ISSN 0288-4534

CODEN: KPPJAD

Powder andParticle Journal

N o . 41 ( 2 0 2 4 )Hosokawa Powder Technology Foundation

KONA Powder and Particle Journal No. 41 (2024)

About this Journal
KONA Powder and Particle Journal is an international, interdisciplinary, and Diamond Open-Access journal that focuseson publishing articles on powder and particle science andtechnology.
This journal is a refereed scientific journal with an annual
publication history dating back to 1983. It is distributed freeof charge to researchers, members of the scientific community, universities, and research libraries worldwide by the
Hosokawa Powder Technology Foundation, established by
the late Mr. Masuo Hosokawa in 1991.
The Chinese character “粉” featured on the cover of thejournal is pronounced as “KONA” in Japanese and signifies“Powder.” The calligraphy for this character is attributed to
the late Mr. Eiichi Hosokawa, the founder of Hosokawa
Micron Corporation.

About the CoverControlling the fate of free-charge carriers generated in colloidal semiconductor nanocrystals through the sequential absorption of photons at photoexcited conditions determines
the device performances of various applications including
light-emitting diodes, photodetectors, sensors and theranostic
photothermal agents. Defects, usually as non-radiating channels generated during crystal growth, interfere with efficientenergy conversion between absorbed energy and other forms
such as luminescence, photocurrent, and heat. In this paper,
we report on a reliable method to investigate the origin of
defects for identification and to inactivate them with uniquesurface chemistry approaches and doping techniques to maximize device performance. Review on this topic is discussed
on pp. 172–182. <https://doi.org/10.14356/kona.2024001>

https://www.jstage.jst.go.jp/browse/kona

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)
T. Ogi(Hiroshima 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)
S. Mukaigawara(Hosokawa Powder Tech. Foundation, Japan)
L. Cui(Hosokawa Micron Corp., Japan)
European / African Editorial Board
Chairman
A. Kwade(TU Braunschweig, Germany)
D. Barletta
G. Biskos
M.-O. Coppens
A. Mainza
M.G. Rasteiro
D.L. Schott
Secretariat
S. Sander
S. Wilk(Univ. of Salerno, Italy)(The Cyprus Institute, Cyprus)(Univ. College London, UK)(Univ. of Cape Town, South Africa)(Univ. of Coimbra, Portugal)(Delft Univ. of Technology, the Netherlands)(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 2024

KONA Powder and Particle Journal No. 41 (2024)

Contents<Editorial>
1

Editor's Preface<Review Papers>
3

Artificial Intelligence and Evolutionary
Approaches in Particle Technology

Whitebox
Known models

Digital twin
Predictive AI
Process control
Genetic programming
Reinforcement
Active learning

Christoph Thon, Marvin Röhl,
Somayeh Hosseinhashemi, Arno Kwade andCarsten Schilde
26 Role of Powder Properties and Flowability in
Polymer Selective Laser Sintering—A Review

Process
optimization
Genetic
algorithms

Simulation/ Physical process/ Data bank

Final Part Properties
Low Porosity

High Porosity

Polymer melt

Polymer melt
Low Flowability

Powder Flowability
High Flowability
Interparticle Forces

Particle Properties

Xi Guo and Brij M. Moudgil
42

Measurement of Fugitive Particulate Matter
Emission: Current State and Trends

Tianyi Cai and Wu Zhou
58

About Modeling and Optimization of Solid
Bowl Centrifuges

Marco Gleiss and Hermann Nirschl

iKONA Powder and Particle Journal No. 41 (2024)

78

Contents

DEM Modelling of Segregation in GranularMaterials: A Review

Ahmed Hadi, Raïsa Roeplal, Yusong Pang andDingena L. Schott
108 A Review of Analytical Methods for
Calculating Static Pressures in Bulk Solids
Storage Structures
Depth

George Dyck, Adam Rogers andJitendra Paliwal
123

High-Pressure Torsion for Highly-Strainedand High-Entropy Photocatalysts

Saeid Akrami, Parisa Edalati, Masayoshi Fuji
and Kaveh Edalati
Applying Bubbling Fluidized-Bed Reactors
for Strongly Exothermic Reactions: Focus on
Methanation

300.0

0.20

α R0
α R3
Bubble Holdup R0
Bubble Holdup R3

250.0

0.18
0.16
0.14

R3

R0

α [W/m²K]

200.0

0.12
0.10

150.0

0.08

100.0

0.06
0.04

50.0
0.0

Philipp Riechmann and Tilman J. Schildhauer

ii

0.02
1.5

2

u/umf

3

4

0.00

Bubble Holdup εb [-]140

KONA Powder and Particle Journal No. 41 (2024)

Contents

151 Current Progress of Experimental and
Simulation Work of Mixing Processes in
Particulate Systems

Xin Jin and Yansong Shen
172

Recent Progress in Controlled Nanostructureof Colloidal Nanocrystal Powders for EfficientLight Emission

Naoto Shirahata
183

Characteristics of Ultrafine Bubbles(Bulk Nanobubbles) and Their Application toParticle-Related Technology

Colloid of gold nanoparticles
Gold nanoparticles synthesized
without ultrafine
bubbles (UFB)

without UFB

with UFB

with UFB

Immediately after synthesis
without UFB

100 nm

100 nmwith UFB

After two days

Keiji Yasuda
197

Synthesis of Anisotropic Metal Oxide
Nanoparticles via Non-Aqueous and
Non-Hydrolytic Routes

Sherif Okeil, Julian Ungerer, Hermann Nirschl
and Georg Garnweitneriii

KONA Powder and Particle Journal No. 41 (2024)<Original Research Papers>
221 Particle Size Measurement Using a Phase
Retrieval Holography System with a GPUEquipped SBC

Yohsuke Tanaka and Dai Nakai
229 Effects of DEM Parameters and Operating
Conditions on Particle Dynamics in a
Laboratory Scale Rotating Disc

Rondinelli M. Lima, Gisele M. Souza,
Rodolfo J. Brandão, Claudio R. Duarte andMarcos A.S. Barrozo
242 Simulation of the Classification of
Manufactured Sands in the Throat Air
Classifier

Horacio A. Petit and Edgardo Fabián Irassar
254

The Mechanism behind Vibration Assisted
Fluidization of Cohesive Micro-Silica

Rens Kamphorst, P. Christian van der Sande,Kaiqiao Wu, Evert C. Wagner,
M. Kristen David, Gabrie M.H. Meesters and
J. Ruud van Ommen

iv

Contents

Contents

265 Lipooligosaccharide Ligands from
Respiratory Bacterial Pathogens Enhance
Cellular Uptake of Nanoparticles
Cell
PAFR

LOS

Mai H. Tu, Timothy M. Brenza,
Margaret R. Ketterer, Morgan Timm,
Benjamin M. King, Michael A. Apicella andJennifer Fiegel% Cells associated with particles

KONA Powder and Particle Journal No. 41 (2024)

40

LOS-coated NPs

30
20
10
0

0

10
20
Incubation time (hours)

30<Information Articles>
280 The 55th Symposium on Powder Technology
282

4th International Hosokawa Powder Technology
Symposium Held in Germany

284 The KONA Award 2022 (Awardees: Prof. Dr. Hidehiro Kamiya
and Prof. Dr. Toshitsugu Tanaka)
286 General Information

2022

Application 1

Application 2

Application 3

Nano- and fine particles powder
process design

Energy and environment
Ash behavior control at hightemperature, dust collection coal,biomass, waste power
generation plant/ PM 2.5, Nanoparticle emission,
ISO standard/ Nano risk, Nano toxicity

Pharmaceutical / DDS, Inhalation
Oral administration using microcapsule
Orally disintegrating tablet
Life and other application/ Cosmetic powder/ Toner for dry copy, ink jet
Food and Energy system
Agriculture + Engineering

OH

HO OH
for
HO P
O
OP
O
/ Fine ceramics O/ Composite, nanoparticles
dispersed polymers/ Lithium ion battery electrode

O
O

3
3

Fundamentals :Characterization and control of adhesion, aggregation / dispersion, andpacking behavior by using interface molecular and nanometer scaled structure design/ Development of new characterization method, such as colloidal probe AFM method and NMR,
/ Molecular design of ligand for nanoparticle dispersion, nanoparticles coating on fine particles.

Particle adhesion and aggregation behavior characterization and control.

Selected research achievements for the KONA Award 2022 (Awardee: Prof. Dr. Hidehiro Kamiya):
Particle adhesion and aggregation behavior characterization and control.

Discrete Particle Modeling and Simulation of Granular Flow2022

─ Pioneering development of numerical prediction of granular flow and gas-solid flow ─
DEM + CFD

Transport

Collision(Hard-particle model)

Contact(Soft-particle model)

Turbulence
Difusion
modification

Clustering

Deterministic
Stochastic (DSMC)
Linear
Hertzian

Soft springmodeling

Fluidization
Heat exchange

MixingSegregation
Aggregation

From analysis to prediction

Selected research achievements for the KONA Award 2022 (Awardee: Prof. Dr. Toshitsugu Tanaka):
Development of discrete particle modeling and simulations of gas-solid flows and granular flows.

vEditorial

KONA Powder and Particle Journal No. 41 (2024) /https://doi.org/10.14356/kona.2024018

Reviewer Appreciation
Makio Naito, Editor-in-Chief

I would like to express my sincere gratitude to the reviewers for their hard work for the KONA Powder and Particle
Journal. Thanks to the generous contributions of the volunteer reviewers as well as the interest of researchers in ourjournal, the number of submissions to this journal has been increasing since its registration on major international
journal platforms, and its level has been further improving. Thanks to their strong support and effort, I am pleased to
inform you that the 2022 Impact Factor (JCR) of the KONA Powder and Particle Journal has increased to 4.1, with
the CiteScore 2022 of 6.8. Because of the limited space of this annual journal, the number of papers published here
is regrettably rather restricted. Therefore, only the papers of good quality will be selected for the journal and further
improved and refined by the reviewing process. Moreover, we have created a channel on J-STAGE Data, to enableauthors to deposit, share and link to the data within their papers that will be or were published in this journal. J-STAGE
Data, which is the data repository provided by the Japan Science and Technology Agency (JST), enables KONA
authors to contribute to the promotion of Open Science with reviewers and editors.
The editorial team of the KONA Journal would like to take this opportunity to gratefully acknowledge the reviewers
here and deeply appreciate their valuable time and professional contributions to the KONA Journal. We apologize to
any reviewers who may have been inadvertently omitted from this list. We are sincerely grateful to all who have beenof any assistance to the publication of this journal.

BISKOS, George
BOURGEOIS, Florent
COPPENS, Marc-Olivier
GOTOH, Kuniaki
GRADON, Leon
HICKEY, Anthony
IRITANI, Eiji

ISHIMOTO, Takuya
KANIE, Kiyoshi
KANO, Junya
KWADE, Arno
MEESTERS, Gabriel
OKUYAMA, Kikuo
SAKKA, Yoshio

SASABE, Shuji
TAVARES, Luis Marcelo
YAMAMOTO, Hiromitsu
YOKOYAMA, Toyokazu
ZHANG, Qiang

Makio Naito
Professor Emeritus, Osaka University
October 2023

vi

Copyright © 2024 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/).

KONA Powder and Particle Journal No. 41 (2024) 1–2/https://doi.org/10.14356/kona.2024019

Editorial

Editor’s Preface
Arno Kwade
Chairman of European/African Editorial Board
Technische Universität Braunschweig, Germany

It is a great honour for me to introduce KONA Powder and Particle Journal No. 41 as the recently appointed Chairof the European and African KONA Editorial Board and the successor of Gabrie Meesters, whom I would like tothank for his great commitment to KONA. Since my doctoral thesis on ultrafine wet milling over 40 years ago, I havebeen part of the journey of particle and powder technology. From predominantly basic research on unit operationsof grinding, granulation, classification, and mixing, including powder flow and particle size characterization, theresearch focus has moved first to in-depth research on the behaviour and interaction of individual particles and more
recently to the deep investigation of full process chains and the production of complex particulate products with optimized properties. During this journey, the design of functional particles and complex particle structures has become
increasingly important, with strong interaction with materials science. An expression of this is the development of
nanoparticle technology, especially in the first decade of this century, and the design of complex particle structures forenergy storage, conversion, and generation, especially in recent years. Accordingly, my research has shifted from theproduction of nanoparticles to the processing and design of energy and pharmaceutical materials, always based on a
deep understanding of particle behaviour within individual processes.
I am very happy that in the post-Corona period, we can meet again with our particle technology colleagues andfriends around the world at conferences or KONA board meetings to further our common interests. It was a pleasure
to meet so many colleagues and friends during 2022 at various conferences, especially the World Congress on Particle
Technology in Madrid (Spain) and Chops in Salerno (Italy), although these conferences were still influenced byCorona as quite a few participants were infected at the conferences. In 2023, the European conference highlight was
PARTEC 2023 in Nuremberg (Germany), with the overarching theme “Particle Technology for Sustainable Products”,
addressing the increasing importance of sustainability and circular economy due to climate change. As the PARTEC
2023 Chair, it was an honour and a pleasure for me to welcome over 500 colleagues from 25 countries, who enjoyedmore than 300 plenary, keynote, regular and flash presentations and experienced the diversity of our research field.
Overall, the presentations at PARTEC 2023 show the importance of particle technology across all industries. Today,
particle technology plays an important role not only in classical industries such as chemical, pharmaceutical, food, andminerals industries but also in dynamically developing industries of energy transition with products such as batteries,
solar panels, and fuel cells. Moreover, numerical simulation and digitalisation are becoming more and more important. Overall, I see a clear trend that particle technology is becoming increasingly interlinked with other technologies
and communities:
First, the importance of design, synthesis, and processing of particulate energy materials rises significantly due tothe global energy transition towards 100% use of renewable energy resources and minimisation of the CO2 footprint.
Even today, the production, processing, and recycling of active materials for lithium-ion batteries require in-depth
knowledge of particle technology in addition to materials science, electrochemistry, and production engineering.
Particle technology is even more important for solid-state batteries, which are the most promising new generation of

Copyright © 2024 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

batteries. Here, particulate solid electrolyte particles made of sulphides, halides, or oxides have to be produced andprocessed together with the active material particles and other components in such a way that high ionic conductivity
and long cycle life are achieved.
Second, the production of innovative products such as battery cells, solid pharmaceuticals, functional coatings, andadditively manufactured products requires close cooperation between particle technology and production engineering,as well as cooperation with relevant technology areas. A relatively new and emerging field is additive manufacturing,where particles need to be produced, tailored, e.g., with nanoparticle coatings, and recycled. In addition to metaland polymer-based particles, mineral particles for additive manufacturing in construction are also current areas ofresearch.
Third, for the numerical simulation and digitalisation of processes and products using particles, the Particle
Technology Community works with scientists and experts from mathematics, physics and computer science. As aresult, numerical simulation using discrete element methods (DEM) is becoming increasingly powerful and can now
simulate even larger industrial problems. In addition, DEM is increasingly being combined with other simulation
methods such as computational fluid dynamics and population balances. More recently, artificial intelligence methods
have been used to predict process results and product properties based on relatively large datasets.
In addition to inter- and transdisciplinary collaborations, I see increasing international collaboration within ourparticle technology community as an important goal for the future, despite the increasing conflicts between countries
that we see today. In addition to international conferences, the funding of joint research projects with partners fromdifferent countries is an important measure and should be strengthened in the future. There are also internationalorganisations such as the International Fine Particle Research Institute (IFPRI) or more technology-oriented associations such as the Global Comminution Collaborative (GCC), the International Delegation on Filtration (INDEFI)or the Association pour l’Etude de la MicroMécanique des Milieux Granulaires (AEMMG). Such global scientificorganisations not only promote international research cooperation but also an intensive exchange between science andindustry, and a general understanding between nations.
This issue of KONA continues the trend of previous issues towards the publication of more high-quality review
articles alongside forward-looking original articles from many countries. Accordingly, journal metrics such as theimpact factor, which is now above 4, have developed very positively recently. The increased focus on high-quality
review papers is sure to further enhance KONA’s reputation. This issue contains 12 high-quality review papers and5 original papers by 54 authors from 11 countries. Five papers focus on modelling and numerical simulation, 2 onenergy materials, and one on additive manufacturing, the three emerging areas mentioned above. Further 5 papers
focus on fundamental operations, including powder and multiphase flow, and 2 papers each on the characterisationand synthesis of particle systems.
Finally, I would like to thank all the authors and reviewers for their hard work in preparing and optimising thepapers, as well as all those who have contributed to the realisation of this “KONA Powder and Particle Journal” Issue
No. 41.

Arno KWADE
Chairman of European/African Editorial Board
Professor, Technische Universität Braunschweig
October, 2023

2

KONA Powder and Particle Journal No. 41 (2024) 3–25/https://doi.org/10.14356/kona.2024011

Review Paper

Artificial Intelligence and Evolutionary Approaches
in Particle Technology †Christoph Thon *, Marvin Röhl, Somayeh Hosseinhashemi, Arno Kwade and Carsten Schilde
Institute for Particle Technology (iPAT), Technische Universität Braunschweig, Germany

Since the early 2010s, after decades of premature excitement anddisillusionment, the field of artificial intelligence (AI) is experiencing
exponential growth, with massive real-world applications and highadoption rates both in daily life and in industry. In particle technology,
Whitebox
Digital twin
there are already many examples of successful AI applications,
Known models
Predictive AI
Process control
Process
for predictive modeling, process control and optimization, fault
Genetic programming
optimization
recognition, even for mechanistic modeling. However, in comparison
Reinforcement
Genetic
Active learningalgorithms
to its still untapped potential and to other industries, further expansionin adoption rates and, consequently, gains in productivity, efficiency,
and cost reduction are still possible. This review article is intended to
Simulation/ Physical process/ Data bankintroduce AI and its application scenarios and provide an overview andexamples of current use cases of different aspects and unit operations
in particle technology, such as grinding, extrusion, synthesis, characterization, or scale up. In addition, hybrid modeling approaches arepresented with examples of the intelligent combination of different methods to reduce data requirements and achieve beneficial synergies.
Finally, an outlook for future opportunities is given, depicting promising approaches, currently being in the conception or implementation
phase.
Keywords: artificial intelligence, genetic algorithms, predictive modeling, hybrid modeling, particle technology

1. Introduction

Artificial intelligence (AI) has reached critical applicability rates in recent years as a consequence of algorithmic
improvements, exponential gains in hardware and data
generation, and increasing improvements in available
infrastructure, such as the current focus on parallelizationand dedicated hardware solutions (ASICs) (LeCun et al.,2015).
Consequently, AI is increasingly being adopted in all
aspects of daily life and throughout industries. In particle
technology, there are many areas in which AI techniques
are already being applied, but dependent on the specificbackground such as the ability to rely on or generate large
amounts of data, security aspects, availability of sensors,
the ability to dynamically interact with processes, etc.,
which lead to varying rates of and requirements for the
adoption of AI. Practical examples encompass a wide variety of areas within particle technology, such as predictivemodeling of grinding results, kinetics, pathways, stability,
mixing and quality, preprocessing of data, shape control inparticle synthesis and parameter control in general, yield
optimization, separation via machine vision, use as soft†*

Received 18 May 2022; Accepted 6 September 2022
J-STAGE Advance published online 15 July 2023
Corresponding author: Christoph Thon;
Add: Volkmaroder Str. 5, D-38104 Braunschweig, Germany
E-mail: c.thon@tu-braunschweig.de
TEL: +49-531-391-65553 FAX: +49-531-391-9633

sensors, improvement of measurement results, surrogate
modeling, etc.
Similarly, AI itself is an umbrella term. This review article builds on more general articles about AI in the context
of process engineering (Thon et al., 2021; Pham and Pham,
1999; Linkens, 1990; Mavrovouniotis, 2012; Lee J.H. etal., 2018) and focuses on the context of particle technology
specifically. Furthermore, general reviews exist, such asof mechanical engineering (Patel et al., 2021), electricalengineering (Sakunthala et al., 2017), material science(Pilania, 2021), and chemical engineering (Schweidtmann
et al., 2021; Ashraf et al., 2021; Venkatasubramanian,2019). On the other hand, focused review articles for
specific subfields within particle technology exist, such as
for related autonomous processes (Nirschl et al., 2022) or
nanoparticle drug formulations (Uhlemann et al., 2021),
although, to date, no conclusive overview of the field ofparticle technology has been provided, which is the focusof this review article.
In this paper, prominent techniques such as neural nets
or evolutionary algorithms are discussed and an overview
of hybrid modeling techniques and examples of ensemble
methods (the intelligent combination of techniques) areprovided.
In the following, based on a short summary of important
AI methods, examples of applying AI to important aspectsof particle technology, including unit operations such as
grinding, synthesis, modification, or classification, as well

Copyright © 2024 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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Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

as particle analysis and scale up procedures, are reviewed.
Finally, an overview is provided to depict near-term opportunities and untapped potentials.

2. Introduction into Artificial Intelligence
AI encompasses a wide spectrum of techniques likeartificial neural networks (ANN), genetic algorithms (GA),
Hidden Markov Models, and others. First introduced at the
famous 1956 Dartmouth conference with the goal to emulate general human cognitive capabilities, AI experienced
three major waves of enthusiasm and disillusionment asa consequence of initial hype and overestimations on one
side and hardware and software limitations on the other.
It was not until the early 2010s that AI experienced major
breakthroughs in real-world applications and adoption
rates, with the most common AI techniques used today
being deep neural networks (DNNs) (Ongsulee, 2017).
Fig. 1 depicts the basic principle of ANNs. Being
loosely inspired by the brain, the fundamental building
blocks are neurons, which are connected. The neurons arearranged in layers, with the first layer being denoted as the
input layer and the last one as the output layer. Each input
and output node is a representation of an input and output,
e.g., a parameter of a process, a pixel in an image, or aword in a text. Inputs can be binary, integers, or continuous
values. Between these two layers are a number of hidden
layers, in which the modeling is taking place. Between theneurons of neighboring layers, the different connections
have dedicated weights associated with them.
As can be seen in the right part of Fig. 1, which iszoomed out for one representative neuron, inputs are
multiplied with their respective weights and consequently
summed. The summed weight is further processed via an
activation function; the resulting value is then propagatedforward to successive neurons of the following layer.
Through different activation functions, linearities, non-linearities, or other patterns can be introduced, with theresulting behaviors of the overall system changing, suchas spiking neural nets appearing, etc. Being a hierarchicalstatistical system, ANNs have generalizing capabilities
because they process more complex patterns of a higher
order on the basis of detected lower-order patterns ofmore limited complexity. The goal of AI training is theiterative optimization of connection weights in order to
get correct outputs for given inputs based on training data.
The classical approach for this is backpropagation in which
after each iteration, a loss function is determined for the
successive analytical approximation and adjustment of theweights. Specifically, for large AI systems with an abundance of training data, backpropagation is the classicallyused training approach, although it has a strong tendency
toward overfitting in cases with limited data sets. Alternative systems exist, which are focused on training withmore limited data, such as genetic reinforcement learning(GRL), which was described in greater detail in Thon etal. (2022b) and can be described as the training of AI via a
genetic algorithm.
In addition, there exists a plethora of techniques toprevent overfitting, which can also be combined to achieve
more general models, such as regularization, feature
selection, early stopping, layer removal, or dropout. Regularization encompasses techniques to prevent the trainingof too complex models, e.g., through the use of penalties orchanges in the neural network architecture upon training.
L1 (Lasse regularization) and L2 (ridge regression) modify
the penalties in training by adding an absolute value or thesquared value of magnitude (Demir-Kavuk et al., 2011).
In dropout, as another regularization method, neurons are
randomly deactivated during training to prevent excessive
co-adaptation (Srivastava et al., 2014). Feature selection
is another method to prevent overfitting by restricting
the training to only the most relevant features in case of

y
x

0

0

2

2

1

∑

Hiddenlayer I

Hiddenlayer II

Output
layer

Input

…

…

Input
layer

1

Weightyyxx

Activation
function∑()

Summation()
Treshold

OutputFig. 1 Depiction of a neural net, with an input, an output, and two hidden layers. Connections between neurons of layers feature dedicated weights.
Incoming inputs from former layer neurons are summed and multiplied according to an activation function, e.g., to introduce non-linearities.

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Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

the initial number of features in the data being too large(Hawkins, 2004). Early stopping describes the techniquesof evaluating training over epochs with independent data(also termed “hold up”) to quit training once a degradationof the loss becomes detectable, indicating overfitting anda loss in generalizing capabilities (Ying, 2019). Crossvalidation, as a more advanced version of hold up, allows
for training and evaluation with independent data, withvarying assignments of the data groups to train and test
data, allowing all data to be used for training (Schaffer,1993).
GAs are another tool often used, specifically for optimization tasks but also for modeling and regulatory
applications. As shown on the left in Fig. 2, GAs are a
population-based approach that mimics Darwinian evolution in biology.
As a starting point, a population of parameter sets (usually consisting of a fixed and, throughout the population,
identical number of entries with variable values) is evaluated according to a stated goal. If, for instance, processparameters in a stirred media mill are to be optimized withregard to maximum energy efficiency, production yield,or the optimal combination of both KPI, measures for the
optimization goal or for multiple goals are established, andthe optimizable parameters are defined. Evaluation of thespecified goals of initial parameter sets is performed for theselection of the most suitable parameter sets. A subsequentcrossover of selected areas in the parameter space results in
a new generation with mixed attributes from the previous
generation. In addition, mutations are induced at specified
rates to increase the “genetic” diversity of the populationand to avoid optimization within local minima. Throughiterative execution over multiple generations, eventually(under constant environmental conditions), a steady stateis reached with ideally close to optimal parameter settings.
Genetic programming, shown on the right side of Fig. 2,
applies the same steps for the iterative evolution of decision tree structures or decision trees. These tree structures
can represent computer programs, with respective operators and numbers being the nodes in the tree, to automatically develop code. Alternatively, the trees can represent
mathematical operators for the evolution of fitting equation
systems for the description of data sets for modeling tasks.
The technical term for genetic programming, when applied
to formulas, is symbolic regression. In principle, all treebased tasks can be optimized with genetic programming.
A more detailed summary of underlying AI techniques
can be found in more general review papers (Krishna etal., 2018; Rupali and Amit, 2017; Raschka et al., 2020;
Rahmani et al., 2021; Thon et al., 2021).
Key criticisms of AI are usually its inherent lack oftransparency and its empirical nature. Different methods
exist to address these issues including explainable AI(XAI), reverse engineering strategies, and hybrid or graybox modeling. Often, respective disadvantages of different
AI methods can be compensated for by the intelligent combination of different methods such as ensemble methods
or hybrid approaches (Stosch et al., 2014). For instance,
as seen in Fig. 3, predictive neural net modeling as a blackbox can be utilized in order to create a model with generalizing capabilities for the subsequent prediction of virtual
training data for genetic programming.
The generalizing capabilities of genetic programmingare more limited than those of neural nets. The techniqueis, however, intrinsically more transparent. In conjunction
with additional methods, genetic programming even allows

Genetic Algorithmd) Mutation ofindividuals

Genetic Programminga) New
population^

2

Mutatione) Cycle ofgenerations

0
3

1
0

2
3

+
5

^
y

+
sin

y

3

x

b) Evaluationand selection

y

x

5

7

x

^
y

x

3

x
√

sin

/
√

+

√

Crossover

+

sin

+
c) Reproduction
and crossover

/
x

√
x

/
3

√

x

*
x

1

Combination of formula components

Fig. 2 Two common evolutionary algorithms, genetic algorithms and genetic programming, emulating Darwinian evolution on technical use cases.
On the left, settings for a fixed set of parameters are optimized via successive generations of parameter mutations, crossover between candidates, andthe evaluation and selection of suitable candidates in the population. On the right, the same principles are applied in genetic programming, but on thebasis of decision trees of theoretically unconstrained complexity, representing computer code, rule sets, or mathematical equations, with the latter
one called symbolic regression. Reprinted from Ref. (Thon et al., 2022b) under the terms of the CC-BY 4.0 license. Copyright: (2022) The Authors,published by Elsevier.

5

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Semiparametric model
Parallel model

Serial model

b

White
box
Model
c=f(b)

Combinatorial modelcaaModular model

Review Paperc
White boxModelc=f(b)

Fig. 3 A combination of predictive models based on neural nets to gain generalizing capabilities and genetic programming for the subsequent
deduction of more transparent mathematical models, in which equation modeling is performed on the basis of predictions from the neural net model.
Examples are provided for the results of both approaches, with blue columns marking training data and orange lines the respective model prediction.
Furthermore, hybrid modeling approaches are depicted, as well as serial and parallel semi-parametric models, modular models, and a possible combinatorial approach.

for the incorporation of physical relations (Udrescu et al.,2020).
In addition, as can be seen in Fig. 3, for an exemplary
data foundation (blue), the generated equation represents
a simpler (more transparent), though still accurate, relation
when compared to neural net predictions (orange lines).
Similarly, McKay et al. (1996) proposed a nonlinear
dynamic model using two different paradigms. In thiswork, they tried to compare the recurrent neural networkand the genetic programming algorithm to build a viscosity
model. They showed that in this particular case, the genetic
programming model has simpler transparency than neuralnetworks.
Beyond the combination of neural nets and evolutionary
approaches, hybrid models, shown in the lower part of
Fig. 3, are well suited for mechanistic modeling and theincorporation of existing empirical or white-box models.
In a serial approach, a white-box model can be applied topreprocessed data from AI or in a direction opposite to thepreprocessed data based on known relations for subsequent
AI modeling. Running in parallel, black-box AI models

6

can be used to compensate for the shortcomings of existing
white-box models by merely modeling the divergence
between white-box model predictions and reality. The parallel AI can, for instance, be broken down again via genetic
programming to iteratively extend the white-box model(Thon et al., 2021, 2022b; Evans et al., 2019). In modular
hybrid models, an interconnected system of sub-AI models
is set up to mirror the known structure of its physical counterpart and facilitate the modeling effort. Combinations ofhybrid models are possible, such as a mixture of a serial
and a parallel semiparametric model, as Psichigios andUngar (1992) demonstrated.
In addition, a wide variety of reverse engineering strategies exist to deconstruct or deduce the inner workings oftrained AI systems, such as sensitivity analysis, dropout,deconvolution, rationalization, LIME, GAM, gradientdescent, neuron activation, etc.

3. Applications of AI in particle technology

Table 1 provides an overview of AI applications forvarious categories of particle technology unit processes.

Characterization/
analysis

Operations

Fit rheology model via a genetic algorithm
Electron transfer properties (e.g., Femi energy) dependent on the
structural and morphological features of silver nanoparticles

Finke et al.

Sun et al.

Mechanistic modeling; GA
Predictive modeling

Predictive modelingSize estimation in grinding (ball mill, spiral classifier)

Dai et al.

Rheology

Predictive modelingand classification

Online soft sensor: Particle size estimation for hydrocyclone
overflow, combined with ball mill and classifier

Tie et al.

Combined
SystemSize estimation in classification via a classifier (ball mill, spiralclassifier)Hoseinian
et al.
Dai et al.

Preprocessing anddata rectification forprocess control
Predictive modeling
Hybrid based on
preexisting process
knowledge
Hybrid ANN-GA;
predictive modeling
Predictive modelingClassification/
RemovalData rectification through image recognition of a physical stirred
reactor coupled with a CFD simulation to act as the surrogate
model for process control
Prediction of chemical equilibrium states effecting precipitation
Prediction of process kinetics for flocculation, crystallization, andfracture to model the kinetics of nucleation, growth, shrinkage,
fracture, and agglomeration
Prediction of ion flotation-based Zn(II) removal

Khaydarov
et al.

Process optimization
Classification andpredictive modeling
Hybrid model;
process control
Predictive modelingfor process control

Modeling and processoptimization

Predictive and mechanistic modeling
Predictive modeling

Task (process and AI)
Classification

Leal et al.
Nielsen etal.

Process control of fat addition for feed pellets for animals

Ittiphalin
et al.

Zhang et al.

Li et al.
Chen et al.

Pellegrino
et al.

Process parameter-dependent prediction of TiO2 nanoparticle
morphology, size, polydispersity, and aspect ratio in hydrothermal
synthesis. Reverse engineering for optimal process conditions
Identify likely synthesis patterns of gold nanoclusters
Prediction and classification of energy expressions on the basis of
structure and energy in ZnO nanoparticle morphologies
Control particle distribution in a cobalt oxalate synthesis process

Predictive modeling of kinetic energies in a wet stirred media millwith subsequent mechanistic modeling via genetic programming
Particle size estimation in grinding (ball mill, spiral classifier)Thon et al.

Dai et al.

Description
Fault recognition in the grinding process

ExampleQu et al.

Aggregation

MixingSynthesis

Grinding
Simulation

Data foundation
Experiments

ANN (three layers)

Hybrid; ANN: random
vector functional-link
network
Modular-hybrid: 2x
ANN; radial basis
function networks
Hybrid; ANN: random
vector functional-link
networkGA

ANN-GANA
NA

DNN (YOLO model);serial hybrid model

LLM, BPNN

NA

NA
NA

Experiments

Experiments

Experiments

NA

Experiments

NA

Preexisting database of casestudy
Mixed experimental andsimulation setup
Simulations
Experimental(sensor data)

NA

NA
NA

Hybrid; ANN: random NA
vector functional-link
networkNANA

AI type
ANNs: BP, LSTM,
Auto-Encoder
ANN, GA, GP

Table 1 Overview (state July 2022) of some examples for AI in particle technology with the author, a broad description, the task, and used AI types, as well as the data foundation, sorted according to a classification
scheme featuring unit operations, and tasks. Abbreviations are defined in the nomenclature before the references.

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25
Review Paper

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Review Paper

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

The four classical unit operations of grinding, separation/
classification, mixing, and agglomeration/granulation are
elaborated on later in this paper, as well as bottom-up
methods such as particle synthesis and characterization
methods, including rheology.
Unit operations in particle technology are sometimes
applied in isolation. In industrial operations, however,they are often combined and interconnected in complex
plants. Possible assemblies vary to a significant degree,with some being more prominent. As can be seen in
Fig. 4, the characterization of educts, products, or intermediary products can accompany the entire production
flow and is often performed online during processes,
as is modeling and the scale-up of operational steps.
Mixing is an important operation embedded in the beginning of a process, repeated throughout the process
flow, or integrated into other unit operations. Grinding
as a top-down approach and synthesis as a bottomup approach are typical operations for the production ofparticles. Stabilization/functionalization, agglomeration, or
precipitation are unit operations often performed successively after the production of primary particles, although
integrative approaches such as parallel stabilization during
grinding or synthesis are also often applied. After production, primary particles, agglomerates, or aggregates can
be classified, e.g., according to their size, morphology, orother characteristics and can be further processed, e.g., via
extrusion. This brief overview is intended as a simplified
representation of inter-dependencies and how unit operations can be integrated and connected in practical applications. The individual unit operations and accompanying
steps are discussed in regard to the application of AI andevolutionary approaches.
In this regard, descriptions are given together with the
AI-specific tasks (predictive modeling, classification, optimization, etc.) and the used types of AI (ANNs, genetic,
algorithms, and hybrid models, etc.), as well as the training
data source (experiments, simulations, data banks, etc.).
In the following, the respective examples for the different
categories are described in more detail.
At the time of this review article, numerous works existed. However, distribution throughout the different tasks

and unit operations is inhomogeneous, with many mostly
small-scale academic applications and a much smaller
number of practical industrial adoption cases for large
connected systems or plants. Distribution between individual unit operations and accompanying steps is similarly
inhomogeneous, with most publications focusing on initial
unit operations such as grinding, while examples for later
unit operations such as extrusion are rarer. Furthermore,most of the published works with real-world applicability
are very recent since they became possible only in the last
five years, with more publications appearing closer to thedate of this review. This indicates that there is significant
untapped potential in the field for the adoption of AI techniques and aligns well with adoption rates in most otherindustries, with some featuring more advanced adoption
rates.

3.1 Grinding
A plethora of techniques for the creation of productparticles exists, as is elaborated on in subsequent chapters.
Grinding is the most widespread technique used throughout many industries for a large variety of materials andproducts. As a top-down approach, it is suitable to costeffectively process large quantities of material. With a developmental history almost as old as human cultural history
itself, starting with the grinding of grains, modern available
equipment is well established, sophisticated, and available
in a large variety for more specialized applications such asmechanochemical reactions.
AI-based preprocessing can, for instance, be applied
as an initial step for predictive modeling. Going to moresophisticated systems, modular predictive models allow for
the modeling of more complex plants, which can be aided
by hybrid modeling and white-box model combinations.
Based on this optimization, anomaly detection and mechanistic modeling can be utilized, and examples for theseincreasingly sophisticated models are provided.
Dimensionality reduction: As depicted in Fig. 5, oneof the first possible tasks that can be performed in an
AI process flows is data preprocessing and dimensionality reduction, for instance, through the use of auto
encoders. With grinding, Qu et al. (2017) trained a hybrid

Characterizations

EductsMixingSynthesis
Grinding

Stabilization
Agglomeration
Precipitation

Classification
Extrusion

Modeling and Scale-up

Fig. 48

Unit operations in particle technology.

Products

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

auto-encoder-Softmax network and an RNN-LSTM network for fault recognition in a grinding system. One thousand experimentally gained data points were used, with 30
dimensional input features.
Via the reduction of the number of involved dimensions,the complexity of the data set can be drastically reduced,
making successive conventional or AI modeling easier. In
Qu et al., dimensionality can be reduced from 30 dimensions (such as temperatures, pressures, sounds, vibrations
in different regions of the grinding system) to 24 (Qu et al.,2017). Input and output layers are set up identically. The
encoder side is intended to reverse engineer and reconstruct
the previously decoded representations from the input side
as accurately as possible. The narrowing of intermediaryhidden layers enforces more compact representations with
reduced features.
Predictive modeling of single units: Based on experimental data, the authors of this review article trained pre-Output layer
Decoder
Hidden-Intermediate
representation
Encoder
Input layer

Fig. 5 Autoencoder networks with an encoder and a decoder side areoften used for dimensionality reduction, as smaller hidden layers act
as a bottleneck, forcing intermediary representations with narrower
features.

Review Paper

dictive models (see Fig. 6), for grinding Quarz in a stirred
media mill (PM1 from Bühler). Input process parameterssuch as tip speed, solid content, and fluid volume flow werevaried, and additional values for torque and power were
measured. An ANN was then optimized and trained.
The cumulative particle size distributions (PSD) werenormalized and represented via a discretized grid, withthe distribution curve being in a grid cell translating toa cell value of 1 or 0. The resulting binary values wereassigned to the output nodes of the net. Consequently,
for novel input parameter settings, heat-maps can begenerated to mark the likelihood that the respective
cumulative curve will pass through a cell. During postprocessing, resulting scatter plots were derived and converted to approximation cumulative curves. In the rightlower part of Fig. 6, predicted X50 (describing the particle
size within the distribution, at which 50 percent of particles
are smaller and 50 percent larger) values are depicted over
the respective X50 values of the independent evaluation data
points. The approach was further optimized for the prediction of fitting parameters, which is elaborated on later in thecontext of describing a predictive breakage tester AI.
Modular predictive hybrid modeling of plants: Dai etal. proposed a parallel semiparametric hybrid particle size
estimation model, in combination with a random vector
functional-link network (RVLFN), with the examined
grinding circuit mainly consisting of an interconnected system comprising a ball mill and spiral classifier (see Fig. 7).
The ball mill’s first principle model was based on population balance equations; the classifier was described by its
own mechanistic model (Dai et al., 2015).
Based on these these underlying models, sensitivity

PSD…

Inputs x:
TorqueTip speed t
Solid content
Volume �lowTime
Net power

Outputs y:[0, 0, 1, . . . ]Fig. 6 Predictive model for experimental grinding results. For varied process parameters such as tip speed and solid content, resulting particle sizes
were predicted, with one output for X50 (marking the particle size in the distribution, where 50 percent of the particles are smaller and 50 percent arelarger) and the other a heat map for the representation of the entire particle size distribution (original work by author).

9Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

analysis and pre-predator optimization algorithms were
utilized in order to reduce parameter optimization complexity. For the resulting error, the RVFLN was trained tocompensate for the first principle method’s error, resulting
in an overall system with significantly higher accuracy (Daiet al., 2015).
Similarly, Tie et al. (2005) demonstrated PSD estimation via an on-line soft sensor for a setup consisting of ahydrocyclone overflow and a multitude of accompanying
machines such as ball mills in two connected semi-circular
grinding circuits, as shown in Fig. 8.
The underlying basic structures were divided utilizing a
modular approach, while the classifier and hydrocyclone
were described with respective ANNs.
The percentages of solids in the mass flow with diameters smaller than 75 μm, M75, were modeled via AI in dependence of masses, concentrations, and respective flows
for both the cyclone and spiral classifier. Mass percentage
was furthermore modeled in dependence of multiple relevant parameters such as shaft power and recycle slurryflow rate. Sump modeling was based on a first principle
population balance equation approach. The respective nets
acting as functions in the overall setup (f1 for the cyclone
and f2 for the classifier) were both radial basis function
networks (RBF). In addition to the described models for themodeling of nominal process conditions, an adaptive neuro

Fig. 7 Milling setup consisting of a bin, a feeder, the ball mill, and aspiral classifier. Reprinted with permission from: Ref. (Dai et al., 2015).
Copyright: 2015 B.V.

Review Paper

fuzzy inference system (ANFIS) model was trained for the
upstream prediction of anomalies and malfunction, e.g., incase of excessive slurries. A subsequent fuzzy logic coordinator was applied for model connection (Tie et al., 2005).
Simulation-based modeling and breakdown formechanistic modeling: AI modeling based on simulations
is an approach with distinct advantages due to the couplingof two numerical approaches, as they can autonomouslyinteract with one another unsupervised (for instance in
active learning (AL), etc.). As an example, on the basis of
two-way coupled computer fluid dynamics - discrete element method (CFD-DEM) simulations with Rocky DEM
and Ansys Fluent, a representative slice of a wet operated
stirred media mill was simulated with varying tip speeds
and grinding bead diameters (Thon et al., 2022a; 2022b).
Heatmaps were extracted, with each voxel representing the
averaged values of relative velocities. Fig. 9 depicts theresults of the approach.
On the left side of Fig. 9, heatmaps for an independent
test case not used in training are shown, with the prediction
respectively depicted over the independent simulation
result. Averaged values in 14 layers, as well as the density
distribution of the entire domain for relative velocity,
demonstrate the good fit of the result. The results also
proved the advantages of GRL, as the models achieved significantly more accurate results in comparison to classicalbackpropagation. Finally, genetic programming was applied to identify a fitting equation to describe the relationship between the relative velocities 99 of colliding beads(99 percent had smaller energies) and the absolute tip speedof the stirrer. Depicted in the lower right corner of Fig. 9,
parameter settings in random order are depicted, with redmarking the equation and black the evaluation data.
The methodology was further improved and adapted to avertical dry stirred media mill (original unpublished workby the author), demonstrating its universality, regardless of
wet and dry states in grinding. In this case, a vertical mill
was numerically investigated in Rocky DEM. Restitution
coefficients and friction coefficients were determined andcalibrated beforehand. Stirrer tip speed, grinding bead size,and grinding aids for the variation of powder flowability

Fig. 8 Investigated connected setup with multiple machines used for PSD prediction with machine learning in a soft-sensor approach. Reprinted withpermission from Ref. (Tie et al., 2005). Copyright: 2005 B.V.

10

Review Paper

Translational velocity mean [m/s]Particle number distribution/ Histogram [-]Pre_Kinetic_Distribution
Test_Kinetic_Distribution
Histogramm / predicted
Histogramm / Test

Cumulative distribution function [-]Line distance [-]Simulation

AI prediction

AI prediction
Simulation

Kernel Density Estimation [-]Line distance [-]Simulation

AI prediction

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

AI prediction
Simulation

Layers [-]Translational velocity mean [m/s]Fig. 9 A predictive mill model trained on the basis of a coupled CFD-DEM simulation. The simulation and prediction domain were discretized forthe predictive modeling of the relative velocities of colliding beads as a measure of kinetic energy via heatmaps. Comparisons between AI predictions
for independent simulation cases, not used for training, are depicted, showing good accuracy. Furthermore, a comparison of averaged values for characteristic regions in 14 layers of the mill, as well as for the entire domain, is provided. Finally, with genetic programming, a fitting equation describing
the relationship of relative velocity 99 with tip speed was identified, being depicted for 22 parameter combinations in random order. Adapted from
Ref. (Thon et al., 2022b) under the terms of the CC-BY 4.0 license. Copyright: (2022) The Authors, published by Elsevier.

(considered via the preliminary determined calibration
parameters in the simulation) were chosen as input parameters, and the relative velocity voxels of the discretizedmill domain (with a resolution in x, y and z of 46 × 22 × 22
voxels) were considered the output (see Fig. 10).
Adding to the previous approach, a convolutional neuralnet (CNN) was applied. CNNs are an approach often usedin image recognition, in which pooling layers are introduced, with a sliding box sampling and condensing thefeatures of a wider discretized domain, which can be seenin the upper part of Fig. 10. It can be seen that the predicted
result comes close to the real values, which is also evident
in the density distribution curve with close to convergent
curves.
The main advantage of predictive AI as an intermediarymodeling tool lies in its generalizing capabilities. Basedon evaluated generalizing models, AI predictions can beobtained in inference for a large parameter space of highresolution whose systematic simulation, if performed completely, can take significantly longer. In the case of the wet
mill simulation, systematic AI predictions were generated
in the order of seconds, which would have taken time in therange of years with conventional CFD-DEM simulations(Thon et al., 2022a; 2022b).

3.2 Synthesis
AI can be used for the prediction of various particle
properties and required synthesis pathways, as well as forthe investigation of their correlations. Furthermore, AI canbe used for synthesis control and optimization to achieve
desired results.
Energy growth prediction: Chen and Dixon (2018)
developed a machine learning (ML) approach to identify
the relationships between the structure and surface andfragment energy in ZnO nanoparticle morphologies inbottom-up growth using nanoparticle fragments and derived means for the classification and energy expression of
the nanoparticles. The focus was on the ML-based investigation of the stability, phase transition, and growth patternsof clusters, ultra-small nanoparticles, and bulk-sized particles based on fragment geometries and energy parameters.
Shape prediction: Pellegrino et al. (2020) demonstrated
an ML approach for the predictive modeling of TiO2
nanoparticle morphology, the size, polydispersity, andaspect ratio in dependence of process parameters such as
the initial concentrations of Ti (TeoaH)2, additive concentrations of TeoaH3 as the shape controller, pH values,
temperature in a hydrothermal synthesis process. Inversely,
it can be used as a reverse-engineering approach to predict
optimal process conditions for specific desired product11

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Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Max & average
pooling

max pooling46 layers

averagepooling
110 mmLayer 13Layer 18

initialoptimized

test

optimized
test

Probability density distribution [-]initialParticle number distribution/ Histogram [-]Layer 6

Relative velocity [m/s]Fig. 10 Predictive models for relative velocities in a vertical dry stirred media mill with an improvement of the modeling approach by applying
convolutional neural nets with integrated pooling layers. Very accurate models can be trained with heatmaps depicted for an independent evaluationcase (test), with the initial prediction based on backpropagation and the final prediction following GRL. Both the side and top views of heatmaps andthe whole domain density distribution show good alignment (original work by author).

characteristics in terms of aspect ratio, morphology, andsize.
Process control for shape (hybrid): Zhang et al.
(2012) demonstrated a hybrid model to control particle
size distribution in a cobalt oxalate synthesis process,with the first principle model capturing the known processcharacteristics with a partial least-squares (PLS) regression
ML model to compensate for its shortcomings, with the
combined model being capable to model the PSD evolution. The reaction took place in a two-reactor system being
connected in series, with the first acting as an ammonium
oxalate dissolver and the second being responsible for the
successive crystallization.
Process optimization for yield: Jose et al. (2021)
demonstrated that AI in the form of multi-object ML optimization can also be used for the optimization of productyields with the example of ZnO nanoparticle synthesis.
The optimization relied on high-yield microreactors andhigh-throughput analysis. In less than 100 experiments, optimization could be achieved, allowing for the continuous
production of 1 kg per day. A scalability assessment of theapproach was conducted.
Prediction, experimental planning, and synthesis-

12

assistance: A dedicated review article that Tao et al. (2021)
conducted focused on the use of AI in the specific context
of nanoparticle synthesis. Examples were provided for theprediction of nanoparticle properties, the assistance of thesynthesis, experimental planning, and data generation in
the contexts of semiconductor, metal, carbon-based, andpolymer nanoparticles. However, most current applicationsin the field focus on the identification and optimization ofexisting procedures, with some targeting the identificationof novel particles and procedures.

3.3 Stabilization and Functionalization
After or during synthesis (e.g., during in-situ stabilization) and in the context of other unit operations (such as
fine grinding, agglomeration, etc.), stabilization is required,as generated product particles would otherwise agglomerate uncontrollably. Identification of suitable stabilizers canbe a laborious task, with the result and emerging properties
often being unpredictable. Here, AI modeling can be auseful tool in predictive identification and the estimationof resulting properties in regard to solution or adsorption
capabilities and resulting stability.
Prediction of solution capabilities: Astray et al. (2017)

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

showed an AI model to predict the solution properties of alkylammonium surfactants such as density, surface tension,and kinematic viscosity as functions of input parameterssuch as concentration, carbon number, and the molecularweight of surfactants, with an R2 of 0.97.
Prediction of adsorption capabilities: Faghiri et al.
(2019) trained an AI model based on least-square vector
machines to predict the surfactant adsorption of sandstone
in the context of oil recovery. Parameters were surfactant
density, concentration, time, and kinetic adsorption density.
Good predictive capability could be reached for kinetic adsorption density, with R2 accuracies for independent testingdata of around 0.96.
Prediction of final stability: Kundu et al. (2016)
focused on stabilization in emulsions. A hybrid multiple
inputs and multiple outputs (MIMO) ANN-GA model
was used to predict the formation and stability of (o/w)
petroleum emulsions during stirring, with parameters being
oil and surfactant concentration, stirrer tip speed, and pH
value. Prediction errors were at around five percent.
Li L. et al. (2020) modeled the stability of Al2O3ethylene glycol nanofluids via an ANN in dependence ofparticle size distribution and velocity ratio (the ratio between Brownian particle velocity and gravitational settling
velocity). The nanoparticles in ethylene glycol were magnetically stirred and positioned in an ultrasonic bath. Input
parameters were thermal conductivity, viscosity, massfraction, and temperature. R2 accuracy ranged between
0.98 and 0.99.

3.4 Precipitation
Precipitation is an approach whereby the addition of
agents or changes in other influencing parameters such astemperature in a solution can rapidly force specific materials out of the solution to form defined, mostly amorphous
particles. In slower dissolution, the resulting particles
crystalize. In both cases, the prediction of suitable conditions and the estimation of the result are difficult tasks,
which usually involve much often intuitive manual experimentation to identify equilibrium states, resulting process
kinetics, and the final amount of precipitate.
Equilibrium prediction: Leal et al. (2020) demonstrated an on-demand ML (ODML) algorithm to predictbased on simulation results new chemical equilibrium
states effecting precipitation in fluids on the basis of the already past chemical equilibrium states in reactive transport
simulations to reduce computing costs based on simulation
results.
Kinetics prediction: Nielsen et al. (2020) described an
AI framework for modeling particle processes based on
preexisting mechanistic process knowledge and sensor data
to estimate underlying process kinetics. Three case studiesof flocculation, crystallization, and fracture were described
and modeled for the kinetics of different underlying pro-

Review Paper

cesses such as nucleation, growth, shrinkage, fracture, andfor agglomeration.
Final precipitate prediction: Hoseinian et al. (2020)
demonstrated a hybrid neural network–genetic algorithmwith first principle models for the prediction of ion flotationbased Zn(II) removal.

