
devices play a crucial role, which determine their overall
performance. Consequently, the exploration of multifunctional electrode materials is still a hot and vital topic.
In order to meet these goals described above, the key
aspects are the innovative design of nanostructured particulate materials and the discovery of fabrication processes
that allow for the synthesis of materials having precisely
tailored morphologies, structures and chemical characteristics. It will also be necessary to search for greener and morecost-effective synthetic processes to allow the widespreadutilization of such devices. Interestingly, natural materials
often tend to adopt spherical shapes because this morphology minimizes surface energy and is highly stable. From anengineering viewpoint, spherical particles also offer many
advantages because they have no sharp edges and are thus
less likely to be damaged during processing. In addition,
the size and regular shape of spheres allow for the effective
ordering of subjects in densely packed states, especiallywhen necks and pores are present. Finally, spheres provide
excellent fluidity (Tian et al., 2019; Cao et al., 2021b). As

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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a consequence of these advantages, carbon-based spheres
have captivated vast attention due to the combined advantages of carbon materials with spherical colloids. Thesematerials can exhibit several unique features, including
regular geometry, high uniformity, good fluidity, superior
packing density, and tunable particle size distribution(Liu et al., 2015; Tian et al., 2019). Despite the enormous
achievements made in many fields, carbon spheres (CSs)continue to emerge and fabricate from smart synthetic
strategies. These intriguing advantages and exciting prospects have stimulated worldwide research activities ofCSs, particularly in energy-related applications. Therefore,
the development and innovation of advanced CSs having
well-defined structural and surface characteristics are expected to play a critical role in the energy field, which provide an opportunity to improve the performance in energy
storage devices with both high power and energy densities.
Although several review articles concerning carbonbased materials (e.g., mesoporous carbon, carbon nanotubes, graphene) have been published over the last decade(Zhang M. et al., 2018; Benzigar et al., 2018; Tian et al.,2019; Kerdnawee et al., 2017; Zhao et al., 2019), thescope of the review covers the critical advances of thenanostructured CSs, focusing on the recent progress inthe fabrication with precisely controlled structures andfunctionalities through smart synthetic strategies. Such
strategies are necessary to achieve better performancein energy storage devices. On this basis, the aim of thepresent review was to offer an overview into the fabri-Fig. 1

198

cation techniques of CSs, with a particular emphasis on
new and promising developments. As a critical point, acomprehensive overview is provided that discusses themost up-to-date strategies (those devised primarily within
the last five years) for the rational synthesis of CSs withdiverse structures. In addition, the utilization of suchmaterials as key components in a wide range of energy
storage applications is explored. Fig. 1 provides a diagram
summarizing various aspects of the nanostructured CSs
considered herein. These aspects comprise fabrication
processes and carbon sources, various methods for thetuning of morphology and architecture, functionalization,
and applications in energy storage devices. To highlightthe above-mentioned important point, the present review
is organized as follows. Firstly, introducing the promising
approaches for the design and synthesis of CSs using threeroutes (e.g., template-free, soft templating, and hard templating) by combining the excellent work from worldwide
researchers. Secondly, overviewing the performance of
CSs with various structures and their composites in energy
storage devices (e.g., rechargeable batteries and supercapacitors). Furthermore, there is still a lack of a clear discussion regarding the specific relationship between carbon
structures and electrochemical performance characteristics.
As a matter of fact, the correlation between the synthesis
strategy, structure, and characteristics of CSs would allow
these materials to be used more extensively in energy storage devices; therefore, their preparation-structure-function
relevance is expected to be understood. Finally, offering

A comprehensive overview of the preparation and functionalization of nanostructured CSs for energy storage applications.

Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

some concluding remarks on the current challenges andperspectives related to nanostructured CS materials, as wellas highlighting the promising future progress in the field ofenergy-related devices.

2. Synthesis of carbon spheres

Various structures of CS materials can be obtainedusing numerous synthesis processes. Fig. 2 shows typicalschemes for preparing nanostructured CSs with different
strategies. These synthesis processes can generally beclassified into three categories: template-free, soft templateand hard template approaches, which will be highlighted inthis section.

2.1 Template-free synthesis of carbon spheres

The large-scale production of CSs will require processes
that are simple, energy efficient, environmentally friendly,
and cost-effective. On this basis, the development oftemplate-free approaches capable of producing CSs having
a wide range of pore sizes would be preferable, as shown in
Fig. 2(a). Three simple and commonly used template-freesynthetic strategies, including hydrothermal carbonization(HTC) method, extension of the Stöber method, and aerosolassisted method will be investigated. Common precursor
materials for the fabrication of CSs in conjunction with
template-free strategies and the respective physical properties of the obtained CSs are summarized in Table 1.
Recently, numerous review articles has been carefully
explained the synthesis of nanostructured CSs using HTC
and extension of the Stöber methods (Liu et al., 2015; Tianet al., 2019). Therefore, in this section, we mainly focusedon the fabrication of nanostructured CSs using aerosolassisted techniques.
Aerosol-assisted techniques, such as spray drying andspray pyrolysis, are cost-effective, simple, continuous andscalable methods for the preparation of CSs (Ogi et al.,2014; Debecker et al., 2018; Leng et al., 2019; Gradon etal., 2020). Because of these advantages, such methods are
frequently used to produce dry powders in the food, pharmaceutical, and chemical industries. Generally, dependingon the operating conditions, solid or hollow sphericalparticles can be produced in the spray process (Cao etal., 2021b; 2021a; Nguyen et al., 2021; 2022). Feng et al.
prepared pomegranate-like carbon microspheres (PCSs) byfabricating monodisperse, submicron CSs via a scalable
template-free spray drying-assisted approach (Feng et al.,2019). In this process, monodisperse 3-aminophenol/formaldehyde (3-AF) resin spheres were initially synthesizedbased on the polymerization of 3-aminophenol and formaldehyde in deionized water without the use of a catalyst. The
monodisperse resin colloids were subsequently spray dried
to generate pomegranate-like resin microspheres during thespray drying process. Finally, after carbonization and potassium hydroxide (KOH) activation, the pomegranate-likeresin microspheres were converted into PCSs. These PCSs
comprised well-defined microspheres with a high specificsurface area (SSA) of 1477 m2/g and a relatively broad size
distribution ranging from 1 μm to 5 μm.
Until now, considering the sustainable developmentof CS materials, the use of biomass as a precursor hasreceived increasing attention due to its renewability, reproducibility, readily accessible, and environmental friendliness. Although biomass has been utilized as a raw material
for the fabrication of carbon materials, there are few reportson the successful production of CSs from biomass-basedmaterials using template-free spray drying method. Cao et
al. has successfully fabricated spherical carbon particles
using a template-free spray drying approach followed by
the carbonization process, utilizing KOH as the activatorand Kraft lignin as the carbon precursor (Fig. 3(a)) (Cao etal., 2021a; 2021b). Adjusting the KOH concentration in thereaction solution was found to allow tuning of the CS structure from dense to hollow, as shown in the TEM images(Fig. 3(b–d)). A possible mechanism for the formation ofcarbon particles is also studied in their research, with theaim of providing a more in-depth understanding of theprocess. In addition, hollow carbon spheres (HCSs) having
a high SSA of 2424.8 m2/g along with a micro-mesoporous
structure were obtained at KOH-to-lignin mass ratios
below 1.5. Very recently, the same group has proposeda fundamental modification of the fabrication process of
porous CSs by employing sodium hydroxide (NaOH) as
the substitute activation agent in place of the more conventionally used KOH (Kitamoto et al., 2022). NaOH offerssome advantages over KOH in terms of less corrosiveness,
low cost, and simple handling procedure, all of which areappealing particularly from an industrial point of view. The
results indicate that the structure of carbon particles can betuned from a compact to hollow structure, and the surface
textural characteristics can be easily adjusted by changing
the amount of NaOH. The significant achievements andongoing efforts in this field suggest that these processes
will allow the development of advanced carbon materials
and the high value-added utilization of Kraft lignin as apromising material for potential applications.
In the spray pyrolysis process, droplets of the liquid
precursor are formed via ultrasonication (He and Wang,
2019; Ogi et al., 2020; Septiani et al., 2021; 2022; Le et al.,2022) and promptly carbonized at high temperatures under
the protection of inert gases (Balgis et al., 2015; 2017). The
size and morphology of the resulting CSs can be adjusted
by changing the reaction conditions, reactant concentration
ratios, and carbonization temperature. In recent years, there
have been several publications regarding the synthesis of
CSs using a template-free spray pyrolysis process. Guo et
al. developed a novel strategy for producing hierarchicalporous CSs by utilizing coal as the raw material in conjunction with ultrasonic spray pyrolysis (Guo et al., 2017). In

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Fig. 2200

Synthetic strategies for the preparation of CSs using (a) template-free, (b) soft template, and (c) hard template approaches.

N-ACSs a

N-CSs

ACSs

PCSs f

PCSs

PCSs

N-CSs

N-HCSs i

1

2

3

4

5

6

7

8

PCSs

15

Spray pyrolysis +Carbonization

Glucose andglucosamine

GlucoseCoal

Spray pyrolysis +Activation

Spray pyrolysis

ChitosanSpray drying +Carbonization

Coal

Sodium lignosulfonate

Spray drying +Carbonization

Spray pyrolysis

Kraft lignin

Kraft lignin

Kraft lignin

Spray drying +Carbonization

Spray drying +Carbonization

Spray drying +Carbonization

3-Aminophenol andformaldehyde

Resorcinol andformaldehyde

Extended the
Stöber method

Spray drying +Activation

3-Aminophenol andformaldehyde

GAML gExtended the
Stöber method

HTC + Activation

StarchStarchHTC

HTC + Activation

XyloseGlucoseGlucosePrecursor

HTC + Activation

Surfactant-assisted
HTC

HTC b + Activation

Process

Hollow structure with large number of mesopores within the shell

Honeycomb-like hierarchical porous structure including micro-,meso-, and macropores

Porous structure composed of micro- and mesopores

Hierarchical porous structure composed of micro- and mesopores

Mesoporous structure with unique bimodal pore size distribution

Hierarchical porous hollow structure comprised micro-, meso- andmacropores

By changing the NaOH concentration, the structure can be adjusted
from a compact to hollow structure

Porous hollow structure composed of micro- and mesopores

Dense structure with abundant micropores and small amounts ofmesopores

Pomegranate-like structure and rich packing voids with abundantmicropores

Micro-/mesoporous structure. Different inner structures (solid, yolkshell, hollow) can be obtained depending on the changed polymerization
degree of internal RF resin—< 1, 1–5, 100—

1.7–2.5~ 4, 7

1–7, 10–100

0.7, 2.4, 20–100

0.7, 2.4

0.7

—

—

327

1443–1837

589–635

547–948645–1292

963–1342

761–1513

1536–2425

936–1233

1477

501

946
1263

—
—

61–356<2

Microporous structure

1746–2478

1.8–2.1

Porous structure with dominant micropores and low content of
mesopores

1973

393–973

2675

844

1579

SSA (m2/g)

3.9

2–3

—

< 1.5

1.6

dpore (nm)Porous structure with high content of micropores and sufficientmesopores

Hierarchical porous structure with coexistence of micro- and mesopores

Micro-mesoporous structure

Microporous structure

Abundant microporous structure

Structure

0.13

1.23–1.40

0.38–0.43

0.23–0.52

0.33–1.29

0.72–1.05

0.34–1.01

0.75–1.57

0.31–0.44

0.83

0.29

0.68

0.51

—

0.87–1.24

0.89

0.18–0.27

1.62

0.35

0.66

Vtotal (cm3/g)

Supercapacitors

Supercapacitors

Supercapacitors

Supercapacitors

LSBs

Supercapacitors

Supercapacitors

Supercapacitors—

Supercapacitors

Supercapacitors

PIBs hSupercapacitors

Supercapacitors and
SIBs

Supercapacitors(Qu et al., 2018)(Tang et al.,2018)

(Lv et al., 2020)(Guo et al.,2017)

(Zhou H. et al.,2018)

(Pang et al.,2018b)(Kitamoto et al.,2022)

(Cao et al.,2021a)(Cao et al.,2021b)(Feng et al.,2019)

(Zhang L. et al.,2019)

(Wang S. et al.,2020)

(Hao et al.,2018)

(Zhang J. et al.,2020)

(Lu et al., 2018)(Sun et al.,2020)

(Zhang H. et al.,2019)

LIBs d and SIBs e
Supercapacitors(Xiang et al.,2018)

Reference

LSBs cApplications

CSs; g GAML: 2-keto-l-gulonic acid mother liquor; h PIBs: Potassium-ion batteries; i N-HCSs: Nitrogen-doped hollow CSs; j N-YS-CSs: Nitrogen-doped yolk-shell CSs; k N-SCSs: Nitrogendoped solid CSs; l HPCSs: Hollow porous CSs; m MCSs: Mesoporous CSs; n N,S-PCSs: Nitrogen, sulfur co-doped porous CSs.

a N-ACSs: Nitrogen-doped activated CSs; b HTC: Hydrothermal carbonization; c LSBs: Lithium-sulfur batteries; d LIBs: Lithium-ion batteries; e SIBs: Sodium-ion batteries; f PCSs: Porous

N-HCSsMCSs m14

18

HPCSs l13

N,S-PCSs n

PCSs

12

PCSs

HCSs

11

16

CSs

10

17

Pomegranatelike CSs

9

N-SCSs k

N-YS-CSs j

Material

No.

Table 1 Summary of the conditions used for the template-free synthesis of functionalized CSs, the textural parameters of these materials and their potential applications.

Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

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Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

Fig. 3 (a) An illustration of the synthesis of CSs from Kraft lignin via a spray drying process; Transmission electron microscopy (TEM) images ofcarbon particles generated using KOH/lignin mass ratios of (b) 0.17, (c) 0.67, and (d) 1.33.

this process, the raw coal is chemically oxidized by a highly
concentrated mixture of sulfuric acid and nitric acid, suchthat numerous functional groups are introduced, including –C=O, –OH, –NH2, –SO3H, and –NO2. Subsequently,
the as-prepared coal oxide can be readily dissolved in an
alkaline solution to obtain a transparent precursor solution.
The pyrolysis temperature was found to have a significant
effect on the pore architecture and SSA of the CSs generated using this process. As the temperature was increased
from 700 to 1000 °C, micro-/mesoporous structures wereobserved in these CSs with the SSA and total pore volumeof the samples rapidly increasing from 547.2 to 948.5 m2/g
and from 0.23 to 0.52 cm3/g, respectively. To further improve the electrochemical properties of carbon materials,
the same group was developed multiple heteroatoms N/S
doping porous CSs through ultrasonic spray pyrolysis
method employing l-Cysteine as nitrogen and sulfur precursor along with coal as the carbon precursor (Lv et al.,2020). The results reveal that the homogeneous doping of
N and S elements not only improves the hydrophilicity of
the CSs but also provides more active sites, resulting in an
increase in specific capacitance.

2.2 Soft template-assisted liquid phase/aerosol
process

Soft templates are typically organic molecules, macro-202

molecules or supramolecules that are easily decomposed atrelatively low carbonization temperature (below 400 °C).
The synthesis of porous CSs using the soft templatetypical involves several key steps (shown in Fig. 2(b)):(i) the dissolution of the template molecules and carbon
precursor in a suitable solvent, (ii) the soft templates turninto micelles during solvent evaporation and simultaneousinteraction of these micelles with the carbon precursor via
hydrogen bonding as well as hydrophobic, hydrophilic orelectrostatic interactions, and (iii) the heat treatment of the
precursor/template composite in an inert atmosphere withcomplete decomposition of the soft template. Various carbon precursors and soft templates that are frequently usedin the preparation of CSs, as well as the physical propertiesof the resulting materials, are summarized in Table 2.
Over the past decade, there has been significant process
in the direct synthesis of mesoporous CSs from amphiphilic molecules such as surfactant or block copolymer
self-assembles with carbon precursor, which is stabilized
by thermal treatment. Different carbon precursors, such as
phenolic resin or their derivatives, carbohydrates, nitrogenand carbon-rich compounds (e.g., dopamine), and naturalpolymers (e.g., cellulose, chitin and lignin) are commonlyused for the fabrication of mesoporous CSs. Tang et al. prepared porous CSs with sub-micrometer particle size (about
300 nm) using the linear block copolymer PS-b-PEO as

N-MCSs a

N-MCSsN-MCSsPCSs dN,O-CSs e

N-MCSsN-PCSsYS-CSs f

N,O-YS-CSs h

N-HMCSs

1

2

3

4

5

6

7

8

9

10

APCSs

N-PCSs

N-MCHSs

12

13

14

Pluronic F127

Soft template + HTC

CTABSoft template + HTC

Soft template + HTC

Soft template + HTC

Soft template

Soft template(Extended the Stöber
method)Interconnected porous hollow structure (meso- andmacropores)

3-Aminophenol and formaldehyde

Gemini l

Hierarchical porous structure including micro- andmesopores.

3-Aminophenol and amphiphilic aliphatic aldehydes[C12mim]Br k

Hollow structure with interconnected pores

3-Aminophenol and formaldehyde

Increasing EtOH/H2O ratio, the structures were adjusted from irregular to hollow to core-shell.
Abundant mesopores under appropriate EtOH/H2O ratio

By changing amount of EDA, mesoporous yolk-shelland hollow structures are obtained

Resorcinol and formaldehyde
Phenol and formaldehyde

Yolk-shell structure comprised dense core and porous
shell layer (micro- and mesopores)

Hierarchical porous yolk-shell structure

Porous structure with many micropores and few
mesopores

Porous structure composed of large mesopores

Hierarchical micro-/mesoporous structure with interconnected and worm-like pores

Hierarchical porous structure consisting of interconnected and worm-like pores

Resorcinol and formaldehyde

Resorcinol and formaldehyde

Phenol and formaldehyde

Melamine and formaldehyde

3-Aminophenol and formaldehyde

Larch sawdust (biomass)

Mesoporous structure with uniform mesopore size
(5 nm)

Core-shell structure with abundant mesopores in thecore

Dopamine
2-Aminophenol and formaldehyde

Interconnected hierarchical porous structure (microand mesopores). Mesopores are well-distributed on
the sphere surface

Structure

Dopamine

Precursor

DDAB jCTAB

CTAC iCTAB gSoft template

Soft template(Extended the Stöber
method)Pluronic F108

Soft template + HTC

Pluronic F127

Pluronic F127

Soft template

Soft template

Pluronic F127

PS-b-PAA c

PS-b-PEO b

Template

Soft template +Pyrolysis

Soft template

Soft template +Activation

Process< 2, 5–20,
20–110

3.7–6.3, 25

—

2.9

2.6

2.4

—

1.5, 2–10—

1.2, 6.7–8.1

0.6, 2–5

—

5

14.8

4.2–16.0

dpore
(nm)

1215–1517

812–1040

259–1746

544–1065

1169

2001

440–996543–7031481

791–883

2012–3203

659–760

439

449

397–2320

SSA(m2/g)

1.12–3.22

0.64–1.54

0.50–1.94

0.57–1.54

0.88

1.86

—

0.59–1.47

0.90

1.3–1.5

0.99–1.93

0.44–0.67

0.33

0.60

0.29–1.41

Vtotal(cm3/g)Supercapacitors

Supercapacitors

Supercapacitors

Supercapacitors

Supercapacitors

PIBs

PIBs

Supercapacitors

SIBs

Supercapacitors

Supercapacitors

Supercapacitors

SIBs

Supercapacitors

Applications(Li et al., 2020)(Jia et al., 2022)(Li et al., 2021)(Du et al., 2020)(Liu C. et al.,2016)

(Chong et al.,2021)

(Zhang H. et al.,2018)

(Liang et al., 2019)(Guo et al., 2021)(Liu S. et al., 2016)(Song et al., 2019)(Wang et al., 2018)(Mao et al., 2020)(Tang et al., 2017)

Referencea N-MCSs: Nitrogen-doped mesoporous CSs; b PS-b-PEO: Polystyrene-block-poly(ethylene oxide); c PS-b-PAA: Polystyrene-b-poly(acrylic acid); d PCSs: Porous CSs; e N,O-CSs: Nitrogen,oxygen co-doped CSs; f YS-CSs: Yolk-shell CSs; g CTAB: Cetyltrimethylammonium bromide; h N,O-YS-CSs: Nitrogen, oxygen co-doped yolk-shell CSs; i CTAC: Cetyltrimethylammonium
chloride; j DDAB: Didecyldimethylammonium bromide; k [C12mim]Br: 1-dodecyl-3-methylimidazole bromide; l Gemini: Pentane-1,5-bis(dimethylcetyl ammonium bromide).

HCSs

11

N-YS-MCSs

Material

No.

Table 2 Summary of the conditions used for the soft template synthesis of functionalized CSs, the textural parameters of these materials and their potential applications.

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Fig. 4 (a) An illustration showing the fabrication of monomodal and bimodal porous CSs through the self-assembly of dopamine, PS-b-PEO BBCPs,
and Pluronic F127; (b) pore size distribution of mesoporous CSs. Reprinted with permission from Ref. (Fei et al., 2020). Copyright 2020, American
Chemical Society. (c) An illustration showing the formation of N-doped mesoporous carbon nanospheres corresponded to TEM images with different
morphologies and mesostructures using a versatile nano-emulsion assembly method. Reprinted with permission from Ref. (Peng et al., 2019). Copyright 2019, American Chemical Society.

the soft template and dopamine as the carbon source inconjunction with KOH activation (Tang et al., 2017). The
size of mesopores could be precisely tuned from 4 to 16 nm
by using PS-b-PEO copolymers having different molecularweights as pore forming agents in this process. Despite
such prior work, the fabrication of mesoporous CSs withlarge pore size from soft templates as a means of providing
efficient pathways for electrolyte diffusion remains a greatchallenge. On this basis, Fei et al. explored the fabricationof nitrogen-doped mesoporous CSs having ultra-large mesopore sizes (up to 24.5 nm) by utilizing PS-b-PEO brushblock copolymers (BBCPs) along with a small moleculesurfactant (Pluronic F127) as the template and dopamine as
the carbon and nitrogen source followed by carbonizationand thermal decomposition of the polymer templates, as
illustrated in Fig. 4(a) (Fei et al., 2020). Mesoporous CSs
with bimodal mesopore size distributions (having apexes
at 6.5 and 24.5 nm) were confirmed by the peak in thedV/logD curve (red line curve) and the cumulative porevolume is calculated by summing individual pore volumes(black line curve), as shown in Fig. 4(b). Specifically,
BBCPs corresponded to the large mesopores and the204

Pluronic F127 template was responsible for the small mesopores. This work demonstrated a unique and adaptableapproach for synthesizing CSs with a precisely tailoredmorphology and bimodal pore sizes for potential applications in energy storage. Peng et al. reported the fabricationof highly uniform mesoporous CSs with adjustable poresizes and structures (e.g., smooth, golf ball, multichambered, and dendritic nanospheres) (Fig. 4(c)) (Penget al., 2019). In this work, Pluronic F127 was utilized
as a soft template, dopamine was employed as a carbon
and nitrogen source, and organic molecules (e.g., 1,3,5trimethylbenzene) played a critical role in the evolution ofpore size and also affected the interfacial interactions between the Pluronic F127 and dopamine. The CSs producedby this process had an ultra-large pore size of 37 nm, small
average particle size of 128 nm, high SSA of 635 m2/g, andhigh nitrogen content of 6.8 wt%.
In some studies, ordered mesoporous CSs have been
fabricated by a simple one-step process. Song et al. synthesized hierarchical porous CSs using Pluronic F127 as a soft
template and biomass (e.g., larch sawdust) as the carbon
precursor via a spray pyrolysis method (Song et al., 2019).

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The morphology, particle size, and porosity of these CSs
could be simply adjusted by varying the amount of F127 in
the reaction system. The obtained porous CSs were foundto have a high SSA up to 760.3 m2/g and mesopore sizes
that could be adjusted within the range of 10–40 nm. In addition to block polymers, surfactants such as Triton X-100
have also been employed as the soft template to synthesize
HCSs (Zhou et al., 2017). The HCSs obtained from thisprocess had a SSA of approximately 893.3 m2/g and a total
pore volume of 0.76 cm3/g.
Yolk-shell CSs with a unique core@void@shell structure have attracted tremendous interests as a consequenceof their hierarchical pore architectures that are present in acontrolled arrangement within each single particle (Zhang
H. et al., 2018). Liu et al. prepared nitrogen-doped yolkshell mesoporous CSs via a soft template approach, employing ethylenediamine (EDA) as both a nitrogen source
and a base catalyst in a Stöber-silica/carbon assembly
system (Liu C. et al., 2016). The resulting nitrogen-doped
yolk-shell mesoporous CSs possessed a uniform mesopore
size of 2.6 nm and a high SSA of 2001 m2/g. Shu et al.
designed nitrogen-doped yolk-shell CSs with tunable mesoporous surfaces and particle sizes based on a soft templateapproach (Shu et al., 2018). The fabrication process used
RF resin as the carbon precursor, melamine as the nitrogenprecursor, cetyltrimethylammonium bromide (CTAB) as
the pore-forming template, TEOS as the skeleton structure
and ammonia as the catalyst. Because the rate of hydrolysis
of TEOS was slower than the rate of polymerization ofmelanin resorcinol formaldehyde (MRF) in the initial stageof this process, a CTAB/MRF formed as the core and SiO2/
CTAB/MRF formed as the shell. The subsequent carbonization of these particles at a high temperature produced ahollow core-shell structure and nitrogen-doped yolk-shell
CSs were obtained after the SiO2 template was removed
using an NaOH solution.

2.3 Hard template-assisted liquid phase/aerosol
process

The hard template approach, also known as the nanocasting approach, is commonly used materials with relatively rigid structures to guide the growth of materials
based on providing limited space (Fuji et al., 2013; Li et al.,2016; Cao et al., 2020) such as SiO2 (Wang H. et al., 2020),
polystyrene latex (PSL) (Arif et al., 2016), poly(methyl
methacrylate) (PMMA) (Ye et al., 2016) and calcium carbonate (CaCO3) (Ni et al., 2019). Several key processing
steps are involved in the preparation of CSs using a hard
template strategy, as shown in Fig. 2(c). These include (i)the fabrication of sphere-shaped hard template materials,(ii) the impregnation of these hard templates with a carbon
precursor, (iii) the carbonization of the resulting carbon
precursor/template composite in an inert atmosphere at ahigh temperature, and (iv) the removal of the hard template

via thermal decomposition or chemical etching methods,
depending on the nature of the hard template. One of the
key factors influencing the architecture and pore distribution is the dispersion of the hard template in the precursor
solution. The resulting materials will possess rich poreswith good connectivity if the template is well-dispersedin the precursor. Therefore, developing a method for enhancing the dispersion of hard template in the precursor
solution is highly desirable (Ogi et al., 2017). Various CSs
having different textural features have been prepared using
different hard templates and the details of the materials andstructural parameters are provided in Table 3.
SiO2 spheres have been extensively applied as hard
template to prepare CSs because they are inexpensive,
have easily controllable sizes ranging from nanometer tomicrometer and exhibit remarkable thermal stability together with a high degree of uniformity. Solid SiO2 spheres
are usually fabricated via the Stöber method, based on thehydrolysis of a SiO2 precursor in a mixture of water andalcohol with ammonia as the catalyst (Stöber and Fink,
1968). HCSs can be obtained by coating of different carbon
precursors on the surface of these SiO2 particles followedby carbonization and etching process. Wang et al. synthesized N-doped HCSs using a simple interfacial sol-gel
coating process, utilizing colloidal SiO2 as the template,resorcinol/formaldehyde as the carbon precursor and EDA
as both the nitrogen precursor and base catalyst (Wang etal., 2017). These HCSs possessed a uniform size of approximately 120 nm in diameter along with porous shells asthin as 10 nm. Density functional theory has demonstrated
that N-doping could change the binding sites and enhance
the adsorption of PF6− ions in the carbon matrix. Zhang etal. developed a simple one-pot approach for synthesizing
mesoporous HCSs with large controllable pore size basedon a surfactant-free system (Zhang et al., 2016). Using a
Stöber process, the reaction between resorcinol, formaldehyde and a TEOS/tetrapropyl orthosilicate (TPOS) mixture
generated SiO2@SiO2/RF spheres without the addition of acationic surfactant. In comparison with traditional TEOSbased system, the incorporation of TPOS, which undergoes
hydrolysis and condensation rate more slowly than TEOS,
provided better control over the formation of SiO2 core andprimary particles. The pore sizes in the mesoporous HCSs
could be precisely tuned over the range from micropores toa size of 13.9 nm by adjusting the TEOS/TPOS or ethanol/
water ratio.
PSL spheres with uniform sizes and good dispersion
have been widely used as hard template, partly because
these materials are readily removed by thermal annealing
at 400 °C (Arif et al., 2016) or dissolution using organic
solvents (Chen et al., 2016a). Balgis et al. synthesizednanostructured carbon particles via ultrasonic spray pyrolysis based on the self-assembly behavior of phenolic resin
and PSL particles (Balgis et al., 2014; 2015). By adjusting

205

206

UHCSs aN,P-HPCSs c

N-HPCSs

N-HMCSs d

N,P-PCSs e

N-HCSs

N,P,S-PCSs f

HPCSs

N-HMCSs

N-HCSsHierarchical PCSs

N,O-HCSs

S,N-HCSs h

S,N-MHCSs

N-HCSs

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Hard template

Hard template

Hard template

Hard template +Activation

Hard template

Hard template

Hard template

Dopamine
3-Aminophenol andformaldehyde

Cu2O sphere

Resorcinol andformaldehyde

PyrrolePhenolic resin

CaCO3 sphere

PMMA sphere

PS sphere

PS sphere

3-Aminophenol andformaldehyde

PS/PAN g

SiO2 sphere

PS sphere

Furfuryl alcohol

Anilinem-Phenylenediamineand formaldehyde

Melamine andformaldehyde

SiO2 sphere

SiO2 sphere

Hard template

Hard template

SiO2 sphere

Hard template

SiO2 sphere

Hard template

Dopamine

Dopamine

SiO2 sphere

SiO2 sphere

Dopamine

PTCDA bPrecursor

SiO2 sphere

SiO2 sphere

Template

Hard template

Hard template +Activation

Hard template

Hard template

Process

Micro-/mesoporous hollow structure with thin carbon shells(15–84 nm)

Interconnected hollow sphere structure with mesopores

Hollow spheres with microporous structure

Hierarchical hollow porous structure with abundant microand mesopores

Hollow structure: macropores covered by a thin carbon shell
Porous structure: macropores exposed on the surface

Hollow structure with micro-mesopores in the shells

Hollow structure
Mesopores in the shells

Mesoporous structure with macro-hollow core

Hierarchical porous structure with numerous pores within
spheres

Hollow structure (shell thickness ~25 nm). Micropores exist
in the shell, while meso-/macropores correspond to the hollow
cavityHollow sphere with interconnected micro-, meso-, macroporous structure

Hollow structure with abundant mesopores

Hollow structure with abundant ultramicropores and reasonable supermicro- and mesopores

Hollow structure with abundant micropores

Hollow structure with non-porous shells, ultrathin shell
thickness

Structure

1.2–1.55, 25—

0.5–15

2.8, 128

—

4.1

2–4

0.5–1, 2–4, 32< 2, 20–70

2.6, 3.7

9.1

0.5, 0.8, 1.3, 2.7

0.5–2

—

dpore (nm)19–112

397

486

558–1036

70–96

365–772807

2489

358–1258

2044

563–720457

860–1789

185–67710SSA(m2/g)

0.07–0.34——

0.66–1.01

—

—

0.87

1.45

0.68–1.96

1.60

0.44–0.56

1.04

0.63–1.18

0.31–1.64

0.12

Vtotal(cm3/g)Supercapacitors

SIBs

SIBs

Supercapacitors—

Supercapacitors

Supercapacitors

Supercapacitors

Supercapacitors

Supercapacitors

Supercapacitors

LSBs

Supercapacitors

SIBs

LSBs

Applications(Zhang D. et al., 2020)(Ni et al., 2019)(Ye et al., 2016)(Chen et al., 2017)(Balgis et al., 2015)(Arif et al., 2016)(Chen et al., 2016b)(Zhou M. et al., 2018)(Yan et al., 2018)(Xu et al., 2021)(Zhang et al., 2017)(Zhang et al., 2015)(Song Z. et al., 2018)(Wang H. et al., 2020)(Song J. et al., 2018)

Referenceh S,N-HCSs: Sulfur, nitrogen co-doped hollow CSs.

hollow mesoporous CSs; e N,P-PCSs: Nitrogen, phosphorus co-doped porous CSs; f N,P,S-PCSs: Nitrogen, phosphorus, sulfur co-doped porous CSs; g PS/PAN: Polystyrene/polyacrylonitrile;a UHCSs: Ultrathin hollow CSs; b PTCDA: perylene-3,4,9,10-tetracarboxylic dianhydride; c N,P-HPCSs: Nitrogen, phosphorus co-doped hollow porous CSs; d N-HMCSs: Nitrogen-doped

Material

No.

Table 3 Summary of the conditions used for the hard template synthesis of functionalized CSs, the textural parameters of these materials and their potential applications.

Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

Fig. 5 TEM images of carbon particle formation prepared via spray pyrolysis with (a) hollow CSs (using positively charged PSL), the video isavailable publicly at https://doi.org/10.50931/data.kona.21014023 and (b) porous CSs (using negatively charged PSL), the video is available publicly at
https://doi.org/10.50931/data.kona.21014023. Reprinted with permission from Ref. (Balgis et al., 2014). Copyright 2014, American Chemical Society.
Illustrations showing the formation of (c) unimodal and (d) bimodal macroporous CSs, SEM and TEM images of bimodal porous CSs made using
PSL particle sizes of (e, h) 230 and 40 nm; (f, i) 230 and 90 nm; and (g, j) 230 and 120 nm. Reprinted with permission from Ref. (Balgis et al., 2017).
Copyright 2017, American Chemical Society. (k) An illustration of the shell growth on the surface of PSL particles, TEM images of HCSs prepared at
3-aminophenol/PSL mass ratio of (l) 1, (m) 10, and (n) 15 with 63 nm PSL particles in each case. Reprinted with permission from (Arif et al., 2016b).
Copyright 2016, Elsevier.

207

Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

the repulsive or attractive forces between the phenolic
resin and PSL particles, the morphology of the preparedcarbon particles can be precisely controlled. Strong electrostatic attraction between the highly positively charged
PSL and phenolic resin resulted in hollow carbon particles(Fig. 5(a)), whereas the electrostatic repulsion occurredin the presence of negatively charged PSL formed porous
carbon particles (Fig. 5(b)). The mechanism has been proposed in Fig. 2(c) and the detailed explanations are demonstrated in Videos S1 and S2 (available publicly at https://doi.org/10.50931/data.kona.21014023). Furthermore, thefabrication of hierarchical bimodal macroporous carbon
nanospheres with interconnected pores (with applications
as a catalyst support) using a spray pyrolysis techniquewith phenolic resin as the carbon source and PSL spheres as
the template was also introduced, as shown in Fig. 5(c–d)(Balgis et al., 2017). The morphology of the resulting carbon particles was greatly influenced by changing the sizeof PSL particles, as shown in the SEM and TEM images(Fig. 5(e–j)). A bimodal macroporous structure can enhance the surface area available to accommodate Pt loading
without sacrificing active site accessibility. As a means ofimproving the electron-transfer characteristics of carbon,
Arif et al. fabricated HCSs having high-nitrogen content
based on microwave-assisted polymerization together withcarbonization process, using PSL as the template and3-aminophenol as the carbon source (Arif et al., 2016). The
plausible mechanism of shell formation and the chemicalbond between PSL particles and 3-aminophenol during thenucleation process are demonstrated in Fig. 5(k). By altering the 3-aminophenol/PSL mass ratio, the carbon shell
thickness could be precisely adjusted from 14.2 to 66.6 nm,
while the particle size could be easily controlled from 58.2
to 320 nm by changing the PSL particle size (Fig. 5(l–n)).
Another approach for producing porous CSs is by
dissolution of the PSL using organic solvents. Chen etal. proposed a strategy for synthesizing mesoporous
HCSs based on the “dissolution-capture” approach (Chenet al., 2016a). Polystyrene (PS) spheres serving as the
template were prepared by emulsion polymerization andthen coated with mesoporous SiO2. The PS cores were
subsequently dissolved in tetrahydrofuran to form hollow
cavities and was captured by the mesopores of the SiO2
shells. The as-captured PS was then crosslinked based on
Friedel-Crafts alkylation and was utilized as the carbon
source. After carbonization and etching to remove SiO2,
mesoporous HCSs with a uniform mesopore size (4.1 nm)
were obtained.