3.5 Mixing
Most processes require homogenous distributions, although on some occasions, controlled inhomogeneities canbe desirable. Therefore, mixing is an important aspect, also
due to energetic reasons. In particular, mixing parameters
should be chosen to not exaggerate energy intake, which
can cause inefficiencies, excess wear, or product degradation, etc. In addition to the modeling of the process, control
modeling and monitoring are highly significant, as mixing
is, in many processes, a highly influential parameter that
often requires active and dynamic adaptations or continuous control during the process.
Qualitative and quantitative monitoring: Bowler etal. (2020) showed ML-based monitoring of a mixing process with ultrasonic sensors in which classification models
were applied for the general mixing capabilities of materials and regression models for the progress monitoring for
mixing time.
Predictive real-time digital twins: Khaydarov et al.
(2020) demonstrated a hybrid surrogate model withbubble recognition and online video processing in aphysical stirred tank reactor for the enhancement of aparallel-running AI-based surrogate model for a CFD simulation, as depicted in Fig. 11. The trained surrogate model
could be implemented on a near real-time running edgenode, with online AR displaying the result for a human operator. As resource-efficient real-time modeling with direct
feedback to human or machine interfaces is a desirable goal
beyond mixing, the use case is interesting for other unit
operations or plant operations. Furthermore, the given casedemonstrates a quick mostly numerical AI-based digitaltwin for the real-time processing of the inner mechanismsof a parallel-running process, yet with periodic alignment
to physical reality via direct measurement feedback (which
can be a soft-sensor approach).
Mixing additive control prediction: Ittiphalin et al.
(2017) demonstrated the addition of fat to a mixing processof feed pallets for animals. Among others, a backpropagation neural network was trained considering moldsize, total percentage of fat, percentage of additional fat,percentage of fibers, and pallet shelf-life as inputs for theestimation of additional fat to be added. In order to achieve
an optimum process for increasing the production rate anddeteriorating pallet shelf-life with an increased fat content,
optimal fat quantities can be added during the dynamic
mixing process.

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Review Paper

Fig. 11 Online surrogate modeling of a mixing process via a CFD surrogate model with direct feedback via an AR interface to a human operator
running in near real time due to AI assistance. Feedback of the physical process was integrated into the approach via machine vision. Reprinted withpermission from from Ref. (Khaydarov et at., 2020). Copyright 2020 B.V.

3.6 Aggregation
As described in precipitation and stabilization, primaryparticles can aggregate, agglomerate, or flocculate, which
can be desirable under controlled circumstances. As in
the previous operations, AI can assist in the prediction ofsuitable conditions and kinetics or the estimation or results.
In addition to the prediction of specific given processes, it
can also be used to identify suitable novel pathways withinlarge parameter spaces.
Hybrid predictive kinetics modeling: Nielsen et al.
(2020) described an AI framework for modeling particle
processes based on preexisting mechanistic process knowledge and sensor data to estimate the underlying process
kinetic rates of constitutive equations based on state variables and control actions. Depending on the investigatedphenomena in the model, kinetic rates included one ormore of the variables of nucleation, growth, shrinkage,
agglomeration, and breakage. Three case studies, namely
flocculation, crystallization, and fracture, were described
and modeled for the kinetics of different underlying processes such as nucleation, growth, shrinkage, fracture, andagglomeration.
Aggregation pathway identification: Li J. et al. (2019)
described the accelerated synthesis of gold nanoclusters
with AU(III) to Au(I) reduction and subsequent coordination via thiolate ligands for complexation, considering all
relevant parameters to identify likely patterns for the successful synthesis of nanoclusters using deep learning (DL).
Combined structure and pathway prediction: Wahl
et al. (2021) demonstrated a ML-based approach to predictboth the aggregate particles (polyelemental heterostructures) via colloidal self-assembly, as well as the way inwhich they formed, using a technique called a diffuse map.
The approach proved capable of predicting many body
phenomena and long-term assembly dynamics.

14

3.7 Extrusion

Another significant process in particle technology is
extrusion. In this process, particles, and eventually, a fluid
are first conveyed and then mixed. In the second step, thecompression and shearing of a viscous mass under highpressure and temperature take place, then the mass isfinally pressed through a shape-giving opening. The parameters are highly interdependent, and the selection of optimal operating conditions is usually time-consuming andincreasingly costly. Consequently, the quality control of the
extruded material is associated with various challenges, including the control of process parameters such as velocity
and temperature or of the final product quality. Therefore, amore intelligent method needs to be developed. To achieve
this goal, AI methods can be beneficial, in addition to being
able to close the gap between often used simulations andaccurate but costly experiments. In the following extrusion
processes, pure polymers are described as the AI methods
can be used similarly for particle-filled polymers.
Curing property prediction: Marcos et al. (2007)
demonstrated the prediction of the extrusion propertiesof rubber, relying on compound composition and mixing
conditions during extrusion. Despite being applied tomolten rubber in this case, extrusion is often based on
granular material, e.g., in many extrusion processes forbattery production, wherefore the general approach is alsoof interest in the context of particle technology. Online information about specific curing properties can be predicted
via integration into the process, eliminating waiting time
for laboratory results and increasing product traceability.
Velocity control: Martínez-de-Pisón et al. (2010) presented a dynamic model for extrusion velocity control,
which was capable of controlling manufacturing processes
based on previously successful start-ups with data about
pressure, temperature, and velocity. The complexity ofthis task was due to the open-loop control system and theconstant alterations inherent to the process.

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Temperature control: Taur et al. (1995) proposeda fuzzy PID temperature controller for the temperature
control of the extruder cylinder using if-then rules andmembership functions.
Tsai and Lu (1998) developed a predictive fuzzy PID
controller with single-loop monitoring for extruder cylinder temperature control.
Quality control: Liu X. et al. (2012) proposed dynamicgray-box modeling based on a soft-sensor approach tocontrol the quality of polymer extrusion. In this article, anattempt was made to use a gray box to provide real-timemonitoring of the quality of the extrudate in order to reduce
the setup time and improve the operation of the system.
In this work, a soft-sensor structure was combined with afeedback observer to estimate the viscosity and control the
final quality of the material.
Simulation-experiment translator: Bajimaya et al.
(2007) used an ANN to predict the process parameters in
extrusion. Simulation analyses are usually unable to take
into account the boundary conditions of the manufacturing process, which is why their results remain mainly
theoretical. On the other hand, conducting experiments is
usually expensive and time-consuming, which is why theuse of AI methods can reduce the gap between simulation
and real data. The trained AI algorithm acted as a transfer
tool to translate the theoretical variables gained from finite
element method (FEM) simulations such as stress, strain,
and shearing into realistic measures for extrusion pressure,temperature, and velocity.

3.8 Classification

Classification has many applications in particle technology that can be performed analytically to determine distributions or properties or as physical separation according
to the respective properties. There exist many separation
categories, with some being easier to implement, such as
the classification of compact particles according to size,e.g., via sieving. For other aspects such as morphology orcomposition, sorting can be difficult. In such cases, manualhuman involvement or application-specific hardcoded
automation is often required, but this can be expensive,
unreliable, and inflexible and can limit the quantities thatcan be processed. Consequentially, the application of AI isa viable alternative in such instances.
Analytical classification with machine vision:Gonçalves et al. (2010) demonstrated an ANN-based technique of analytically classifying microscopic wear particles. Binarized particle images were obtained, and features
such as area, perimeter, width, height, diameter, circularity,
ferret diameter, elongation, and aspect ratio were obtained
via ImageJ. Supervised classification reached accuracies ofaround 95 percent.
Massarelli et al. (2021) used computer vision and ML
to analytically (i.e., with no physical separation involved)

Review Paperclassify and count particles depending on dimensional size
and morphology. The particles could be classified under
supervision according to predetermined groups or without
supervision to act as a measure for sample representativeness and in order to identify hidden features.
Physical separation: In addition to mere analyticalclassification, physical sorting can be performed, as
Kattentidt et al. (2003) demonstrated, for the sorting ofbulk solids. In an online operation, AI-based quality control
was performed with two optical sensors for the separationof recycled glass. Distinguishing features were dimensionality, surface area and volume, texture, morphology,
homogeneity, conductivity, and spectral reflection. Finally,
a separation unit sorted the bulk material into dedicatedfractions.
The AI-based sorting of particles, also in the context ofrecycling, has the potential to be a crucial pillar for a circular economy. For instance, Wilts et al. (2021) demonstrated
AI-based waste sorting via a robotic arm and a sorting beltwith a multitude of sensors (metal detector, high-resolution
RGB camera, NIR sensor, VIS sensor).
Furthermore, Peršak et al. (2020) demonstrated thesorting of transparent plastic granulates based on computer
vision and subsequent pneumatic air separation. The plastic particles had sizes of 2–4 mm, sorting accuracy for 9classes equaled 90 percent, and for two classes, it was 100
percent.

3.9 Characterization and Analysis
In almost all cases, after or during unit operations, thecharacterization of properties and their distribution isrequired. Sometimes, these can easily be gathered, whilein other cases, directly obtaining the required data can bechallenging or impossible due to limitations in measurement equipment, physical constraints, accessibility during
operations, etc. Often, analysis is possible but limited inregard to the executable quantity due to the nature of themeasurement technique or the hardware. In all these cases,
AI can be a useful technique to use with parallel runningpredictive or surrogate models, as these can be enriched bylimited physical information, as previously shown in thecase of mixing (Khaydarov et al., 2020). In addition to use
in soft sensors, AI and evolutionary approaches can be used
to enhance measurement results through post-processing or
to derive mechanical descriptions of the measured properties and their influencing parameters.
AI as a soft sensor: Reliable predictive models can also
be used to directly replace measurement, or as additional
assistance in the context of soft sensors.
An important characterization method for particle breakage is the particle breakage function, which can be determined via a breakage tester, as described in Böttcher et al.
(2021) (see Fig.12).
In the breakage tester, rotational velocity and gap size

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Review Paper

Fig. 12 A predictive model for a breakage tester to characterize the particle breakage function. Successful prediction capabilities were achieved for
acting normal forces, power intake, and resulting particle size distribution. Further processing can be done for transference into existing breakage
function models, with AI enabling reduction in the physical measurement effort (original work by author). Left upper picture reprinted with permission
from Ref. (Böttcher et al., 2021). Copyright 2021 B.V.

can be adjusted, while torque and normal force between therollers are measured. Based on GRL for rotational velocity,with gap size and initial particle size as input parameters,a predictive model was trained for the prediction of power,
force, and resulting particle size via two fitting parameters.
Both, the prediction of power and force achieved highaccuracies, with an R2 in both cases of 0.97.
Sun et al. (2017) successfully trained a three-layer neuralnet for the prediction of electron transfer properties such as
the Femi energy of silver nanoparticles and successfully
identified correlations with structural and morphologicalfeatures.
Measurement improvement: Furat et al. (2022) applied
generative adversarial networks (GANs) to retrospectively
super-resolve images of particle based Li-ion electrodes
for the quantification of features such as the identificationof cracks in aged cathodes. Consequentially, the trade-off
between resolution and field of view in the SEM imagingof bulk-pictures was significantly reduced.
Mechanistic analysis measure modeling: GAs provide
another AI method that is often used for optimization
tasks by mimicking the process of evolution involving themutation, selection, and reproduction of system states andparameters. Finke et al. (2021) used a GA for the identification and optimization of suitable model parameters for a
viscosity model of nanoparticulate suspensions.

3.10 Other Applications
A method with great potential for future applications is

16

a combination of nature-based or mathematical algorithms
and AI.
Drilling and blasting through hybrid models: Onepossible hybrid approach was introduced by Mojtahedi
et al. (2019), which was developed for the prediction ofparticle size distribution in mining and is presented below.
Drilling and blasting are the most widely used methods in mining for rock crushing. In order to describe thefragmentation, the ANFIS method was combined withthe firefly algorithm (FA). By extending a fuzzy inference
system (FIS) with an artificial neural network (ANN), thesystem becomes self-learning and can solve nonlinear
complex problems. In addition, the combination of ANFIS with the FA provides an ability to determine optimalvalues for complex nonlinear problems. More precisely,
the FA describes a nature-based algorithm of firefly social
behavior, where the convergence speed of the algorithm
is high and the number of iterations for an optimal result
is low. The objective of the ANFIS-FA was to predict theD80 (the 80 percent passing size at which 80 percent of theparticles are smaller) value for different input parametersof the resulting particle distribution, using a data set from88 blasting operations at the Shur river dam in Iran. For
further validation of the predictions, the ANFIS-FA method
was contrasted with linear multiple regression (LMR)
methods alongside the test data set.
Six different hybrid models were created, each differingin the choice of input parameters used. Each of these models achieved higher R2 values with a minimum of 0.856 in

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Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

predicting the D80 value than the LMR method, with 0.669
in relation to the test data set.
In summary, combining AI methods with existing algorithms has been demonstrated to significantly improve
the accuracy of particle size predictions (Mojtahedi et al.,2019).

Predictive AI systems have the potential for massive savings in computing power and extensions of the investigableparameter space, yet they represent customized solutions
that need to be performed repeatedly for new use cases, as
was done for the dry operated vertical mill simulation after
the modeling of the wet simulation.
More universally applicable AI solutions exist forsome simulation methods in the form of surrogate models, although development is still in an early state.
Calzolari et al., using tensor basis neural networks(TBNN) and CNNs (discussed in terms of the verticalmill), described CFD surrogate models (see left side ofFig. 13), demonstrating the time extrapolation of flow
fields and AI upscaling based on previously learned
physical data to derive super-resolved fields for lowresolution fields and physics-informed DL turbulencemodel enhancement (Calzolari and Liu, 2021).
For the discrete elements method, the authors (Thonand Schilde) are currently working on a surrogate model
to train small-scale unit cells with particle positions andforces from DEM for incorporation in a respective smallscale unit cell surrogate model for later application on large
domains. By discretizing the large domain cyclically andre-meshing the unit cell grid, the dynamics for larger domains are to be represented by the surrogate model and predicted as a new initialization state for subsequent iteration.
The Initial training of output 3D particle positions overtime is promising, with minuscule errors in individual time
steps and a mean error of 0.5 percent per spatial dimension(0.1 nm error in a 20 × 20 × 20 nm unit cell).

4. AI Surrogate Models
Sophisticated computer simulations are increasingly
aiding particle technology in all covered domains. Depending on the time and length scale, different simulation
techniques can be applied, such as the Finite Elements
Method (FEM) for the investigation of the inner structuraland mechanical behaviors of solid objects, Computer Fluid
Dynamics (CFD) for the simulation of fluid flows, theDiscrete Elements Method (DEM) for the investigationof the interactions among many particles and boundaries,
or population balance simulations (PB) for the numericalmodeling of particle-based processes. The methods canbe coupled for multi-scale investigations or in CFD andDEM to investigate particulate systems within fluids, suchas air or water. Interaction can be mono- or bidirectional,
depending on the required accuracy and computational
demands. Coupled CFD-DEM simulations are often used
to numerically investigate particles in fluidic environments(Li J. and Tao J., 2018; Schrader et al., 2019; Zhu et al.,2020; Beinert et al., 2018; El-Emam et al., 2019). Suchsimulations are often demanding in terms of time and computing resources, limiting the space, time, or accuracy thatcan be investigated.

DEM surrogate models

Inferece on large scale

Training on small scale

CFD surrogate models

Input Positions
 Forces

Small scale simulation

Output New
positions

AISurrogate
Model

Discretization in AI inference and New positioning
moduleinterpolation and discretization

Cyclic Iteration (time steps)

Fig. 13 Depiction of surrogate models for CFD simulations to quickly get accurate results, which would take unaided CFD significantly longer. On
the right side, a current approach for a surrogate model for DEM is shown, in which a predictive model of a 3D tesseract is trained for a small-scale unit
for later iterative inference in a larger discretized domain with remeshing. The left part is a reprint of Calzolari and Liu (2021) under the terms of the
CC-BY 4.0 license. Copyright: (2021) The Authors, published by Elsevier. (The right part is an original work by the author).

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5. AI-based design and scale-up
In addition to modeling, the optimization or control ofindividual processes such as synthesis, grinding, analysis etc., the design, modeling, and control of plants andcomplex networks as a whole, as well as layout and optimization, including the design of the underlying processes
and units, is of major economic significance. Using AI for
design, layout, optimization, or scale-up can lead to significant savings and improved efficiency.
Other than for the task or unit’s specific applications,elaborated on earlier in the publication, adoption of AI inparticle technology in this respect is still in an early stage.
However, there are some examples, more advanced examples, and real-world applications in other fields, with theused approaches being transferable to particle technology,e.g., the use of GAs for the autonomous design of heatexchangers, which can be easily adopted for the design ofmilling geometries.
Generally, two major approaches can be distinguished,
the design and optimization of dedicated production
systems, processes, unit operations, plants, and logistics
infrastructure for dedicated goals or the intelligent dynamic
identification, connection, and management of suitable(potentially decentralized) infrastructure already in existence.
The former method of dedicated design tasks can beused for basic operations or if products will be required in
the long term and in large quantities, making applicationspecific inflexible yet efficient high-throughput systems asuitable option.
With GAs, Wisniewske (2004) demonstrated an optimal
design of reinforcing fibers within a composite, while Jattiet al. (2013) optimized the milling of aluminum alloys. For
the design of process units, RatnaRaju and Nandi (2013)used GAs for the construction of heat exchangers whileconsidering pressure drop and effectiveness.
In contrast to evolutionary techniques, Lee X.Y. et
al. (2019) used deep reinforcement learning for microfluidic flow sculpturing, finding it to be superior to a GA
approach. Similarly, with classical AI techniques suchas neural nets, Sarkar et al. (2019) demonstrated an autonomous design of a compressor rotor and optimization
through AL.
Specifically, GANs, in which two competing AIs traineach other, are useful for the design process. In this approach, a generator learns from training examples in orderto produce similar results, while a discriminator AI evaluates the results and learns through feedback to improve theaccuracy of its evaluation. Being mostly known from deep
fakes, the generation of realistic images, they can also be
applied to generate solutions for given problems. Via theiterative interaction of generator and critique AI, the two
networks improve each other’s capabilities.
Chen and Fuge (2019) demonstrated GANs for design

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synthesis, the generation of hierarchical designs in conjunction with generative models. Similarly, Oh et al. (2019)
applied GANs for the generation of 2D wheels. Nobari etal. (2021) demonstrated autonomous design synthesis forconstrained 3D geometry generation for different purposes
via deep generative models and constraint GANs.
In addition to the complete execution of the design
process, AI can be used in a semi-automated manner toassist manual design endeavors. For instance, Khan et al.
(2019) discussed iterative design adjustment in the construction endeavor in a loop with user feedback. Similarly,
Deshpande and Purwar (2019) demonstrated the solution
generation of mechanisms and synthesis for abstract inputs.
Raina et al. (2019) applied transfer learning to incorporate historical design strategies from past cases into
AI systems. Combining these approaches in the future, aclosed loop of adjustment with continued learning based onhuman feedback can be implemented.
Similarly, GA-, ANN-, and GAN-based design or theevolution of design strategies can be applied for processesor production units in particle technology, such as in thedesign of mill geometries and operating conditions or forthe morphological features of product particles.
In regard to the second major variant in design, optimization and scale-up, the intelligent management and interconnection of existing infrastructures, units, systems, andprocesses, a key foundation are digital twins, which can bepredictive AI models, as already discussed in this publication. Cyber-physical systems, which include sensors, communication, computation, and control elements, are closely
associated with digital twins as virtual representations ofphysical systems. Ideally, they rely on a digital twin formonitoring, control, and autonomous decision-making(Lee E.A., 2008; Tao et al., 2019). Together with cloudcomputing, big data, and the internet of things (IoT) as thefoundation, both cyber physical systems and digital twins,
which can rely heavily on AI, are important components ofthe concept of smart factories and industry 4.0.
As already discussed, in a fixed production environment,
management and control are vital for the dynamic interconnection and allocation of resources, units, processes,
or entire plants in a decentralized (potentially global)
manner while considering the available infrastructure andits flexibility, intelligent supply chain management anddecision-making are of paramount importance. Toorajipour
et al. (2021) performed a more detailed review of AI
in supply chain management and successive decisionmaking, focusing on marketing (including sales, pricing,segmentation, consumer behavior, decision support, direct
marketing, industrial marketing, new products, etc.), logistics (containing container terminal operations, inbound,logistics automation, sizing, etc.), production (assembly,
integration, product driven control, etc.), and the supply
chain itself (facility location, supplier selection, supplyChristoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

chain network design, risk management, and inventory replenishment), with references to specific examples. Supply
chain management and autonomous decision making areimportant aspects that mirror, to a large degree, processcontrol in a fixed system, although challenges in a dynamic
decentralized system can strongly outweigh those in a fixedsystem.
In terms of the online monitoring of large connected
production systems, the company Hosokawa Alpine (2019)
applies an ML-based platform intelligent software assistant(ISA) for the maximization of efficiency and quality.
Regarding the supply chain network design underconsideration of profit maximization, Zhang et al. (2017)
demonstrated a competition-based intelligent physarum
solver.
Wang et al. (2012) demonstrated autonomous collaboration between heterogeneous actors (multi-robotic systems)
and AI to solve complex formation performance in a dynamic environment.
In regard to the dynamics of product life-cycles andsupply chain networks and the resulting inbound fluctuations, Knoll et al. (2016) demonstrated predictive inboundlogistics planning.
Kwon et al. (2007) presented an integrated multi-agent
framework to solve collaboration tasks in supply chain
management in regard to supply and demand uncertainties.
In cases when human supervision is desired, Kasie et al.
(2017) demonstrated a decision support system combining
AI and discrete-event simulations (DES), as well as database management.
Applied to particle technology, the existing production
sites addressed earlier in this review can be intelligently
connected in a dynamic, decentralized, self-organizing
structure based on digital twins and cyber physical systems. Based on AI market prediction, as well as autonomous real-time monitoring and decision making, the costand efficiency benefits of smart factories and industry 4.0
can be adopted more progressively in the field of particle
technology.

6. Conclusion
In conclusion, AI techniques are already used in allareas of particle technology. Predictive modeling, often thefoundation for successive AI applications or conventional
post-processing, is already universally represented for allprocess types, although the degree of adoption varies. AI
methods or evolutionary approaches, which are often usedin combination, are furthermore applied for the optimization or set up of process control.
AI can perform or assist in scale-up, as trained AI
models can be transferred between different scales throughpre-training. In regard to classical simulations such as
DEM, FEM, or CFD, AI can aid in numerical investigation.
Surrogate models are increasingly being investigated in

Review Paper

various fields as quick-to-apply alternatives to classicalsimulations such as DEM for particulate systems. Often,
the simulation counterpart is used as a training foundation
for the training of surrogate models. Consequently, throughsurrogate models, the investigation of systems such as large
stirred media mills with trillions or quadrillions of particles
or more and their inner workings can be possible, ordersof magnitudes being larger than what could be done withstate-of-the-art simulations. Inversely, surrogate models
can be performed significantly faster than real simulations,allowing for real-time or faster numerical investigationsof parallel-running physical processes or plants, allowing
for the predictive control of complex milling or reaction
processes or of entire plants. Since simplifications or approximations need to be done often as compromises need
to be made between accuracy and computing performance,e.g., simpler contact models or focusing on grinding beads
without product particles, such simplifications can become
unnecessary with surrogate models, allowing for greatertransparency and realism.
Regarding the criticism of AI’s black-box nature, there
are techniques to increase the transparency of AI models,in particular the use of genetic programming (GP) or
reverse-engineering strategies, to transfer AI models into
more transparent representations to facilitate, among
others, the mechanistic modeling endeavor in particle
technology.
In summary, there are examples in most domains ofparticle technology, although widespread adoption has not
yet been achieved. Based on the existing examples, ranging
from proof of principles to successfully applied economic
adoptions, with the exponential increase in general capability, AI adoption will be a major determinant of economic
competitiveness and survivability in this and the following
decade.

7. Outlook
Looking towards the future, the author sees promisingareas and ideas to address untapped potential and remaining shortcomings.
In Fig. 14, a possible framework is depicted, extending apreviously published hybrid modeling approach now called
hybrid regression evolutionary network (HyREN) (Thon etal., 2022b).
1) HyREN: Being marked by the blue square, training
data from either experiments or experimentally validated
simulations (e.g., via DEM calibration or the execution andcomparison of identical mill experiments and simulations)
are used for parallel hybrid modeling, also called gray-box
modeling. In this way, the parallel-running black-box AI
only models the divergence between predefined white-box
models, e.g., an existing physical or mechanistic modelfrom the literature, and only compensates for the whitebox model’s shortcomings, with major tendencies being

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Fig. 14 Outlook for a comprehensive modeling system combining classical AI techniques such as neural nets with evolutionary approaches such as
genetic algorithms and programming in conjunction with hybrid modeling. Integrated optimization and the establishment of control decision trees canbe generated based on underlying hybrid models acting as digital twins. Furthermore, dimensionality analysis, equations, and physics recognition canbe added to improve the underlying modeling capabilities and improve transparency and physical reasonability. The middle blue box is a reprint fromThon et al. (2021), as are the lower boxes for genetic algorithms and programming (Thon et al., 2022b), both under the terms of the CC-BY 4.0 license.
Copyright: (2021) The Authors, published by Wiley.

defined by the transparent and established white-box model.
Successive GPs can be performed to break down the blackbox model component into more transparent representations to iteratively extend the white-box model. Dependingon whether human intervention will be involved, the extensions can be empirical or mechanistic. Based on this as an
outlook, several additional extensions are proposed, which
are currently being developed.
2) Genetic reinforcement learning: One shortcomingof AI models is usually their large training data requirements. The GRL training technique has already addressed
this shortcoming. Despite GRL’s advantages, unknown
regions in the parameter space in terms of the available
training data can still compromise the generalizing capabilities of the model.
3) Active search and request: e.g., the comparison of
the systematic predictions of independently trained AIs,
can reveal parameter combinations of uncertainty in orderto close these gaps with a minimum amount of additional
data acquisition (via targeted simulations or experiments).
In regard to hybrid modeling and the iterative extensionof white-box components, additional approaches can increase physical significance.
4) Dimensionality analysis: As shown in green, GP canbe augmented through dimensionality analysis to ensure
the mathematical and physical consistency of units.
5) AI-driven regression: Furthermore, initialization
with suitable equations, derived from AI detection basedon the (n-dimensional) shape of the data, can facilitate the20

process.
6) AI-driven physics recognition: Furthermore, empirical models can be transferred to mechanistic models
in a semi-automated manner by generating a large poolof equations containing known physical or mechanistic
equations. Resulting from generations of crossover andmutations, and involving tree structures from this equationpool, recognition AIs can be trained to later be applied
to the identification of white-box equations or respective
fragments in data from real use cases.
7) Digital twins for optimization and control: Based on
the trained models (black, gray, or white box), subsequent
techniques such as GA, GP, or AL, etc. can be performed
and considered to be safe to interact with and quickto-apply virtual process representations (AI-based digital
twins) for optimization and control.
8) Online Learning: Further development opportunities
and the application of data-driven methods in process,formulation, and material modeling are manifold, such as
online learning in which AI is constantly running in parallel
to processes or data banks to update its internal modelingand dynamically adapt to changing circumstances (e.g.,anomalies such as the blocking of particles in a grinding
process or changes in the product formulation). Similar toGAs, ALs allow for the optimization of defined goals.
9) Autonomous AI modularization: Via un- or selfsupervised learning, the use of modular neural nets or
dropout, automated identification and the decomposition of process and production engineering issues into

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sub-processes and sub-mechanisms for better (hybrid)modeling and control of the entire process chain can beachieved. For instance, a complex production plant withvarious sensors and actuators can be autonomously broken
down into closed unit systems, which can then be interconnected to represent the entire plant, simplifying the modeling effort while increasing the transparency of the model.
10) An Expert Management System for AI: As
different AI tools (neural nets, evolutionary algorithms,
Hierarchical Hidden Markov models, etc.) provide varying
advantages and disadvantages, the best results can usually
be achieved via the intelligent combination of methods
either in the form of joint applications or the fusion of
methods (ensemble methods). In the future, based on a comprehensive toolbox of methods, a self-supervised expertmanagement system can be established for the optimal
allocation and interconnection of ML methods.
Since there exist real-world examples in particle technology of almost all unit operations in terms of predictivemodeling and, increasingly, optimization and control
via AI, the mostly untapped design, scale-up, modeling,
optimization, control, and monitoring of large, highly connected systems such as entire plants or decentralized production facilities are likely to be the next steps in adoption.
11) AI-driven unit and geometry design: Furthermore,
the design of individual components such as stirrer geometries can be facilitated. That is, the geometry design itself
can be performed numerically, e.g., via AL or the use of
GAs along predefined constraints, as can be the optimization of process parameters.
12) Surrogate models: Through the use of surrogate
models, quick iteration and prototyping are possible. Forprocess control, AI models can be used as quick-to-apply
digital twins (predictive/surrogate models) that run fasterthan real time and have the potential to improve automationand the autonomous development of sophisticated control
schemes.
13) Brain-derived AI: Finally, current AI is still loosely
inspired by biological nerve systems. Recent developments in neuroscience and the successive adoption of AI,
specifically in regard to neuroscientific theory about the
mammalian neocortex, as found in Hawkins et al. (Holeand Ahmad, 2021; Hawkins and Ahmad, 2016; Hawkins
et al., 2017, 2018; Lewis et al., 2019), can allow for moregeneral levels of intelligence compared to the currently
narrow AI. These advancements harbor the potential toallow for desired features currently investigated in thecontext of classical AI such as online learning (permanent model update), distributed voting AIs (robustness,
generalizing capabilities), or reference frames (Hinton,2022). This can allow for more general AI systems, which
can apply and detect learned patterns, strategies, andsolutions in the context of novel or other types of unit
operations via analogies. Applied to particle technol-ogy, being a wide field involved in many industries, theautonomous transfer and detection of common patterns
and solutions can allow for the identification of nonobvious solutions, e.g., the use of Michaelis–Menten kinetics for the dispersion of nanoparticles (Schilde et al., 2010).

Acknowledgments
We would like to thank the company ESSS (Florianópolis,
Brasil) as the developer of Rocky DEM for the close
research cooperation and provision of the research license. We especially thank Jan-Phillipp Fürstenau of
CADFEM GmbH (Hanover, Germany) for the technical support. The authors want to thank the Deutsche
Forschungsgemeinschaft (DFG) in the context of the
Heisenberg Program SCHI 1265/19-1 “Digital methods forcomplex systems in process and production engineering”
and the SPP 2364 “Autonomous Processes in Particle
Technology”.

Nomenclature
AI
ANFIS
ANN
ASIC
BP
BPNN
CFD
CNN
DEM
DL
DNN
FA
FEM
FIS
GA
GAM
GAN
GP
GRL
IoT
LIME
LMR
LSTM
MIMO
ML
ODML
PB
PID
PLS
PSD
RVLFN
SEM

Artificial intelligence
Adaptive neuro-fuzzy inference system
Artificial neural network
Application-specific integrated circuit
Backpropagation
Back propagation neural network
Computer fluid dynamic
Convolutional neural network
Discrete elements method
Deep learning
Deep neural network
Firefly algorithm
Finite elements method
Fuzzy inference system
Genetic (or evolutionary) algorithm
Generalized additive model
Generative adversarial network
Genetic programming
Genetic reinforcement learning
Internet of things
Local interpretable model agnostic explorations
Linear multiple regression
Long short-term memory
Multiple inputs and multiple outputs
Machine learning
On-demand machine learning
Population balance
Proportional integral derivative (controller)
Partial least-squares regression
Particle size distributions
Random vector functional-link network
Scanning electron microscope

21Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

TBNN
XAI
YOLO

Tensor basis neural networks
Explainable AI
You only look once (object detection)

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Review Paper

Authors’ Short Biographies
Christoph Thon
Christoph Thon studied bio- and chemical engineering at the Technische Universität Braunschweig
and the Ostfalia University of Applied Sciences from 2011 to 2017 and finished with his master’s
thesis, “Investigation of the stress distribution through laminar flows by using CFD for scaletransfer of kneading processes”. His doctoral research, under the supervision of Prof. Dr.-Ing.
Carsten Schilde at the Institute for Particle Technology (iPAT), focuses on the investigation of grinding processes in mills through computer simulations and the application of artificial intelligence.

Marvin Röhl
Marvin Röhl studied mechanical engineering at the Technische Universität Braunschweig from2013 to 2020 and graduated with his master’s thesis, “Modeling of a drying and structure formation
process via coupling of DEM simulation and methods of artificial intelligence”. His doctoral thesis,
under the supervision of Prof. Dr.-Ing. Carsten Schilde at the Institute for Particle Technology (iPAT)
deals with the development of an intelligent controlling approach to an electrode coating processusing machine learning methods.

Somayeh Hosseinhashemi
Somayeh Hosseinhashemi studied computational science and engineering at the University of
Rostock from 2017 to 2020 and graduated with her master’s thesis, “Numerical investigation ofpressure ripple in a rotary vane pump to determine optimization potentials with the software
StarCCM+” at Robert Bosch Automotive Steering Company. Her doctoral thesis, under the supervision of Prof. Dr.-Ing. Carsten Schilde at the Institute for Particle Technology (iPAT), addresses thedevelopment of an intelligent and autonomous paste manufacturing process using machine learning
approaches.

Arno Kwade
Prof. Dr.-Ing. Arno Kwade studied mechanical engineering at TU Braunschweig and University ofWaterloo, finishing his PhD in 1996, receiving the Heinrich-Büssing-Price. He is head of the Institutefor Particle Technology, Scientific Director of the Battery Lab Factory, spokesman of the Center of
Pharmaceutical Engineering at TU Braunschweig, division head of the Fraunhofer Project Center
for Energy Storage and Systems, member of the National Academy of Science and Engineering,
chairman of the ProcessNet and EFCE working parties “Comminution and classification”, vice chairman of the Advisory Board Battery Research Germany, and chair of the PARTEC 2024, with 300+
publications, a Google Scholar H Index of 40, and over 6000 citations.

Carsten Schilde
Prof. Dr-Ing. Carsten Schilde studied bioengineering at the Technische Universität Braunschweig
and completed his PhD thesis on “Structure, Mechanics and Fracture of Nanoparticulate Aggregates” under Prof. Dr.-Ing. Arno Kwade. Later, he worked at the Institute for Particle Technology
as a research associate and was appointed Assistant Professor for “Particle Simulation & Functional
Structures” in 2017. In 2019, he received the Friedrich-Löffler award. His research focuses on the investigation of particulate systems and structures using computer simulation and artificial intelligence.
His main application fields are pharmaceuticals and composite materials. With over 60 publications,
he has a Google Scholar H Index of 18 and over 1000 citations.

25

KONA Powder and Particle Journal No. 41 (2024) 26–41/https://doi.org/10.14356/kona.2024012

Review Paper

Role of Powder Properties and Flowability in Polymer Selective
Laser Sintering—A Review †Xi Guo and Brij M. Moudgil *Department of Materials Science and Engineering, Center for Particulate and Surfactant Systems (CPaSS), University of Florida, USA

Polymer selective laser sintering (SLS) is an additive manufacturing
Final Part Properties
technology that involves the melting of a selected area of particles on
High Porosity
Low Porosity
the powder bed. A 3D component is then printed using layer-by-layer
sintering of the powder bed. SLS is considered one of the mostpromising technologies applicable to a variety of applications,
Powder Flowability
particularly for manufacturing customized design products with highPolymer melt
Polymer melt
geometric complexity, such as patient-specific designed implants,
Low Flowability
High Flowability
surgical tools. Currently, only a small number of polymers are available
that are suitable for SLS due to the complex multiple physicalInterparticle Forces
phenomena involved. Therefore, it is critical to develop new materials
in order to fully realize the potential of SLS technology for
manufacturing value-added customized products. For a given material,
Particle Properties
the quality of powder spreading in SLS plays a key role in printing
performance and is a precondition for new material development. The
aim of this review is to (1) present flowability characterization methods
suitable for SLS, (2) examine the influence of powder properties and flowability on laser–material interaction and the quality of the final
part, and (3) discuss the methods adopted in the literature to improve the quality of powder spreading.
Keywords: polymer selective laser sintering, powder property, flowability, powder packing

1. Introduction
Additive manufacturing (AM) is a group of solid
freeform fabrication methods used to build 3D objects
layer by layer based on computer-aided design files. The
layer-by-layer building process, which is the opposite of
traditional subtractive manufacturing, enables additive
manufacturing to create highly complex structures. AM
was introduced in the late 1980s (Ligon et al., 2017), primarily for building complex structure prototypes. With thedevelopment of technology, AM nowadays is capable ofbuilding products with various materials (polymers, metals, ceramics), processing desired properties and shapes.
The fundamental principle of AM process is to form
parts by joining materials together. In polymer additive
manufacturing, this is achieved through thermal reaction or
chemical reaction, or both. Current processes in polymer
AM include material extrusion, material jetting, powder
bed fusion, binder jetting, vat photopolymerization, andsheet lamination (ISO/ASTM, 2021). In selective laser
sintering (SLS), a laser is used to selectively melt or fuseparticles in the powder bed, layer by layer. SLS is one of

†

*

26

Received 25 July 2022; Accepted 22 September 2022
J-STAGE Advance published online 29 July 2023
Corresponding author: Brij M. Moudgil;
Add: Gainesville, FL 32611, USA
E-mail: bmoudgil@perc.ufl.edu
TEL: +1-352-846-1194 Fax: +1-352-846-1196

the most widely used AM technologies due to its high levelof accuracy, high productivity in manufacturing complex
structures, and good mechanical properties (Kruth et al.,
1998; Lupone et al., 2022).
To address the unmet needs in various industries, researchers have investigated the processability of several
polymers including Polyamides (PA) (Beitz et al., 2019;
Benedetti et al., 2019; Berretta et al., 2014; Drummer et al.,2010; Soe, 2012; Starr et al., 2011; Vasquez et al., 2011,2014), Polypropylene (Ituarte et al., 2018; Kleijnen et al.,2016, 2017; Tan et al., 2021; Wegner, 2016; Zhu et al.,2016), Polyetheretherketone (PEEK) (Benedetti et al.,2019; Berretta et al., 2014, 2016), Polyethylene (Drummer
et al., 2010), Polyoxymethylene (Drummer et al., 2010),
Poly-butylene terephthalate (Schmidt et al., 2016), Polyε-caprolactone (Williams et al., 2005), Polystyrene (Yan etal., 2011), Thermoplastic polyurethanes (Dadbakhsh et al.,2016; Vasquez et al., 2014; Verbelen et al., 2017; Yuan etal., 2017), Thermoplastic elastomers (Vasquez et al., 2014),
etc. However, it is worth noting that the SLS process currently has a limited number of commercially available
polymers. PA-related materials account for over 95 % of
the market (Goodridge and Ziegelmeier, 2017; Stansbury
and Idacavage, 2016). Semi-crystalline polymers such as
Polyamide 12 (PA12) are the material of choice in SLS for
the following reasons, (1) sharp melting peak to ensure
homogeneous melting, (2) large melting temperature (Tm)

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

to degradation temperature (Td) window to enable fullmelting, (3) a rapid viscosity drop once the crystalline region disappears enabling rapid particle coalescence(Drummer et al., 2010). The limited material option is a
result of the high powder production cost, lower final partmechanical properties, and dimensional accuracy compared to the traditional manufacturing processes.
The physical phenomena involved in SLS include particle spreading and deposition, laser material interaction,polymer melting and coalescence, and polymer crystallization. Each phenomenon spans different time scales and has
a significant impact on the final product properties(Bierwisch et al., 2021; Lupone et al., 2022). An in-depth
understanding of these phenomena is the key to improving
the final part properties and expanding the suitability ofcommercial powders for SLS applications.
A SLS process schematic and a process temperature
profile are depicted in Fig. 1. The process temperature
profile is embedded in a semi-crystalline polymer differential scanning calorimetry (DSC) graph to provide a better
understanding of polymer heating and cooling behavior
during SLS.
The SLS process initiates with a preheating step. An inert gas is continuously purged into the printing chamberfrom this step and throughout the entire printing process.
As shown in Fig. 1 diagram A, the polymer powder on the
powder bed is preheated to a temperature below the polymer’s Tm. This temperature is defined as the printing temperature. The preheating step holds significant importancefor several reasons: (1) it stabilizes the temperature of theentire instrument, (2) it minimizes the energy required for
melting the polymer in the target area, (3) it reduces thetemperature gradient between the melt area and the chamber air to prevent curling (Soe, 2012). It should be notedthat during the preheating stage, since the bed temperature

is close to the onset of melting temperature, the unmelted
powder may undergo physical and chemical changes after
printing (Goodridge et al., 2010; Kuehnlein et al., 2010)
and could reduce its reusability. The thermal properties andflowability of the polymer play essential roles in the preheating phase (Berretta et al., 2014; Drummer et al., 2010;
Tan et al., 2021; Verbelen et al., 2017).
The second phase is laser scanning (B in Fig. 1). Laser
scanning of the target area starts once the preheating temperature is stabilized. CO2 laser (10600 nm wavelength) is
the most common laser used in polymer SLS systems because of its high absorptance and low penetration depth in
the polymer (Jones, 2013). When the polymer molecular
structure interacts with the laser beam, which typically hasa size of 500 μm for a CO2 laser, heat is generated due tothe vibration of the polymer’s molecular structure. The
generated heat raises the temperature above the melting
point, but below the Td, as illustrated by point 2 in Fig. 1.
Upon heating, the polymer particles melt, resulting in viscous flow and coalescence. After laser scanning, the molten
area rapidly cools down to the printing temperature (point
3 in Fig. 1) (Greiner et al., 2019). Once the layer scanningis completed, the printing bed will move down one layer
and a new layer of powder is deposited on the printing bed
by a blade or roller. The laser scanning step is repeateduntil the designed part is fully printed (C in Fig. 1). During
the preheating phase as well as the later phases where there
is no polymer melting involved, powder deposition is primarily governed by powder–powder interactions (A1 in
Fig. 1). During the scanning phase, where there is polymer
melt on the top layer, powder deposition is mainly governed by powder–polymer melt interactions (B1 in Fig. 1).
Polymer flowability, optical properties, thermal properties,
and rheology are the main factors that impact the laser
scanning phase (Kruth et al., 2007; Laumer et al., 2016a;

Laser Beam

1

2

A

B

4

C

Recoating Blade

A1

Recoating Blade

Heat Flow

Roller

B1

3

4

1

2

D
3
Recoating on powder surface

Polymer melt

Recoating on melt surface

Fig. 1

Temperature

Polymer SLS process schematic.

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Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

Verbelen et al., 2016, 2017; Vock et al., 2019a; Wudy et al.,2015).
After the completion of a printing job, the entire machine, including the printed parts, undergoes a gradualcooling process until it reaches room temperature. The
slow cooling process allows the polymer chains to form acrystalline structure (point 4 in Fig. 1). Polymer crystallization plays an essential role in this step (Drummer et al.,2019; Goodridge and Ziegelmeier, 2017; Verbelen et al.,2016).
Fig. 2 illustrates the interplay between operational parameters of the SLS process, particle properties and material properties. Powder properties such as size, shape, size
distribution, and surface roughness, not only determine its
flowability but also affect the thermal and optical propertiesof the powder bed (Bierwisch et al., 2021; Myers et al.,2015).
An in-depth understanding of powder flowability is essential for designing a successful SLS material and productdevelopment scheme. However, due to the specific spreading mechanism involved in SLS, it is challenging to directly apply existing knowledge from traditional particle
science research to SLS systems. The present review aims
to provide an overview of the role of powder properties,
particle deposition, and flowability characteristics in designing a successful SLS system. Experimental analysis,
characterization methods, and computational modeling arereviewed to provide the current state of polymer powderresearch. Although it is challenging to establish comprehensive quantitative correlations between powder properties, powder deposition, process parameters, and final partproperties, the review strives to enhance the understandingof the factors that influence the SLS powder deposition
process and ultimately establish the sciencebased guidelines for new material development rather than
the trial-and-error approaches currently practiced.

2. Flowability overview
In SLS, the powder bed packing density is directly
linked to the achievable part density (Schmid et al., 2015).
Despite some applications where porous parts are desired(Yan et al., 2017), the main objective of powder depositionin SLS is to transfer the powder from a feed container to theprinting bed to form a homogenous and densely packed
layer. Therefore, a good flowing powder is a necessary
condition to constitute a suitable SLS material. Powder
flow properties and flowability are commonly used to evaluate the suitability of a powder for SLS. Additionally, thenewly deposited powder layer should exhibit uniform
thickness. According to Prescott et al. (2000), powder flow
properties are specific bulk characteristics and can be measured. Powder flow properties depend on the particle size
distribution, particle shape, surface morphology, density,
electrostatic charge, and environmental factors such astemperature and humidity. Powder flowability, on the other
hand, refers to the ability of a powder to flow in a desiredmanner in a specific piece of equipment (Prescott andBarnum, 2000), and depends on powder flow properties,
underlying surface layer morphology and spreading (recoating) mechanism. During recoating on top of the polymer melt scenario, the melt viscosity and surface tension of
the molten layer also impact the powder flowability.
From the force perspective, the flowability of SLS powder is influenced by inter-particle forces and external forces

Final Part Properties
Laser Parameters
Temperature
Layer Thickness

SLS Flowability

Layer Deposition

Rheological
Properties

Interparticle Force /Process Conditions
Temperature Layer Thickness
Humidity
Recoating Strategy
Gas flow

Powder
Properties
Particle Size Distribution
Shape
DensitySurface morphology

Fig. 2

28

Strength
Dimension
Porosity

Printing Parameters

Rheological
Properties

ThermalProperties

OpticalProperties

Melt Viscosity
Surface Tension

Heat Capacity
Conductivity
Latent Heat

Reflectance
AbsorptanceTransmittanceChemical Properties
Chemical Structure
Molecular Weight
Crystallization
Kinetics

Graphical representation of the relationships between material properties, printing parameters and final part properties.

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2.1 Flowability characterization

due to mechanical interlocking and layer recoating. Van der
Waals force, electrostatic force, liquid bridge formation(capillary force), and gravity are the main inter-particle
forces between spherical particles (Suzuki, 2019). The vander Waals forces are based on electric dipoles of atoms andmolecules. Their magnitude depends on particle size, distance and surface characteristics/morphology (Schulze etal., 2008). Liquid bridge force is formed by the surface
tension of the liquid between particles. Considering that theprinting temperature of the majority of SLS semicrystalline polymers is above 100 °C and inert gas is commonly used, the liquid bridge force can be neglected.
Electrostatic forces are generated by different electric potentials of particle surfaces (Schulze et al., 2008). Triboelectric charging of polymer powder occurs when thepolymer particles come into contact with materials of different dielectric constants (e.g., a metal component)(Tanoue et al., 1999). Due to their low conductivity, it iscommon practice to allow polymer powder to remain undisturbed for at least 12 hours before printing to reduce/
dissipate any electrostatic charge generated during powder
preparation. However, triboelectric charging during powder deposition is inevitable. Although the charge build-up
between particles improves powder spreading by reducing
the attractive van der Waals force and liquid bridge forces,
it leads to low packing density (Hesse et al., 2019). Understanding the relationships between these different forces iscritical to explaining some of the flowability issues encountered in SLS.

During the material development stage, powder flowability needs to be characterized and optimized before
printing. Although the flowability characterization may not
represent the powder flow conditions in the SLS process
because of the different force and flow mechanisms employed, it can still provide a good indication of the powder
flow behavior. Table 1 gives an overview of the flowability
characterization methods that have been used in polymer
SLS research.

2.2 Flowability measurement techniques
The two most used methods are the angle of repose andHausner ratio measurement techniques. Although different
setups can be used to measure the angle of repose(Schwedes, 2003), the static angle of repose is widely used
in SLS (Berretta et al., 2014; Montón Zarazaga et al., 2022;
Tan et al., 2017). In the static angle of repose measurements, powder freely flows through a funnel onto a plate.
The angle between the developed pile and the plate is defined as angle of repose (Spierings et al., 2016). A small
angle of repose represents good flowability. The Hausnerratio is defined as the ratio between the tapped and loosebulk density of the powder. It describes powder compressibility and flowability. Geldart et al. (2006) found a linear
relationship between angle of repose and Hausner ratio.
Although being widely used in SLS (Arai et al., 2017,2019; Chatham et al., 2019; Schmid et al., 2013; Wegner,
2021; Ziegelmeier et al., 2015), the Hausner ratio wasfound to be inaccurate in predicting the flowability ofhigh-density polyethylene composite and polypropylene

Methods

Table 1 list of powder flowability characterization methods in polymer SLS.