3. Applications of carbon spheres
3.1 Rechargeable batteriesWith the rapid development of electric vehicles andportable electronics, there have been increasing demands
for the development of lithium-ion batteries (LIBs) hav-208

ing improved energy density, longer cycle life and fastercharging rate. Graphite has traditionally been used as an
anode material but provides a limited theoretical capacity(372 mAh/g) and a rate capability that cannot satisfy therequirements for enhanced performance, especially in thecase of novel sodium-ion batteries (SIBs) or potassium-ion
batteries (PIBs). Therefore, the development of new
carbon-based materials for battery electrodes is critical.
CSs are of particular interest among the variety of promising carbon materials because their small particle sizes andhighly exposed surfaces ensure reduced charge transfer
resistance during metalation/de-metalation. In addition,
an isotropic spherical structure ensures a homogeneous
distribution of current flow and allows a highly packed
particle arrangement, resulting in higher volumetric energy
densities. When employing CSs as an anode material for
metal-ion batteries, large SSA, high pore volume, and good
conductivity are crucial to obtaining the required metal-ion
storage capacity and stability. Specifically, a high SSA
provides for good contact with the electrolyte to sustain anelevated metal-ion flux through the interface, a large porevolume allows for metal ion and volume changes during
metalation, while good conductivity ensures efficient electron transport.
The capacities and rate performances of anodes based on
CSs have been significantly improved using two principal
strategies. One such strategy involves doping single ordual heteroatoms (e.g., O, N, B, S, P) into carbon materials
to modulate the electronic and chemical structures. Suchmodifications can lead to enlarged interlayer distance,enhanced electronic conductivity, improved metal ion/electrolyte absorption and more active sites such that metal ion
storage and cycling performance are enhanced (Long et al.,2017). In addition, the incorporation of such atoms modifies the pore structure. A hierarchical structure is desirable
because micropores promote the reversible intercalation/
de-intercalation of metal ions to afford high storage capacity, whereas meso-/macropores improve metal ion transfer
kinetics and electrolyte mobility (Zhang J. et al., 2020). A
representative example of these strategies was provided byZhang et al., who synthesized nitrogen-doped CSs (N-CSs)
through a HTC method followed by treatment with NH3, in
addition to the use of glucose as the carbon precursor and
CTAB as a surfactant (Fig. 6(a)) (Zhang H. et al., 2019).
The resulting material showed excellent lithium/sodium
ion storage capacity which delivered reversible capacitiesof 578 and 281 mAh/g for LIBs and SIBs, respectively,
at 20 mA/g. As shown in Fig. 6(b), when incorporated in
LIBs, the N-CSs exhibited a capacity of 168.1 mAh/g at
a high current density of 5 A/g, with a retention of 89.7 %even after 4,000 cycles. In contrast, the capacity of CSs(without nitrogen doping) was only 63.8 mAh/g, which
was significantly lower than that of N-CSs. Similarly,
in the case of trials with SIBs incorporating the N-CSs

Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

Fig. 6 (a) An illustration showing the formation process of nitrogen-doped CSs (N-CSs), (b) cycling stability of N-CSs and CSs for LIBs at 5 A/g, and(c) cycling stability of N-CSs and CSs for SIBs at 1 A/g. Reprinted with permission from Ref. (H. Zhang et al., 2019). Copyright 2019, Elsevier. (d) An
illustration showing the fabrication of SiOx@C core-shell particles, (e) galvanostatic charge-discharge profile, and (f) cyclic performance of SiOx@C
particles as a LIB anode at different cycles. Reprinted with permission from Ref. (Cao et al., 2019). Copyright 2019, American Chemical Society.

(Fig. 6(c)), a high capacity of 163 mAh/g was retained
after 500 cycles, whereas the CSs showed a lower specificcapacity of 52 mAh/g at 1 A/g. These results indicate that
nitrogen doping can improve the conductivity of CSs andprovide additional active sites, both of which are crucial forenhancing electrochemical performance.
Interestingly, certain non-carbon materials provide large

theoretical capacities when used as battery anodes, including Si, Sn, Bi, P as well as various transition metal oxides(e.g., phosphides, sulfides, selenides) (Long et al., 2017).
Even so, anodes made of these materials tend to undergo
agglomeration and volume expansion/shrinkage during
metalation/de-metalation and so exhibit poor cycling
performance. CSs represent a means of addressing these209

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problems and high-capacity carbon-based anode materials(e.g., SiOx/C, Fe3O4/C) have been intensively investigatedin attempts to achieve high energy densities. Cao et al. reported the synthesis of carbon-coated SiOx (SiOx@C) coreshell particles through a sol-gel process (Cao et al., 2019).
This work was based on the simultaneous hydrolysiscondensation of tetramethyl orthosilicate (TMOS) together
with the polymerization of 3-aminophenol and formaldehyde in the presence of ammonia as a base catalyst andCTAB as a cationic surfactant, followed by the carbonization process (Fig. 6(d)). The resulting SiOx@C particles
were investigated as an anode material for LIBs and exhibited a reversible capacity of 509 mAh/g at 100 mA/g withapproximately 80 % capacity retention after 100 cycles(Fig. 6(e–f)). This significantly improved electrochemical
performance can be attributed to the structure of the material. The core-shell structure of these particles ensured optimal contact with the carbon matrix, while the round shapeof carbon shell was highly resistant toward stress. These
factors improved the conductivity of SiOx and exerting thefunction of carbon.
On the cathode side, porous CSs have been successfully
used as hosts for conductive sulfur in lithium-sulfur batteries (LSBs). The highly porous structure of these materials
can accommodate a high proportion of sulfur while alleviating its isolating nature, thus facilitating the transport ofelectrons. Most studies show that sulfur is stored preferentially in the pores of carbon shells rather than in the interiorof CSs, which is not conducive to inhibiting the shuttle
effect and attaining high energy density. The process of
sulfur material passing through the porous shell is difficult
due to the capillary condensation effect and the absenceof strong adsorption potential inside the carbon shell. As
a result, the rational design of well-defined interior structure of CSs is an efficient method to suppress the “shuttle
effect” of lithium polysulfides (LiPSs). Xu et al. proposed
the fabrication of mesoporous nitrogen-doped yolk-shellCSs (N-YS-CSs) as novel sulfur encapsulators for LSBs
through a Stöber method (Xu et al., 2018). The appliedresorcinol-formaldehyde was acted as a carbon precursor
and CTAB as a template to form the interior yolk and exterior shell under different hydrolysis polymerization rate.
The N-YS-CSs@S cathode exhibited a high reversible capacity of 909 mAh/g at 0.2 C even after 500 cycles, which
was higher than core-shell type architectures (323 mAh/g).
The yolk-shell structure can effectively fulfill the synergistic effect of physisorption and chemisorption for LiPSs,
and comprehensively absorb the LiPSs within the pores. In
addition, the inner void between the yolk and shell provides
sufficient cavity to accommodate the volume expansion
during cycling, which contributes to the stability of thecycling performance. This strategy provides new ideas forthe development of cathode structures for practical applications in LSBs. The structures, synthesis processes and210electrochemical performance characteristics of CSs andactive material/CS composites used as electrode materials
in rechargeable batteries are summarized in Table 4.

3.2 Supercapacitors

Typically, porous CSs have been employed as electrodematerials in commercial electrical double-layer capacitor(EDLC) devices (Zhang M. et al., 2018). The sphericalstructure of these materials can decrease the distance thatelectrolyte ions are required to migrate so that an electric
double-layer easily forms between the electrode andelectrolyte. The capacitance of EDLCs is directly related
to the contact area of the electrode with the electrolyte. In
principle, increasing the pore volume, SSA and electricalconductivity of porous CS electrode will enhance charge
accumulation in the electrical double layer formation andso improve capacitance (Pang et al., 2018b; Tang et al.,2018). It is generally considered that the presence of micropores in carbon materials significantly increases the SSA,
which in turn plays an important role in enhancing specificcapacitance. However, some investigations have discovered that ultrahigh SSAs may result in decreased volumetric capacitance as a consequence of relatively low packing
densities (Kitamoto et al., 2022; Pang et al., 2018a). For
this reason, it is critical to adjust pore sizes to maximize theion-accessible SSA while decreasing dead volume. Boththeoretical and experimental studies have confirmed thatthe highest capacitance is obtained in the case that the poresize (primarily micropores) is well matched to the ion sizeof the liquid electrolyte, because this scenario maximizes
charge accommodation. However, the slow ion diffusionand desolvation in sub-nanometer micropores mean thathigh capacitance can only be realized at low or moderatecharge-discharge rates, which limits the power density andrate capability. This issue can be resolved by incorporating
macro-/mesopores into microporous carbon structures. In
such cases, the mesopores can act as reservoirs to shorten
ion transport pathways and so boost ion diffusion, allowing for high capacitance retention during rapid charging/discharging (Wang et al., 2018; Zhang et al., 2017). Thus,
optimizing the proportions of micro-, meso-, and macropores in a porous CS electrode is critical to attaining highspecific capacitance and rate performance.
The incorporation of heteroatoms (e.g., N, O, S, B,
P) into porous CSs also plays a vital role to improve theelectrochemical performance. Several studies have been
conducted on the use of porous CSs doped with a variety ofheteroatoms such as nitrogen (Xiong et al., 2017; Zhang L.
et al., 2019), sulfur (Liu et al., 2017), or nitrogen co-dopedwith sulfur (Lv et al., 2020), oxygen (Chen et al., 2017;
Liu S. et al., 2016), and phosphorus (Zhang et al., 2017)as electrodes in supercapacitors. Such modifications provide additional active sites for reversible redox reactions
and so raise the overall capacitance of the sample. Yan etN-CSs

PCSs

CSs

2

3

4

Soft template

Soft template

N-MCSsN-MCSsYS-CSsN,O-YS-CSs

UHCSs

N,P-HPCSs

N-HMCSs

S,N-HCSs

S,N-MHCSs

SiOx@C core-shell

Fe3O4@N-PCSs

Se@N-MCSs

Sn4P3-C core-shell

Bi@C nanospheres

SnS QDs@N-CSs

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

HTC

HTC

HTC +
Phosphorization

Soft template

Hard template

Hard template

Hard template

Hard template

Hard template

Soft template +HTCSoft template(Stöber method)

Soft template

Soft template

MCSs

Spray drying +Carbonization

Extended the
Stöber method

HTC + Activation

5

N-CSs

HTC + Activation

N-ACSs

1

Surfactant-assisted
HTC

Process

No. Material

—

—

—

Pluronic F127

PS-b-PAA

Dopamine andtin(II) chloride

Glucose andbismuth nitrate

Glucose andpotassium stannate

Dopamine

Dopamine

3-Aminophenol
and formaldehyde

Dopamine

CaCO3 sphere
CTAB

Resorcinol andformaldehyde

Dopamine

PMMA sphere

SiO2 sphere

Dopamine

PTCDA

SiO2 sphere
SiO2 sphere

Resorcinol andformaldehyde

Resorcinol andformaldehyde

Melamine andformaldehyde

Dopamine

Chitosan

3-Aminophenol
and formaldehyde

Resorcinol andformaldehyde

StarchGlucoseGlucosePrecursor

CTAB

CTAB

Pluronic F127

PS-b-PAA

—

—

—

—

—

Template(Chong et al., 2021)

KPF6 in DEC/EC/PC(Veerasubramani et al., 2020)

1 M NaClO4 in EC/PC + 10 % FEC
SIBs

280 mAh/g at 100 mA/g

(Zhu et al., 2020)

1 M NaPF6 in DME

400.3 mAh/g at 200 mA/g
SIBs

1 M NaClO4 in DMC/EC + 5 % FEC

680 mAh/g at 500 mA/g

(Choi et al., 2018)(Sun et al., 2021)(Mao et al., 2019)(Cao et al., 2019)(Ni et al., 2019)(Ye et al., 2016)(Zhang et al., 2015)(Wang H. et al., 2020)

SIBs

1 M LiPF6 in DEC/EC + 2 % VC

1 M NaClO4 in DEC/EC + 5 % FEC

1 M LiPF6 in DEC/EC

1 M LiPF6 in DMC/EC/EMC

1 M NaClO4 in DEC/EC + 2 % FEC

1 M NaClO4 in DEC/EC + 2 % FEC

1 M LiTFSI and 0.1 M LiNO3 in DOL/DME

1 M NaClO4 in DEC/EC + 5 % FEC

700 mAh/g at 500 mA/g

378 mAh/g at 100 mA/g

1240 mAh/g at 100 mA/g

509.2 mAh/g at 100 mA/g

240 mAh/g at 500 mA/g

185 mAh/g at 500 mA/g

1656 mAh/g at 0.2 C

302 mAh/g at 100 mA/g

1305 mAh/g at 0.2 C

473.7 mAh/g at 20 mA/g

(Song J. et al., 2018)(Zhang H. et al., 2018)

0.8 M KPF6 in DEC/EC

1 M LiTFSI and 0.1 M LiNO3 in DOL/DME(Guo et al., 2021)

1 M NaPF6 in DEC/EC

373 mAh/g at 60 mA/g
314 mAh/g at 50 mA/g

(Mao et al., 2020)

1 M NaClO4 in PC + 5 % FEC

251 mAh/g at 100 mA/g

(Wang S. et al., 2020)(Zhou H. et al., 2018)

0.8 M KPF6 in DEC/EC(Zhang J. et al., 2020)

1 M LiTFSI in DOL/DME

1163 mAh/g at 0.2 C

241 mAh/g at 25 mA/g

1 M NaClO4 in DEC/EC

1 M NaPF6 in DMC/EC(Zhang H. et al., 2019)(Xiang et al., 2018)

1 M LiTFSI and 0.1 M LiNO3 in DOL/DME
1 M LiPF6 in DMC/EC/EMC

Reference

Electrolyte

LIBs

SIBs

LIBs

LIBs

SIBs

SIBs

LSBs

SIBs

LSBs

PIBs

PIBs

SIBs

SIBs

LSBs

PIBs

210.3 mAh/g at 50 mA/g

281 mAh/g at 20 mA/g

SIBs
SIBs

578 mAh/g at 20 mA/g

1003.7 mAh/g at 0.1 C

Reversible capability

LIBs

LSBs

Battery

Table 4 Structures, synthesis processes, and electrochemical performance characteristics of CSs and active material/CS composites intended for use as electrode materials in rechargeable batteries.

Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

211

212

HTC + Activation

ACSs

PCSs

PCSs

N-HCSs

1

2

3

4

HCSs

PCSs

HPCSs

N,S-PCSs

PCSs

N-HCSsN-MCSsN-PCSsPCSs

N,O-CSs

N-PCSs

N-HMCSs

6

7

8

9

10

11

12

13

14

15

16

17

HCSs

APCSs

N-PCSs

N-MCHSs

N-HPCSs

N,P-PCSs

N-HCSs

N,P,S-PCSs

N,O-HCSs

N-PCSsN-HCSs

18

19

20

21

22

23

24

25

26

27

28

N-YS-MCSs

Pomegranate-like CSs

5

Hard template

Hard template

Hard template + Activation

Hard template

Hard template

Hard template

Hard template + Activation

Soft template + HTC

Soft template + HTC

Soft template

Soft template

Soft template (Extended the Stöber method)

Soft template + HTC

Soft template + HTC

Soft template + Spray pyrolysis

Soft template + Activation

Soft template + Activation

Spray pyrolysis + Carbonization

Spray pyrolysis + Activation

Spray pyrolysis

Spray drying + Carbonization

Spray drying + Carbonization

Spray drying + Carbonization

Spray drying + Activation

Cu2O sphere

CaCO3 sphere

PS sphere

SiO2 sphere

SiO2 sphere

SiO2 sphere

SiO2 sphere

Gemini[C12mim]Br

DDAB

CTAB

CTAC

Pluronic F108

Pluronic F127

Pluronic F127

Pluronic F127

PS-b-PEO

—

—

—

—

—

—

—

3-Aminophenol and formaldehyde

Dopamine

PyrroleAnilinem-Phenylenediamine and formaldehyde

Melamine and formaldehyde

Dopamine

3-Aminophenol and formaldehyde

3-Aminophenol and amphiphilic aliphatic
aldehydes

3-Aminophenol and formaldehyde

Phenol and formaldehyde

Resorcinol and formaldehyde

Phenol and formaldehyde

3-Aminophenol and formaldehyde

Larch sawdust (biomass)

Dopamine

Dopamine

Glucose and glucosamine

GlucoseCoal

Sodium lignosulfonate

Kraft lignin

Kraft lignin

3-Aminophenol and formaldehyde

263.6 F/g at 0.5 A/g

6 M KOH

6 M KOH

1 M H2SO4

473 F/g at 0.2 A/g
270 F/g at 0.5 A/g

6 M KOH

6 M KOH
535 F/g at 0.2 A/g

274 F/g at 0.5 A/g

EMIBF4TEABF4/AN

234 F/g at 0.5 A/g

6 M KOH

6 M KOH

6 M KOH

221 F/g at 0.5 A/g

208 F/g at 0.5 A/g

353 F/g at 0.5 A/g

240 F/g at 0.2 A/g

6 M KOH

6 M KOH
181 F/g at 1 A/g

6 M KOH
256 F/g at 0.2 A/g

178 F/g at 1 A/g
265 F/g at 0.5 A/g

1 M H2SO4

6 M KOH
300 F/g at 1 A/g

6 M KOH
365 F/g at 0.5 A/g

6 M KOH
309 F/g at 0.1 A/g

6 M KOH
338.8 F/g at 0.2 A/g

1 M TEABF4/AN

111 F/g at 1 A/g
433 F/g at 0.5 A/g

1 M H2SO4
EMIMBF4170 F/g at 1 A/g

1 M TEABF4/AN

80 F/g at 0.5 A/g
266 F/g at 0.2 A/g

6 M KOH

240 F/g at 0.5 A/g

6 M KOH

7 M KOH

284 F/g at 0.1 A/g
308 F/g at 1 A/g

6 M KOH

6 M KOH

6 M KOH

6 M KOH

66.5 F/g at 10 mV/s

31.8 F/g at 0.2 A/g

338 F/g at 1 A/g

160 F/g at 1 A/g

196 F/g at 1 A/g

6 M KOH

6 M KOH

303.7 F/g at 0.04 A/g

1 M H2SO4

331.4 F/g at 0.5 A/g

238.1 F/g at 1 A/g
369.8 F/g at 1 A/g

Electrolyte
1 M Na2SO4

Specific capacitance

N-SCSsResorcinol and formaldehyde

GAML

StarchXylosePrecursor

242 F/g at 1 A/g

—

—

—

—

Template

N-YS-CSs

Extended the Stöber method

HTC + Activation

HTC + Activation

Process

No. Material

Table 5 Structures, synthesis processes, and electrochemical performance characteristics of porous CSs as electrode materials in supercapacitors.
Reference(Zhang D. et al., 2020)(Guo et al., 2018)(Chen et al., 2017)(Yan et al., 2018)(Xu et al., 2021)(Zhang et al., 2017)(Song Z. et al., 2018)(Li et al., 2020)(Jia et al., 2022)(Li et al., 2021)(Du et al., 2020)(Liu C. et al., 2016)(Liang et al., 2019)(Liu S. et al., 2016)(Song et al., 2019)(Xiong et al., 2017)(Tang et al., 2017)(Qu et al., 2018)(Tang et al., 2018)(Lv et al., 2020)(Pang et al., 2018b)(Kitamoto et al., 2022)(Cao et al., 2021a)(Feng et al., 2019)(Zhang L. et al., 2019)(Hao et al., 2018)(Zhang J. et al., 2020)(Sun et al., 2020)

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Kiet Le Anh Cao et al. / KONA Powder and Particle Journal No. 40 (2023) 197–218

al. fabricated N,P,S-codoped hierarchically porous CSs(N,P,S-PCSs) by utilizing silica colloid as a hard template,polyaniline as both a carbon and nitrogen precursor, phyticacid as a phosphorus precursor, and ammonium persulfate
as a sulfur precursor (Yan et al., 2018). The well-dispersed
pores provide a facile ion transportation path within theelectrode. The resulting N,P,S-PCSs exhibited both highgravimetric and volumetric specific capacitance valuesof 274 F/g and 219 F/cm3 at 0.5 A/g, which revealed asuperior electrochemical performance. After 10,000 cycles,
the N,P,S-PCSs samples showed the long-term cyclingstability with 95 % initial capacitance retention at 10 A/g.
This is mainly attributed to the uniform doping of N, P, andS as well as the hierarchically porous structure of the N,P,SPCSs. Table 5 summarizes the supercapacitor performanceand the main characteristics of porous CSs prepared byvarious synthetic processes.

4. Conclusions and outlook

Experimentation with spherical particles having attractive structures and multiple functions has proceeded
for many years and these materials are expected to playa significant role in future energy sources. This review
provides an overview of the latest advances and recent
progress in the synthesis of CSs, with a particular focus
on the applications of these materials in electrochemicalenergy storage. There have been significant advances in
the fabrication of CSs with well-designed, controllable
morphologies, desirable textural properties and adjustablefunctionalities. The primary techniques for fabricating CSs
comprise template-free, soft template and hard templateapproaches, all of which are discussed in detail herein toprovide a clear understanding of the capability of eachsynthesis route in terms of controlling structural features.
It is vital to obtain specific chemical compositions andmorphologies when fabricating CSs with targeted functionalities related to potential applications in rechargeable
batteries and supercapacitors. This review demonstrates
that CSs have been extensively employed because the porosity and other structural parameters of these materials arereadily tuned. As such, CSs are of particular interest withregard to addressing critical issues related to rate capability
and cycling stability, as well as facilitating their applicationin emerging energy storage devices. Despite the obvious advantages and significant achievements summarized
above, CSs still face numerous challenges that need to beaddressed before practical applications in energy storage
devices are feasible. A number of suggested future research
directions related to the advancement of this field of study
are detailed below.
(1) The electrochemical performance of CSs requires
further improvement to allow practical usage. As an example, the spherical carbon-based materials having core-shellstructures with porous shells are especially appealing as

anode materials because they are able to host a number ofactive species that provide the electrical conductivity required for stable cycling and efficient use. It is important to
prepare CS materials with pore structures that are tunable
within the micropore and mesopore size ranges and to construct hierarchical structures by connecting micropores tomesopores and macropores. These features would improve
the transport of ions and provide abundant active sites toincrease the ion accessible SSA, leading to enhance theperformance of energy storage devices. It will also be important to consider the packing density of CSs because thisis a critical parameter that is rarely investigated. It is worth
noting that the packing density of CSs is generally low(< 0.5 g/cm3). CSs typically possess hollow and porous
structures, which result in improving the mass-ratio of thepowder and energy density but decrease the volumetric energy density of the device. Consequently, some important
applications of CSs are limited.
(2) The large-scale industrial applications of CSs are still
restricted by the cost and the scale of production, which isvastly different from the estimated and makes them delayedin their adoption. For instance, to enhance the SSA andporosity, highly corrosive chemical activation treatment(e.g., KOH, H3PO4) followed by the removal of impurities is required. These processes typically consume extra
energy and produce significant waste streams. Therefore,
green chemical engineering processes are essential to thesustainable fabrication of porous CSs for energy storage
devices. Future research may focus on the fabrication of
CSs using less toxic and greener chemical activating agentsat a reasonable cost.
Furthermore, it is important to note that the selection ofcarbon precursor has a pronounced effect on the performance of CSs. Biomass-based porous CSs are a promisingmaterial for emerging applications. However, biomass
usually contains pollutants and other impurities. Importantquestions remain unanswered concerning the impacts ofusing these materials to construct CSs. How will these
impurities migrate and transform during the preparation
and application of CSs? What is the effect of these contaminants on the properties and performance of CSs? Answers
to these questions must be determined to advance the use of
biomass as a precursor for CS materials.
(3) The development of reliable synthetic pathways
is still an emerging requirement and may be required toallow the scalable production of CSs with control overtheir structural properties. The innovation and simplicityof the new green synthesis protocol will be a prerequisite
to produce CSs in conjunction with reduced costs, minimal
environmental impact, and improved quality to facilitate
their usage in large-scale energy storage devices. The useof green activation strategies typically provides enhanced
yields. However, these approaches have been underdeveloped and more detailed studies are needed for their

213

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optimization and advancement.
(4) Heteroatom doping is generally used to modify thesurface chemistry of carbon materials but overdoping canvitiate the porous structure and an excess of some atoms
can impair the electrical conductivity of the carbon. The
future development of porous CSs will require an indepth understanding of the structure-function relationshipto allow the formation of desired structure and surface
chemistry that meet the demands associated with various
applications.
(5) The rapid evolution of CSs has been associated withadvances in energy storage technologies. However, there
remains an incomplete understanding of the relationships
between structure and the electrochemical properties ofCSs. More advanced characterizations in conjunction withnumerical simulations may provide better insight into theconnection between structures and electrochemical properties or reaction mechanisms.
In the future, there is likely to be increasing interest in
CS materials for specific scientific and industrial applications and these materials will be used more frequently tosupport the development of energy storage devices. A better understanding of structure-performance relationships isalso anticipated. Moreover, greener and simpler synthesis
methods with reduced environmental impacts will be
developed to enable the full use of renewable resources.
There is no doubt that many opportunities are hidden, andthe researchers should investigate more on utilizing thetremendous potential of functionalized CSs in the applications discussed herein. The progress achieved to date is
expected to inspire future research and the authors hopethat this review paper will accelerate further work toward afuture sustainable society.

Acknowledgements

This work was supported by a JSPS KAKENHI Grant
Number 19H02500, International Network on Polyoxometalate Science at Hiroshima University, the JSPS
Core-to-Core Program, the Hosokawa Powder Technology
Foundation, and the Information Center of Particle Technology, Japan. We thank Michael D. Judge, MSc, from
Edanz Group for editing a draft of this manuscript.

Data Availability Statement

The video data of hollow carbon particle formation
prepared via ultrasonic spray pyrolysis using phenolic resin(−) and PSL (+) is available publicly in J-STAGE Data
(https://doi.org/10.50931/data.kona.21014023).
The video data of porous carbon particle formation
prepared via ultrasonic spray pyrolysis using phenolic resin(−) and PSL (−) is available publicly in J-STAGE Data
(https://doi.org/10.50931/data.kona.21014023).

214

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Authors’ Short Biographies
Kiet Le Anh Cao
Kiet Le Anh Cao received his B.Sc. from University of Science, Vietnam National University, Ho
Chi Minh City in 2014 and M.Sc. from Hanyang University, Korea in 2018. He obtained his Ph.D.
degree in Chemical Engineering from Hiroshima University in 2021. He is currently working as a
post-doctoral researcher in the Department of Advanced Science and Engineering, Hiroshima University, Japan. His research focuses on the rational design and development of novel nanostructured
materials for energy storage and conversion.

Ferry Iskandar
Ferry Iskandar studied transport phenomena at Hiroshima University, Japan, where he earned his
doctorate. From 2007 until 2010, he worked as a faculty staff of Hiroshima University’s Departmentof Chemical Engineering. He is currently an Associate Professor at the Department of Physics at theInstitut Teknologi Bandung in Indonesia. Since 2016, Ferry Iskandar has also been a researcher at theIndonesian Research Center for Nanosciences and Nanotechnology. His broad research interests include material synthesis and physical characterisation, with an emphasis on nanostructured materials
and their applications in the fields of photoenergy conversion and energy storage, including batteries
and capacitors.

Eishi Tanabe
Eishi Tanabe is a senior researcher of Hiroshima Prefectural Technology Research Institute. He received his bachelor’s degree and master’s degree of science in physics from Hiroshima University in
1993 and 1995, respectively. He started his career with electron microscopy in Hiroshima Prefecture
Industrial Research Center in 1995. He received his Ph.D. degree in material science from Shimane
University in 2005. His current research interests are TEM and FIB-SEM Tomography.

Takashi Ogi
Takashi Ogi is a Full Professor in the Department of Advanced Science and Engineering, Hiroshima University. He received his Ph.D. degree in Chemical Engineering in 2008 from Hiroshima
University. He was an Assistant Professor at Osaka Prefecture University from 2008–2010 and
Associate Professor at Hiroshima University from 2015–2021. His current research interests include
synthesis of nanostructured particles materials via liquid phase and aerosol process. Especially, hefocuses on the development of rare earth free/less nanomaterials, and recovery of rare metal using
biosorption.

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KONA Powder and Particle Journal No. 40 (2023) 219–235/https://doi.org/10.14356/kona.2023008 Original Research Paper

Computational Fluid Dynamic Modelling of Fully-Suspended
Slurry Flows in Horizontal Pipes with Different Solids
Concentrations †Gianandrea Vittorio Messa 1*, Qi Yang 1, Maria Graça Rasteiro 2, Pedro Faia 3,
Vaclav Matoušek 4, Rui C. Silva 2 and Fernando Garcia 2
1 Department of Civil and Environmental Engineering, Politecnico di Milano, Italy

2 Chemical Process Engineering and Forest Products Research Centre, Department of Chemical Engineering, University of Coimbra, Portugal

3 Center of Mechanical Engineering, Materials and Processes, Electrical and Computer Engineering Department, University of Coimbra, Portugal
4 Department of Hydraulics and Hydrology, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic

Abstract
This research work is a significant step toward further understanding of the β-σ two-fluid model for the simulationof fully-suspended slurry flows in pipeline systems, with the goal of enhancing its potential for scientific researchand engineering applications. Particularly, the focus of the study is the characterization and handling of the twomain empirical coefficients of the model, namely, β and σ, which require case-specific tuning based on a given setof experimental data. Reference is made to the relevant case of slurry transport in horizontal pipes with infinite
length. The influence of β and σ on different features of the fluid dynamic solution has been extensively investigated,considering also the role played by the specific testing conditions. Based on these findings, a procedure fordetermining appropriate values of β and σ has been developed, which requires only two experimental measurements,
namely the concentration profile from a test at moderate slurry concentration, and the hydraulic gradient fromanother test in which the same slurry flows at high concentration. The procedure has been satisfactorily tested against
published experimental data on pipe transport of fine glass bead and sand slurry.
Keywords: Computational Fluid Dynamics, slurry pipelines, hydro-transport, two-fluid modelling, model
calibration

1. Introduction

Pipeline transport of solid particles in the form of slurryis frequently encountered in several engineering fields,such as mining, chemical, oil and gas, pharmaceutical,
food, among others. In addition, understanding the physicalmechanisms and the complex phenomena occurring in aslurry pipe is an extremely challenging task, as well is thedevelopment of mathematical models for their description.
Therefore, it is not surprising to see that slurry pipe flows
have been driving the efforts of researchers for several
decades.
From a technical point of view, there are a few relevant
parameters which characterize slurry pipe flows. Probably
the key role is played by the hydraulic gradient, im (pressure drop in meters of water per pipe unit length), whichquantifies the energy dissipation produced by the flow,
and, thus, dictates the selection of the pump capacity. The†*

Received 31 January 2022; Accepted 17 March 2022
J-STAGE Advance published online 4 June 2022
Corresponding author: Gianandrea Vittorio Messa;
Add: 20133 Milano, Italy
E-mail: gianandreavittorio.messa@polimi.it
TEL: +39-02-2399-6287

hydraulic gradient for a certain solid volumetric concentration, Cvi, is often expressed as a function of the averageslurry velocity, Vm, estimated by the ratio between thevolumetric flow rate of the mixture, Qm, and the pipe crosssection area, A. When plotted, the trend of im versus Vm, fora certain Cvi, is called pipe characteristic curve. Actually,
two types of concentrations can be defined for slurry pipe
flows: one is the already mentioned Cvi, or in-situ concentration, which is the ratio between the volume of solids andthe volume of slurry in a certain part of the system; theother is the delivered concentration, Cvd, which is the ratio
between the volumetric flow rate of the solids, Qs, and thatof the mixture, Qm. Finally, another important parameteris the deposition limit velocity, Vdl, which is the thresholdvalue of average slurry velocity at which solids deposition
starts to be observed.
The values of the just mentioned technical features andparameters, as well their relations, are strongly related with
the internal structure of the flow. This has been usuallyquantified through several flow regimes, whose names
and definitions are different in the literature. In this study,reference will be made to the well-established classification reported in the textbook of Wilson et al. (2006).

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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Homogeneous flow (Fig. 1a) is typically composed byvery fine solids, smaller than say 40 microns, flowing athigh velocity. In this regime, the distribution of particles isvirtually uniform along the flow. The (Reynolds-averaged)
velocity is axisymmetric across the pipe cross section andthere is no local slip between particles and carrying fluid
velocities. Slurries with particles in the range between
200 microns and up to 1.5 % of the pipe diameter show aheterogeneous behavior (Fig. 1c). In this regime, the particles tend to get separated from the carrying liquid. Thisa

Cv [–]Uz [–]bcdeFig. 1 Main flow regimes of slurry pipe flow: (a) homogeneous flow;(b) pseudo-homogeneous flow; (c) heterogeneous flow; (d) partiallystratified flow; (e) fully stratified flow. The sketches denoted by Cv andUz indicate the typical solid volume fraction and Reynolds-averaged
fluid velocity profiles along the vertical diameter. In the sketches on theleft, the color of the particles indicates the key mechanism driving theflow: grey = particle–fluid interactions; green = quick particle–particle
collisions; red = enduring particle–particle contacts.

220

produces greater non-uniformity of the distributions of thesolids concentration and velocity, especially at low velocities. In the case of coarse sands and fine gravel (size bigger
than say 800 microns), heterogeneous flow becomes more
distinctly stratified with a detectable sliding bed. Finally,
fully stratified flow occurs when large and rapidly settlingparticles travel in the lower part of the pipe by ether saltation or as a moving bed (Fig. 1e). Threshold conditions occur between the three main flow patterns described above.
These are the pseudo-homogeneous regime (Fig. 1b),in-between the homogeneous and the heterogeneous ones,
and the partially stratified regime (Fig. 1d), in-between theheterogeneous and the fully stratified ones. The pseudohomogeneous regime is the focus of the present work. Thispattern takes place when particles in the range in between
40 to 200 micron (typically fine sand) flow at relatively
high velocities, and it is characterized by a moderate, yetclearly detectable, vertical concentration gradient and aslightly asymmetrical velocity profile.
Several sorts of models have been developed for the prediction of slurry parameters. Traditionally, they fit in two
main categories. The first includes models based on integral(cross-section averaged) quantities which produce empirical or semi-empirical correlations for relevant parameterssuch as im or Vdl, as was done by Durand (1953) andThomas (1979). The second category is that of mechanismbased layered models: Wilson (1976) developed one, starting from a simplified description of the inner structure of
the flow.
Indeed, empirical and mechanistic models have beenand still are a powerful tool for slurry pipeline design
and management, as well as for researchers. At the sametime, an intrinsic limitation is that, despite taking the basic
transport mechanisms into account, these models are notable to characterize them at the local level. This does notallow deep insight into the flow, nor exploring geometries
and flow conditions different from those considered forthe calibration of the model coefficients and parameters.
Numerical simulations based on Computational Fluid
Dynamics (CFD) might allow overcoming such limits,
since CFD modelling is inherently local and distributed.
As discussed in Messa et al. (2021b), the CFD approachrequires switching from a macroscopic description of theflow, i.e. the flow regimes in Fig. 1, on which traditionalslurry models are based, to a particle-scale description
based on the key physical mechanisms driving the flow at
the local level. These are interaction between the particles
and the turbulent fluid, quick particle–particle collisions,and long-lasting inter-granular contacts. As it is indicatedby the color of the particles in Fig. 1, homogeneous andpseudo-homogeneous flow are driven by particle–fluid
interactions, whereas fully stratified flow is contactdominated. Conversely, all the three basic mechanisms
play a role in heterogeneous and partially stratified flow.