Angle ofrepose(ASTM
D6393)

Hausner ratio(ASTM D748109) / Carr index(ASTM D6393)

Shear cell
(ASTM
D6773)

TensiletesterPowder rheometer (ASTM
D7891)

Revolution powder
analyzer

Modified film applicator

References

Parameters

camera▪Angle ofrepose▪ Hausner ratio▪ Carr index (C)- loose bulk
density- tapped bulk
density▪ Shearforce▪ Yield loci

▪ Adhesionforce▪ Basic flowability energy▪ Specific energy▪ Conditioned
bulk density▪ Avalanche angle▪ Surface fractal
volume▪ Expansion ratio▪ Degree of coverage▪ Packing density▪ Packing ratio(Berretta etal., 2014;
MontónZarazaga etal., 2022;
Tan et al.,2017)

(Arai et al., 2017, (Ruggi et
2019; Chatham et al., 2020a,al., 2019; Schmid 2020b)
et al., 2013, 2019;
Wegner, 2021;
Ziegelmeier etal., 2015)(Schmidt etal., 2014)(Ziegelmeier etal., 2013, 2015)(Amado et al., 2011;
Amado Becker, 2016;
Chatham et al., 2019;
Sassaman et al.,2022)

(Laumer et al., 2016b;
Van den Eynde et al.,2015; Van den Eynde
et al., 2017; Verbelen
et al., 2016)

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Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

Review Paperof the most important indices of the rotating powder. The
flow of powder in this method is considered closer to SLS
process conditions than the other techniques (Spierings etal., 2016). The high temperature measurement capability ofthis method can provide more insights into powder flow
behaviors under SLS temperature conditions (Amado
Tapped Bulk Density  Loose Bulk Density
Carr Compressibility
100
Becker,2016).
Tapped Bulk Density
To provide a direct assessment of the powder flowability,(1)
Tapped Bulk Density  Loose Bulk Density
Carr Compressibility
100some researchers modified a film applicator into a powder
Tapped Bulk Density
spreader with a similar powder spreading mechanism as in
SLS (Laumer et al., 2016b; Verbelen et al., 2016; Van den
In addition to the Carr Compressibility, the measurement
Eynde et al., 2015). A basic powder spreader enables visualof (1)
Carr Angle of Repose is also described in the ASTM
observation of the spreading condition. Van den Eynde et
D6393 (2021) to test the flowability and floodability ofal. (2015) set up a powder bed plate on a balance so thatbulk
solidsusing
Carr
Indicesmethod.
1)
layer weight and packing density could be measured in situ
Tensile strength can also be used to measure SLS powto assess powder flowability. Furthermore, a powder
der flowability (Schmidt et al., 2014). This method requires
spreader with high-temperature capability (up to 140 °C)
a tensile tester and a smooth loose powder bed. The tensile
testing head is coated with jelly. The test starts with thewas developed (Van den Eynde et al., 2017). This modificatesting head in contact with the powder surface, the firsttion not only facilitated the characterization of powder forlayer of the powder in the container adheres to the jellyrecoating purposes but also allowed for the evaluation ofcoated testing head. When the powder container moves
melt surface recoating for low melting temperature polydownward, the force between the stamped powder layer
mers.
and the remaining powder is measured. The measured force
is a combination of gravity and adhesion force between the
2.3 Comparative features of various techniques
powder particles. This method provides a good insight into
With the availability of various characterization meththe fundamental interparticle force relationship. However,ods, it is important to understand their relationships, advantages, disadvantages, and differences. Ziegelmeier et al.
it is limited in indicating a suitable powder flow behavior.
(2013) compared cohesion, flowability and packing effiShear testers are commonly used to evaluate the powder
ciency of polymer powders with different characterization
flowability under well-defined shear conditions. Powder
methods—rotational revolution powder analyzer, powder
rheological properties like cohesive strength and wall fricrheometer and the Hausner Ratio technique. Different partion under shear can be measured in a shear tester to repreticle size distributions of a cryogenically ground polyuresent flowability. Ruggi et al. (2020a, 2020b) characterizedthane powder and standard PA12 were used to study the
SLS polymers with a modified high-temperature ring shear
effect of both particle size distributions and particle shape.
tester. Among different types of shear testers, ring shear
The revolution powder analyzer showed the best reproductesters not only provide homogeneous shear displacementible and reliable results under all experimental conditions(Schulze et al., 2008), but they also have the capability of
examined.
low normal stress measurements (Vock et al., 2019b). Although the force conditions in ring shear testers are closer
Krantz et al. (2009) investigated the relationship beto SLS printing conditions compared to other shear testers,
tween different characterization techniques using two difthe flow mechanism of the shear tester is different from thatferent formulations of powders with particle sizes ranging
encountered in SLS (Schmidt et al., 2019). Another characbetween 22 to 31 μm. Characterization methods included
terization method involving shear stress is the powder rheangle of repose, powder rheometer and revolution powderometer. It measures the resistance of a powder to flow
analyzer. A linear relationship between angle of repose andagainst an upward or downward rotating helix under differavalanche angle was discovered regardless of powder forent consolidation conditions. Under specific consolidation
mulation. This is because both techniques involve similarand shear conditions, the powder rheometer and shear cellforce conditions. Additionally, a linear relationship wasdata correlate well with each other (Freeman, 2007).
discovered between angle of repose and cohesion (powder
More recently, the rotational revolution powder analyzer
rheometer characterization index) for individual powder
has become more popular (Amado et al., 2011; Amado
types. However, the trend line for different powders did not
Becker, 2016; Chatham et al., 2019; Sassaman et al., 2022).
overlap because of the different forces encountered in the
It consists of a transparent rotating drum and a camera that
two techniques. The authors concluded that matching therecords the powder flow conditions in the drum. The
measured and process force conditions is critical for reliAvalanche angle, which is the maximum angle of the powable flowability characterization. Furthermore, they emder surface before an avalanche occurs, is considered one
phasized that utilizing multiple characterization techniques
composite materials (Laumer et al., 2016b). Anothermethod that is similar to Hausner ratio but less common in
SLS is the Carr index(C) (Schmid et al., 2019), which is theCarr Compressibility as referenced in ASTM D6393(2021) and can be expressed as,30

Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

provides a more comprehensive understanding of powder
flowability compared to relying on a single techniquealone.
The above discussion highlights that each flowability
characterization technique focuses on examining powder
flowability from a specific perspective related to a particular flow mechanism. The modified film applicator simulates the SLS flow behavior; however, the setup is noteasily accessible. Therefore, it is important to obtain a better understanding of powder flowability by applying multiple flowability characterization techniques during the newmaterial development.

3. Role of powder properties

3.1 Particle size distribution (PSD)
To achieve high surface quality and printing accuracy,
smaller particle sizes are theoretically preferred. However,for fine powders, van der Waals forces are significantly
greater than gravity which causes powder agglomeration
issues leading to nonuniformity of layer thickness, andparticle packing inhomogeneity during flow (Krantz et al.,2009). While polydisperse powders can benefit particle
packing in free flow conditions (McGEARY, 1961), it isoften limited by polymer powder production methods.
From the laser material interactions perspective, a narrow
PSD is preferred to obtain homogeneous melting (Berretta
et al., 2014; Wegner, 2021). With the above considerations,a particle size range of 45 to 90 μm is usually preferred(Goodridge and Ziegelmeier, 2017), although some authors
have reported this range to be from 20 to 80 μm (Schmid etal., 2015; Vock et al., 2019b).
Beitz et al. (2019) sieved PA12 into three grades: PA12
Coarse (D50 = 56.80 μm), PA12 Original (D50 = 51.14 μm)
and PA12 Fine (D50 = 46.03 μm). All grades had an identical level of PSD span, which is defined as (D90 − D10)/D50.
The powders were characterized by Hausner ratio and used
for laser printing under the same conditions. The threegrades of PA12 showed no difference in either flowability
or surface roughness of the printed parts. However, it isimportant to note that this conclusion should be consideredvalid only within the particle size range examined in thisstudy. A larger difference in D50 could lead to different results.
As part of a flowability study by Van den Eynde et al.
(2015), standard PA12 powder with a mass-median diameter of 59 μm was compared with a smaller average particle
size PA12 (around 42 μm). The PSD span of standard PA12
(0.84) was higher than the smaller PA12 (0.58). A customized powder spreader setup that simulated the SLS spreading process was used to characterize flowability. The
smaller PA12 showed lower packing density. Compared to
the results from Beitz et al., it is inferred that the span of
PSD plays a vital role in flowability; with a similar PA12
particle size in both studies, a smaller span could result in

Review Paper

lower packing density.
It is noteworthy that the effect of PSD span has also beenreported in a review primarily focusing on ceramic andmetal powders (Vock et al., 2019b). With increasing PSD
span, the printed part density and surface quality increased,
but the mechanical properties decreased. This indicates thata higher PSD span improves particle packing and part
density, however, it can lead to inhomogeneous melting
and reduced mechanical properties. However, in a comparison between two commercially available PA12 powders,Schmid et al. (2017) suggested that powder with a smaller
span exhibits better flowability and high packing density.
Even though the different inter-particle forces could lead to
opposite results, more systematic studies are needed to
improve the understanding of the flow mechanism during
the polymer SLS process.
Multiple studies have been conducted to identify a suitable PSD for SLS. Ziegelmeier et al. (2015) compared theflowability and printability of thermoplastic polyurethane(TPU) and thermoplastic elastomer Duraform Flex (DF).
Except for the original particle size fraction, two additional
fractions were obtained by sieving out particles below
25 μm or 45 μm. The overall packing density, flowability,
printed part surface roughness, ultimate tensile strengthand the elongation at break were found to be higher with adecreasing number of fine particles. It was concluded thatthe gravitational forces increase with particle size, resulting
in reduced cohesiveness and improved flowability. Similarly, Schmid et al. (2015) compared two powders withsimilar volume distribution but different number distribution. The powder with a significant number of small particles (below 20 μm) failed the processability testing due toa higher ratio of cohesion to gravity forces.
On the contrary, other researchers reported the benefitsof small particles for powder flow. Verbelen et al. (2016)
compared PA6, PA11 and PA12 from different manufacturers with PA11 consisting of rough particles with sharpedges and a large fraction of smaller particles(D10 = 20.6 μm). Surprisingly, the rough PA11 had a higher
packing density. One of the reasons the authors concluded
was that the large fraction of smaller particles allowed
more efficient packing at the given layer thickness. Although no quantified values were reported, this study alongwith the previous two studies emphasized that a smaller
number of fine particles in the distribution promotes powder flow possibly due to the ball bearing effect, while anexcess number of fine particles hinders flow due to agglomeration.
It should be noted that the PSD directly impacts the SLS
printing results by changing the powder bed packing density. Indirectly, PSD changes the thermal and optical properties of the powder bed. The powder thermal conductivity
is significantly lower than the bulk because of the high porosity of powder beds (Gong et al., 2013). Sillani et al.

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reported a positive linear relationship between packing
density and thermal conductivity for several SLS polymer
powders (Sillani et al., 2021). The porosity also changes
powder optical properties which in turn affects the amountof heat energy generated by laser–material interaction. In
an infrared spectroscopy study, quantitative infrared directional–hemispherical reflectance spectra were obtained using a commercial integrating sphere (Myers et al., 2015).
The results suggested that the optical reflectivity drops withincreased particle size for most wavelengths. With a higher
optical reflectivity, higher laser energy is needed to compensate for the energy loss. Overall, the change of powder
optical reflectivity and thermal conductivity resulting fromthe PSD should be considered when selecting the laser
power and laser scanning rate for a given material.

3.2 The impact of Particle morphology

In free-flowing particle packing, particles with highroundness and low roughness exhibit reduced mechanicalinterlocking and increased packing density (German, 1989;
Hettiarachchi and Mampearachchi, 2020). Thus, particle
morphology is important for the SLS process as well
(Schmid et al., 2014; Van den Eynde et al., 2015; Verbelen
et al., 2016).
In the spreading study conducted by Van den Eynde et al.
(2015), medium roundness standard PA12, medium roundness PA12 with smoother edges, high roundness monodisperse Polystyrene (PS) and low roundness TPU werecompared with a customized powder spreader. The highroundness PS exhibited the highest packing density andlayer smoothness. Smoother edge PA12 had a higher packing density than the standard PA12. The low roundness
TPU had the lowest packing density and resulted in incomplete layers. Accordingly, the high roundness and low
roughness were emphasized as the optimal particle morphology conditions for SLS.
However, the high roundness and low roughness particles are not always available for SLS due to the limitationof polymer powder production methods. The current commercially available SLS powder is produced by three main
methods: co-extrusion of two non-compatible polymers,
precipitation of polymer solutions and cryogenic milling oflarge polymer granules. Co-extrusion methods produce
spherical and smoother particles. However, only limited
polymers can be produced by this method, one example is
Polypropylene from IRPD AG. The most popular potato
shape PA12 powders, such as PA2200 from EOS GmbH, isproduced by the precipitation method. Cryogenic milling
can process a wide range of polymers by milling below
glass transition temperatures. However, milled particles
result in irregular shapes and rough surfaces due to therandom mechanical force application during the size reduction process. Cryogenic milling also generates a large
amount of fine particles which reduces the powder flow-

32

spherical particles

potato shape particles cryogenic milled particles

Fig. 3 Typical SLS powder particle shapes (Reprinted with permission
from AIP Conference Proceedings “open access”, Schmid et al., 2015).

aerosol inflow

N2
sintered
metal platesheath air
outletaerosol
outletheatingzones

Fig. 4 Schematic drawing of downer reactor (Reprinted with permission from Procedia Engineering “open access”, Sachs et al., 2015).

ability (Schmid et al., 2014). Some typical SLS powder
particle shapes are shown in Fig. 3.
In addition to the commercially available methods, multiple techniques have been developed in the literature to
improve flowability by generating high-roundness particles.
Berretta et al. (2014) found that virgin PEEK particles
did not yield a smooth powder bed during powder deposition. The powder was annealed at a temperature above its
glass transition temperature for 24 hours in an airventilated oven, left to cool down naturally and sieved. The
thermal treatment improved particle roundness and flowability significantly, ultimately enabling a smooth spreading within the printing system.
Downer reactor as shown in Fig. 4, which utilizes theeffect of the surface tension of the molten polymer, is commonly used to smooth irregular polymer particles (Sachs etal., 2015). Schmidt et al. (2014; 2016) demonstrated theReview Paper

Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

rounding process of polystyrene (PS) and polybutylene
terephthalate (PBT) powder and introduced a procedure to
generate spherical particles for SLS. The polymer microparticles were produced by wet grinding first. Then theparticle roundness was improved by passing them in aheated downer reactor. Finally, hydrophobic fumed silica
was mixed with the polymer powder to further improve theflowability by reducing the interparticle van der Waals
forces.

4. The impact of processing conditions
During layer deposition, factors such as layer thickness,spreading speed, spreading tool, and temperature are someof the important operational parameters that affect SLS
process performance. The impact of these process conditions has also been reported in the literature.

4.1 Layer thickness

For given powder flow characteristics, the desired printing layer thickness depends on different product requirements. A smaller layer thickness is desired to improve
printing resolution, but it is limited by the particle size
discussed in the previous section. On the other hand, alarger layer thickness reduces printing time. The decreased
printing time improves manufacturing efficiency and reduces overall cost. In general, the layer thickness in polymer SLS ranges from 80 to 150 μm. During the depositionof the subsequent layer, melting of the previous layer
causes shrinkage in the bed thickness. New powder needs
to form a smooth layer on top of the sintered bed. Static anddynamic wall effects are the other main mechanisms ofvoids formation during the sintering process (Chen et al.,2019). The static wall voids in SLS occur among particles,
printing bed and spreading tool due to the round shape of
the particles. On the other hand, dynamic wall voids arecaused by the arches formed between particles during the
spreading motion. In the scenario where particle size andshape cannot be modified to improve packing, increasing
the layer thickness can improve the packing density by reducing both the static and dynamic wall effects. Dechet etal. (2020) utilized a layer thickness of 1.5 times the D90,
corresponding to around 300 μm, in PLLA development.
Although this significantly thicker layer provided promising flowability, it compromised the printing resolution.

4.2 Spreading blade/roller
Blade and roller (counter-rotating with respect to theadvancement direction) are the most common powderspreading tools in SLS systems. The design and movementof the spreading tool also affect the powder packing condition.
Beitz et al. (2019) compared three blade geometries in
PA12 printing, including flat, round and sharp bottom. The
flat bottom blade shape resulted in the most uniform anddense powder bed due to the larger horizontal contact zone
between the blade and the powder bed.
Haeri et al. (2017) simulated the spreading tool and its
speed using discrete element simulation and validated thepredicted values with experimental results. The use of aroller produced better bed quality compared to the flat bottom blade because of the adequate contact of a roller, which
prevented particle dragging. It was also found that thelower roller translational speed produced better powder
bed quality compared to a higher speed. It should be notedthat non-spherical (rod-shaped) particles were used in thiswork which might not represent the spreading conditions ofspherical or potato shape particles.
Drummer et al. (2015) investigated the effect of spreading speed with both blade and roller systems. The reproducibility of three spreading speeds (125 mm/s, 250 mm/s,
500 mm/s) was analyzed. An optimal spreading speed of
250 mm/s was found independent of spreading tools. The
optimal speed was based on the curling phenomenon due tothe temperature gradient after laser scanning as shown in
Fig. 5. With a low speed, the long roller and molten areainteraction time led to significant cooling of the molten
area. Consequently, curling got worse and the warped layer
became stuck to the roller and failed to print. With highspeed, the varying compression force could shift theslightly curled section and fail the print.
Niino and Sato (2009) proposed a powder compaction
method by using a two-steps roller spreading technique asshown in Fig. 6. Compaction was carried out by using aroller of whose rotation speed was controlled independentof its traversing speed. After a traditional roller spreading
and layer formation, the powder bed moved up and an additional roller spreading step using the same roller was applied to compact the powder bed. This additional process
improved the packing density of the powder bed by a factorof 20 % and reduced residual porosity by 30 %.

V<

Fig. 5

V=

opt

opt

V>

opt

The effect of powder recoating speed on molten areas.

Fig. 6

Powder bed compacting step.

33

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Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

4.3 Other in-process factors
In addition to the powder properties and layer spreadingmechanisms, there are other factors in the printing processthat also affect powder flowability. One of the most crucial
factors is the elevated temperature during the SLS process.
The effect of elevated temperature on the flowability isthreefold. (1) The polymer particles become softer thereby
introducing additional forces by changing contact area andfriction between particles during powder flow. (2) Humidity induces cohesive forces between the particles andstrongly affects the flow of powders (Rescaglio et al.,2017). For the polymer with a melting temperature above
100 °C, the moisture in the powder evaporates during printing and liquid bridge force gets minimized. Both changes
under the elevated temperature cannot be captured with themost powder flowability measurements. (3) The extended
long time under elevated temperatures causes changes in
the properties of unused powder, which affects its flowability during recycling.
Van den Eynde et al. (2017) examined PA12 flowability
under elevated temperatures with a customized powder
spreader. A slight increase in the packing condition wasobserved above the glass transition temperature, indicatingthat the softening of the polymer increased the powder
compressibility. Similarly, Amado et al. (2014) comparedPA12 and a co-polypropylene using a modified revolution
powder analyzer. The results showed PA12 has reduced
surface fractal and improved flowability when processtemperatures were above its glass transition temperature.
Ruggi et al. (2020a; 2020b) evaluated PA12 flowability
with a high-temperature annular shear cell. The followability was characterized using the “free-flowing range”
method (ASTM D6773). The results showed that the flowability improved from 25 °C to 100 °C, then reduced from
100 °C to 160 °C. The liquid bridges present at 25 °C were
proposed as the main limitation of the free flow. The flowability increased with reduced moisture from 25 °C to
100 °C. From 100 °C to 160 °C, powder became cohesive
towards close to melting temperature because the softened
polymer induced more contact points and contact area
which reduced the flowability.
In the earlier discussion of morphology, it was mentioned that PEEK flowability can be optimized by hightemperature annealing. However, high-temperatureannealing is not always desired. In a SLS print, most of thepowder is not melted. It is important to recycle the unmelted
powder to reduce the production cost (Dotchev and Yusoff,2009). The recycling process involves sieving and storing
the unmelted powder, which is then mixed with fresh powder for the next cycle of use. The purpose of powder mixing
is to neutralize the changes in the properties of used powderthat occur as a result of prolonged exposure to elevatedtemperatures. The mixing rate depends on the extent ofproperty change in the used powder, and not all the poly-34

mers can be recycled (Dadbakhsh et al., 2017; Mwania etal., 2021). The change in the chemical structure, thermal
properties and rheology properties of the used polymer
have been well researched in the literature, especially for
PA12 (Dooher et al., 2021; Kuehnlein et al., 2010; Wudy etal., 2014). Mielicki et al. (2015) reported a higher PA12
particle shape deviation for particle size bigger than
100 μm and longer aging times. Dadbakhsh et al. (2017)
observed an increased cracking in used PA12 from scanning electron microscope images. The cracks might becaused by the evaporation of remaining alcohol and absorbed moisture and/or the subsequent expansion/
shrinkage steps of the process cycles. From the powder
flowability perspective, Wegner et al. (2014) reported reduced flowability of used polypropylene (PP) based on the
Hausner-ratio method. On the contrary, Yang et al. (2021)
reported an improved flowability of used PA12 based on
the powder rheometer measurement. The discrepancy inflowability results could be attributed to the different measurement methods employed, where the simulated forced
flow in a powder rheometer may yield different outcomescompared to free flow and tapped-based Hausner ratio
methods.
In our study, both new and used PA12 were evaluatedwith the Hausner-ratio method. As shown in Table 2, new
PA12 exhibits a lower Hausner-ratio compared to used
PA12, which suggested a reduced flowability of used PA12.
The used PA12, on the other hand, has a higher tapped
density. Comparing this with the higher flowability in thepreviously mentioned PA12 powder rheometer measurement results, there could be a similarity between the tapped
force during tapped density measurement and the simulated forced flow in the powder rheometer measurement.
Systematic studies of the triboelectric phenomena inpolymer SLS are rare. Recently, Hesse et al. (2019) investigated the triboelectric charge of fresh and used PA12. A
powder spreading setup with the electrostatic voltmeter
was used to compare both fresh and aged PA12. The electrostatic charge build-up of used PA12 was found to besignificantly higher than the fresh PA12. The degradationof an antistatic agent in the powder, or the electrical properties change due to the post-condensation were the reasonscited for the observation according to the author. This

Table 2 Hausner ratio of new and used PA12. The raw data on the
Hausner ratio is available publicly at https://doi.org/10.50931/data.
kona.23741511

Material

Loose bulk
density(g/cm3)

Tapped bulk
density(g/cm3)Hausner ratio

New PA12

0.44

0.51

1.15 ± 0.01

Used PA12

0.42

0.52

1.23 ± 0.02

Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

finding is important for the analysis of powder flowability
and the development of powder recycling strategies.

5. Additives

Another approach to improve or modify material flow
properties is combining additives with established SLS
materials (Arai et al., 2018; Gulotty et al., 2013; Mousah,
2011; Sivadas et al., 2021; Tolochko et al., 2000; Yan et al.,2009; Yuan et al., 2019). Additives, especially hydrophobic
fumed silica, have been widely used as flow aids to improve powder flowability (Berretta et al., 2014; Laumer etal., 2013; Schmidt et al., 2014, 2016; Verbelen et al., 2016).
The presence of the flow aids reduces the interparticle vander Waals forces (Schmidt et al., 2014) and the effects ofelectrostatic charges (Lexow and Drummer, 2016).
Considering the significantly small particle size of flow
aids, they have a large surface-to-volume ratio, and only asmall percentage needs to be added to the polymer matrix.
The optimal amount of flow aids is of great interest.
Laumer et al. (2016b) investigated the effect of flow aids
content in high-density polyethylene (PE-HD, D50 = 57 μm)
and polypropylene (PP, D50 = 100 μm). Weight concentrations of 0.0 %, 0.1 %. 0.25 % and 1.0 % of nano-scaled
fumed silica were mixed with the polymer by dry mixing.
The Hausner ratio and degree of coverage were measured
to evaluate flowability. The degree of coverage measurement was done by a customized powder spreader. The results showed that the addition of fumed silica improved
overall flowability. However, the concertation effect differed between the two characterizations. The Hausner ratio
increased with increasing concentration, with 0.1 % as the
optimal concentration. On the other hand, in PE-HD degreeof coverage testing, saturation was observed at 0.25 % anddegree of coverage decreased above this concentration.
The proposed explanation was that the increased amount offumed silica after saturation caused the separation of fumed
silica and polymer particles, and/or the fumed silica agglomeration reduced flowability. On the contrary, no satu-

Fig. 7

Review Paper

ration was found in the PP system. The degree of coveragecontinuously increased with the increase in fumed silica
amount, which could be attributed to the different particle
sizes and inter-particle forces between the two polymers.
Similarly, Kleijnen et al. (2019) reported the effect offume silica concentration ranging from 0.005 % to 0.5 % ina PBT (D50 = 41.5 μm) powder system. Hausner ratio and arevolution powder analyzer were used to characterize theflowability. The results showed that the flowability did notchange below 0.01 wt%, increased from 0.01 wt% to
0.1 wt%, then reached a plateau. Additionally, SEM images
revealed that at 0.5 wt% of additive, the polymer surfaces
were fully covered and aggregates were formed, which
could justify the flowability plateau.

6. White spot
Besides the lack of a comprehensive understanding of
powder flow mechanisms discussed in the previous sections, there are two other major areas that require further
investigation.
Most of the existing powder flowability studies wereconducted to improve particle packing. However, there aresituations where a low packing density can be advantageous, such as when reducing printing time or achieving a
porous product is desired. According to Beer-Lambert law,
the laser intensity along with the powder depth as it transmits into a media decays exponentially (Osmanlic et al.,2018; Xin et al., 2017). A low particle packing density allows for a slow decay of the laser, which leads to deeper
melting depth compared to a high powder packing density
bed as shown in Fig. 7. A larger layer thickness can be
employed in this condition which reduces the overall printing time. However, there are two potential penalties forprinting with a low packing density. First, the high laser
scattering caused by low packing density could lead to issues with printed part dimension, such as over-sintering.
Second, extremely high melting depth could result in highsurface temperatures, potentially leading to polymer

Laser–powder interaction under different packing densities (Reprinted with permission from Polymers “open access”, Osmanlic et al., 2018).

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Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41
Table 3 The effect of powder properties and process conditions on SLS–other polymers.
SLS process
Factors
Powderproperty- PSD

Optimal range+ Span+ D50+ Fine particles

Powderproperty- Morphology

Recoating

Process
conditions

Particle
additives

Flowability

Layer smoothness+ Roundness

High roundness(Berretta et al.,2014; Haeri etal., 2017; Van
den Eynde etal., 2015)+ Roughness

Low roughness(Van den Eynde
et al., 2015)+ Layerthickness

1. Detailed
design ofexperimentto find out
the optimal
PSD

0 (PA12) (Beitz etal., 2019)+ (PA12) (Vanden Eynde etal., 2015)

0 Surface roughness (PA12) (Beitz
et al., 2019)− (TPU, TPE)
(Ziegelmeier et al.,
2015)
− (Schmid et al.,2015)

− (TPU, TPE)
(Ziegelmeier etal., 2015)+ (PA11, PA12)(Verbelen et al.,2016)

− Tensile strength(TPU, TPE)
(Ziegelmeier et al.,2015)
− Surface roughness (TPU, TPE)
(Ziegelmeier et al.,2015)
0 Shore hardness(TPU, TPE)
(Ziegelmeier et al.,2015)

2. Improved
polymer
powderproduction
methods+ (PEEK) (Berretta
et al., 2014)+ (Haeri et al., 2017)+ (PA12) (Vanden Eynde etal., 2015)+ (Haeri et al.,2017)
+ (PA12) (Vanden Eynde etal., 2015)− (PA12) (Vanden Eynde etal., 2015)+ Blade flatness

Flat (Beitz etal., 2019)+ (PA12) (Beitz
et al., 2019)+ Roller insteadof blade

Roller (Haeri etal., 2017)+ (Haeri et al.,2017)

+ Recoating
speed

250 mm/s(Drummer etal., 2015)− (Simulation)(Haeri et al.,2017)+− (PA12)(Drummer etal., 2015)+ (PA12) (Amado etal., 2014)+− (PA12) (Ruggi etal., 2020a, 2020b)+ ReuseFresh Powder(Wegner andÜnlü, 2016)+ Concentration

0.25 wt%
Fumed silica in
HDPE, 1 wt.%
Fumed silica in
PP (Laumer etal., 2016b)
0.1 wt% Fumed
silica in PBT(Kleijnen et al.,2019)

Researchneeds− Homogeneous
melting [49]+ (PLLA) (Dechet etal., 2020)+ (HDPE, PP)(Laumer et al., 2016b)+ Temperature

SLS printed part
(Caused byflowability change)+ (PA12) (Vanden Eynde etal., 2015)− (PA12)(Schmid et al.,2017)

45 to 90 μm(Goodridge andZiegelmeier,2017).
20 to 80 μm(Schmid et al.,2015; Vock etal., 2019b)(General guild
line, No
experimental
data support)

Packing density+ (PA12) (Beitz
et al., 2019)

3. Lowpackingdensity andlarge layer
thickness
scenario
4. Powder–polymer melt
recoatingscenario+ (PA12) (Vanden Eynde etal., 2017)− (PP) (Wegner andÜnlü, 2016)+ (PA12) (Yang etal., 2021)

5. Aging
effect on
powderpropertychange indifferent
polymersystems+− (Fumed
silica in HDPE,
PP) (Laumer etal., 2016b)(Fumed silica in
PBT) (Kleijnen
et al., 2019)“+” denotes a positive effect with an increased factor, “−” a negative effect with an increased factor, “0” for no effect. “+−” denotes a
positive, then a negative effect with an increased factor. “()” denoted the polymer used in the cited work.

36

Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

degradation. The detailed impact of packing density, layer
thickness and laser parameters on polymer coalescence is
not yet well understood and requires further systematic investigations.
Although studies have been conducted on the relationship between powder flow conditions and printed partproperties, there remains a significant knowledge gap regarding powder–polymer melt recoating, as shown in
Fig. 1 diagram B1. This fundamentally differs from thewidely investigated powder flow behavior on flat powder
surfaces, which is a significantly different environment
than the relatively uneven polymer melt surfaces in realsystems. From a powder flowability perspective, most researchers have analyzed the flowability based on a designated single-layer thickness. However, the layer thickness
would be different after melting. The powder packing density is about 40–50 % (Van den Eynde et al., 2015). During
melting, the powder layer sinks due to the filling of gaps
between particles. This phenomenon leads to powder layerheight changes for the next powder deposition. In the caseof metal SLS, this powder layer height is 4–5 times thedesigned layer thickness (Wischeropp et al., 2019). The
dramatic increase in the layer thickness could change powder flow behavior. Furthermore, Mielicki et al. (2015) discovered that the temperature of the polymer melt, 10
seconds after laser exposure, is close to the printing temperature, indicating that the polymer is in a viscous flow
undercooling stage during the next layer powder deposition. Powder deposition on a surface under viscous flow
conditions encounters different force conditions compared
to deposition on a cooled solid surface. From a polymer
rheology perspective, most of the research has focused on
the polymer melt coalescence (Haworth et al., 2013;
Mielicki et al., 2012), except for a study by Wudy et al.
(2015) who investigated the effect of different polymer
melt surface tension conditions on the powder bed formation. The opposite scenario, where powder flows on thepolymer melt, remains unexplored.

7. Conclusion and future perspectives
Polymer selective laser sintering is a powder-based additive manufacturing technology. In this paper, we have presented a review focusing on one of the most important
aspects of SLS: powder flowability. The powder flowability determines layer packing and the final printed partproperties. Besides a brief discussion of the powder flowability characterization methods used in polymer SLS, theprimary objective of the review was to provide a better
understanding of the powder flow mechanism during theprinting process. The powder properties such as PSD andparticle morphology play important roles in flowability.
The effect of powder properties and process conditions
on SLS process performance, based on the literature citedin this review, is summarized in Table 3. Some factors ex-

Review Paper

hibit contradictory effects when viewed from different
perspectives., e.g., PSD span improves packing but results
in inhomogeneous melting. There is also a delta between
the findings for polymer and metal/ceramic regarding theeffect of PSD as discussed in the previous section. The
current research on polymer SLS is constrained by thelimited variety of powder properties options i.e., availability of spherical, and smooth particles with controllable
particle sizes. It is essential to improve existing polymer
powder production methods and develop new methods sothat the desired powder properties criteria for optimal printing conditions can be developed.
As discussed in the above sections, all material properties contribute to a successful SLS print. Regarding thepowder properties and flowability:• A smooth powder layer formation is the precondition of
the layer-by-layer process. It is influenced by powder
flowability, morphology and process conditions. The useof an optimal PSD (45–90 μm or 20–80 μm), highroundness particles or an appropriate concentration offlow aids often leads to an acceptable layer formation.
• The powder packing density is influenced by the same
factors as powder flowability and can be improved byutilizing an appropriate PSD, high roundness particles
and an optimal concentration of flow aids. The packing
density affects the thermal and optical properties of thepowder bed, which cause a direct impact on the laser–material interaction as shown in Fig. 7. By tuning laserparameters, process conditions and adding absorption
intensifiers (Laumer et al., 2013), successful print properties can be achieved even with powder beds that have
low packing densities.
In addition to experimental studies, simulations andmodeling efforts are needed to provide further insights todevelop meaningful guidelines for achieving optimal powder properties and successful process performance.
Considering that PA and its composites account for 95 %of the SLS polymer commercial market, there is a need todevelop other polymers to meet diverse product property
requirements and expand the range of SLS applications.
The current understanding of polymer processing phenomena, including flowability, is primarily based on PA12. As acommon approach, investigating the printing mechanismof PA12 and using it as a basis for developing guidelines
for new materials is often employed. Without the development of universally quantified criteria for powder/material
properties, the trial-and-error method will continue to be
the most common approach for new material developmentin the industry. It is also critical to expand the investigationof other polymers, from feasibility tests to fundamental
failure mode analysis, to gain a deeper understanding of the
SLS process.

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Xi Guo et al. / KONA Powder and Particle Journal No. 41 (2024) 26–41

Data Availability Statement
The raw data on Polyamide 12 Hausner ratio is available
publicly in J-STAGE Data (https://doi.org/10.50931/data.
kona.23741511).

Acknowledgments

The authors acknowledge the financial support of the
National Science Foundation Center of Particulate andSurfactant Science (CPaSS) and the industry members(NSF Award #1362060). Any opinions, findings, and conclusions or recommendations expressed in this material arethose of the authors and do not necessarily reflect the viewsof the National Science Foundation or the CPaSS industry
members.

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Authors’ Short Biographies
Xi GuoXi Guo received his bachelor’s degree in process equipment and control engineering from the
Beijing University of Chemical Technology in 2014. He went to graduate school at the University of
Florida and received his master’s degree in mechanical engineering in 2016. Afterward, he worked
for Johnson and Johnson as a materials engineer. In 2022, he completed his doctoral degree under theadvisement of Dr. Brij M. Moudgil, Department of Materials Science and Engineering, University of
Florida. His current research focus is on polymer selective laser sintering.

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, andremoval of microbes from surfaces, synthesis of functionalized nanoparticles, antiscaling and surfactant mediated corrosion inhibitors, photocatalytic degradation of hazardous microbes, and nanotoxicity. He has published more than 400 technical papers and has been awarded over 25 patents. He isa member of the U.S National Academy of Engineering.

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KONA Powder and Particle Journal No. 41 (2024) 42–57/https://doi.org/10.14356/kona.2024008

Review Paper

Measurement of Fugitive Particulate Matter Emission:
Current State and Trends †Tianyi Cai and Wu Zhou *Institute of Particle and Two-phase Flow Measurement, Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering,
University of Shanghai for Science and Technology, China

Fugitive particulate matter (FPM) refers to a mixture of solid particles
and liquid droplets that are released into the air without passing throughconfined flow equipment. These emissions of FPM can originate fromnatural processes and anthropogenic activities. FPM emissions are an
important source of PM2.5. Precisely measuring the size, concentration,and other properties of such particulate matter is crucial for effectively
controlling emission sources and improving air quality. However,compared with particulate matter emission from stationary sources, it isdifficult to monitor the FPM effectively owing to its dispersive andirregular emissions. Traditional measuring methods for FPM are basedon sampling, which is a point monitoring approach and can be timeconsuming. In recent years, several new techniques based on opticalprinciples, image-based processes and low-cost sensors have beendeveloped and applied for FPM measurement, with the advantages ofspatial and time resolutions. The current state and future development
of FPM measurements are reviewed in this paper.
Keywords: fugitive particulate matter, air quality, light scattering, image-based measurement, low-cost sensor, real-time

1. Introductions
Particulate matter (PM) has drawn massive attention
owing to its great impact on human health and wealth(Amoushahi et al., 2022; Chen et al., 2019; Hassan et al.,2022), environment, and global climate (Hassan et al.,2020; Mutlu, 2020; Yang et al., 2021). Fugitive particulatematter (FPM), which refers to a mixture of solid particles
or liquid droplets that are not discharged into the atmosphere through confined flow equipment (e.g., stack, chimney, pipe, vent, or duct), contributes a great portion to thetotal outdoor PM emission. Unlike particulate matter emitted from definable and stationary point sources, FPM isgenerally discharged from everywhere, such as anthropogenic land use, urban traffic, industrial activities, winderosion, vegetation growth, and other natural processes.
The emission intensity of FPM is different from site to site,
and it often fluctuates temporally and spatially. Accordingly, it is challenging to develop the time- and spaceresolved measurement for FPM emission but beneficial tounderstand its generation, track its migration, evaluate its
effects and damage on human beings and the environment,
†

*

42

Received 25 July 2022; Accepted 19 October 2022
J-STAGE Advance published online 17 June 2023
Corresponding author: Wu Zhou;
Add: 516 Jungong Rd., Shanghai 200093, China
E-mail: zhouwu@usst.edu.cn
TEL: +86-021-5527-7764 FAX: +86-021-5527-6854

and consequently figure out reliable mitigation measures toreduce FPM.
The objective of this review is to provide an overview of
the current state and trends in measuring fugitive particulate matter. We will first introduce the main sources and key
properties of FPM, analyze its effects and the current measurement standards on it. Then, we will provide a critical
overview of the conventional methods and discuss the new
trends for FPM measurement. Finally, we will propose abrief outlook on the future of FPM measurement.

1.1 Sources of FPMs
Typically, sources of FPMs can be separated into two
categories: natural processes and anthropogenic activities.
Common natural sources include soil & desert dust (Hassan
et al., 2016; Vos et al., 2021), wildfire smoke (Sedlacek etal., 2018), volcanic ash (Sasaki et al., 2021), sea sprayaerosol (February et al., 2021), and pollen & fungi grains(de Weger et al., 2020). The geomorphologic and geologic
conditions mainly contribute to the compositions and distribution of the natural FPMs. For instance, arid regions areplagued by wind-blown soil, and coastal areas are familiar
with sea salt-based or biogenic-enriched aerosols. Comparing with natural processes, human activities create more
FPMs, mainly derived from traffic & transportation (Kuhns
et al., 2010; Pervez et al., 2018; Wang H. et al., 2023),
heavy constructions (Noh et al., 2018), agriculture &Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Review Paper

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
Table 1 Fugitive particulate matter sources and activities.

Table 2 Description of common particle size bases.

SourceName

Description

TSP

Total Suspended Particulate, as measured by thestandard high-volume air sampler, has a relatively
coarse size range. It was the basis for the previous
primary National Ambient Air Quality Standards of
the U.S. (NAAQS) for PM and is still the basis of
the secondary standard.

SP

Suspended Particulate, which is often used as a surrogate for TSP, is defined as PM with an aerodynamic diameter no greater than 30 μm.

IPInhalable Particulate, which is defined as PM with
an aerodynamic diameter no greater than 15 μm, canget into the human body by breath.

PM10

PM with an aerodynamic diameter no greater than
10 μm. It is the current primary NAAQS for particulate matter, representing the particle size range ofthe greatest regulatory interest. It is also known as
Thoracic particles since it may reach the upper partof the airways and lungs.

PM10–2.5

PM with diameters generally larger than 2.5 μm andsmaller than or equal to 10 μm. It is also known as
Coarse particles and is primarily generated frommechanical operations.

PM2.5

PM with an aerodynamic diameter no greater than
2.5 μm. It is also known as alveolar particles, indicating that it can penetrate more deeply into thelungs and may reach the alveoli. It is called Fine
Particulate (FP) as well.

UFP

Ultra Fine Particulate, which is defined as PM with
an aerodynamic diameter no greater than 0.1 μm,
can penetrate tissues and enter the bloodstream.

Activity

From natural processes
soil & desert dust

windy weather / sandstorm

wildfire smoke

forest fire / prairie fire / hill fire

volcanic ash

volcanic eruption

sea spray aerosol

air bubbles bursting at the air-sea
interfacepollen & fungi grainspollination / spore dispersal

From anthropogenic activities
traffic & transportation

roads / automotive exhaust / loading
and offloading

heavy constructionsindustrial & commercial building /road, bridge and other public constructions

agriculture &livestock operations

straw burning / grain harvest / storage
and distribution / pasture mowing /animal feedmining & quarrying

blasting / crushing / conveying andstorage / heavy equipment operations

metal processing

sintering / blasting / casting / plating

residential combustion cook / heating / cigarettes / incense

livestock operations (Gladding et al., 2020; Liu Y. et al.,2022), mining & quarries (Chang et al., 2010; Froyd et al.,2019), the metal industry (Zhang et al., 2022), and residential combustion (Nugraha et al., 2021). The main sources
and activities that contribute to the FPM emissions arelisted in Table 1.

1.2 Main properties of FPMs
1.2.1 Critical dimension and size distribution
Particle size has a great impact on the suspension, transport and distribution of FPM in the atmosphere. The residence time of submicron particles is on the order of
102–103 h, assuming the absence of precipitation. Particles
with diameters between 1 μm and 10 μm can exist in the airfor several days (Esmen and Corn, 1971). The typical residence time for 10 nm particles is about 15 minutes owing
to their primarily coagulation with larger particles (Kim etal., 2021). The potential drift distance of particles is also
related to particle size as well as the initial injection heightof the particle and the intensity of atmospheric turbulence.
Fugitive dust larger than about 100 μm is likely to settle out
near the source under a typical wind speed of 16 km/h.
Particles in the range of 30 μm to 100 μm are likely to undergo impeded settling without extra wind uplift. Smaller
particles with much slower gravitational settling velocities
are much easier to travel following atmospheric turbulence.
Particles propelled higher from the source also travel far-ther. For instance, volcanic ash can have an intercontinental
diffusion (Ansmann et al., 2011).
Particle size also has a major influence on the transportof particulate in the human respiratory system since thedeposition efficiency of particles in the respiratory tract
depends upon their size. Large particles can be hindered by
the nasal hair (rhinothrix), medium particles can reach theupper respiratory tract, and small particles can deposit on
alveoli or even invade the blood-circulatory system.
PM10 (particulate matter with a diameter of 10 μm or lessin size) and PM2.5 (particulate matter with a diameter of
2.5 μm or less in size) are currently common size bases forparticulate matter. However, some other particle size bases,
listed in Table 2, are used in research as well.
Different sources contribute to various size distributionsof FPM. It is generally a narrow size distribution and smallmean size for biomass-burning smoke, which is dominatedby submicron particles (Wang F. et al., 2019). However, thesize of smoke particles varies along different combustion
phases (Ordou and Agranovski, 2019). Particles generated
by mechanical force are normally widely distributed in size

43Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57(Zhang et al., 2022). Sea spray aerosols can range from a
few tens of nanometers to tens of micrometers (February etal., 2021). Moreover, the size distribution of some FPMs
may show bimodal or multimodal patterns (Chang et al.,2010; Chen et al., 2019).

1.2.2 Mass concentration, emission rate and emission
factors
PM10 and PM2.5 mass concentrations (γm) are the mostcommonly used criteria to evaluate air quality. The criteria
can further be separated into 24-hour average values andannual mean values. Most countries and many internationalorganizations have issued their standards or recommend
thresholds for PM10 and PM2.5 mass concentrations, as
listed in Table 3. However, no current standards are set up
for FPM.
Before determining the mass emission rate (ṁ), the volume flow rate of the flue (V ) should be measured. Then, themass emission rate can be calculated by multiplying themass concentration of the measured pollutant by its volume
flow rate, as shown in Eqn.
(11)(1):m  γmV(1)

The mass emission factor (Ḟ) is a representative value
indicating the emission intensity of a pollutant generated(1)
from a specific activity. It can be calculated following
Eqn. (2):

m
F 
a

(2)

where a represents the activity rate.
The mass emission factor is usually expressed as the(2) of the pollutant divided by the unit weight (g kg–1),
weightvolume (g m–3), distance (g km–1), or duration of the activity that emits the pollutant (g h–1).

Review Paper

genic substances. Wind-blown dust from dried land ismainly dominated by minerals, especially quartz. Volcanic
ash contains more metallic minerals, such as Al2O3, Fe2O3
and MgO. Construction, mining and quarrying, which utilize stones and ores, contribute to the mineral dust emission. Metal processing generates both metal-rich andmineral particles. Hence, mineral dust is the major PM
component at a global scale (Minguillón et al., 2013). Sea
spray aerosols are little droplets containing inorganic salts(e.g. NaCl) or biogenic substances. Animal- and vegetationrelated particles belong to biogenic ones. Soot is mainly
generated from biomass or fuel combustion, including
wildfire, residential combustion, and working engines.
Non-exhausted PM from traffic, originating from tire wear,
brake use and road dust resuspension, mainly consists ofmineral particles. Since the reduction of exhaust emissions
is performed worldwide, non-exhausted PM emissions will
dominate traffic-related emissions in the near future.

1.2.4 Shape and morphology
Mineral dust typically has complex irregular shapes derived from its intrinsic crystal orientation and the force
loading from crushing and abrasion (Kalashnikova and
Sokolik, 2004; Nousiainen, 2009). Metallic particles,
which are discharged from high-temperature processes likeburning and metal cutting, are usually spherical (Sanderson
et al., 2014). Soot is always amorphous and forms chainlike aggregates during its aging period (Adachi et al.,2010). Pollen and other biogenic particulates are often
nonspherical (Amina et al., 2020), although it is commonly
assumed that pollen grains are spherical (Theilade et al.,1993). Inorganic salts, including NaCl, sulfates and nitrates, are basically hydrophilic. Therefore, inorganic
aerosols contain moisture at relatively high humidity levels, resulting in a spherical shape. Otherwise, the shape
may be irregular (Ueda, 2021).

1.2.3 Chemical components
Although the composition of FPM highly depends on its
source, it can be roughly classified into five categories:
minerals, metallic particles, soot, inorganic salts and bio-

1.2.5 Optical properties
Complex refractive index (m) is the fundamental parameter to describe FPM, expressed in Eqn. (3).
m  n  iηTable 3 Criteria for PM10 and PM2.5.

PM2.5

PM10
Criteria

Annual(μg m–3)

Daily(μg m–3)

Annual(μg m–3)

Daily(μg m–3)

WHO

15

45

5

15

USA

—

150

12

35

China

70

150

35

75

EU

40

50

25

—

Japan

—

—

15

35

Korea

50

100

15

35

44

(3)

where the real part, n is the refractive index indicating thedegree of light dispersion through the media, and the imag(3)inary part, ƞ is the absorption coefficient, donating the lightattenuation when it propagates through the media. If a particle can absorb light, its ƞ is positive.
Among all the common FPMs, black carbon (BC) is themost light-absorbing component with an imaginary part
ranging from 0.4 to 0.8 (Kelesidis and Pratsinis, 2019).
Organic carbon (OC), also known as brown carbon (BrC),
absorbs light as well (Zhang et al., 2017). Mineral andmetallic particles both slightly absorb light (Ebert et al.,
2002; Kandler et al., 2007).

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The extinction coefficient (bext) of FPM includes thescattering coefficient and absorption coefficient (as detailedin Section 2.3), and it can be converted to visibility (Vis) byadapting the Koschmieder equation (Cao et al., 2012; ten
Brink et al., 2018; Xiao et al., 2014):
Vis 

k
bext

(4)

where k is a constant assumed to be 3.912.
Single scatter albedo (SSA) is defined as the ratio of(4)particle-scattering
efficiency to the total extinction efficiency. It
is a key parameter in radiative transport models for estimating
aerosol direct radiative forcing (Jeong et al., 2020).

1.3 Effects and damage from FPMs
1.3.1 Human health
Short-term and long-term exposure to particles increases
the risk of developing cardiovascular and respiratory diseases, as well as lung cancer (Brook et al., 2010). Outdoor
PM2.5 is the fifth-largest risk factor for premature mortality
worldwide (Feigin et al., 2016). Infants, the elderly, andoutdoor workers are the most likely to be affected. The
health effects of FPMs depend considerably on their size.
Coarse PM reaches the upper part of the airways and lungs,
while fine PM can penetrate more deeply into the lungs andmay reach the alveoli. UFP can even directly translocate
into the bloodstream and cells (Martinelli et al., 2013).
Chemical composition issues in the health effects as well.
Exposure to metal or metal-containing dust increases therisk of cardiovascular dysfunction (Ain and Qamar., 2021).
Black carbon, as a universal carrier of various toxic constituents, has a strong association with premature mortality(Wang Y. et al., 2021). Inhalation of sand and concrete dust
can lead to silicosis, an irreparable form of lung damage(Hoy and Chambers, 2020). Additionally, inhalation ofpollen and other biogenic aerosols can induce allergy andrespiratory problems (D’Amato et al., 2020).

1.3.2 Wealth and safely
FPMs impair visibility. Due to the prominent ability oflight extinction, fine PM contributes more than gas pollutants to visibility degradation (Yu et al., 2016). On roads,
visibility reduction increases the risk of accidents and escalates the costs associated with vehicle cleaning and equipment maintenance. FPM emissions also result in thecancellations of flights and avoidance of outdoor activities(Guo et al., 2021). For example, sudden volcanic ash discharges directly affect aircraft engines, causing them tostall and posing a threat to human and cargo safety (Prata,
2008). Heavy wind erosion of soils and dust storms diminish land values. Excessive emissions can also damage
vegetation and reduce crop production. Burning smoke andflammable fine powders can cause fire or blast damage if
ignited.

Review Paper

1.3.3 Environment and global climate
Various FPMs contribute differently to climate forcing
owing to their distinct optical properties. For instance,
black carbon aerosols strongly absorb solar radiation, leading to a warming effect (Bond et al., 2013). In contrast,
mineral dust cools the atmosphere due to its ability to increase radiation reflection into space (Choobari et al.,2014). Besides, FPMs also have an indirect effect on the
climate by modifying the properties of clouds given thatparticles act as cloud condensation nuclei (Williamson etal., 2019). For instance, soot is an efficient ice-nucleating
particle that influences cirrus formation at the tropopause.
Although the relatively short residence time and highlyvariable spatial distribution in the atmosphere further complicate the estimation of net effects of FPMs on the global
climate, scientists are trying to include FPM emissions into
global atmospheric models (Philip et al., 2017).

1.4 Current standards on FPM emissions
The most widely used method for PM measurements isbased on sampling, and mass concentration, emission rateand emission factors are the highly concerned parameters.
The International Organization for Standardization (ISO)has released the measurement standards for PM in the atmosphere and from the stationary source (ISO 11771-2010/ ISO 10155-1995 / ISO 9096-2017), and many countries
have all set domestic standards. Since there are no specificstandards for FPM, the above-mentioned ones are used for
FPM measurements. In addition, some monitoring methods
are recommended for FPM emissions by some institutions.
The US Environmental Protection Agency (EPA) Method
22 “Visual Determination of Fugitive Emissions” was proposed. Through this method, viewers position themselvesto have an unobstructed view of potential emissions. If
visible emissions are observed, the viewer measures theduration of time that the visible emissions were present
during the observation period. Method 22 is often used in
the regulation of fugitive emissions from toxic materials
that escape during material transfer (i.e. asbestos removal),
escape from buildings housing material processing or handling equipment (i.e. asbestos presence in building demolitions), and/or are not captured by an exhaust hood or are
emitted directly from process equipment. Method 9, known
as the “Visible Determination of Opacity of Emissions
from Stationary Sources,” involves the recording of readings as a percentage of opacity in terms of black and whiteparticulate plumes or dense clouds of matter. While
Method 9 is used extensively to sight and control visible
emissions from stationary and non-stationary industrial
processes, it is also used to measure visible emissions fromunconfined places such as roads and bulk material storage
areas.

45

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

2. Conventional methods for FPM measurement

As mentioned in Section 1.4, sampling methods universally accepted for PM measurements are equally applicable
to FPM measurements. However, due to the temporary andunsteady-in-position of FPM emissions, it is essential to
implement well-performed sampling strategies and consider the use of multiple measurements to gather comprehensive information about FPM emissions.

2.1 Sampling strategies and conditions
2.1.1 Upwind-downwind method
Upwind-downwind sampling requires a high density ofvertical and/or horizontal measurements to determine thePM emission rate. Measurements of FPM emission rates
are usually performed using this method, which has been
described in detail in the previous study (Cowherd et al.,1974). For instance, particulate emission rates from gravel
processing sites and traffic-busy roads were calculated on
the basis of a mass balance approach using the upwind/
downwind technique, respectively (Chang et al., 2010;
Etyemezian et al., 2004).

2.1.2 Wind tunnel method
The wind tunnel method is suitable for measuring fugitive dust emissions from dried land. To conduct thismethod, a wind tunnel with a working section of several
tens of meters in length, several meters in width, and heightis required. A blower works at the tunnel entrance and PM
monitors sample the wind-blown dust along the tunnel toevaluate the dust emission properties. This method has
been successfully applied to measure fugitive emissions
from soil surfaces (Fitz et al., 2021; Gao et al., 2020; Roneyand White, 2006).

Review Paper

2.2 Filter-based techniques
2.2.1Direct gravimetric analysis
In direct gravimetric analysis, a filter is weighted by anelectronic microbalance before and after sampling underconstant temperature and humidity conditions. The masschange is used to calculate the degree of PM contamination(Lei et al., 2020). The collected filter can be used for offlinechemical component analysis (Guo et al., 2021).
However, despite providing accurate and reliable data,
the direct gravimetric method is time-consuming and inapplicable to FPM measurement.

2.2.2 Tapered element oscillating microbalances(TEOM)
Unlike the direct gravimetric method, the TEOM method
uses a hollow glass tube as a microbalance for real-time
mass loading measurement. Particles continuously depositon the glass tube, increasing its inertia and consequently
tuning its oscillation frequency. This frequency change isconverted to an electrical signal, which is amplified for
detection.
The TEOM method offers the advantage of high time
resolution (2 seconds) (Sullivan et al., 2017), but it should
be noted that this method is sensitive to temperature andmoisture. Therefore, the incoming sample flow is heatedand kept constant. Depending on these measuring conditions, it is difficult to precisely measure the semi-volatile
particles. A filter dynamic measurement system (FDMS) is
specially designed to overcome this drawback. TEOM instruments equipped with FDMS are operated alternately
between a measuring cycle and a reference cycle. The massloss of the glass tube in the reference cycle is used to correct the output in the measuring cycle, thereby increasing
the accuracy of the TEOM results (Grover, 2005).

2.1.3 Isokinetic sampling method
The isokinetic sampling method, which means the sampling velocity is the same as the FPM/airflow, is employed
to prevent incorrect lower or higher particle concentrations
owing to anisokinetic effects from particle bypassing or
swarming, respectively.

2.1.4 Size-selective inlet and cascade impactor
A size-selective inlet removes larger particulates and allows the particulate smaller than the designed size to pass.
By applying a size-selective inlet, PM10, PM2.5 or UFP canbe separated from the pollutant flow (Su et al., 2020). Cascade impactors operate on the principle of curvilinear motion of particles in the aerosol stream, and as a result,particles of various sizes are divided and collected on different plates. In this way, the size distribution of the sampling FPM is obtained (Manojkumar and Srimuruganandam,2022).

46

2.2.3 Beta attenuation monitoring (BAM)
The main principle of BAM is based on Lambert–Beer’slaw. When airflow passes through the filter tape, particles
are left on it. The beta radiation is attenuated by the attached particles before reaching the detector on the otherside of the filter tape. The attenuation is an exponentialfunction of the deposit amount and it is roughly not affectedby particle composition or other properties. Hence, themass concentration of particles can be examined. However,
the moisture in the air may cause an error, so a heater isnecessarily equipped at the sample inlet. Typically, theBAM method can be used for continuous online measurement, providing the mass concentration data hourly(Mueller et al., 2013). Moreover, offline chemical analysis
can carry out on the used filter tape (Raja et al., 2017).

2.2.4 Offline characterization

Filter-based offline characterization has low time resolutions and fails to reflect the dynamic nature of the fugitive

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Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
Sideward 90°

Sideward 90°

Forward 0°

Sideward 90°

Forward 0°

×1

α = 0.5m = 1.33

α = 1.0m = 1.33

α = 2.0m = 1.33

Sideward 90°

Sideward 90°

Sideward 90°

× 10

× 10

Forward 0°

×1

× 1 Forward 0°

Forward 0°

α = 4.0m = 1.33

× 10
× 1 Forward 0°

α = 5.0m = 1.33

α = 6.0m = 1.33

Fig. 1 Diagram of scattered light intensities of different size. α = πD/λ, is a dimensionless particle size, where D is the mean particle diameter, m isparticle refractive index.

pollution sources, nevertheless, it gives more information
about the particulate.
Electron microscopy (EM) techniques are valuable todetect and quantify the shape and size of particles(Gelencsér et al., 2011; Omidvarborna et al., 2018).
Ion chromatography (IC) is performed to examine theconcentrations of the water-soluble ions in the particulate(Guo et al., 2021; Hu et al., 2019; Tian et al., 2020; Xiao etal., 2014; Zou et al., 2018).
Elements and trace metals, including Ca, Sc, Ti, V, Fe,
Co, Zn, and Mo, were analyzed by X-ray fluorescence(XRF) spectrometry (Gao et al., 2015; Tian et al., 2020;
Xiao et al., 2014) or Inductively Coupled Plasma-Mass
Spectrometry (ICP-MS) (Hu et al., 2019; Philip et al.,2017).
The concentrations of OC and element carbon (EC) are
analyzed by a thermal/optical carbon analyzer (Guo et al.,2021; Hu et al., 2019; Lei et al., 2020; Tian et al., 2020;
Xiao et al., 2014; Zou et al., 2018).
In general, filter-based techniques are the most technically mature. However, the sampling conditions should betightly controlled to reduce the effect of sampling conditions on accuracy (Le and Tsai, 2021).

2.3 Optical-based techniques
2.3.1 Basics of light scattering, absorption andextinction
The scattering pattern of a particle refers to its scattering
efficiency as a function of the angle. It depends on the particle size, shape, refractive index, and the wavelength (λ) of
the excitation light. The distribution of the scattering light
as a function of a dimensionless particle size parameter, α,
assuming the refractive index equals 1.33, is demonstrated
in Fig. 1.
It is shown that the smaller the particle is (compared to

dl

I0

I

Light pathL
Fig. 2

Diagram of light extinction.

the excitation wavelength), the more spherical the scattering pattern is. For large particles (e.g. α ≥ 4.0 in Fig. 1),
most of the scattered intensity deviates with smaller angles,
and side lobes appear as the diameter increases. Besides,
the polarization of scattering light is also influenced by theparticle properties, which are not shown in Fig. 1. Therefore, the particle size, shape, component, and concentration
can be predicted using the information from the scattering
signature theoretically. Mie theory (Hergert and Wriedt,2012) is the most common description of light scattering.
Despite its simple hypothesis that a uniform sphere is illuminated by a monochromatic plane wave, this theory has
proved to be quite robust and applicable in particle size andconcentration prediction.
When light passes through a cloud of particles, its intensity (I0) is attenuated because of the scattering and absorption effects of the particles, as seen in Fig. 2.
Assuming the particles disperse uniformly in the illuminated area, and then the transmitted light intensity (I) canbe described with Eqn. (5).

47

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

I  I 0 e bext L(5)where L is the path length of the light beam through theparticles.
(5)
The extinction coefficient (bext) can be further divided
into two parts, the scattering coefficient (bsca) and the absorption coefficient (babs).
bext, as a function of the particle number, diameter D,
wavelength of the incident light λ, and the complex refractive index m, can be computed by Mie theory.

2.3.2 NephelometerA nephelometer is the most commonly used opticalequipment for PM measurement. The pollutant air is sampled into the measuring chamber of the nephelometer, illuminated by a light beam. The scattering light is detected bya light detector set to one side of the light source. Despite
the intensity is also related to the refractive index, shape,and size distribution of the measured particles, nephelometers are calibrated to a known particulate, then use anenvironmental-related factor to compensate for the error
accordingly. After calibrating to a known particulate, thePM mass concentration can be evaluated from the scattered
light intensity.
The DustTrakTM (TSI Co., Ltd.) is a classicalnephelometer-type tool to determine the mass concentration in real time. The DustTrak aerosol monitors (model
8520, TSI Co., Ltd.) were run in the kitchens and livingrooms of the selected sites to measure fine particulate matter (Sidra et al., 2015).
Portable In Situ Wind Erosion Laboratory (PI-SWERL)
is a portable wind tunnel surrogate, equipped with
DustTrak (Etyemezian et al., 2007). It is contained in an
open-bottomed, cylindrical chamber operated by adirect-current motor and set parallel to the soil surface. By
adjusting the rotation speed, the desired high winds withknown wind shear are created and the soil and dust particles are lifted. With the application of PI-SWERL, the behavior of FPM emissions from unpaved roads, farmlands,
and deserts were studied without the deployment of flux
tower systems (Kuhns et al., 2010; Sweeney et al., 2021;
Vos et al., 2021).
Thanks to the calibration method, the measurement accuracy depends on the assumption that the properties of
FPM are constant, except for the concentration. However,FPM emissions are complicated and diverse. Single- angle
scattering measurement can result in unreliable results. To
solve this problem, SHARP-5030i (Thermo Scientific Co.,
Ltd.) measures PM2.5 mass concentrations based on theprinciples of particle light scattering (nephelometer) andbeta attenuation (Su et al., 2020). The beta attenuation data
can be used to calibrate the nephelometer.

48

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2.3.3 Particulate counter
Optical particle counter (OPC) and condensed particle
counter (CPC) are both used to measure particle number
concentration and size distribution. OPC is based onsideward light scattering. The aerosols pass through thelight-illuminated measuring spot particle by particle andthe induced light scattering signal is detected by the lightdetector simultaneously. The particle size is a function of
the scattering light intensity and the particle number iscounted during the measuring period. Consequently, theparticle number size concentration is achieved. CPC introduces condensation equipment before OPC, inducing theparticle size growth by absorbing liquid, and therefore it
can measure smaller aerosols with diameters of a few nanometers.

2.4 Electrical methods
2.4.1 Electrical low-pressure impactor (ELPI)The ELPI (Dekati Ltd.) operates at 30 LPM and ambientparticles are charged when the air sample passes through aunipolar charger. The charged particles are size-classified
by aerodynamic diameter following the same principle as
the cascade impactor mentioned in Section 2.1. The electrical currents arising from particle deposition are measured
simultaneously from 12 stages with a multichannel electrometer in real time. Therefore, the ELPI is capable ofmeasuring the particle number concentration over a wide
size range (7 nm–10 μm) with 1–2 s resolution (Holmén etal., 2007).

2.4.2 Differential mobility analyzer (DMA)

The intake particles are electrically charged first andseparated by deflection since the particles with different
sizes have different electrical mobility. If it is combinedwith the CPC technique, known as scanning mobility particle sizer (SMPS), the lower limit of detection (several
nanometers) is reached. Fast mobility particle sizer (FMPS)
is another derived technique from DMA. It combines DMA
with an electrometer to improve the time resolution to one
second.

2.5 Real-time chemical characterization
The mass spectrometry techniques are quickly developed for the real-time chemical characterizations of ambient particulates, including aerosol mass spectrometer(AMS), high-resolution time-of-flight AMS, aerosol chemical speciation monitor, single-particle mass spectrometers,
and aerosol time-of-flight mass spectrometer. The comparison between these techniques is well illustrated in Ref.
(Wang F. et al., 2021).
However, continuous measurements at high time resolution remain impractical, and like most offline methods,
volatile or reactive aerosol species can change before
analysis.

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Conventional methods for FPM measurement all belong
to point measurement techniques since they require a sampling process, which means that the space resolutions ofthese techniques are poor. Rigorous sampling strategies
and multi-measuring equipment should be arranged to reveal the spatial distribution and movement of FPM. However, it is impractical to perform this arrangement
considering the equipment is cumbersome and expensive.
To achieve the high space- and time-resolved investigationof FPM, some cheap, portable, and remote sensing techniques are being developed.

3.1 Open-path optical techniques
3.1.1 Light detection and ranging (Lidar)
Lidar is an active light scattering detection method,
based on the analysis of the radiation backscattered from atarget. This technique is often used in climate research tomeasure altitude-dependent aerosol extinction. For instance, the distribution and movement of volcanic ash wereobserved with the ground-based lidar systems of theEuropean Aerosol Research Lidar Network (EARLINET)(Ansmann et al., 2011). The strong and long-lasting stratospheric aerosol perturbation was also monitored all over
Europe with EARLINET (Baars et al., 2019).
Lidar remote sensing has been introduced to measure theFPM near the ground. A scanning lidar system was applied
to measure FPM emissions from different industrial
sources simultaneously, determining the potential of lidar
for tracking and differentiating plumes from various sites(Pal et al., 1998). The reliable detection of early fire wasaccomplished using lidars operating at 355 nm, 532 nm,
1064 nm, and 1540 nm, and the maximum detection distance relied on the wavelength (Lavrov et al., 2003). A
three-wavelength scanning lidar system was built to simultaneously monitor FPM emissions from agricultural operations and road traffic. The mass concentration and size
distribution were inversed assuming a bimodal lognormal
size distribution and calibrated by standard facilities, including optical particle counter, PM10 and PM2.5 samplers,
multistage impactors, and an AMS (Zavyalov et al., 2006).
Traditional lidars are monostatic, requiring expensive
electronics to measure the time of flight of the backscattered light and its corresponding distance. The overlap effects between the backscattered signal and receiver field ofview near ground levels may impact the detection reliability for the nearby pollution. To avoid these disadvantages,
a bistatic lidar, named CLidar, was established. In the
CLidar, a laser source and a camera fitted with a wide-angle
lens were separated several hundred meters away fromeach other. The laser transmits light vertically into the atmosphere, and the scattering light was imaged by the camera, as shown in Fig. 3. Owing to this setup, the altituderesolution is excellent near ground level, providing an effi-

Laser beam

3. New trends for FPM measurement

Laser

θ

z

PM2.5 MonitorDdθ CCD camera

Acquisition and control computer
Fig. 3 Schematic of CLidar system. Reproduced from Ref. (Tao et al.,2016) with permission from Copernicus Gesellschaft mbH.

cient detector of pollution at low altitudes. The aerosol extinction coefficient along the altitude was inverted from theintensities of pixels. A grilling activity was successfully
monitored since the aerosol extinction increased from0.03 km–1 with no grilling to 0.085 km–1 (Kabir A. et al.,2018).
Moreover, the CCD-based scattering Lidar system (including sideward scattering and backward scattering) isverified to be useful to measure particle mass concentrationand size distribution of cooking fumes and open smoldering as well (Wang W. et al., 2019; Zhou W. et al., 2020).

3.1.2 Hybrid optical remote sensing (h-ORS)
The h-ORS was mainly developed by the University of
Illinois to measure FPM emissions from military-related
activities, including back blasts from firing artillery, open
burning and detonation, tracked and wheeled vehicle operation, and helicopter landing (Du et al., 2011; Varma et al.,2008; Yuen et al., 2013, 2015, 2017). It combines micropulse Lidar (MPL), open-path Fourier Transform Infrared
spectroscopy/Laser transmissometer (OP-FTIR/OP-LT),
and/or filter-based sampling techniques (DustTrak orTEOM). The measurement system is schematically demonstrated in Fig. 4.
2-D PM mass concentration profile and mass emission
factors can be calculated using the h-ORS system. First, the2-D MPL data obtained using scanning mode were converted to the 2-D light extinction profiles with the LIDAR
inversion algorithm. Second, the 2-D PM mass concentration profiles were determined from the 2-D light extinction
profiles by multiplying a factor. This factor refers to themass concentration at the reference point (as the blue point
shown in Fig. 4) divided by the local light extinction obtained in the first step. The mass concentration at the reference point can be derived from the light extinction
calculation performed by OP-FTIR/OP-LT or the result
from DustTrak and TEOM, respectively. Last, the massemission factors can be easily calculated if the wind speed
and direction are recorded.

49

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Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
Bell 210 HelicopterWind

Withdraw

Approach

100 ⁓ 150 m

3m

Measurement surface
MPL paths

Dust plume
300 m

130 m

LIDAR

Reflective
targetScanning
MPL
OP-FTIR &OP-LT

Note: map not to scale

OP-FTIR and
OP-LT paths

Retroreflector

Fig. 4 Schematic of h-ORS system. The blue point represents the site for DustTraks or TEOM equipment. Reproduced from Ref. (Du et al., 2011) withpermission from the American Chemical Society.

Contrastingbackground

Retroreflector

Wind direction

Fugitive dust plume

Digital
CameraLaser
Transmissometer

Sun
Fig. 5 Schematic of the camera-based light extinction system. Reproduced from Ref. (Du et al., 2013) with permission from Elsevier Ltd.

3.2 Image-based optical techniques
3.2.1 Camera-based detection
Biomass burning, especially wildfire, should be monitored and detected as soon as possible to avoid the expansion of combustion. As mentioned in Section 3.1, Lidar isuseful to detect fire smoke in its early stage. It is demonstrated here that video monitoring is also effective for thissmoke early sensing (Jakovčević et al., 2013). The camerarecorded video was segregated into a photographic sequence, then region segmentation and categorization,
motion detection, and chromatic and shape analysis were
performed on these photos to finally discriminate firesmoke from fog, clouds, and other stuff.
Besides, the road dust from busy traffic can be monitored
by measuring the opacity of fugitive plume emissions. A
camera-based light extinction method was described forquantifying the opacities of FPM (Du et al., 2013). Thismethod was validated by performing simultaneous measurements using a co-located laser transmissometer, as
shown in Fig. 5. Moreover, the values of the R, G, and B

50

Fig. 6 The prototype of picture-based filter system. Reproduced from
Ref. (Carretero-Pena et al., 2019) with permission from MDPI.

channels, and the grey value were all consistent with theopacities from the OP-LT measurement.

3.2.2 Photographic filter analysis

The direct gravimetric analysis of the filter is timeconsuming and strict to the operating procedure. Picturebased filter analysis takes pictures of the filter before andafter use and evaluates the mass of particles deposited on
the filter depending on the difference of pictures accordingly. A smartphone is both easy to be attained and easy tooperate and therefore it is a good option to perform theanalysis. A prototype of such a system was proposed recently (Carretero-Pena et al., 2019), as displayed in Fig. 6.
A testing box was designed to provide stable lighting

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Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

MSS

Drone

EZ-0048

K30

B5W-LD0101

PPD42NS

TM (LCS)PMSA0031

SLR-429M-RS2
RASP-TSL7

Bluetooth

RASP-TSL7
Raspberry Pi 3
Model BOWL-LPB10010

CM-15-6

DS1307OWL-LPB10010

BME280

TSL2591

RVS

CloudInternet
Fig. 7

SI1145

KP-9250

Laptop PC

RM (LCS)
LoRa(LPWA)

Schematic of the integrated system for in-situ measurements. Reproduced from Ref. (Madokoro et al., 2021) with permission from MDPI.

conditions, in which a stable LED strip was equipped. The
saturation parameter of the picture was proved to be able toestimate the mass concentration of dust deposited on thefilter.

3.3 Low-cost, compact, mobile and connectablesensor system
Low-cost PM measurement techniques most commonly
rely on the light-scattering-based (nephelometer or OPC)
principle, which is available for below 50 USD (Kuula etal., 2019; Yi et al., 2018). PMS7003 (Plantower Co., Ltd.)and SDS011 (Nova Fitness Co., Ltd.) sensors, based on
OPC and nephelometry, respectively, were confirmed precision and were linearly correlated with TEOM data(Kaźmierczak et al., 2018). However, these two sensors
both had a bias concerning PM2.5 concentrations obtained
from TEOM, indicating the importance of calibration of
low-cost devices before use. PMS7003 was capable ofmeasuring PM10 and PM2.5 with a measurement range from0 to 500 μg m–3, signal response of less than 1 s, and resolution of 1–2.5 μm and 2.5–10 μm, respectively (CamarilloEscobedo et al., 2022). SDS011 was successfully applied inresidential biomass combustion monitoring (Nugraha et al.,2021). The data obtained from SDS011 sensors were reliable, but it may cause errors since high RH (over 80 %)negatively affected the sensor response (Liu et al., 2019).
Usually, there is a trade-off between the accuracy of measurement and the cost of the sensor, which are both vital to
the wide and proper use of low-cost sensors (Castell et al.,2017).

3.3.1 Drone-based measurement and sensors
networks
Mobile measurements used to be carried out on airplanes(Froyd et al., 2019; Hobbs et al., 2003; Taubman, 2004;
Zhang et al., 2017). The drone is small and exquisite. It canquickly fly to the place where FPM emission happens, fa-

cilitating the monitoring of FPM. The fugitive dust at thequarry sites was measured using a Sniffer4D moduleequipped drone, providing measurements at 1 s intervals
through a light scattering method (Kim et al., 2021). The
volcanic ash was detected remotely by drone-based sensors(Sasaki et al., 2021). Small, lightweight, and cheap sensors
were integrated for multiple measurements of atmosphericphenomena and related environmental information. For insitu local area measurements, a long-range wireless communication module with real-time monitoring and visualizing software applications was integrated as established inFig. 7 (Madokoro et al., 2021).

3.4 Real-time laser-induced breakdown
spectroscopy
Laser-induced breakdown spectroscopy (LIBS) has been
proven to perform elemental microanalysis of PM on filterswith high sensitivity (ppm-level) (Marina-Montes et al.,2021). The technique indicates the possibility of automatedchemical analysis collaborating with conventional filterbased measurements in quasi-real time.
Moreover, online LIBS in open space detection has also
been demonstrated in several field measurements, showing
great potential for rapid monitoring and control of particulate emissions generated in public areas, such as toxic dust
in a hostile environment of the steel industry (Girón et al.,2018), cigarette smoke in houses (Zhang et al., 2020), andincense smoke near around temples (Qu et al., 2021).
LIBS is a useful and promising tool to determine the elemental composition of FPM in situ. However, this technique is not sufficient for obtaining information on specificparticulate sizes because of the breakdown of all particulates.

4. Summary and outlook
FPM has contributed a great portion to the total outdoor
PM emissions, and it will account for more than PM

51

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Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

emissions from stationary point sources, given that serious
regulations and effective control measures are applied to
the latter. However, the FPM emissions are irregular,
highly dispersive, and intensity-fluctuated. The precise
measurement of FPM is challenging but worthy, yet there iscurrently no uniform regulation or standard measurement
procedure in place. In this work, we first introduced thesources of FPMs both natural and artificial, including soil
& desert dust, wildfire smoke, volcanic ash, sea spray aerosols, pollen and fungi grains, traffic and transportation activities, heavy constructions, agriculture and livestock
operations, mining and quarrying, metal processing, andresidential combustion. Although the sources are diverse,
the FPM can be sorted into five main categories: minerals,
metallic particles, soot, inorganic salts, and biogenic substances. Each category possesses distinctive properties,
resulting in varying effects on human health and the environment. Next, we provided an overview of conventional
PM measurement methods. Sampling is necessary for thesemethods, with the upwind–downwind technique widelyadopted. Filter-based methods are accurate, but the time
resolution is generally poor (except for TEOM). Additionally, the filter can be used for offline chemical analysis. We
also discussed two conventional optical methods, namely
the nephelometer and particle counter, and demonstrated
the basics of light scattering. Furthermore, we briefly explained electrical methods and real-time chemical characterization techniques. Conventional PM measurement
belongs to point measurement which can provide scarce
information about the spatial distribution of FPM. In response, open-path and image-based technologies have beendeveloped. Lidar technology, for example, is an easyperformed method to detect FPM. The light extinction coefficient can be derived from the Lidar data to evaluate thedegree of pollution. A camera-based bistatic Lidar has been
proposed to avoid expensive time-of-flight electronics inmonostatic Lidar. Additionally, several hybrid remote sensing strategies combing Lidar with sampling gravimetricanalysis have been illustrated. Image-based optical methods have also been used to detect FPM, especially firesmoke. Low-cost sensors show great promise for widespread FPM monitoring because they are light and small,and can be equipped on drones for dynamic measurement.
Moreover, real-time LIBS holds great potential for in-situ
chemical analysis.
By far, the mass concentration is the most concernedparameter in FPM measurement. Other factors such as size
distribution, shape, and complex refractive index are lessmentioned. Light-scattering-based methods have the potential to reveal more information about particles by performing multi-angle, multi-wavelength, and polarization/
depolarization measurements. However, these measurement systems are much more complicated, and may not be
feasible for field measurements. The image-based optical52

methods are promising for particle measurement since thespatial and temporal light signals (often scattering light)
can be recorded simultaneously. Besides, machinelearning/deep learning algorithms can be applied to decouple the relationship between light patterns and particle
clouds. It is feasible to build an FPM monitoring network
using low-cost, compact, mobile, and connectable sensors.
Individuals equipped with portable sensors can contributeto the data collection process, and drone-based sensor systems can effectively monitor dangerous or hard-to-reachedplaces, enhancing the overall effectiveness of the monitoring network.

Acknowledgments
Financial support from the National Key Research andDevelopment Project of China (2017YFC0211500) and theShanghai Sailing Program (22YF1429600) is gratefully
acknowledged.

Nomenclature
AMS

Aerosol mass spectrometer

BAM

Beta attenuation monitoringBC

Black carbon

BrC

Brown carbon

CPC

Condensed particle counter

DMA

Differential mobility analyzer

EARLINET

European Aerosol Research Lidar Network

EC

Element carbon

ELPI

Electrical low-pressure impactor

EM

Electron microscopy

EPA

U.S. Environmental Protection Agency

FDMS

Filter dynamic measurement system

FMPS

Fast mobility particle sizer

FP

Fine particulate

FPM

Fugitive particulate matterh-ORS

Hybrid optical remote sensingIC

Ion chromatography

ICP-MS

Inductively coupled plasma-mass spectrometry

IP

Inhalable particulate

ISO

International Organization for Standardization

LIBS

Laser-induced breakdown spectroscopy

Lidar

Light detection and ranging

LPM

Liter per minute

MPL

Micro pulse Lidar

NAAQS

National Ambient Air Quality Standards of the
U.S.

OC

Organic carbon

OPC

Optical particle counter

OP-FTIR

Open-path Fourier transform infrared spectroscopy

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

OP-LT

Open-path laser transmissometer

PI-SWERL

Portable in-situ wind erosion laboratory

PM

Particulate matter

PM2.5

Particulate matter with a diameter of 2.5 μm orless in size

PM10–2.5

PM with diameters generally larger than 2.5 μm
and smaller than or equal to 10 μm

PM10Particulate matter with a diameter of 10 μm or lessin size

SMPS

Scanning mobility particle sizer

SP

Suspended particulate

SSA

Single scatter albedo

TEOM

Tapered element oscillating microbalances

TSP

Total suspended particulate

UFP

Ultra fine particulate

WHO

World Health Organization

XRF

X-ray fluorescenceapolluting activity rates

bext

extinction coefficient (Mm–1)

bsca

scattering coefficient (Mm–1)

babs

absorption coefficient (Mm–1)

D

particle diameter (μm)Ḟ

mass emission factorI

transmitted light intensity (J)I0initial light intensity (J)kKoschmieder constant (-)

L

path length of the light (m)ṁmass emission rate (μg h–1)mcomplex refractive index (-)

nrefractive index (-)

Vvolume flow rate of flue (m3 h–1)γmmass concentration (μg m–3)

Vis

visibility (km)(21)α

dimensionless particle size parameter (-)λ

wavelength of extinction light (nm)ƞabsorption coefficient (-)

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09.014

Authors’ Short Biographies
Tianyi Cai
Tianyi Cai received his bachelor’s degree and a Ph.D.in Engineering from Southeast University in2014 and 2020, respectively. He is now a lecturer in the School of Energy and Power Engineering at
the University of Shanghai for Science and Technology. His research interests include particles andtwo-phase flow measurement, heterogeneous reaction flow measurement. He also researches ontheoretical modeling of the heterogeneous reactions related to various carbon capture and utilization
processes.

Wu ZhouProf. Wu Zhou received her degree from Nanjing University of Science and Technology in 2006
and a Ph.D.in Engineering from Southeast University in 2011.She is now an professor in the Schoolof Energy and Power Engineering at the University of Shanghai for Science and Technology. Her
research interests include particles and two-phase flow measurement, combustion measurement,
mainly by image-based methods. She is also interested in mathematical modelling of the above processes. She is a member of Youth Council of the Chinese Society of Particuology.

57

KONA Powder and Particle Journal No. 41 (2024) 58–77/https://doi.org/10.14356/kona.2024010

Review Paper

About Modeling and Optimization of Solid Bowl Centrifuges †Marco Gleiss * and Hermann Nirschl
Institute of Mechanical Process Engineering and Mechanics, Karlsruhe Institute of Technology (KIT), Germany

Many processes involve solid bowl centrifuges as a solid–liquid separation step,
typically used for clarification, thickening, classification, degritting, mechanicaldewatering, and screening. In order to operate solid bowl centrifuges safely, withminimum resource consumption and reduced setup times, modeling and optimization
are necessary steps. This is a challenge due to the complex process behavior, which can
be overcome by developing advanced physical models and process analysis. This review
provides an overview of solid bowl centrifuge applications, their modeling, andaddresses future optimization potentials through digital tools. The impact of dispersed
phase properties such as particle size, shape, surface roughness, structure, composition,and continuous liquid phase is the reason for the lack of generally applicable models.
Laboratory-scale batch sedimentation centrifuges are used to predict material behavior
and develop material functions describing separation-related properties such as
sedimentation, sediment build-up and sediment transport. The combination of material
functions and modeling allows accurate simulation of solid bowl centrifuges fromlaboratory to industrial scale. Since models usually do not cover all influencing
variables, there are often deviations between predictions and the real process behavior.
Gray-box modeling and on-line or in-situ process analytics are tools to improve centrifuge operation.
Keywords: solid bowl centrifuges, material characterization, multiphase flow, process modeling, gray-box modeling, process analytics

1. Introduction

The separation of fine particles from liquids plays animportant role in daily products such as chemicals, pharmaceuticals, healthcare products, ingredients, beverages,
proteins, food or consumer goods, see Table 1 (Anlauf,
2007). Solid–liquid separation is an essential step in theprocessing and handling of products that are often obtainedby precipitation, crystallization, or comminution.
The two main principles of solid–liquid separation are
filtration and centrifugation. Filtration requires a filter medium that retains particles, while centrifugation relies on arotating bowl.
Solid bowl centrifuges are sedimentation centrifuges anduse the physical principle of sedimentation, based on adensity difference between the solid and liquid phases. In
the industry, there are several types of centrifuges that allow the separation of colloids or particles with several
millimeters in size. Typical machines are solid bowl peeler
centrifuges, tubular bowl centrifuges, disk stack centrifuges, or decanter centrifuges.
Fig. 1 shows the categorization of different centrifuge
types in terms of cut size, which is the particle size that has
an equal chance of being in the underflow or overflow. The
†

*

58

Received 14 May 2022; Accepted 8 September 2022
J-STAGE Advance published online 30 June 2023
Corresponding author: Marco Gleiss;
Add: Straße am Forum 8, 76131 Karlsruhe, Germany
E-mail: marco.gleiss@kit.edu
TEL: +49-721-608-42426 FAX: +49-721-608-40405

influencing variables are numerous, and the cut size depends not only on the process conditions and the centrifuge
geometry, but also on the properties of the dispersed andcontinuous phases. Furthermore, the limits can be arbitrarily shifted by suspension pre-treatment such as coagulation or flocculation.
For a spherical particle, in a creeping flow and infinitely
diluted Newtonian fluid, the Stokes’ settling velocity isvalid (Stokes, 1850): ρs  ρl C g x 2
vs 

(1)

18 μl

Here, ρs is the density of solid phase, ρl is the density ofliquid, g is gravity, x is the particle diameter, and μl is the(1)
dynamic
viscosity. The Stokes’ settling velocity can be derived from an equilibrium of forces consisting of buoyancy,
centrifugal, and drag force, neglecting the influence of inertia and assuming small particle Reynolds number Re < 0.1.
For semi-batch or continuous solid bowl centrifuges,
modeling requires the linkage between process conditions,geometric dimensions and material properties (Ambler,1952). One approach to scale-up of solid bowl centrifuges
is Sigma theory, which requires a significant amount ofpilot testing, making the design process time-consuming
and costly.
Increasing computational power and improvements insensor technology have led to the development of morecomprehensive and detailed models and measurement
techniques in recent years (Gleiss et al., 2020; Hammerich

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Marco Gleiss et al. / KONA Powder and Particle Journal No. 41 (2024) 58–77

Review Paper

Fig. 1 Classification of different centrifuge types depending on the operation mode and applicable particle size range. Adapted from Ref. (Spelter etal., 2010) with permission from Elsevier, Copyright 2010.
Table 1 Overview of different applications of solid bowl centrifuges.

Centrifuge type

Field of application

Tubular bowl centrifuge

Biotechnology a, b
Nanotechnology c, d, eBattery materials f, g

Disc stack centrifuge

Protein separation h
Bio fuels i, j
Pharmaceuticals k, l
Oil industry m

Decanter centrifuge

Food industry n, o
Biotechnology p
Wastewater treatment q, rProtein separation s, t
Minerals processing u, va(Spelter et al., 2010), b(Rathore et al., 2015), c(Kohsakowski et al., 2019),d(Flegler et al., 2016), e(Flegler et al., 2018), f(Sinn et al., 2020), g(Wolf etal., 2021), h(Byrne et al., 2002), i(Najjar and Abu-Shamleh, 2020),
j(Arcigni et al., 2019), k(Kempken et al., 1995), l(Jungbauer, 2013),
m(Khatib et al., 1995), n(Caponio et al., 2014), o(Schubert et al., 2018),
p(Lopes et al., 2013), q(Wakeman, 2007), r(Ginisty et al., 2021), s(Haller etal., 2021), t(Haller and Kulozik, 2019), u(W.F. Leung, 2004), v(Merkl etal., 2012)

et al., 2018). The aim of this review is to provide an overview of the current developments in the field of modelingand optimization of solid bowl centrifuges.
First, the process behavior of solid bowl centrifuges is
explained in more detail. This is followed by a descriptionof the separation properties and their characterization

based on laboratory centrifuges. Finally, different modelingstrategies are discussed.
In this context, process modeling enables the simulationof the process behavior and provides a better understandingof the separation-related behavior of solid bowl centrifuges. Furthermore, this review highlights the capabilities
of dynamic and gray-box modeling. Gray-box modelingcombines physical and data-driven modeling. Direct coupling here means the integration of process analytics tomeasure particle properties on-line or in-situ, since dynamic modeling usually cannot capture all physical effects
with sufficient accuracy.

2. Process behavior of solid bowl centrifuges
The process behavior of solid bowl centrifuges varies
considerably depending on the type of centrifuge and affects the operation. The reasons for this are manifold. The
most important influencing variables can be divided into
three groups: centrifuge geometry, material properties andprocess conditions.

2.1 Batch and semi-batch centrifuges
Analytical and ultracentrifuges have a high degree of
flexibility due to batch operation. The process time can be
adjusted according to the sedimentation behavior, but islimited to small volumes and laboratory scale.
Compared to batch sedimentation centrifuges, semibatch centrifuges have an overflow weir that forms a liquid
pond. This allows continuous clarification of the liquid
phase. Centrifugal acceleration is the driving force forparticle separation. As a result, the accumulated solids forma liquid-saturated sediment on the rotor wall. The sediment59

Marco Gleiss et al. / KONA Powder and Particle Journal No. 41 (2024) 58–77

build-up influences the flow behavior by shortening theresidence time.
For example, tubular bowl centrifuges are semi-batch
operating machines and have a slim rotor. Conventional
bearing systems provide high relative centrifugal numbersof C = 70,000 with special magnetic bearing technology of
C = 120,000 (Konrath et al., 2016). The relative centrifugal
number is an important value indicating the ratio of centrifugal acceleration to gravity. Due to the high relative centrifugal force, tubular bowl centrifuges are typically used
for separation and classification of colloidal particles, bacteria, or human blood. Disk stack centrifuges operate either
in semi-batch or continuous mode. Manual removal of
sediment from the rotor is required for semi-batch operation, which is commonly used for dilute suspensions.

2.2 Continuous centrifuges

Decanter and disk stack centrifuges are continuously
operating machines that have a discharge mechanism forboth the clarified liquid (overflow weir and peeling disk)
and the sediment.
Disk stack centrifuges consist of a conical rotor andconically inclined disks, forming a number of small flow
channel which significantly shortens the settling distance.
As a result, biological particles such as microorganisms,
yeast, mammalian cells or algae can be separated. It is
crucial that these substances have only a small density difference to the liquid phase. This fact makes it necessary tohave a high centrifugal force. Discharge can be either intermittent, pressure controlled or through nozzles. The choiceof discharge mechanism depends on the material properties
and operating conditions. Typically, discharge of materials
in disk stack centrifuges requires a flowable sediment.
The main components of a decanter centrifuge are a feed
tube, a cylindrical conical bowl, and a screw conveyor. The
screw conveyor transports the collected solids to the sediment discharge located in the conical section of the machine. The material behavior of the sediment has asignificant influence on the performance of the centrifuge.
Incompressible sediments exhibit a constant pressure gradient resulting in a constant sediment porosity. The centrifugal force leads to undersaturation of the sediment after it
leaves the liquid pond.
In contrast to the aforementioned behavior, interparticle
forces, which are dominant for finely dispersed particles
influence the sediment formation process in a different way.
Here, the sediment is compressible and high capillary pressures occur within the sediment resulting in saturation of
S = 1. The compressive forces in the sediment lead to anonlinear porosity gradient. Insufficiently compacted compressible sediments tend to flow easily and interfere withsediment transport.
To overcome this issue, a plain disk is welded at theconveyor hub between the feed zone and the conical part

60

Review Paper(Records and Sutherland, 2001). The cake baffle disk has asmaller diameter than the centrifuge bowl and creates a gap
which serves for the pre-compaction of the material. This
ensures a highly compacted non-flowable sediment during
the operation.
Another important function of the cake baffle is to improve separation by preventing turbulence and resuspension of previously separated particles. Furthermore, it ispossible to increase the pond level, which has a positiveeffect on the separation efficiency, especially for fine particles with low settling velocity.

2.3 Flow conditions

The flow conditions in solid bowl centrifuges are crucial
for achieving the desired separation efficiency. In semibatch and continuous solid bowl centrifuges, the liquid
pond moves almost like a rigid-body. In practice, however,
the tangential velocity is smaller than rigid-body motion
due to friction losses or insufficient pre-acceleration of thefeed. One way of describing the real tangential velocity
analytically is a power law that relates the theoretical andactual values as a function of radial position (Gösele, 1968;
Reuter, 1967). When deviations from the rigid-body motion are present, internals such as the screw conveyor,
disks, internals or deflectors improve the pre-acceleration(Romani Fernández et al., 2010).
The axial velocity in solid bowl centrifuges occurs due
to mass conservation between the feed and centrate. In theliterature, two limiting cases are distinguished: a plug flow
and a boundary layer flow. A uniform flow profile acrossthe entire liquid pond characterizes the plug flow (Ambler,
1959; Stahl and Langeloh, 1983). In comparison, theboundary layer flow consists of two different zones: afast-flowing layer near the gas-liquid interface and a stagnant layer below (Leung, 1998). For a particle entering thestagnant layer, the influence of axial velocity is negligible.
Both flow profiles do not accurately represent the real
flow behavior. This has been demonstrated by recent experimental and numerical studies based on Laser Doppler
Anemometry (LDA) and Computational Fluid Dynamics(CFD) (Spelter et al., 2011). The advantage of both methods is the ability to study the flow behavior in a solid bowl
centrifuge in space and time (Romani Fernández et al.,2009).
Fig. 2 (I) illustrates the simulation of a mixture of water(blue) and air (red), its distribution in a vertical solid bowl
centrifuge type STA at a constant flow rate of 150 L/min
and a rotational speed of 2550 rpm. The method investigated here was the Volume-of-Fluid (VOF) method which
considers the physical behavior of immiscible liquids
where surface tension acts on both phases and forms a clear
phase boundary between gas and liquid. Coriolis andcentrifugal forces were included in the conservation of
momentum in the form of volumetric forces (Hirt et al.,

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Review Paper

Fig. 2 Left: (I) Contours of volume fraction for air (red) and water (blue) (II) Contours of tangential velocity at 2550 rpm and 140 L/min inlet flow.
(III) Axial velocity of water versus radius for different angular positions (0°, 30°, 45°, 60°) at the height of z = 0.3 m. Operation conditions are
150 L/min and 2550 rpm. Adapted with permission from Ref. (Romani Fernández et al., 2010) under the terms of the CC-BY 4.0 license. Copyright(2010) the Authors, published by Hindawi.

1981). The simulation indicates a sharp gas-liquid interface
and the formation of a liquid pond. Furthermore, the liquid
splits into smaller droplets due the pre-acceleration(Romani Fernández et al., 2010).
A closer look at Fig. 2 (II) depicts a contour plot of thetangential velocity. The increase of tangential velocity occurs due to the previously mentioned rigid-body motion of
the rotor.
The resulting axial flow profile for the investigated vertical solid bowl centrifuge at different angles is shown in
Fig. 2 (III). A detailed analysis of the axial velocity suggests that neither plug flow nor boundary layer flow ispresent. This is due to the absence of slip on the inner wallof the rotor and reduced friction at the gas-liquid interface.
Decanter and disk stack centrifuges have a different flow
behavior compared to vertical solid bowl centrifuges. The
screw conveyor and the disk stack counteract a pure axial
flow. Here, a complex flow behavior is present, which not
only depends on the centrifuge geometry, but also on theflow rate and centrifugal acceleration. In the case of a decanter centrifuge, the screw conveyor and the overflow
weir form a liquid pond. The main flow occurs along theformed helical channel. The mentioned flow direction wasinvestigated by color tracer experiments in a transparent
decanter centrifuge (Faust, 1985). The tracer observation
indicates insufficiently pre-accelerated liquid in the feed
zone, where the liquid is deflected towards the conical part,reverses and flows in the direction of the overflow weir.
In disk stack centrifuges, friction between the disks
dominates which results in hydrodynamic instabilities(Janoske et al., 1999). The flow through the disks is usuallybased on a superposition of tangential, radial, and axial
velocities. This causes the particle to settle on a threedimensional trajectory along the formed disk channel toward the centrate (König et al., 2021). Disk spacing withaxial stands between two disks minimizes the effect of
centripetal force on the particle movement and has a posi-tive effect on the separation efficiency.
Predicting local and temporal changes in flow behavior
is very difficult, and so far, there is no CFD model that cansimulate the flow conditions of an entire disk stack centrifuge. The reasons for this are the operating conditions, thedifficult centrifuge geometry, the high pressure and velocity gradients, and the momentum exchange between thesolid and liquid phases.

2.4 Residence time behavior

Another promising technique to evaluate the flow behavior of solid bowl centrifuges, is the indirect measurement of
the residence time distribution. This method allows to investigate the residence time of fluids or particles in any
kind of semi-batch or continuous apparatus. Fig. 3 illustrates three methods for characterizing the residence time
behavior of a machine. The tracer experiment (Fig. 3 (I)) is
the simplest method with small experimental effort and low
cost for the measurement equipment.
Saturated sodium chloride solution is a tracer that increases the conductivity of the continuous liquid phase after injection, and a sensor at the centrate tracks the change
in conductivity until no change occurs. To avoid phaseseparation during conductivity measurement, the operator
must ensure that the density of the sodium chloride solution
is close to the density of the continuous phase.
The method has been successfully applied to verticalsolid bowl, tubular bowl and decanter centrifuges. Residence time measurements for vertical and tubular bowl
centrifuges show a broad distribution. This distributiondiffers significantly from the behavior calculated by plug orboundary layer flow (Konrath et al., 2015; Romani
Fernández, 2012). In the case of a decanter centrifuge, thevolume flow and the solids content have a significant influence on the residence time distribution (Frost, 2000).
Another method of indirectly measuring flow behavior is
to experimentally characterize system behavior (Fig. 3 (II))

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Table 2 Comparison of hindered settling functions to describe the settling behavior of a concentrated slurry with uniform settling velocity.

AuthorHindered settling function

Richardson et al. av  vSt 1    RZ

Michaels et al. b v  vSt 1  max 

n

n

(11)
μ

Ekdawi et al. c  l maxv  vSt 1    1  max (12)a(Richardson et al., 1954), b(Michaels et al., 1962), c(Ekdawi etal., 1985)(13)

Fig. 3 Schematic representation of three methods to determine residence time behavior. (I) Tracer experiment, (II) Characterization ofsystem behavior, (III) Numerical simulation. Reprinted with permission from Ref. (Gleiß, 2018) under the terms of the CC-BY 4.0 license.
Copyright: 2018, the Authors, published by KIT scientific publishing.

by examining a step response following a sudden change involumetric flow rate or solids volume fraction during
feeding. Compared to tracer experiments, step response
measurements require considerably more equipment. An
experimental setup consists of two storage tanks for two
feed suspensions with different solids volume fractions anda three-way valve to cause a sudden change in the feed
composition. Time-dependent analysis of the step response
can be performed by time sampling of the centrate flow andgravimetric measurement of the solids volume fraction(Gleiss et al., 2017). A more automated method of measuring step response is to integrate a turbidity sensor on thecentrate for on-line prediction of solids content (Konrath etal., 2014).
The third promising approach to characterize the residence time behavior is CFD simulation (Fig. 3 (III)),
which requires a model to evaluate the flow behavior insolid bowl centrifuges (Gleiß, 2018). Numerical simulations are usually not possible without calibration with experimental data, as CFD relies on many assumptions, suchas turbulence models.

3. Separation behavior
The selection and operation of solid bowl centrifuges
depends not only on the flow conditions, but also on thematerial properties, which are influenced by the particle
and liquid phase properties. Particle size, particle shape,
particle interactions, solids content, densities, and liquid
viscosity play an important role. Moreover, a distinction
must be made between the properties of individual particles, agglomerates and bulk materials (Hammerich et al.,2019a). When evaluating the separation process in batch or62

semi-batch centrifuges, the sedimentation behavior andsediment build-up are important parameters. For continuous centrifuges, sediment transport and discharge must also
be taken into account (Karolis et al., 1986). It is not possible to draw general theoretical conclusions about the separation process in solid bowl centrifuges because theinfluencing variables at the particle level are manifold.

3.1 Settling behavior

Sedimentation is the settling of particles in a surrounding
liquid phase. Typically, sedimentation can be divided into
four areas: free settling, cluster formation, cluster breaking,and hindered settling (Bhatty, 1986). Free settling meansthat the particles do not interfere with each other and settle
under Stokes’ conditions. Moderate increases in solids
content lead to clustering and faster settling. At a criticallevel, the clusters are destroyed by the upward flow, reducing the settling rate. Further increase in solids content leads
to slower settling due to increased momentum exchange.
Theoretical prediction of the described behavior as a function of particle properties is time-consuming and requires
resolved simulations at the particle level (Romaní
Fernández et al., 2013).
Experimental investigation of the influence of particle
and continuous phase properties is therefore more effective. In practice, empirical material functions are commonly used to describe the influence of the solids content
on the settling velocity. A large number of researchers havestudied the settling behavior of monodisperse (Richardson
et al., 1954) and polydisperse particles (Al-Naafá et al.,
1989; Beiser et al., 2004; Ettmayr et al., 2001; Ha et al.,2002). This is summarized in Table 2. One approach todescribe hindered settling is based on (Richardson et al.,1954) and is valid for monodisperse suspensions, where theexponent nRZ depends on the particle Reynolds number.
The approach by Michaels et al. (1962) treats flocculated
systems and considers two correction factors (n, ϕmax) for
hindered settling. The approach by Ekdawi et al. (1985)
takes into account the influence of maximum packing andliquid viscosity.

Marco Gleiss et al. / KONA Powder and Particle Journal No. 41 (2024) 58–77
Table 3 Comparison of different empirical and semi-empirical equations describing compressive yield stress as a function of solids volume
fraction.

AuthorCompressive yield stress

Auzerais et al. a

ps  aBuscall et al. b

ps = a(ϕ – ϕgel)mGreen et al. c  
(14)p p 

Landman et al. d

 
 2
 1
ps  p1 (15)  gel Usher et al. e a max   b    gel  
ps    gel(16) s1  gel 

contacts. The common practice is to fit the material functions to experimental data which are based on batch centrifugation test on a laboratory scale.

4. Material characterization

nmax  

p2

Review Paper

Laboratory and pilot scale trials are essential for the design and optimization of solid bowl centrifuges. However,
due to the amount of material and time required, it is advisable to keep the experimental effort of pilot tests to a minimum. For this purpose, a variety of laboratory centrifuges
are available to measure material-related properties such as
particle size distribution, sedimentation behavior and sediment compressibility. These are described in more detail in
the following sections.


 1

p

k

a(Auzerais et al., 1988), b(Buscall et al., 1988), c(Green et al.,1996), d(Landman et al., 1995), e(Usher et al., 2013)(17)
For dilute suspensions and polydisperse particles, thesettling velocity is not uniform. Rather, a settling velocity
distribution can be observed in the experimental characterization (Balbierer et al., 2019). This makes the development of material functions much more complex, as theinfluence of different particle fractions or species on thesedimentation behavior must be taken into account.

3.2 Sediment formation process

Particles accumulate on the rotor wall after sedimentation and form a liquid-saturated sediment, which allows thetransfer of normal and shear forces due to permanent particle contacts. A saturated sediment behaves either incompressible or compressible while forming.
Incompressible sediments consist mostly of coarser particles x > 10 μm. In this size range, inertial forces dominate, whereas in compressible sediments the influence ofinterparticle forces increases. If the particles are uniformly
distributed in the sediment, incompressible materials havea constant porosity. In contrast, a compressible sedimentexhibits a porosity gradient based on compressive forces inthe sediment. Compressibility modeling combines compressive yield stress and solids volume fraction, wherecompressive yield stress is defined as the integral strengthof a particle network.
Table 3 compares different material functions to modelcompressive yield stress depending on solids volume fraction. All presented models are empirical or semi-empiricalequations which use fit parameters (a, b, k, m, n, p1, and p2)
and characteristic value such as gel point ϕgel or maximum
packing density ϕmax. The gel point determines the transition from a suspension to a sediment (Skinner et al., 2016).
Here, the material behavior changes abruptly, since forces
are transferable in the sediment due to permanent particle

4.1 Particle size distribution

The particle size is an imperative parameter for the evaluation and design of solid bowl centrifuges. Since the particles are not uniformly distributed, it is helpful to measurea particle size distribution (PSD).
Since most particles are non-spherical, the concept ofequivalent diameter is applied to calculate particle size.
The equivalent diameter is a reference value to the real
particle diameter and depends on the measurement technique. Examples are the Sauter’, the Feret’, and the Stokes’
diameter. For solid bowl centrifuges, it is desirable to use
sedimentation-based methods for particle size analysis. All
sedimentation methods have in common that the determination of the equivalent diameter is based on the Stokes’
velocity.
Typically, sedimentation methods are divided into incremental or cumulative methods. For the incremental method,
three main principles are conceivable: the pipette, thephoto-, and the X-ray sedimentation method (Hayakawa etal., 1998). The cumulative method also differentiates threemeasuring principles: the manometer centrifuge, the sedimentation balance, and the imbalance centrifuge (Yoshida
et al., 2001).
Two analytical centrifuges based on the incremental
method are CPS disk centrifuge and LUMiSizer® analytical centrifuge (Detloff et al., 2007; Neumann et al., 2013).
The CPS disk centrifuge uses the well known principleof differential centrifugal sedimentation (DCS). The particle size is analyzed by applying a density gradient in therotating disk. After injection of a small amount of suspension, the particles migrate in radial direction depending on
their individual settling velocity. Particles are counted by a
CCD sensor which measures the signal of a monochromatic laser beam.
In contrast to the DCS, the measuring principle of the
LUMiSizer analytical centrifuge is the spatial and temporalmeasurement of absorbance in a rectangular cuvette(Detloff et al., 2007). Both methods use Stokes’ settling
velocity to calculate the equivalent particle diameter. This

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20

C=21
18

Sediment height (mm )supposes additional physical parameters such as the solid
density, liquid density, liquid viscosity, and the refractive
index of both phases.
There are other physical principles to measure particle
size or shape distributions, such as laser diffraction, ultrasonic spectroscopy, X-ray scattering or dynamic light scattering. Details on the measuring principles can be found in(Frank et al., 2022).

C=42
C=212

16

C=302
14

C=214612
10
8Real�ve
centrifugal
number

6
42

4.2 Settling behavior measurement

The use of measuring cylinders in the Earth’s gravitational field is one way to measure the sedimentation rate.
For concentrated suspensions, experiments usually show aclear phase boundary between the clarified liquid and thesedimenting solids, indicating hindered settling (Lester etal., 2005). Modern instruments measure the settling velocity automatically (Horozov et al., 2004). To reduce measuring time, the settling behavior of finely dispersed particles
can be determined using analytical centrifuges (Detloff etal., 2007). According to the measuring principle of particle
size measurement, the temporal and spatial variation ofabsorbance along the radial position of a cuvette in the
LUMiSizer is used to calculate the settling velocity distribution from experimental data.