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There is no doubt that CFD has great potential as a toolfor investigating slurry pipe flows. The local nature of a
CFD model, in fact, opens up the possibility of (virtually)
investigating geometries with no constraints in size andcomplexity, providing much more information when compared to laboratory testing. At the same time, this approachis also not free from criticisms. Depending on the modelling approach and type of flow, CFD simulations might
require large computational costs, which could become
practically unaffordable. For instance, describing particle–
particle interactions in the Lagrangian framework is not
currently feasible for the typical concentrations of slurry
pipelines, making Eulerian-based methods (i.e. two-fluid
model and mixture model) the preferred or even the only
viable route for engineering computations. Apart from that,it must be noted that all CFD models for slurry flows areapproximated in nature, and they include several closure
parameters, and coefficients which, practically speaking,cannot be decided based on other criteria than the fittingof experimental data for certain conditions. This means
that, in the end, CFD modelling cannot dismiss laboratory(or field) testing, but the two approaches should act in asynergistic way. And that is where the main challenges of
CFD modelling arise, especially but not exclusively, in thecontext of its application to slurry pipelines.
As pointed out in Messa et al. (2021b), CFD modellingmight have different scopes and impact. On the one hand,interpretative models are calibrated for specific flow conditions and then used to infer local and difficult-to-measurefeatures of the flow under the same testing conditions only.
This is the case, for instance, of a model whose calibration
procedure uses a set of hydraulic gradient and concentration profile measurements, employed to gather informationon the particle velocity distribution for the same experimental conditions. On the other hand, predictive models
allow predicting one or more features of the flow outside
the calibration conditions. Interpretative models are suitable for achieving a comprehensive and detailed pictureof the flow overcoming the limitations of experiments;
thus, they are very useful in research, in advanced design,
and in the management of existing systems. Conversely,
predictive models can be a fundamental tool for the designof new systems.
Although scientific research on the CFD usage in modelling of slurry pipe flows dates back to the 1980s, with
an impressive increase in the number of published papers
during the last 5–10 years, there is still a long way to go
in the exploitation of its potential of application in thisfield. Among the most significant studies in the field, thoseauthored by Roco and Shook (1983), Ling et al. (2003),
Ekambara et al. (2009), Kaushal et al. (2012), Capecelatro
and Desjardins (2013), Messa et al. (2014), Uzi and Levy
(2018), Messa and Matoušek (2020) shall be mentioned.
The interested reader is referred to Messa et al. (2021b) for

a comprehensive review of the state-of-the-art in the field.
The aforementioned review paper concluded that most of
the existing models might be regarded as interpretative,
even if some works do not meet the proper verificationand validation requirements, being the initial code verification stage via convergence testing one of them. For
sure, very few models proved or even looked predictivein the sense previously introduced. This is not surprising,
because the demonstration of the predictive capacity of amodel requires going well beyond its evaluation with theexisting experimental database, but also the extrapolation
to new sets of conditions. Two different procedures havebeen followed in previous studies. The former consists ofcalibrating the model by referring only to a subset of theexperimental database, while employing the remaining
sets of data for validation. An attempt is this sense wasreported, for instance, in Messa and Matoušek (2020). The
second and different strategy was adopted in Ekambara
et al. (2009) and Messa et al. (2014), and consists of theverification that the same combination of calibration coefficients allows for a reasonably good agreement with thelargest possible experimental data available in the measurements database, which basically means that calibration andvalidation are no longer two distinct phases.
For about ten years, research work has been carriedout at Politecnico di Milano on the development of CFD
models for the simulation of slurry pipe flows. The main
outcome of this research, achieved with important contributions from Concentration, Heat, and Momentum Limited(CHAM Ltd, London) and from Institute of Hydrodynamics of the Czech Academy of Sciences, is the alreadymentioned β-σ two-fluid model. Whereas the first efforts
were devoted to the definition of the model formulation(Messa and Malavasi, 2015), recently the focus has been
directed mostly to the assessment of the predictive capacityof the model.
The research work illustrated in the present paper fits
into this trend and represents an important step towards theusability increase of the two-fluid model in an industrial
context. The role played by the two main tuning coefficientsof the model, β and σ, has been established through an
extensive sensitivity analysis. The goal was to understandwhich features of the CFD solution are mostly affected by
β and σ, taking into account also the physical conditions of
the simulated problem (pipe diameter, bulk velocity, in-situ
concentration, etc.), in order to develop an appropriate
procedure for calibration of the two parameters.
The remaining of the paper is divided into three sections,followed by the conclusions. In Section 2, the main features of the β-σ two-fluid model and of the CFD setup are
briefly illustrated. In Section 3, the results of the sensitivity
analysis are presented, and the procedure for calibrating βand σ is proposed. Finally, in Section 4, three examples
of application of the calibration strategy are presented

221Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

and discussed, referring to glass bead and fine sand slurryexperimental data reported in the literature.

2. Mathematical model

evaluation of the friction parameter μ m , which is expressed as a function of the volume fraction of the solid
phase, through the following empirical correlation,[23]
 2.5 

1
(5)
1




β
 β  1  Φs built and based on the formal analogy with the correlationof Cheng and Law (2003) for the viscosity of the mixtureof (5)
colloidal suspensions. Although μ m (and, thus, also β)does not appear explicitly in Eqns. (1–4), it comes intoplay twice in the β-σ two-fluid model. Firstly, it is used
to evaluate the viscosity of the solid
phase, μs, assuming[23]
that μl, μs, and μ m are related with each other through thefollowing equation

2.1 Overview of the β-σ two-fluid model μ m μlexp 
The β-σ two-fluid model is an extension of the IPSA
(Inter-Phase Slip Algorithm) of Spalding (CHAM, 1994a)for turbulent slurry flow. Being a two-fluid model, it isbased on the Eulerian-Eulerian approach, in which bothphases are interpreted as interpenetrating continua andsolved in the Eulerian, cell-based framework. The reader
is referred to Messa and Matoušek (2020) for the complete
set of conservation equations and closures. Only the essential features are summarized here.
The fundamental conservation equations of the β-σ twofluid model are for the mass and momentum of the twophases. Under steady-state conditions, the ones consideredin this study, their formulation is as follows: μt  Φl ρlU     l Φl 
 σ

 μt

  Φs ρsV     s Φs 
 σ

(1)
  Φl ρlUU  ΦlP    Φl  μl  μlt  U

(2)

 μt

 Φl ρl g  M l     l U Φl 
σ



(1)
(2)

(3)

  Φs ρsVV  ΦsP    Φs  μs  μst  V

(3)

 μt

 Φs ρs g  M s     s V Φs 
σ



(4)

where: Φl, U, μl, and μlt are the locally averaged volume
fraction,
the locally averaged velocity vector, the viscosity,(4)
and the eddy viscosity of the liquid phase, respectively;
Φs, V, μs, and μst are [21]the corresponding variables for thesolid phase; P is the locally averaged pressure, shared by
the phases; g is the gravitational acceleration vector; andMl and Ms are[22]the generalized drag term, which will be
discussed later. Clearly, Φl and Φs sum up to a unit value.
A key feature of the β-σ two-fluid model, inheritedfrom the original IPSA, is the presence of phase diffusion
terms, which are the last terms on the right-hand side in
Eqns. (1–4). The origin of these terms has been discussed
in Messa et al. (2021b) on the grounds of the previous
derivations of Burns et al. (2004): they basically arise fromthe modelling of the correlations between the fluctuating
velocities and the fluctuating volume fractions, and theyaccount for the effect of particle turbulent dispersion. The
coefficient, σ, used to calculate the diffusion coefficientof the phase diffusion terms, is called turbulent Schmidt
number for volume fractions, and it is one of the two main
calibration parameters of the β-σ two-fluid model.
The second calibration coefficient, β, appears in theμ m μlΦl  μsΦs
(6)[23]
The solid phase viscosity μs, in turn, appears both in the
momentum equation for that phase (Eqn. (4)) and in the(6)wall boundary condition for the solid phase, which will
be briefly illustrated later in Section 2.3. In addition to theevaluation of μs, μ m is used to calculate the generalizeddrag terms, Ml and Ms. These two terms account for the exchange of momentum between the two phases within each
local control volume,[23] and, considering the action-reaction
principle, they are equal in intensity and opposite in sign. In
the β-σ two-fluid model, it is assumed that the inter-phasemomentum exchange is uniquely due to the drag force, andso Ml and Ms are given byM s  M l 3Φs ρlCd U  V U  V 
4d p

where dp is the volume-equivalent particle diameter, and Cd
is the drag coefficient. As in many two-fluid models, Cd is(7)
obtained through the Schiller and Naumann (1935) correlation for a single spherical particle. However, a distinguishing feature in the β-σ model resides in the fact that the inputof the Schiller and Naumann’s correlation is not the usual
particle Reynolds number, but a modified Reynolds number based on ρl, dp, |U-V|, and on the friction parameter μ m .
The rationale behind this definition is the need to accountfor the increased flow resistance encountered by a single
particle at high local solid concentration. Additionally,it[23]
is a simple way to account for the effect of particle shapethrough the empirical parameter β.
The eddy viscosities, μlt and μst , are obtained fromtwo-phase turbulence models. Particularly, μlt is calculated
through the two-phase extension of the k-ε standard turbulence model for high Reynolds
number flows, which is
[22]
[21]
embedded in the PHOENICS code (CHAM,
1994b). Thus,[21]
μlt is obtained through the solution of two partial differential equations for the turbulent kinetic energy per unit massof the carrier fluid, kl, and its dissipation rate per unit mass,εl. Conversely, μst is directly obtained from μlt through the[21]
following algebraic constraint

[22]
222

(7)

[21]

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

μlt
μt
 s
ρl
ρs

(8)

2.2 Applicability conditions(8)
The applicability conditions of the β-σ two-fluid model
have been individuated in Messa and Matoušek (2020).
The first comes from the wall boundary condition of thesolid phase, and it imposes that:
d pB

dp
0.25

μl 
 ρlVm D 
0.039




ρlVm 
 μl 


0.5

 30

(9)

where d pB is the ratio between dp and the viscous lengthscale(9) estimated using the Blasius correlation for the coefficient of frictional resistance of turbulent single–phaseflows insmooth straight pipes (Schlichting, 1979), and D[24]
is the pipe diameter. The two other constraints quantify thecondition in which the flow must be dominated by particle
turbulent interactions, without any role being played byparticle–particle interactions. In Messa and Matoušek(2020), such condition has not been related to any of theflow regimes in Fig. 1, but it was referred to as “fully
suspended flow”. However, it could be reasonably claimed
that the flow regime in which the β-σ two-fluid model isapplicable, is the pseudo-homogeneous one (Fig. 1b).
Mathematically speaking, the two constraints are:Vm  1.5VdlT(10)Φs  0.45
(10)

(11)

where VdlT is the estimate of the deposition limit velocity
obtained(11) from the correlation of Thomas (2015).

2.3 CFD[25]setup

The computational domain and the boundary conditions
imposed are the same as in Messa and Matoušek (2020),
namely, a cylindrical volume 120D long, bounded by an
inlet, an outlet, and a solid wall (Fig. 2).
At the inlet, uniform distributions have been imposed
Outlet120D

for the axial velocities of the two phases, U zin Vzin Vm , fortheir volume fractions, Φsin  1  Φlin  C vi, for the turbulent
kinetic energy of the liquid, k lin , and for its dissipation
rate, εlin . The values of the last two parameters[37] have been
set based on a turbulence
intensity of 0.05 and on a length[38]
scale of 0.07D, as explained[26]in Messa and Matoušek(2020).
At
theoutlet,thepressureis set to zero, as well as[27]
the normal gradient of all solved variables. At the walls,
the shear stress of the liquid, τ lw, as well as the values of
the turbulent parameters in the near-wall cells, k lw andεlw , are obtained from the equilibrium wall function option,
available in PHOENICS (CHAM,[28] 1994c). Conversely, thewall shear stress of the solid phase on the surface[29]of thenear-wall cells, τ sw, was obtained through the following[30]
expression, implemented in PHOENICS through a userdefined function:
[31]
2
(12)
τ sw  Φsw ρs ss V 
where the V  is the resultant velocity of the solid phaseparallel to the wall at the first grid node, Φsw is the solid
volume(12) fraction in the near-wall cells, and ss is the friction
factor of thesolid phase, given by the following implicit[32]
equation
[33]
ss 

ln 2



κ2
E  Resw ss

where κ = 0.41 is the von Karman constant, E is a roughness parameter, set at 8.6 as recommended for smooth(13)and Re w is a wall Reynolds number defined in terms
walls,sof ρs, V  , μs, and of the normal distance of the first grid
point to the wall, δ (Fig. 3a). Since μs is obtained from μlt(Eqn. (6)) [34]which, in turn, is a function of β (Eqn. (5)), it is
not surprising
to see that this coefficient plays a role in the[35]
predictions of the frictional losses. Evidence of this claim[21]
will be given later.
In this study, the used code; PHOENICS version 2018
(CHAM, 2002) and all settings of the solution algorithm(FV formulation, differencing schemes, relaxation factors,
convergence criteria, etc.) were the same as detailed in
Messa and Matoušek (2020). Similarly, the grid was astructured one in cylindrical-polar coordinates, consistingof 30, 30, and 200 subdivisions along the azimuthal, radial,
and axial directions, respectively (Fig. 3b). The azimuthalab
Near-wall cell

D

(13)



First grid node
Uǁ

Inlet

Solid wallrzθFig. 2δ

Axis

Computational domain and boundary conditions.

k lw , ε lw

Vǁ

τ lw
τs w
Fig. 3

Sketch of (a) a near wall cell and (b) of the computational mesh.

223

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

and axial subdivisions were uniform, whereas the radial
ones were progressively reduced when moving away fromthe pipe axis. Thus, the smallest radial subdivisions were
those adjacent to the pipe wall. On the grounds of thefindings in Messa et al. (2021a), their size was set equal to
60 times the viscous length scale obtained by the Blasiuscorrelation for turbulent single-phase flows in straightpipes. This implies that, although the total number of cells
was the same for all simulations, their distribution over thepipe section was changed according to the inlet velocity,
Vm. Note that, with the cylindrical-polar mesh, additionalboundary conditions are needed along the pipe axis, whichwere of zero-flux type.

3. Analysis of the effects of β and σ on the
key features of the CFD solution anddevelopment of a calibration strategy

Three necessary steps for proving that a model has a predictive capacity are (i) the identification of the calibration
coefficients, (ii) the assessment of their role on the different
features of the solution, and (iii) the development of a precise calibration strategy. The β-σ two-fluid model has two
main calibration coefficients, β and σ. Note that the numberof empirical parameters becomes much higher if one
includes the numerical constants in the turbulence model.
However, dealing with a large number of tuning coefficients would make the calibration difficult and the model
less robust; therefore, the turbulence model constants were
simply set with the default values for single-phase flow
(Launder and Spalding, 1974), and the β-σ two-fluid model
has been regarded as a two-parameter model.
Once the calibration parameters have been identified,their role in the different features of the CFD solution
must be established. This requires not only to specify therange of variability of β and σ and to select the featuresof the solution of most interest, but also to define thephysical conditions for the sensitivity analysis. In fact, βand σ might play different roles depending on the specificcharacteristics of the slurry flow subject of investigation,i.e. for different levels of concentration, pipe diameter,
bulk mean velocity etc. Starting from this last task, it could
be observed that the physical conditions characterizing aslurry pipe flow are defined by the pipe diameter, D, thepipe roughness, the slurry velocity Vm, the in-situ concentration Cvi, the particle properties, and the properties of thecarrier fluid. In principle, the sensitivity analysis upon βand σ should be carried out within a suitable range of all thephysical features above mentioned, and, for each physicalcondition, different values of β and σ should be considered. However, this would result in an unnecessarily large
number of simulations and, therefore, some simplifications
were made. Firstly, the pipe walls were considered hydraulically smooth, thereby neglecting the pipe roughness. The
choice was made because, in its original formulation, the224

β-σ two-fluid model is unable to account for pipe roughness. Indeed, a rough wall function option is available in
PHOENICS for the liquid phase, but no study has been
made for the solid phase. Additionally, the pipe roughness
in the experiments used for calibrating and validating theβ-σ two-fluid model was generally very low (Kaushal andTomita, 2007; Matoušek, 2002; Schaan et al., 2000). Secondly, also the particle properties, particle size and particle
density were kept fixed in the sensitivity analysis, on the
grounds of the following considerations. On one hand,
typical materials used in slurry pipe flow experiments
include glass beads and natural sand, whose densities arerather close to each other. On the other hand, preliminary
simulations run at the beginning of this research work
indicated that the role played by β and σ is substantially
unaffected by the value of particle diameter within the typical range where this model applies, say 100 μm to 200 μm.
Based on the above, the sensitivity analysis was performed
considering particles with density of 2650 kg/m3 and sizeof 150 μm. Particle shape is very different for glass beadand sand, and also among sand grains. However, particle
shape is not set a priori in the β-σ model, but only indirectly
through the empirical parameters β and σ.
Finally, also the properties of the carrier liquid have notbeen varied (density, viscosity, rheological model) but thecharacteristic features of water have been taken, namelyρl = 998.23 kg/m3, μl = 0.001 Pa∙s, and Newtonian rheology. It is expected that reasonably small variations in termsof ρl and μl will not alter the conclusions of the sensitivity
analysis. The study of the effect of the rheological model
will be addressed in future research, as it will require developing a non-Newtonian formulation of the β-σ two-fluid
model, currently unavailable.
Based on the considerations above, in the selection of
the physical conditions of the sensitivity analysis, reference
was made only to the pipe diameter, D, to the slurry velocity, Vm, and to the in-situ concentration, Cvi. For each of thethree parameters, two “extreme” values have been considered to span the broadest possible range of applicability ofthe β-σ two-fluid model, defined by the three applicability
constraints in Section 2.2. In particular, the pipe diameter
was either 50 mm (“small” pipe) or 500 mm (“large” pipe);
the bulk velocity was either 2 m/s (“low” velocity) and
4.5 m/s (“high” velocity); the in-situ concentration was either 0.05 (“low” concentration) or 0.40 (“high” concentration). Only the “high” velocity was considered in the large
pipe runs, as the 2 m/s value does not fulfil the constraint
Vm 1.5VdlT (Eqn. (10)). Thus, the total number of combinations was equal to six, as summarized in Table 1. For eachof the six aforementioned scenarios, different combinationsof β and σ were considered. Particularly, σ was either 0.50,[36]
0.75, or 1.00, whereas β was either 0.50, 1.50, 2.50, or 3.50.
The effects of varying σ were studied for β = 2.50, whereasthat of varying β was investigated for σ = 0.75.

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

The target variables of the sensitivity analyses were
those of most interest in slurry pipeline systems, namely,
the hydraulic gradient, im, the chord-average volume fraction profile, the vertical profile of the locally-averaged fluid
velocity, and the distributions of the wall shear stresses of
the two phases over the pipe circumferences. Note that thevertical profile of the locally-averaged velocity of the solid
phase has not been investigated, as the axial slip between
the two phases was very small for all simulated cases, as
typical of the pseudo-homogeneous regime.
Due to space limitations, only the most significant results will be provided in the form of tables and figures, but
all of them are available upon request to the corresponding
author. Table 2 summarizes the values of hydraulic gradient, im, as obtained for all the combinations of β and σ andall testing conditions in Table 1. The results highlight thatthe effect of σ on im is negligible or even lacking, whereas
β affects im but only at high concentration (cases C3, C4,C6). More generally, the effect of β at low concentration
was found to be negligible not only in terms of hydraulicgradient, but also in terms of all other features of the flow
under investigation, namely, concentration profile, velocity
profile and wall shear stresses distributions. This is not
surprising, if one considers that β plays a role in the model

Table 1 Simulation scenarios in the sensitivity analysis. The other physical parameters were ρl = 998.23 kg/m3, μl = 0.001 Pa∙s, ρs = 2650 kg/m3,
dp = 150 μm.

Case ID

D [mm]Vm [m/s]Cvi [-]

C1

50

2

0.05

C2

50

4.5

0.05

C3

50

2

0.40

C4

50

4.5

0.40

C5

500

4.5

0.05

C6

500

4.5

0.40

through its appearance in the formula for the friction parameter μ m (Eqn. (5)) and, according to the mathematicalformulation employed, the effect of β on μ m is very limited
at low Φs.
For alllevels of concentration, the predicted concen[23]tration profiles are affected by the valueof σ, as shown[23]
in Fig. 4b, d for the two exemplary cases C1 and C6. Increasing σ will increase the vertical concentration gradient,which is a clear consequence of the phase diffusion terms
in the mass conservation equations (Eqns. (1) and (2)). As
already mentioned, no effect of β can be detected at low
concentration, as shown in Fig. 4a for case C1. Conversely,β was found to significantly affect the concentration profile
at high concentration, as seen in Fig. 4c for case C6.
The predicted velocity profiles appear substantiallyinsensitive to the values of β and σ for all the testing conditions. The greatest effect is observed when varying β athigh concentration, but the change in the velocity values isstill rather moderate (Fig. 5).
The wall shear stresses of the two phases are, in general,
affected by both β and σ. At high concentration, an obvious
increase in τ sw is evident for increasing β (Fig. 6a), whichis the direct consequence of the higher friction factor ss
produced by a higher wall Reynolds number Resw (Eqn.
(13)). At both[31]low and high concentration, varying σ will
modify the individual distributions of τ lw and τ sw (Figs. 6b,c). However, comparing the curves in Figs. 6b,[34]c with thecorresponding volume fraction profiles (shown in Fig. 4b
for case C6 at β = 2.5 only) suggests [28]that the[31]variations inτ lw and τ sw mainly arise from the effect of σ on the volume
fraction in the near-wall cells rather than from a direct
influence of σ on the friction factors of the two phases. It[31] to note that, although σ has an effect on theis interesting[28]
individual wall shear stresses distribution, this parameter
was found to have practically no influence on the hydraulicgradient (Table 2), which is related with the average valueof their sum:Table 2 Effect of β and σ on the predicted hydraulic gradient for the six testing conditions in Table 1.

Model parameters
β

2.5

0.50

1.50

2.50

3.50

σ

0.50

0.75

1.00

0.75

C1

0.079

0.080

0.082

0.080

0.080

0.080

0.081

C2

0.331

0.333

0.335

0.332

0.332

0.333

0.333

C3

0.198

0.199

0.199

0.144

0.162

0.199

0.293

C4

0.825

0.825

0.825

0.607

0.680

0.825

1.203

C5

0.023

0.023

0.023

0.023

0.023

0.023

0.023

C6

0.051

0.051

0.051

0.040

0.044

0.051

0.071

im [-]

225

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235
1

β = 0.5
β = 1.5
β = 2.5
β = 3.5

0.6
0.4
0.2

0.05

0.1

0.15

0.2

〈Φs⟩γ [-]

1

0
0.34

0.36

Fig. 4

0.38

0.4

0.46

Case C4
σ = 0.75

β = 0.5
β = 1.5
β = 2.5
β = 3.5βFig. 5

0.6

0.8

1

Uz/Vm [-]

1.2

1.4

Effect of β on the predicted fluid velocity profile for case C4.

 D 2  D 2π w
ρl g im  π τ l  τ sw  dθ
 4  2 0

(14)

Based on the results of the sensitivity analysis, a strategy
was proposed for the calibration of the two parameters β(14)and
σ. The strategy is arranged into two steps in sequence.
Firstly, in a first-attempt, β is chosen and an appropriate
σ is determined by referring to the concentration profile
data at low concentration. Secondly, using this value of σ,a more appropriate β is found by referring to the hydraulic%%%%%%%%%%%%%%%%%%%%%%%
226

0.05

0.1

0.15

0.25

0.2

〈Φs⟩γ [-]

Case C6
β = 2.5

0.8
0.6
0.4

0
0.34

σ

σ = 0.50
σ = 0.75
σ = 1.00
0.36

0.38

(d)
0.4

0.42

0.44

0.46

〈Φs⟩γ [-]

0.2
0
0.4

0

Effect of β and σ on the predicted chord-averaged concentration profile for cases C1 and C6.

0.8y/D [-]

0.44〈Φs⟩γ [-]

1

0.4

0.42

(b)

0.2

(c)

β

σ

1

y/D [-]y/D [-]

0.4
0.2

0.6

0.25

0.6

β = 0.5
β = 1.5
β = 2.5
β = 3.5

σ = 0.50
σ = 0.75
σ = 1.00

0.4

0Case C6
σ = 0.75

0.8

0.6

0.2

(a)
0

Case C1
β = 2.5

0.8y/D [-]y/D [-]

0.8

01

Case C1
σ = 0.75

gradient data at high concentration. Therefore, in order tocalibrate β and σ, one needs at least one experimentally
determined concentration profile at low concentration (say10 %) and one measured value of hydraulic gradient at highconcentration (say 30–40 %). In the two calibration tests,
the same type of particles shall be used, but no specificconstraints are given in terms of mixture velocity and pipe
diameter, provided that the three applicability conditionsof the β-σ model are fulfilled. As it will be demonstrated in
Section 4, the two calibration tests might have different Vm.
In principle, the pair of values of β and σ obtained from thiscalibration procedure should allow for reasonably accurate
prediction over the entire range of Cvi spanned by the two
calibration tests, and that they are also appropriate overthe entire range of Vm determined by the two conditions
defined by Eqns. (9) and (10) (which does not depend on
the Cvi).
The rationale behind the proposed strategy is the solutionof the β-σ two-fluid model is practically insensitive to thevalue of β for low concentration cases, as shown in Table 2(cases C1, C2, C5) and Figs. 4a and 6a, whilst, for highconcentration cases, the hydraulic gradient is not affectedby σ but only by β (Table 2, cases C3, C4, C6). However,for the same high concentration cases, β does not influence
only the hydraulic gradient, but also the concentration

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235
τlw

τsw

12

8
6
4
2
0

10

60

120

180

240

300

6

(b)
0

60

120

θ [°]
τlw

τsw

β = 0.5
β = 1.5
β = 2.5
β = 3.5τlw

90

Case C6
σ = 0.75

β(c)y/D [-]

60

30

τsw

0

60

120

240

300

360

180

240

300

360

θ [°]

σ = 0.50
σ = 0.75
σ = 1.00

Case C6
β = 2.5

(d)

60

σ
30

σ

β
0

180

θ [°]

τlw, τsw [Pa]90

Case C1
β = 2.5

σ

σ

4

0

360

σ = 0.50
σ = 0.75
σ = 1.00

8

2

(a)
0

τsw

12

Case C1
σ = 0.75

τlw, τsw [Pa]

τlw, τsw [Pa]

10

τlw

β = 0.5
β = 1.5
β = 2.5
β = 3.5

0

0

60

120

180

240

300

360

θ [°]

Fig. 6 Effect of β and σ on the predicted distributions of the wall shear stresses of the two phases for cases C1 and C6. According to the coordinate
system in Fig. 1, θ = 0 and θ = 180° indicate the crown and the invert of the pipe, respectively.

profile (Fig. 4c). Therefore, the effects of σ and β cannot be
fully decoupled, as requested by the proposed calibration
methodology; as a result, deviations might be expected forhigh concentration cases in terms of concentration profile.
Strengths and weaknesses of the calibration strategy, and of
the β-σ formulation from a more general perspective, will
be discussed in the next section. Three verification tests
will be presented, employing experimental data reported in
the literature.

4. Verification of the calibration strategy
against experimental data

4.1 Experiments by Kaushal and Tomita (2007)
on fine glass-bead slurry

The calibration strategy was firstly applied to the flow
conditions extracted from the experimental tests on fineglass-bead slurry flow performed by Kaushal and Tomita(2007). The experiments were performed in a pipe witha diameter of 54.9 mm using glass beads with density of
2470 kg/m3 and mean particle diameter of 150 μm. The
narrow particle size distribution enabled the assumption of

mono-disperse particles, inherent to the two-fluid modelling approach. The ranges of average velocity and in-situ
concentration considered in the present study were 2 to
5 m/s and 10 to 40 %, respectively, disregarding the data at
Vm = 1 m/s and those at Cvi = 50 % because they were outof the range of applicability of the β-σ two-fluid model. For
each condition, Kaushal and Tomita (2007) provided thehydraulic gradient and the concentration profile. In order
to measure the latter quantity, they used a sampling probe
and a γ-ray radiometric device, detecting full consistencyof the two measurement techniques for fine glass beads.
The concentration profiles obtained with the γ-ray device,
which were the chord-average ones, were considered forcomparison with the CFD simulations.
The first-attempt value of β was 0.5, and the case
Vm = 4 m/s and Cvi = 10 % was chosen for the calibrationof σ. As it is shown in Fig. 7a, the predicted concentration
profile with σ = 0.5 was in very good agreement with theexperimentally determined for this case, and additional tests
confirmed the same finding for the other flow velocities
with Cvi = 10 %. However, it must be observed that the227Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235
1(a)exp. data
CFD: β = 0.5, σ = 0.25
CFD: β = 0.5, σ = 0.50
CFD: β = 0.5, σ = 0.75
CFD: β = 0.5, σ = 1.00

0.6

0

25

0

0.05

0.1

0.15

〈Φs⟩γ [-]

(b)

0.6

Cvi = 40%
Cvi = 30%

0.4

Cvi = 20%
Cvi = 10%

0.2

0.2

0

0

0.1

Glass bead slurry
Vm = 3 m/s
20
Cvi = 40%

0.8

15

0.6

10
5
0
10-6

β = 0.25

β = 0.001
2.6% deviation

10-4

β [-]

10-2

100

Fig. 7 Calibration for glass bead slurry (data from Kaushal and Tomita2007): (a) calibration of σ based on the measured concentration profile
for Vm = 4 m/s and Cvi = 10 % (β = 0.25); (b) calibration of β based on themeasured hydraulic gradient for Vm = 3 m/s and Cvi = 40 % (σ = 0.50).

uncertainty of the concentration data was not considered,as this information is difficult to estimate and it was notreported by the experimenters. Indeed, the consistency ofthe concentration profiles obtained by the sampling probe
and the γ-ray device indicates the reliability of the data, but
it is well known that local concentration values might bequite inaccurate close to the pipe bottom. This leads to theconclusion that it is not possible nor meaningful to seek foran “optimum” value for σ in a strict sense but, rather, thegoal is to find “reasonably accurate” value of σ.
Once σ was defined as 0.5, an appropriate value of β wasdetermined by referring to the measured hydraulic gradientfor Vm = 3 m/s and Cvi = 40 %. Fig. 7b shows the percentage deviation between predicted and measured im for different values of β. The CFD model overestimates im for every
β. Particularly, for β ranging from 10–5 to 0.25, the calculated hydraulic gradient is minorly affected by the valueof β, both in absolute terms (im varies from 0.2437 m/m
to 0.2483 m/m), as well as in terms of relative deviationfrom the experimental value (which is between 0.70 %and 2.60 %). The impossibility to find an “optimum” valueof β which fully matches the experimentally-determinedhydraulic gradient suggests that there exists some intrinsic

0.2

(b)

0.7

calculated im [m/m]Deviation from i exp
m [%]

30

σ

Glass bead slurry
Vm = 4 m/s
Cvi = 10%

(a)

0.8

0.4
0.2

228

1

y/D [-]y/D [-]

0.8

exp. data
CFD: β = 0.25, σ = 0.50
CFD: β = 0.001, σ = 0.50

0.3

0.4

〈Φs⟩γ [-]

0.5

+15%

-15%

0.5
0.4
0.3
0.2

β = 0.25
β = 0.001

0.1
0

0

0.2

0.4

0.6

measured im [m/m]0.8

Fig. 8 Validation of the model with β = 0.25 and σ = 0.5 (continuouslines, filled points) and β = 0.001 and σ = 0.5 (dotted lines, unfilledpoints) for glass bead slurry (data from Kaushal and Tomita 2007): (a)concentration profiles for Vm = 3 m/s (b) hydraulic gradient.

limitation in the β-σ formulation, which prevents its ability
to capture accurately the physical processes driving slurryflows at high concentration. Nonetheless, in this first test
case, the accuracy of the β-σ two-fluid model in terms of
hydraulic gradient is high (<2.60 %) for every β between
10–5 and 0.25. Considering that the simulation convergence
was more and more difficult to achieve as β decreased,probably because of issues in the numerical evaluationof μ m through Eqn. (5), initially it was decided to takeβ = 0.25. Such value did not produce any convergenceissue, and resulted in a 2.6 % overestimation of the hydraulic[23] gradient in the calibration case, which was judged asatisfactory enough level of agreement.
The conjecture underlying the proposed calibration
strategy is that the pair of values σ = 0.5 and β = 0.25 allow
for reasonably accurate prediction of concentration profileand hydraulic gradient for Cvi between 10 % to 40 % (thatis, within the calibration conditions) and for Vm between
1.41 m/s and 5.95 m/s (as obtained from Eqns. (10) and(9), respectively). This could be partially verified throughGianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

3.5) and, additionally, that the horizontal axis is relatively
narrow in Fig. 4c. Similar considerations can be made to
explain why, in the parity plot in Fig. 8b, the effect of β on
the predicted hydraulic gradient is very moderate, and visible only for a couple of points, whereas Table 2 indicatesa higher degree of influence for cases with Cvi = 40 % (C3,C4, C6). The comparison in Fig. 8 indicates that even theimpossibility to identify an “optimum” β does not affectthe overall degree of accuracy of the β-σ model, thus its engineering effectiveness. It also gives strength to our claimthat the poor agreement between measured and calculated
concentration profiles at Cvi = 40 % is not a consequenceof an inappropriate value of β, but, rather, is due to some
intrinsic modelling limitation.

4.2 Experiments by Matoušek (2002) on fine sandslurryA second verification of the calibration procedure was
performed by referring to fine sand slurry flow experiments
reported in Matoušek (2002), in which the pipe diameter
is of 150 mm and the solids have a density of 2650 kg/m3
1

exp. data (series 1)
exp. data (series 2)
CFD: β = 0.5, σ = 0.50
CFD: β = 0.5, σ = 0.75
CFD: β = 0.5, σ = 1.00

y/D [-]

0.8
0.6
0.4
0.2 Fine sand slurry
Vm = 4.24 m/s
Cvi ≈ 14%
0
0
0.05
0.1
30

Deviation from i exp
m [%]validation with respect to all other testing conditions reported at the beginning of the present section (14 cases intotal), for which however, the velocity range is a little bit
narrower (2 m/s to 5 m/s).
With β = 0.25 and σ = 0.5, reasonable agreement was
obtained in terms of the concentration profile, as exemplified through the continuous lines in Fig. 8a for Vm = 3 m/s
and increasing Cvi. The β-σ two-fluid model is able
to accurately predict the concentration profiles up to
Cvi = 30 %. Conversely, for Cvi = 40 %, the experimental
data suggested a reverse slope close to the pipe invert,
and this effect is not captured by the CFD model, resulting in rather poorly predicted concentration profile. This
give strength to the claim that there exists some inherent
inability of the β-σ two-fluid model to capture the physics
of highly concentrated slurry flows at high velocity, whichcannot be remedied simply by adjusting β. Particularly,
the absence of the lift force in generalized drag term could
explain why the β-σ two-fluid model is not able to reproduce the reversal in the slope of the concentration profile
observed experimentally, as already guessed by Kaushal
et al. (2013). Additionally, it is not excluded that particle–
particle collisions could take place for slurry flows at
Cvi = 40 %, even at high flow velocity. The β-σ formulation
is not able to account for these effects, which is instead a
peculiar feature of two-fluid models based on the Kinetic
Theory of Granular flow, such as those used by Ekambara
et al. (2009) and Kaushal et al. (2013).
Beyond these considerations, the parity plot in Fig. 8b
indicates the capacity of the β-σ two-fluid model with
β = 0.25 and σ = 0.5 in predicting the hydraulic gradientwith satisfactory accuracy (the filled points correspondto this combination of β and σ). The maximum absolutedeviation between calculated and measured im is 17.5 %,and the mean absolute deviation is 8.70 %.
A weak point in the analysis here above relies in the
choice of the value of β = 0.25, which appears quite arbitrary. As already noticed, the points in Fig. 7b indicate thatevery value of β within 10–5 and 0.25 produces broadly
the same hydraulic gradient prediction, which is also
close to the measured value (deviation < 2.60 %), but thenumerical convergence becomes more and more difficult
as β decreases. In order to investigate the actual impact of βon the predictive capacity of the β-σ two-fluid models, thesimulations were repeated with β = 0.001 and σ = 0.5 andthe new results are shown in Fig. 8 using dotted lines andunfilled points. No differences are detected in terms of concentration profile. At first glance, this finding might appear
in contrast with the results of the sensitivity analysis shownin Fig. 4c, indicating an influence of β on the concentration
profile for case C6 in which Cvi = 40 %. However, theinconsistency might be explained considering that thetwo values of β compared in Fig. 8a (0.001 and 0.25) arelower than the range of β in the sensitivity analysis (0.5 toFine sand slurry
Vm = 4.25 m/s
25
Cvi = 34%

σ

(a)
0.15

〈Φs⟩γ [-]

0.2

0.25

0.3

20
15
10

β = 0.50
6.3% deviation

β = 0.25

5
0
10-3

(b)
10-2

10-1

β [-]

100

Fig. 9 Calibration for fine sand slurry (data from Matoušek 2002):(a) calibration of σ based on the measured concentration profile for
Vm = 4.24 m/s and Cvi = 14 % (β = 0.50); (b) calibration of β based on themeasured hydraulic gradient for Vm = 4.25 m/s and Cvi = 34 % (σ = 0.75).
Both repetitions are shown in plot (a), whether the mean hydraulic gradient between the two repetitions is considered in plot (b).

229

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

230

exp. data (series 1)
exp. data (series 2)
CFD: β = 0.25, σ = 0.75
CFD: β = 0.50, σ = 0.75

1Vm ≈ 6 m/s

(a)

0.8

y/D [-]

0.6

Cvi ≈ 33%

0.4

Cvi ≈ 26%

0.2
0Cvi ≈13%
0

0.1
0.3

calculated im [m/m]and a mean particle diameter of 130 μm. Only six testing
situations fulfill the applicability conditions of the β-σ twofluid model, corresponding to two values of Vm (≈4.2 m/s
and ≈6 m/s), and three levels of Cvi (≈14 %, ≈28 %,≈33–34 %). For the six cases, the experimenter provided
hydraulic gradient and chord-average concentration profiles by repeating the tests twice.
The concentration profile for Vm = 4.24 m/s andCvi ≈ 14 % was used to decide an appropriate σ, using afirst-attempt β equal to 0.5. The findings in Fig. 9a indicatethat a good match with the experimental data is obtainedfor σ = 0.75 and σ = 1, and the former value is used in theremaining simulations. Afterwards, a suitable value forβ was determined from the measured hydraulic gradientfor Vm = 4.25 m/s and Cvi ≈ 34 %. The points in Fig. 9b
show some similarity with the corresponding ones for fineglass-bead slurry, shown in Fig. 7b. In both cases, in fact,
the calculated im increases with increasing β, and showsa plateau for low values of this coefficient. However, adifference exists in terms of the deviation between thepredicted and the calculated im within the plateau region,
which is around 0.7 %–2.6 % in the previous glass-beadcase (Fig. 7b, in which Cvi = 40 %), and around 6 % for thepresent fine sand case (Fig. 9b, in which Cvi = 34 %). This
indicates that, in the fine sand case, the inherent limitationsin the β-σ formulation for highly concentrated slurries havean obvious impact on the accuracy of the hydraulic gradient predictions despite the lower value of Cvi (34 % insteadof 40 %). As a first-attempt, it was decided to use the samevalue of β of the glass-bead case, that is, β = 0.25, whichcorresponds to an overestimation of the hydraulic gradientof about 6.3 %.
With β = 0.25 and σ = 0.75, the predicted concentration
profile was in a good agreement with the experimental data
in Matoušek (2002) for all the five conditions (note thatthe sixth profile had been already used for calibrating σ).
Exemplary results are reported for Vm ≈ 6 m/s in Fig. 10a,using continuous lines to denote the CFD predictions
with β = 0.25 and σ = 0.75. In this second test case, theconcentration profile is well predicted even for the highest
value of Cvi (34 %), in contrast to the findings of the firstvalidation example (grey-black series in Fig. 8a, where theCvi was 40 %). This suggest that the limitations of the β-σformulation affect different features of the CFD solution,
depending on the testing conditions. In the first validation
example, the hydraulic gradient was well predicted but
the concentration profile was not. In the second test case,the situation seemed to be the opposite. The deviation interms of hydraulic gradient prediction is within 15 % forfive points out over six, and equal to 9.4 % on average(filled points in Fig. 10b). This indicates that the predictive
capacity of the calibrated model can be satisfactory for theapplications, although obviously a six-point population is
not sufficient to provide accurate statistics.

0.2

(b)

0.3

〈Φs⟩γ [-]

0.4

0.5

+15%

0.2

-15%

0.1
β = 0.25
β = 0.50

0

0

0.1

0.2

measured im [m/m]0.3

Fig. 10 Validation of the model with σ = 0.75 and β = 0.25 (continuouslines, filled points) and β = 0.50 (dotted lines, unfilled points) for finesand slurry (data from Matoušek 2002): (a) concentration profiles (bothrepetitions are shown); (b) hydraulic gradient (experimental data are theaverage over the two repetitions).

As for the glass bead case, also in the fine sand one
the first-attempt value of β is quite arbitrary, owing to thepresence of a plateau in Fig. 9b. Thus, it was decided toinvestigate how the choice of a different value of β affects
the predictive capacity of the model. The new value of β
was 0.50, which, in Fig. 9b, is outside the plateau region
and produces an overestimation of the hydraulic gradientof about 7.5 %. In Fig. 10, the CFD results obtained with
β = 0.50 and σ = 0.75 are shown using dotted lines orunfilled points. Similarly to what has been obtained in Section 4.1, changing β did not produce appreciable variationsin terms of the concentration profiles and, except for a couple of points, even for the hydraulic gradient. Once again,
the lack of influence of β even for the highest concentration
observed in Fig. 9 is not inconsistent with the findings ofthe sensitivity analysis shown in Table 2 and Fig. 4. In
fact, this can be explained through a number of reasons,such as: (1) the two values of β in Fig. 9 (0.25 and 0.50)

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

are quite close to each other and below the range used in
the sensitivity analysis (0.5 to 3.5); (2) the horizontal axis
in Fig. 10a is much more restricted than that in Fig. 4c; (3)the maximum Cvi in Fig. 10, equal to 34 %, is lower thanthe maximum Cvi in the sensitivity analysis, equal to 40 %.

4.3 Experiments by Schaan et al. (2000) on Lane
Mountain sand slurry

In the final verification test, use was made of datareported in Schaan et al. (2000). Among the experiments
described in the paper, calculations are directed to those
performed in a 158.5 mm pipe using Lane Mountain sand.
This type of sand is composed of small grains (medianparticle size is 90 μm) with high angularity, which produce
significant frictional losses when flowing at high concentration. The density of the particles is the typical value forsand, that is 2655 kg/m3. Hydraulic-gradient data and a few
chord-averaged concentration profiles were provided for
Vm between 1 m/s and 5 m/s and three levels of Cvi between
about 14 % and about 39 %.
The case Vm = 3 m/s and Cvi ≈ 14 % was considered
for determining an appropriate value of σ by imposing
the agreement between the calculated and the measured
1y/D [-]
0

0.05

0.1

Lane Mountain sand
Vm = 3 m/s
Cvi ≈ 39%

40

Deviation from i exp
m [%]

σ

20

〈Φs⟩γ [-]

0.2

0.25

0.3

β = 3.75

-40

(b)
1

2

β [-]

0
0.2

0.25

3

4

Fig. 11 Calibration for Lane Mountain sand slurry (data from Schaan
et al. 2000): (a) calibration of σ based on the measured concentration
profile for Vm = 4.24 m/s and Cvi = 14 %; (b) calibration of β based on themeasured hydraulic gradient for Vm = 3 m/s and Cvi = 40 %.

0.3

(b)

0.25

-20

0

Cvi ≈ 32%

0.4

0.3

0

-60

Cvi ≈ 37%

0.2

(a)
0.15

0.6

calculated im [m/m]y/D [-]

0.4
Lane Mountain sand
Vm = 3 m/s
Cvi ≈ 14%

(a)

0.8

0.6

0.2

exp. data
CFD: β = 3.75, σ = 0.75

1exp. data
CFD: β = 0.5, σ =0.50
CFD: β = 0.5, σ =0.75
CFD: β = 0.5, σ =1.00

0.8

0concentration profile. Based on the obtained results, shownin Fig. 11a, it was chosen to set σ equal to 0.75. Then,
Vm = 3 m/s and Cvi ≈ 39 % were used to decide on theβ value. The relative deviation from the experimentally
determined hydraulic gradient versus β, shown in Fig. 11b,
suggests a similar trend to those of the two other test cases,
if ones considers that the horizontal axis in Fig. 11b is
not in log-scale and that the range of β was narrower. The
tendency of the points towards some limiting deviation
for lower β is evident, which confirms the modelling
limitations of the β-σ formulation for high concentration.
Nonetheless, here it was possible to identify a value of β(that is, 3.75) which fully matches the measured hydraulic gradient. Thus, the coefficients values σ = 0.75 andβ = 3.75 were judged appropriate for Lane Mountain sandflow in a 150 mm pipe, with Cvi between 14 and 39 % (asobtained from the two calibration cases), and Vm between
1.79 m/s and 10 m/s (as obtained from Eqns. (10) and (9),respectively).
Validation was performed with respect to all other datareported in Schaan et al. (2000) fulfilling the conditions

0.35〈Φs⟩γ [-]

0.4

0.45

0.5

+15%

0.2

-15%

0.15
0.1
0.05
0

0

0.1

0.2

measured im [m/m]0.3

Fig. 12 Validation of the model with σ = 0.75 and β = 3.75 for Lane
mountain sand (data from Schaan et al. 2000): (a) exemplary comparison of concentration profiles for Vm = 3 m/s; (b) parity plot of predicted
vs measured hydraulic gradient.

231

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

above. The accuracy was satisfactory in terms of bothconcentration profiles and hydraulic gradient, as shown in
Fig. 12.
As a final note, the value of β in the third validation
example is significantly higher than that of the other two.
This might be a consequence of the high angularity of theLane Mountain sand particles used in the experiments of
Schaan et al. (2000). This suggests that, as already guessedin one of the first papers concerning the β-σ two-fluid models (Messa and Malavasi, 2015), the parameter β might berelated with the effect of particle shape. Nonetheless, a fullunderstanding of the features of the slurry flows dictating
the values of β and σ has not been achieved yet, and it will
drive the future research efforts of the authors.

5. Conclusions and open questions

The present research work marked a step forward in theuse of the β-σ two-fluid model as an engineering-effective
tool with predictive capability. Particularly, the focus wason the analysis of the role played by the two main tuning
coefficients of the model, namely, β and σ. The main findings are as follows:● The value of σ mainly affects the slope of the predictedconcentration profile, without any influence on the hydraulic gradient and on the velocity field. The value of β
is important for slurries with high in-situ concentrationand it affects both the hydraulic gradient and the solid
concentration profile, but not the velocity field. The
role played by β and σ on the different features of the
CFD solution does not depend on flow velocity and pipe
diameter.
● A procedure has been proposed for deciding the valuesof β and σ through experimental calibration. It consists
firstly in evaluating σ based on a single concentration
profile at low in-situ concentration (say around 10 %),followed by the calculation of an appropriate β based ona single hydraulic gradient point at high slurry concentration (say around 30–40 %). This strategy relies on the
negligible effect of β on the solution of the β-σ two-fluid
model at low slurry concentration.
● The calibration procedure was verified based on experimental data reported in the literature, referring toboth glass bead and sand slurry. The validation study
indicated some degree of robustness of the calibrated
parameters with respect to changes in velocity, particle
size, and in-situ concentration. It has also been shown
that, although in some cases it might not be possible
to find a value of β which fully matches the measured
hydraulic gradient for the high concentration calibrationcase, selecting different values of β within a reasonable
range does not affect the overall performance of the β-σ
two-fluid model. The level of accuracy appears generally
satisfactory for the application requirements but it might
result in poor predictions for highly concentrated slurries

232(in-situ concentration higher than about 35 %).
At the same time, some unresolved issues demand forfurther research. Two examples are given below.
Firstly, the calibration yielded for σ = 0.50 and σ = 0.75
intended for the validation tests on glass beads and the two
types of sand, respectively. Bearing in mind that no optimum value of σ could be obtained in a strict sense, due tothe uncertainty of the local volume fraction measurements,
in order to provide a better characterization of σ it would be
important to understand the origin of this difference. Is it
due to the different material (glass beads versus sand) and/or to the different pipe size (54.9 mm versus 150 mm)? Or
do other factors come into play? Similar open questions
can be made for β, which appears related with the shape of
the particles, but still lacks of a comprehensive characterization.
Additionally, the data in Figs. 7b, 9b and 11b, indicate
that, for highly concentrated slurries, the hydraulic gradient is insensitive to β below a certain threshold. This is alimitation of the β-σ formulation, which appears uncapableof reproducing all the physical mechanisms driving thetransport of massive amounts of particles, thus demanding for further improvement. At the moment, the impactof this limitation varies according to the specific testing
conditions. In the first two validation examples (glass beadslurry and fine sand slurry), it led to the already mentioned
impossibility of finding an optimum β which matches theexperimentally determined hydraulic gradient. Indeed,such limiting deviation is more significant for the finesand case (≈6 %, Fig. 9b) rather than for the glass beadone (<2.6 %, Fig. 7b), where, however, inaccuracies areobserved in terms of the concentration profile (Fig. 8a,grey-black series). In the last validation example, whichrefers to slurry with highly angular particles, a best value ofβ was identified (Fig. 11b) and, based on the available data,
the predictions are accurate in terms of both concentration
profile and hydraulic gradient (Fig. 12).

Acknowledgements

The research at University of Coimbra was sponsoredby national funds through FCT—Fundação para a Ciência
e a Tecnologia, under projects UIDB/00102/2020 and
UIDB/00285/2020.

Data Availability Statement

The data analysis file and the data supporting the findings of this study are available in J-STAGE Data
(https://doi.org/10.50931/data.kona.19861708).

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Authors’ Short Biographies
Gianandrea Vittorio Messa
Gianandrea Vittorio Messa studied Civil Engineering (Hydraulics) at Politecnico di Milano, where
he received the M.Sc. cum laude and the PhD with honors in 2009 and 2013, respectively. Since
2021, he is Associate Professor at Politecnico di Milano, where he teaches courses of Fluid Mechanics and Hydraulics. His main research interest is the numerical modelling of particle-laden flows andrelated phenomena, such as slurry pipe transport and slurry erosion of pipeline components.

Qi Yang
Qi Yang is currently a Ph.D. candidate in Civil and Environmental engineering at Politecnico di
Milano. His doctoral work is focused on the CFD modelling of slurry flow in pipelines, including
development and application of Eulerian-Eulerian two-fluids models.

Maria da Graça Rasteiro
Maria da Graça Rasteiro graduated in Chemical Engineering at the University of Coimbra, didpost graduate studies in Loughborough University, UK, and obtained a PhD in Chemical Engineering/Multiphase Processes, in 1988, at the University of Coimbra, under the co-supervision ofProfessor Brian Scarlett. She is presently an Associate Professor with Aggregation in the Chemical
Engineering Department of the University of Coimbra in Portugal. She is a member of the working
groups on Particle Characterization and Particle Mechanics of the European Federation of Chemical
Engineering, and is presently a member of the Portuguese Engineers Association and a founding
member of the Portuguese Society for Engineering Education (SPEE). Her research interests havealways been in the field of Particle Technology and she started the first Portuguese laboratory on
particle characterization. Presently, she has got research interests in the field of particle aggregation,
development of natural polymers for particle flocculation including microplastics, rheology of particle suspensions, solid/liquid flow modelling including fiber flow modelling, and on experimental
techniques for flow visualization including tomographic techniques.

Pedro M. Faia
Pedro M. Faia received his degree in Electrical Engineering (1990), his M.S. in Automation(1994) and his Ph.D in Electronic Materials (2003) from the University of Coimbra. He is an Auxiliary Professor at the Department of Electrical Engineering and Computers of the University of
Coimbra where he teaches. His research interest’s concern: chemical and electrochemical sensors;
impedance spectroscopy technique and applications; electric and ionic materials characterization;
Electrical Impedance Tomography for multiphase flow visualization; Instrumentation.

Václav Matoušek
Václav Matoušek is Full Professor of Water Engineering and Water Management at Faculty of
Civil Engineering of Czech Technical University in Prague. He received the Master Degree in Civil
Engineering at this university in 1986 and the PhD in Mechanical (Dredging) Engineering fromthe Delft University of Technology, the Netherlands, in 1997. His research focuses primarily on
two-phase flows with a special attention to pipeline transport of slurries, slurry pumping, rheologyof mixtures, flow of rheologically active slurries, sediment transport in open channels and rivermorphology.

234

Gianandrea Vittorio Messa et al. / KONA Powder and Particle Journal No. 40 (2023) 219–235

Authors’ Short Biographies
Rui César Silva
Rui César Silva has a background in chemical engineering from the University of Porto and in2016 attained his PhD approval from the University of Coimbra, under the theme Solid-liquid suspension flow in pipes: modelling and experimental investigation where the first Portuguese electrical
tomography system was successfully constructed. He worked at Hovione Farmaciência, a pharmaceutical CDMO, from 2016 until 2021, acting as technical scientific leader on several projects with
topics ranging from particle engineering, hollow-fiber separations, chemical process modelling anddata science. During his tenure at this company, he was one of fastest members to achieved seniority
and lead projects to commercialization, through process performance qualification, in record time.
He authored several works presented at scientific conferences and served as a tutor on both Masterand PhD thesis on hollow-fiber crystallization and particle size reduction technologies. Presently, heworks in R&D Process Development and Scale-up at Amyris Portugal.

Fernando A. P. Garcia
Fernando A. P. Garcia was graduated as a chemical engineer in the Instituto Superior Técnico of
the Technical University of Lisbon. He obtained a Master degree in Biological Engineering in Chemical Engineering and then a PhD degree in Chemical Engineering in the University of Birmingham,UK. He started his academic career in the University of Coimbra, first as an assistant demonstrator
in the areas of fluid dynamics and transport phenomena. After his doctorate he became an AssistantProfessor and later an Associate Professor. Apart from the transport phenomena, his main interests
are in the area of biocatalysis and downstream operations of fermentation processes and in treatment
of industrial effluents. He is a member of several scientific institutions. He served as secretary of the
ESAB, the European Section of Applied Biocatalysis of the European Federation of Biotechnology
and later as a member of the Executive Board.

235

KONA Powder and Particle Journal No. 40 (2023) 236–249/https://doi.org/10.14356/kona.2023010 Original Research Paper

Evaluation of a Coating Process for SiO2/TiO2 Composite
Particles by Machine Learning Techniques †Taichi Kimura 1*, Riko Iwamoto 2, Mikio Yoshida 2, Tatsuya Takahashi 3,
Shuji Sasabe 3 and Yoshiyuki Shirakawa 2*
1 Applied Chemistry, Graduated School of Science and Engineering, Doshisha University, Japan

2 Department of Chemical Engineering and Materials Science, Faculty of Science and Engineering, Doshisha University, Japan
3 Hosokawa Micron Corporation, Japan

Abstract
In this study, in order to optimize a fabrication process for SiO2/TiO2 composite particles and control their coating
ratio (CTi), regression models for the coating process were constructed using various machine learning techniques.
The composite particles with a core (SiO2)/shell (TiO2) structure were synthesized by mechanical stress under various
fabrication conditions with respect to the supply volume of raw materials (V), addition ratio of TiO2 (rTi), operation
time (t), rotor rotation speed (S), and temperature (T). Regression models were constructed by the least squares method(LSM), principal component regression (PCR), support vector regression (SVR), and the deep neural network (DNN)
method. The accuracy of the constructed regression models was evaluated using the determination coefficients (R2)
and the predictive performance was evaluated by comparing the prediction coefficients (Q2). From the perspectiveof the R2 and Q2 values, the DNN regression model was found to be the most suitable model for the present coatingprocess. Moreover, the effects of the fabrication parameters on CTi were analyzed using the constructed DNN model.
The results suggested that the t value was the dominant factor determining CTi of the composite particles, with theplot of CTi versus t displaying a clear maximum.
Keywords: coated composite particles, machine learning techniques, neural network, coating ratio, SiO2/TiO2

1. Introduction

Composite particles synthesized by compounding techniques have been extensively utilized in various fields(Al-Salihi H.A. et al., 2019; Karger-Kocsis J. et al., 2014;
Pierpaoli M. et al., 2019). Since powder properties playa key role in the suitability for a particular application(Kimura T. et al., 2020), selecting the optimal combinationof particles is crucial for the performance of the composite
particles (Deki Y. et al., 2018). For example, composite
particles consisting of oxides, such as SiO2 and TiO2, areutilized in cosmetics and ceramics because of their superior
physical properties including optical and electrical properties (Adebisi A.A. et al., 2016; Himoto I. et al., 2016). Consequently, it is necessary to control the powder properties
during the fabrication of composite particles.
In the synthesis of composite particles, compounding by
mixing and dispersion, which involves convection, shear,and diffusion processes, is extensively applied. These
Received 11 March 2022; Accepted 12 May 2022
J-STAGE Advance published online 25 June 2022
* Corresponding Author: Taichi Kimura and Yoshiyuki Shirakawa;
1,2 Add: 1-3 Tatara-Miyakodani, Kyotanabe, Kyoto 610-0321, Japan
E-mail: cyjf1702@mail4.doshisha.ac.jp;
yshiraka@mail.doshisha.ac.jp
TEL: +81-774-65-6569 FAX: +81-774-6842†

236

compounding techniques require a combination of impact,
compression, and friction to overcome the aggregation
forces. (Kim K. et al., 2016). In many cases of conventional
processes using mixers and mechanical compounding machinery, premixing and compounding in other equipment
is necessary because the mechanism of mixing of a single
device is biased toward convection, shear, or diffusion(Thongnopkoon T. et al., 2018). Several fabrication methods for composite particles have been reported, althoughachieving control over the powder properties of the particles during the fabrication processes remains challenging
owing to the complex relationships between the powder
properties and fabrication conditions (Matsuoka Y. et al.,2021). Hence, it is essential to develop methods of predicting these complex relationships to control the powder
properties of the composite particles.
Data with complex correlations are often analyzed bystatistical and machine learning methods (Kaneko H. and
Funatsu K., 2015; Wada S. et al., 2021; Zhao Z. et al.,2018), such as the least-squares method (LSM), principal
component regression (PCR), and support vector regression (SVR) (De Backer A. et al., 2021; Tran H. et al., 2018;
Zhang Z. et al., 2021). In addition, a deep neural network(DNN) analysis has proved useful in a variety of fields,
including agriculture (Cai Y. et al., 2019; Qui Z. et al.,Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

2018), environmental studies (Ghatak M.D. and Ghatak A.,
2018; Tanzifi M. et al., 2018), and medicine (Basheer I.A.
and Hajmeer M., 2000; Horie Y. et al., 2019; Stokes J.M.
et al., 2020). DNN analysis is one of the typical machinelearning methods and aims to replicate the neural circuitsof the human brain by a mathematical model using multiple
artificial neurons. A DNN learns by adjusting parameters in
the model and can predict complex correlations (Basheer
I.A. and Hajmeer M., 2000; Zhang G. et al., 1998).
In previous studies (De Backer A. et al., 2021; Tran H.
et al., 2018; Zhang Z. et al., 2021), regression models wereconstructed to examine the relationships between several
explanatory variables and the objective variables using
various methods, including LSM, PCR, and SVR, andtheir predictive performance was improved by considering
multiple explanatory variables. For example, Matsuoka etal. (Matsuoka Y. et al., 2021) investigated the relationships
between the operating conditions and the physical properties of oral solid dosage tablets during a continuous manufacturing process using a DNN model, which successfully
predicted the physical properties of the tablets from theoperating conditions with high accuracy. In another study,
regression models were constructed to estimate the volume
loss of AA7075/Al2O3 composites during wear test at various operating conditions using the LSM, SVR, and DNN
methods, and the performance of the machine learning
was compared with statistical analysis for this challenging
situation involving complex correlations between the explanatory and objective variables (Aydin F., 2021).
In this study, the optimization of prediction techniques
for the complex correlation between powder properties
and fabrication conditions by constructing regression
models using several methods was investigated. Furthermore, parameters affecting the powder properties of SiO2/
TiO2 composite particles were analyzed with a focus on
the coating ratio (CTi) of the composite particles. The
SiO2/TiO2 composite particles were synthesized using a
powder processing system (NOB-MINI, HOSOKAWA
Micron Co., Japan). Because of the balanced effects ofcompression, shear, and impact on individual particles, thefabrication apparatus used in this study enables particle
design and particle processing, such as compounding,surface modification, and spheronization. To analyze thecorrelations between the fabrication conditions and CTi ofthe SiO2/TiO2 composite particles, the supply volume (V),
addition ratio of TiO2 (rTi), operation time (t), rotor rotation
speed (S), and temperature (T) were varied. On the basis of
the experimentally determined CTi values of the composite
particles prepared under various fabrication conditions,regression models for CTi of the composite particles wereobtained from the machine learning methods. Furthermore,
the optimized models were used to predict CTi values of thecomposite particles under various fabrication conditions.

2. Experimental and construction methods
for regression models
2.1 Materials and experimental procedure

The composite particles were prepared from SiO2 (MT150W, Tayca Co., Japan), as the core particle and TiO2
(Silsic T-1 (S-1), Yamamori Tsuchimoto Inc., Japan) as theshell particle. The SiO2 and TiO2 particles were supplied
to the experimental apparatus (NOB-MINI, HOSOKAWA
Micron Co., Japan). The supply volume of the raw materials (V) and the addition ratio of TiO2 (rTi) were varied in theranges of 25–125 mL and 5.0–15.0 wt%, respectively. The
operation time (t) was set to 1–15 min. The rotation speed(S) and temperature (T) were set to 2,200–5,700 min–1 and
295–319 K, respectively. Under all experimental conditions, the electric current was maintained at constant valueof 3.5 A. In total, the SiO2/TiO2 composite particles weresynthesized under 29 sets of experimental conditions, assummarized in Table 1. The surface morphology of the
SiO2/TiO2 composite particles was examined by scanningelectron microscopy (SEM; SU3500 II; Hitachi High-Tech
Science Co., Japan), and the Si and Ti distributions were
measured using an energy-dispersive X-ray spectrometer(EDS; Ultim Max, Oxford Instruments Co., Japan) connected to the SEM. The mapping time was set at approximately 120 s. The coating ratio (CTi) of the composite
particles was calculated according to Eqn. (1) from theintegrated areas of Si (SSi) and Ti (STi) in the elemental
mapping images by using imaging software (WinROOF;
Mitani Corp., Japan):
CTi =

S Ti
S Si + S Ti

(1)

2.2 Construction of regression model(1)
The regression models were constructed as described in
Sections 2.2.1–2.2.4. The fabrication parameters (V, rTi, t,
S, and T) were employed as the explanatory variables toserve as the input values. CTi was chosen as the objective
variable to serve as the output value. The 29 samples
were split into training to construct the regression models (21 samples) and test data to evaluate the predictive
performance of the constructed models (8 samples). The
input and output values (z) were auto-scaled by following
Table 1 Summary of fabrication conditions for the SiO2/TiO2 composite
particles.

Experimental condition
Supply volume (V) [mL]

25–125Addition ratio of Ti (rTi) [wt%]5.0–15.0

Operation time (t) [min]1–15

Rotation speed (S) [min–1]

2,200–5,700

Temperature (T) [K]

295–319237

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

Eqn (2):

z' =

(2)

zμ
σ

where z′ denotes the auto-scaled value, and μ and σ represent the mean and standard deviation of each explanatory(2)
variable, respectively.
The determination coefficient (R2) was used to evaluate
the accuracy of the regression models constructed fromthe training data. The predictive performance of the constructed models was assessed by calculating the predictive
coefficient (Q2) using the test data. The root mean squareerror (RMSE) for each model was also calculated. The
formulas used to calculate the R2, Q2, and RMSE are given
in Eqns. (3)–(5): ε LSM, 1 εLSM, 2ε LSM =    ε LSM, m (6) β LSM (1) 
β
(2) 
(6)
β LSM =  LSM 



β
(n) 
 LSM (7)

 y1 y 
y=  2
(7)
  
y 
 m

(8)

n

R2 = 1i=1(yicalc.  yi ) 2ni=1(ymean  yi ) 2

(3)

n

(3)2
i=1(yipred.  yi ) 2
Q =1
ni=1(ymean  yi ) 2

RMSE =(4)

1 n calc.,pred.
 yi ) 2(yin i =1(4)(5)

LSM modeling is a construction method that involves
determining the coefficients (βLSM) that minimize thesum-of-squares error (SLSM) between calculated values(ycalc.) and experimental values (y). When the numbers ofsamples and explanatory variables are m and n, respectively, the vector of error (εLSM), βLSM, y, and matrix of
explanatory variables (X) can be expressed as shown in
Eqns. (6)–(9):238x1(n) x2 (n) 
xm (n) (9)Furthermore, the ycalc. and y vectors are given by Eqns. (10)and (11), respectively:

where yi, yicalc., and yipred. represent the experimental value,
the calculated value using the training data, and the pre(5) value using the test data, respectively, the subscript
dictedi denotes the i-th sample, and ymean is the average of theexperimental values.
The R2 value indicates the accuracy of model construction because it expresses the degree of agreement between
the experimental values and the calculated values whentraining data are substituted into the models constructedfrom training data. Meanwhile, the Q2 value reflects thedegree of agreement between the experimental values andthe predicted values when test data are substituted into
the models constructed from training data. The maximum
value for both R2 and Q2 is 1.0, and values closer to 1.0
indicate a higher quality model. The RMSE has a positive
value and is used to evaluate errors in the numerical prediction, where a smaller value indicates superior predictive
performance (Barrasso D. et al., 2015).

2.2.1 Least-squares method (LSM) x1(1) x1(2)  x (1) x (2) 
2
(8)
X= 2



 x (1) x (2)  mmy calc. = XβLSM(9)y = XβLSM + ε LSM

(10)
(11)

Because smaller errors between the ycalc. and y vectors were(10)
desired, the βLSM vector minimizing the SLSM matrix, which(11)
is determined by the square sum of the εLSM vector, wassought by partial differentiation of Eqn. (11), to afford therelationship shown in Eqn. (12):X T XβLSM = X T y(12)Furthermore, when the inverse matrix of the XTX matrix
was multiplied from the left of both sides of Eqn. (12),(12)
the βLSM vector was optimized as shown in Eqn. (13), thus
constructing the LSM model:

βLSM = (X T X ) -1 X T y(13)For simple relationships, such as linear and quadratic
functions consisting of a small number of parameters(13)
serving as explanatory variables, the LSM is a suitable
method. Because the order of the explanatory variables was
set to 1, the LSM model constructed in this study is a liner
regression.

2.2.2 Principal component regression (PCR)

PCR modeling is a construction method in which explanatory variables are transformed into principal components that are uncorrelated with each other. The algorithm
for PCR model construction consists of the following two
steps (Hotelling H., 1957).
In the first step, principal component analysis (PCA)
is conducted. When the score vector (tPCR) is defined as

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

a linear combination of the X matrix, the tPCR vector is
obtained as described by Eqn. (14):(14)

t PCR = XpPCR

where pPCR is the weight of the linear combination, whichis referred to as loading.
(14)
Because PCA is performed by maximization of thescore, the sum-of-squares score (SPCA) is maximized by using the Lagrange multiplier (GPCR) expressed in Eqn. (15):yi  f (x i ) ε = max ( 0, yi  f  x i   ε )(18)Minimization of Eqn. (17) affords a regression modelwith a satisfactory balance between generalization capabil(18)ity and ability to adapt to training data. When an x vector is
inputted, a y value is predicted by Eqn. (19):N

y = f  x =  αi  αi * K  x i , x + uSVR

(19)

i= 1

where K is a kernel function, and uSVR is a constant. As
m
 n

 n

GPCR = S PCA  λ   ((p j ) 2  1) = (ti ) 2  λ   ((p j ) 2  1)the kernel function for this study, the radial basis function(19) given by Eqn. (20) was adopted:
 j= 1 i= 1j =1kernel

m
 n

 n

S PCA  λ   ((p j ) 2  1) = (ti ) 2  λ   ((p j ) 2  1)(15) i = 1
j =1
 j= 1(15)

where j represents the number of explanatory variables. n is
the number of solutions of the equation represented by the
λ value. The tPCR vector with the n-th largest variance of λis defined as the n-th principal component, and the principal component matrix (T) is given by Eqn. (16):t PCR, 1(1)t(2)PCR, 1T=t(m) PCR, 1t PCR, 2 (1) t PCR, 2 (2) 


(
)
m

t PCR, 2

2.2.3 Support vector regression (SVR)

An SVR model is constructed using a support vector
machine (SVM) for regression analysis. In SVR modeling,a kernel trick along with SVM is applied to construct nonlinear models. The original form of the SVR minimizes thematrix (SSVR) shown in Eqn. (17), which is related to thevectors of error and coefficient in SVR:N
1
w 2 + C  yi  f (x i ) ε
2
i= 1

(17)

where f and w denote the SVR model and a weight vector,
respectively, ε is a threshold, C is a penalty term that(17) the trade-off between the model complexity andcontrols
training errors, and N is the number of training data. The
second term of Eqn. (17) is the ε-insensitive loss function,
as defined in Eqn. (18):(20)

where γ represents a turning parameter for controlling thewidth of the kernel function, and αi and αi* in Eqn. (19)(20)
are obtained from Eqns. (17) and (18) by minimizing the
Lagrange multiplier (GSVR), as expressed in Eqn. (21):
GSVR =NN
1 N N
K ij (αi  αi *)(α j  α j *)   yi αi  αi * + ε αi  αi *
2 i= 1 j= 1
i= 1
i =1

t PCR, n (1) 
N
N
(2) 
1 N N
t PCR,Gn
K ij (αi  αi *)(α j  α j *)   yi αi  αi * + ε αi  αi * = (16)SVR
2 i= 1 j= 1


i= 1
i =1
(21)

(
)
m
t PCR, n 
and the α and α * values are subject to

The PCR model was constructed by adopting the T(21)matrix as the explanatory variables in a similar manner as
described
for LSM in Section 2.2.1, and the relationship(16)
between the output variables and the feature components
was optimized by varying the number of principal components (NP.C.) within the range of 1–5.
For processes with a correlation between the explanatory variables, the PCR method is considered the optimal
selection because the impact of explanatory variables withdependent relationships on the accuracy of the regression
model is reduced.