4.3 Sediment characterization

Batch sedimenting centrifuges are also suitable for theexperimental characterization of sediment formation. There
are several methods that differ in the amount of measurement required and the complexity of data post-processing.
The simplest method is to centrifuge a sample at constant
speed and measure the solids volume fraction gravimetrically. The volume-averaged compressive yield stress canbe derived from the analysis of the sediment at different
speeds. However, information about the porosity gradientin the sediment is lost by this method. Alternatively, twodifferent measurement setups in combination with batch
sedimenting centrifuges allow to measure the local solids
volume fraction as a function of sediment height.
The centrifuged sediment can be divided into a defined
number of layers after centrifuging. The solid volume fraction of each layer is then measured gravimetrically. A disadvantage of this method is the destruction of the sediment.
Non-invasive X-ray measurements are another promising technique for the analysis of local sediment structures.
Examples for measuring cake structures with X-rays are
the LUMiReader X-Ray or micro-computed tomography(μCT) (Peth, 2010). Both measurement principles are limited to the Earth’s gravitational field, which requires a twostep measurement procedure based on centrifugation andsubsequent X-ray measurement (Sobisch et al., 2016).
Fig. 4 illustrates the experimental result of the mentioned two-step procedure for solids volume fraction depending on sediment height. The suspension studied was a

64

0
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Solids volume fraction (-)

Fig. 4 Spatial distribution of solids volume fraction in a limestone
sediment as a function of centrifugal acceleration. The measurement
requires a two-step measuring procedure. First, suspension is centrifuged with the LUMiSizer analytical centrifuge at a speed. Subsequently, the sediment structure is investigated with X-Ray measurementwith the LUMiReader X-Ray.

mixture of limestone and water, which was first centrifuged
and then measured in the LUMiReader X-Ray.
Closer examination of the measured data serves to study
the influence of the relative centrifugal number on thecompressibility of the formed sediment. Two effects areobservable. First, the solids volume fraction increases toward the bottom of the cell. Second, the solids volume
fraction of the sediment shifts to a higher value with increasing centrifugal acceleration.
However, this method is only applicable to sedimentsthat exhibit plastic behavior. This means that the sedimentdoes not tend to elastic recovery after reducing the centrifugal force (Hammerich et al., 2019b). Typically, biologicalsystems tend to behave elastically due to the existing cellstructure (Usher et al., 2013).
Additionally, the sediment transport in decanter centrifuges leads to a superposition of shear yield and normal
stress (Erk et al., 2004). Shear stress moves and rearranges
the particles in the sediment, resulting in denser packing(Hammerich et al., 2020; Radel et al., 2022).
To study the influence of shear yield stress on the sediment compressibility, several measurement principles canbe found in the literature. One method is to measure theeffect of shear in a vane-under-compressional-loading rheometer (Höfgen et al., 2020). Here, a cylinder at the top of
the rheometer produces a normal stress on the sediment,
while at the same time a vane rotates at a constant angularvelocity and introduces a shear yield stress. Furthermore,
modified ring shear cells are suitable to analyze sedimentswith a saturation of S = 1. This involves the modification of
the ring shear cell by incorporating semi-permeable membranes at the top and bottom to allow the liquid to escapeduring the experiment (Erk et al., 2004; Hammerich et al.,2020).

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5. Modeling of solid bowl centrifuges

During process development, accurate estimation ofcentrifuge type and size with the lowest possible productand resource consumption is essential. It is also importantto optimize existing solid bowl centrifuges to increase
productivity and minimize energy consumption and maintenance.
The following section provides an overview of different
methods for predicting centrifuge size and process behavior. A variety of approaches can be found in the literature.
This review focuses on the scale-up using analytical equations such as sigma theory, process modeling for real-time
simulation, CFD simulations of solid bowl centrifuges, andthe extension of process modeling to gray-box models toimprove the predictive accuracy of process models(Menesklou et al., 2021a).

5.1 Steady-state models

The first published theory to describe clarification insolid bowl centrifuges is the Sigma theory (Ambler, 1952).
Sigma theory is based on several assumptions. These include spherical particles in a creeping flow, uniform flow,
and Stokes settling. Also, the approach neglects insufficient
pre-acceleration or turbulences, resulting in the assumptionof uniform distribution of the solid at the feed. Furthermore, hydrodynamic interactions between solid and liquid
which are present for high solids content and the sedimentbuild-up are also not considered (Leung, 1998).
The basis of the original Sigma theory is a shallow pondwith (rw/rb ≥ 0.75). Here, rw is the weir radius and rb is thebowl radius. Extensions are available for deep pond solidbowl centrifuges (rw/rb ≤ 0.65) (Records and Sutherland,2001). To predict the clarification, the theory assumes thata solid bowl centrifuge removes the cut size with a probability of 50 %. With the above assumption, the flow rate ofa solid bowl centrifuge can be calculated by the product ofsettling velocity in the earth’s gravity field and the Σ-value
which is defined as an equivalent clarification area:

Qf  2 Σ vg

(2)

Looking more closely at Table 4, the Σ-value considersthe(2)
influence of the centrifuge geometry by integrating theweir radius, the bowl radius, and the centrifuge length.
Additionally, the Σ-value takes the angular velocity into
account. At this point it should be noted, that there are further modifications of the Sigma theory, which can be foundin the literature (Ambler, 1952; 1959). Due to the previously mentioned assumptions underlying the Sigma theory,
it is not reasonable to use this approach for designing thecapacity of a solid bowl centrifuge. Instead, Sigma theory
is applied to transfer for a geometrically similar machine toa larger scale with known experimental pilot scale data. For
tubular bowl and decanter centrifuges, the radial dimension
is defined by the bowl radius and the weir radius. For disc

Table 4 Overview of Σ-value for different types of solid bowl centrifuges. According to Ref. (Ambler, 1961).

Centrifuge type

Sigma value

Batch sedimenting centrifuges

ΣTubular bowl or decanter
centrifuges (shallow pond)

Σ

(18)

ω 2V
 2r 2 
4.6 log  rw  rb 

πlω 2
g

rb 2  rw 2 
 2r 2 ln  2 b 2  rb  rw Tubular bowl or decanter
centrifuges (deep pond)πlω 2 rb 2  rw 2 2g
r2
ln  b 2 (19) rw 

Disc stack centrifugesΣΣ

2πnω 2
rdo 3  rdi 3 cot θ3g(20)(21) is defined by r r
stack centrifuges the radial dimensiondo, diand θ, which are the outer radius, inner radius and inclination angle of the disc channel.
In terms of practice, this means that pilot tests are essential for the selection and scale-up of solid bowl centrifuges.
For the description of two geometrically similar machines
the following equation is valid:
Qf 2
Σ
 2
Qf1
Σ1

(3)

The integration of correction factors ξ1 and ξ2 allows totransfer for non-geometrically similar machines:
(3)
Qf 2
ξ Σ
 2 2
(4)
Qf1
ξ1 Σ1
Another mathematical model for the scale-up of decanter centrifuges is the g-volume, which is a modified(4) of the sigma theory. Similar to the Sigma theory, theformg-volume also allows the comparison of geometrically different decanter centrifuges. The g-volume is the ability of adecanter to clarify a suspension, but is not a measure of theavailable surface area as in the Sigma theory, but of theavailable centrifuge volume.
Therefore, the g-volume approach considers the influence of sediment build-up on the clarification process(Stahl, 2004). The dimensionless Leung number has beendeveloped for spin tube, disk, tubular bowl, and decanter
centrifuges. Leung number depends on the properties ofsuspended particles, liquid and design parameters (Leung,2004).

5.2 CFD simulation

CFD is based on the conservation of mass and momentum, also known as the Navier-Stokes equations. The Navier-Stokes equations can only be solved using
numerical methods such as the finite volume method. This

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requires a computational grid. The advantage of CFD is theability to simulate temporal and spatial changes in velocity
and pressure. The optimization of solid bowl centrifuges is
an interesting task in terms of process behavior and energy
consumption. However, due to the presence of several
phases (gas, liquid and solid), the simulation of the material
behavior is very challenging. In addition, high pressure andvelocity gradients lead to very small time steps.
When simulating multiphase flows, CFD distinguishes
between the Euler-Euler and Euler-Lagrange methods. The
Euler-Euler method treats the continuous and disperse
phases as interacting continua. A volume-averaged dragforce, allows to model the momentum exchange between
several phases. In contrast, the Euler-Lagrange method
uses a Euler model to estimate the behavior of the continuous phase. The dispersed phase is modeled by Newton’s
equation of motion. The implementation of a drag force isessential to calculate the momentum exchange between
discrete particles and the liquid phase.
One problem with the computational time of the EulerLagrange method is that sediments in solid bowl centrifuges consist of billions of particles. Simulating this large
number of particles is not possible with today’s computing
power.
One promising method to solve this issue is to reduce thedomain size. For solid bowl centrifuges, this means that
only specific parts of the machine such as the infeed wheel
or discharge zone are simulated (Bell et al., 2016;
Törnblom, 2018).
A suitable method for simulating the velocity and pressure field of gas-liquid flows without taking into accountthe influence of particles is the Volume-of-Fluid (VOF)
method (Hirt et al., 1981). VOF is mostly used to simulate
gas-liquid interfaces by incorporating the surface tension

Review Paper

as volume force into conservation of momentum (Romani
Fernández et al., 2009). The VOF approach is well suitedfor the optimization process of solid bowl centrifuges withthe target of minimized friction loss of the rotating liquid
pond to ensure rigid-body motion (Romani Fernández etal., 2010). Furthermore, the coupling of CFD with the discrete element method (DEM) is also used to study the
temporal change of sediment build-up (Romani Fernández
et al., 2013). DEM uses additional forces for particle andparticle–wall interactions by considering nonlinear contact
models.
Another method to study the process of sedimentbuild-up in solid bowl centrifuges is based on an extended
mixture model, as this involves significantly lower computational costs compared to Euler-Euler or Euler-Lagrange
models (Hammerich et al., 2018).
The extended mixture model does not solve the mass andmomentum conservation for each phase separately, but
considers the suspension as a mixed phase. The algorithm
is based on different rheological models for the suspension
and the sediment. Power laws are suitable to consider the
influence of solids volume fraction on suspension viscosity(Quemada, 1977). Whereas sediments in many cases havea yield point which depends for compressible materials on
the solid volume fraction (Erk et al., 2004).
Fig. 5 (I) shows the simulation of the sediment formation process for a constant time step in a tubular bowl centrifuge for two scenarios: a cohesive non-flowable (1.) anda pasty flowable material (2.) (Hammerich et al., 2019a).
The rheological behavior of the sediment has a significant
influence on the sediment distribution. For easily flowablematerials, shear forces at the sediment surface result in asediment transport and a uniform distribution of theaccumulated solids. For cohesive materials, the friction

Fig. 5 (I) Contour plot of axial velocity and sediment solids volume fraction in a tubular bowl centrifuge type Z11 for two materials with different
rheological behavior. 1. Cohesive non-flowable sediment. 2. Pasty, flowable sediment. (II) Residence time distribution F as a function of flow numberτ for three cases: empty machine (a), sediment formation process of a cohesive sediment (b) and a pasty sediment (c). Adapted with permission from
Ref. (Hammerich et al., 2019a). Copyright 2019, Wiley.

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within the sediment is high and a slope angle has formed,
leading to higher axial dispersion and back-mixing in theflow domain.
Additionally, Fig. 5 (II) compares the residence time
distribution F depending on the flow number τ for threedifferent scenarios. The flow number is the ratio of elapsed
time to mean residence time and allows to compare theresidence time distribution of different centrifuges and operating conditions.
Further investigations are devoted to the flow field in
disk stack centrifuges. For biological systems, the shear
stress in the feed and discharge zone is crucial to keep thestress on microorganisms low and the vitality high, such as
in mammalian cell cultures (Shekhawat et al., 2018). To
predict the separation behavior of a disk stack centrifuge,detailed information about the flow and particle trajectories
within two disks is necessary. A suitable simulation method
is direct numerical simulation (DNS) which can be used to
predict flow instabilities that significantly affect particle
trajectories (Janoske et al., 1999b). Furthermore, CFD is apromising method to compare different centrifuge types
such as disk stack and tubular bowl centrifuges(Esmaeilnejad-Ahranjani et al., 2022).
The complex geometry of a decanter centrifuge with therotating screw conveyor and the cylindrical-conical bowl
makes it difficult to study the process behavior. Few studies
have focused on olive oil and polymer production (Zhu etal., 2013; 2020). Current studies assume stationary rotation
and apply a multi reference frame (MRF) (Zhu et al.,2020). MRF is a stationary approach to treat rotation of asystem and does not consider the transient nature of therelative motion between the screw conveyor and the centrifuge bowl.
In summary, some numerical studies are available. However, some simplifications have been made here in the areaof turbulence modeling and momentum exchange between
solid and liquid.

5.3 Process modeling

In contrast to CFD, the goal of process modeling is tohave a simplified model that can predict the dynamic behavior with minimal computational effort. Ideally, processmodels should be faster than real-time simulation to enablefurther applications such as advanced process control (Sinnet al., 2020).

5.3.1 Batch sedimenting centrifuges

A widely used approach to simulate the sedimentation-consolidation process for batch sedimenting centrifuges is based on a 1D advection-diffusion equation(Bürger et al., 2001; Garrido et al., 2003). These 1D models
assume highly concentrated suspensions where hinderedsettling is present to neglect the influence of segregation(Kynch, 1952).

Review Paper

Essential material properties are the flux density function, the effective solid stress, the density of solid and liquid phase, and the liquid viscosity (Garrido et al., 2000). A
major limitation of 1D models is that they are not suitablefor capturing the physical behavior caused by internal disks
in disk stack centrifuges, sediment transport in decanter
centrifuges, and particle classification for low concentrated
suspensions in tubular bowl centrifuges. Models that consider the geometry and the influence of the particle size
distribution are necessary to describe the above process
behavior.

5.3.2 Tubular bowl centrifuges
The modeling of the technical separation in tubular bowl
centrifuges is complex, since the flow conditions and material properties have a significant influence on the process
behavior. One promising strategy to simulate the process
behavior of tubular bowl centrifuges is a pseudo-2D compartment approach. The idea of compartmentalization is to
distribute the particles along the axial length of the rotor
based on the calculated sedimentation path, see Fig. 6 (II)(Spelter et al., 2013). Compared to steady-state models
such as the Sigma theory, the pseudo-2D compartment
model predicts the temporal change of sediment build-up
and the degree of filling. The degree of filling is the ratio of
sediment to centrifuge volume and provides information
about the current filling status. One limitation of thepseudo-2D compartment model is the modeling of the particle motion which is based on the assumption of a plug
flow neglecting the influence of the turbulences and backmixing (Spelter et al., 2013).
The extension of pseudo-2D to 2D compartment modeling addresses the previously discussed limitation by accounting for nonideal flow behavior by interconnecting adefined number of compartments to approximate the residence time behavior of tubular bowl centrifuges (Gleiss etal., 2020). Fig. 6 illustrates the differences between the twopresented algorithms. The 2D compartment model divides
the tubular bowl centrifuge into two zones: the sedimentation and sediment zone. Both zones consider different material streams and material functions to capture the physicalbehavior of the suspension and sediment. The upper zone is
the sedimentation zone. Compartments of the sedimentation zone consist of one incoming and two outgoingstreams. The feature of the algorithm is to calculate theparticle classification along the rotor for a predefined number of particle classes. A more detailed description of thealgorithm can be found in (Gleiss et al., 2020).
The sediment zone is located below the sedimentation
zone to calculate the sediment build-up for incompressible
and compressible materials. A compartment of the sediment zone consists of one ingoing stream which represents
the separated solids from the sedimentation zone. The relationship between compressive yield stress and solids

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Fig. 6 (I) Schematic representation of a tubular bowl centrifuge. (II)Pseudo 2D-compartment modeling according to Ref. (Spelter et al.,2013) with the discretized sediment zone. (III) 2D-compartment modeling of a tubular bowl centrifuge based on the interconnection of compartments which are based on the sedimentation and sediment zone
(Gleiss et al., 2020).

volume fraction is used to compute sediment compressibility which can be measured in batch sedimenting centrifuges as mentioned earlier in Section 4.3.
It should be noted that the sediment influences the separation process, as the available free flow cross-section is
reduced and results in a shorter residence time.
Fig. 7 depicts the comparison between simulation andexperiment for the sediment distribution of colloidal silica
particles at a flow rate of 0.2 L/min and two different centrifugal numbers of C = 19,200 (I) and C = 38,500 (II) fora tubular bowl centrifuge type Z11 from CEPA. The flow
direction is from left to right. As already mentioned, particles accumulate at the inner wall of the rotor. Due to thepolydispersity of the particle system and different settling
velocities, the sediment accumulation is not uniform, but
has a constant slope along the axial length of the tubular
bowl centrifuge. A closer look at the relative centrifugal
numbers C = 19,200 and C = 38,500 shows different timeintervals for the filling process. For C = 38,500, the rotor
fills faster compared to C = 19,200, because the centrifugal
force leads to faster settling rates and therefore faster solids
accumulation. Analytical equations such as the Sigma theory (see Section 5.1) do not allow to estimate temporalchanges in solid bowl centrifuges. However, this is neces-

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Fig. 7 Comparison between simulation and experiment for sediment
distribution along the axial length Lax of the rotor for colloidal silica
particles at a constant flow rate of 0.2 L/min. (I) C = 19,200, (II)C = 38500.

sary to achieve a constant separation performance, especially for tubular bowl centrifuges.

5.3.3. Decanter centrifuges

The modeling of decanter centrifuges requires the consideration of sediment transport as one of the most important influencing variables during operation. As mentioned
earlier, the material properties of the sediment are critical,
and a distinction must be made between incompressible
and compressible sediments. For incompressible materials,
friction between the material, the screw conveyor and thecentrifuge bowl is dominant. Friction in the conical part ofthe machine has the highest impact on the screw torque(Bell et al., 2014; Reif and Stahl, 1989). After the material
leaves the liquid pond, undersaturation occurs which increases the frictional forces. The required power consumption can be determined from four components: feed
acceleration, product transport, windage and power transmission losses (Bell et al., 2014).
In contrast, compressible sediments have a totally different transport behavior. The pasty consistency leads to considerable difficulties during sediment transport when
applying unfavorable operating conditions. This can lead tototal failure of the machine. However, so far only simplified material functions and models can be found which
represent the rheological behavior of compressible
sediments with good accuracy (Karolis et al., 1986). As

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previously described, the material has a yield point depending on solids volume fraction (Erk et al., 2004; Mladenchev,
2007).
For decanter centrifuges, the concept of 2D compartment modeling is also a suitable approach to calculate theprocess behavior (Gleiss et al., 2017). However, the approach needs to be extended to the process behavior of decanter centrifuges. Fig. 8 (I) shows the structure of a 2D
compartment model for countercurrent decanter centrifuges. In contrast to tubular bowl centrifuges, the simulation domain is an unrolled screw channel which is formedby the screw conveyor and the overflow weir. Furthermore,
Fig. 8 (II) illustrates the detailed structure of a single compartment with the inlet and outlet flows (Menesklou et al.,2020). The 2D compartment model for decanter centrifuges considers two additional material streams in the sediment zone to model the sediment transport by screw

Review Paper

conveyor system (Menesklou et al., 2020). The applicability of the 2D compartment approach for scale-up purposes
was tested using different decanter centrifuges. Fig. 9 (I)depicts the comparison between simulation and experiment
for the solids mass fraction in the centrate for three countercurrent decanter centrifuges of different sizes, which are
not geometrically similar. With increasing speed, a higher
centrifugal force occurs and the solids mass fraction of thecentrate decreases. Another application of 2D compartment
modeling is to simulate the solids mass fraction of the sediment, see Fig. 9 (II) (Menesklou et al., 2021b). The material functions were determined at laboratory scale andintegrated into the 2D compartment model. The successful
simulation of different machine sizes is a first step awayfrom many pilot tests towards a predictive design andscale-up starting from laboratory tests to significantly
reduce the number of pilot tests.

Fig. 8 (I) Schematic representation of a countercurrent decanter centrifuge and unrolled helical screw channel which is the basis of the 2Dcompartment model. (II) Ingoing and outgoing streams for the sedimentation and sediment zone of one compartment in the 2D compartment approach.
Adapted from Ref. (Menesklou et al., 2020) with permission from Elsevier, Copyright 2020.

Fig. 9 (I) Comparison between simulation and experiment for solids mass fraction of the centrate for three countercurrent decanter centrifuges ofdifferent size which are not geometric similar. (II) Comparison between simulation and experiment for the mean solids mass fraction of the concentrate
for three countercurrent decanter centrifuges of different size which are not geometric similar. The investigated product was finely dispersed limestone.
Reprinted from Ref. (Menesklou et al., 2021b) under the terms of the CC-BY 4.0 license. Copyright: (2021) The Authors, published by MDPI.

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A further application of a decanter centrifuge is the particle classification, where the local flow behavior is crucial
for accurately predicting the particle size distribution in thecentrate. While the 2D compartment model assumes a uniform flow profile and ideal back-mixing, extensions of theapproach take into account the local flow profile and theinfluence of screw conveyor motion on the flow (Bai et al.,2021a).

5.3.4 Disk stack centrifuges

The process modeling of disc stack centrifuges is currently limited to the use of analytical equations and does
not consider process dynamics. Existing models are steadystate and divide disk stack centrifuges into different zones:
the pre-acceleration zone, the disk stack zone and the resuspension zone (König et al., 2020). However, it is also conceivable to apply the 2D-compartment model also to disk
stack centrifuges. This would have the advantage of a uniform modeling strategy for different solid bowl centrifuges
which would improve comparability and thus the selectionof the optimal centrifuge type especially for industries
where no centrifuge experts are not available.

5.4 Gray-box modeling

The idea of gray-box modeling is to combine white-box
and black-box models. White-box models represent thephysical laws, i.e. conservation of mass, momentum, andenergy. An example of a white-box model is the 2D compartment model introduced in Section 5.3. Black-box
models are mostly data-driven algorithms (e.g. support
vector machines, linear, nonlinear regression, or neuralnetworks) that are trained and validated with experimental
data (Xiong et al., 2002). Data acquisition can be accomplished by integrating off-line, on-line, or in-situ measurement techniques to determine particle properties at theprocess. On-line and in-situ measurement has tremendous
potential for the safe and cost-effective operation of solidbowl centrifuges and is necessary to collect data for graybox models. Gray-box models are suitable for applications

Review Paper

where white-box models are not accurate enough. For solidbowl centrifuges in particular, there is a complex relationship between centrifuge geometry, material properties andprocess conditions that has not been adequately captured
by white-box models. At the same time, data-driven algorithms require a huge amount of experimental data which isoften not available due to the costs of measurement equipment. Furthermore, black-box models are not extrapolatable and are difficult to adapt to new conditions without
enough new experimental data.
Two basic structures of a gray-box model are the paralleland serial structures which are compared in Fig. 10. The
advantage of the parallel structure is that the white-box andblack-box models can be easily combined into a gray-box
model. If the white-box model has a poor accuracy theblack-box model takes over to simulate the process. A disadvantage of the parallel structure is that both models do
not work simultaneously. Depending on the accuracy of theprediction, either the white-box or the black-box model isused for the calculation (Menesklou et al., 2021a). In thecase of a white-box model with a poor accuracy, the graybox model will be a pure black-box model, which needs ahuge amount of data.
An improvement to the aforementioned drawback is theserial structure which uses the black-box model to be able
to improve the uncertainties of the white-box model(Nazemzadeh et al., 2021). For example, it is conceivable
to use the black-box model to predict the influence of flow
conditions in a serial gray-box model structure or to correct
separation-related properties, such as the hindered settlingfunction, through on-line learning algorithms. However,this is challenging task and currently part of the research in
the field of particle technology.
Fig. 11 shows experimental results of the solids massfraction in the centrate of a pilot scale decanter centrifuge
and simulation data based on a developed gray-box model
based on a parallel structure. In this study, the white box
model is the previously discussed 2D compartment modeland the black box model corresponds to a neural network

Fig. 10 Comparison between the two main structures of a gray-box model which is typically based on a deterministic and data-driven model. (I)Parallel structure (II) Serial structure.

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Fig. 11 Comparison between white-box and gray-box modeling for theinvestigated centrate mass fraction of a pilot-scale decanter centrifuge
as a function of flow rate and confidence interval at constant speed. The
model system is a limestone-water suspension. Reprinted from Ref.
(Menesklou et al., 2021a) under the terms of the CC-BY 4.0 license.
Copyright 2021, the Authors, published by MDPI.

with one input, hidden and output layer. The training procedure of the neural network consists of a training and a test
phase. The training data includes pilot test data for material
properties measured at the feed, centrate and concentrate
and the relevant process conditions.
Another important point for the application of a graybox model with parallel structure is the confidence interval
necessary to allow the intervention of the black-box model
when the white-box model has a poor accuracy. For thecase presented in Fig. 11, there is a deviation of the whitebox model at higher speeds. The smaller the confidence
interval is set, the earlier the correction of the white-box
model prediction by the black-box model occurs. As mentioned earlier, this allows the prediction of process behavior for decanter centrifuges to be improved by integrating
experimental data.

6. Process analytics

Process monitoring is playing an increasingly importantrole in optimizing the operation of solid bowl centrifuges toachieve a higher product quality and shorter setup times. To
avoid reject rates or exceeding tolerance limits, quality
control is moving from off-line to on-line, or in-situ applications. On-line measurements typically use a bypass toguide the material into the measurement system. In contrast
to off-line measurements, on-line detection requires both asampling system and often a dilution station to measureparticle properties with high accuracy. The following section summarizes several measurement methods for on-line
and in-situ characterization of particle and bulk properties
that have been applied to solid bowl centrifuges.

6.1 On-line measurement of particle properties

In tubular bowl centrifuges, the flow conditions changeover time due to the accumulated solids. To maintain consistent product characteristics, the rotor speed or flow rate
must be adjusted during the centrifugation process. How-

Fig. 12 Comparison of sensor data and gravimetric measurement forthe product loss as a function of time for colloidal silica for the noncontrolled case (n constant) and the controlled case (n controlled). For atubular bowl centrifuge type Z11. Reprinted from Ref. (Konrath et al.,2014) with permission from Elsevier. Copyright 2014.

ever, in order to change the rotor speed, it is essential tomonitor the particle properties at the centrate. For dilute
suspensions, light scattering sensors are suitable to determine the solids volume fraction on-line (Konrath et al.,2014). To integrate such a system into the process, calibration with the initial suspension is required. Typically, a dilution series is necessary, which can be characterized by
off-line measurements.
Fig. 12 illustrates the comparison of the product loss forthe controlled (n controlled) and the non-controlled (nconstant) scenario for a tubular bowl centrifuge type Z11.
The controller design is based on the step response after
changing solids volume fraction at the feed. A closer lookat Fig. 12 reveals a fundamentally different process behavior for the non-controlled and the controlled case.
While the product loss increases with time for n =constant, since the machine fills continuously, the productloss remains constant for n = controlled. In this case, thedecreasing residence time is compensated by increasing therotational speed. Light scattering sensors can also be used
to detect turbidity in the centrate of decanter and disk stackcentrifuges to monitor centrate clarification and control theprocess to maintain product quality (Leung, 2007).
Another measurement system for on-line analysis of total suspended solids is UV-Vis spectroscopy. Unlike lightscattering sensors, UV-Vis spectroscopy does not operate ata constant wavelength, but uses the full range of ultraviolet
and visible light to produce an absorption spectrum. UVVis spectroscopy also requires extensive calibration basedon a dilution series for the measured suspension. A
promising technique to correlate the measured UV-Vis datawith the solids volume fraction is the use of multivariate
regression which is typically based on generalized ordinary
least squares (Winkler et al., 2020). Another application of
on-line UV-Vis spectroscopy is to detect the composition of71

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Marco Gleiss et al. / KONA Powder and Particle Journal No. 41 (2024) 58–77

binary mixtures (Winkler et al., 2021). One example is theon-line measurement of polymethyl methacrylate (PMMA)
and zinc oxide (ZnO) in an aqueous system. Both particle
systems consist of submicron particles. For composition
prediction, both components must scatter a UV-Vis signal
at different wavelengths. By directly coupling UV-Vis datawith product-related properties such as the product loss, theUV-Vis sensor acts as a soft sensor that directly outputs thequality of a classification, or fractionation process (Winkler
et al., 2021).
Fig. 13(a) shows the comparison between off-line andon-line measurement of the temporal change of the productloss for the fractionation of PMMA and ZnO. The results
indicate a good agreement between reference values andthe prediction also for the variation of the centrifugal number. Additionally, Fig. 13(b) illustrates the prediction of thetemporal change of the product loss during the classification of a suspension consisting of PMMA and water.
Another method of recording the change in solids volume fraction in the centrate over time is to use a measuring
cell with a light source and a camera system. The measured
data can be evaluated using a convolutional neural network(CNN). Prediction requires a pre-trained CNN that has
been trained with experimental data (Bai et al., 2021b). The
presented deep learning algorithm is a hybrid neural network based on a CNN and a short-term memory network(LSTM). The LSTM helps to ensure that previous data is
not forgotten, when searching for the correct gradientsduring the new training period (Bai et al., 2021b). The

measuring system can be applied to a low concentrated
suspension with a solids mass fraction of up to 0.5 wt %,which allows to detect the clarification in the centrate. Online measurement of particle size distribution is challenging, but there are several methods such as laser diffraction,
ultrasonic extinction, and X-ray. The practicality of an instrument depends on the particle properties, the solids volume fraction, and the process itself. If the measurement
time is less than the residence time, the instrument can beused directly for process control after successful calibration. However, if the measurement time is longer than theresidence time, the direct coupling of process control andmeasurement is challenging due to the different timescales. Furthermore, most particle size measurement techniques are only suitable for a defined particle size range,
making experiments irreplaceable.

6.2 In-situ measurement of slurry properties

The in-situ measurement of particle properties in solidbowl centrifuges is very challenging due to the high centrifugal acceleration and requires costly sensors, but provides detailed information about the local physicalbehavior. Several aspects are necessary for the applicationof in-situ sensors in solid bowl centrifuges. First, gaps areneeded inside the rotor to install the sensors. Therefore, it isnecessary to calculate the influence of the gaps on the mechanical strength of the rotor. Second, signal transmission
requires a transmitter, e.g., Wi-Fi, to transmit the measured
signal to the receiver.

(a)

(b)

(c)

Fig. 13 (a) Comparison between soft-sensor prediction and reference for the temporal change of product loss for a binary mixture of polydisperse
PMMA and ZnO particles for a tubular bowl centrifuge type Z11. (b) Comparison between soft-sensor prediction and reference for the temporal changeof product loss P PMMA for a polydisperse PMMA suspension at a constant relative centrifugal number C (c) Temporal change of relative centrifugal
number for the two illustrated experiments (a) and (b). Here, the solid line (expF) represents the centrifugal number for the case of a binary mixture(a) and the dashed line (expC) the PMMA system (b). Reprinted from Ref. (Winkler et al., 2021) under the terms of the CC-BY 4.0 license. Copyright2021, the Authors, published by MDPI.

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Review Paper

rotor of a tubular bowl centrifuge type GLE from CEPA.
The rotor consists of a glass fiber reinforced plastic (GRP).
The adapted rotor is necessary for the undisturbed MRI
measurements of sediment distribution. The analysis of
sediment involves a contrast agent such as carbon black(CB). CB generates a white contrast during MRI measurement, see Fig. 14 (II). A closer look at Fig. 14 (II) depicts
that the temporal filling process starts from the right (inflow) to the left (centrate). The axial flow and the simultaneous classification of the particles along the rotor explain
the non-uniform sediment build-up according to the previously mentioned CFD results and process simulation.
In summary, in-situ measurement methods provide very
accurate insight into the physical behavior of solid bowl
centrifuges, but experiments are time-consuming andequipment costs are high, limiting their use to research.

7. Conclusions
Fig. 14 (I) glass fiber reinforced plastic (GBP) tube and original rotorof tubular bowl centrifuge GLE from CEPA GmbH. (II) MRI analysis ofrotor with longitudinal and lateral cross section. Flow direction fromright to left. Adapted with permission from Ref. (Stahl et al., 2008).
Copyright 2008, Wiley.

Wireless electrical resistance (WERD) is a technique toinvestigate sediment thickness at high relative centrifugal
number (Prayitno et al., 2020). The measurement system
records the change in electrical conductivity of the liquid
phase in-situ by integrating multiple pairs of electrodes
along the cylindrical conical bowl. The measured data canbe used to indirectly calculate the solids volume fraction in
the sediment.
An extension of this method is the Periodic Segmentation Technique in Wireless Electrical Resistance Detector(psWERD), which enables in-situ prediction of material
functions such as the hindered settling function for decanter centrifuges. The periodic segmentation technique isnecessary due to the influence of relative motion between
the screw conveyor and the centrifuge bowl. The screw
conveyor disturbs the measurement signal, due to the different material and is filtered by the periodic segmentation
technique (Prayitno et al., 2022).
Another promising non-invasive and in-situ analysis of
the sediment distribution is the Magnetic ResonanceImaging (MRI), which is an imaging technique based on
the physical principle of nuclear magnetic resonance(Hardy, 2011). This is done by placing the rotor of thecentrifuge in the tube of the tomograph.
In contrast to WERD or psWERD, the size of the tomography limits the application of MRI to small solid bowl
centrifuges. An example of MRI is the study of sediment
distribution in tubular bowl centrifuges (Spelter et al.,2013; Stahl et al., 2008). Fig. 14 (I) presents an adapted

This work presented an overview of research in the fieldof modeling and optimization of solid bowl centrifuges.
Modeling on different time and length scales helps to better
understand the physical behavior of solid bowl centrifuges
and optimize their operation. One of the major challenges
in the selection and development of suitable models arises
from the influence of distributed particle properties.
Conventional steady-state methods for the design ofsolid bowl centrifuges are based on several assumptions
and are not predictive without several pilot scale experiments. The transfer from pilot to industrial scale is usually
only possible for geometrically similar machines in combination with decades of experience of the manufacturers. A
counter idea to conventional methods is to apply processmodeling and to consider material functions for separationrelated properties such as sedimentation, sediment build-up,
and sediment transport. The measurement of the material
properties is not carried out on the pilot but on a smaller
laboratory scale, with well-established laboratory centrifuges. Furthermore, CFD helps to gain a better understanding of the usually inaccessible parts of a solid bowl
centrifuge. At the same time, CFD can be used to optimize
different centrifuge geometries, such as the feed accelerator
or other parts of the machine.
However, since any form of modeling is based on assumptions and therefore does not completely reproduce thephysics, there will always be deviations. This is wheregray-box models have great potential to successively improve the modeling of solid bowl centrifuges. Data-driven
methods such as supervised learning and neural networks
allow an intensive analysis of process data.
Nevertheless, this requires the measurement of particle
properties on-line or in-situ by integrating process analytics. Various measurement techniques such as turbidity or
UV-Vis sensors, camera systems, laser diffraction, ultrasonicextinction and X-ray are available. However, due to the73Marco Gleiss et al. / KONA Powder and Particle Journal No. 41 (2024) 58–77

high cost of measuring equipment, individual testing of theparticle system is necessary.
In summary, useful tools for the modeling and optimization of solid bowl centrifuges are available and their application has already been demonstrated in research. The task
now is to further improve the algorithms and measurement
methods. With further developments and the interconnection of gray-box models, process analytics, and advanced
process control, the overriding goal of autonomous centrifugation will be possible in the future.

Acknowledgments
The authors gratefully acknowledge the funding by
German Research Foundation (DFG) in the priority program SPP 1679 “Dynamic simulation of interconnected
solids processes” and the Federal Ministry of Economic
Affairs and Climate Action in the program IGF—Industrial
Collective Research.

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Authors’ Short Biographies
Marco Gleiss
Marco Gleiss has studied Chemical Engineering at the Karlsruhe Institute of Technology (KIT)and subsequently became a doctoral student at the Institute of Mechanical Process Engineering and
Mechanics (KIT), where he got his Ph.D in Chemical Engineering and became Senior scientist. His
research interest is in the field of solid–liquid separation, on the digitization of filtration and centrifugation, the development of digital twins, and autonomous processes.

Hermann Nirschl
Hermann Nirschl received his Ph.D. in fluid mechanics from the Technical University in Munich
in 1994. In the years between 1997 and 2003 he was responsible for process engineering developments for the 3M company. From 2003 on he is Professor for Mechanical Process Engineering at the
KIT. The focus of the research is on particle technology with a special emphasis on separation processes, numerical simulation and the development of particle characterization technologies.

77

KONA Powder and Particle Journal No. 41 (2024) 78–107/https://doi.org/10.14356/kona.2024017

Review Paper

DEM Modelling of Segregation in Granular Materials:
A Review †Ahmed Hadi ‡, Raïsa Roeplal ‡, Yusong Pang and Dingena L. Schott *Department of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, The Netherlands

Segregation control is a challenging yet crucial aspect of bulk material handlingprocesses. The discrete element method (DEM) can offer useful insights into
segregation phenomena, provided that reliable models are developed. The main
challenge in this regard is finding a good balance between including particle-level
details and managing the computational load. This is especially true for industrial
applications, where multi-component flows consisting of particles with various
irregular shapes and wide size distributions are encountered in huge amounts. In thiswork, we review the state of the art in DEM modelling of segregation in industrial
applications involving the gravity-driven flow of dry, cohesionless granularmaterials. We start by introducing a novel scientific notation to distinguish between
different types of mixtures. Next, we review how parameters for mixture models are
determined in the current literature, and how segregation is affected by material,
geometric and operational parameters based on these models. Finally, we review
existing segregation indices and their applicability to multi-component segregation.
We conclude that systematic calibration procedures for segregation models are
currently missing in the literature, and realistic models representing multicomponent mixtures have not yet been developed. Filling these gaps will pave theway for optimising industrial processes dealing with segregation.
Keywords: segregation, granular materials, discrete element method, granular
flow, DEM calibration

1. Introduction
Particulate materials are ubiquitous on Earth and are thesecond-most common materials handled in the industry(Richard et al., 2005). Nowadays, almost all commodities
are composed of, and/or derived from granular materials
through agriculture, mining, chemical, and/or mechanicalprocessing (Coulson, 2012). Particulate materials being
handled in the real world exhibit complex behaviour arising from the fact that almost all of them are either mixturesof different sizes, shapes, densities, or combinations
thereof. Taking into account the market-driven demand forincreasing production, lowering costs and the developmentof new sustainable products and systems, it is important tostudy and unravel the complex behaviour of granular materials, as well as to advance the technologies related to producing, processing, and transporting them (Rosato andWindows-Yule, 2020).
Segregation, also referred to as reverse mixing orde-mixing, is a phenomenon happening in moving granular
†

‡

*

78

Received 5 April 2023; Accepted 6 June 2023
J-STAGE Advance published online 15 September 2023
Authors with equal contribution
Corresponding author: Dingena L. Schott;
Add: Delft 2628 CD, The Netherlands
E-mail: d.l.schott@tudelft.nl
TEL: +31152783130

materials in which particles with similar properties, e.g.,size, density and shape, tend to collect in certain parts of a
mixture. Except for a few specific applications in mining
and agricultural engineering (Zhang et al., 2004), segregation is generally considered an undesirable occurrence affecting product homogeneity in a negative way. A
well-known example is the blast furnace, where segregation adversely influences the distribution of materials at theburden surface, which has a detrimental effect on the bed
permeability (Yu and Saxén, 2010). This negative effect on
permeability introduces an inconsistency in the pressure
drop which leads to the inefficient use of reductant gas andresulting in both economic and environmental consequences (Bhattacharya and McCarthy, 2014). This example, along with several cases of other industries (e.g., foodprocessing and pharmaceuticals industries) highlights theimportance of increasing knowledge about the underlying
roots of segregation, as well as investigating how segregation is influenced by various factors.
According to de Silva et al. (2000), there are thirteen
segregation mechanisms in general. However, some ofthese mechanisms are either rare or special cases of others.
Therefore, past researchers attempted to condense these
segregation roots in different ways (Carson et al., 1986;
Tang and Puri, 2004; Williams, 1991). Based on the size,
Williams mentioned the four main mechanisms as

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

trajectory, percolation, the rise of coarse particles due tovibration (or Brazil-nut effect) and elutriation (Rhodes,
2008; Williams, 1991). In addition to these size-based
mechanisms, the buoyancy mechanism in which the difference in density drives segregation should be considered for
multi-component mixtures (Ottino and Khakhar, 2000).
Having said that, multiple mechanisms might take place
simultaneously, making the prediction of the segregation
pattern extremely challenging. For instance, in the case of
the mixture of small light and large heavy particles, percolation and buoyancy mechanisms oppose each other andthe overall segregation behaviour of such a mixture is notknown a priori (Jain et al., 2005). Furthermore, since segregation only occurs for particles that are in motion—due toeither shearing, by means of moving walls (such as in ashear cell or bladed mixer) or as a result of gravitational
force (as is the case in hoppers) or vibration—it can be deduced that the formation of segregation patterns depends
not only on the properties of the components relative toeach other, but also on the system configuration and thedegree and type of agitation imposed on the material. Although having knowledge of the mentioned mechanisms
can shed light on the segregation behaviour of particulate
mixtures, it is necessary to investigate each case independently to determine the dominant segregation mechanism(s).
Researchers have attempted to experimentally observe
segregation phenomena in common applications since theearly ’70s in order to first understand why and how granular mixture components tend to separate spatially during
different types of agitation (Gray, 2018) and subsequently
developed models capable of capturing the observed phenomena. Given the complexity of the segregation problem,
the general approach was to scrutinize the effects of different material properties separately by considering material
mixtures differing only in either size (Duffy and Puri, 2002)
or density (Shlnohara and Mlyata, 1984) and, ultimately,
the combined effect of size and density (Jain et al., 2005).
This allowed researchers to systematically observe and incorporate the effect of each material property in mathematical models. However, there are several limitations
associated with the experimental investigation of segregation. First, despite the recent advances in measurement
techniques (Asachi et al., 2018; Bowler et al., 2020), extracting data on the composition of a granular mixture is
still not a trivial task. Second, it is nearly impossible toobtain all the information on a particle scale which canshed light on the macro-scale granular behaviour. Finally, it
is expensive and time-consuming to systematically study
the effect of various factors on segregation in experiments.
As computational models gained popularity, the Discrete
Element Method (DEM) initially introduced by Cundall
and Strack (1979) became a widely used tool for simulating
granular phenomena. The main advantage of DEM is that

Review Paper

any mixture of materials—whether they have different
sizes, densities, shapes, or combinations thereof—can bemodelled to provide detailed, particle-level insight on segregation patterns, which is both difficult and expensive toachieve experimentally and is not yet possible with mathematical models for segregation. This is highly relevant
since most materials encountered in industry are of thiscomplex nature. Hence, DEM has expedited researchers’
ability to predict segregation in multi-component mixtures,
irrespective of the application, and can therefore be considered a practical tool for modelling and optimising industrial
processes. However, until now it is unclear to what extent
the existing DEM models of segregation are representativeof the complex multi-component mixtures often encountered in real-world applications. Moreover, a DEM model
can accurately represent granular material behaviour only
if its parameters are reliably chosen, i.e., the model is calibrated and validated.
The objective of this paper is to provide an overview ofthe state of the art in modelling the segregation behaviourof complex multi-component mixtures using DEM. Specifically, this review assesses to what extent the existing DEM
studies of segregation in cohesionless materials represent
actual mixtures encountered in industry. Furthermore, thereliability of these existing studies in terms of their approach to calibration and validation is evaluated. The paper
is structured as follows. For the sake of consistency, a novel
way of defining and using consistent terminology for thedescription of mixtures is proposed in Section 2. Then in
Section 3, we first evaluate different ways of analysing
segregation employed by past studies including their applicability to multi-component mixtures. Next, the approachof past DEM studies on segregation for determining DEM
parameters is critically reviewed. Thereafter, an overview
of the methods used for the validation of DEM models ofsegregation is presented and evaluated. Finally, the resultsof past DEM studies on the effect of several factors on
segregation in different systems are reviewed in Section 4.
Section 5 concludes the review and provides recommendations for future work.
The scope of this paper is limited to DEM-based studies
of gravity-driven segregation of dry and cohesionless granular materials. Cohesionless (or free-flowing) granularmaterials are of interest because they are more susceptible
to segregation compared to cohesive particles (Schulze etal., 2008). Studies on fluidised segregation are excluded
since investigating such a phenomenon is conducted using
the coupled CFD-DEM approach, which does not fit in thisreview. Also, applications such as rotating drums and various types of mixers in which gravity is not the only sourceof energy causing segregation are beyond the scope of thisreview.

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2. Terminology

3. Discrete element method (DEM) forsegregation

For consistency and to make a clear distinction between
various terminologies used in this paper regarding mixtures, Fig. 1 is presented. In this figure, a component isdefined as a material with constant particle density. In casea mixture is composed of more than one component, it isgenerically referred to as a multi-component or more specifically, as two-component, three-component, etc. bymentioning the exact number of components. Moreover,the particles constituting each component can be mono- topoly-sized and/or mono- to multi-shaped. In this regard,referring to complex mixtures composed of several components is not yet straightforward. To overcome this issue, we
present in Eqn. (1) a novel way to define multi-component
mixtures:nC  nk 1 (ik S / jk Sh)

3.1 Overview of DEM

A DEM model calculates the forces and moments of inertia acting on all particles and subsequently uses Newton’s
second law to compute their positions at each time step
through numerical integration. The interaction forces between particles and their surroundings are determined using contact models such as the Linear spring-dashpot(Luding, 2008) and Hertz-Mindlin model (Zhu et al.,2007), which are the most widely used for cohesionlessgranular materials. The model inputs can generally be divided into three categories: (i) morphological parameterssuch as particle size and shape distributions; (ii) material
parameters such as particle density (ρ), shear modulus (G)and Poisson ratio (ϑ); and (iii) interaction parameters suchas sliding and rolling friction coefficients (μs and μr, respectively) and the restitution coefficient (CoR).
The first step in developing a DEM model is generally
measuring morphological parameters, and determining
how they will be included in the model. In industrial applications, millions of particles with many irregular shapes
and wide size distributions are typically handled. From acomputational standpoint, modelling the true size andshape distributions for such applications can be very challenging for several reasons (Marigo and Stitt, 2015;
Roessler and Katterfeld, 2018; Sakai, 2016). First of all,tracking a large number of particles and their mutual interactions demands huge amounts of computational power,
even for simulating a few seconds of a simple process.
Secondly, modelling the actual size distribution can prolong simulation times significantly since the numericaltime step is determined by the smallest particle in the flow.
Finally, realistic (non-spherical) particle shapes can be(1)

where n is the number of components and the compositionof each component is specified in the corresponding paren(1) That is, i is the number of sizes in kth component
thesis.
kwhich could be 1, 2, 3, M or P representing a mono-, binary-, ternary-, multi- or poly-sized component. Similarly
representing the shape, jk might take the value of 1, 2, 3 orM denoting a mono-, binary-, ternary- or multi-shaped
component. For instance, 2C[(2S/1Sh + (MS/1Sh)] represents a two-component (2C) mixture composed of a binary-sized (2S) mono-shaped (1Sh) component mixed with amulti-sized (MS) mono-shaped (1Sh) one. This formula
will be consistently used in this review to refer to different
types of mixtures in a concise way.

Complex mixture composition

C1

...

Ci

...

Cn

Par�cle density = constantPar�cle sizePar�cle shape

Mono-sized

Mono-shaped

Binary-sized

Binary-shaped

Ternary-sized

Ternary-shaped
1

Poly-sized

...

1

N

2

Multi-shaped

Frequency

Multi-sized2

Particle diameter

Fig. 180

Terminology to describe various types of mixtures.

N...

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modelled using multi-spheres or superquadrics which require computationally expensive algorithms (Soltanbeigi etal., 2018). Several solutions have been proposed to reduce
computational effort such as downscaling the full threedimensional system to a 2D representation or simulating a
portion using periodic boundaries (Yang et al., 2014; 2015)
using larger, so-called “coarse-grained” or “upscaled” particles (Coetzee, 2019; Lommen et al., 2019; Roessler andKatterfeld, 2018), ignoring small sizes of the full particle
size distribution (PSD—also referred to as “scalping” or“cut-off” (Cleary and Sawley, 2002)—and reducing theshear modulus (G), although the latter should be carried out
with caution as it might cause serious errors (Lommen etal., 2014). Besides all these techniques, DEM code development in conjunction with parallel computing techniques
using high-performance clusters (HPC) look promising toovercome the high computational time in due course(Marigo and Stitt, 2015).
The next step is determining the remaining model parameters such that the simulated flow behaviour matches
the real behaviour to an acceptable degree (Marigo andStitt, 2015). When it comes to modelling segregation, theparameters should be determined in a way that not only the

global behaviour (e.g., angle of repose, mass flow rate, etc.)
but also the local behaviour, i.e., spatial concentration ofcomponents, are captured. The reliability of DEM predictions depends on the proper selection of a contact model fora specific application and the values assigned to the model
parameters, given the simplifications which have been
made for computational reasons. Model validation is therefore an important final step for both verifying and demonstrating the model’s credibility.
We encountered 63 papers on the segregation of thegravity-driven flow of cohesionless granular materials in
the literature. These studies are mostly focused on segregation during hopper filling and discharge, chute flow andheap formation and are summarised in Table 1. Thesestudies have been categorised into four groups based on
their approach to obtaining DEM parameters. In the remainder of this section, we will review the studies from
Table 1 with respect to the methods used to quantify segregation (Section 3.2) and model development practices(Sections 3.3 and 3.4).

3.2 Methods for assessing segregation
Since DEM allows tracking the position of each

Table 1 Overview of DEM studies for segregation (LSD = Linear spring dashpot, HM = Hertz-Mindlin, n.s. = not specified, N/A = notapplicable, *for the definition and notation of the mixture type see Eqn. (1) in Section 2). (continued on next page)

II. Parameter assumption

I. Parametric sensitivity studies

Group

SourceSoftwareContact
model

Mixture type*

Material(in experiments)

Particle shape(Tripathi and Khakhar, 2011; 2013)

n.s.

LSD

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

N/A

Sphere(Ketterhagen et al., 2008)

n.s.

LSD

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Sphere(Pereira and Cleary, 2013)

n.s.

LSD

2C[(1S/1Sh) + (1S/1Sh)]

Sphere(Panda and Tan, 2020a; 2020b)

LIGGGHTS

HM

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Sphere(Huang et al., 2022)

n.s.

n.s.

1C[(2S/1Sh)]

Sphere(Yu and Saxén, 2014)

EDEM

HM

1C[(3S/1Sh)]

Sphere(Xu et al., 2019)

n.s.

HM

3C[(MS/1Sh) + (MS/1Sh) + (MS/1Sh)]

Sphere(Li et al., 2022)

n.s.

HM

2C[(2S/1Sh) + (2S/1Sh)]

Sphere(Vuilloz et al., 2021)

LMGC90n.s.

1C[(2S/1Sh)]

Sphere(Zhao et al., 2022)

PFC3D

LSD

1C[(3S/1Sh)](Tao et al., 2013)

n.s.

n.s.

1C[(2S/2Sh)]

SoybeanEllipsoidal,
corn-shaped,
cylinder, spherical(Shirsath et al., 2015)

n.s.

LSD

2C[(1S/1Sh) + (1S/1Sh)]

Glass beads

Sphere(Xu et al., 2017)

n.s.

n.s.

1C[(2S/1Sh)]

Alumina spheres

Sphere(Zhang et al., 2018)

n.s.

HM

1C[(2S/1Sh)]

Plastic pellets, rape seeds

Sphere(Mantravadi and Tan, 2020)

LIGGGHTS

HM

1C[(2S/1Sh)]

Glass beads

Sphere(Mio et al., 2020)

n.s.

Voigt

1C[(MS/1Sh)]

Sintered ore, coke

Sphere(Zhang et al., 2014)

n.s.

HM

1C[(MS/1Sh)]

Coke, iron ore

Sphere(Zhao et al., 2018)

LIGGGHTS

HM

1C[(PS/1Sh)]

Glass beads

Sphere(Zhao and Chew, 2020b)

LIGGGHTS

HM

1C[(PS/1Sh)]

Glass particles

Sphere, ellipsoid,
cylinder, cuboid(Kumar et al., 2020)

LIGGGHTS

HM

1C[(PS/1Sh)]

Glass beads

SphereSphere81

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Table 1 Overview of DEM studies for segregation (LSD = Linear spring dashpot, HM = Hertz-Mindlin, n.s. = not specified, N/A = notapplicable, *for the definition and notation of the mixture type see Eqn. (1) in Section 2). (continued from previous page)

IV. Parameter calibration/ measurement

III. Parameters from literature

Group

82

SourceSoftwareContact
model

Mixture type*

Material(in experiments)

Particle shape(Ketterhagen et al., 2007; 2009)

n.s.

LSD

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Glass beads, cast steel
shot

Sphere(Ketterhagen and Hancock, 2010)

n.s.

LSD

1C[(2S/1Sh)]

Glass beads

Sphere(Kou et al., 2015; 2018)

n.s.

HM

4C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)+ (1S/1Sh)]

Pellet, ore, coke, flux

Sphere(Cliff et al., 2021)

MFiX

LSD

1C[(2S/1Sh)]

Mustard seed

Sphere(Yu and Saxén, 2013)

EDEM

HM

1C[(2S/1Sh)]

PelletSphere(Liao et al., 2023)

n.s.

n.s.

2C[(1S/1Sh) + (1S/1Sh)]

Lump ore, pellet

Sphere, clumped
spheres(Asachi et al., 2021)

EDEM

HM

3C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)]

TAED, BP, EPG(detergent powder)

Sphere, clumped
spheres(Tian et al., 2022)

n.s.

HM

1C[(3S/1Sh)]

Coke

Sphere, clumped
spheres(Yu et al., 2018)

EDEM

HM

1C[(2S/1Sh)]

PelletSphere(Xu et al., 2018b)

n.s.

HM

1C[(2S/1Sh)]

Mung bean

Sphere(Mandal and Khakhar, 2019)

n.s.

LSD

1C[(2S/1Sh)]

N/A

Spheres, dumbbells(Zhao and Chew, 2020a)

LIGGGHTS

HM

1C[(1S/2Sh)]

N/A

Ellipsoids,
cylinders, cuboids(Zhang et al., 2021)

n.s.

HM

1C[(2S/1Sh)]

Coke

Sphere(Yu and Saxén, 2010)

EDEM

HM

1C[(3S/1Sh)]

Pellets

Sphere(Yu and Saxén, 2012)

EDEM

HM

1C[(3S/1Sh)]

Pellets, coke, steel ball

Sphere, clumped
spheres(Kou et al., 2013)

n.s.

Voigt

1C[(3S/1Sh)]

Coke

Sphere(Wu et al., 2013)

n.s.

HM

1C[(3S/1Sh)]

SinterSphere(You et al., 2016)

n.s.

HM

1C[(3S/1Sh)]

Coal

Sphere(Xu et al., 2021)

n.s.

n.s.

1C[(MS/1Sh)]

Coke

Sphere(Kou et al., 2019)

n.s.

HM

3C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)]

Sinter, pellet, lump ore

Sphere(Chibwe et al., 2020)

LIGGGHTS

HM

2C[(MS/1Sh) + (1S/1Sh)]

Sinter, pellet

Sphere(Hong et al., 2021)

n.s.

HM

3C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)]

Sinter, pellet, ore

Sphere(Z. Zhang et al., 2020)

EDEM

HM

1C[(2S/1Sh)]

Acrylic spheres

Sphere(Izard et al., 2021)

EDEM

HM

1C[(2S/1Sh)]

SinterClumped spheres(Alizadeh et al., 2017)

EDEM

HM

2C[(1S/1Sh) + (1S/1Sh)]

TAED, BP (detergent
powder)Clumped spheres(Kim et al., 2020)

EDEM

HM

2C[(3S/1Sh) + (3S/1Sh)]

Sinter, briquette

Sphere, clumped
sphere(Qiu and Pabst, 2022)

PFC3D

LSD

1C[(MS/1Sh)]

Waste rockSphere(Wang et al., 2023)

LIGGGHTS

HM

2C[(1S/1Sh) + (1S/1Sh)]

Steel and aluminium
spheres

Sphere(Barik et al., 2023)

LIGGGHTS

HM

1C[(3S/1Sh)]

Microcrystalline cellulose(MCC)

Sphere(Mio et al., 2008a; 2009; 2010; 2012)

n.s.

Voigt

1C[(3S/1Sh)]

Sintered ore

Sphere(Nakano et al., 2012)

n.s.

Voigt

1C[(3S/1Sh)]

SinterSphere(Bhattacharya and McCarthy, 2014)

n.s.

HM

1C[(3S/1Sh)]

Polystyrene spheres

Sphere(Li et al., 2017)

n.s.

HM

1C[(MS/1Sh)]

Iron ore

Sphere(Li et al., 2019)

LIGGGHTS

HM

1C[(MS/1Sh)]

Iron ore

Sphere(Shimosaka et al., 2013)

n.s.

LSD

1C[(MS/1Sh)]

Glass bead, sand, alumina

Sphere(Combarros et al., 2014)

LIGGGHTS

HM

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Aluminium oxide

Sphere, cylinders(Combarros et al., 2016)

LIGGGHTS

HM

1C[(2S/1Sh)]

Sand

Sphere(Terui et al., 2017)

n.s.

Voigt

2C[(1S/1Sh) + (1S/1Sh)]

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individual particle, it becomes relatively simple to assessthe degree of segregation within a system. This can be donein different ways. One approach is to divide the system into
subdomains and subsequently determine how different
mixture components are distributed in the subdomains bymeans of plots. Gradient plots are useful for visualising the
distribution of a single mixture component in a system. For
example, a gradient plot as in Fig. 2(a) has been used tovisualise how small particles within a binary mixture were
distributed after filling a hopper (Xu et al., 2017). Since
they were dealing with a binary mixture, showing the distribution of one component was sufficient to demonstrate
how well the material was mixed in the hopper. Whendealing with multi-component mixtures, monitoring only
one component’s distribution is no longer sufficient, andother types of plots can be used. Fig. 2(b) shows an example of line plots for monitoring the radial distribution offour different particle sizes in a blast furnace. The methodof plots is beneficial as it provides two important insights:(1) whether or not different mixture components are evenly
distributed and (2) where the different components areconcentrated in the case of uneven distribution.
Another method is to use an index which reflects thedegree of segregation based on its value. There are generally two types of segregation indices: grid-dependent andgrid-independent (Bhalode and Ierapetritou, 2020). In thefirst type, the system is divided into subdomains, similar to
the method of plots. The distribution of one mixture component (referred to as the tracer) is evaluated, and the segregation index is then determined through statistical(a)

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analysis of the tracer distribution. If the tracer is distributed
evenly, then the material is considered to be well-mixed,
and this is reflected by the value of the segregation index.
In the second type, the value of the segregation index isdetermined by considering how the tracer particle is distributed within the system as a whole, rather than in subdomains. The tracer’s distribution is generally evaluated on a
distance or contact basis, as described by Bhalode and
Ierapetritou (2020). The index value reflects how spreadout the tracer particles are in the whole system.
The benefit of using an index is that it allows for a quantitative comparison of segregation for different scenarios
by means of a single value. This might be more difficult to
do with the method of plots, which provides a more qualitative comparison. However, it is generally known that
grid-dependent indices depend on the chosen size of the
subdomains (Cho et al., 2017; Jin et al., 2022; Rosato andWindows-Yule, 2020), which makes it difficult to judge thedegree of segregation based on the index value. For thisreason, researchers often investigate how sensitive theirsegregation predictions are to the grid size. While thegrid-independent indices do not have this shortcoming,
they are often computationally intensive, especially for industrial applications where millions of particles are involved.
The majority of literature from Table 1 used the plot
method to quantify segregation, with only a few used segregation indices. These indices are presented in Table 2
and are grouped by the calculation method (grid dependent/
independent). The equations for calculating the indices are(b)

Fig. 2 (a) Gradient plot for demonstrating how a single component is spatially distributed inside a system, where white colour represents a wellmixed status and red and blue colours denote segregated areas. Reproduced with permission from Ref. (Xu et al., 2017). Copyright 2017, Elsevier. and(b) line plots for different coke sizes, showing how they are distributed inside a blast furnace after being charged using various chute angles Reproduced with permission from Ref. (Zhang et al., 2014). Copyright 2014, Elsevier.

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Table 2 Common segregation indices used in DEM studies of Table 1. Legend: refer to the Appendix for symbol definitions. Subscripts: “t” refers to“tracer” when a single tracer is used; ti refers to the i-th tracer when multiple tracers are used(*).

No.

Segregation index

SourceValues(mixed–segregated)

Classification0–(**)

Variance-based / single tracer

0–1

Variance-based / single tracer

0–(**)

Variance-based / single tracer

1–0(***)

Variance-based / single tracer

0–(**)

Variance-based / single tracerj

0–1

Distance-based / two tracers

logσ 02  logσ 2logσ 02  logσ r2

1–0

Variance-based / single tracer

1–0

Contact-based

COM final COM initial
δ

0–0.5

Distance-based / single tracerl  lminlud  lmin

1–0Distance-based / single tracer

Grid-dependent
1(Panda and Tan, 2020a; 2020b)
SI 

2

m

j
2
m N
 j 1  y tj  y t  
N

SI 
[31]
y t 1  y t 2(Li et al., 2022)

3(Mantravadi and Tan, 2020)

[32]
SI 
4

(Mantravadi and Tan, 2020)

5(Zhang et al., 2014), (Ketterhagenand Hancock, 2010)

SI 

[33]
[34]

SI 

 x tj  xtj  m  
 j 1  xt  xt  avg.m 1(Zhao et al., 2018)7(Combarros et al., 2014)

SI 

[36]

Grid-independent

 x tjm j 1 
 xt

σ σ
[37]
SI  02(Zhang et al., 2018)2

σ 02  σ 2
σ 02  σ r2
 xj   t   xt  avg.
m mj 1 xt1  h j 
1jm
 j 1 xt 2  h j 
SI 
[35]
2 x t 2  x t1
1
x t1

6

8

j
m
 j 1 c t  c t 

2

(*)

2

σ 02  σ r2

9(Mandal and Khakhar, 2019)10(Chibwe et al., 2020)

SI 

[38]
SI 

[39]

(*) The readers are referred to Table A1 in the Appendix for detailed equations on calculating component concentrations of indices 1–8.

(**) For these indices, the maximum value of SI depends on the mixture composition, as the equations contain the variables c , y and xt ttwhich represent the concentration of the tracer[40]in the entire mixture.

(***) In these equations, σ2, σ 2 and σ 2 are calculated by0

σ 2  in1ry 1  y t  y ti  y t  2 , 
σ 2 y 1  y  , σ 2  t

n 1

0

t

t

r

Ni

[41] [42]given along with [44]the range of values that can be assigned to[43]each index. Additionally, the[45]type of segregation index
based on the classification according to Bhalode and Ierapetritou (2020) is indicated. It is important to note that,similar to the method of plots, the single-tracer approach isonly useful when considering a binary mixture. We havetherefore also indicated whether the segregation indices are
based on the distribution of a single tracer. The common
symbols used in index no. 1–8 from Table 2 are defined in84

the Appendix, and for index no. 9–10, the reader is referred to the respective references.
We conclude that all grid-dependent indices in Table 2
are based on a statistical analysis of a single tracer, which is
not surprising since they were applied to studies of binary
mixtures. In studies of three or more particle types(Bhalode and Ierapetritou, 2020), the plot method was used
to visualize the degree of segregation. For indices which
can be applied to multi-component segregation, the reader

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is referred to the work of Cho et al. (2017), who studiedsegregation in a mixer.

3.3 Determination of DEM parameters forsegregation

The literature mentioned in Table 1 has been categorizedbased on the way in which the authors dealt with model
parameters. We identified the following four categories:
I. Parametric sensitivity studies. This group of studies
carried out parametric sensitivity studies to identify thecritical factors (i.e., model, material, geometric and operational parameters) affecting the segregation behaviour of
mixtures in different systems. Hence, parameter values inthese studies are systematically varied. Since they are notaimed at modelling a specific material, calibration andvalidation are not performed in these studies.
II. Parameter assumption. The second group contains
studies in which the authors assumed parameters without
any justification or referring to related resources (for instance chosen by experience). Although these studies attempted to justify the parameter values by comparing theDEM results with physical tests, this is insufficient because
more than one parameter set can yield the same bulk behaviour (Roessler et al., 2019). Furthermore, major errors
were reported in some cases (Tao et al., 2013), which could
be the consequence of not calibrating the parameters.
III. Parameters from literature. Many DEM studies on
segregation have taken either all or merely a selection of
the parameters from the literature (Asachi et al., 2021;
Chibwe et al., 2020; Cliff et al., 2021; Hong et al., 2021;
Ketterhagen et al., 2007, 2009; Ketterhagen and Hancock,2010; Kou et al., 2013, 2015, 2018, 2019; Liao et al., 2023;
Mandal and Khakhar, 2019; Tian et al., 2022; Wu et al.,2013; Xu et al., 2018b, 2021; You et al., 2016; Yu et al.,2018; Yu and Saxén, 2010, 2012, 2013; Zhang et al., 2021;
Zhao and Chew, 2020a). This approach has the same shortcoming as the previous group, since the parameters are
obtained from resources that have not conducted calibration. Furthermore, it is unclear if the material under study issimilar or exactly the same as in the literature (Zhang et al.,2021).
IV. Parameter calibration/ measurement. Several past

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studies have established DEM parameters through either
direct measurements or bulk calibration (Alizadeh et al.,2017; Barik et al., 2023; Bhattacharya and McCarthy,
2014; Combarros et al., 2014; Combarros Garcia et al.,2016; Izard et al., 2021; Kim et al., 2020; Li et al., 2019,2017; Mio et al., 2010, 2009, 2008a, 2012; Nakano et al.,2012; Qiu and Pabst, 2022; Shimosaka et al., 2013; Terui etal., 2017; Wang et al., 2023; Z. Zhang et al., 2020). Each ofthese approaches comes with a set of advantages and disadvantages as summarized in Table 3. The calibration approaches of the studies in this group are summarized inTable 4. All calibration tests presented in this table capture
only the global behaviour of granular materials (e.g., angleof repose) and none of them has calibrated the DEM model
for local behaviour using a segregation test.
Calibration of DEM models against experimental data
ensures that the model captures the material behaviour andtherefore provides credibility (Coetzee, 2017). However,calibration can be very time-consuming. This is especiallytrue for multi-component mixtures since the number ofmodel parameters increases with the number of components in the mixture. Sensitivity studies enable us to explore the importance of the model parameters for inclusionin the calibration step. Table 5 presents a summary of theresults of the existing DEM sensitivity studies on segregation. As can be seen, the significance of DEM parameters
depends on the system and the granular flow regime being
studied.
Fig. 3 visually presents deeper insight into studies listed
in Table 1 to highlight the percentage of studies with respect to the approach for determining DEM parameters,
reproducibility and the types of the mixtures being studied.
According to Table 1, not all past DEM studies havedetermined the parameters in a proper way. To highlight
this with respect to segregation, Fig. 3(a) shows the percentage of studies in groups II to IV, where only studies ingroup IV include calibration. It should be noted that group
I is excluded since calibration is not necessary for parametric sensitivity analysis. As can be seen, 66 % of the existingstudies have omitted the calibration of the DEM model.
This is relevant since calibrated DEM models are required
if they are intended to be used as predictive tools that yield

Table 3 Pros and cons of two main approaches to determining DEM parameters (Coetzee, 2017; Marigo and Stitt, 2015; Wang et al., 2022).

Advantage

Disadvantage

Direct measurement+++

Independent of the contact model
Independent of the DEM code
Maintaining physical meaning

–
–
–
–

Mostly limited to mm-sized particles
Not practical for irregular particles
Not practical for all parameters
Not considering the stochastic nature of the parameters

Bulk Calibration+

 ompensating for the inaccuracy of –C
the parameters–
Obtaining values for all parameters ––Probability of losing physical meaning on particle level
DEM code dependency
Probability of not resulting in a unique parameter set
Challenging in the case of a high number of parameters+

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Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107
Table 4 Detailed calibration approach of studies in group IV of Table 1.

SourceDirect measurement

Bulk calibration test

(Z. Zhang et al., 2020)

μs,pp, μs,pw, CoR—(Izard et al., 2021)

μs,pw, ρs

μs,pp (angle of repose)(Alizadeh et al., 2017)

CoR, μs,pw, particle shape

The number of spheres in clumped approach, μr (angle ofrepose), μs,pp (sliding process),(Kim et al., 2020)

PSD, ρs, CoR, μs, E

μr (angle of repose)(Qiu and Pabst, 2022)

PSD

μs, μr (angle of repose, both at lab and field scales)(Wang et al., 2023)

ρs, CoR—(Barik et al., 2023)—

μs (silo discharging)(Mio et al., 2008a; 2009; 2010; 2012) PSD, ρs

μs (shear test), μr (particle rolling test)(Nakano et al., 2012)—μr (angle of movement)(Bhattacharya and McCarthy, 2014)

PSD, ρs

Tuning the contact model(Li et al., 2017)

PSD, ρs, G

μs, μr (angle of repose)(Li et al., 2019)

PSD, ρs, G, μs, μr—(Shimosaka et al., 2013)—

μr (inclined plate test)(Combarros et al., 2014)

PSD, ρs

μs, μr (sensitivity analysis + dynamic and static angle of repose)(Combarros Garcia et al., 2016)

ρs, D50

μs, CoR (sensitivity analysis + shear test, angle of repose anddrop fall test)(Terui et al., 2017)—

μs (stationary bed angle), μr (angle of repose)

Table 5 A summary of the results of DEM sensitivity studies on the significance of model parameters for segregation.

DEM parameter

Range (source)

SystemSignificanceParticle-particle sliding friction (μs,p-p)(0.0–0.5) (Ketterhagen et al., 2008)

Hopper discharge

High

Particle-wall sliding friction (μs,p-w)

Particle-particle rolling friction (μr,p-p)(0.01–0.9) (Yu and Saxén, 2010)Low(0.15–0.95) (Z. Zhang et al., 2020)

High(0.1–1.0) (Zhang et al., 2018)

Hopper fillingHigh(0.3–0.9) (Li et al., 2022)

Blast furnace throat

Low(0.0–0.5) (Ketterhagen et al., 2008)

Hopper dischargeHigh(0.01–0.9) (Yu and Saxén, 2010)High(0.05–0.85) (Z. Zhang et al., 2020)High(0.0–0.045dc) (Ketterhagen et al., 2008)

Hopper discharge(0.05–0.9) (Yu and Saxén, 2010)
Particle-wall rolling friction (μr,p-w)

Low
Low

(0.001dp–0.2dp) (Zhang et al., 2018)

Hopper fillingHigh(0.0–0.045dc) (Ketterhagen et al., 2008)

Hopper discharge

Low(0.01–0.9) (Yu and Saxén, 2010)

High

Particle-particle coefficient of restitution (ep-p)(0.2–0.94) (Ketterhagen et al., 2008)

Hopper filling

High

Particle-wall coefficient of restitution (ep-w)(0.2–0.9) (Ketterhagen et al., 2008)

Hopper filling

High

Local damp

(0.2–0.8) (Qiu and Pabst, 2022)

Pile formation

Low

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(a)

(b)

(c)
7%

20%
34%

23%

40%
60%

70%

46%

II. Parameter assumption

Not specified

III. Parameters from literatureSpecifiedSingle-component
Two-component
Three-component and more

IV. Parameter calibration/measurement
Fig. 3Distribution of past DEM-based studies of segregation by (a) approach of calibration, (b) DEM software and (c) types of mixtures.

reliable results (Coetzee, 2017). Even though the studies ingroup IV have applied calibration to some extent, not all ofthem have carried it out in a systematic way (cf. Table 4).
In several studies, only a portion of the parameters has beencalibrated (Nakano et al., 2012; Shimosaka et al., 2013).
Also, in several cases, a single experiment is used to determine the values of more than one parameter (Izard et al.,2021; Li et al., 2017). As shown by Wensrich and Katterfeld(2012), this approach leads to ambiguity since an infinite
combination of parameter values might lead to the samebulk behaviour.
There are good examples of systematic calibration of
DEM models with respect to bulk material behaviour forvarious operational conditions in the literature (Coetzee,
2016; Do et al., 2018; El Kassem et al., 2021; Marigo andStitt, 2015; Mohajeri et al., 2020; Roessler et al., 2019).
When it comes to the calibration of models for segregation,
several authors have shown that bulk calibration also ensures that the local material behaviour is accurately captured. For example, Li et al. (2017) calibrated the friction
coefficients of iron ore pellets using a repose angle test andverified that the calibrated model could reproduce the experimentally observed size segregation in simulations.
Similarly, Izard et al. (2021) modelled the size segregationof sinter particles after determining the model parameters
through bulk calibration. However, it has not yet been addressed to what extent the DEM parameters obtained
through bulk calibration (e.g., dynamic and static angle ofrepose) can be employed to accurately replicate the segregation of multi-component mixtures, which is essentially a
local occurrence. Furthermore, the systematic calibrationof mixture models has received little attention. One of the
few studies where mixture calibration was performed is thework of Alizadeh et al. (2017). The authors modelled thesegregation of a detergent mixture during heap formationand found that the segregation behaviour was not accurately captured by their model, which was calibrated using
a combination of direct measurement and bulk calibration.
They attributed the model’s inaccuracy to the fact thatspheres were used to model the particle shape. When using
clumped spheres to approximate the actual particle shape,
the predicted segregation behaviour was in good agreementwith the experiments. Other researchers have also modelled the segregation of mixtures, but the interaction parameters between mixture components were not mentioned(Combarros et al., 2014; Terui et al., 2017).
Fig. 3(b) illustrates the various DEM software that has
been used by past researchers to investigate segregation. It
is notable that 60 % of the studies have not specified theDEM software they employed. This is highly importantbecause not only the same DEM parameters might be defined in different ways in various software, but also models
may be differently implemented, making the parameterscode-dependent (Coetzee, 2017; González-Montellano etal., 2012). It means that the calibrated parameters of thesestudies cannot be reliably and directly used by others to
reproduce the work or to model the same material. Therefore, it is essential to specify in detail the DEM code orsoftware used in future studies.
The different types of mixtures that have been modelled
and studied are shown in Fig. 3(c). A vast majority of paststudies (i.e. 70 %) have been dedicated to singlecomponent mixtures i.e., with the general form of 1C[(iS/jSh)] (see Section 2), most of which have modelled simple
binary- or ternary-sized mixtures with spherical particles(i.e., 1C[(2S/1Sh)] or 1C[(3S/1Sh)]). In addition, a few

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studies considered two or three components (Chibwe et al.,2020; Combarros et al., 2014; Hong et al., 2021; Ketterhagen
et al., 2007; Kou et al., 2019; Li et al., 2022; Panda and Tan,
2020a; Pereira and Cleary, 2013; Shirsath et al., 2015;
Terui et al., 2017; Xu et al., 2019), but they also simplified
the mixtures by modelling mono-sized and mono-shaped
components. In conclusion, although past DEM studies
have attempted to shed light on segregation in granularmaterials, they have failed to represent the real-world complex mixtures which are mostly multi-component witheach component having a size distribution and different
particle shapes (Gao et al., 2021).
As can be seen in Table 1 (columns Mixture type andParticle shape), the particle shape and size distribution
have often been simplified, mainly to prevent high computational time. The effect of particle shape on segregation is
not crystal clear yet as can be illustrated by seemingly
contradictory results in the literature. For instance, Yu andSaxén (2014) asserted that particle shape has an insignificant effect on the size segregation during charging anddischarging from a hopper since spherical particles yielded
the best agreement with the experimental results ofStandish (1985). It is notable that they used the same DEM
parameters for both spherical and non-spherical particles,
which can lead to misinterpretation. In another study, Alizadeh et al. (2017) concluded that modelling non-sphericalparticles as spherical with rolling friction underestimates
the segregation extent during heap formation. Despite thisconflict, Lu et al. (2015) and Combarros Garcia et al.
(2016) concluded that particle shape has to be modelled as
accurately as possible to ensure that the DEM model is accurate and predictive. Similarly, particle size distribution
has usually been approximated either by using a limited
number of sizes or scalping (cf. Section 3.1). However,since particle size is the most crucial parameter affecting
segregation (Williams, 1976), size distribution should bemodelled as precisely as possible. As stated by Coetzee(2017), particle shape along with the particle size distribution should be explicitly included in the calibration process
and they have to be determined prior to other parameters.

3.4 Validation of DEM models for segregation
The parameters obtained through calibration must be
independent of the application (Coetzee, 2016). Therefore,
once the model is calibrated, it should be capable of accurately reproducing other experiments. In order to provethis, the results of the DEM simulation have to be compared with either experimental tests, analytical or “wellestablished” numerical results related to segregation which
are available in the literature. If a “good agreement” is observed, the model is validated. Regardless of theabove-mentioned approaches being adopted for determining the DEM parameters, the studies in groups II, III and IV
of Table 1 (except for parametric studies in group I) at-

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tempted to validate their DEM model for segregation. The
validation techniques found in the literature overview ofTable 1 can be classified into two main categories as illustrated in Table 6: (1) using other research results as thebenchmark and (2) using first-hand experimental results.

3.4.1 Validation using other work’s results
In this approach, existing experimental, mathematical or
numerical (i.e., DEM) results on segregation are used tovalidate the DEM model. Although adopting this approachfacilitates the process of developing a DEM model, it suffers from several drawbacks. Firstly, some of these references have not particularly investigated the local behaviour(i.e., segregation) of granular materials and as a result, evenin the case of an acceptable agreement, one cannot makesure the DEM model is valid for segregation. Secondly, insome cases, the size distribution of the DEM model differsfrom the source study against which the model has beenvalidated. For example, Zhao et al. (2018) compared theresults of the model for mono-sized particles and used thesame model for modelling the segregation behaviour of
granular materials with log-normal size distribution. However, according to Coetzee (2017), if particle size in themodel changes, the model parameters must be recalibrated, i.e., the previous parameters’ values are notvalid anymore. Thirdly, some studies employed a different
setup in DEM than the experiment in the benchmark, whichmight significantly affect the simulation results. For instance, Wu et al. (2013) simulated a virtual factory insteadof a conveyor belt in the original study (Kajiwara et al.,1988). While simplifying real-life scenarios is a common
practice among researchers, it’s crucial to ensure that thevelocity field is accurately mimicked, as it has a significant
impact on segregation.
All in all, the approach of using other work’s results tovalidate the DEM model can have serious shortcomings. In
other words, the model can be validated in this way only if
all the details are accurately documented. This is often
challenging since some data may have been omitted due toconfidentiality. In addition, this approach is limited to specific materials (e.g., glass beads, iron ores), because there isa shortage of experimental data on the segregation of various kinds of granular materials in the literature.

3.4.2 Validation by conducting experiments

Unlike the above-mentioned category which relied on
existing data in the literature, other studies have conducted
experiments themselves and used first-hand experimental
data to validate the DEM model. Two types of experiments
have been carried out (cf. Table 6): the flow-related measurements of the granular materials, i.e., global/bulk behaviour, or segregation-related measurements, i.e., local
behaviour.
Examples of the first type of experiment in which

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Table 6 Validation approaches of past DEM studies on segregation (cf. Table 1 for the type of mixture in each study).
Approach for validation
Other work’s results

First-handexperimental results

SourceExperimental(Liao et al., 2023; Wu et al., 2013; T.F.
Zhang et al., 2020; Zhang et al., 2018;
Zhao et al., 2018; Zhao and Chew,
2020b)

DEM(Kumar et al., 2020; Mandal andKhakhar, 2019; Mantravadi and Tan,
2020)

Mathematical(Ketterhagen et al., 2008; Ketterhagenand Hancock, 2010; Yu and Saxén,
2013; Zhao and Chew, 2020a)

Flow-related(bulk behaviour)

Segregation(local behaviour)

The mass fraction of the whole particles discharged
from the hopper(Tao et al., 2013)

The velocity profile of particles using particle tracking
velocimetry (PTV)(Shirsath et al., 2015)

Angle of repose measured in practice(Kou et al., 2015; Tian et al., 2022)

The discharge time of the particles from the hopper(Xu et al., 2017; 2018b)

Burden profile(Kou et al., 2013; Yu and Saxén, 2013)

Mass distribution of “all” particles in the radial direction in furnace throat(Kou et al., 2013)

Laser grid (Gao et al., 2010) measurement of burden
falling trajectories(Zhang et al., 2014)

Bed height(Li et al., 2019)

Invasive

Non-invasive

Sampling spaces composed of 24
cuboid-shaped cells inside thehopper(Combarros Garcia et al., 2016)

The discontinuous start-stopsampling method (Standish andKilic, 1985) has been mostly
adopted(Ketterhagen et al., 2007; Yu and Saxén,
2010; Z. Zhang et al., 2020)

Including a number of samplingboxes at the bottom to capture thetrajectory segregation of materials(Bhattacharya and McCarthy, 2014; Mioet al., 2008b, 2009, 2010, 2012; Yu andSaxén, 2012)

Heaps are divided into a number of
sub-regions in the horizontal and/ orvertical direction(Li et al., 2017; Mio et al., 2020; Qiu
and Pabst, 2022; Terui et al., 2017; Youet al., 2016)

Using rotating sampling table withpie slicing configuration(Cliff et al., 2021)

Measuring mass flow rate of fineparticles using Copley flowability
tester BEP2 model(Barik et al., 2023)

Sampling in the horizontal andvertical direction of the sinter cooler(Izard et al., 2021)

Sampling box in QPM segregation
tester(Combarros et al., 2014)

Taking pictures and counting thenumber of different components(Yu et al., 2018)

Colouring equal-sized components
and using image analysis to analyse
segregation(Combarros et al., 2014)

Taking pictures and using image
processing to measure the fractionof components(Alizadeh et al., 2017; Wang et al.,2018)

physical measurements related to global behaviour havebeen carried out are listed in Table 6. With respect to segregation, this approach fails to provide detailed spatial andtemporal or “local” information about segregation such as
the fraction of different components. Although this approach can be adequately adopted to validate DEM models

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for “flow behaviour” on the bulk level, it cannot necessarily
prove the validity of the model for segregation. In otherwords, to validate the models for segregation, component
fractions are required to obtain local information and assess
segregation.
The second type of the studies relied on segregationrelated measurements. As can be seen in Table 6, there are
two main ways of measuring segregation, namely invasive
and non-invasive methods (Huang and Kuo, 2014). Invasive methods for evaluating segregation simply involve
taking a number of samples, separating the components ineach sample and weighting each component to obtain its
mass fraction. Several sampling methods have been
adopted by researchers for different systems as listed inTable 6. Although all the mentioned invasive methods are
relatively simple and can provide valuable information
about the local composition of granular mixtures, they havea number of disadvantages. First and foremost, invasive
methods disturb the original structure of the mixture which
affects the accuracy of the measurement. Moreover, particles from locations other than the target points might becollected during sampling (Muzzio et al., 1997). And, as
mentioned above, components in each sample must be
separated to further calculate their fractions. This can bestraightforward for components differing in size, however
for multi-component mixtures with overlapping particle
sizes, this can be challenging and time-consuming. Terui etal. (2017) successfully employed the gravity separation
method to separate sinter and coke particles based on thedensity difference. However, besides being time-consuming,
this method is not applicable to all types of materials.
The mentioned drawbacks of invasive methods pushed
the search for non-invasive techniques as an alternative.
Because they do not disturb the granular structure, thesetechniques have been increasingly employed in recent
years, especially in the powder blending industry (Nadeem
and Heindel, 2018). There are several comprehensive review papers in the literature explaining the pros and cons aswell as the applications and limitations of these methods indetail (Asachi et al., 2018; Bowler et al., 2020; Huang andKuo, 2014; Nadeem and Heindel, 2018). Because of theinherent limitation of those techniques, not all of them areapplicable to the segregation analysis of any granular mixture. For instance, the electrical conductivity method,(a)

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electrical capacitance tomography (ECT), positron emission particle tracking (PEPT) and magnetic resonance imaging tomography (MRI) cannot be readily utilised toanalyse the mixing and segregation of all multi-component
mixtures (Asachi et al., 2018). This is because the electricalconductivity method and ECT work based on the noticeable difference in electrical conductivity and permittivity,
respectively, which is not the case in all multi-component
mixtures (Asachi et al., 2018; Shenoy et al., 2015). Also, alimited number of tracers is tracked in PEPT, which is notsuitable to measure segregation, and in MRI, the component should be coated with oil to be detectable, whichmight significantly affect its flow behaviour (Stannarius,
2017). Other techniques such as Raman spectroscopy,
near-infrared spectroscopy (NIR) and acoustic emissions
which are feasible for multi-components, are conducted onvery small samples (Bowler et al., 2020), which makes
them unsuitable for coarser particles. Image analysis is another existing non-invasive method which has been widelyused due to its simplicity and low cost. The main advantage
of image analysis over other techniques is that it is applicable to multi-component mixtures. However, this method islimited to surface analysis, it requires a transparent vessel
and the components must differ in colour (Asachi et al.,2018). This method has been successfully employed by anumber of studies to provide experimental results for thecalibration and validation of DEM models of segregation.
Yu et al. (2018) took photos of the heap, divided the heap
into sub-regions radially and counted the number of small
and coarse particles in each region to analyse segregation.
Adopting a more robust approach, Wang et al. (2018) andAlizadeh et al. (2017) employed image processing techniques to measure local concentrations of mixture components at the surface of the heap. To be fully consistent, theyused the same approach in DEM by taking snapshots of the
granular system and applying image processing (seeFig. 4).
In conclusion, all the methods being adopted to validate
DEM models come with a set of advantages and disadvantages. Although the use of existing results in the literaturefacilitates the validation, it is often difficult to precisely
replicate the experimental setup and sometimes, the otherstudy has not itself been validated. Additionally, utilisingexisting results is confined to a few materials which have(b)

Fig. 4 An example of using image analysis to measure segregation in (a) experiments and (b) DEM. Reproduced with permission from Ref. (Alizadeh
et al., 2017). Copyright 2017, Elsevier.

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already been investigated. To overcome these obstacles,
carrying out experiments and using first-hand results would
be beneficial. However, it should be noted that the experimental measurements should provide detailed spatial information for segregation on a local level supplemented by
global level or bulk behaviour such as mass flow rate. The
segregation-related measurements can be performed using
either invasive or non-invasive methods. Image analysis asa non-invasive method is considered superior to invasive
techniques since it not only maintains the structure of the
granular materials but also is applicable to multi-component
mixtures, provided that the components differ in colour.

4. Results of DEM-based studies on
segregation

The work we presented in Table 1 in the previous section will be elaborated in detail here. We gather and analyse
the findings of those studies on how segregation is affectedby adjusting material properties, system configurations andoperational parameters.

4.1 Material properties

standing of the effect of particle size and especially particle
size distribution (PSD) on segregation.
In the case of binary-sized mixtures, i.e., mixtures in theform of 1C[(2S/1Sh)], the effect of size distribution can beinvestigated by varying either the size ratio or the massfraction of the components. It is generally accepted that alarger size ratio leads to more pronounced segregation(Bridgwater, 1994). Fig. 6 shows the investigated ranges ofsize ratio in the DEM-based studies of binary-sized
free-flowing mixtures in various systems. For hoppers andin one of the earliest attempts to study segregation using
DEM, Ketterhagen et al. (2007) concluded that the extent
of segregation significantly increases for size ratios greaterthan 1.9. Using the velocity difference between small andlarge particles as an indicator of percolation, T.F. Zhang etal. (2020) investigated the segregation during conical hopper discharging and found that in their case, percolation
was not dominant for size ratios smaller than 6.0 (i.e., corresponding to the velocity difference of 0.07 mm/s). Z.
Zhang et al. (2020) observed that the percolation which
occurred during the discharge of a wedge-shaped hopper
near the wall and the bottom of the hopper was eliminatedGenerally, any difference between the particles of a particulate system can cause segregation. For free-flowing
granular materials, it has been proven that particle size,
density and shape affect segregation more than other factors such as surface roughness and elasticity (Tang andPuri, 2004). The studies listed in Table 1 are analysed andthe effect of these factors are presented in the following
paragraphs. To systematically discuss the results in the literature, we present Fig. 5 which illustrates the possiblecombinations of different particle properties and Table 7
which shows the relevant subsection addressing the corresponding type of mixture.

Size (A)Density (B)(A+B)(A+B+C)
(A+C)

(B+C)

Shape (C)4.1.1 Particle size
Particle size is unequivocally the most influential property contributing to segregation among the other particle
properties (Williams, 1976). This is why numerous research studies have been dedicated to gaining a full under-

Fig. 5Different possible combinations of particle properties.

Table 7 Corresponding subsection for different types of mixtures.

SectionType of mixture
SymbolNotation

4.1.1 Particle sizeA

1C[(iS/1Sh)]i = 2, 3, …, M, P

4.1.2 Particle density

B

nC[(1S/1Sh) + ⋯ + (1S/1Sh)]

n = 2, 3, etc.

4.1.3 Particle shapeC

1C[(1S/jSh)]

j = 2, 3, etc.

4.1.4 Combinations of particle size,
density and shape(A+B)

nC[(i1S/1Sh) + ⋯ + (inS/1Sh)]

n = 2, 3, etc. & i = 2, 3, …, M, P

(A+C)

1C[(iS/jSh)]i = 2, 3, …, M, P & j = 2, 3, etc.

(B+C)

nC[(1S/j1Sh) + ⋯ + (1S/jnSh)]

n = 2, 3, etc. & j = 2, 3, etc.

(A+B+C)nC[(i1S/j1Sh + ⋯ + (inS/jnSh)]

n = 2, 3, etc. & j = 2, 3, etc.

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SourcePereira and Cleary (2013)
Li et al. (2022)
Xu et al. (2018)
Zhang et al. (2017)
Mandal and Khakhar (2019)
Panda and Tan (2020)
Tripathi and Khakhar (2011)
Tao et al. (2013)
Yu and Saxen (2010)
Xu et al. (2017)
Z. Zhang et al. (2020)
T.F. Zhang et al. (2020)
Ketterhagen et al. (2007)
Ketterhagen et al. (2008)
1

Fig. 6

2

3

4

5

6

8

9

10

11

12

13

14

15

Size ratio

Size ratio ranges in the past DEM studies on segregation.

for a size ratio lower than 1.2. Varying size ratios from 1.3
to 4.0, Yu and Saxén (2010) observed significant segregation even for the minimum ratio of 1.3. Unlike these studies, Ketterhagen et al. (2008) found that in the case of thehopper bottom wall angle of 15° (as opposed to 90°), no
considerable segregation was observed for all size ratios
tested.
In addition to hoppers, several studies have examinedthe effect of size ratio on segregation in other systems suchas inclined planes, chutes, the throat of the blast furnace
and V-blender. In their work on the segregation of binary
mixtures down an inclined plane, Tripathi and Khakhar(2011) found that varying size ratio does not affect the extent of segregation. In the case of a chute, Panda and Tan(2020a) observed that although the stream-wise, crossstream and vertical velocities all increase with increasing
size ratio, the degree of segregation did not increase significantly in the stream-wise direction. Li et al. (2022) studied
the particle segregation in the throat of the blast furnace.
For this, they charged a layer of binary-sized cokes followed by a layer of binary-sized ore particles. Varying thesize ratio equally for coke and ore particles, they observedthat larger size ratios result in a more segregated state,
which was more evident for the coke mixture. In their study
on the de-mixing of binary-sized mixtures during discharge
from a V-blender, Pereira and Cleary (2013) observed thatthe segregation extent becomes significant for large size
ratios, i.e., 3 and 4.

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7

Apart from the particle size ratio, the mass fraction offine particles has been proven to have a significant impact
on segregation (Tang and Puri, 2004). Artega and Tüzün(1990) proposed a model for binary-sized mixtures to determine the fine mass fraction (xf) for which segregation via
percolation becomes feasible during hopper discharge.
They claimed that percolation no longer occurs when thesurface area of the large spheres is completely covered bysmall spheres, implying that percolation only occurs if themass fraction of finer particles is lower than the limiting
value defined as xL,crit = [4/(4 + ØR)], where ØR is the size
ratio. This model is applicable to free-flowing materials of
approximately spherical shape and is independent of thehopper geometry. While the findings of Ketterhagen et al.
(2008) and Z. Zhang et al. (2020) are in accordance withArtega and Tüzün’s model, Xu et al. (2017) observed segregation for mass fractions higher than the abovementioned xL,crit. Nevertheless, all of these studies agree
that increasing the fine mass fraction decreases segregation(Barik et al., 2023; Ketterhagen et al., 2008; Tian et al.,2022; Xu et al., 2017; Zhang et al., 2018; Z. Zhang et al.,2020).
While in most of the DEM studies a limited number ofparticle sizes has been used, Zhao et al. (2018) and Kumar
et al. (2020) used a continuous distribution of particle size,i.e. poly-sized mixtures with the general form of
1C[(PS/1Sh)]. Zhao et al. (2018) studied the discharge of
mixtures with a log-normal size distribution from a conicalAhmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

hopper. As shown in Fig. 7, they examined the effect of thewidth of size distribution, defined as the ratio of standard
deviation to mean diameter (σ/µ) on segregation and concluded that the higher the width, the greater the size segregation will be. This agrees with observations on the impactof size ratio on the segregation of binary-sized mixtures, as
the size ratio in binary-sized mixtures is essentially thesame as the width of size distribution in poly-sized mixtures.
In another study, Kumar et al. (2020) studied the discharge from a hopper of mixtures with the Rosin-Rammler
size distribution as follows:
n

 x 
R
1  exp    
  x  (2)with size distribution’s width (n) and location parameters(x′). It was found that the width has more impact on the(2)segregation than location parameters. Moreover, they conducted a comparative analysis between the log-normal and
Rosin-Rammler particle size distribution (PSD) with thesame width and observed that the latter showed a higher
segregation extent. They also reported that the extent ofsegregation is increased with more fine particles which
contradicts the observations in past studies on the segregation of binary-sized mixtures (Ketterhagen et al., 2008; Xuet al., 2017; Zhang et al., 2018; Z. Zhang et al., 2020). This
conflict may lie in the fact that comparing a continuous size
distribution with a binary-sized mixture requires selectionof the right cut-off size between fine and coarse particles,
which is not always a trivial task.
To date, a large number of segregation-related studies
focused on the effect of particle size and mass fraction inbinary-sized mixtures. Generally, it has been found that
reducing the size ratio as well as increasing the mass fraction of fine particles lessen segregation. With respect to thesize ratio, however, there is no unanimous agreement either
on the critical size ratio which promotes segregation or on

Fig. 7 Different log-normal size distributions. Reproduced with permission from Ref. (Zhao et al., 2018). Copyright 2017, John Wiley and
Sons.

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the effect of increasing the size ratio on segregation in
general. Overall, there is no single limiting size ratio above
which segregation happens. Also, there is no unanimous
agreement on the effect of increasing the size ratio on segregation. The reason behind this is twofold. Firstly, apartfrom size ratio, other factors such as material properties(e.g., mass fraction), geometric, and operational parameters
also play a crucial role in segregation phenomena. Secondly, percolation rate measurements in shear cells haveshown that segregation can occur for all size ratios, andlarger size ratios only serve to accelerate its occurrence(Bridgwater, 1994; Bridgwater et al., 1978; Tang and Puri,2005). Similar to the size ratio, the effect of the mass ratioof fine particles on segregation is not consistent, leading to
the conclusion that the results of one study cannot be applied to all cases since other factors (e.g., geometrical andoperational parameters) play a key role as well.

4.1.2 Particle density
The difference in particle density is another known factor to cause segregation. As mentioned in Section 2, thedensity of particles in a single material is assumed to beconstant. Therefore, the difference in particle density isequivalent to having different materials. For instance, what
is referred to as a “binary-density” mixture is basically a
two-component mixture with the notation of
2C[(1S/1Sh) + (1S/1Sh)].
While a number of earliest experimental studies on the
mixing and segregation of granular materials argued thatparticle density does not affect segregation considerably(Vallance and Savage, 2000; Williams, 1976), some otherssuggested that the difference in density should be taken into
account in segregation analysis (Bridgwater et al., 1978;
Drahun and Bridgwater, 1983; Khakhar et al., 1999). There
has been, however, little systematic analysis of densitydriven segregation in free-flowing granular materials using
DEM. Seil et al. (2012) investigated the segregation of
mixtures discharging from two hoppers. They observedthat the heavier particles tend to gather in the centre areas
and that the extent of segregation is linear to the density
ratio. In another study on an inclined plane, Tripathi and
Khakhar (2013) observed that heavy particles sink to near
the base, and light particles form a layer close to the freesurface with a mixed region in between, which was confirmed in other studies (Panda and Tan, 2020a; Wang et al.,2018, 2023). Furthermore, they showed that higher density
ratios result in stronger segregation.
In conclusion, particle density has been shown to affectsegregation to some extent. However, the significance of its
effect compared to other factors such as the difference inparticle size is unclear as yet. More effort on densityinduced segregation in various systems is required to pave
the way for developing DEM models for multi-component
segregation.

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4.1.3 Particle shape
Only a few of the studies found focused on modelling theeffect of particle shape on the extent of segregation. Thesestudies can be classified into two groups. The first group
includes studies in which the mixtures are composed of two
particle shapes to investigate shape-induced segregation.
Using the multi-sphere approach, Mandal and Khakhar(2019) studied the flow of mixtures of spherical andnon-spherical particles of the same volume down a roughinclined plane (cf. Fig. 8). They found that the geometricmean diameter (i.e., dg = λ1/3d, where λ is the aspect ratio
and d is the diameter of the constituent spheres) is a good
representative of the effective size for irregularly shaped
particles, and the extent of segregation of two species depends on the ratio of the geometric mean diameter of particles. Moreover, it was observed that at the steady state, theparticles with a larger geometric mean diameter accumulate near the free surface.
Zhao and Chew (2020a) studied the flow behaviour ofbinary-shaped mixtures during discharge from a hopper.
They modelled seven different particle shapes (a sphere,
two ellipsoids, two cylinders, and two cuboids (as shown in
Fig. 9), all with an aspect ratio between 0 and 2 and thesame volume. Considering various combinations of theseparticle shapes, they observed that mixtures of cylinders
and cuboids exhibited the least segregation and the ellipsoids mixed with cylinders were the most segregated mixtures.
Second, studies in which the segregation behaviour ofdifferent mixtures, each component composed of a particular shape, is analysed. Zhao and Chew (2020b) compared
the discharging behaviour of mixtures with log-normal
PSD composed of ellipsoids, cylinders or cuboids and observed that ellipsoids show a very similar segregation behaviour to spheres. The cuboids and ellipsoids exhibit thelowest and the highest segregation extent, respectively.
Concluding, very little attention has been paid toshape-induced segregation in DEM-based studies. Past
studies have demonstrated that the difference in particle
shape itself can induce segregation (Mandal and Khakhar,

Fig. 8 Different combinations of spherical and non-spherical particles.
Reproduced with permission from Ref. (Mandal and Khakhar, 2019).
Copyright 2019, AIP Publishing.

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2019; Zhao and Chew, 2020a). However, it is unclear how
particle shape induces segregation and specifically, whichshape characteristics are more significant for segregation.
Therefore, further work needs to be done to shed light on
shape-induced segregation. Challenging herein are thecomputational expense of irregularly shaped particles andthe fact that particle shape is not independent of particle
size. This is very important since if only the segregationinduced by particle shape is of interest, the “effective”
particle size should be the same to avoid misinterpretation(Shekhar et al., 2023). Therefore, we second that attempts
should be made to establish the criteria for finding the “effective” size of non-spherical particles for segregation (Yuet al., 2020).

4.1.4 Combinations of particle size, density andshape
While the preceding subsections were focused on studies
examining variations in a single particle property (such assize, density, or shape), a limited number of research works
investigated mixtures with multiple properties that vary simultaneously. To systematically discuss them, these studies
are grouped below using the symbols in Fig. 5 and Table 7:(A+B). Most of the studies varied the particle size anddensity at the same time. Two different scenarios can be
distinguished: 1. a mixture of (small/heavy) and (large/
light) particles, in which the percolation and buoyancy
mechanisms work together, or, 2. a mixture composed ofsmall/light and large/heavy particles where percolation andbuoyancy mechanisms oppose each other. Fig. 10 illustrates these two scenarios. As can be seen in Fig. 10(a), in
the first scenario, both percolation and buoyancy mechanisms act together and (small/heavy) particles sink into the
mixture. However, in the second scenario as in Fig. 10(b),the dominant segregation mechanism is not known a priori
and should be studied case by case. Past studies indicatedthat in the first scenario, reducing the size ratio and/or
density ratio leads to less segregation and could be used asa way to suppress segregation (Terui et al., 2017; Xu et al.,2017, 2018a). For the second scenario, Ketterhagen et al.

(a)

(b)

(d)

(e)

(f)

(g)

(c)

Fig. 9 Different particle shapes modelled. Reproduced with permission from Ref. (Zhao and Chew, 2020a). Copyright 2020, John Wileyand Sons.

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107(a)Review Paper(b)

Fig. 10 Two scenarios for particles differing in both size and density (solid black colour indicates heavier particles), (a) first scenario; a mixture of(small/heavy) and (large/light) particles, and (b) second scenario; a mixture of (small/light) and (large/heavy) particles.

(2008) varied the density ratio from 0.33 to 3.0 for a size
ratio of 4.3, and no significant segregation was detectedduring discharge from a wedge-shaped hopper. Similarly,
Tao et al. (2013) increased the density ratio from 0.5 to 2.0
for a mixture with a size ratio of 4.0 and observed that
segregation was negligible.
(A+C). In attempts to explore the simultaneous effect ofparticle size and shape on segregation, several studies varied the particle shape of binary-sized mixtures, i.e. mixtures in the form of 1C[(2S/2Sh)]. Mandal and Khakhar(2019) studied the flow of mixtures of non-spherical particles where the two particle types differ in both volume (i.e.,size) and shape, and observed that the segregation extent
increases for larger geometric mean diameter ratios. Tao etal. (2013) simulated binary-sized mixtures with different
particle shapes including spherical, corn-shaped, cylindrical and ellipsoidal particles. It was observed that particle
shape has a significant effect on the segregation extent withthe most segregated system for ellipsoidal and the least for
spherical particles.
(A+B+C). A few studies examined the segregation in
mixtures differing in particle size, density and shape. Liaoet al. (2023) investigated the segregation behaviour oflump ore and pellets that differed in size and density. To
examine the impact of particle shape on segregation, they
simulated three different shapes of lump ore particles,
namely spheres, cylinders, and schistous particles andconcluded that particle shape has a significant effect onsegregation, with spheres showing the least amount ofsegregation. In the mixture of sinter and briquette, Kim etal. (2020) changed both the shape and mass ratio of thebriquette and observed that while the shape had little effecton segregation, a mass ratio higher than 20 % led to significant segregation.
Despite previous efforts to investigate the influence ofvarious material properties on segregation, there remains asignificant knowledge gap. While the majority of naturaland industrial mixtures are multi-component, i.e. (A+B+C)
in Table 7, most studies have concentrated on binary or
ternary-sized mixtures consisting of spheres. Therefore, amore comprehensive and rigorous approach towards material properties that takes various combinations of size,
density and shape into account is required to accurately

predict segregation patterns and occurrences.