SSVR =K  x i , x = exp (  γ ( x i  x ) 2 )i

 0  αi *
α j  C(21)i

i = 1, 2, …, N

(22)

N

 αi  αi * = 0
(22)

(23)

i= 1

and Kij in Eqn. (21) is
K ij = K  x i , x j 
(23)

(24)

In SVR modeling, the hyperparameters (C, ε, and γ values) have to be set beforehand. The hyperparameters were(24)
selected by a comprehensive grid search (Kaneko H. and
Funatsu K. et al., 2013).
The SVR method is suitable when it is necessary toconstruct a regression model to predict processes involving
nonlinearities and negligible error ranges.

2.2.4 Deep neural network (DNN)

A neural network (NN) model is constructed by optimizing the hyperparameters, such as the numbers of hidden
layers (NH.L.) and neurons (NNeur.), the activation function,and the number of learning times (NL.T.). An NN with an
NH.L greater than two is referred to as a DNN. In this study,
the NH.L. and NNeur. values were each varied in the range of
1–10. As the activation functions, the sigmoid function,
tanh function, and rectified linear unit (ReLU) function
were compared. The sigmoid function has a long history
as an activation function for NN models. The tanh function
has been reported to learn faster than the sigmoid function

239

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249(Ismail H.Y. et al., 2019; Shirazian S. et al., 2017). In recent
years, the ReLU function has seen increasing use because
of several advantages such as reduced gradient vanishingand faster calculation (Roggo Y. et al., 2020). The sigmoid
function, tanh function, and ReLU function are expressedin Eqns. (25), (26), and (27), respectively:h x =

1
1+ e  x(25)

h  x =e x  e x
e x + ex

(26)

(25)
(x > 0)x
h  x = (27)0(
x  0)
(26)
In all cases, the stochastic gradient descent method was
adopted as the optimization method. NL.T. was varied in the(27)
range of 30–3,500. The hyperparameters are summarizedin Table 2.
For systems in which the explanatory variables and objective variables are intricately related, the DNN method is
an appropriate selection.

Table 2 Summary of the hyperparameter ranges used to optimize the
DNN method.

Parameter
Number of hidden layers (NH.L.) [-]1–10

Number of neurons (NNeur.) [-]1–10

Activation function

SigmoidHyperbolic tangent
Rectified linear unit

Loss function

Mean square error

Optimization method

Stochastic gradientdescent

Learning times (NL.T.) [-]30–3,500

t = 1 min3. Results and discussion

3.1 Fabrication of SiO2/TiO2 composite particles

To investigate the effects of the fabrication conditions
on CTi of the SiO2/TiO2 composite particles, the particles
were synthesized under 29 sets of conditions by varying thevalues of V (25–125 mL), rTi (5.0–15.0 wt%), t (1–15 min),S (2,200–5,700 min–1), and T (295–319 K). V, rTi, t, S, andT were set to include the maximum and minimum values
within the operable range of experimental manipulations.
The SEM and EDS images of the SiO2/TiO2 composite
particles for t values of 1 and 10 min are shown in Fig. 1
to demonstrate the surface morphology and the state of the
TiO2 coating on the SiO2 particles. The V and rTi values
were 25 mL and 5.0 wt%, respectively. The S and T values
were varied with an increase in the t value. From the EDS
observations, the CTi values of the composite particles were
calculated using Eqn. (1). The data obtained from the fabrication experiments are listed in Table 3. In addition, thedata subjected to auto-scaling using Eqn. (2) are listed inTable 4. During the construction of the regression models
using the machine learning methods, data from 21 of thefabrication experiments were used as training data. The
data from the remaining eight fabrication experiments wereused as test data to evaluate the predictive performance ofthe constructed models.

3.2 Model construction using machine learning
methods

The regression models were constructed using fourmachine learning methods, namely, LSM, PCR, SVR, and
DNN. Regression analysis was performed to predict CTi ofthe SiO2/TiO2 composite particles depending on the valuesof V (25–125 mL), rTi (5.0–15.0 wt%), t (1–15 min), S(2,200–5,700 min–1), and T (295–319 K), which were input
as explanatory variables. CTi of the composite particles was
set as the objective variable to serve as the output value.
The accuracy of the constructed regression models with

Si

Ti

Si

Ti

25 µm
t = 10 min

25 µm
Fig. 1

240

SEM and EDS images of the SiO2/TiO2 composite particles obtained at t values of 1 min and 10 min.

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249
Table 3 Experimental data for the relationship between the fabrication conditions and CTi of the composite particles.

Run

V [mL]

rTi [wt%]t [min]S [min–1]

T [K]

CTi [%]

1

25

5.0

1

5,500

295

34.6

2

25

5.0

3

5,600

316

57.1

3

25

5.0

5

5,500

308

35.2

4

25

5.0

10

5,600

317

40.5

5

25

5.0

15

5,700

317

41.0

6

50

5.0

10

3,500

305

44.5

7

50

5.0

15

3,500

306

35.5

8

100

5.0

10

2,500

302

37.1

9

100

5.0

15

2,500

303

45.7

10

50

10.0

10

3,700

307

39.5

11

50

10.0

15

3,700

308

42.5

12

100

10.0

10

2,600

303

46.7

13

100

10.0

15

2,600

304

45.1

14

75

5.0

10

2,800

304

52.7

15

75

5.0

15

2,800

304

36.2

16

125

5.0

10

2,200

301

61.5

17

125

5.0

15

2,200

301

40.0

18

100

5.0

3

2,500

310

34.7

19

100

5.0

5

2,500

313

37.6

20

100

5.0

10

2,500

315

39.6

21

100

10.0

3

2,600

312

43.6

22

100

10.0

5

2,600

315

53.1

23

100

10.0

10

2,600

316

53.0

24

100

15.0

3

2,800

315

49.3

25

100

15.0

5

2,800

317

57.2

26

100

15.0

10

2,700

319

63.9

27

50

15.0

3

3,800

319

59.8

28

50

15.0

5

3,800

315

52.1

29

50

15.0

10

3,800

316

63.8

respect to the training data was evaluated by calculating theCTi' = 0.0669V ' + 0.616rTi' + 0.0215t ' + 0.0532S ' + 0.242T 'R2 values according to Eqn. (3), and the predictive perfor(28)CTi' = 0.0669V ' + 0.616rTi' + 0.0215t ' + 0.0532S ' + 0.242T 'mances of the constructed models with respect to the test
where V′, rTi′, t′, S′, and T′ denote the auto-scaled values
data were compared using the Q2 values calculated from(28)
of each fabrication parameter and CTi′ is the auto-scaled
Eqn. (4). The errors of the regression models were evalu(28)value of CTi of the SiO2/TiO2 composite particles. Hence
ated by calculating the RMSE values according to Eqn. (5).
the coefficients in Eqn. (28) reflect the influence of the corresponding parameter on CTi of the composite particles. All
3.2.1 Construction of the LSM modelof the coefficients were positive, indicating that increasing
The LSM model represents the relationship between thethe value of each fabrication parameter increased CTi of the
explanatory variables and objective variable (Stojanovic B.
composite particles. Furthermore, the contribution of each
et al., 2016). When the order of the explanatory variables
parameter to CTi of the composite particles was calculated
is 1, as in this study, the relationship derived by the LSM isby comparing the absolute values of the coefficients. The
linear. Thus, the regression model constructed by the LSM
contributions of the parameters decreased in the followaccording to Eqns. (6)–(13) is expressed in Eqn. (28):
ing order: rTi (61.6 %) > T (24.2 %) > V (6.69 %) > S

241

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249
Table 4 Auto-scaled data for the relationship between the fabrication conditions and CTi of the composite particles.

Run

V [mL]

rTi [wt%]t [min]S [min–1]

T [K]

CTi [%]

1

–1.55

–0.82

–1.74

1.91

–2.24

–1.28

2

–1.55

–0.82

–1.30

2.00

0.96

1.18

3

–1.55

–0.82

–0.85

1.91

–0.34

–1.21

4

–1.55

–0.82

0.26

2.00

1.05

–0.63

5

–1.55

–0.82

1.37

2.08

1.07

–0.58

6

–0.78

–0.82

0.26

0.12

–0.74

–0.20

7

–0.78

–0.82

1.37

0.12

–0.52

–1.18

8

0.78

–0.82

0.26

–0.77

–1.20

–1.01

9

0.78

–0.82

1.37

–0.77

–1.10

–0.07

10

–0.78

0.43

0.26

0.30

–0.46

–0.75

11

–0.78

0.43

1.37

0.30

–0.20

–0.42

12

0.78

0.43

0.26

–0.68

–1.06

0.05

13

0.78

0.43

1.37

–0.68

–0.86

–0.13

14

0.00

–0.82

0.26

–0.50

–0.89

0.70

15

0.00

–0.82

1.37

–0.50

–0.86

–1.11

16

1.55

–0.82

0.26

–1.04

–1.32

1.66

17

1.55

–0.82

1.37

–1.04

–1.30

–0.69

18

0.78

–0.82

–1.30

–0.77

–0.02

–1.27

19

0.78

–0.82

–0.85

–0.77

0.43

–0.95

20

0.78

–0.82

0.26

–0.77

0.84

–0.74

21

0.78

0.43

–1.30

–0.68

0.41

–0.30

22

0.78

0.43

–0.85

–0.68

0.79

0.74

23

0.78

0.43

0.26

–0.68

0.98

0.73

24

0.78

1.68

–1.30

–0.50

0.76

0.33

25

0.78

1.68

–0.85

–0.50

1.16

1.19

26

0.78

1.68

0.26

–0.59

1.37

1.92

27

–0.78

1.68

–1.30

0.39

1.41

1.47

28

–0.78

1.68

–0.85

0.39

0.84

0.64

29

–0.78

1.68

0.26

0.39

1.02

1.91

Predicted value [%]

70

LSM

60

50

40

R2 = 0.617
30

30

40

50

60

70

Actual value [%]Fig. 2 Relationship between the actual and predicted CTi values for the
LSM regression model.

242(5.32 %) > t (2.15 %).
The relationship between the actual CTi values obtained
from the experimental data and the calculated values
obtained from the LSM regression model is presented inFig. 2. The input values were 0.617 and 5.99 %, respectively. In general, the accuracy of a constructed regression
model increases as the R2 value approaches 1.0 and the
RMSE value decreases. The low R2 value was attributed to
the features of LSM.
The correlation represented by the LSM is a linear variation of CTi of the composite particles with respect to five
fabrication parameters (Arioli M. and Gratton S., 2012;
Zhang Y. and Fearn T., 2015). Thus, when the correlation
between the fabrication parameters and CTi of the composite particles is not linear, this nonlinear relationship cannot

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

Predicted value [%]

70
60
50
40

R2 = 0.0144

30
70

Predicted value [%]PCR - 2

PCR - 1R2 = 0.560

PCR - 3

PCR - 5

PCR - 4

60
50
40
30

R2 = 0.583
30

40

50

60

Actual value [%]Fig. 3R2 = 0.616
70 30

40

50

60

R2 = 0.617
70 30

40

50

60

70

Actual value [%]Actual value [%]Actual and predicted CTi values for the PCR regression model with various NP.C. values.

be adequately expressed by a regression model based on
the LSM (Arioli M. and Gratton S., 2012; Zhang Y. andFearn T., 2015). Moreover, if the fabrication parameters
are highly related to each other, the coefficients in Eqn. (28)
could be anomalous owing to instability in the analytical
calculations and the inaccuracy of the regression equation(Arioli M. and Gratton S., 2012; Zhang Y. and Fearn T.,2015).

Table 5 Hyperparameters used in the SVR regression model.

C

2–5, 2–4, …, 29, 210

16 candidatesε

2–15, 2–14, …, 2–1, 20

16 candidatesγ

2–20, 2–19, …, 29, 210

31 candidates

The PCR regression model constructed with NP.C. of 5 isexpressed in Eqn. (29):CTi = 0.635t PCR,1 + 0.249t PCR,2 + 0.0690t PCR,3 + 0.0549t PCR,4 + 0.0221t PC

3.2.2 Construction of the C
PCR= model
0.635t(29)
Ti
PCR,1 + 0.249t PCR,2 + 0.0690t PCR,3 + 0.0549t PCR,4 + 0.0221t PCR,5
In an effort to deal with the inaccuracy and instabilityof the regression model due to the correlation between(29) tPCR,i represents the i-th principal component obwhere
the fabrication parameters as described in Section 3.2.1,
tained by PCA. The effects of the principal components(29)
the parameters were converted to principal components
on CTi of the composite particles are expressed by each
uncorrelated with each other by using PCA as expressed
coefficient. The obtained R2 and RMSE values were 0.617
in Eqns. (14) and (15). Because the fabrication parametersand 5.99 %, respectively. The fact that all of the principal
highly related to each other were removed in advance, thiscomponents were applied to the construction of the regresallowed for prediction of CTi of the composite particles by asion model implies that the correlation between each fabcombination of fabrication parameters with low correlation
rication parameter prior to PCR processing was not strong(El Ghaziri A. and Qannari E.M., 2015).
(El Ghaziri A. and Qannari E.M., 2015).
The number of principal components (NP.C.) was varied
When all of the principal components were used to conwithin the range of 1–5, and the relationship between the
struct the regression model, the values calculated from theactual CTi values obtained from the experimental data and
PCR regression model were identical to those calculated
the calculated values obtained from the PCR regression
from the LSM model, because the application of the lastmodels for different NP.C. values are presented in Fig. 3.
principal component means that any effect of the fabricaThe R2 values for each regression model are also indicated.
tion parameters was not removed. Hence, the contribution
When the data points are closer to the dotted line of y = x inof any correlation between the fabrication parameters to
the plots, the R2 values for the PCR regression models apthe low accuracy of the LSM regression model discussed
proach 1.0. Because the R2 values increased with increasin Section 3.2.1 was small. Thus, in an attempt to improve
ing NP.C., all five of principal components were applied tothe accuracy of the regression models, we next considered
the construction of the PCR regression model.
the possibility of a nonlinear correlation between the243Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

fabrication parameters and CTi of the composite particles.

of the possibility of a nonlinear correlation between the
fabrication parameters and CTi of the composite particles
resulted in little change in the accuracy of the regression
model. Therefore, we next considered the possibility ofmore complex correlations between the fabrication parameters and CTi of the composite particles.

3.2.3 Construction of SVR model

To consider a nonlinear correlation between the fabrication parameters and CTi of the composite particles, a regression model was constructed using SVR according to Eqns.
(17)–(24). Regression models using SVR are constructedby minimizing the structural risk. The hyperparameters (C,ε, and γ) of the SVR model were optimized by adapting thecomprehensive combination from the candidates listed inTable 5 by exploring hyperparameters with maximum R2
values in verification results. The C, ε, and γ values in the
optimized SVR model were 2–5, 20, and 2–2, respectively.
The relationship between the actual CTi values obtained
from the experimental data and the calculated values obtained from the SVR regression model is shown in Fig. 4.
The R2 and RMSE values were 0.591 and 5.80 %, respectively. The former value is slightly lower than that obtained
for the LSM model (0.617), indicating a lower accuracy. In
contrast, the RMSE value was slightly smaller for the SVR
model, indicating a high accuracy.
This comparison based on the R2 and RMSE values suggests that the SVR and LSM models had similar accuracy.
Thus, under the conditions of this study, consideration

Predicted value [%]70

3.2.4 Construction of DNN model

To consider more complex correlations between the
fabrication parameters and CTi of the composite particles,
a regression model was constructed using a DNN. For
this model, the hyperparameters (NH.L., NNeur., activation
function and NL.T.) were optimized by exploring which
hyperparameters afford the highest R2 values in the verification results.
The variation of the R2 values with NH.L., NNeur., and
NL.T. is plotted in Fig. 5. In the case of NH.L., as shown in
Fig. 5a), the R2 values were almost constant for NH.L. values
in the ranges of 1–5 layers and 6–10 layers but increasedslightly when NH.L. was increased from 5 layers to 6 layers.
This increase in the R2 values with an increase in NH.L. from
5 layers to 6 layers is caused by improved fit of the relation
between the fabrication parameters and CTi of the composite particles. In the case of NNeur., as shown in Fig. 5b), theR2 values tended to increase with increasing NNeur. in therange of 1–6 neurons, after which the R2 values remained
almost constant irrespective of NNeur.. This increase in theR2 values with increasing NNeur. in the range of 1–6 neurons
is caused by improved fit of the relation between the fabrication parameters and CTi of the composite particles. The
minimal variation of the R2 values in the NNeur. range of
6–10 neurons was attributable to the sufficiently good fit at
the NNeur. of 6 neurons. Finally, NL.T. was varied in the rangeof 30–3,500 times. As shown in Fig. 5c), the R2 values rapidly increased as NL.T. was increased from 30 to 700 times.
Then, as NL.T. was increased from 700 to 1,500 times, theR2 values increased more gradually. At NL.T. values above
1,500, the R2 values remained almost constant irrespective of NL.T.. Comparison of various activation functions
revealed that the tanh afforded the highest R2 value, as

SVR

60

50

40

R2 = 0.591
30

30

50

40

60

70

Actual value [%]Fig. 4 Relationship between the actual and predicted CTi values for the
SVR regression model.
1.0

R2 [-]

0.8
0.6
0.4
0.2
0

a)
0

2

4

6

NH.L. [-]
Fig. 5

244

8

b)
10 0

2

4

6

NNeur. [-]

8

c)
10 0

700

1,400 2,100 2,800 3,500

NL.T. [-]

R2 values with variation in a) NH.L., b) NNeur., and c) NL.T. for optimizing the DNN regression model.

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

summarized in Table 6. Hence, the optimized hyperparameters for the DNN regression model were an NH.L. of six
layers, an NNeur. of six neurons, an NL.T. of 1,500 times, anda tanh activation function.
The relationship between the actual CTi values obtained
from the experimental data and the calculated values
obtained from the DNN regression model is plotted inFig. 6. The R2 and RMSE values were 0.941 and 2.19 %,respectively. Comparison of the results obtained for the
DNN, LSM, and SVR regression models revealed that the
DNN model displayed the highest accuracy, as indicated byits high R2 value and low RMSE value.

Table 6 Relationship between the R2 values and activation functions for
optimizing the DNN regression model.

Activation function

R2 [-]

Sigmoid0.0294

tanh

0.596

ReLU

0.152

Predicted value [%]

70

DNN

60

50

3.3 Comparison of constructed models

The LSM, PCR, SVR, and DNN regression models wereused to predict CTi of the SiO2/TiO2 composite particles
under various fabrication conditions based on the test data.
Moreover, the predictive performances of the constructed
models were evaluated by comparison of their Q2-values.
The relationship between the actual CTi values obtained
from the experimental data and the calculated values
obtained from the LSM, SVR, and DNN models for the
training data are plotted in Fig. 7. Furthermore, to verify
the predictive performances of the constructed models,
the relationships between the actual CTi values obtained
from the experimental data and the predicted values obtained from the three models for the test data are shownin Fig. 8. The results for the PCR regression model are
excluded from these plots because they were identical tothese obtained from the LSM model. The horizontal andvertical axes show the actual values obtained from theexperimental data and the predicted values calculated fromthe training data or test data, respectively. The R2 and Q2
values become closer to 1.0 as the data points approach thedotted line of y = x in the plots. The R2 and Q2 values foreach regression model are summarized in Table 7, alongwith the RMSE values for the training data (RMSEtrain) andtest data (RMSEtest).
Comparison of the R2 values for the three regression
models revealed that the DNN model had the highest
accuracy. Similarly, the DNN model displayed the highest
Q2 value, indicating the best predictive performance. For

40

70

Q2

30

30

50

40

60

70

Actual value [%]Fig. 6 Relationship between the actual and predicted CTi values for the
DNN regression model.

Predicted value [%]

70

R2

60

LSM
SVR
DNN

60

LSM
SVR
DNN

50

40

30

50

30

40

50

Actual value [%]60

70Fig. 8 Comparison of the predictive performance of the various regression models by Q2 values based on the test data.

4030

Predicted value [%]R2 = 0.941

Table 7 Summary of the R2, Q2, and RMSE values for the various
regression models.
30

40

50

60

70

Actual value [%]Fig. 7 Comparison of the accuracy of the various regression models by
R2 values based on the training data.

R2 [-]

RMSEtrain [%]Q2 [-]

RMSEtest [%]

LSM

0.617

5.99

–0.510

9.28

SVR

0.591

5.80

0.280

7.97

DNN

0.941

2.19

0.767

3.26

245

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

3.4 Analyzing the effects of fabrication conditions
on CTi of SiO2/TiO2 composite particles by the
DNN regression model

The DNN regression model was applied to analyzed therelationship between the fabrication conditions and CTi ofthe SiO2/TiO2 composite particles. The application range of
the DNN regression model with respect to the five fabrication parameters was V = 25–125 mL, rTi = 5.0–15.0 wt%,t = 1–15 min, S = 2,200–5,700 min–1, and T = 295–320 K.
Each fabrication parameter serving as an input value wasdivided into 20 points over the corresponding range, andCTi of the SiO2/TiO2 composite particles was predictedusing the DNN regression model.
The calculated effects of the fabrication parameters on

246

60
V
rTi
t
S
T

50

CTi [%]

a regression model with high accuracy and predictive performance, higher R2 and Q2 values are required (Gurgenc
T. et al., 2020). The high R2 and Q2 values of 0.941 and
0.767 obtained for the DNN regression model in this study
demonstrate the successful construction of a regression
model with high accuracy and predictive performancefor estimating CTi of SiO2/TiO2 composite particles. The
RMSE values for the training data and test data were
2.19 % and 3.26 %, respectively. The average CTi of the
SiO2/TiO2 composite particles in the experimental data was
46.3 %. In comparison, the RMSE values for the training
and test data were sufficiently small. Thus, the constructionof a regression model for predicting CTi of SiO2/TiO2 composite particles under various fabrication conditions was
successfully realized by using the DNN method.
The DNN regression model displayed the highest accuracy in this study because the DNN method considers more
complex correlations between explanatory variables andobjective variables, including nonlinearities, by varying
NH.L. and NNeur.. The LSM regression model attempts topredict the CTi values by calculating a formula based on
simple relationships involving the explanatory variables.
Meanwhile, the PCR regression model has similar features
to the LSM model because the main difference between thetwo methods is the replacement of explanatory valuables
with principal components. The SVR regression model
is constructed by using explanatory variables converted
to support vectors by processing with kernel functions.
Because the support vectors of SVR models are used in thesame manner as the principal components of PCR models,SVR models possess similar characteristics to LSM and
PCR models. Therefore, the constructed SVR regression
model also predicted the CTi values from a calculation formula involving only simple relationships. In contrast, theapplication of the DNN method to construct a regression
model leads to predictions based on complex correlations
between the explanatory variables and objective variables
because of the numerous hidden layers and neurons inherent to this approach.

40

30
-3.0

-1.5

0

1.5

3.0

Experimental conditions [-]Fig. 9 Calculated effects of the fabrication parameters on CTi of thecomposite particles.

CTi of the SiO2/TiO2 composite particles are plotted inFig. 9. Comparison of the resulting curves revealed thatthe maximum gradients decreased in the following order:t > T > V > rTi > S. Because a higher gradient indicates alarger influence of the fabrication parameter on CTi of thecomposite particles, these results suggest that the effectsof the fabrication parameters on CTi decrease in the sameorder. The V, rTi, t, S and T values under the base condition
when varying each parameter were 100 mL, 5.0 wt%,
5 min, 2,500 min–1, and 303 K, respectively.
The plot of CTi versus t revealed a clear maximum. CTi ofthe composite particles initially increased with increasing t
owing to the gradual coating of TiO2 onto SiO2 over time.
However, at higher t values, CTi of the composite particles
decreased as a result of exfoliation of the surface coating.
Upon varying T, CTi of the composite particles markedly decreased with increasing T in the low-T range thenremained almost constant with increasing T in the high T
range. These findings suggest that lower T values are beneficial for enhancing CTi of SiO2/TiO2 composite particles.
The variation of V initially had little effect on CTi of thecomposite particles, which remained almost constant withincreasing V in the low-V range. At higher values of V, CTiof the composite particles decreased with increasing V,
which was ascribed to a decrease in the contact frequency
per single particle.
Examining of the relationship between rTi and CTi of thecomposite particles revealed that CTi slightly decreased
with increasing rTi at lower rTi values. However, at higherrTi values, CTi increased with increasing rTi, which was
attributed to the increased proportion of TiO2 particles withrespect to SiO2.
Finally, upon increasing S, CTi of the composite particles
slightly increased. This was ascribed to the progress of
coating TiO2 onto SiO2.

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

4. Conclusion

In this study, SiO2/TiO2 composite particles with different CTi values were synthesized under various conditions(V, rTi, t, S, and T). To optimize the fabrication process of
SiO2/TiO2 composite particles, regression models wereconstructed to predict CTi of the composite particles using
the LSM, PCR, SVR, and DNN approaches. Furthermore,
the regression model with the highest accuracy and predictive performance was employed to analyze the effectsof the fabrication parameters on CTi of the SiO2/TiO2
coated composite particles. The obtained results can besummarized as follows:
1) SiO2/TiO2 composite particles were fabricated by coating TiO2 onto SiO2 at various V, rTi, t, S, and T values.
2) Comparison of the constructed regression models withrespect to the training data revealed that the DNN regression model displayed the highest accuracy.
3) Comparison of the constructed regression models withrespect to the test data indicated that the DNN regression model exhibited the best predictive performance.
4) Analysis of the effects of the fabrication parameterson CTi of the SiO2/TiO2 composite particles using the
DNN regression model revealed that t was the mostinfluential factor governing CTi of the SiO2/TiO2 composite particles.

Acknowledgement

This study was financially supported by the HOSOKAWA
Powder Technology Foundation (No. 20502), Osaka,
Japan.

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Authors’ Short Biographies
Taichi Kimura
Taichi Kimura is a Ph.D. student at Doshisha University since 2020. He received his Bachelor andMaster of Engineering degrees from Nihon University in 2017 and 2020, respectively. His researchinterests are analysis and design of particle fabrication processes using machine learning.

Riko Iwamoto
Riko Iwamoto received her Bachelor of Engineering from Doshisha University in 2022. Her
research interests were composite particle synthesis by mechano-chemical processes and materials
designs using data science. She works for a company involved in data analysis since 2022.

248

Taichi Kimura et al. / KONA Powder and Particle Journal No. 40 (2023) 236–249

Authors’ Short Biographies
Mikio Yoshida
Dr. Mikio Yoshida has been a Professor at Doshisha University since 2022. He received his Ph.D.
degree from Doshisha University in 2005. After that, he worked as a post-doctoral researcher at
Okayama University. He was employed as an Assistant Professor of Okayama University in 2007,
and as an Associate Professor of Doshisha University in 2016. He was a scientific committee memberof the 9th World Congress on Particle Technology (WCPT9) 2022 in Madrid, Spain. He has been an
editorial board member of Advanced Powder Technology since 2017.

Tatsuya Takahashi
He received his Bachelor of Engineering degree, and Master of Engineering in Physics from
Kwansei Gakuin University, Japan in 2013, and 2015, respectively. From 2015, he works Instituteof Powder Engineering and ICT Digital Promotion Division, Hosokawa Micron Co., Ltd., Japan.
His research topics cover area in semiconductor materials engineering technology, powder analysis
technology.

Shuji Sasabe
Shuji Sasabe is a Division Manager of ICT Digital Promotion Division of Hosokawa MicronCorp. He received Master’s degree in electrical engineering from Nippon Institute of Technology,
Japan in 1991. He has been engaged in research on powder processing and powder characterization,and has been involved in the development of many powder characterization instruments. He is engaged in the promotion of Digital transformation.

Yoshiyuki Shirakawa
Dr. Yoshiyuki Shirakawa is a Professor at Doshisha University. He received his Ph.D. in 1993
from Niigata University. He started an academic carrier at Niigata University as a post-doctoral
student for JSPS Research Fellowship Sciences in 1993 and worked at Osaka and Doshisha Universities. His research interests are composite particle synthesis by crystallization and mechano-chemical
processes and materials designs using computer simulations.

249

KONA Powder and Particle Journal No. 40 (2023) 250–261/https://doi.org/10.14356/kona.2023005 Original Research Paper

On the Similarity of Austin Model and Kotake–Kanda Modeland Implications for Tumbling Ball Mill Scale-Up †Ecevit Bilgili
Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, USA

Abstract
The aim of this theoretical investigation is to seek any similarities between the Austin model and the Kotake–Kanda(KK) model for the specific breakage rate function in the population balance model (PBM) used for tumbling ball
milling and assess feasibility of the KK model for scale-up. For both models, the limiting behavior for small particle
size-to-ball size ratio and the extremum behavior for a given ball size are described by “power-law.” Motivated bythis similarity, specific breakage rate data were generated using the Austin model parameters obtained from the labscale ball milling of coal and fitted by the KK model successfully. Then, using the Austin’s scale-up methodology, thespecific breakage rate was scaled-up numerically for various mill diameter scale-up ratios and ball sizes of 30–49 mm
and coal particle sizes of 0.0106–30 mm. PBM simulations suggest that the KK model predicts identical evolution of
the particle size distribution to that by the Austin model prior to scale-up. Upon scale-up, the differences are relatively
small. Hence, modification of the exponents in the Austin’s scale-up methodology is not warranted for scale-up with
the KK model. Overall, this study has established the similarity of both models for simulation and scale-up.
Keywords: ball milling, breakage, population balances, Austin model, Kotake model, scale-up

1. Introduction

Tumbling ball mills have been commonly used in a multitude of industries, especially the minerals industry (Austin
et al., 1984). Population balance modeling (PBM) enables
engineers to simulate, design, control, and scale-up various
particulate processes (Randolph and Larson, 1988; King,
2001), including the tumbling ball milling (Austin et al.,
1984; Prasher, 1987). The size-discrete, time-continuous
form of the PBM (Sedlatschek and Bass, 1953) for a wellmixed ball mill operating in batch mode is expressed as
i 1

dM i

S i M i   bij S j M j with M i 0 
M i,ini
dt
j 1

(1)

where i and j are the size-class indices running up to Ns(sink size class). Here, t, Mi, Si, and bij represent time, mass(1)
fraction, specific breakage rate parameter, and breakage
distribution parameter, respectively. Eqn. (1) also assumes
first-order (linear) breakage kinetics and has been successfully used for simulating the milling of a wide variety ofmaterials in different types of mills (Austin et al., 1984;
King, 2001). Readers are referred to Bilgili and Scarlett(2005) and Capece et al. (2011) for an extensive review
and a PBM framework for treatment of nonlinear breakage†

Received 14 February 2022; Accepted 1 March 2022
J-STAGE Advance published online 23 April 2022
Add: Newark, NJ 07102, USA
E-mail: bilgece@njit.edu
TEL: +1-973-596-2998 FAX: +1-973-596-8436

250

kinetics.
It has been experimentally well-established that Si varies
significantly with design and operating conditions for agiven material to be ground in ball mills (Austin et al.,1984). The celebrated Austin model describes how Si varies
as a function of particle size xi:

S i  aT xiα

1
1   xi μT Λ(2)Here, aT and μT are parameters that strongly depend on the
milling conditions, the material, and the ball size, whereas(2)
α and Λ are positive constants that largely depend on theproperties of the material. Austin et al. (2007) assert that
Λ can be satisfactorily taken as 3 for most materials. Some
researchers assumed Λ = 3 (e.g., Bwalya et al., 2014) or
fitted Λ to find it within ±10 % of this value (Rogers etal., 1986; Petrakis et al., 2017); yet others found notably
different values: 4.74 (Chimwani et al., 2013) and 2.4(Shahcheraghi et al., 2019). Traditionally, these parameters
have been estimated via either direct calculation of Si by
one-size-fraction method (Austin and Bhatia, 1971–72) or
the optimization-based back-calculation method (Klimpel
and Austin, 1977) using experimental data from small or
pilot scale ball mills. As balls are an integral element of
the ball mill design, the impact of ball size dB on aT andμT has been extensively studied (Austin et al., 1976a;
1984; Katubilwa and Moys, 2009). The Austin model canbe recast into the following form with explicit ball size
dependence (e.g., Katubilwa and Moys, 2009, with slight

Copyright © 2023 The Author. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

differences in formalism):ξ

S i  a0 xiα

d B,0 d Bη Λ
1  xi  μ0 d B d B,0  (3)

where a0 and μ0 are the values of aT and μT for specific ballsize dB,0 in the small/pilot scale batch milling experiments,
while η and ξ are constant ball-size exponents. For a mix(3)ture of balls, the specific breakage rate is calculated fromthe mass fraction of each ball size MB, p and its corresponding Si, p as follows:P

S i   M B, p S i, p
p 1

(4)

where D, J, U, and ϕC denote the mill diameter, the fractional mill filling by the balls, the void filling fraction, andthe fraction of actual rotation speed compared to the critical
speed NC. The subscript T refers to the parameters used in
the small-scale batch test, which are used to fit the model
parameters in Eqns. (2) and (3). Typical scale-up exponents
are N0 = 1, N1 = 0.5, N2 = 0.2, N3 = 2, and N4 = 0.2 (Austin
et al., 1984). However, other values of N3 were also usedsuch as 1 (Yildirim, 1999) and 1.2 (Austin et al., 2007).
One should note that N0 and N3 should be taken as ξ and ηof Eqn. (3), respectively, from a study on the ball size impact on Si at the test (T) scale, which is a preferred approach(Mulenga, 2017).
While the Austin model in Eqn. (2) or (3) has been themost widely used kinetic model for ball milling, Kotake etal. (2002, 2004) developed the following kinetic model forthe breakage of narrowly sized feed with size xf:

Scale-up of tumbling ball mills from small-scale batchmills to industrial scale mills has been successfully im(4)plemented
within the context of PBM (Austin et al., 1984;
Yildirim et al., 1999; Chimwani et al., 2014; De Oliveira
and Tavares, 2018). In this approach, empirical equations
 C x 
S1 C1d Bm xfα exp   2 n f 
are used for determining the dependence of the breakage
(10)
dB 

parameters on ball/mill dimensions and operating variables at the lab-scale and large-scale industrial mills. It
which we briefly refer to as the KK (Kotake–Kanda)
is important to emphasize that scale-up from a batch millmodel for the sake of simplicity. The parameters C1 andto continuous large-scale mills entails modification of theC2(10)
strongly depend on the milling conditions and material
breakage parameters (Si and bij) as well as consideration ofproperties, whereas α is a positive constant that largely
the residence time distribution and internal–external clasdepends on the properties of the material; m and n aresification in the continuous mills (Herbst and Fuerstenau,
material-dependent, ball size exponents. Kotake et al.
1980; Austin et al., 1984; Rogers and Austin, 1984; De(2002, 2004) and Deniz (2003) demonstrated the fitting
Oliveira and Tavares 2018). Although each one of thesecapability of this model for ball milling of a multitude of
aspects is important, this study focuses on the scale-up ofminerals. The KK model, Eqn. (10), is appealing as it has 5
the specific breakage rate Si.
parameters as opposed to the Austin model with 6 parameUpon scale-up, Si is known to change significantly,
ters, when ball sizes are explicitly considered. The millingwhereas bij or its cumulative counterpart Bij is generally
studies, where the back-calculation approach involved
assumed to remain invariant unless ball size/type changes
more than a few breakage parameters to be estimated, havesignificantly (Herbst and Fuerstenau, 1980; Austin et al.,shown that the accuracy decreases as the number of param1984). In accounting for scale-up related changes, theeters to be estimated increases (Klimpel and Austin, 1977;
Austin’s methodology scales-up Si in Eqn. (2) via correcKwon and Cho, 2021). However, to the best knowledge oftion factors K1–K5 that depend on the design parameters of
the author, the KK model has not been used for the PBM
the dry ball mill and operating conditions as follows:
simulation of ball milling of a natural feed with a wide size
particle size distribution (PSD), and no scale-up has been
1
*
α
(5)
performed using it. More importantly, the KK model has
S i  aT xi
K 2 K 3K 4 K 5Λ
1  xi K1 μT 
never been compared to the Austin model.
The present theoretical investigation seeks answers
N3
N2
N0 D   dB  d B,T (6)tothe following questions: (i) Are there any similarities
K1  , K2   dB 
(5)  DT   d B,T 
between the Austin model and the KK model despite theirdifferent mathematical forms? (ii) Is the Austin’s scale-up
N1  D methodology (scale-up correction factors) applicable to

D
if
3.81
m
 

  DT (7)
the KK model? (iii) Do these models predict similar
K3  
N1
N1  N 4
time-wise evolution of PSD in ball mills before and after(6)  3.81   D 
if D  3.81 m
 D   3.81 
scale-up? To be able to answer these questions, we first T 
generated synthetic specific breakage rate data using the1  6.6 J T2.3
(8)
Austin model, whose parameters were obtained from the
K4
exp 1.2 U  U T 
1  6.6 J 2.3
lab-scale ball milling of coal. Next, this data was fitted by(7)
the KK model to determine its parameters. Then, using  C 0.1  1  exp 15.7 C,T  0.94 
the Austin’s scale-up methodology, the specific breakage(9)
K5  (8)   C,T  0.1   1  exp 15.7 C  0.94 

(9)

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Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

rate was scaled-up numerically (virtual scale-up) using
both models for various mill diameter scale-up ratios D/DT
from 4 to 10, ball sizes dB of 30–49 mm, and coal particle
sizes xi of 0.0106–30 mm. PBM simulations assumed
plug flow and invariance of bij in the large-scale mills andincorporated the scaled-up Si values. The PBM simulations
were performed for the small-scale mill and the large-scalemills. Upon scale-up, we find that the differences in bothmodels’ predictions are relatively small. Instead of justusing N1 = 0.5 and N2 = 0.2 for the virtual scale-up with
the KK model, which is based on the Austin’s scale-up
methodology, we also fitted these exponents to the Si values
generated by the Austin kinetic model, keeping the otherscale-up exponents the same. Comparison of the PSD evolution prediction by the KK model (with/without modified
N1 and N2) and the Austin model will allow us to find theanswers to the above-mentioned questions.