4.2 System configurations
In addition to the material properties, system parameters
play a key role in the segregation phenomena occurring inparticulate systems because they influence the motion of
the particles. In gravity-driven handling processes, twomain pieces of equipment can be distinguished: hopper/siloand chute/inclined surfaces. A review of these systems in
the context of segregation is presented in the following
paragraphs.

4.2.1 Hopper
Segregation during hopper discharge has been studied
since the early ’80s when most studies were experimental.
Ketterhagen et al. (2007) were one of the first to model
segregation during cylindrical hopper discharge using
DEM in 2007. Since then, a number of DEM studies haveinvestigated the influence of geometric properties on theextent of segregation by varying the outlet width, the slopeof the bottom wall and the smoothness of the walls.
Two main flow regimes are distinguished during hopper
discharge: mass flow, where the entire body of mass inside
the hopper is in motion during discharge, and funnel flow
(Jenike, 1967; Saleh et al., 2018; Schulze et al., 2008);
funnel flow is less desirable as it imposes asymmetric pressure distribution and causes significant segregation which
can be harmful to both hopper structural stability and particulate homogeneity (Saleh et al., 2018). The flow mode ismainly dependent on the bottom wall angle (θc) and wall
friction angle (ϕx). Smoother and steeper walls (i.e., low θc)most likely induce a mass flow regime and are less likely to
induce segregation. This was also confirmed in several
DEM studies (Ketterhagen et al., 2009, 2008; Saleh et al.,2018; Yu and Saxén, 2010).
Several studies used DEM to gain a deeper insight into
segregation during hopper discharge. T.F. Zhang et al.
(2020) varied the angle of the hopper wall from 15° to 90°
and observed that larger angles (i.e., flatter walls) lead tofaster percolation of small particles, increasing their tendency to gather at side walls (cf. Fig. 11(a)). Similarly, Z.
Zhang et al. (2020) showed that the increase in the hopper
angle promotes segregation in the bottom regions (red95Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

regions in Fig. 11(b)). However, they found that with larger
angles, the percolation near the wall region decreases. Thisdisagreement might be because of the different size ratio,wall condition (i.e. boundary walls in T.F. Zhang et al.
(2020)), or a different method of assessing segregation (cf.
Section 3.2). Also, it is remarkable that in T.F. Zhang et al.
(2020), the hopper outlet size of 3dl (where dl is the diameter of the large particles) is used, which does not satisfy
fundamental hopper design principles (Schulze et al.,2008) and can affect the segregation results.
Stating that the bottom angle of the hopper is a decisive
factor in the flow mode, Huang et al. (2022) proposed an
optimised design which increases the mass flow zone andas a result, achieves less size segregation (cf. Fig. 12).
In addition to the hopper wall angle, the outlet size can

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also affect segregation during hopper discharge.
Ketterhagen et al. (2008) found that the increase in outlet
width reduces the segregation of mixtures discharging fromwedge-shaped hoppers. Although Z. Zhang et al. (2020)
confirmed that the percolation in the bottom part of thehopper reduces in the case of a bigger outlet, a higher segregation degree near hopper walls was observed which canbe related to wall effects. Taking the effect of both the
hopper outlet and wall angle into account, Ketterhagen etal. (2008) claimed that segregation conditions remain thesame for hoppers with the same aspect ratio of hopper
height to hopper width.
To investigate the effect of flow-correcting inserts, Cliffet al. (2021) compared the segregation behaviour ofbinary-sized mixtures during discharge from a hopper with

(a)

(b)

Fig. 11 The effect of hopper wall angle on size segregation in two studies: (a) Reproduced with permission from Ref. (T.F. Zhang et al., 2020). Copyright 2020, Elsevier. and (b) Reproduced from Ref. Z. Zhang et al. (2020), used under Creative Commons CC-BY-NC-ND License.

Fig. 12 The effect of different hopper design on segregation during hopper discharge at different discharging fractions (md /mt). Reproduced withpermission from Ref. (Huang et al., 2022). Copyright 2022, Elsevier.

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and without an insert (cf. Fig. 13(a)). They stated that in
the case of a hopper with an insert, lower velocity gradientsat the free surface lead to less segregation (cf. Fig. 13(b)).
While most of the works mentioned above studied concentric hoppers, Ketterhagen and Hancock (2010) compared the segregation of binary-sized mixtures in eccentricand concentric hoppers (cf. Fig. 14(a)). They found thatwhile the eccentric hopper had slightly less segregation, thesegregation profiles around the outlet were completely different (cf. Fig. 14(b)). Additionally, they modified the design of the eccentric hopper, resulting in improved
segregation.
In conclusion, designing for mass flow is beneficial to
avoid size segregation during hopper discharge. This canbe achieved by steeper and smoother walls. However, several studies observed insignificant effects of these factors
on segregation. The reason for the conflict between thesestudies is that besides geometric parameters, material properties and operational parameters can influence particle
velocities and segregation. This makes it challenging tocompare the findings of different studies and arrive at adefinitive conclusion. Therefore, a more robust approach is(a)needed to investigate the simultaneous effect of different
geometric factors on segregation and its dependence onmaterial properties. Moreover, the work found evaluatedonly single-component mixtures, and the effect of geometric design parameters in the case of multi-component segregation is missing.

4.2.2 Chutes
Chutes are often used to transfer materials from one
piece of equipment to another. They can have different
cross-sectional shapes (e.g., semicircular, rectangular),lengths, curvatures and wall roughness. While the chuteangle may be regarded as a system configuration parameterin certain cases, e.g., transfer chutes, it is considered an
operational parameter in dynamic systems such as blast
furnaces. Therefore, we will be discussing the chute angle
later in Section 4.3.2 of this paper.
Segregation can occur simultaneously in three directions: in the direction of the flow (longitudinal/stream-wise
segregation), from the chute to the free surface (vertical/
normal segregation) and from one side wall to the other(horizontal/cross-stream-wise segregation). During chute(b)

Fig. 13 (a) The cross-sectional view of the hopper with insert, (b) Difference in the velocity gradient between the hopper with (right) and without(left) insert. Reproduced with permission from Ref. (Cliff et al., 2021). Copyright 2021, The Royal Society.

(a)

(b)

Fig. 14 (a) Concentric and eccentric hopper, and (b) Segregation profile in the eccentric hopper. Reproduced with permission from Ref. (Ketterhagenand Hancock, 2010). Copyright 2010, Elsevier.

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flow, segregation can happen in two stages. Firstly whenthe material is inside the chute, gravitational forces, centrifugal and Coriolis forces, inter-particle friction and particlewall friction forces act on the particles (Zhang et al., 2014).
Mio et al. (2008b) found that particles segregate inside thechute with smaller particles at the bottom wall of the chuteand larger ones on top. In the second stage, the material
leaves the chute and trajectory segregation causes larger
particles to travel further (Mio et al., 2008b). The effect ofchute-related geometric parameters on segregation was researched by employing either a simple inclined plane(Zhao et al., 2022) (to minimise the effect of side walls) ora real 3D chute (Panda and Tan, 2020a).
With respect to the effect of the cross-sectional shape ofthe chute, Kou et al. (2019) investigated the effect of threedifferent shapes, namely semi-circular, trapezoid, and rectangular on the segregation of ferrous burden (i.e., pellet,
sinter and lump ore) forming a heap in the blast furnace
throat. They found that the chute cross-sectional shapesignificantly affects the radial size distribution of materials
and the effect is intensified with larger chute angles andhigher rotating speeds. Moreover, unlike trapezoid andrectangular chutes, the semi-circular chute was reported tomaintain a uniform radial size distribution regardless of thechute angle and rotating speed. Panda and Tan (2020a;
2020b) focused on the segregation inside the chute andcompared the segregation between rectangular andsemi-circular chutes. It was observed that the rectangularchannel resulted in slightly higher segregation in thestream-wise direction, while there were barely any differences in cross-stream and vertical directions.
In terms of the impact of chute length, the observations
revealed that since segregation occurs within the chute, anincrease in the chute length leads to a greater extent ofsegregation (Bhattacharya and McCarthy, 2014; Kou et al.,2013; Yu and Saxén, 2012; Zhang et al., 2014). Bhattacharya
and McCarthy (2014) determined the critical chute length
for trajectory segregation of ternary-sized mixtures discharging from a hopper on to the chute, using both theoretical equations and simulations which yielded 4.0 and
5.4 m, respectively. They claimed that with other parameters fixed, there is significant segregation in the samplingboxes located under the chute for the chute longer than thecritical length. For the radial segregation of multicomponent mixtures of sinter, pellet and ore, Hong et al.
(2021) observed that the effect of chute length is negligible.
Mantravadi and Tan (2020) examined the effect of bend
angle (i.e. curvature) in periodic flow inversions (seeFig. 15)—an idea initially introduced by Shi et al. (2007)—
on reducing the size segregation of binary-sized mixturesand observed that a bend angle between 120 and 150 degrees leads to the minimum extent of segregation.
Zhou et al. (2016) varied the wall roughness by using thewall friction coefficient and found that a higher base rough-98

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Fig. 15 The periodic flow inversion with a bend angle of θ. Reproduced with permission from Ref. (Mantravadi and Tan, 2020). Copyright 2020, Elsevier.

ness significantly accelerates segregation. However, in thecase that base friction was larger than inter-particle friction,no effect on the flow regime and segregation was observed.
Shirsath et al. (2015) studied the effect of chute wall roughness on the segregation of binary-density mixtures, i.e.,2C[(1S/1Sh) + (1S/1Sh)]. They implemented the roughness
artificially by modelling a number of particles at chutewalls, attributing the ratio of the diameter between surface
particles and flowing particles (ranging from 0.5 to 2.0) to
the degree of roughness. They reported that wall roughness
has a considerable effect on segregation as it is negligible
for the smooth (but frictional) chute wall. While it was observed that the degree of segregation increases with wallroughness, this increase plateaus when the diameter of wallparticles exceeds the flowing particle size. However, it isunclear to what extent the method of modelling wall roughness in this work is representative for roughness at micro
scale.
Concluding, the geometric aspects of chutes including
cross-section shape, length, curvature and wall roughness
can significantly influence segregation. Generally, it has
been found that longer chutes with rougher walls promotesegregation.

4.3 Operational parameters

Besides the material properties and system configurations discussed above, operational parameters are also
among the influencing factors on segregation. In order togain a deeper insight, related studies are reviewed in thefollowing subsections under different categories based on
the system type, namely hopper, chute and other systems.

4.3.1 Hopper filling operations

Various hopper-related operational parameters, including filling method, filling angle, and filling position havebeen studied in the past. Several studies investigated theAhmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

influence of various hopper filling methods, i.e., different
components/sizes being charged in separate layers or in a
pre-mixed state, on the segregation after hopper discharge(Chibwe et al., 2020; Ketterhagen et al., 2008, 2007; Mio etal., 2010; Yu and Saxén, 2010). Although Ketterhagen et al.
(2008), Yu and Saxén (2010) and Chibwe et al. (2020) usedslightly different setups and filling patterns, their conclusion was that the initial filling method significantly affects
the segregation.
Apart from the filling method, the filling position was
found to significantly influence segregation. Wu et al.
(2013) investigated the effect of burden apex (i.e., the highest point on the hill formed when materials are charged into
the hopper) on the segregation during hopper charging anddischarging. It was found that the burden apex mostly influences the particle size distribution during charging but has
little effect during discharging. In the case of a Paul-Wurthhopper, Zhang et al. (2021) also observed that the filling
position has a significant effect on the segregation and there
was strong segregation in the case of the right and left filling positions (cf. Fig. 16(a)). Moreover, they observed thata filling angle closer to the vertical position (cf. Fig. 16(b))

Review Paperand a larger charging outlet (cf. Fig. 16(c)) reduced theextent of segregation.
Cliff et al. (2021) showed that a continuous filling operation, as opposed to batch processing, significantly reduces
the amount of segregation because segregation mostly occurs in the last 25 % of hopper discharge.
Concluding, the filling method, filling position and filling angle significantly affect segregation downstream (i.e.,during hopper discharge). This means that all the handlingsteps should be accurately modelled in order to achieve a
reliable assessment of segregation.

4.3.2 Chute operations
With respect to chute-related operational parameters,
several studies have investigated the effect of chute angle(θ) (Jing et al., 2015; Kou et al., 2019, 2018, 2013; Mio etal., 2009; Nakano et al., 2012; Tripathi and Khakhar, 2011;
Xu et al., 2018b; You et al., 2016; Zhang et al., 2014), tilting direction (Terui et al., 2017), rotating speed (ω) (Kouet al., 2019, 2013; You et al., 2016), fill level (Izard et al.,2021; Panda and Tan, 2020a), the number of chute rotations(Mio et al., 2012) and installation of a damper (Mio et al.,

(a)

(b)

(c)

Fig. 16 The effect of (a) filling position, (b) filling angle and (c) hopper outlet on segregation during hopper charging. Reproduced with permission
from Ref. (Zhang et al., 2021). Copyright 2021, Elsevier.

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2008b). Some of the parameters are schematically illustrated in Fig. 17.
It should be noted that in a rotating chute, particles are
subjected not only to gravitational forces but also to Coriolis and centrifugal forces (Shirsath et al., 2015). The relative impact of inertial and Coriolis forces is quantified by
the Rossby number (Eqn. (3)):

Ro 

νa
2ωL cos θ(3)where νa, ω, L, and θ denote the flow velocity, chute rotation rate, chute length and chute angle, respectively. In the(3)of Ro ≫ 1, the effect of the rotation rate can be necase
glected. For instance, Shirsath et al. (2015) varied the chuterotation rate from 4 to 16 rpm and observed Ro larger than1, concluding that the flow was gravity driven. Also, theFroude number (Eqn. (4)) can be used to quantify the relative significance of the centrifugal force compared to thegravitational force (Shirsath et al., 2015):
Fr 

ω 2 L cos θg(4)

In the case of Fr ≪ 1, the gravitational force is dominant,preventing the particles from escaping out of the chute.
(4)
The chute angle is an important flow control parameteras it is used to increase or decrease the flow velocity. Past
DEM studies have investigated the effect of chute angle on
the segregation occurring either inside the chute or whenparticles flow out and form a heap. Regarding the segregation happening inside the chute, Tripathi and Khakhar(2011) observed that the shear rate and extent of segregation in a vertical direction increased with a lower angle.
Similarly, Jing et al. (2015) found that segregation occurs
faster as the angle decreases, which was expected since therate of acceleration increases as the chute is steeper. However, the final (converged) segregation degree was similar

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for different chute angles and, surprisingly, the converged
state was reached at approximately the same time for different angles.
For segregation occurring after discharging from chutes,
several studies have found that reducing the chute angle
intensifies the segregation in different systems, namely
blast furnace throat (Kou et al., 2019; Mio et al., 2009; Xuet al., 2018b; Zhang et al., 2014), feed bed of sinteringmachine (Nakano et al., 2012), COREX melter gasifier(Kou et al., 2015, 2013; You et al., 2016). However, Zhang
et al. (2014) varied the chute angle from 21° to 37° andobserved that the radial segregation index (RSI) would notnecessarily decrease (cf. Fig. 18). Also, Kou et al. (2013)
observed that increasing the chute angle from 10° to 30°
first leads to a decrease of segregation of large particles andthen to an increase.
In modern blast furnaces equipped with the Paul-Wurth
Bell-Less Top® charging system, as well as in the COREX
process, a rotating chute is used to distribute materials in
the circumferential directions of the furnace throat(Cameron et al., 2019; Kou et al., 2013). In addition to thepreviously discussed chute angle, other operational parameters such as the rotating speed, tilting direction (i.e. changing the angle from low to high or vice versa as shown in
Fig. 17), and the number of rotations have been studied
with respect to segregation. Terui et al. (2017) modelled amixture of coke and sinter and two types of tilting directions, namely conventional tilting (i.e., from wall to centre
direction) and reverse tilting (i.e., from centre to the wall
direction). They found that with reverse tilting, coke particles are less segregated as shown in Fig. 19. The rotating
speed of the chute was found to have a negligible effect onsegregation compared to the chute angle (Kou et al., 2019,2013; You et al., 2016). Mio et al. (2012) observed that anincrease in the number of chute rotations intensifies
segregation, leading to a larger mean particle size near theθReverse tilting
Conventional tilting

Fig. 17 Schematic view of a rotating chute and related operational parameters: chute angle (θ), rotating speed (ω).

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Fig. 18 Change in radial segregation index (RSI) with different chuteangle. Reproduced with permission from Ref. (Zhang et al., 2014).
Copyright 2014, Elsevier.

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centre of the blast furnace.
The volume (or mass) flow rate through a chute is another operational parameter which is generally varied on an
on-demand basis. For a given chute design, increasing/decreasing the flow rate results in a higher/lower fill levelof the chute. Panda and Tan (2020a) found that the increasein the fill level causes the small and large particles to move
together, i.e., decreases the velocity difference, resulting inlower segregation in the cross-stream and vertical directions of the chute. Although it was observed that the filllevel does not affect velocities in a stream-wise direction,
segregation was decreased because of less available spacefor percolation due to denser packing. In a study on sintercooler charging systems, Izard et al. (2021) investigated theeffect of the fill level of the chute (i.e. 20 %, 50 % and
90 %) on the segregation pattern in the trolleys and foundthat while a filling ratio smaller than 50 % imposes an increasing size heterogeneity in the trolley, no change in the
segregation pattern was observed for a filling ratio larger
than 50 %.
In conclusion, the operational parameters for operating
moving chutes including chute angle, tilting direction, rotating speed and fill level, affect the segregation to someextent. Based on the studies reviewed here, it appears thatchute angle and fill level may be more significant than theother parameters. However, further research is needed toconfirm this hypothesis and to fully understand the complex nature of segregation in granular mixtures discharging
from moving chutes. Nevertheless, all the parameters havethe potential to be adjusted with the purpose of reducing or(a)controlling segregation.

4.3.3 Other systems
In addition to hoppers and chutes, other operationrelated factors have been investigated in the context of thegravity-driven segregation of cohesionless materials. This
includes the falling height in the stockpile (Kou et al.,2013; Zhang et al., 2017), the method of “discharge” froma v-blender (which is essentially a gravity-driven flow)(Pereira and Cleary, 2013), the bottom base shape of the
COREX furnace (cf. Fig. 20) (Kou et al., 2018) and feed
rate as well as rill plate angle in an iron ore sinter strand (Liet al., 2019). Hence, in every industrial system, there areseveral operational parameters that can affect segregation.
Detecting these parameters and studying their effect utilising DEM can pave the way to optimise the industrial processes with respect to segregation. This is highly
advantageous because, in industrial applications, it is notalways possible to control segregation by modifying material properties and system configurations.

5. Conclusion
This review paper presents an overview of the state of
the art in the DEM modelling of the segregation behaviourof complex multi-component mixtures in applications involving the gravity-driven flow of dry, cohesionless granular materials. First of all, a novel scientific notation has
been introduced to ensure consistency and accurately describe different types of mixtures. The main findings of thisreview can be summarised as follows:(b)Fig. 19 Cross-sectional view of the radial distribution of a coke-sinter mixture for (a) reverse tilting and (b) conventional tilting. Reproduced withpermission from Ref. (Terui et al., 2017), used under Creative Commons CC-BY-NC-ND License.

(a)Fig. 20

(b)

(c)

(d)

Different bottom base shape of the COREX furnace. Reproduced with permission from Ref. (Kou et al., 2018). Copyright 2018, Elsevier.

101Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107● Although most mixtures existing in nature and industrial
settings are considered multi-component (i.e., they contain particles differing simultaneously in size, density
and shape), relatively few studies have investigated thesegregation behaviour of such mixtures.
● Despite the fact that calibration is an essential part ofdeveloping a reliable DEM model, most of the past studies have omitted it. This is especially true when it comesto models of multi-component mixtures. For instance,
the effect of interaction parameters between components
has not yet been studied in detail. Furthermore, it is notclear whether calibrated parameter values obtained for acertain mixture composition are applicable to other compositions as well.
● The effect of size distribution and particle shape on segregation has not yet been fully understood. Considering
the fact that including small particles and irregular particle shapes in the model increases computational time
considerably, the effect of approximating real-world
mixtures with a finite number of particle sizes and spherical particles should be comprehensively studied.
● When quantifying segregation experimentally, noninvasive techniques such as image analysis are generally
more suitable than conventional invasive methods (i.e.,sampling and weighing), since non-invasive techniques
do not disturb the mixture structure and are applicable tomixtures differing in both size and density.
● Results of past DEM studies on segregation show thatthe effects of different parameters influencing segregation, i.e., material properties, system configurations andoperational parameters, should be considered together
since they are highly intertwined.
● Compared to material properties and system configurations, operational parameters are relatively easier tochange. Hence, to reduce and control segregation in industrial applications, operational parameters are preferential.

Acknowledgements
This research was carried out under project number
T18019 in the framework of the Research Program of theMaterials innovation institute (M2i) (www.m2i.nl) supported by the Dutch government. Also, this work was carried out as part of the “Industrial Dense Granular Flows”
project, which received funding from the Dutch ResearchCouncil (NWO) in the framework of the ENW PPP Fundfor the top sectors and from the Ministry of Economic Affairs in the framework of the “PPS-Toeslagregeling”.
The authors would like to extend their acknowledgements to dr.ir. Jan van der Stel and dr.ir. Allert Adema from
Tata Steel IJmuiden for their invaluable contributions tothis review paper.

102

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In Table 2, we summarized the segregation indices usedin the literature. These indices quantify to which degree thetracer particle is homogeneously distributed within the
mixture. For indices no. 1–8, the concentration of tracer

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16026
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particles is determined in each sub-domain, which can be
expressed in terms of the number, mass or volume fraction,
as indicated in Table A1.

Table A1 Equations for calculating the concentration of different types of particles (type-A or type-B) on number, mass and volume basis. Legend: N =number of particles, m = mass of particles, V = volume of particles.

Number-based
Fraction of particle type-A

NA
N
where N = NA + NB

N  A N

Mass-basedmm  A  A
mVolume-based

VA
V
where V = VA + VBV  A 

where m = mA + mB

mB
m

V

Fraction of particle type-B

N  B  B[11]Nm  B 
[12]

Total (number/mass/volume) fraction

ϕN = (ϕN)A + (ϕN)B = 1

ϕm = (ϕm)A + (ϕm)B = 1

ϕV = (ϕV)A + (ϕV)B = 1[15] m t i[16]  Vt i

Relative fraction of tracer particle in bin i[14] N  i


yti

t

N t iNwhere t = A or BN tRelative fraction of tracer particle in the entireyt  N t[17]
system
N

106


xti

 m  tmwhere t = A or B
xt 
[18]

m t m tmV  B  B
[13]
V

i

cti 

V
where t = A or B

V t
ct 
[19]

V

where t = A or B

where t = A or B

where t = A or B

[20]

[21]

[22]

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

Review Paper

Authors’ Short Biographies
Ahmed Hadi
Ahmed Hadi earned a BSc in Civil Engineering from Isfahan University of Technology in 2015.
He then pursued an MSc in Geotechnical Engineering at the University of Tehran, graduating in
2018. During his master’s degree, he specialised in the DEM modelling of shear banding in granularsoils. Currently, Ahmed Hadi is a PhD candidate at the GranChaMlab@TUDelft. His current research project focuses on the DEM modelling of multi-component segregation in the blast furnace
charging system.

Raïsa RoeplalRaïsa Roeplal pursued a master’s degree in Mechanical Engineering at the University of Twente
in 2016. During that time, she studied the mixing of cohesive particles in paddle mixers and became
fascinated with the segregation behaviour of granular materials. She joined the GranChaMlab@
TUDelft at Delft University of Technology in 2020, in pursuit of her PhD degree. She is currently
developing a DEM model to study the flow and packing behaviour of blast furnace mixtures during
furnace charging. Her main focus areas in this regard are: high-velocity dense granular flows, industrial granular flow modelling, multi-component segregation and mixture calibration.

Yusong Pang
Yusong Pang received his MSc degree in Electrical Engineering in 1996. In 2000 he started working at Practic B.V. and Seaview B.V., the Netherlands, for industrial production life cycle management. After his PhD research of intelligent belt conveyor monitoring and control in 2007, he was
employed by the Advisory Group Industrial Installations of Royal Haskoning, the Netherlands, as anexpert in material handling. In 2010 he was appointed Assistant Professor in the section of Transport
Engineering and Logistics, Delft University of Technology, the Netherlands. His research focuses on
the intelligent control for large-scale material handling systems and logistics processes.

Dingena Schott
Dingena Schott is a Full Professor at TU Delft on Machine Cargo Interaction Engineering. She
obtained her PhD in 2004 from TU Delft on the homogenisation of bulk materials in mammoth silos.
In 2007 she started the GranChaMlab@TUDelft to characterise, model, calibrate and validate granular materials for enabling the simulation-supported design of cargo handling equipment on an industrial scale. Since then, she has worked on developing calibration frameworks and modelling
particle-based systems in various design contexts; including terminal designs for particulate materials, as well as an award-winning new grab design. Her main research interests include: machine-cargointerfaces, simulation-supported design, biomass materials and energy-transition-driven handlingand logistics.

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KONA Powder and Particle Journal No. 41 (2024) 108–122/https://doi.org/10.14356/kona.2024013

Review Paper

A Review of Analytical Methods for Calculating Static
Pressures in Bulk Solids Storage Structures †George Dyck 1, Adam Rogers 2 and Jitendra Paliwal 1*
1 Department of Biosystems Engineering, University of Manitoba, Canada
2 AGCO Corporation, Canada

The Janssen equation is a widely used method for calculating pressures in bulk storage
structures. This review explores the historical legacy of Janssen’s equation and its applications
in both planar and three-dimensional structures. Our focus is on the limitations of the original
formulation of Janssen, extensions made to avoid these deficiencies, and alternative models
that have been developed. The motivation behind these modifications is to improve therepresentation of shear stress within a grain bin in both the horizontal and vertical directions.
Modifications to Janssen’s basic assumptions include the vertical-to-horizontal stress ratio(k), the coefficient of friction between the wall and the stored bulk material (μ), internal angleof friction (ϕ), and bulk density (ρ). We also discuss recent developments in pressure theories,
which have provided new insights into pressure fields in bulk storage bins. These modern
approaches include the continuum elastic theory and microscopic theory. Finally, we discuss
recent developments in pressure theories which provide new insights into the storage of bulk
solids. Overall, this review provides a comprehensive overview of the Janssen equation andits historical development, limitations, and extensions, as well as recent advancements inpressure theory that offer a more accurate representation of pressure fields in bulk storage
structures.
Keywords: powder, particle, bulk solids, Janssen effect, stress distribution, granular matter

1. Introduction
A bulk solid is an aggregate material consisting of solid
particles and fluids (gases/air). Considered as a whole, thematerial has properties common to both fluids and solids,i.e., it can change its shape to fill volumes like a liquid but
responds to internal shear stresses like a solid (Walker,
1966). Bulk solids occupy an intermediate position on the
spectrum between liquids and solids. These materials exhibit many novel, counter-intuitive properties, for example
the stress (pressure) distribution within the material when it
is stored in bins. This behavior is associated with “theJanssen effect”, as pressure is commonly described by H.A.
Janssen’s equation. The Janssen effect predicted that thebottom of a bin did not experience the full force of theweight of the commodity stored below some critical height.
This phenomenon resulted from the friction between the
sidewall and the stored material: the walls carried some of
the material weight. Knowledge of internal and wall friction is essential to Janssen’s theory. Coulomb’s work on
friction and failure of soils due to shearing led to the firstunderstanding of the relationship between internal stress†*Received 31 May 2022; Accepted 22 March 2023
J-STAGE Advance published online 29 July 2023
Corresponding author: Jitendra Paliwal;
Add: Winnipeg, Manitoba, MB R3T 2N2, Canada
E-mail: j.paliwal@umanitoba.ca
TEL: +1-204-474-6700

108

Depth

and the orientation of failure planes within a material. Rankine synthesized Coulomb’s work and Mohr’s descriptionof failure planes and provided an analysis of material failure. This paper will first discuss the historical developments leading up to pressure models before discussing
Janssen’s work, and then investigate important refinements
and critiques of the model over the past century. There arethree modern works with a similar historical perspective on
the topic of pressures in bulk storage structures. The firstwas the translation of Janssen’s entire original paper into
English, 111 years after its original publication and a century’s worth of citations (Sperl, 2006). This paper contains
original data and figures as well as a short commentary thatspeaks to the legacy of the original publication. The second
and third publications were conference papers by A.W.
Roberts presented during the PARTEC Congress held in
Nuremberg, Germany, 21–23 March 1995. He provided a
detailed description of Janssen’s work, and a review of thepast 100 years of bulk solids research (1895–1995; Roberts,
1995). The work was formally rewritten and later published(Roberts, 1998). The historical section is indebted to Milo
Ketchum’s “The Design of Bins, Walls and Grain Elevators”(Ketchum, 1911). While this paper provided a novel interpretation of Janssen’s equation, the alternative models havenot been as resilient to time as Janssen’s solution. Furthermore, the paper was written in 1998, and advancements incomputational technologies have allowed for new

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

George Dyck et al. / KONA Powder and Particle Journal No. 41 (2024) 108–122

approaches to modeling bulk solids, which were before“insoluble problems” as noted by Jenike (1961). There will
be a brief overview of the experiments covered in this literature review for historical context. Information about thepressures in a bin can also provide insight into a wide rangeof physical phenomena. The pressure field affects bulk
solid’s packing, flowability, porosity, and tortuosity, toname a few. For example, Janssen along with empirical
packing equations has been used to predict the densitychange within a bin (Thompson et al., 1991; Bhadra et al.,2015, 2018; Turner et al., 2016; Cheng et al., 2017).

2. Historical Pressure Theories
This section focuses on a review of pressure models forgrain storage bins. Historically, the “Janssen Effect” wasknown before Janssen’s mathematical description, noted
anonymously by Gotthilf Heinrich Ludwig Hagen. Hagen’s
account is the oldest published theory of pressure in bulk
materials, 40 years before Janssen in 1852 (Hagen, 1852).
Hagen attempted to predict saturation pressure with respect
to height (sand). The paper was translated into English by
Tighe et al., which provided a detailed view of this seminal
work (Tighe and Sperl, 2007). Hagen suggested that thefriction on the side of the wall acted in the upward direction, opposing to the gravitational force. The pressure felt
by a “disk which is easily movable but seals tightly” wasgiven by the expression,

σ disc  r 2 πρ g y  2rρ g μy 2

(1)where r is the radius of the disc (bin), ρ the bulk density, y
the height of bulk material, and μ the friction coefficient.
(1)This equation reached a maximum at a characteristicheight—related to the diameter of the bin, height, and friction coefficient—and began to decrease afterwards, amathematical prediction but physically incorrect; Hagen
stated that the pressure followed this curve until this maximum was reached. After, the bin pressure remained constant at this maximum, which was a similar behavior to the“Janssen Effect”. The difference from Janssen’s formulation was that Hagen used a quadratic to explain the change
in pressure, whereas Janssen’s approach solved a differential equation whose solution was an exponential function,
which allowed for an asymptotic approach to the maximum
pressure. A noteworthy reference found in Hagen’s work
regarded an observation Huber-Bernand published in 1829,
likely the first published research on the so-called “Janssen
Effect,” which was highlighted in both Hagen and Janssen’s
publications. Huber-Bernand noticed that when he filled acontainer with eggs, added sand in the voids between theeggs extending several inches above the eggs, as well as a
25 kg weight on top, the eggs placed at the bottom of thecontainer remained unbroken, even though all this weightshould crush them. He concluded that only a portion of thepressure was transmitted to the bottom of the container,

Review Paper

indicating some kind of saturation depth (Sperl, 2006),(Tighe and Sperl, 2007). A Welsh engineer Isaac Roberts
published a crucial pre-Janssen study that observed an asymptotic trend in bulk solid bin pressures. He claimed thatthe pressures at the bottom of bins stopped increasing at theheight of two diameters, which was an empirical demonstration of the Janssen effect (Roberts, 1883). He conducted
two experiments on this topic. The first was prone to measurement errors due to the mechanical nature of the scales
used, an issue other researchers (e.g. Prante) would struggle with and overcome; the second experiment produced
reliable results that were widely referenced (Ketchum,1911). Ten years later, H.A. Janssen formulated his seminal
equation analytically modeling Roberts’s work, although
the work was not cited. Both Hagen and Roberts worked onmathematical descriptions of stresses of earth pressure
systems and experimental data from agricultural commodities. H.A. Janssen would be the first to formulate a simple
way to calculate pressures in deep bins, the standard reference for well over a century on the topic. This equation’s
influence was enormous and has been used in almost all
design codes and standards in the world, such as theEuropean bulk storage building codes (European Standard
EN 1991-4, 2006). The expression was also used in thecalculation of apparent weight for bin capacities by the
ASABE (American Society of Agricultural and BiologicalEngineers; ASABE, 2005). Janssen’s work has been usedby a vast community of researchers and industry because it
explained the complicated phenomenon of bin pressures
via a straightforward expression (Eqn. (7)). Janssen was anengineer from Germany; not much is known about the individual and his work outside of this one publication, a seminal paper for the science of bulk solids. Roberts postulates
that it was likely his work and records were destroyed
during the Second World War (Roberts, 1995). The life andwork of Janssen is accessed through his 1895 paper,“Experiments on Corn Pressure in Silo Cells.” One year
after its publication, an English translation of Janssen’s
abstract was published in the Proceedings of the Institutionof Civil Engineers, the work often cited as Janssen (1896).
Soon, the equation was added to the building practices
outline in the “Des Ingenieurs Taschenbuch or The Hütte Das Ingenieurwissen”, a book of references for practicing
engineers, initially written in German and now translated
into many languages (Ketchum, 1911). During this time,
many aspects of the work were validated, critiqued, andmodified, which is discussed later in this review. It should
also be noted that M. Koenen worked on a similar theory ofpressure but published it a year after Janssen. Koenen’s
work is challenging to find because it was never translated
from German to English, and the conclusions were passed
down by secondary literature. His most significant contribution suggested the lateral to vertical pressure ratio could
be calculated using established equations, such as Rankine’s

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George Dyck et al. / KONA Powder and Particle Journal No. 41 (2024) 108–122

active pressure state (Eqn. (14)), while Janssen experimentally determined its value (Reimbert M. and Reimbert A.,
1976; Schulze, 2008; Koenen, 1896). Koenen explicitly
connected Janssen’s work to the Mohr-Coloulmb failure
criterion by relating it to the principal stresses acting on amass. It is an interesting historical point that Janssen referenced two other works incorrectly, which Sperl addressedin the translation of Janssen’s original document (Sperl,
2006). The first was a fellow German researcher, C. Arndt,
and was used to discuss the then-new developments ingrain storage in North America. The paper’s title is “TheSilos of Galati and Braila”, which are Romanian cities that
hold the oldest concrete grain bins. These were built in thelate 1880s, designed by an engineer Anghel Saligny, andwere two of the earliest examples of reinforced concrete
bins. Janssen described these bins as “...iron-strengthened
brickwork, and in six-fold profiles like honeycombs. The
grain was introduced through the upper end of the cellthrough a hatch. For discharge, stock transfer, or embarkation, the bottoms of the cells provided close-able openings”(Sperl, 2006). This sounds similar to modern grain storage
bin design; Janssen saw these as the future of bin design
and wanted to provide mathematical models for predicting
pressures. The second citation was Hagen, who has been
discussed thoroughly above (Sperl, 2006). Ketchum discussed the design of concrete bins in Chapter XVIII of his
text “The Design of Bins, Walls and Grain Elevators”. Unfortunately, he did not mention Saligny and only discussed
the concrete bins of North America.

2.1 Quantitative description of the Janssen effectJanssen began with a force-balance equation of a thin
horizontal layer of bulk material in a deep bin. Note twothings: first, Janssen was investigating deep bins but thetheory can be applied to shallow bins (Xu and Liang,2022). Second, the following is not directly Janssen’s formulation but a modernization of the derivation for claritythat was made popular by Ketchum, but is essentially thesame (Ketchum, 1911). The first difference lies in incorporating a hydraulic radius RH, which generalized the container geometry from Cartesian (rectangles) to arbitrary
shapes. Secondly, in Janssen’s work, he expressed K = μk
and solved for K experimentally. The lateral pressure ratio,k, is defined as the ratio of the horizontal stress to the vertical effective stress. Within a filled container (bin), the force
balance on an infinitesimal thin disk element of granularmaterial in the vertical direction is given as:
Aσ y  g ρA d y  A σ y  d σ y   τ w C d y(2)where A is the cross-sectional area and C the circumferenceof the bin, ρ the bulk density of the material, g the gravita(2)tional acceleration, σx and σy the horizontal and verticalstresses, respectively, and τw the shear stress acting on thematerial at the wall, which opposes the gravitational force.

110

Here, x and y are the horizontal and vertical coordinates,
respectively. It should be noted that the depth of the stored
material is given by y, increasing in the downward direction. The shear stress is related to the normal stress in thehorizontal direction through the wall friction coefficient μ
as follows:τ w  σ x μ  σ x tan w(3)with μ = tanϕw given by the wall friction angle ϕw. Finally,
the ratio of horizontal (σx) to vertical stress (σy) is assumed(3)
to be constant,

k

σx
σy

(4)

Defining the hydraulic radius

A
(4)
 RH
C

(5)

substituting Eqns. (3) and (4) into Eqn. (2) yields theJanssen differential equation,
(5)
dσy
μk

σ y  gρ(6)dy
RH
Solving for vertical pressure givesμyk(6) ρ g R 


H
RH
σy 
1
e



μk 


(7)

Janssen’s equation may also be derived from the fundamental(7) (standard) static equilibrium equations for an infinitesimal element of material in 2D Cartesian or cylindricalcoordinates. For a 2D Cartesian coordinate system, theequilibrium equations on an infinitesimal element of material in the vertical and horizontal directions are given as:
σ y
y



τ xy

 gρ

x

(8)
σ x τ xy

0
x
y
(8-1)
For the axisymmetric conditions where a cylindrical coordinate system (r,θ,z) is used, the following equilibrium
equations(8-2) are used:σ z τ rz τ rz


 gρ
r
z
r
(9)
σ r τ rz

0
r
z
(9-1)
These formulae were used by Rahmoun et al. (2008) and
Schillinger & Malla (2008) among others to derive pres(9-2)equations. The equilibrium approach is wellsure
established in the field of continuum mechanics (Negi andOgilvie, 1977), and Janssen’s results can be obtained fromthese expressions, with certain assumptions. For example,
assuming the shear stress is related to the normal stress byReview Paper

George Dyck et al. / KONA Powder and Particle Journal No. 41 (2024) 108–122

equation τrz = μσr and using the ratio of principal stresses,
σr/σz = k, we obtain(10)

τ rz  z  μkσ z  zCombining Eqns. (9) and (10) with the definition of thehydraulic radius RH = b/2 reproduces the Janssen’s differ(10)Eqn. (6).
ential

2.2 Lateral pressure ratio k
In addition to producing the equation that bears his
name, Janssen also gathered experimental data to validate
his model for calculating pressures in bulk storage structures (Eqn. (7)). With novel instruments and measuring
techniques, Janssen measured μ, the pressure exerted on thebottom of the bin (σy) and had planned to measure the horizontal force on the wall (σx) but failed to succeed due tothe arching effects of the material and the construction of
the apparatus. He suggested an improvement that wasadapted by Jamieson in 1906. To solve Eqn. (6), Janssen
assumed that k was a constant, an assumption that many
would question (Ketchum, 1911). Typically, the verticalto-horizontal stress ratio k is assumed—unrealistically—tobe constant throughout a bin, in the range 0 for rigid solids
and 1 for liquids. For granular materials, the Rankine coefficient is often used which gives the stress ratio in terms of
the internal angle of friction ϕ,k

1  sin 
1  sin (11)

In fact, even with the assumption of constant k there is
still a wide variety of values cited throughout the literature.
(11)
This
lack of consensus exists because k is not a fundamental, innate physical property of bulk solids; it is an emergent result of equilibrium, arising from the “fluid” nature ofbulk materials. The value of k is not realistically expected
to be constant within a storage structure at all. The simplifying assumption is made because Janssen’s equation
works well within a narrow column of material, but thevariation of k through a large volume of bulk material has
not been well-studied experimentally. It has been difficult
to include a varying k analytically. Coulomb, Rankine andJanssen all made the simplifying assumption of constant0 < k < 1. Coulomb’s approach produced a stress ratio
which related lateral and vertical pressure of soil, and wasdetermined by using the Moore-Coulomb failure criterion(Labuz and Zang, 2012) as follows:cos 2   ω
ka 

2


cos 2 ω sin w  β 1 (12)

sin w    sin   β cos w  ω cos ω  β (12)

cos 2 (  ω)
kp 

2


cos 2 (ω) sin w  ω 1 sin w    sin   β cos w  ω cos  β  ω 

(13)where ka is the active pressure ratio (the bulk solid exerts(13)
pressure on the wall), kp the passive pressure ratio (the wall
exerts pressure on the bulk solid), ϕw the friction angle ofwall, ω the slope of the wall, ϕ the internal angle of friction,and β the angle of the backfill surface. Rankine provided asimplification of the above equations by assuming a frictionless and non-adhesive interface between the verticalwall and the bulk solid. Under such conditions the following stress ratios can be formulated (Rankine, 1856),π (14)
k a  tan 2   
4 2
π 
(15)
k p  tan 2   
4 2
(14)
Note that ka, shown in Eqn. (14), is mathematically
identical to Eqn. (11). Rankine’s formulation of k was oneof (15)the first theories of pressure for shallow bins, because
the pressure distributions within these structures behave
analogously to retaining walls. The assumptions fail when
the bin height is increased, as the pressure exerted on thebottom and sides of the bin no longer fit the observations
because the bin walls were assumed to be frictionless (Xuand Liang, 2022). This phenomenon is a property of bulk
solids, but not considered until Janssen. Pleissner convincingly established the variation of k at the beginning of the20th century. Many different numerical methods have adopted simplified versions of the Rankine formulation (14).
For example, the Eurocode (European Standard EN 19914, 2006) usedk  1.10  1  sin (16)

A mathematical model to account for vertical and radialchanges in stresses and k is discussed in detail later throughthe(16)
work of Zhang et al. (1998a). They provided a literature
review on the topic in the context of agricultural commodities, up to the publication date, and reported that there wasno consistent agreement on how pressure, and the resultingk, varied radially. Rusinek investigated the variation inpressure of rape seeds using a hydraulic pressure transducer to determine the mean normal stress and mean shear
stress on the wall, as well as the vertical pressure distribution, and found that k was significantly lower in the middleof the bin than the wall, and when wall friction increased,so did k (Rusinek, 2003). Horabik et al. discussed the behavior of k within bins (Horabik and Rusinek, 2002), andreported that the pressure ratio was mostly dependent on
the internal angle of friction but also the shape of the seed(particles), and inversely related to moisture content; theexperimental uniaxial compression test produced similar

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values to theoretical predictions given by Eqn. (16). Qadir
et al. (2010) showed experimentally that k increased withthe ratio of the individual grain and diameter of the bin. Sun
et al. presented four independent approaches to k and applied them to deep and shallow bins. The results werecompared with experimental data and three national standards (Sun et al., 2018). They also compared different failure criteria to the typical Mohr-Coulomb criterion andrecommended the Lade-Duncan (L-D) criterion to be used
to calculate wall pressure. Uniaxial compression tests
showed, for cereal grains, k decreased with increased moisture content (Horabik and Rusinek, 2002), whereas forcalcareous sand, using a similar procedure, increased watercontent initially increased k, which reached a maximum
and eventually declined again (Wang et al., 2020). Numerical studies that used discrete element methods suggested
that k did not change with polydispersity (Wiącek andMolenda, 2014). Zhang et al. applied a two-parameter
failure criterion that was typically used for soils to determine failure points (Zhang et al., 1994). When both parameters were nonzero, the lateral to vertical stress ratio
increased with the vertical stress. At the same time, in otherconfigurations, it was observed to be roughly equal to that
determined from the Mohr-Coulomb failure criterion. Xu& Liang considered the elastic deformation of the bin walls
in both static and dynamic states and demonstrated that the
Rankine formulation of k would underestimate lateralforces (Xu and Liang, 2022). Back et al. studied the effectof friction on the pressure ratio k. Specifically, they focusedon particle-particle friction, which the lateral pressure ratio
was associated with through analytical arguments (Back,2011). They established that k increased with particle–
particle friction and packing.

2.3 After Janssen
The reaction to Janssen’s work was immediate, andmany people rushed to both validate and critique his analysis in the decades after the paper was published.

2.3.1 Wilfrid Airy: 1898
Airy noted at the beginning of his paper both the worksof Isaac Roberts and Janssen, although he admitted he was
not able to find any, “Tables of coefficients of friction ofgrain, wither on grain, or wood, iron, or brickwork...”(Airy, 1898). Moreover, the reference to Janssen was to theEnglish translation of the abstract (Janssen, 1896), which
was the reason why Airy did not have access to the friction
coefficients. The translation only included the dimensionsof the test cell, the equation describing the vertical pressureof grain, empirical data on corn depth and the resulting side
pressure. Regardless, he noted these were reasonable investigations on the pressure of wheat (Janssen investigatedcorn) on the bottom of small bins. In the conclusion, Airycommented on the geometry of Roberts and Janssen’s work

112

and the limitation of “small bins”, precisely how it would
adjust the coefficient of friction on the rupture plane. Unfortunately, this topic was addressed by Janssen only in theoriginal German publication. Moreover, Airy cited a textbook by Weisbach to describe “semi-fluids” for which, “thepressure on the side of a bin was the maximum pressure
due to a wedge-shaped mass of the grain which might besupposed to separate from the general mass, and the angleof slope of the wedge-shaped mass which exerted the maximum pressure had to be determined” (Airy, 1898;
Weisbach, 1849). The Mohr-Coulomb failure criterion wasused as the basis for the pressure ratio. A slightly different
approach was the specific application to “fluid–
solids”, which are known in the modern literature as “bulksolids”. Airy was a contemporary of Janssen and took adifferent approach to modeling pressure, applicable to bothshallow and deep bins. Airy defined the critical height hL interms of the coefficient of friction on the rupture planeμ′ = tanϕ as well as the coefficient of friction between thegrain and the bin wall μ such that2

1   μ 
hL  D μ  μμ  μ 



(17)

Airy then defined two regimes for shallow and deep bins.
Case 1: Shallow Bin, h < hL
(17)
2


1
σ x  ρy (18)
 μ  μ  μ  1   μ 2 Case 2: Deep Bin, h ≥ hL


ρD 
σx 
1 
μμ 


(18)




2h
 μ  μ  1  μμ D
1   μ2(19)where h was the height of grain bulk solid pile, D the diameter of the bin and y the depth of grain measured from thesurface(19) of the pile. Airy started with the Mohr-Coulomb
failure criterion, which was used to calculate the static
condition of the wedge shape above the failure plane. The
proof and resulting equations were complicated, and although the deep bin equation produces similar results to
Janssen’s. Airy’s approach was not widely used due to thedifficult calculation (Ketchum, 1911).

2.3.2 Milo Ketchum: 1907 and others
Milo Ketchum wrote a book “The Design of Bins, Walls
and Grain Elevators”, which exhaustively covered how tobuild all aspects of a modern elevator for the early part ofthe 20th century. It is an interesting text for a historical
picture of the turn of the century in bin design and still
provides a relevant pedagogical explanation of practices
and equations. In chapter XVI, Ketchum discussed both

George Dyck et al. / KONA Powder and Particle Journal No. 41 (2024) 108–122

Janssen and Airy’s proposed solutions to grain bin pressure(Ketchum, 1911). It is from this text that we find the formulation of Eqn. (7). In Janssen’s original text, the formulation was different, but only superficially. However,
Ketchum’s form is dominant because its derivation was
clearly articulated in his text. Ketchum’s work was important because it formalized Janssen’s theory in a text discussing proper engineering practices and organized data thatengaged with Janssen’s theory. Ultimately, he concluded
that Airy’s equations were too complicated for practical use
and recommended a graphical method for obtaining solutions. Ketchum provided summaries of many researchers
who responded directly to Janssen’s work, including
Roberts, Prante, Toltz, Jamieson, Luft, Pleissner, Bovey,
Lufft, and Pleissner. A.W. Roberts provided citations to all
the original works discussed by Ketchum (Roberts, 1995;
Roberts, 1998). This was the primary channel for understanding the development following Janssen’s efforts.
As for the development after Janssen's efforts, a few are
mentioned below to establish issues with Janssen’s conclusions. A.W. Roberts provided a good summary of the work,
which extended much beyond Ketchum’s analysis (Roberts,
1995; 1998). Prante (1896) observed that the flow of bulk
solids increased pressure as much as four times static values. Still, the results might have been unreliable due to theexperimental apparatus, although the general trend wascorrect. These experiments established that grain removal
should be symmetrical, otherwise this pressure increase on
one side of the bin could bring about bin failure. Toltz(1903) utilized Prante’s work while designing a grain elevator to account for fluid pressure within the facility. Toltz
repeated Prante’s experiment but changed the techniqueused to measure the deflection of steel plates and calculated
the resulting pressure during filling and emptying. These
results were considered reliable and confirmed that staticand dynamic pressures were different, with dynamic pressures being significantly higher. Jamieson (1903) validated
Janssen’s model and calculations of lateral pressure ratio
and also showed pressure changes in static and dynamic
states with symmetric and asymmetric outlets. Furthermore, Jamieson’s measurement device was complex, andallowed for both vertical and lateral pressures to be measured; his work added robustness to Janssen’s theory.
Pleissner (1906) conducted many experiments throughout
1902–1905 that showed k was not a constant in a bin andchanged for different commodities. This was one of
Janssen’s assumptions, which most researchers adopted.
Other experiments conducted by Bovey (1904) and Lufft(1902–1904) showed variability in k but less comprehensively than Pleissner. Finally, it should also be noted thatoutside of the enormous project of writing “The Design ofBins, Walls and Grain Elevators,” Ketchum conducted experiments, which showed that the flow of bulk solids was
independent of the grain height (the height of a bulk solid

Review Paperpile). This phenomenon is commonly known throughBeverloo’s model, published in 1961 but is also found in
the second half of Hagen’s work and is commonly called
the Hagen-Beverloo Equation (Beverloo et al., 1961).