2. Theoretical approach

2.1 Experimental data and model calibration

The KK model can be cast into the following form,
which is valid for any arbitrary particle size xi

 C x 
S i C1d Bm xiα exp   2n i  dB 

(11)and logarithmic transform, a square-root transform of the
response (Si) led to the best overall fitting and simulation
results when the KK model with the estimated parameters
were compared with the Austin model. Hence, only the KK
model parameters obtained by fitting after the square-root
transform of the response (Si) are presented. The optimization was performed via the Marquardt–Levenberg methodin Minitab version 21.1.

2.2 Virtual scale-up

A virtual scale-up of the specific breakage rate Si was
performed from the small-scale batch mill to the largescale continuous ball mills. Using the Austin’s scale-up
methodology, the specific breakage rate Si obtained experimentally for the Austin model (Katubilwa and Moys, 2009)
and the KK model with the estimated parameters (refer to
Section 2.1) were scaled-up numerically for various milldiameter scale-up ratios D/DT from 4 to 10, ball sizes dBof 30–49 mm, and coal particle sizes xi of 0.0106–30 mm.
For simplicity, K4 and K5 are set to 1 in this study for the
scale-up with both models regardless of any particular values of J, U, and ϕc values because K4 and K5 are independent of dB/dT ≡ dB/dB0 and D/DT, which were varied in thisstudy. Hence, the scaled-up Si, i.e., Si*, was calculated byξd B,0 d B K 3
Note that both Eqns. (3) and (11) are explicit in dB andS i *  a0 xiα
Λxi, which enables us to discern any similarities or differ(12)


xi
(11)

1


η
ences between the two models by analyzing the limitingN μ0 D DT  2 d B d B,0  
behavior for small particle size-to-ball size ratio and theextremum behavior for a given ball size. To demonstrate
C 2 xithat the KK model can predict the same Si as that predicted
(13)
S i * C1d Bm xiα exp  K
N2 n  3
(12)
D
D
d




T
B
by the Austin model, we need to have Si parameters froman experimental data set on batch ball milling. Hence, we
The above equations incorporate K1, K2, and K3 directly,
considered the experimental work on the batch ball millingwhich depend on dB/dB0 and D/DT. First, Eqn. (12) was(13)
of a South African coal (Katubilwa and Moys, 2009). Theyused
to generate the Si* by the Austin model for all scale-up
fitted and/or set the Austin model parameters as follows:
scenarios with various dB/dB0 and D/DT. These scaled-up
a0 = 0.48 mm–0.81∙min–1, μ0 = 19.27 mm, dB,0 = 38.8 mm,
data were taken as the “synthetic scale-up data” against
α = 0.81, Λ = 3, ξ = 1, and η = 1.96. Using this set of pawhich the scale-up with the KK model was compared.
rameters, a synthetic data set for Si was generated by theThen, two different virtual scale-up approaches for theAustin model using Eqn. (3) for ball sizes of 30–49 mm
KK model were used. In the first approach, the standard
with increments of 1 mm and coal particle sizes of 0.0106–
scale-up exponents of the Austin’s scale-up methodology,
30 mm with a geometric progression ratio of 21/13. As thei.e., N1 = 0.5 and N2 = 0.2, along with N4 = 0.2 were used
purpose of this study is to examine theoretical similarity ofin Eqn. (13) to predict the Si*. However, as there is no prior
the KK model to the Austin model, we did not fit the KK
scale-up study with the KK model, one cannot assume
model to the experimental data directly, but to the synthetic
that the Austin’s scale-up exponents N1 and N2 are directly
Si data generated by the Austin model to minimize any bias.
applicable for use in Eqn. (13). Hence, in the second apBoth models have been shown to fit various ball millingproach, we fitted Si* values generated by Eqn. (12) for eachdata successfully in the milling literature; the question we
D/DT from 4 to 10 and ball sizes dB of 30–49 mm using
seek to answer here is not how well they can fit specificEqn. (13) and minimizing the following standard error SE:
experimental data. Therefore, the parameters of the KK
2N**
S Austin,model, Eqn. (11), were estimated by minimizing the sumi  S KK,i

i 1
(14)
SE 
of-squared residuals between the KK model prediction
N  Npand the synthetic data generated by the Austin model for
the South African coal. In comparison to no transformwhich allowed us to estimate N1 and N2. An additional fit



(14)
252



Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

was performed to minimize SE for all D/DT cases simultaneously. Here, N and Np refer to the total number of data
points and parameters estimated, respectively. Then, thesesimultaneously estimated values of N1 and N2 were used to
predict Si* for each scale-up scenario separately.

2.3 PBM simulations

about the same South African coal for which Si parameters
were reported in Section 2.1:γBij   xi 1 x j   1     xi 1 x j 

β

(15)

Here, 0 < ϕ < 1 is a breakage constant, and γ and β are
breakage exponents. For the South African coal, ϕ, γ, andβ were(15) reported to be 0.51, 0.53, and 3.2, respectively. It
is well-known that these parameters are largely material
dependent, and are relatively insensitive to the milling conditions, except the ball size (Austin et al., 1984; Prasher,
1987; King, 2001). In a more elaborate model, one could
incorporate the impact of ball size on Bij by making γ dependent on the ball size (Austin et al., 2007). In the present
simulations and scale-up, we assumed that Bij remain invariant to ball size and scale-up, similar to what Chimwani
et al. (2014) implemented. Using the Bij values from Eqn.
(15), we calculated bij via bij = Bij – Bi+1j.
The initial PSD for the small batch mill simulation andthe feed PSD for the continuous mill simulation were taken
the same: a Gaussian PSD with a mean size of 20 mm and astandard deviation of 2 mm. The Gaussian PSD was generated using the function “normpdf” in MATLABTM version
9.11. Considering the time scale of the batch milling experiments (Katubilwa and Moys, 2009), we simulated 0.5, 1,
2, 4, and 8 min of milling time (or residence time). Anotherrationale for the selection of 8 min is the following: powerstation boilers usually demand 60–70 % of coal particles
below 75 μm (Prasher, 1987). Our exploratory simulations
suggest 8 min residence time in the large-scale mills could
meet this demand approximately. In all PBM simulations,the number of size classes Ns and the geometric progression
ratio were set as 320 and 21/13, respectively, which yields
grid-independent simulation results. The set of ODEs in
Eqn. (1) along with either Eqns. (3), (11), and (15) for
the small batch mill or Eqns. (12), (13), and (15) for thelarge-scale continuous mill was solved using the function“ode15s” in MATLAB, which is a highly accurate, variable
order–step-size ODE solver (Shampine et al., 2003). The
relative error tolerance and the absolute error tolerance
were set as 10–4 and 10–6, respectively.

The scale-up from a small batch mill or a pilot-scalemill to a large-scale industrial continuous mill is a complex
endeavor, entailing considerations of the residence time
distribution (RTD), internal classification, and externalclassification in the continuous mill circuits (Herbst andFuerstenau, 1980; Austin et al., 1984; Rogers and Austin,
1984; De Oliveira and Tavares, 2018) besides scale-upof the specific breakage rate. However, any considerationof these factors in the scale-up process would complicate
the analysis to discern the differences between the Austin
model and KK model in the scale-up of Si. Hence, we
followed the simplest approach for the modeling of an
open-circuit continuous mill by assuming plug flow behavior and disregarding any internal classification in the mill.
This simple approach purposefully isolates the problemof “breakage kinetics” in scale-up and allows us to focus
on the discrimination of the KK model from the Austin
model within the context of simulation and scale-up. The
plug-flow is a viable assumption for some industrial scaleball mills (Austin, 1973). For example, a 10 tanks-in-seriesmodel represented the RTD data for a large-scale cementball mill (Austin et al., 1975). The plug-flow assumption
was also adopted by other researchers in the ball millscale-up (Chimwani et al., 2014). Developing a sophisticated PBM for a continuous ball mill circuit with internal–
external classification is not within the scope of this study.
Note that Eqn. (1) is applicable to mills operating inbatch mode as well as plug-flow continuous mode. One
can replace time t with contact time t* = y/u for any axial
position y in the mill with u denoting the average axial velocity of the particles: u = L/τ = LF/Mh, where τ, L, F, andMh stand for the space-time (the average residence time),
length of the mill, inlet mass flow rate, and mass hold-up at
steady state, respectively. So, after replacing t with t*, one
can solve the set of ordinary differential equations (ODEs)
3. Results and discussionin Eqn. (1) with the initial condition Mi(0) = Mi,ini, with
3.1 Austin model vs. Kotake–Kanda model
Mi,ini representing the feed PSD, and predict the steadyDespite the differences in their mathematical forms, bothstate PSD at t* = τ. Henceforth, although we use the above
the Austin model and the KK (Kotake–Kanda) model haveformalism, for the sake of simplicity, we will still denotesimilar limiting behavior and extremum behavior. Let us
time as t in the results, but keeping in mind, t represents
first consider the limiting case of xi << dB, for which Eqns.
retention time in the batch mill and contact (residence) time(3) and (11) reduce toin the plug-flow continuous mill at the steady state.
ξα
S i a
C1d Bm xiα
In the PBM simulations, we used the S
(16)
0 xi d B,0 d B  and S ii values in
Section 2.1 for the small-scale batch mill simulations andSi* values, as described in Section 2.2, for the continuous
respectively, because the dominator of Eqn. (3) and expomilling. The following cumulative breakage distribution
nential term in Eqn. (11) approach 1. Clearly, both models(16)parameters Bij were taken from Katubilwa et al. (2011)provide power-law dependence of Si on xi and dB in this253

Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

limit. Note that xi << dB has not been strictly defined andxi/dB < O(10–2) is reasonable for this limiting behavior. To
prevent a possible confusion, we assert that this limiting
behavior is not a practically irrelevant asymptotic limit.
As demonstrated by Austin et al. (1976b) and De Oliveira
and Tavares (2018), the whole milling kinetics could begoverned by this limiting behavior if the feed or initial size
is much smaller than the ball sizes used. The KK model canbe made equivalent to the Austin model by imposingξC1  a0d B,0and m  ξ(17)

as well as the sameness of α in both equations in terms of
the limiting behavior.
(17)
Let us now consider the extremum behavior of Si by
setting dS/dx = 0 after taking the equivalent particle
size-continuous forms of Eqns. (3) and (11). This analysis
yields the particle size xm at which Si is at a maximum andhas the following Sm:
1

η

xm 
μ0 α  Λ  α Λ d B d B,0  with Λ  αα
 α 
Λ 1 S  Λ  αm(18)
xm  α C2  d Bn
a0 μ0α ααS m  C1e α α C 2  d Bαn  m
(19)

dB 

Λ   d B,0 (18)αη  ξ

(19)
(20)
(21)

for(20)the Austin model and the KK model, respectively.
Similar(21) to the limiting behavior, both models predict a
power-law dependence of xm and Sm on dB. However, theexponents for the limiting behavior and the extremum behavior are different. One can make both models equivalent
in terms of the extremum behavior alone, i.e., identical xm
and Sm, by setting the KK parameters as
(22)

ξ

C1 a0e α 1  α Λ d B,01/ ΛC2 α μ0  Λ α  1

η
d B,0

(23)

n =(22)
η, m = –ξ, and same α in both equations.
An interesting theoretical question arises as to what
happens(23) if the equivalence conditions, i.e., either Eqn.
(17) for the limiting behavior or Eqns. (22)–(23) for theextremum behavior are imposed on the KK model. We notethat the conditions imposed on C1 are incompatible becauseeα(1–α/Λ) = 1 is overly restrictive and cannot be satisfied,
except for α = 0, which is untenable. If one imposes theequivalence in terms of the extremum behavior, then C1
provided by Eqn. (22) will be similar to C1 provided by
Eqn. (17) in terms of the power-law dependence with thesame exponent ξ. However, Eqn. (22) will overpredict Si by
a factor of eα(1–α/Λ) in the limit xi << dB. This factor equals
1.641 (64.1 %) for the coal considered in Section 2.1,
which is not acceptable.
The upshot of the above theoretical analysis is that the254

KK model is similar, but not equivalent or identical, to
the Austin model in terms of the limiting behavior and theextremum behavior. It is impossible to ensure equivalencyof these models in terms of both limiting behavior in thesize domain xi << dB and the extremum behavior in theneighborhood of xi = xm. Owing to the power-law dependence of Si on xi and dB in the size domain xi << dB and thatof xm and Sm on dB, we hypothesize that both models could
predict similar Si while satisfying the limiting behavior
and the extremum in some approximate statistical sense.
Note that they may differ significantly in the size domain
xi << dB and near the extremum. This hypothesis can betested by first fitting the KK model parameters to the Si
generated by the Austin model and then analyzing the resulting Si profiles as well as these models’ prediction of thetemporal evolution of the PSD. To this end, the synthetic
Si data generated by the Austin model, whose parameters
were obtained by fitting to the ball milling data on a South
African coal (refer to Section 2.1), were fitted by the KK
model. The parameters of the KK model are presented inTable 1. The sum-of-squared errors was 4.08 min–1, andthe standard error SE of the fit was 3.69 × 10–2 min–0.5. The
parameters had low standard errors: all below 8 %. Clearly,
the KK parameters in Table 1 will not satisfy the requirements of Eqn. (17) and Eqns. (22)–(23), which, respectively, ensure equivalency in terms of the limiting behavior
and the extremum behavior. These strict conditions are notrequired for the models to be similar both qualitatively andquantitatively when experimental errors in determining Si
are considered (see later discussion).
Fig. 1 presents the specific breakage rate Si data generated by the Austin model for the South African coal and its
fitting by the KK model, which led to the estimated parameters in Table 1. While there are minor deviations, the KK
model estimated the Si profiles and the extremum behavior
reasonably well. The KK model exhibited a slightly depressed, shifted peak as compared with the Austin model.
Both models predict a cliff or falling off Si for xi > xm,
which can be explained by the inability of the balls to nip
or capture the particles effectively. In view of Eqn. (16),the values of ξ = 1 and m = –1.444 suggest that the smallerball sizes are effective for xi << dB because for the smaller
particles, the number frequency of the collisions with theTable 1 The KK model parameters obtained from fitting to the synthetic
data generated by the Austin model.

Parameter

Value

Unit

SE (%)

C1

107.5

mm0.494∙min–1

5.81

m

–1.444–1.10

α0.9496

–

0.242

C2

370.6

mm1.364

7.30

n

2.364

–

0.876

Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

beads govern the specific breakage rate, which is suggested
to be scaled by the inverse of dB, as signified by ξ = 1(Austin et al., 1984). Overall, both models’ phenomenological representation of the breakage kinetics appears to bequalitatively and quantitatively similar.
The location of the extremum points in Fig. 1 are given
by xm–Sm, which depend on the ball size dB and are obtained via Eqns. (18)–(21) for the Austin model and the KK
model, respectively, as follows:xm 
1.063 10 2 d B1.96 and S m 
0.3423d B0.588

(24)

xm 
2.562  10 3 d B2.36 and S m 
0.1439d B0.801

(25)

(24)
Fig.
2 depicts the variation of xm and Sm with dB based on
Eqns.
(25)(24) and (25). According to the Austin model, each
3.6
Data Generated by Austin ModeldB = 30 mm
dB = 34 mm
dB = 38 mm
dB = 42 mm
dB = 46 mm

Specific Breakage Rate, S (min-1)

3.2
2.8
2.4

Kotake Model Fit
dB = 30 mm
dB = 34 mm
dB = 38 mm
dB = 42 mm
dB = 46 mm

2.0
1.6
1.2
0.8
0.4
0.0
100

1000

10000

Particle Size (µm)

30

3.5Austin Model
Kotake Model

25

3.0
2.5

20

2.0
15
Austin Model
Kotake Model

10

5

1.5
1.0

30

35

40

45

Ball Size, dB (mm)50

The Maximum Specific Breakage Rate
Parameter, Sm (min-1)

Particle Size at which Specific Breakage Rate
Parameter is Maximum, xm (mm)Fig. 1 Size dependence of Si for various ball sizes: fitting by the KK(Kotake) model to the synthetic data generated by the Austin model.

0.5

Fig. 2 Dependence of Sm and xm on the ball size: Austin model with
Eqn. (24) and the KK model with Eqn. (25).

ball size most effectively breaks a certain particle size.
Large balls are the best for breaking coarse particles; smallballs are the best for breaking small particles. For example,
46 mm balls are most effective for breaking ~20 mm particles; yet, they are the worst for breaking <~8 mm particles;
30 mm balls are most effective for the latter. Practically,
for a feed with wide PSD, the common practice is to use a
mixture of different ball sizes (Austin et al., 1984; Prasher,
1987; Katubilwa and Moys, 2009).
As can be seen from Fig. 2, the KK model provides asimilar ball size dependence of Sm and xm to that by theAustin model although there is some deviation. The mean
relative deviation for Sm and xm were 8.18 % and 7.31 %,respectively, while the maximum relative deviation for Sm
and xm were 13.2 % and 16.1 %, respectively. One interesting trend is that as dB increased, the Sm deviation of the KK
model from the Austin model decreased, while the xm deviation increased, which counter each other. This observation
along with <10 % mean relative deviation overall imply
that the deviations may have small impact on the PBM
simulation and scale-up results. This is indeed the case, as
will be demonstrated next through PBM simulations before
and after scale-up.
To develop a deeper understanding of the ball size effects,
one must resort to Discrete Element Modeling (DEM) of
tumbling ball mills. DEM provides significant fundamental
insights and microdynamic information such as collisionenergies among the balls–particles–mill wall/liners andtheir frequencies (Tavares, 2017; Rodriguez et al., 2018).
In an excellent critical analysis, Rodriguez et al. (2018)
pointed out some fundamental learnings from the DEM:
(i) Collision energies involving events with only particles(particle–particle and particle–liner) are not sufficiently
high to cause breakage of particles and (ii) a fraction of
the collisions inside the ball mill do not involve particles;
hence, they do not directly contribute to particle breakage.
In agreement with the above, the multi-scale PBM–DEM
modeling of the experimental tumbling ball mill (Kotake
et al., 2002) by Capece et al. (2014) predicted the Si profile
similar to those in Fig. 1. Moreover, their simulations
suggest that for a given narrow feed size, an effective ballsize that maximizes the specific breakage rate exists. Thiseffective ball size appears to maximize collision frequency
with the particles (ball–particle collisions) with sufficiently
high impact energy above a minimum threshold impact energy that is particle size-dependent. Such minimum impact
energies required to break individual particles under impact
loading should be determined experimentally (Tavares andKing, 1998; Vogel and Peukert, 2003; Tavares, 2007).
While the analysis of the data in Figs. 1 and 2 suggeststhat the KK model is similar to the Austin model andthey both provide similar Si, xm, and Sm, one would argue
about the deviations. At this juncture, it is imperative tomention the errors involved in the determination of Sm and255

Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

xm. For example, Kotake et al. (2002; 2004) described anaveraging procedure to determine Sm and xm because of
the experimental scatter around the maxima. The experimental deviations were certainly greater than the <~10 %Sm and xm deviations of the KK model from the Austin
model observed here. Moreover, the 85 % of the fitted S1values by the KK model were within a band of ±20 %,with 15 % of the values outside this band (Kotake et al.,2004); similarly notable deviations can be seen in Deniz(2003). Most importantly, a long-due study (Shahcheraghi
et al., 2019) clearly demonstrated that at 95 % confidence
level, the experimentally determined Si parameters of theAustin model had 21.7–34.1 % uncertainty, whereas the
Bij parameters had 7.8–17.8 % uncertainty. Hence, the deviations shown in Figs. 1 and 2 between the Austin modeland the KK model would most likely be within the rangeof experimental uncertainty when the two models are fitted
to the same experimental data with repeats. This has neverbeen done in the experimental ball milling literature. In thisstudy, we will confirm the similarity of the two models via
PBM simulations next.
A batch ball milling process was simulated by PBM,
as described in Section 2.3 to assess if the deviations
between the two models’ Si values could cause significant
difference in the simulated time-wise evolution of the PSD.
To the best knowledge of the author, this is the first study
in which a batch milling process has been simulated by
the KK model. Fig. 3 clearly illustrates that despite the Si
deviations illustrated in Fig. 1, the two models simulated
the temporal evolution of the PSD almost identically. The

1.0

Cumulative Fraction Undersize

0.8

0.6

Historically, the Austin kinetic model has been usedalong with Austin’s methodology for ball mill scale-up(Austin et al., 1984). As the KK model has never been used
for ball mill scale-up, it is doubtful if it can be integrated
with the Austin’s scale-up methodology. In Section 2.2, wedeveloped the scaled-up specific breakage rate parameterSi* using this methodology. To the best knowledge of theauthor, this is the first attempt to use the KK model for
scale-up, albeit virtual. This virtual scale-up will allow
us to assess if the KK model could provide (or fit to) Si*
similarly to the Austin model upon scale-up. Table 2
presents the virtual scale-up scenarios and the standard
error SE, as per Eqn. (14). Four diameter scale-up ratios
D/DT with ball sizes of 30–49 mm and coal particle sizesof 0.0106–30 mm were considered. The Austin model, via
Eqn. (12), was used to produce the synthetic scale-up Si*
data to which the Si* generated by the KK model, via Eqn.
(13), will be compared (SU1–4 in Table 2). In SU1–SU4,
standard values of the scale-up correction exponents, i.e.,
N1 = 0.5 and N2 = 0.2 were used. In SU5–SU8, N1 and N2
were estimated by fitting Eqn. (13) to the synthetic scale-up
Table 2 Standard error SE calculated or estimated by Eqn. (14) with
the KK model estimation and the synthetic scale-up data generated
by the Austin model. The analytical data is available publicly at
https://doi.org/10.50931/data.kona.19596367

Kotake Model

Scale-upno.

D/DT (–)

N1 (–)

N2 (–)SE (min–0.5)

SU1

4

0.5

0.2

5.79 × 10–2

SU2

6

0.5

0.2

6.90 × 10–2

SU3

8

0.5

0.2

7.78 × 10–2

SU4

10

0.5

0.2

8.47 × 10–2

SU5

4

0.455

0.239

5.07 × 10–2

SU6

6

0.455

0.241

5.56 × 10–2

SU7

8

0.455

0.242

5.88 × 10–2

SU8

10

0.455

0.242

6.07 × 10–2

SU9a

4

0.455

0.241

5.07 × 10–2

SU10a

6

0.455

0.241

5.56 × 10–2

SU11a

8

0.455

0.241

5.88 × 10–2

SU12a

10

0.455

0.241

6.07 × 10–2

t = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 mint = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 min

0.2

Initial PSD

100

1000

Particle Size (µm)

10000

Fig. 3 PBM simulation of the time-wise evolution of the cumulative
PSD in a batch mill with an initial Gaussian PSD (mean size: 20 mm,
SD: 2 mm) and dB = 38 mm.

256

3.2 Virtual scale-up with the KK modelAustin Model

0.4

0.0
10

maximum deviations in the PSDs estimated by the KK
model and the Austin model are within a few percentages.
In view of the above discussion and the simulation results,
it is clear that the PBM with either the KK model or theAustin model yields a similar temporal evolution of the
PSD in a batch mill prior to scale-up. The KK model has
one less parameter than the Austin model (5 vs. 6); its use
may be advantageous for parameter estimation.

The SE of simultaneous fitting with SU9–12 was
5.66 × 10–2 min–0.5.
aEcevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

Scaled-Up Specific Breakage Rate, S* (min-1)

10

8

D/DT = 4
Austin Model, N1 = 0.5, N2 = 0.2
dB = 30 mm
dB = 38 mm
dB = 46 mm

6

Kotake Model,
N1 = 0.5, N2 = 0.2
dB = 30 mm
dB = 38 mm
dB = 46 mm

4

Kotake Model,
N1 = 0.455, N2 = 0.241
dB = 30 mm
dB = 38 mm

2

0
100

dB = 46 mm

1000

10000

Particle Size (µm)

Fig. 4 Comparison of Si*generated by the Austin model and the KK
model with two sets of N1–N2 (D/DT = 4).
14

Scaled-Up Specific Breakage Rate, S* (min-1)

data for each D/DT separately. In SU9–SU12, they were fitted to the synthetic scale-up data for all D/DT data together.
Fitting of the scaled-up specific breakage rate parameter
Si* by the KK model to the synthetic scale-up Si* data
(Austin model) either one-at-a-time for each scale-up
scenario (SU5–8) or simultaneously for all scenarios(SU9–12) led to almost identical exponents of the scale-up
correction factor: N1 = ~0.46 and N2 = ~0.24, which deviated only by 8 % and 20 % from the standard values of theAustin’s scale-up methodology, i.e., N1 = 0.5 and N2 = 0.2(SU1–4). Hence, one set of modified N1 and N2 can be used
for the scale-up of Si of the KK model. When the SE of
SU9–12 was compared with that of SU1–SU4, the modified N1 and N2 reduced SE by 12 %, 19 %, 24 %, and 28 %,respectively, for D/DT values of 4, 6, 8, and 10.
At the small-scale, the fitting of the KK model to thesynthetic Si data for its parameter estimation has a SEof 3.69 × 10–2 min–0.5. Upon scale-up, the SE values inTable 2 for SU1–4 are 57 %–130 % higher than thatassociated with the parameter estimation. The situation
is somewhat better when the newly estimated, modified
N1 = 0.455 and N2 = 0.241 are used. The SE values for
SU9–12 (upon scale-up) are 37 %–65 % higher than thatassociated with the parameter estimation. Clearly, the Si
differences between the KK model and the Austin model
become more pronounced upon scale-up whether the standard Austin scale-up correction exponents or the modified
exponents are used. While the SE increased upon scale-up,
as intuitively expected, in absolute terms, the SE values arestill low which will become apparent once the deviations
are presented in visual form next.
Figs. 4 and 5 present the synthetic Si* data by the Austin
model and Si* provided by the KK model (SU1 and SU3)
and fitted by the KK model (SU9 and SU11). The data fromthe other scale-up scenarios were not presented in graphicalform for the sake of brevity. It suffices to state that Figs. 4
and 5 are representative of the general scale-up trends forall scenarios.
A cursory look at Figs. 4 and 5 vs. Fig. 1 reveals theremarkable impact of the scale-up on the specific breakage
rate. Upon scale-up and an increase in D/DT, the specificbreakage rate increases, which accords well with the experimental ball milling literature (Austin, 1973; Austin etal., 1984). N1 captures the increase in mill power as milldiameter increases (Austin, 1973). Sm and xm also increase,which suggests the maximum point moves to the right andits peak is heightened. With an increase in D/DT, the cliff
after xi > xm becomes less pronounced and the abnormal
breakage region shrinks. In fact, for dB = 46 mm, the maximum point almost disappears. Obviously, for a higher D/DT
value, the balls are dropped from a greater height, and they
hit the particles at a much higher impact force. According
to a semi-theoretical analysis (Hayashi and Tanaka, 1972),
the impact force for free-falling balls scales with D0.5. A

12

D/DT = 8
Austin Model, N1 = 0.5, N2 = 0.2
dB = 30 mm
dB = 38 mm

10
8
6
4

dB = 46 mm
Kotake Model,
N1 = 0.5, N2 = 0.2
dB = 30 mm
dB = 38 mm
dB = 46 mm
Kotake Model,
N1 = 0.455, N2 = 0.241
dB = 30 mm
dB = 38 mm
dB = 46 mm

2
0
100

1000
10000
Particle Size (µm)

Fig. 5 Comparison of Si* generated by the Austin model and the KK
model with two sets of N1–N2 (D/DT = 8).

recent advanced DEM simulation study (De Carvalho etal., 2021) provides a more fundamental explanation. Their
DEM simulations point out a significant expansion of thecollision frequency–collision energy envelope as the ball
milling is carried out at larger scales: from batch to pilot
and then to industrial mills. They determined the specificcollision frequency to be 2404 collisions/kg∙s, 2746 collisions/kg∙s, and 3795 collisions/kg∙s at the batch, pilot,
and industrial scales, respectively. Overall, higher impact
forces, collision energies, and specific collision frequency
explain the higher specific breakage rates upon scale-up

257

Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

fundamentally.
The differences in Si* between the Austin model andthe KK model, with/without the modified N1 and N2 exponents, illustrated in Figs. 4 and 5 seem to be unremarkableand most likely statistically insignificant when actual ball
milling data are considered. The readers are referred to thediscussion and the references in Section 3.1. However,proving this point with comparative experimental study
is beyond the scope of this study. We pose a different, yet
quite relevant question: can the Si* differences depicted in
Figs. 4 and 5 translate into significant differences in termsof the PSD evolution with residence time in a continuous
mill?
Fig. 6 illustrates that the PSD shifts from the Gaussian
feed PSD to the left monotonically along the axial direction of a continuous mill with D/DT = 4, which is scaled
from the small batch mill whose PSD evolution is shownin Fig. 3. The time refers to retention time in the batchmill (Fig. 3) and the residence time in the continuous mill(Fig. 6), which corresponds to different axial locations in
the mill. A cursory look at Fig. 6 vs. Fig. 3 reveals that the
product becomes much finer upon scale-up, which is inagreement with the higher specific breakage rate parameter
upon scale-up (Si* in Fig. 4 vs. Si in Fig. 1). A larger diameter mill with D/DT = 8 (Fig. 7) exhibits similar behavior;
the PSDs are finer as compared with those in Fig. 6, which
again can be attributed to the respective Si* values in
Figs. 4 and 5.
Let us now answer the question posed earlier: the Austin
model and the KK model, with either the standard scale-up

1.0

exponents N1 = 0.5 and N2 = 0.2 or the modified scale-up
exponents estimated in this study, i.e., N1 = 0.455 andN2 = 0.241, predict very similar PSDs in a plug flow continuous mill. Although the differences increased slightly
upon scale-up to a larger mill with D/DT = 8 (Fig. 7), both
the Austin model and the KK model estimated similar
PSDs, with a max. deviation of 4 % at 10 μm in the cumulative mass fraction.
An interesting finding from the continuous mill simulations presented in Figs. 6 and 7 is that the modificationof the Austin’s scale-up exponents N1 and N2 was notwarranted for the integration of the KK model with theAustin’s scale-up methodology. The KK model with themodified scale-up exponents showed less deviation fromthe Austin model than that with the standard exponents at
the earlier residence time values. However, this trend wasreserved at the later residence time points and at the exitof the mill (t = τ = 8 min). Despite the lower SE values of
the Si* associated with the modified scale-up exponents inTable 2, these differences in the SE values did not seem tomake a significant difference in terms of the PSD change
along the mill axis and the product PSD, as proven by thesimulation results in Figs. 6 and 7. Longer residence times
were not considered for two reasons. First, the product
PSD (t = 8 min) in the large-scale continuous mills reached
the desired target approximately for coal applications:
60–70 % below 75 μm. Second, the Austin model parameters were estimated using the one-size-fraction data froma ball milling study on South African coal (Katubilwa andMoys, 2009; Katubilwa et al., 2011). In these studies, theD/DT = 4

1.0

Austin Model
N1 = 0.5, N2 = 0.2
t = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 min

0.8

0.6

0.4

0.2

0.0
10

Initial PSD

100

Kotake Model
N1 = 0.5,
N2 = 0.2
t = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 min
Kotake Model
N1 = 0.455, N2 = 0.241
t = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 min

1000

Particle Size (µm)

10000

Fig. 6 The variation of the PSD at different residence times of the particles (different axial locations) in a continuous ball mill with plug-flow,
D/DT = 4, and dB = 38 mm.

258

Cumulative Fraction Undersize

Cumulative Fraction Undersize

0.8

D/DT = 8

Austin Model
N1 = 0.5, N2 = 0.2
t = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 min

0.6
Kotake Model
N1 = 0.5, N2 = 0.2
t = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 min

0.4

Kotake Model
N1 = 0.455, N2 = 0.241
t = 0.5 min
t = 1 mint = 2 mint = 4 mint = 8 min

0.2

0.0
10

Initial PSD

100

1000

Particle Size (µm)

10000

Fig. 7 The variation of the PSD at different residence times of the particles (different axial locations) in a continuous ball mill with plug-flow,
D/DT = 8, and dB = 38 mm.

Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

smallest feed size fraction studied contained 300–425 μm
coal particles. Hence, both the Austin and the KK models
extrapolate Si of the finer particle range (< 300 μm) basedon the coarser particles’ breakage kinetics. While this is acommon practice, we suspect that the α parameter in bothmodels could be sensitive to the particle size domain in themodel calibration. Hence, the slightly higher deviation of
the KK model from the Austin model for particles smaller
than 100 μm (refer to Fig. 7) could be indirectly linked tothe model calibration and the slight differences in the αvalue (α = 0.81 for the Austin model and α = 0.95 for the
KK model).

4. Concluding remarks and outlook

This theoretical study presents a comparative analysisof the Austin kinetic model and the Kotake–Kanda (KK)
kinetic model in terms of their prediction of the specificbreakage rate parameter as a function of particle size andball size. Both models have been shown to be similar
in terms of their limiting behavior and the extremum
behavior because both behaviors are governed by powerlaw relationships. Mathematical requirements have beenformulated to ensure equivalency of both models underthese conditions; however, ensuring equivalency for bothbehaviors through these derived conditions is shown to be
severely restrictive or incompatible. Hence, to investigate
their similarities further, synthetic specific breakage rate
Si data were generated by the Austin model and fitted by
the KK model reasonably well. The PBM simulations of abatch (reference) mill have demonstrated almost identical
PSD evolution, confirming the similarity of the two models. Then, a virtual scale-up from the batch reference mill
to a continuous plug-flow mill with different D/DT has been
performed. Using the Austin’s scale-up methodology alongwith the Austin model, the scaled-up specific breakage rate
parameter Si* has been determined. The KK model has also
been scaled-up using the Austin’s scale-up methodology
with and without modified scale-up correction exponents.
Also, the large-scale continuous mill operation has been
simulated via PBM with the Si*. This study has overall
demonstrated that (i) the KK model is similar, but notequivalent, to the Austin model, (ii) the KK model and theAustin model provide similar PSD evolution after scale-up,(iii) the modification of the Austin’s scale-up exponents
N1 and N2 is not warranted, and (iv) the Austin’s scale-up
methodology is applicable to the KK model as well as theAustin model.
This theoretical study has generated additional questions
and ideas for further research. A well-designed experimental ball milling study, using both one-size fraction method
and the optimization-based back-calculation method on
several natural feed PSDs, should be conducted. The
models should be discriminated based on their goodnessof-fit, parameter uncertainties and confidence intervals,

and statistical significance. Such studies could reveal if
the KK model has some advantages in terms of parameter
uncertainties over the Austin model as the former has one
less parameter than the Austin model. Such a future study
is especially important to the calibration of the KK model(parameter estimation) as the currently used method with
one-size-fraction milling experiments is conducive to accumulation of experimental errors. Finally, it is hoped that
the Kotake–Kanda model will be used within the Austin’s
scale-up methodology for ball mill scale-up in the future.

Acknowledgements

The author would like to thank his PhD student
Mr. Nontawat Muanpaopong for preparing the figures andproof-reading the manuscript. This theoretical research did
not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors.

Data Availability Statement

The data analysis file and the data supporting thefindings of this study are available in J-STAGE Data
(https://doi.org/10.50931/data.kona.19596367).

NomenclatureaT

model parameter in the Austin model (mm–α∙min–1)

a0

value of aT for specific ball size dB,0 (mm–α∙min–1)

bij

breakage distribution parameter (–)

Bijcumulative breakage distribution parameter (–)

C1

model parameter in the KK model (mm–(m+α)∙min–1)

C2

model parameter in the KK model (mmn–1)

dB

ball size (mm)dB,0reference ball size (mm)Dmill diameter (m)DEM

Discrete Element ModelingF

inlet mass flow rate (kg∙min–1)

Jball filling fraction (–)

KK

Kotake–Kanda

K1–K5 Austin’s scale-up correction factors (–)
Lmill length (m)m, nball-size exponents in the KK model (–)

MB, pmass fraction of ball size with index p (–)

Mhmass hold-up (kg)Mimass fraction in size class i (–)

Ntotal number of data points (#)

NC

critical speed (rpm)Np

total number of parameters estimated (#)

Nstotal number of size classes, sink size class (#)

N0–N4 Austin’s scale-up correction exponents (–)
ODE

Ordinary Differential EquationP

total number of ball sizes in a ball mixture (#)

259

Ecevit Bilgili / KONA Powder and Particle Journal No. 40 (2023) 250–261

PBM

Population Balance Modeling

PSD

Particle Size Distribution

RTD

Residence Time DistributionSDstandard deviation (mm)SEstandard error (min–0.5)Sispecific breakage rate parameter (min–1)

Smmaximum specific breakage rate parameter (min–1)

Si*

scaled-up specific breakage rate parameter (min–1)

Si

specific breakage rate parameter for a ball mixture(min–1)SU

scale-upt[31]time (min)t*contact or residence time (min)uaverage axial velocity (m∙s–1)

U

void filling fraction (–)

xparticle size (mm)xffeed particle size (mm)xmparticle size at which specific rate parameter is maximum(mm)yaxial position in a continuous mill (m)Greek lettersαparticle size exponent in both the Austin model and the
KK model (–)β, γ

breakage distribution exponents (–)ξ, ηconstant ball-size exponents in the Austin model (–)Λ

abnormal breakage exponent in the Austin model (–)μT

a size parameter in the Austin model (mm)μ0value of μT for specific ball size dB,0 (mm)τ

space-time or average residence time (min)ϕbreakage distribution constant (–)ϕCfraction of operating rotation speed compared to critical
speed (–)

Subscripts
i, jsize-class indicesini

initialm

maximumpball size index

T

test condition as reference for the scale-up

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Author’s Short Biography
Ecevit Bilgili
Dr. Ecevit Bilgili is a Professor, AIChE Fellow, master teacher, and associate chair of the
Chemical and Materials Engineering department at NJIT. He holds a PhD in chemical engineeringfrom Illinois Institute of Technology, Chicago, USA. His Particle Engineering and Pharmaceutical
Nanotechnology Lab has been conducting research in designing formulations and processes for highvalue-added products like pharmaceuticals with enhanced functionalities. His research entails development, optimization, intensification, and scale-up of various particulate processes such as milling,
granulation, and drying. Dr. Bilgili also models various milling processes via combined DEM–PBM.
He currently serves as the Associate Executive Editor of Advanced Powder Technology journal.

261

KONA Powder and Particle Journal No. 40 (2023) 262–276/https://doi.org/10.14356/kona.2023011 Original Research Paper

A Hybrid PBM-DEM Model of High-Pressure Grinding Rolls
Applied to Iron Ore Pellet Feed Pressing †Victor A. Rodriguez 1, Túlio M. Campos 1, Gabriel K.P. Barrios 1,
Gilvandro Bueno 2 and Luís Marcelo Tavares 1*
1 Department of Metallurgical and Materials Engineering, Universidade Federal do Rio de Janeiro, Brazil
2 Vale S.A., Complexo de Tubarão, Brazil

Abstract
Mathematical models of high-pressure grinding rolls (HPGR) have attracted great attention, owing to their role inoptimization of operating machines as well as in the design and selection of new ones. Although population balance
models (PBM) and the discrete element method (DEM) have been used in this task, both suffer from important
limitations. Whereas PBMs have challenges associated to the prediction of operating gap and to the validity ofseveral of its assumptions in different formulations in the literature, application of DEM has its own challenges, inparticular when fed with distributions containing large amounts of fines. This work proposes a hybrid approach inwhich the coupling of DEM to particle replacement models and multibody dynamics is used to predict operating
gap, throughput and power, as well as providing information along the rolls length that is used in PBM to predictthe product fineness. The hybrid approach is then compared to both DEM and a PBM (Modified Torres and Casali),demonstrating similar results to the later when applied to simulating a pilot-scale machine operating under different
conditions, but improved prediction when applied in scale-up to an industrial-scale HPGR.
Keywords: high-pressure grinding rolls, discrete element method, population balance method, modelling,simulation

1. Introduction

Since the introduction of the high-pressure grindingrolls (HPGR) technology, various authors have proposed
different approaches to its mathematical modelling, which
may be classified as predominantly empirical, phenomenological or mechanistic.
Schönert (1988) was the first to propose relationships
among key performance variables, involving geometricalvariables of the HPGR such as rolls diameter and length;
operating variables as rolls velocity, working gap, nipangle, working pressure and material variables such as
flake density. Based on experimental evidence, Austin et al.
(1993; 1995) and Morrell et al. (1997), proposed relationships to predict the porosity of the product flakes, as well as
the working gap as a function of the specific grinding force,
including a correction factor for the effects of rolls surface,feed top size, scale of the machine, moisture content androlls speed.
In regard to prediction of product fineness, Klymowsky†*

Received 18 April 2022; Accepted 23 May 2022
J-STAGE Advance published online 2 July 2022
Corresponding author: Luís Marcelo Tavares;
Add: COPPE/UFRJ, Cx. Postal 68505, CEP 21941-972, Rio deJaneiro, Brazil
E-mail: tavares@metalmat.ufrj.br
TEL: +55 21 2290-1544 (ext. 246)

262

and Liu (1997) and Morrell (2004; 2010; 2022) developed
work indices that characterize empirically the relationshipbetween the energy that is applied by an HPGR to a particle
bed and the resultant product fineness. Application of the
HPGR work index to predict the specific energy consumed
by the machine demands the use of correction factors thatreflect the effect of top feed particle size, fines, moisture
content, mode of operation (open- or closed-circuit), material strength, and roll surface. More recently, Chelgani etal. (2021) and Tohry et al. (2021) proposed yet another empirical approach that is based on the so-called Explainable
Artificial Intelligence (EAI). Although it requires large databases originated from monitoring variables in industrialscale plants, it demonstrated to be able to predict accurately the 80 % passing size in the product and the powerdraw using linear and nonlinear multivariable assessment,
being useful in optimizing a machine in operation. These
approaches may be regarded as predominantly empiricalsince they do not rely on a consistent description of theunderlying physics of machine operation.
Phenomenological models have been widely used to
predict the HPGR product particle size distribution. Different authors have developed approaches based on the selfsimilarity concept (Fuerstenau et al., 1991; Lim et al.,1996), different formulations of the population balance
model (PBM) (Austin et al., 1993; Dundar et al., 2013;

Copyright © 2023 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

Thivierge et al., 2022), including those considering different breakage zones (pre-crushing, edge and compression
zones) and mechanisms (Daniel and Morrell, 2004; Morrell
et al., 1997), which are often characterized using the pistonand-die, besides laboratory- and pilot-scale HPGR tests(Rashidi et al., 2017).
Torres and Casali (2009) proposed a PBM formulationof the HPGR that refined the description of the edge andcentral products, relying on an empirical parabolic variation of axial pressure profile along the rolls. In their model,
the specific breakage rates are a function of the specificenergy input. As such, good predictions of product fineness
rely on the ability of the model to properly predict HPGR
throughput and power. Based on a large number of experiments, Campos et al. (2019a) identified some limitationsof the Torres and Casali approach when applied to a pilotscale HPGR processing iron ore pellet feed. The authors
proposed modifications of the expressions originally used
to describe HPGR throughput, associated to the material
ejection from the edge of the rolls, as well as to the power
consumption equation. Additional modifications were also
proposed and validated in industrial-scale HPGRs, including a breakage saturation model and an equation to accountfor variations of the axial pressure profile (Campos et al.,2021a).
One important application of mathematical models is in
sizing and scale-up of HPGRs (Daniel and Morrell, 2004;
Morrell et al., 1997; Schönert, 1988), where limitations
are recognized when they deal with prediction of HPGR
throughput (Banini et al., 2011), working gap and productfineness (Daniel and Morrell, 2004). Many of the mentioned scale-up studies are based on HPGR testing in pilotand industrial-scale machines. However, experimental tests
require a large mass of material even at the laboratory scale(Rashidi et al., 2017). The piston-and-die test is adopted bymany researchers to study interparticle breakage and obtain
information of the compressibility and breakage responseof the particle bed. Models based on the PBM and pistonand-die tests include the description of the energy absorption in different size classes of a compressed particle bed(Liu and Schönert, 1996) and the creation of new surface
area and particle bed porosity (Dundar et al., 2013; Esnault
et al., 2015) to describe the HPGR product particle size distribution. In addition, the piston-and-die test has also beenused to predict and scale-up HPGR performance variables(Davaanyam et al., 2015; Hawkins, 2008; Nadolski et al.,2011; Pamparana and Klein, 2021).
The application of mechanistic models to simulate
comminution machines has been increasing in recent years.
Mechanistic models are meant to describe in detail theeffect of operating and design variables since they can decouple material from machine contributions in the process.
This is accomplished by both characterizing and modellingin detail each of the breakage mechanisms, almost invari-

ably using the Discrete Element Method (Weerasekara etal., 2013). However, simulation of large-scale industrial
comminution machines using DEM deals with challenges
that include the large number of elements (up to several
million particles), boundaries containing large numbers ofelements, wide particle size ranges and need to couple withother tools describing the additional relevant physics of thesystem, which include particle breakage, dynamic geometry interactions, solid-fluid interactions, etc. (Cleary, 2004).
In particular, laboratory- and pilot-scale DEM simulations of HPGRs allowed to describe the effect of feed size
distribution, roll dimensions and speed, applied specificforce, besides features such as lateral confinement, rollwear profile and feed segregation. These have then beenused to predict performance variables that include throughput, working gap, power consumption and compressive
force profile along the rolls, besides skewing (Barrios andTavares, 2016; Cleary and Sinnott, 2021; Nagata et al.,2020; Quist and Evertsson, 2012; Rodriguez et al., 2021;
2022a; 2022b).
However, prediction of size distribution of the product remains a great challenge in DEM simulations of HPGRs. In
this regard, two publications are worth mentioning. Herbst
and co-workers (2011) combined Metso’s proprietary Fast
Breakage simulation approach in DEM, experimental datafrom piston-and-die tests and the energy-based population
balance model as alternatives to ore characterization andequipment selection by HPGR pilot-plant testing. The
validated physics-based model was then successfully used
to predict the performance of industrial-scale HPGRs using
the proposed energy-based scale-up method. Althoughpromising, their approach did not account for the axial
force and throughput distribution along the rolls.
Cleary and Sinnott (2021) simulated an industrial-scale
HPGR using DEM including a particle replacement model(PRM) to predict the variation of flow, pressure and particle
breakage response along the rolls. Yet, the authors recognized that it is not possible to include all fine particles in the
DEM simulations owing to the large computational effort,
so that a minimum cut-off size was applied to represent the
HPGR product, representing the fines as additional mass inthe finest resolved size class. While good predictions werereached, such approach is not suitable to predict HPGR
operation with very fine feeds.
The present work proposes a hybrid modelling approach, through which HPGR throughput and power aredescribed as a function of roll axial position for a given set
of design and operating conditions through coupled DEMMBD-PRM simulations (Rodriguez et al., 2022a; 2022b),
whereas size reduction is also predicted as a function ofroll axial position using PBM considering the breakage
distribution and selection functions. The approach has been
applied to a base case, consisting of a pilot-scale HPGR in
size reduction of iron ore pellet feed with 80 % passing size

263

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as fine as 103 μm, from which model parameters have been
fitted. The approach was validated by comparing model
predictions to data from the HPGR operating under different specific forces and roll velocities. The greatest potentialof the model has then been demonstrated by predicting theperformance of an industrial-scale HPGR based on breakage parameters calibrated at pilot-scale.

2. Modelling background
2.1 Proposed hybrid model

The model consists of linking outputs from DEM
simulations to the population balance model (PBM).
This later is represented by the energy-specific selection
function proposed by Herbst and Fuerstenau (1980), a nonnormalizable breakage distribution function used byCampos et al. (2019a) and selected expressions proposedby Torres and Casali (2009). Following this approach,
DEM simulations allow to capture the effect of design andoperating variables on the HPGR performance, while the
PBM model allows dealing with the entire range of particle
sizes fed to the machine.
As presented in detail in recent publications by theauthors (Rodriguez et al., 2022a; 2022b), outputs provided
by the DEM simulations correspond to total power (PDEM),
total throughput (QDEM), compressive axial force profile in
the region between the gap and the nip angle (Fk), and axial
mass flowrate profile in the region between the gap and thenip angle (Vk).
Following the PBM approach proposed by Torres andCasali (2009), the roll length is discretized in NB sections.
In each of the k sections, the mass of material contained inevery size class i is given by:
v z,k

i 1
d
wi, k  z  S j,k bij w j,k  z  Si,k wi,k  z
dz
j 1

(1)

where wi,k is the fraction of material in size class i andsection k, z is the distance between the beginning of the(1)
compression
zone and the extrusion zone, vz,k is the material flow velocity in the z direction for each section k, Si,k isthe breakage rate for each size class i and section k, and bij
is the breakage function in distributed form.
Two functions must be known in order to solve the resulting system of differential equations: bij and Si,k. Campos
et al. (2019a) and Faria et al. (2019) observed that, forsome Brazilian iron ores, the non-normalizable breakage
function proposed by King (2001) was required to describe
their response, being given by:
γ

β

x 
x 
Bij   i   1     i 
 xj 
 xj 
γ

Bi–1,j – Bij. The specific selection function can be calculated using the polynomial equation given by Herbst andFuerstenau (1980):
 sE x 
  x ln  iE   ξ1 ln  i   ξ 2 ln  i sx
 1  x1 
 1 

(3)

where s1E (t/kWh), ξ1 and ξ2 are the parameters to be fitted
from a base case, xi is the representative size given by(3)xi  xi 1xi and x1 is a reference size. In the present workx1 was[21]set to 1 mm. Following the Torres and Casali modelling approach, Eqn.
4 calculates the breakage rates (Si,k),
[22]
[23]
giving[24][24]
P
S i, k  siE k(4)Mk
where Pk and Mk are the power profile and the mass ofsolids profile along the roll length gathered from the DEM(4)simulations.
Power profile is hereby calculated by normalizing the compressive force profile (Fk) and multiplying
it by the total HPGR power, with both variables gatheredfrom the DEM simulation (Eqn. 5). The throughput profile in Eqn. 6 is then calculated on the basis of the total
throughput and mass flowrate of solids profile from DEM:
(5)

Fk
Pk  PDEM

NB j 1F j
Vk
Qk  QDEM(6)N j B1V j(5)
where PDEM is power consumption (kW), QDEM is total
throughput (t/h), and Fk is the compressive force profile(6) predicted by DEM. Given their direct relationship to(kN)
power, compression forces (Fk), provided by DEM simulation, are used in Eqn. 5. In the hybrid model approach, themass flowrate profile of solids (Vk) is then defined on thebasis of the mean particle residence time (λk) and the total
mass (Mk) in each block (k) along the rolls:
Vk 

(7)

Mk
λk

The solution of Eqn. 1 allows calculating the fraction
retained in each size class of every block (k) following the(7) and Casali model approach, which is based on the
TorresReid’s analytical solution (Reid, 1965):i
P pi,k  Aij, k exp  s Ej k 
Qk 

j 1

(8)

where Aij,k is a matrix given by:

for xi  ωx x η x 
Bij   i   i   1     i 
ω
x


(2)-1
 j
 xj 

(2)β

for xi  ωwhere γ, β, ϕ, ω and η are fitting parameters and bij =0 i 1 bil S l, kAij, k  l  j
Alj, kS i, k  S j , k

 IP
i 1
 f i  l 1Ail , k

(8)

(2)-2
264

2

(9)


i  j
i  j
i  j (9)

Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

where is the fraction retained in each size class i in the feed.
Finally, the overall product size distribution is calculated
from the weighted average product size distributions ofeach section k and is given as:

piHPGR 

1

NB

(10)

 pi,k Qk
QDEM
k 1

2.2 Modified Torres and Casali model(10)
In the present study, the Modified Torres and Casali(MTC) model (Campos et al., 2019a) represents the purely
phenomenological model that is compared to the proposed
hybrid approach. A summary of its main expressions andthe comparison with the corresponding ones in the hybrid
model is presented in Table 1. Throughput and powermodels in the Modified Torres and Casali model required
fitting four parameters (φ, υ and τ for the throughput modeland κ for the power model). That was done on the basis ofa selected base case. The throughput profile is assumed as
uniform. On the other hand, whereas the Torres and Casali(2009) model assumes a parabolic pressure profile along
the rolls, the MTC model allows for different profiles,
given evidence from both experiments and DEM simulations (Cleary and Sinnott, 2021; Rodriguez et al., 2021).

3. Materials and methods
3.1 Experimental

In the present study, the material selected is an iron ore
concentrate processed in the production line of the Com-

plexo the Tubarão (Vitoria, Brazil) and used in the production of iron ore pellets. The iron ore concentrate, originallyfrom the Itabira region, in the Iron Quadrangle from Minas
Gerais (Brazil), is essentially composed of hematite withminor amounts of quartz as contaminant (Campos et al.,2019b).
Experiments consisted in both pilot-scale tests and an industrial survey. Pilot-scale tests were carried out in an RPR
03.6 – 100/32 roller press manufactured by KHD Humboldt Wedag AG with 1 m-diameter rolls (Fig. 1). A sample
of 100 tons was collected from the production line in the
HPGR feed located in the pre-grinding stage (Campos etal., 2019b). The feed moisture content was 7.5 %, which
is very similar to the value practiced in industrial-scale
operations, although close to the maximum value allowablefor pressing iron ore concentrates (Van der Meer, 1997).
A total of eight experiments were carried out varying thespecific force (2.5 and 3.5 N/mm2) and the peripheral
roll velocity (0.20, 0.35, 0.50 and 0.70 m/s). Throughput,
power consumption, specific energy, operating gap, rollvelocity and operating pressure were obtained from thesupervisory system. During each test, a sampling apparatus
was introduced underneath the rolls to collect samples fromfive parallel sections of the discharge. The apparatus wasintroduced manually and allowed characterizing the axial
variation of the product fineness along the rolls (Campos
et al., 2019b). Additional pilot-scale tests were carriedout with a blend composed of iron ore concentrates fromthe Iron Quadrangle, normally used to feed the pelletizing

Table 1 Summary of expressions and approaches used for estimating the key performance variables in both the Modified Torres and Casali model(Campos et al., 2019a; Campos et al., 2021a) and the proposed hybrid model.

Model
Throughput

Modified Torres and Casali

Hybrid approach100 
Q  LU g ρg xg  100  δ Mass flow sensor underneath the working gap in DEM
simulationsτχg  U δln    υD  U max φThroughput profileUg 

xcUρa 
xg ρg

{31}

Uniform, with step bypass flow

{32}
Power consumption
Power distribution
along the rolls καip P  2Fsin 
U
 2 

Pk 

4 100 1  cos nπ  μ n 2π 2
sin nπy k 2n eπ n 1{33}
Breakage modelpiHPGR 

1 NB
 pi, kN B k 1

Eqn. 7 Extracted from NB sections positioned in thecompression zone (i.e. between the working gap andnip angle) along the rolls in the DEM simulations. Eachsection moves as a plugP  4πτ DEMU
Force profile extracted from NB bins located in thecompression
zone (i.e. between the working gap and nip{34}
angle) along the rolls in the DEM simulations
piHPGR 

1NB pi,k Qk
QDEM
k 1

{35}

D: roll diameter, L: roll length, U: roll velocity, Ug: material velocity; ρa: bulk density ρg: flake density, xc: critical size, xg: operating gap,
δ: the percentage of material ejected by the edge of the rolls; F: compressive force, αip: nip angle, τDEM: torque on the rolls extracted
from DEM simulation, ω:{36}angular velocity of the roll, κ: nip angle parameter, φ,{37}υ and τ: throughput model parameters, μ: power profile
parameter, ȳk: normalized average position, NB: number of sections along the roll length. *Only used in simulations of industrial-scalemachines

265Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

Fig. 1

Pilot-scale HPGR (left) and industrial-scale HPGR (right) used in the experimental campaigns.

Table 2 Summary of the main design and operating conditions of
HPGRs in the experimental campaigns.

Variables

Pilotscale
study

Scale-up study
Pilot

Industrial

Roll diameter (m)

1.0

1.0

1.4

Roll length (m)

0.32

0.32

1.6

Specific force (N/mm2)

2.5–3.5

2.5

2.5

Operating pressure (bar)37.0–52.0 40.0

60.9

Rolls velocity (m/s)

0.20–0.70 0.32

1.30

Operating gap (mm)6.8–8.2

13.8

6.0

Maximum roll velocity (m/s) 0.90

0.90

1.95

Feed 80 % passing size (μm) 103

143

143

Feed % passing 45 μm

28

26

26

Feed BSA (cm2/g)

450 ± 10490 ± 10 470 ± 10

plant in question (Campos et al., 2021a). A previous study
demonstrated relatively minor differences in pressing response between the samples (Campos et al., 2021b), making comparisons valid between them. Operating conditions
and main feed characteristics are presented in Table 2.
In addition, industrial-scale data were obtained from asurvey on an HPGR manufactured by KHD/WEIR (Fig. 1).
The machine has 1.4 m-diameter rolls, with 15.9 mm diameter studs, and cheek plates. During the survey the HPGR
operated at a specific compressive force of 2.5 N/mm2
and roll velocity of 1.3 m/s. Performance variables of the
HPGR were captured using the supervisory system. Samples from the feed and product were collected from belt
cuts for size analysis.
Particle size distributions from samples of the feed andproduct were measured by laser scattering in a Malvern
MastersizerTM 2000 (Malvern Instruments Inc), whereas
Blaine specific surface area (BSA) was measured in a PCBlaine-Star (Zünderwerke Ernst Brün GmbH).

266

3.2 Coupled DEM-MBD-PRM simulations

Simulations using DEM were carried out coupled tomultibody dynamics (MBD), describing the translation of
the movable roll, and particle replacement method (PRM),
following the approach proposed and adopted as part ofearlier studies by the authors (Rodriguez et al., 2021;
2022a). In the present work, DEM simulations were carriedout using the commercial software Altair EDEM version
2020.3. The Hertz–Mindlin contact model was used, inwhich the normal force component is described on thebasis of Hertz contact theory, whereas the tangential force
model is based on Mindlin-Deresiewicz work (Mindlin
1949; Mindlin and Deresiewicz 1953). Furthermore, theseparticles are allowed to break owing to the use of a spherebased particle replacement (PRM) strategy, recently proposed by Tavares et al. (2021) and coded in an application
programming interface (API). Finally, a two-way coupledmodel based on MBD and DEM was implemented to calculate the dynamics of the floating roll as initially proposed
elsewhere (Edwards et al., 2013; Barrios and Tavares,
2016). Since this DEM-MBD coupling was embedded in
the EDEM software, the use of co-simulation tools became
unnecessary.

3.2.1 Material properties

Due to the large number of particles demanded in HPGR
simulations, the authors selected a coarser particle size
distribution, with a top size 8 mm, to represent the ironore pellet feed. This approach was already successfully
used in this application in previous studies (Rodriguez etal., 2021; 2022a). From this and bench-scale tests, which
included measurement of the static and dynamic angles
of repose (Rodriguez et al., 2022a), material and contact
parameters were calibrated (Table 3). Addition material
properties were estimated on the basis of a piston-and-die
test, resulting in the selection of values of Poisson’s ratioof 0.25, particle density of 3,011 kg/m3 and shear modulus of 2 GPa (Rodriguez et al., 2022a). Finally, valuesof breakage model parameters were taken from previous
publications (Rodriguez et al., 2021; 2022a). The same set

Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276
Table 3 Hertz–Mindlin contact parameters used in the DEM simulations.

CoefficientOre-steelOre-oreRestitution

0.15

0.20

Static friction

0.49

0.55

Rolling friction

0.47

0.51

of material, contact and breakage parameters were used tosimulate both pilot- and industrial-scale HPGRs.

3.2.2 Equipment setup

At first, computer Aided Designs (CAD) of the HPGR
units simulated were generated. The translational dynamics
in the floating roll was described by a force controlled in
the longitudinal direction “x”, while only the kinematicsof the rotating movement in both rolls with a fixed angular velocity was described. Being a one-dimensionalmodel, it does not allow for skewing of the rolls, studied
by Rodriguez et al. (2022b). The rolls movement started
only when DEM particles filled the hopper from a particle
factory located above the rolls. From this point on in thesimulation, new particles were added continuously until
steady-state conditions were reached, and then analysis
were performed.
The hopper level is an important aspect in DEM simulation of HPGRs, since particles located in this region
only apply pressure to the particle bed sitting between therolls (Cleary and Sinnott, 2021; Nagata et al., 2020). In
the present study, the recommendation from Cleary and
Sinnott (2021) was followed, where the minimum mass inthe hopper (mhopper) is given by:mhopper  LhW 2 ρaf(11)

where Lh is the length along the diameter of the rolls andW the width of the feed hopper, and ρaf is the bulk density(11)of the feed. During simulations, this minimum mass ofparticles filling the hopper was maintained approximately
unchanged.
Even though the present approach already used virtual
particles that are larger than those in the real operations(upscaled particles), maintaining the hopper level to meet
Eqn. 11 imposes a major computational challenge when
dealing with simulations of industrial-scale machines. As
such, in order to reduce the number of elements requiredin the simulation, the final portion of the hopper silo wasfilled with particles of even coarser size (60 mm diameter)and density (10,000 kg/m3)—called column-pressure particles—so as to match the same pressure that would havebeen generated by piling up additional feed particles. Fig. 2
illustrates this scheme, where these column-pressure particles are placed without interfering with the rolls. It is worth
mentioning that, although these particles move downwards

Fig. 2 Representation of industrial HPGR simulation using the coupled DEM-MBD-PRM with both feed (upscaled) particles and columnpressure particles.

as rolls rotate, the short simulation times required to reachsteady state conditions prevented them from ever contacting the rolls.

3.2.3 Measurement of key variables in DEM
simulations

As part of the hybrid simulation approach, the DEM
simulations directly provide throughput and power of the
HPGR, as informed in Table 1. Indeed, total throughput
between the rolls was estimated with a cylindrical geometry sensor positioned underneath the working gap. The
total power was calculated based on the torque required torotate the rolls multiplied by the rotation frequency. The
mass flowrate in each block (Vk) and axial compressive
force (Fk) profiles were estimated from a grid bin locatedin the compression zone, presented in detail elsewhere(Rodriguez et al., 2022a).
With the aim of providing an additional comparison withboth phenomenological and hybrid approaches, the size
distribution from the DEM simulations using PRM wasalso computed. The approach used to obtain the particle
size distribution using the Tavares PRM can be found elsewhere (Tavares et al., 2021).

3.3 PBM parameter estimation

The proposed Hybrid model was implemented in
MatlabTM (version R2021a, Mathworks Inc.), which is theplatform also used in predictions using the Modified Torres
and Casali model. Initially, the parameter optimization
was performed on a base case test, which consisted in
the pilot-scale experiment with a peripheral roll velocityof 0.35 m/s and specific compressive force of 2.5 N/mm2(reference test #1 in Table 4). Breakage function parameters (Eqn. 2) were estimated as part of previous studies

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by Campos et al. (2019a), being also used in the present
work. The three remaining parameters in Eqn. 3 were thencalibrated from reference test #1.
Parameter optimization for DEM simulations, used in
power and mass flowrate predictions in the Hybrid model
was conducted as part of an earlier work (Rodriguez etal., 2022a), whereas in the case of the Modified Torres
and Casali model, reference tests #1 and #2 (Table 4)
were used. Two parameters from the throughput model(Table 1) were kept constant and equal to values from aprevious study (φ = 100 and τ = 0.1), since they were foundto be valid for a range of machines (Campos et al., 2019a).
The single fitting parameters for the nip angle model (κ)
and the remaining parameter for the throughput model(υ) were then calibrated on the basis of the reference tests
in Table 4. This was used to ensure that the model wasproperly calibrated to capture variations when the HPGR
was operating under different conditions. In order to ensure
a parabolic pressure profile as assumed by Campos et al.
(2019a), parameter μ from Table 1 was set to 0.05. From
the fitted parameters, prediction of the remaining pilotscale experiments as well as the industrial-scale survey was
performed.
The optimal parameters were fitted using the function
fminsearch from MatlabTM (version R2021a, MathworksInc.), which allows finding the minimum of unconstrained
multivariable function using the derivative-free method.
The objective function consisted of the sum of squaresof the differences in the logarithms of the experimental
and fitted cumulative particle size distribution values ofreference test #1.
Comparison between predictions by the different models and the experimental results were carried out fromcomputations of the relative absolute deviation given by
εABS = 100|varExp – varCalc|/varExp, where varExp is the exTable 4 Operating conditions of the reference tests.

Variable

Reference test
#1

#2*

Specific force (N/mm2)

2.5

3.5

Operating pressure (bar)

38.6

48.5

Operating gap (mm)

8.2

7.4

Roll peripheral velocity (m/s)

0.35

0.50

Throughput (t/h)

19.9

26.8

Power consumption (kW)

47.2

66.1

Specific energy (kWh/t)

2.37

2.47

Passing 45 μm in product (%)

52.3

52.0

Product BSA (cm2/g)

800

820

*Additionally used to calibrate the power and throughput
model in the Modified Torres and Casali model.

perimental variable and varCalc is the calculated variable.
Besides power, throughput and product size distribution,
Blaine specific surface area (BSA) was also analyzed. Predictions of BSA from simulated product size distributions
were computed using the approach described elsewhere(Campos et al., 2019b).

4. Results and discussion
4.1 Parameter fitting

Different base cases were used for predictions ofthroughput and power in each approach. The DEM-MBDPRM approach, used in the proposed Hybrid model, wasable to make predictions of power and throughput of thepilot HPGR campaign. In that procedure, Rodriguez et al.
(2022a) based their results on reference test #2 (Table 4).
No particular calibration was carried out using industrial
data. On the other hand, selected parameters of the power
and throughput equations (Table 1) for the Modified Torres
and Casali model (Campos et al., 2019a; Campos et al.,2021a) were calibrated from reference tests #1 and #2 inTable 4, resulting in the values of υ = 155 and κ = 3.2. The
different values for these parameters from those previously
fitted on the basis of data from pressing dry pellet feed fines
may be attributed to the effect of moisture content.
In predicting size reduction, the optimal parameters
fitted for the Hybrid model and the Modified Torres andCasali model are shown in Table 5. Both the non-normalizable breakage and the selection functions are illustrated inFig. 3, which show that nearly identical specific selection
functions were obtained from the parameter optimization
procedure. The results compared to the experimental data
are presented in Fig. 4, demonstrating that good agreement
was reached between model and experiments, with valuesof the objective function of 0.038 for the Hybrid modeland 0.040 for the MTC model. Results allow to conclude
that the non-normalizable breakage function parametersproposed in a previous study (Campos et al., 2019a) remain
valid in the present work. A closer examination of predicted product BSA shows that good agreement between

Table 5 Fitted selection and breakage function parameters.

Equation

Parameter

MTC

Hybrid model

Breakage function(Eqn. 2)

γ

0.896

0.896

β

5.461

5.461

ϕ

0.857

0.857

η

0.46

0.46

ω (mm)

0.013

0.013

ξ1

–0.491–0.492

ξ2–0.191–0.190s1E (t/kWh)

0.260

0.247

Selection function(Eqn. 3){38}
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Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

fitted and predicted values for size reduction was obtained,
since absolute deviations from measurements were 0.7 %and 0.5 % for the Modified Torres and Casali and Hybrid
models, respectively. It is worth mentioning that values ofBreakage function

1

0.1

0.01

0.001

0.0001
0.001

0.01

0.1

1

10

Particle size (mm)Selection function (t/kWh)

1

0.1

0.01
MTC model
Hybrid model
0.001
0.001

0.01

0.1

1

10

Representative size (mm)Fig. 3 Non-normalizable breakage function of selected feed particle
sizes (top) and comparison between specific selection function fitted for
the Hybrid model and for the Modified Torres and Casali (MTC) model(bottom).

10

1

0.1
0.001

Feed
ExperimentHybrid model
MTC model
0.01

0.1

4.2 Pilot-scale HPGR simulations

Based on thecalibrations from Section 4.1, both mod[24]elling approaches were applied to predict the remaining
pilot-scale tests (Table 2) pressing iron ore concentrates.
Initially, Fig. 5 compares measured and predicted throughputs, highlighting the good agreement between both model
approaches and experiments. As discussed by Rodriguez etal. (2022a), good predictions were achieved using DEMMBD-PRM simulations with average absolute deviationof 7.2 %, with larger overestimations for tests at higher
throughputs (peripheral roll velocity of 0.70 m/s). Predictions of the Modified Torres and Casali model, on the other
hand, show a smaller deviation of 4.6 %, underestimating
values for roll peripheral velocity of 0.70 m/s.
A comparison between measured and predicted power
consumptions in the pilot-scale HPGR using the Hybrid
model and the MTC model is presented in Fig. 6 (top).
Very good agreement is evident between the Hybrid modeland experiments, with mean absolute deviations of 7.9 %.
Likewise, the MTC model, with κ = 3.2 fitted based on
the two pilot-scale tests (Section 4.1), also exhibited good
agreement with experiments, with mean deviation of
9.5 %. Likewise, similar deviations of the specific energy
predictions were observed in Fig. 6 (bottom), being equal
to 7.2 % for the Hybrid model and 8.2 % for the MTC
model. Those estimates are very important for both approaches since the predicted product size distributions are
directly influenced by them.
Fig. 7 shows experimental results and predictions of
product size distribution from two selected tests run at theroll peripheral velocity of 0.50 m/s. Reasonable agreement
is evident, demonstrating the acceptable ability of both
50

Predicted throughput (t/h)

Cumulative passing (%)

100

the specific selection function in Table 5 differ from thosereported in earlier studies (Campos et al., 2019a) primarily
due to setting x1 = 1 mm in Eqn. 3.

40
30
20
10
0

1

Particle size (mm)Fig. 4 Comparison of fitted and experimental product size distributions of base case (reference test #1).

MTC model
Hybrid model

0

10

20

30

40

50

Experimental throughput (t/h)
Fig. 5 Comparison between measured and predicted throughput in thepilot-scale HPGR using the Hybrid and the Modified Torres and Casali
models.

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100

120

Predicted power (kW)100

Cumulative passing (%)

MTC model
Hybrid model

80
60
40

10

1

Feed
ExperimentHybrid model
MTC model

20
0

0.1
0.001
0

20

40

60

80

100

0.1

1

100

4
MTC model
Hybrid model

Cumulative passing (%)

Predicted Specific energy (kWh/t)

Experimental power (kW)

3

2

1

1

2

3

4

Experimental Specific energy (kWh/t)
Fig. 6 Comparison between measured and predicted power (top) andspecific energy consumption (bottom) in the pilot-scale HPGR for the
Modified Torres and Casali and Hybrid models. Filled markers correspond to the base cases used in model calibration.

modelling approaches in predicting product size distribution when operating conditions are varied. In applicationof HPGRs to iron ore concentrate pressing, it is routine
to check the percent passing the 45 μm sieve to assess thecoarser part of the product size distribution, whereas theBlaine specific surface area (BSA) is strongly influenced
by its finer portion. As such, Fig. 8 presents a comparison
between experimental and predicted values for both indicesfor all tests performed in the pilot-scale HPGR. In general,
Hybrid model predictions are in only reasonable agreementwith experiments, with values of average absolute deviations of 5.5 % and 6.2 % for passing in 45 μm and the BSA,
respectively. Marginally higher deviations were observed
for the MTC model, where average absolute deviations of
5.9 % were obtained for the percentage passing the 45 μm
sieve and 6.7 % for the BSA.
A more detailed inspection is also possible from Fig. 9
with a relationship between specific energy consumption
and the product BSA. Unlike expected, experimental BSA
values did not show clear trend with specific energy. In

270

0.01

Particle size (mm)

120

10

1

0.1
0.001

Feed
ExperimentHybrid model
MTC model
0.01

0.1

1

Particle size (mm)Fig. 7 Comparison between predicted and experimental product size
distributions in the pilot-scale HPGR with roll peripheral velocity of
0.5 m/s and specific compressive forces of 2.5 N/mm 2 (top) and 3.5 N/
mm 2 (bottom).

general, although recent works showed no improvementin size reduction given the increment in roll peripheral
velocity (Campos et al., 2019b; Van der Meer and Leite,2019), it is worth mentioning that HPGR operations withhigh moisture content as in the present work would provide
some acceleration on the compression zone and increase
the proportion of material ejected from the edge of therolls. These features can, at least in part, be responsible forthe dispersion of the data in Figs. 8 and 9.
On the other hand, Fig. 9 shows that results from thetwo models exhibited a clear and direct improvement in the
BSA with the increase in specific energy. These results canbe explained by the modelling approach adopted in bothmodels, which relies on breakage rates (Eqn. 4) that arecalculated from the product of the specific selection function and the ratio between the power consumption and theholdup. As such, although DEM provides a more detailed
description, the use of the PBM limits the capability of themodel in providing improved descriptions of the effect ofoperating variables when compared to the phenomenological (MTC) model.

Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276
70
65

2.5

60

Power (kW)Predicted < 45 µm (%)

3.0

MTC model
Hybrid model

55
50
45

2.0
1.5
1.0

40

0.5

35

0.0
-18

30

30

35

40

45

50

55

60

65

70

Experimental < 45 µm (%)

10.0

Mass flow (t/h)

Predicted BSA (cm2/g)

MTC model
Hybrid model

1000
900
800
700

400
400

600

800

1000

1200

Experimental BSA (cm2/g)
Fig. 8 Comparison of experimental and predicted passing the 45 μm
sieve (top) and the BSA (bottom) using both the Hybrid and the Modified Torres and Casali models. Filled markers correspond to the basecases used in model calibration.

950

BSA (cm2/g)

900
850

Campos et al. (2019b)
Experiments
MTC model
Hybrid model

800
750
700
650
600
0.0

0

6

12

18

2.5 N/mm2 - 0.35 m/s (MTC model)
3.5 N/mm2 - 0.50 m/s (MTC model)
2.5 N/mm2 - 0.35 m/s (Hybrid model)
3.5 N/mm2 - 0.50 m/s (Hybrid model)

-12

-6

0

6

12

18

Roll axial position y-axis (cm)

500

1000

-6

1.0

0.1
-18

600

-12

Roll axial position y-axis (cm)

1200
1100

2.5 N/mm2 - 0.35 m/s (MTC model)
3.5 N/mm2 - 0.50 m/s (MTC model)
2.5 N/mm2 - 0.35 m/s (Hybrid model)
3.5 N/mm2 - 0.50 m/s (Hybrid model)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Specific energy (kWh/t)
Fig. 9 Relationship between product BSA and specific energy consumption for the tests carried out in the pilot-scale HPGR. For comparison, data are also included for the experimental campaign carried out
by Campos et al. (2019b).

Fig. 10 Comparison between the Hybrid model and MTC model ofthe power (top) and mass flowrate (bottom) profiles for the pilot-scale
HPGR. Vertical dotted lines identify the projection of the rolls length.

4.3 Axial variation in pilot-scale HPGR

Similarities and important differences appear on how
the two model approaches deal with the axial variation of
power and mass flow in the HPGR, as illustrated in Fig. 10.
Regarding the power profile, as mentioned in Table 1, aparabolic pattern is associated to the MTC model with thehighest value at the centre of the rolls, vanishing to zero towards the edges. DEM simulations show a similar pattern,
but with a more marked drop towards the edge of the rolls.
Greater differences are found in the throughput profiles,
whereas the MTC model gives uniform values between
the rolls and a marked step increase at the by-pass region,
due to the material ejected beyond the length of the rolls(Fig. 10 bottom). On the other hand, the mass flowrates inthe Hybrid model do not show the by-pass effect, since its
values are computed within the particle bed. In addition,
this approach results in higher values towards the centre of
the rolls and lower values towards its edge.
As explained in Section 3.1, samples were collectedunderneath the rolls to capture the axial variation of theparticle size distribution along the rolls. This is an important aspect in HPGR operation since products with coarser
particle sizes are known to appear close to the edge of therolls (Campos et al., 2019b; Lubjuhn and Schönert, 1993;

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Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

Passing in 45 µm (%)

2.5 N/mm2

90
80
70

1600

Feed
ExperimentHybrid modelHybrid model (average)
MTC model

1400
1200

BSA (cm2/g)

100

60
50
40

800
600
400

30
20
-18

1000

-12

-6

0

6

12

200
-18

18

Roll axial position y-axis (cm)Passing in 45 µm (%)

3.5 N/mm2

90
80
70

1800

Feed
ExperimentHybrid modelHybrid model (average)
MTC model

1600
1400

60
50

1200

30

400
-6

0

6

12

Roll axial position y-axis (cm)

0

6

12

18

18

Feed
ExperimentHybrid modelHybrid model (average)
MTC model

800
600

-12

-6

1000

40

20
-18

-12

Roll axial position y-axis (cm)

BSA (cm2/g)

100

Feed
ExperimentHybrid modelHybrid model (average)
MTC model

200
-18

-12

-6

0

6

12

18

Roll axial position y-axis (cm)Fig. 11 Comparison between experimental and predicted values for the percentage passing of the 45 μm sieve and BSA using the Hybrid and theModified Torres and Casali models for pilot-scale tests at a peripheral roll speed of 0.50 m/s.

Morrell et al., 1997; Torres and Casali, 2009), in spite ofefforts that have been made to prevent it (Van der Ende etal., 2019; Herman et al., 2013). In order to check the abilityof the models to describe the variation of product size
distribution along the length of the rolls, predictions of selected tests are compared to experiments in Fig. 11 for thepilot-scale HPGR operating at the roll peripheral velocityof 0.50 m/s. Good agreement is evident for both test conditions simulated, in particular for the Hybrid model. Also, as
already discussed by Campos et al. (2019a), the parabolic
shape profile demonstrates a reasonable description for thepilot-scale HPGR studied in pressing iron ore concentrates.

4.4 HPGR Scale-up

At first, a snapshot of the DEM-MBD-PRM simulationof the industrial scale HPGR is presented in Fig. 12.
Around 2.5 million particles were processed in the simulation, in which the reduction in particle size becomes
clear as the bed of particles move towards and throughthe gap. A summary of the simulation results is presented
in Table 6, which shows that the Hybrid model provided
reasonable predictions of total power, with a deviation of
17.8 % from the experimental results, with an even closer

272

Fig. 12 3D rendered DEM simulation image of the industrial-scale
HPGR with particles colored by size operating at specific force of 2.5 N/
mm 2 and roll velocity of 1.30 m/s. Column-pressure particles coloredin gray.

prediction of throughput, with a deviation of only 2.0 %.
Greater deviations are, however, observed when comparing
predictions using the modified Torres and Casali model toexperiments, with 12.3 % deviation in the case of power

Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

Table 6 Key variables of industrial-scale HPGR with both modelingapproaches, with deviations (%) from experiments in parentheses

MeasureSurveyMTCPower (MW)

1.17

1.31 (12.3) 1.37 (17.8)

Throughput (t/h)

520

905 (74.1)

Spec. energy (kWh/t)

2.24

1.45 (35.5) 2.70 (20.2)

Prod. % <45 μm

48.842.5 (13.1) 52.0 (6.6)

Prod. BSA (cm2/g)

890

680 (23.5)

hypothetical case was then simulated. This condition
consisted in matching the experimental specific energy
in each model and make prediction of the industrial test.
These results are presented as dashed lines in Fig. 14, with
reductions in absolute deviations to 6.2 % and 6.9 % for the
MTC and Hybrid models, respectively.
A final comparison is shown in Fig. 15, where predictions

Cumulative passing (%)

100

10
Feed
ExperimentHybrid model1
0.001

Cumulative passing (%)

and 74.1 % in the case of throughput. This significant
overestimation of throughput may be explained by thegreater proportion of material ejected by the bypass zone
in the model prediction. Similar mass transfer in the centreof the roll was predicted by both models, with around 9 %larger from that estimated from the DEM simulation as partof the hybrid model.
A comparison of the pressure profiles in pilot- andindustrial-scale HPGRs predicted using DEM, shownin Fig. 13, demonstrates that the pressure profile in theindustrial-scale machine is more constant in the central
portion of the rolls. Indeed, the DEM simulation was able
to capture this effect that was already reported experimentally by some authors (Campos et al., 2021b; Morley,2010).
On the basis of the parameters calibrated from a basecase test from the pilot-scale test campaign, presented in
Section 4.1, predictions of product size distribution of
the industrial HPGR survey were carried out using boththe Hybrid and the Modified Torres and Casali models.
Predictions are presented in Fig. 14, where the Hybrid
model shows good agreement with experiments, with anaverage absolute deviation of 8.0 %. The poor predictionof throughput (Table 6), which directly affects the specificenergy, led to the high value of absolute deviation (15.8 %)for the Modified Torres and Casali model. An additional

1
0.001

100

Predicted < 45 µm (%)

Relative compressive force (-)

0.8
0.6
0.4

0.0
-0.55

Pilot scale
Industrial scale
-0.35

-0.15

0.05

Feed
Experiment
MTC model
0.01

0.25

1

0.1

Particle size (mm)Fig. 14 Comparison of predicted and experimental product size distribution using the Hybrid (top) and the Modified Torres and Casali models(bottom) of the industrial HPGR operating at 2.5 N/mm 2 and 1.30 m/s.
Dashed lines correspond to hypothetical case matching the experimental specific energy in each model.

858 (3.6)

1.0

1

10

Hybrid509 (2.0)

0.1

Particle size (mm)

100

90

0.2

0.01

80
70
60

MTC model (Industrial)
Hybrid model (Industrial)
PRM (Industrial)
MTC model (Pilot)
Hybrid model (Pilot)
PRM (Pilot)

50
40
30
20
10

0.45

Relative axial (y-distance)from center of the roll (-)
Fig. 13 Relative compressive force profile along the roll in pilot- andindustrial-scale HPGRs obtained from DEM-MBD-PRM simulations.
Pilot-scale HPGR operating at 2.5 N/mm 2 and rotating at 0.32 m/s.

0
30

35

40

45

50

55

60

65

70

Experimental < 45 µm (%)
Fig. 15 Comparison of experimental and predictions of 45 μm of the
product size distribution in both pilot and industrial scale, including the
PRM.

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Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

of percentage passing the 45 μm sieve for all tests arecompared to those obtained using the particle replacement
model (PRM) in DEM. As demonstrated in the earlier
work (Rodriguez et al., 2022a), the PRM was unable toreach quantitative agreement with experiments. However,
although biased, its predictions were more aligned withexperiments than both Hybrid and the MTC models, thus
demonstrating its great future potential.

5. Conclusions

In order to deal with the limitations of DEM-MBD-PRM
approach in predicting quantitatively the fineness of ironore pellet feed in the HPGR product, a Hybrid model was
proposed. The model used throughput and power, besides
mass flowrate and force profiles predicted along the rolls
obtained from DEM simulations as input to a population
balance model (Modified Torres and Casali model), fitted
on the basis of a base case test.
The approach was able to predict the product particle
size distributions of a pilot-scale HPGR operating at different roll velocities and pressures in size reduction of ironore pellet feed, but with predictions that were comparable
to those obtained using the Modified Torres and Casali
model. Advantages of the Hybrid model in this case would
only appear if changes are made to the machine in question,such as replacement of cheek plates by flanges or changes
in stud patterns, for instance. In these cases, the Hybrid
model would be able to capture their effect, whereas thephenomenological model would not.
The Hybrid model demonstrated to provide superior
predictions of throughput, power and product fineness thanthe phenomenological approach when used to predict theperformance of an industrial-scale HPGR when the models
were fitted using data from pilot-scale tests. This demonstrates the great potential of the Hybrid model to be used inscale-up and design of HPGRs in the future.

Acknowledgements

The authors would like to thank Vale S.A. for financial
and technical support to the research, as well as permission
to publish the work. We also thank the additional financial
support by the Brazilian research agencies CNPq (grant
number 151862/2022-2 and 313425/2021-3), FAPERJ(grant number E-26/202.574/2019) and CAPES (finance
Code 001 and grant number - 88881.188860/2018-01).

Data Availability Statement

The data analysis file and the data supporting the findings of this study are available in J-STAGE Data
(https://doi.org/10.50931/data.kona.20129942).

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in HPGR grinding, in: SAG Conference 2019, pp. 1–9.
Van der Meer F.P., Roller press grinding of pellet feed. Experiences of KHD in the iron ore industry. In: Proceedings
from the AusIMM Conference on Iron Ore Resources andReserves Estimation, Perth WA Australia, 1997.
Van der Meer F.P., Leite I.A., Aspects of HPGR in iron ore pellet
feed preparation, in: 6th Brazilian Ore Agglomeration Symposium, São Paulo, 2018, pp. 102–115. DOI: 10.5151/2594357X-31516
Weerasekara N.S., Powell M.S., Cleary P.W., Tavares L.M.,
Evertsson M., Morrison R.D., Quist J., Carvalho R.M.,
The contribution of DEM to the science of comminution,
Powder Technology, 248 (2013) 3–24. DOI: 10.1016/j.
powtec.2013.05.032

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Victor A. Rodriguez et al. / KONA Powder and Particle Journal No. 40 (2023) 262–276

Authors’ Short Biographies
Victor Alfonso Rodriguez
Victor Rodriguez received his Materials Engineering degree from Universidad del Valle in Colombia. He then spent four years working as an R&D engineer in a company focused on refining,
manufacturing and recycling precious metals. After that, he joined Universidade Federal do Rio deJaneiro (UFRJ), where he was awarded master’s and doctorate degrees for research dealing withmechanistic modeling and simulation of ball mills and HPGRs, respectively. Victor is currently a
post-doctoral fellow at the Laboratório de Tecnologia Mineral (LTM/COPPE/UFRJ) and his researchinterests include discrete element methods, mechanistic modeling and material characterization.

Túlio Moreira Campos
Túlio Campos is a Metallurgical Engineer graduated at UFRJ and is currently pursuing a master’s degree in Metallurgical and Materials Engineering at the Alberto Luiz Coimbra Institute for
Postgraduate Studies and Engineering Research (COPPE/UFRJ). Túlio is a research assistant at theLaboratório de Tecnologia Mineral (LTM/COPPE/UFRJ) and works with mathematical modelling ofcomminution, process simulations, particle breakage, grinding circuit optimization, multicomponent
operations and digital twin technology. Túlio is interested in developing and applying advanced,
practical and robust process models to improve and integrate total process simulation as a tool for
innovation in the minerals industry.

Gabriel Kamilo Pantoja Barrios
Gabriel Barrios is a Chemical Engineer graduated at Universidad del Valle in Colombia, withmaster’s and doctorate degrees from UFRJ in Brazil. He is currently a post-doctoral fellow at theLaboratório de Tecnologia Mineral of the Alberto Luiz Coimbra Institute for Postgraduate Studies
and Engineering Research (LTM/COPPE/UFRJ). His research interests include particle characterization, mathematical modelling and simulation of mineral processing equipment, simulation using thediscrete element method and geometallurgy. He has experience in experimental work in metallurgicallaboratory, mineral processing plant surveying, and computational simulation.

Gilvandro Bueno
Gilvandro Bueno is a Master Engineer at Vale S.A. He is a Metallurgical Engineer, graduated fromthe Universidade Federal de Ouro Preto and has a master’s degree in Metallurgical and Materials
Engineering from UFRJ. Prior to his current engagement he worked at Taboca Mineração S.A., Votorantim Cimentos S.A. and Samarco S.A., with a total of 30 years of industrial experience in mineral,cement and pelletizing processes.

Luís Marcelo Tavares
Luís Marcelo Tavares is a Professor at UFRJ in Brazil. He received his bachelor’s degree in
Mining Engineering (honors) and his master’s degree from Universidade Federal do Rio Grande
do Sul. He was awarded a Ph.D. degree in Extractive Metallurgy at the University of Utah. He has
been a member of the faculty of UFRJ since 1998, where he is head and founder of the Laboratóriode Tecnologia Mineral and has been Department chairman. His research interests include particle
breakage, advanced models of comminution and of degradation during handling, DEM, physicalconcentration, classification, iron ore processing and development of pozzolanic materials. He is afounding member and currently president of the Global Comminution Collaborative (GCC) and hasreceived several awards from the Brazilian Association of Metallurgy, Materials and Mining (ABM).

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KONA Powder and Particle Journal No. 40 (2023) 277–278/https://doi.org/10.14356/kona.2023018

Information Article

The 54th Symposium on Powder Technology and Special
Lecture for the 30th Anniversary of the Establishment of HPTF
The 54th Symposium on Powder Technology organized by Hosokawa Powder Technology Foundation took place at Imperial Hotel Osaka, in Japan on Monday, September 5, 2022. It was held by the planning of Council of Powder Technology,
Japan and with the sponsorship of Hosokawa Micron Corporation. Nearly 180 people from the industries and universities
attended this symposium. The theme of the symposium this year was “Frontiers of Powders and Powder Processing forSDGs”. There were seven lectures including three special ones given by the 2019 KONA Awardee Prof. Makio Naito, 2020
KONA Awardee Prof. Brij M. Moudgil and 2022 Queen Elizabeth Prize winner Dr. Masato Sagawa. This 54th symposium
was scheduled to be held two years ago, but due to the COVID-19 pandemic, this time it was held after three year interval
in a hybrid style (on-site and online) for the first time.
At the beginning of the symposium, Mr. Yoshio Hosokawa, the president of the Foundation, gave the opening address
mentioning that this symposium was started in 1968, more than 50 years ago and has been continued almost annually sincethen. The Hosokawa Foundation established 30 years ago took over the role of organizer from Hosokawa Micron Corporation. Prior to the lectures, the KONA Award presentation ceremony was held and the plaques of KONA Award were handedfrom the president to the 2019 KONA Awardee Professor Makio Naito (Osaka Univ., Japan), the 2020 KONA Awardee
Professor Brij M. Moudgil (Univ. of Florida, USA) and one of the 2021 KONA Awardees Professor Shuji Matsusaka (Kyoto
Univ., Japan). The other awardee of the KONA Award 2021 Professor Wolfgang Peukert (Friedrich-Alexander-Univ. Erlangen-Nuremberg, Germany) was handed the plaque at ICCCI 2022 last November. The KONA Award has been presented to
the researchers who have greatly contributed to the research and development as well as education in the field of Powder andParticle Science and Technology since 1990. It was given originally by Hosokawa Micron Corporation but now is presented
to the researchers from all over the world by the Hosokawa Powder Technology Foundation annually.
The contents of the symposium are shown below. In the morning session, Prof. Naito and Prof. Moudgil gave the KONA
Award lectures, in which they introduced their research results on various materials, in which the structural control andinterfacial design of fine particles and powders have been used to improve their properties and enhance their performancefor a wide range of industrial applications. Then, three leading researchers gave thought-provoking lectures on resources
circulation, solid-state batteries, and direct methane reforming (DMR) reaction using iron-based catalysts to address the
SDGs from the perspective of advanced powder processing and particle bonding technologies, followed by an introductionof latest equipment and technology development with the SDGs in mind from Hosokawa Micron Corporation.
The last lecture was Special Lecture for the 30th Anniversary of the Establishment of HPTF, which was given by Dr.
Masato Sagawa, who discovered and globally commercialized the world’s most powerful permanent magnet, Nd-Fe-B
Sintered Magnets, in which he explained the history of the discovery and development of neodymium magnets, theircharacteristics, and applications for such as electric vehicles, robots and wind generators in an easy-to-understand manner.

At the KONA Award presentation ceremony,
President Hosokawa (Left) and 2019 KONA Awardee Professor
Makio Naito (Osaka Univ., Japan).

At the KONA Award presentation ceremony,
President Hosokawa (Left) and 2020 KONA Awardee Professor
Brij M. Moudgil (Univ. of Florida, USA) .

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The 54th Symposium on Powder Technology and Special Lecture
for the 30th Anniversary of the Establishment of HPTF
Theme: Frontiers of Powders and Powder Processing for SDGs
Date: Monday, September 5, 2022 Place: Imperial Hotel Osaka
Opening address Mr. Yoshio Hosokawa (President of Hosokawa Powder Technology Foundation,
Chairman of Hosokawa Micron Corporation)• 2019-2021 KONA Award presentation ceremony
Session 1 Chaired by Prof. Masayoshi Fuji (Nagoya Institute of Technology, Japan)• Lecture 1 (Special lecture by the 2019 KONA Awardee)“Microstructure Control of Particles and Powders for High Quality Advanced Materials”
Prof. Makio Naito (Osaka University, Japan)• Lecture 2 (Special lecture by the 2020 KONA Awardee)“Surface Engineered Particle Systems for Industrial Applications”
Prof. Brij M. Moudgil (University of Florida, USA)
Session 2 Chaired by Associate General Manager, Dr. Yoshio Sakka (NIMS, Japan)• Lecture 3“Resources Circulation Through Advanced Powder Processing”
Prof. Chiharu Tokoro (Waseda University, The University of Tokyo, Japan)• Lecture 4“Powder Joining Technologies for Realizing Solid-State Batteries”
Dr. Kazunori Takada (National Institute for Materials Science, Japan)
Session 3 Chaired by Prof. Shuji Matsusaka (Kyoto University, Japan)• Lecture 5“Efforts for SDGs by Direct Methane Reforming (DMR) Reaction with Iron-Based Catalysts”
Dr. Toshiki Matsui (Toda Kogyo Corporation, Japan)• Lecture 6“Hosokawa Micron’s Equipment and Technology Development Considering SDGs”
Dr. Kenji Murata (Hosokawa Micron Corporation, Japan)
Session 4 Chaired by Honorary Research Advisor, Dr. Hisao Makino (CRIEPI, Japan)
Special Lecture for the 30th Anniversary of the Establishment of HPTF
• Lecture 7“40 Years of Nd-Fe-B Sintered Magnet Development”
Dr. Masato Sagawa (Advisor, Daido Steel Co., Ltd., Japan)
Closing remarks Emeritus Prof. Hiroyuki Kage (Kyushu Institute of Technology, President of Council of Powder
Technology, Japan)

Symposium on Powder Technology.

278

Special Lecture for the 30th Anniversary of the Establishment of
HPTF (Left: presenter, Dr. Sagawa; Right: questioner, Prof. Fuji).

KONA Powder and Particle Journal No. 40 (2023) 279–280/https://doi.org/10.14356/kona.2023019

Information Article

The KONA Award 2021
The KONA Award has been presented to the researchers who have greatly contributed to the research and development
as well as education in the field of Powder and Particle Science and Technology since 1990. It was given originally by
Hosokawa Micron Corporation but now is presented to the researchers from all over the world by the Hosokawa Powder
Technology Foundation annually. The application for this award requires a specified recommendation form written in English to be submitted to the President of the Hosokawa Powder Technology Foundation. The award candidates are reviewed
by the KONA Award Committee members and the results are reported to the Selection Committee of the Foundation for thenomination of the awardee. It needs to be finally approved at the Board of Directors’ meeting of the Foundation. The KONA
Award is presented at a ceremony in or outside of Japan with a plaque and a prize of one million yen.
There were three candidates recommended from around the world for The KONA Award 2021, and it has been presented
to two researchers this year, namely Professor Wolfgang Peukert of Friedrich-Alexander-University Erlangen-Nuremberg,
Germany and Professor Shuji Matsusaka of Kyoto University, Japan. The KONA Award plaque was presented to Prof.
Matsusaka at the 54th Symposium on Powder Technology on September 5, 2022 and to Prof. Peukert at the 7th International
Conference on the Characterization and Control of Interfaces for High Quality Advanced Materials (ICCCI 2022) in November 2022.
Prof. Peukert has brought particle technology forward like no other European and probably no other professors at allin the last decade. With his forward-looking approaches to focus research activities on particle interactions, he has given
particle technology as an engineering discipline a new direction. From the fundamental and in-depth research of individual
processes in mechanical process engineering, which was predominant 20 years ago, his influence has clearly shifted thediscipline towards process-structure-property relationships and, thus, towards the design of products made of the finest particles, which are often in the nanometer range. He has published his research results in more than 550 refereed publications
with over 11800 citations (according to Scopus as of Oct. 10, 2022), whereby he has achieved an extremely high h-index of
57 for the research subject “process engineering”.
His pioneering concepts have led to the acquisition of the Cluster of Excellence “Engineering of Advanced Materials –Hierarchical Structure Formation for Functional Devices”, which he successfully coordinated for 12 years of funding. He
was also awarded the Gottfried Wilhelm Leibniz Prize (1.55 million €) for his scientific achievements. Within the ProcessNet association (as part of DECHEMA and VDI/GVC), he has been intensively promoting the subject for many years aschairman of the division “Particle Technology and Product Design”. These outstanding achievements are complemented bymany other smaller successes and activities.

At the KONA Award presentation ceremony,
President Hosokawa (Left) and 2021 KONA Awardee Prof. Wolfgang
Peukert (Friedrich-Alexander-University Erlangen-Nuremberg, Germany).

Selected research achievements for the KONA Award 2021 (Prof. Wolfgang Peukert): Particle based product innovations by understanding andcontrolling particle interactions.

279

At the KONA presentation ceremony, President Hosokawa (Left) and 2021 KONA Awardee Prof. Shuji Matsusaka (Kyoto Univ., Japan).

Prof. Shuji Matsusaka has been conducting research at Kyoto University focusing on powder technology, particularly
with regards to the advanced characterization of fine particles in gases, for 32 years. This area of study covers a number offundamentally related topics including electrostatic charging, deposition and re-entrainment, adhesion and agglomeration,and powder flowability and mechanics. He has used the characterization methods to develop novel powder handling systems for feeding, transport, and particle motion control systems. Furthermore, he established the theory and control methodof particle charging based on contact potential difference in an external electric field.
He also developed novel methods using atmospheric pressure plasma jets and the photoelectric effect arising from ultraviolet radiation. In addition, he established a dynamic model for particle deposition layers formed in gas-solid pipe flow,
considering simultaneous particle deposition and re-entrainment. Applying the airflow method, he developed a system measuring the adhesive strength distribution of fine particles to characterize different adhesion forces including van der Waals
forces and electrostatic forces. To analyze and evaluate the flowability of highly cohesive powders, he invented the vibration
shear method, which was effective for micro-feeding of nanoparticles and micron-sized particles. Furthermore, he developed the constant-volume shear method and the vibration-induced fluidization method for characterization and applications.
For the above achievements, he was awarded the APT Distinguished Paper Award in 2018, Best Paper Award of theSociety of Powder Technology, Japan in 2018, SCEJ Award for Outstanding Research Achievement in 2018, Best Paper
Award of the Imaging Society of Japan in 2010, Distinguished Achievement Award of the Information Center of Particle
Technology, Japan in 2002, Best Paper Award of the Society of Powder Technology, Japan in 2002, Iinoya Award of theJapan Association of Aerosol Science and Technology in 1995, among other awards.
In recognition of his research achievements, he was appointed as Editor-in-Chief of Advanced Powder Technology.
During his tenure, he elevated the journal to a top ranking in the field by increasing the impact factor from 1.6 (2013) to
4.2 (2019). To achieve this objective, he made effective use of the financial support provided by the Japanese government
and established overseas editorial boards in Europe and North America. He has been a longstanding proponent of various
international programs.
Prof. Matsusaka is an acknowledged world leader in powder technology and has made significant contributions to theformation of a global network of researchers in the field.

Selected research achievements for the KONA Award 2021 (Prof. Shuji Matsusaka): Advanced characterization of fine particles and the developmentof novel powder handling systems.

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KONA Powder and Particle Journal No. 40 (2023) 281/https://doi.org/10.14356/kona.2023020

Information Article

General Information
History of the Journal
The KONA journal (currently called “KONA Powder andParticle Journal”) was first published in 1983 by the Council of
Powder Technology, Japan (CPT), which had been established in1969 by Hosokawa Micron Corporation as a nonprofit organization
to promote powder technology, in order to introduce excellent
Japanese papers to the world. From the issue of No.8, the CPT
changed its editorial policy to internationalize the KONA journal
and to incorporate papers by authors throughout the world. In
response to this change, three editorial blocks have been organizedin the world: Asian-Oceanian, American and European. The issues
from No.1 (1983) to No.12 (1994) of the KONA were published by
the CPT and the issues from No.13 (1995) by the Hosokawa Powder
Technology Foundation. The policy and system have not changed
even after the Hosokawa Foundation has taken over the publicationfrom the CPT. From the issue of No.27 (2009), publication oftranslated papers has been terminated and only original papers
have been published.
Aims and Scope
KONA publishes review and original research papers in a broad
field of powder & particle science and technology, ranging fromfundamental principles to practical applications. The papers on critical reviews of existing knowledge in special areas are very 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 feature articles invited by the KONA
Editorial Committees.
• Contributed papers
Original review papers and some limited number of original
research papers of high quality submitted to the KONA
Editorial Committees, and refereed by the reviewers and editors.
Submission of Papers
You can submit your paper online via Editorial Manager® for
KONA Online Paper Submission and Peer Review System. For
all further information, please go to the journal’s homepage on
https://www.kona.or.jp/jp/journal/info.html
Publication in KONA is free of charge.
Publication Schedule
KONA is published annually. The publication date is around
January 10th.
Subscription
KONA is distributed free of charge to senior researchers atuniversities and laboratories as well as to institutions and libraries
in the field throughout the world. The publisher is always glad toconsider the addition of names of those, who want to obtain thisjournal regularly, to the mailing list.
Instructions to Authors(1) Language

All submissions should be written in good English. Authors may
choose either British or American English, provided that the chosen
style is used consistently. Authors from non-English-speaking countries
are encouraged to use a professional English editing service toproofread their manuscript.

(2) Data Repository Information

In order to promote open data discoverability and use of research
outputs, this journal encourages authors to submit the data files
supporting your manuscript work, which can be deposited in thejournal’s J-STAGE Data site <https://jstagedata.jst.go.jp/kona> after
acceptance of the paper through the peer-review process.
These data may describe observations, experiments, modeling or
analyses and may take the form of databases, simulations, movies,
large figures or as otherwise appropriate.
If you have any questions or would like more information, pleasecontact the KONA Editorial Office (contact_zainq@hmc.hosokawa.com).

(3) 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 asterisk mark“*” after thename. Telephone and fax numbers and e-mail address should
be provided for the corresponding author.
• Abstract should not exceed 200 words.
• Graphical 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, ISBN: 9780205309023.

- Reference to a chapter in an edited book:

Mettam G.R., Adams L.B., How to prepare an electronic version ofyour article, in: Jones B.S., Smith R.Z. (Eds.), Introduction to theElectronic Age, E-Publishing Inc., New York, 2009, pp.281–304.

- Reference to a journal publication:

Tsuji Y., Tanaka T., Ishida T., Lagrangian numerical simulation ofplug flow of cohesionless particles in a horizontal pipe, Powder
Technology, 71 (1992) 239–250. DOI: 10.1016/0032-5910(92)88030-L

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 ....”(4) Copyright and permission• The original paper to be submitted to KONA has not been
published before in any language or in any journals or media; it
is not submitted and not under consideration for publication inwhole or in part elsewhere.
• Authors are responsible for obtaining permission from the
copyright holders to reproduce any figures, tables and photos
for which copyright exists.
• The KONA Journal applies the Creative Commons Attribution
License to all works published by the Journal. Copyright stays
with the agreed copyright owner, and the Hosokawa Powder
Technology Foundation is granted the exclusive right to publish
and distribute the work, and to provide the work in all forms
and media.
• Users of the journal will be able to reuse the contents in any
way they like, provided they are accurately attributed. No
permission is required from either the authors or the publisher.

Free electronic publication of KONA is available at http://www.kona.or.jp281

The Hosokawa Micron Group will always be the leading global company covering themountain range of Powder Technologies. The Group has been a pioneer in the filed of
powder and particle processing and blown film processing. We provide R&D,
engineering, manufacturing and services in various fields of the world’s majorindustrial markets.

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