2.3.3 Andrew W. Jenike: 1961
Jenike was the first to mathematically describe the flowof bulk materials, which culminated in his book “Gravity
Flow of Bulk Solids” (Jenike, 1961). While the topic of thispaper is static pressure, Jenike developed essential descriptions of flow that determined what future state a system
would take and is thus relevant to this review. Jenike’s
work drew heavily on his contemporaries as well as investigations into mathematical descriptions of plasticity. In
addition to new analytical theories and methods, the adventof computers would make it possible to carry out otherwise“insoluble” mathematical problems. This comment hinted
at the future of bulk solid research and where one finds theforefront of research today, introduced through the work of
P.A. Cundall and O.D.L. Strack, who developed the discrete element method (Cundall and Strack, 1979). The
work “Gravity Flow of Bulk Solids” was outlined into six
parts: 1) the nature of the yield function; 2) differential
equations that described steady-state flow and producepressure, density, and velocity fields; 3) the initiation offlow, which was called incipient failure; 4) necessary criteria for the bulk solid to flow; 5) apparatuses to test flow
properties of bulk solids; and 6) experiments and applications of this flow theory. Jenike categorized different typesof bulk solid flows and how the bin/hopper geometry andother properties could influence flow types. Jenike notedthat there existed no perfect bin; each bin had its own
unique advantages and disadvantages. In part VI, Jenikementioned both Janssen and Ketchum’s work as being themost important contributions to the knowledge of bulk
solids at the turn of the century. Jenike also produced several other important publications. One was written with
J.R. Johanson and J.W. Carson that described bin design,
mass flow, and funnel flow in a four-part series (Jenike etal., 1973a; 1973b; 1973c). Moreover, Jenike summarized
bin loads under different flow states: static, during flow,
and steady flow (Jenike and Johanson, 1969). These later
publications were often cited in both research and engineering practices because they included design formulasand examples of calculations.

2.3.4 Walker: 1966
Walker investigated pressure and arching in hoppers,
using Janssen’s model and calculating a k value in both
static and flow states. His work also was an attempt to determine whether a specific hopper would experience blockage through arching of powder across the outlet. In
addition, he investigated the role cohesion played in thegravity flow in a hopper (Walker, 1966).

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2.3.5 Marcel Reimbert and André Reimbert: 1976
Marcel and André Reimbert wrote a book titled“Silos-Theory and Practice”, which presented a new model
for bin pressures (Reimbert M. and Reimbert A., 1976).
The book was written with the same spirit as Milo
Ketchum’s “The Design of Bins, Walls and Grain
Elevators”. It was ultimately interested in the constructionof grain elevators but diverged by presenting a new theoryof bulk solid phenomenon to inform calculations instead of
aggregating established theories. The book also offeredcritiques on the traditional Koenen-Janssen approach andnoted that the Janssen method needed to assume a constantk. Additionally, Ketchum suggested k as a function of bin
geometry and material depth. The model proposed in thebook produced similar results to Janssen’s. However, asignificant difference between the two was that Reimbert etal. used an empirical equation (hyperbola) to describe thechange in pressure as a function of depth. The equation wasdeveloped for cylindrical silos and can also be used forn-sided polygons. Furthermore, while the authors argued
that k changed throughout a bin, they still assumed it to beconstant in calculation, and the value they took (“the characteristic abscissae”) was calculated at the maximum vertical pressure, which resulted in
D
A


π 
4 tan   tan 2   
4 2

h
3

with the horizontal stress,(22)

where y was the depth of the material.

3.(22)
Comparison
Many models have been proposed in the past 100 years
and the immediate differences between these models are
not intuitive. A quantitative comparison was conducted tocompare the results between Hagen, Janssen, Airy, andReimbert. Each model uses the same properties as shown in
Table 1.
Hagen’s work attempted to recreate the “Janssen effect”
using a parabolic increase in pressure until a maximum wasreached, after which it remained constant. This piece-wise
approach used a quadratic equation to describe the increasein pressure, and constant once the maximum was reached,represented by the dashed line in Fig. 1. Hagen’s model isthe crudest of the historical methods but provided an essential first step; subsequent models predicted very similar

114

Value

Unit

d

6

m

h or y

30

m

ρ

770

kg/m3

k

0.4

N/A

μ

0.7

N/A

ϕ

30

deg

Parameter(20)

where D the diameter of the bin. The maximum stress wasgiven by(20)
ρ gD
σ max (21)
4 tan  

2


y
(21)
σ x  σ max 1    1  A  

Table 1 Model variables and values used to generate Fig. 1, with the bin
diameter d, grain height h or y, mass density ρ, stress ratio k, coefficient offriction μ and the internal angle of friction ϕ.

Fig. 1 Comparing historical bin pressure models. (The data are available publicly at https://doi.org/10.50931/data.kona.23741598)

behavior giving a more gradual increase than Hagen.
Janssen’s model predicted an exponential change in pressure with the material depth that approached an asymptote.
Airy’s model used a complicated geometric approach andapplied the knowledge developed by Coulomb and othersin earth pressure theory to bulk solids in bins. Both Airyand Hagen’s models are separated by a transition point inwhich the pressure changes discontinuously. Reimbert’s
equation found the maximum pressure at the bottom of thebin and modeled the change in pressure using a hyperbolic
approach. Reimbert attempted to create a new variable fork that could account for more factors than the other methods. For example, Janssen assumes a lateral pressure ratio
to remain constant within a particular bin, but we would
expect k to vary from bin to bin as a function of geometry.
Our analysis of these models at different depth/diameter
ratios revealed that the differences in pressure predictions
between the models can be divided into three main regions:low (0–1.5), intermediate (1.5–3), and high (3–5). At thelow h/d ratio region, the equations exhibited the most variation between each other. The following percentage differences were observed: Hagen vs Reimbert, 16.0 %; Hagen
vs Janssen, 31.3 %; Hagen vs Airy, 64.6 %; Reimbert vsGeorge Dyck et al. / KONA Powder and Particle Journal No. 41 (2024) 108–122

Janssen, 15.6 %; Reimbert vs Airy, 50.0 %; and Janssen vsAiry, 35.1 %. In the intermediate region, Reimbert andJanssen closely approached the Hagen asymptote, with
Airy’s equation being 10.0 % lower during this intersection. At the extreme end of the high h/d ratio region, Reimbert and Janssen were very close, within 2.0 % of eachother, while Airy and Hagen’s asymptote were 5.1 % above
and 7.6 % below them, respectively. It is important to notethat there is a 9.9 % difference between the Hagen and
Janssen asymptote, with Janssen predicting greater pressure.

4. New developments in static pressure
theories
While the Janssen equation produces satisfactory results
for static conditions if the “right” parameters are selected,
it does not represent the full pressure field present within abin. Many new models and approaches to the pressure fieldwithin a bin have provided new insights. Such modern approaches include elastic theory (Bräuer, 2006; Ovarlez and
Clément, 2005; Schillinger and Malla, 2008), ordinarystress linearity (OSL) and incipient failure everywhere(IFE) models (Vanel et al., 2000), microscopic theory (Xuet al., 1996), the principal stress cap approach (Matchett,2006), as well as a plethora of numerical methods (Chen, etal., 1999).

4.1 Investigations and modifications of Janssen’s
model
Rigorous validation of Janssen’s model occurred within
the first decade of its publication (Ketchum, 1911; Roberts,
1995). Many modern studies have attempted to validate or
modify Janssen’s model. Di Felice et al. (Di Felice andScapinello, 2010) highlighted several works in the past
decade and identified a literature gap that questioned important parameters in the Janssen model: μ and k. Theirwork showed that while many studies determined the numerical values of these parameters, their physical interpretation was not as clear. Moreover, empirically their study
showed some discrepancies when measuring these parameters. While De Felice et al. highlighted the need to investigate Janssen’s assumptions, others have taken up this task.
Janssen assumed bins to be a static and closed environment, where no forces are transferred from outside thesystem besides the self weight of stored materials. This is
not the case for most bins, which experience external forcesfrom their environments, such as vibrations caused by machinery (Körzendörfer, 2022), earthquakes (Silvestri et al.,2012), intentional flow-inducing inertia forces (Pascot etal., 2020) and bin honking (Buick et al., 2004). Bertho et al.
looked at the applicability of Janssen’s model in dynamic
environments, provided an overview of the current research, and considered the effect of wall movement on
packing (Bertho et al., 2003). They found the classicalReview Paper

Janssen model was valid for bin walls that experienced
several centimeters per second movement. Moreover,
Windows-Yule et al. investigated horizontal wall movement and found that the Janssen model was also valid for awide range of dynamic movement (Windows-Yule et al.,2019). It is worth noting that this topic was often investigated by numerical methods, an important tool for understanding bulk solids but a topic outside the scope of thispaper.
Janssen’s model is a macroscopic model, which treats
the material as a continuum instead of a collection of particles. Bratberg et al. examined the threshold where microscopic behaviors turn into macroscopic behaviors throughnarrow granular columns (Bratberg et al., 2005). They
found that Janssen’s model could not account for small
container diameters. Similarly, for shallow granular pileheights, an interesting result was discovered by Mahajan etal. (Mahajan et al., 2020)—a “reverse Janssen effect”.
Specifically, the wall frictional forces could become compressive, effectively reversing the usual Janssen effect.
Under this condition, the walls increase the effective massof the bulk material at the bottom, which requires modification to Janssen’s equation to predict the changes in effective mass as a function of height. This reverse effect was
only observable when the height of a bulk solid pile wasrelatively small, i.e., h ≤ 30dG with dG the diameter of asingle particle. Thus, we do not generally expect to see thiseffect on large scales such as within bins. The continuum
approach is typically used in analytical models, whereas
numerical models can be applied to both continuum andparticulate materials (Chen et al., 1999; Schulze, 2008).
Many works focused on addressing the inadequacies ofJanssen’s assumptions. Some examples include variablebulk density due to particle packing (Vanel and Clément,
1999; Haque, 2013; Landry et al., 2004), variable angle ofrepose (Pont et al., 2003), horizontal bins (Tang et al.,2021), pressures in the hopper section (Walker, 1966), obstacles in bins (Endo et al., 2017), elastic deformation ofbin walls (Xu and Liang, 2022), moisture content (Zhang etal., 1998b; Chen, et al., 2020), temperature (Lapko et al.,2003), shallow bins (Ooi and Rotter, 1990; Xu and Liang,2022), as well as loading and unloading stresses (Walters,1973), among others (Ayuga, 2008). These modifications
are relevant because they point toward higher fidelity models that can better describe physical systems. Such modifications are potentially important for future industrial
applications as well as advancing development of state-ofthe-art models. While it is not realistic to review the studies
that address all the factors listed above, this paper has selected one significant topic to discuss, namely variable
stress fields.

4.2 Variable stress fields
The most fundamental and controversial assumption in115

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Janssen’s theory is the constant lateral to vertical pressure
ratio, k (discussed at length in Section 2.2). This ratio isfundamentally dictated by the stress field in the bulk solidmass. Many experiments have shown that this ratio is afunction of material properties and location in a bin (Chen,et al., 2020; Liu et al., 2021). In this section, the stress field
is investigated analytically in a 2D Cartesian coordinate
system (x,y), where the y direction points “downward,”
with y = 0 representing the top of the bulk solid surface.
Deriving Janssen’s equation in the Cartesian case requires
the use of a “slip-state” condition throughout the bin suchthat τxy = μσx(y) at the wall. In terms of vertical stress, wehaveτ xy  y  μσ x  y  μkσ y  y(23)

The equilibrium in the vertical direction can be expressedas:(23)
σ y τ xy

 gρ
(24)
y
x
If the stresses are assumed not to vary in the horizontal direction, as in Janssen’s theory, or τxy = τxy(y), the partial(24)
derivativeof shear stress with respect to x vanishes,

τ xy

(25)

0
x

This means the equilibrium equation in the vertical direction is simplified as
(25)
σ y
(26)
 gρ
y
which has the solution of(27)

σ y  g ρy(26)This indicates that the bulk solids behave like a fluid if theshear stress varies only in the vertical direction. Here, the(27)stress changes with depth, just as a fluid does. In this casethe wall friction has no effect on the vertical stress in thematerial. Mathematically, this vanishing of the partial derivative of the shear stress can be observed due to the relationships presented in Eqn. (25). Let’s now consider avariable stress field in the horizontal direction. Specifically,
we postulate a shear stress which varies linearly in thehorizontal direction,

τ xy  x, y x
μσ x  yb(28)

where b is the radius of the bin. Now the partial derivative
term is expressed as
(28)
τ xy
μ
 σ x  y
(29)
b
x
and the equilibrium equation has the form of

(29)

116

dσy

Review Paperμk
σ y  gρ(30)
dybThis equation form is similar to that of Janssen’s differential equation. This means that in order to account for the
Janssen(30) effect, the shear stress in 2D must include radial
dependence. It should be noted that exchanging b to RH in
Eqn. (28) would identically result in the Janssen equation.
The difference between the granular and liquid-like behaviors occurs because the effect of wall friction is not included in the problem when the derivative term drops out.
Deriving the Janssen equation requires a specific choice of
shear stress that links the friction coefficient and the vertical stress through the horizontal to vertical stress ratio
σx(y) = kσy(y). Geometrically, this term naturally arises incylindrical coordinates (r, θ, z) due to the radially dependent volume of infinitesimal cylindrical elements
dV = rdrdθdz. However, this geometrical effect does notoccur in Cartesian coordinates (x, y, z), in which the volume of all infinitesimal elements are independent of theradial coordinate.
Radial variation of the shear stress can be traced back to1948 (Jaky, 1948). In relation to flow problems this termshows up in the work of McInnes (1968) and Savage(1967). Later, it was used by Zhang et al. (1998a) andMilleteet al. (2006) to develop a theory of radial stress behavior in a 2D Cartesian coordinate system and later toexpand to 3D cylindrical coordinates (Rahmoun et al.,2008).
We compare the results of Zhang et al. (1998a) with theusual Janssen approach for a hypothetical bin of 5 m in diameter (Fig. 2). Zhang et al.’s model accounted for the
non-uniformity of stresses in the horizontal and verticaldirections, whereas Janssen’s work only considered change
in the vertical direction. Additionally, in the Zhang model kvaries throughout the bin, whereas variation in k is neglected by Janssen. Zhang’s model suggests that higher
stresses occur at the center of a bin. This may have design
implications for airflow in aeration and drying.
To quantify the differences between the Zhang andJanssen models, we calculated the percentage differences
between their stress values at various depths and distancesfrom the bin center. The analysis revealed a range of percentage differences between the two models in both horizontal and vertical stress distributions. The vertical stress
distribution exhibited more variation than the horizontal, so
we focused on this set of data. At the most distant pointfrom the center of the bin, Zhang’s model predicted lesspressure than Janssen, ranging from 40.0 % to 24.0 %, withthe maximum discrepancy occurring at the 1 m depth anddecreasing afterward without a clear trend. In contrast,
Zhang’s model predicted higher pressure at the center ofthe bin, with differences ranging from 22.5 % to 7.6 %; themaximum discrepancy occurred at the 1 m depth and decreased with depth. At all depths, Zhang’s pressure crossedReview Paper

George Dyck et al. / KONA Powder and Particle Journal No. 41 (2024) 108–122

Fig. 2 Differences between the linear stress variation used by Zhang, Bu and Britton (Zhang et al., 1998a) and the planar Janssen result using the
Rankine coefficient (Eqs. 30 and 11) assuming a bin radius of b = 2.5 m, uncompacted mass density ρ = 834 kg m –3, coefficient of static friction
μ = 0.36 and internal angle of friction ϕ = 22° using h = 1 m. Bin depth is given in the legend. Change in the radial dependence is introduced by thelinear modification to Janssen’s equation. (The data are available publicly at https://doi.org/10.50931/data.kona.23741598)

over Janssen’s between 1.5 and 1.6 meters from the bin
center, and generally, as depth increased, this crossover
occurred closer to the center of the bin. This indicated that
the Zhang model’s predictions could significantly deviate
from those of the Janssen model, depending on the depth
and distance from the bin center. A consistent trend observed across various depths and distances was that
Zhang’s model predicted lower pressure and greater discrepancy on the outside, while the center experienced
higher pressure and relatively less discrepancy comparedto Janssen’s model. This finding further emphasized theimportance of considering non-uniform stress distributions
in grain bin design for optimal aeration and drying performance.
The work of Rahmoun, Millet and de Saxcé (Millet etal., 2006; Rahmoun et al., 2008) included significant expansions over the original Janssen model, specifically applicable to cohesive powder materials. The analysis inMillet et al. (2006) for a planar bin with the 2D Cartesianequilibrium equations (Eqn. (8)) resulted in a differential
equation for the angle between the horizontal axis and theprincipal stress direction. The differential equation was
solved analytically. The model was numerically comparedto Janssen’s equation for low cohesion ∼ 10–6 Pa and theresults were found to agree for the passive and active states.
A similar approach was taken in Rahmoun et al. (2008) inwhich the cylindrical equilibrium equations (Eqn. (9))
were used to produce a more general 3D result. The resulted model could be solved analytically for cohesionlessmaterials, but a numerical approach was required when finite cohesion was included.
An important model used to complement the MohrCoulomb criterion is known as the incipient failure every-

where (IFE), or the rigid-plastic model (Nedderman, 1992;
Wittmer et al., 1996, 1997). The IFE model assumes thatthe material is on the edge of failure throughout the entire
bin. This means that through each point there is some planeover which the shear force is linearly related to the normal
force. The variable vertical to horizontal stress ratio givenby the IFE model has the form of Wittmer et al. (1996)
cot  τ xy  σx 

1  sin 2    2sin  1  α  β 2(31)

cos 2 where α is the vertical to horizontal stress ratio (k–1) and βis (31)the shear stress to horizontal stress ratio,

σ y  ασ xτ xy  βσ x

(32)
(33)

Substituting Eqns. (32) and (33) into Eqn. (31) yields
(32)
(33)
β2
1  sin 2    2sin  1  2
tan 
(34)
α  β cos 2 The α(β) has real solutions if β ≤ tanϕ. This condition is(34) because the stress ratio β is a maximum at the bin
upheldwall β|r=b = μ, so we have generally β ≤ μ ≤ tanϕ. Moreover, under static conditions the vertical stress should exceed the horizontal stress, so we expect α > 1, which selects
the + sign in the quadratic solution. In this case, the variation in α accounts for the change in stress across the bin
radius. Similar to the approach outlined above, Millet et al.
(2006) considered the variation in radial stresses for cohesive materials.

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After Jenike outlined proper designs to avoid arching
and funnel flow, hoppers became a common addition tobins. The stress field in a hopper is variable and Janssen’s
equation is not applicable to hoppers. Walker (1966) andTakami (1975) both used force-balance conditions on aconical slice to describe the stress distribution within aconical hopper and showed vertical and horizontal variations in stresses using Rankine’s formulation of k. Thiswork, similar to Janssen’s theory, considered forces on aplanar differential slice, which was assumed axisymmetric,
which is a reasonable assumption for most cases, but notall. Some bins have eccentric hoppers, which produce a
non-axisymmetric distribution of material and stresses.
While there have been many studies into eccentric discharge of bins in terms of improper flow, there have been
far fewer on structural loads (stresses), but this has changedin the past two decades (Wojcik et al., 2003; Vidal et al.,2006, 2008). A detailed study was reported by Ramírez etal. (2010), which provided a thorough discussion of thebenefits of eccentric hoppers, experimental work, and several numerical models. This work inspired Matchett’s analytical model based on the “principal stress cap” approach,which was capable of accounting for eccentricities in binloads, as well as symmetrical loads (Matchett, 2006;
Matchett, 2020; Matchett and Close, 2021). This research
provided a fundamental start to designing safe eccentric
hoppers.

4.3 Variable density
Janssen’s equation assumed the density of a bulk solid tobe constant throughout the entirety of a bin. There havebeen substantial attempts at numerically incorporatingpacking into pressure models and considering the effect ofparticle impact forces on spatial arrangements of particles(Volfson et al., 2003; Landry et al., 2004; Umbanhowar andGoldman, 2010; Petingco et al., 2022). There are some direct modifications to Janssen’s model to account for density
variations, with varying complexity. Vanel reported thatpacking could affect k and these effects could be quite significant when the porosity changed from 0.43 to 0.39
(Vanel and Clément, 1999). They proposed a modification
to Janssen’s model by introducing a fit parameter to account for inhomogeneous density. Cheng et al. directly
modified Janssen’s equation to account for changes indensity and investigated bins with hopper sections (Chenget al., 2017). They related the degree of packing to theprincipal stress through a quadratic equation and addressed
gaps and ambiguous understandings of pressures in the literature. They concluded that bulk density increased rapidly
with the material depth in the upper cylindrical part of thebin but decreased slowly in the hopper. Haque utilized
hoop stress and hoop pressure, which represent the circumferential stress and force exerted by stored material in acylindrical structure, to analytically describe packing

118

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within a bin (Haque, 2013). Developing a modified
Janssen’s equation, Haque’s model accounted for variations in bulk density when calculating vertical and horizontal pressures within a bin, offering a more comprehensive
understanding of the forces involved in grain storage.

4.4 Non-continuum approaches
4.4.1 Microscopic theory
Bulk solids are discontinuous in nature and microscopic
theory may provide a better alternative to the continuum
approach such as Janssen’s, but with greater complexity.
Bratberg et al. investigated the failure of Janssen’s theory
as the bin and material scale transitioned from macroscopic
to microscopic. A few researchers have investigated the binpressure from the microscopic angle. Xu et al. developed abin pressure model accounting for the discontinuous natureof bulk solids (Xu et al., 1996). Their model was based ona more general microscopic approach by Granik & Ferrarithat used principles of thermodynamics to investigate asystem of doublets (pairs of adjacent particles; Granik andFerrari, 1993). The particles modeled by Xu et al. wereidealized spheres in hexagonal packing and held within 2D
frictionless walls. Under a pair of contact forces within adoublet, the two particles experienced microstrains due todeformation, rotation, and slipping. Microstress (elongation, compression, torsion, and shearing) were calculated
from the contact forces, but torsion and shear microstresses
were found to be negligible. Their model results showedthat Janssen’s results were a special case of their microscopic theory. Moreover, k was only a constant value whenthe model used frictionless walls and rigid particles. Xu
had a deeper examination of the application of microscopic
theories in his Ph.D. dissertation (Xu, 1966). Xu et al.
demonstrated how this microscopic theory could be used to
predict moisture induced stresses within the mass of hygroscopic bulk solids. Zhang et al. further developed this theory to explain discharge loads in bins (Zhang and Britton,
2003). Ferrari et al. compiled a review of doublet microscopic theory (Ferrari et al., 1997). The strength of thistheory was the ability to analytically describe the discontinuous nature of particle assemblies, something that was beyond the scope of the continuum approach.

4.4.2 Granular elasticity
Jiang and Liu gave a brief overview of granular elastic
theory (Jiang and Liu, 2007a), which covered the original
work done by Jiang and Liu (2003). Granular elasticity was
distinct due to its definition of strain, which has two parts.
The first accounted for particle deformation (elastic). The
second accounted for irreversible rolling and slippage (aprocess that also causes heat) of particles and was considered plastic. The irreversible plastic phenomenon was themain contributor to the overall deformations. The energy
within the material under loading could be articulated as aGeorge Dyck et al. / KONA Powder and Particle Journal No. 41 (2024) 108–122

function of strains and static stresses. Elastic strains wereused because particles are compressed and sheared, which
stores energy reversibly. These models are based on a relationship between elastic energy, static stress and the deformation of particles. The stress distributions in a granularmaterial could be determined the same way as in elastic
media. Granular elasticity could account for a bulk solid’s
yield, volume dilatancy, and shear-induced anisotropy.
Jiang & Liu discussed the applicability of this theory tobins and compared it to Janssen’s method. They found thatbins were a good fit for granular elastic theory because allsix stress tensor elements could be calculated. Bräuer et al.
validated granular elasticity for its application to bin stress(Bräuer, 2006). Their model showed a saturation pressurethat matched Janssen’s. The remaining components of thestress tensor were also computed, which Janssen’s theory
could not perform. They assumed a constant k and foundlittle variation in vertical stress radially. Other interesting
works on granular elasticity included a slow movement of
deforming particles (Jiang and Liu, 2007b), which accounted for the infamous stress dip in granular piles(Krimer et al., 2006), showing good agreement with experimental data (Jiang and Liu, 2008). Sun et al. applied
granular elasticity to the mesoscopic scale using numericalmethods, which examined packing, pressure, and force
network structures (Sun et al., 2015). While the study was
focused on mesoscopic objects, there was a possibility that
this approach could be applied to many discontinuous systems.

5. Concluding remarks
New perspectives and viewpoints are often required tosolve stubborn, longstanding problems. This was certainly
the state of the field when Janssen’s approach produced arevolution in describing and predicting the behavior of
granular materials in bins. Even in the modern literature,
Janssen’s model forms the cornerstone of our understanding of pressures in bins. Modifications to Janssen’s model
have been sought to generalize it for describing the observed phenomena to higher accuracy, but the starting point
is usually Janssen. Otherwise, it is the first validation caseto test a new theory against. This ubiquity is a monument toJanssen’s success. Just as a new perspective spurred therevolution that led to Janssen’s insights, a survey of theliterature reveals many new promising approaches.
Despite these successes, there are several limitations ofJanssen’s model. First, the vertical stress is assumed to beconstant across a bin. This is not generally true as the pressure is expected to vary from the center to wall of a bin.
Second, the horizontal stress is assumed to be proportional
to the vertical stress through the factor k, which is constantthroughout a bin. Finally, the density is considered constantthroughout a bin. The first of these assumptions is incorrect. The second statement is ad hoc, and generally it

Review Paper

should require some deeper theoretical justification. The
third assumption is generally incorrect.
In recent years the computational power available toperform DEM simulations has significantly increased, andnumerical methods used to simulate granular properties
have been extensively studied. The discussion of material
properties acquisition and standardization has made DEM
a more viable approach for solving bulk solid problems(Xu et al., 2002; González-Montellano et al., 2011;
González-Montellano et al., 2012; Horabik and Parafiniuk,
2016). Generally, DEM simulations need to limit the number of particles for computational purposes, and the particle
shape sometimes needs to be crudely approximated. However, strategies have been developed to mitigate these limitations (Ramírez-Gómez, 2020). DEM provides thenumerical revolution analogous to the analytical revolution
provided by Janssen.

Data Availability Statement
The data from the pressure models are available publicly
in J-STAGE Data (https://doi.org/10.50931/data.kona.
23741598).

Acknowledgments

We acknowledge the effort of the anonymous referee,whose careful reading of our work helped to focus and refine our manuscript.

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Authors’ Short Biographies
George Dyck
George Dyck is a Biosystems Engineering PhD student from the University of Manitoba, Winnipeg, Canada. His research focuses on the application of digital twin technology in bulk solids storage
systems, with an emphasis on the development of comprehensive and efficient models for thesesystems. George’s work is aimed at advancing sustainable and efficient bulk solids storage practices,making a significant contribution to the field of agricultural engineering.

Adam Rogers
Adam Rogers is a Research Scientist with a doctorate degree in Physics and Astronomy from the
University of Manitoba in Winnipeg, Canada. He has worked with AGCO since 2020, where his focus has been on model development and mathematical analysis. His research interests broadly encompass the physics of granular materials.

Jitendra Paliwal
Dr. Jitendra Paliwal is a Professor of Biosystems Engineering whose expertise lies in the storage,
handling, and quality monitoring of cereal grains, oilseeds, leguminous crops, and their derivatives.
His research on developing hardware and software solutions is widely referred to by the designers ofgrain quality monitoring and assessment instruments. In 2018, he was part of a distinguished team ofindustry and academic researchers, who won the American Society of Agricultural and BiologicalEngineering’s AE50 award for adapting a cancer detection imaging technique for remote monitoringgrain bins.

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KONA Powder and Particle Journal No. 41 (2024) 123–139/https://doi.org/10.14356/kona.2034003

High-Pressure Torsion for Highly-Strained and High-Entropy
Photocatalysts †Saeid Akrami 1, Parisa Edalati 1, Masayoshi Fuji 1,2* and Kaveh Edalati 3,4*
1 Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Japan
2 Advanced Ceramics Research Center, Nagoya Institute of Technology, Japan

3 WPI International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, Japan
4 Mitsui Chemicals, Inc.- Carbon Neutral Research Center (MCI-CNRC), Kyushu University,

Japan

Nowadays, the environmental crisis caused by using fossil fuels and CO2 emissions has
become a universal concern in people’s life. Photocatalysis is a promising clean technology
to produce hydrogen fuel, convert harmful components such as CO2, and degrade pollutants
like dyes in water. There are various strategies to improve the efficiency of photocatalysis sothat it can be used instead of conventional methods, however, the low efficiency of the process
has remained a big drawback. In recent years, high-pressure torsion (HPT), as a severe plastic
deformation (SPD) method, has shown extremely high potential as an effective strategy to
improve the activity of conventional photocatalysts and synthesize new and highly efficientphotocatalysts. This method can successfully improve the activity by increasing the lightabsorbance, narrowing the bandgap, aligning the band structure, decreasing the electron-hole
recombination, and accelerating the electron-hole separation by introducing large latticestrain, oxygen vacancies, nitrogen vacancies, high-pressure phases, heterojunctions, andhigh-entropy ceramics. This study reviews the recent findings on the improvement of theefficiency of photocatalysts by HPT processing and discusses the parameters that lead to these
improvements.
Keywords: photocatalysis, water splitting, oxygen vacancy, nitrogen vacancy, heterojunctions, high-entropy alloys (HEAs),
high-entropy ceramics

1. Introduction
Nowadays, serious environmental crises such as global
warming and the formation of wastewater have forced
humans to find alternatives like using clean fuels withoutcarbon dioxide emissions such as hydrogen, reducing thepollutant gases such as carbon dioxide, and degrading thetoxic pollutants in wastewater through clean strategies(Gaya and Abdullah, 2008). Using photocatalysts is aneffective strategy to deal with these environmental crises
using renewable sunlight energy (Ma et al., 2014). The
function of photocatalysts is the acceleration of reduction
and oxidation (redox) reactions under light irradiation.
Reduction and oxidation reactions occur on the surface ofa photocatalyst using the electrons and holes, respectively(Tong et al., 2012), to promote processes such as dye
degradation (Akpan and Hameed, 2009), water splitting(Maeda, 2011), CO2 conversion (Tu et al., 2014), etc.
In photocatalytic dye degradation, photoexcited†

Received 9 September 2022; Accepted 19 December 2022
J-STAGE Advance published online 25 March 2023
* Corresponding author: Masayoshi Fuji and Kaveh Edalati;
1,2 Add: 3-101-1 Hon-machi, Tajimi, Gifu 507-0033, Japan
3,4 Add: 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
E-mail: fuji@nitech.ac.jp (M.F.); kaveh.edalati@kyudai.jp (K.E.)
TEL: +81-572-27-6811 (M.F.); +81-92-802-6744 (K.E.)

electrons take part in the degradation reaction of dyes (e.g.
rhodamine B, methylene orange, acid red B, etc.) which
normally exist in wastewaters produced by factories,
laboratories and different industries (Akpan and Hameed,2009). Photocatalytic water splitting includes the reduction
to hydrogen and oxidation to oxygen (Maeda, 2011). In
photocatalytic CO2 conversion, photoexcited electrons
contribute to the conversion of CO2 to reactive and usefulcomponents such as carbon monoxide (CO), formic acid(HCOOH), formaldehyde (HCHO), methanol (CH3OH)
and methane (CH4) (Tu et al., 2014). The mechanismof photocatalytic dye degradation, water splitting andCO2 conversion is shown in Figs. 1(a), 1(b) and 1(c),respectively.
A photocatalyst should have some features to support
the photocatalytic reactions including a narrow bandgap,
appropriate band positions to cover the desired reactions,appropriate light absorbance, potential to absorb andactivate the reactants, easy electron transition, and low
recombination rate of electrons and holes (Akpan and
Hameed, 2009; Maeda, 2011; Tu et al., 2014). Furthermore,high specific surface area, low cost, low toxicity and highstability are the other required properties that should
be considered to select a photocatalyst (Tong et al.,2012). Challenging in this field is finding a photocatalyst

Copyright © 2024 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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Saeid Akrami et al. / KONA Powder and Particle Journal No. 41 (2024) 123–139

a

b

c

d

Review Paper

Fig. 1 Schematic illustration of (a) photocatalytic dye degradation, (b) photocatalytic water splitting, (c) photocatalytic CO2 conversion, and (d) the
HPT process.

with sufficient properties for considerable photocatalytic
efficiency compared with the conventional catalytic
processes. TiO2 (Ajmal et al., 2014; Ni et al., 2007; Ola
and Maroto-Valer, 2015), ZnO (Chakrabarti and Dutta,
2004; Lin et al., 2012; Zhao et al., 2019), g-C3N4 (Wenet al., 2017; Ye et al., 2015; Yuan et al., 2016), WO3 (Liu
Y. et al., 2010; Wang et al., 2012; Wang et al., 2019a),
SrTiO3 (Huang et al., 2014a; Iwashina and Kudo, 2011;
Shan et al., 2017), GaN–ZnO (Hagiwara et al., 2017;
Maeda et al., 2005; Ohno et al., 2012) and BiVO4 (Gaoet al., 2017; Sun et al., 2014; Zhou et al., 2010) are someof the most promising photocatalysts which have beenutilized for photocatalytic dye degradation, water splitting
and CO2 conversion. The efficiencies of all these photocatalysts are still low for practical applications, and thus,
various strategies were utilized to enhance the optical andstructural properties and accordingly the photocatalytic
activity of these materials.
Vacancy introduction (Huang et al., 2014b; Lei et al.,2014; Yu H. et al., 2019), strain engineering (Di J. et al.,2020; Feng et al., 2015; Sa et al., 2014), using mesoporous
structures (Li Y. et al., 2010; Pauporté and Rathouský, 2007;
Puangpetch et al., 2009), formation of heterojunctions(Cao et al., 2018; Moniz et al., 2015; Uddin et al., 2012),
nanosheet production (Etman et al., 2018; Tu et al., 2012;
Yu J. et al., 2010) and doping with impurities (Kuvarega124

et al., 2011; Lee et al., 2016; Liu B. et al., 2018) are someof the reported methods which have been effective toimprove the photocatalytic activity. Doping with impurities
is the most common method which is used to improve theactivity by narrowing the bandgap, but it usually causes
recombination effects (Kuvarega et al., 2011).
High-pressure torsion (HPT) as one of the severe plastic
deformation (SPD) methods has been introduced as aneffective process to improve the photocatalytic efficiencyof various materials by introducing vacancies (oxygen
vacancy and nitrogen vacancy), dislocations, nanocrystals,
lattice strain, heterojunctions, high-pressure phases andhigh-entropy ceramics. The HPT method is effective in
reducing the recombination rate of electrons and holes
which are usually observed in doped photocatalysts. The
HPT method decreases the optical bandgap and enhances
the photocatalytic activity of various oxides and oxynitrides including TiO2 (Razavi-Khosroshahi et al., 2016b),
ZnO (Razavi-Khosroshahi et al., 2017b), Al2O3 (Edalati etal., 2019b), MgO (Fujita et al., 2020a), ZrO2 (Wang et al.,2020b), SiO2 (Wang et al., 2020a), LiTaO3 (Edalati et al.,2020a), CsTaO3 (Edalati et al., 2020a), BiVO4 (Akrami
et al., 2022c), Ga6ZnON6 (Edalati et al., 2020b) for dye
degradation, hydrogen production and CO2 conversion.
Furthermore, HPT was successfully used to synthesize
new high-entropy oxides such as TiHfZrNbTaO11 (Edalati

Saeid Akrami et al. / KONA Powder and Particle Journal No. 41 (2024) 123–139

et al., 2020c) and high-entropy oxynitrides such as
TiZrHfNbTaO6N3 (Akrami et al., 2022a) for hydrogenproduction, oxygen evolution and CO2 conversion.
Moreover, it was also utilized to form heterojunctions
in binary and high-entropy composites such as TiO2–
ZnO (Hidalgo-Jimenez et al., 2020) and TiZrNbTaWO12(Edalati et al., 2022b) to improve the photocatalytic
activity for hydrogen and oxygen production.
In this review paper, the influence of the HPT methodon optical properties, electronic structure, electron-hole
separation, migration and recombination rates and photocatalytic efficiency of various photocatalysts is discussed.
The improvement of photocatalytic activity of thesematerials for dye degradation, hydrogen production,oxygen production and CO2 conversion is discussed byconsidering the generation of oxygen vacancies, nitrogenvacancy complexes, high-pressure phases, heterojunctions
and high-entropy ceramics.

2. High-pressure torsion (HPT)The SPD process is typically used to form ultrafinegrained (UFG) and nanostructured materials withenhanced mechanical and functional properties (Estrin and
Vinogradov, 2013; Valiev, 2004). SPD has various methods
including HPT, introduced by Bridgman in 1935 for the
first time (Bridgman, 1935). In this method, torsionalstrain under high pressure is applied to induce large plasticstrain in various ranges of materials. In the HPT process,a disc-shape sample (Bridgman, 1935) that has typicallya 10 mm diameter or a ring sample (Edalati and Horita,2009) is inserted between two anvils under high pressure
and strained by rotation of anvils against each other. A
schematic illustration of HPT is shown in Fig. 1(d). The
applied shear strain (γ) to the sample can be calculated by
the following equation (Edalati and Horita, 2016).

γ

2πrN
h

(1)

where r, N and h are the distance from the center of thedisc or ring, the number of turns and the sample height,(1)
respectively.
In addition to grain refinement, the HPT method is
widely used for hardening of pure metals (Edalati andHorita, 2011; Starink et al., 2013), mechanical alloyingof miscible and immiscible systems (Edalati et al., 2015b;
Edalati, 2019a) and plastic deformation of hard-to-deformmaterials (Edalati et al., 2010a; Edalati et al., 2010b;
Ikoma et al., 2012). Phase transformation, consolidationof powders, the introduction of defects such as oxygen
vacancies and dislocations, narrowing of the bandgap andimprovement of functionalities were frequently reported
after HPT processing (Edalati, 2019d). The HPT method canbe utilized for plastic deformation of oxides and ceramics
which are hard and brittle materials at ambient temperature

Review Paper(Edalati et al., 2022a). The presence of covalent or ionicbonding in ceramics results in their lower grain size after
HPT processing compared to metals. Moreover, suchbonding features result in the formation of large densitiesof defects such as vacancies and dislocations which can
improve the properties and functionality of ceramics.
Despite the high potential of ceramics for various
applications, there are some limited publications on theeffect of HPT on the structure, properties and functionalityof ceramics. A list of ceramics treated by HPT forvarious properties and applications and their relevantpublications are given in Table 1. Table 1 indicatesthat these HPT-processed ceramics, including oxides,
nitrides, oxynitrides and borides, have been investigatedfor photocatalysis, phase transformation, electrocatalysis,
photocurrent generation, dielectric properties, bandgap
narrowing, optical properties, mechanical properties, Liion batteries and microstructural features. Furthermore,
the HPT method was also utilized to synthesize newceramics for various applications. As can be seen in
Table 1, ceramic photocatalysts processed or synthesizedby HPT were used for water splitting (CsTaO3, LiTaO3,
ZrO2, GaN–ZnO, TiO2–ZnO, TiO2, TiHfZrNbTaO11,
TiZrNbTaWO12 and TiZrHfNbTaO6N3), dye degradation(ZnO, MgO, γ-Al2O3 and SiO2), CO2 conversion (TiO2,
BiVO4, TiHfZrNbTaO11 and TiZrHfNbTaO6N3) andoxygen production (TiZrNbTaWO12).

3. Fundamentals and mechanism of photocatalysis

In photocatalysis, which is also called artificial photosynthesis, electrons in the valence band absorb the light
photons, separate from the holes and transfer to the
conduction band of the photocatalyst to form the electronhole charge carriers. The charge carriers then migrate to
the surface of the photocatalyst and finally take part invarious chemical reactions. In this process, both reduction
and oxidation reactions occur on the surface of the photocatalyst, provided that the thermodynamic and kinetic
conditions are satisfied. From the thermodynamic pointof view, a photocatalytic process will perform when the
reduction and oxidation reaction potentials are between thevalence band and the conduction band of a photocatalyst(Tong et al., 2012). For the reduction reactions, potentials
lower than the valence band top are desirable, while for theoxidation reactions, potentials higher than the conduction
band bottom are desirable. From the kinetic point of view,the electron-hole separation should be fast, and theirrecombination should be slow. Here, the fundamentals of
three main photocatalytic reactions are mentioned.

3.1 Photocatalytic dye degradation
In photocatalytic dye degradation, there are two direct
and indirect pathways. In the direct pathway, the dye

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Table 1 Summary of major publications about ceramics treated by HPT and their major applications or features.
Materials

Investigated properties and application

Reference

TiHfZrNbTaO11

Photocatalytic activity for CO2 conversion(Akrami et al., 2022b)

TiZrHfNbTaO6N3

Photocatalytic activity for CO2 conversion(Akrami et al., 2022a)

BiVO4

Photocatalytic activity for CO2 conversion(Akrami et al., 2022c)

TiO2-II

Photocatalytic activity for CO2 conversion(Akrami et al., 2021b)

MgO

Photocatalytic activity for dye degradation(Fujita et al., 2020a)

SiO2

Photocatalytic activity for dye degradation(Wang et al., 2020a)

γ-Al2O3Photocatalytic activity for dye degradation(Edalati et al., 2019b)

ZnO

Photocatalytic activity for dye degradation(Razavi-Khosroshahi et al., 2017b)

TiZrHfNbTaO6N3

Photocatalytic activity for hydrogen production(Edalati et al., 2021)

ZrO2

Photocatalytic activity for hydrogen production(Wang et al., 2020b)

CsTaO3, LiTaO3

Photocatalytic activity for hydrogen production(Edalati et al., 2020a)

GaN–ZnO

Photocatalytic activity for hydrogen production(Edalati et al., 2020b)

TiHfZrNbTaO11

Photocatalytic activity for hydrogen production(Edalati et al., 2020c)

TiO2–ZnO

Photocatalytic activity for hydrogen production(Hidalgo-Jimenez et al., 2020)

TiO2-II

Photocatalytic activity for hydrogen production(Razavi-Khosroshahi et al., 2016b)

TiZrNbTaWO12Photocatalytic activity for oxygen production(Edalati et al., 2022b)

Review on HPT of oxides(Edalati, 2019b)

Review on HPT(Edalati and Horita, 2016)

SiO2, VO2

Phase transformation(Edalati et al., 2019a)

TiO2

Grain coarsening and phase transformation(Edalati et al., 2019c)

ZrO2

Phase transformation by ball milling and HPT(Delogu, 2012)

ZrO2

Allotropic phase transformations(Edalati et al., 2011)

Photocatalysis

Reviews
OxidesPhase transformation

TiO2

Plastic strain and phase transformation(Razavi-Khosroshahi et al., 2016a)

BN

FEM modeling of plastic flow and strain-induced phase transformation(Feng et al., 2019)BN

Coupled elastoplasticity and plastic strain-induced phase transformation(Feng and Levitas, 2017)

Electrocatalysis for hydrogen generation(Edalati et al., 2019c)

Bi2O3

Enhanced photocurrent generation(Fujita et al., 2020b)

TiO2-II

Visible light photocurrent generation(Wang et al., 2020c)

Electrocatalysis
TiO2-II
Photocurrent

Dielectric propertiesBaTiO3

Optical and dielectric properties(Edalati et al., 2015a)

CuO

Dielectric properties(Makhnev et al., 2011)

Bandgap narrowing(Qi et al., 2021)

Y2O3

Optical properties(Razavi-Khosroshahi et al., 2017a)

CuO, Y3Fe5O12, FeBO3

Optical properties and electronic structure(Gizhevskii et al., 2011)

Cu2O, CuO

Middle infrared absorption and X-ray absorption(Mostovshchikova et al., 2012)

CuO, Y3Fe5O12, FeBO3

Optical properties(Telegin et al., 2012)

α-Al2O3

Microstructure and mechanical properties(Edalati and Horita, 2010)

Fe53.3Ni26.5B20.2, Co28.2Fe38.9Cr15.4Si0.3B17.2

Microstructure and mechanical properties(Permyakova and Glezer, 2020)

Lithium-ion batteries(Qian et al., 2018)

Bandgap investigation
ZnO
Optical properties

Mechanical properties

Lithium-ion batteries
Fe3O4
Microstructural features
ZnO

Plastic flow and microstructural instabilities(Qi et al., 2018)

YBa2Cu3Oy

Microstructural investigation(Kuznetsova et al., 2017)

Fe71.2Cr22.7Mn1.3N4.8

Microstructural features(Shabashov et al., 2018)126

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Saeid Akrami et al. / KONA Powder and Particle Journal No. 41 (2024) 123–139(Lu et al., 2021), as shown in Fig. 2. These CO2 adsorption
modes determine the reaction pathway for photocatalytic
CO2 conversion. For instance, the oxygen coordinationmode, with the bidentate bonding of oxygen atoms withthe surface of the photocatalyst, leads to the fabricationof formate anions as an intermediate product and formic
acid as the final product. In the carbon coordination mode,with the monodentate binding of carbon and photocatalyst
surface, the reaction results in carboxyl radical production.
After the adsorption step of CO2 to the surface of thephotocatalyst, the conversion reactions occur via different
pathways.
It was reported that there are three main pathways forCO2 photoreduction: (i) carbene pathway (ii) formaldehyde
pathway and (iii) glyoxal pathway (Habisreutinger et al.,2013; Maeda and Domen, 2007; Wang et al., 2021). The
reactions for these three pathways are presented in Table 2.
In all these pathways, the CO •2 radical is the main intermediate product which is formed by the reaction of

degrades directly by the excited electrons and holes in
the conduction band and the valence band, respectively,
as shown in Fig. 1(a). In the indirect pathway, which ismore conventional than the direct pathway, the electrons
in the conduction band react with the O2 to produce theO •2 radicals. The O •2 radicals then react with the dye toform the reduction products (Li X. et al., 2018). In thevalence band, holes react with H2O to produce •OH, andthe •OH radicals react(11)(11)with the dye to produce the oxidation
products as shown in Fig. 1(a).

3.2 Photocatalytic water splitting
In photocatalytic water splitting, electrons in the
conduction band take part in the reduction reaction to
produce H2, while holes in the valence band take part in theoxidation reaction to produce O2 (Lu et al., 2021), as shownin Fig. 1(b). In most photocatalysts, overall water splitting
does not occur, and the process is limited to either reduction
or oxidation reactions thus a sacrificial agent or scavenger
is usually required to produce electrons or holes. Methanol
and AgNO3 are popular sacrificial agents for photocatalytichydrogen and oxygen production, respectively.

. (12).

δ-C

In photocatalytic CO2 conversion, electrons in the
conduction band along with protons (H+) produced fromwater oxidation in the valence band, take part in various
reduction reactions leading to the formation of CO, CH4
and some other hydrocarbons as shown in Fig. 1(c).
The first step in photocatalytic CO2 conversion is theadsorption of CO2 molecules on the surface of the photocatalyst which occurs in three modes including oxygen
coordination, carbon coordination and mixed coordination

δ-

O

O

3.3 Photocatalytic CO2 conversion

δ-

C

δ-

Oxygen CoordinationO
C

Carbon Coordination

O

δ-

O

O
C

O

O

δ-

δ-

O

C

δ-

O

Mixed Coordination
Fig. 2CO2 adsorption modes on the surface of photocatalysts.

Table 2 CO2 photoreduction pathways (Habisreutinger et al., 2013).

Carbene Pathway

Formaldehyde Pathway

Glyoxal Pathway

(1) CO2 + e– → CO •2(1) CO2 + e– → CO 2

(1) CO2 + e– → CO •2(2) CO •2 + e– + H+ → CO + OH–(2) CO •2 + H+ → •COOH(2) CO •2 + e– + H+ → CHOO–•–(3) CO + e– → CO[21](3) •COOH + e– [21]+ H+ → HCOOH(3) CHOO– + H+[21]→ HCOOH(4) [21]CO•– + e– + H+ → C + OH–(4) [21]
HCOOH + e– + H+ → H3OOC•(4) [21]
HCOOH + e– → HOC•

(5) C + e– + H+ → CH•(5) HCOOH •2 + e– + H+ → HCOH + H2O(5) HOC• + OH– → C2H2O2(6) CH• + e– + H+ → CH2(6) HCOH + e– → H2C•O–(6) C2H2O2 + e– + H+ → H3O2C2•(7) CH2 + e– + H+ → CH •3(7) [24]
H2C•O– + H+ → H2OHC•(7) H3O2C2• + e– + H+ → C2H4O2(8) CH •3 + e– + H+ → CH4(8) H2OHC• + e– + H+ → CH3OH(8) C2H4O2 + e– + H+ → H3OC2• + H2O(9) CH •3 + OH– → CH
3OH
[23]

(9) CH3OH + e– + H+ → •CH3 + H2O(9) H3OC2• + e– + H+ → C2H4O(10) •CH3 + e– + H+ → CH4(10) C2H4O + h+ → H3OC2• + H+

[23]
[23]

•

(11) H3OC2• → CH •3 + CO(12) CH •3 + e– + H+ → CH4

[23]
[23]
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adsorbed CO2 and an electron. In the carbene pathway,
CO •2 converts to CO• and leads to the production of CO,
CH2, CH4 and CH3OH. In this pathway, carbon coordinationis the main binding mode. In the formaldehyde pathway,
CO •2 converts to •COOH to produce HCOOH, CH3OH and(12)

CH4. In this pathway, CO •2 attaches to the catalyst bymonodentate or bidentate binding. In monodentate binding,
either one oxygen atom in CO •2 or one carbon atom in(12)
CO •2 are bonded to the surface(12) of the photocatalyst. In thebidentate mode, both oxygen atoms in CO •2 are bonded tothe photocatalyst surface. (12)In the glyoxal pathway, C2
components are formed by some complicated reactions,(12)though there are limitations to producing
C2 due to the low(12)
selectivity of the photocatalyst. In this pathway, CO •2converts to the formyl radical HOC• to form C2H2O2,
C2H4O2, C2H4O, CO and CH4. In all these pathways, CO2adsorption and activation are key parameters to adjust
the(12)
selectivity of photocatalysts and this should be empoweredby some strategies such as oxygen vacancy generation andstrain engineering (Fu et al., 2020).

4. Impact of HPT on photocatalysis

As given in Table 1, numerous studies in recent years
reported that the HPT method can be used to improve thephotocatalytic activity of ceramics. Bandgap narrowing
for the easier transition of photoexcited electrons from thevalence band to the conduction band, increasing the lightabsorbance in the visible region of light, electronic band
structure alignment, decreasing the recombination rateof electrons and holes and accelerating the electron-hole
separation and migration are some phenomena responsible
for the high activity of photocatalysts processed orsynthesized by HPT. The occurrence of these phenomena
was attributed to the formation of oxygen vacancies,
nitrogen-vacancy complexes, high-pressure phases,
heterojunctions and high-entropy phases which will be
discussed in detail as follows.

4.1 Generation of oxygen vacancies

Oxygen vacancies on the surface have a significant
effect on the photocatalytic activity of materials, since they
can act as active sites to adsorb and activate the reactants
and as trap sites for photoexcited electrons. In contrast,
oxygen vacancies in the bulk of photocatalysts may act
as recombination centers and lead to decreasing photocatalytic efficiency. Oxygen vacancies can also reduce theoptical bandgap by changing the electronic structure ofmaterials via the formation of defect states between thevalence band and the conduction band. The formation ofoxygen vacancies after HPT processing has been evidenced
by several analysis methods such as electron paramagnetic
resonance (EPR), X-ray photoelectron spectroscopy (XPS),
Raman spectroscopy, differential scanning calorimetry(DSC), and X-ray diffraction (XRD) (Fujita et al., 2020a;

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Razavi-Khosroshahi et al., 2016b; Wang et al., 2020a).
Furthermore, changing the color of catalysts to the darker
ones, which are frequently observed after HPT processing,indicates the light absorbance in the visible region due tothe formation of oxygen vacancies as color centers (Fujita
et al., 2020b; Razavi-Khosroshahi et al., 2017b; Wang etal., 2020b). Among the photocatalysts treated by HPT,
Al2O3 (Edalati et al., 2019b), MgO (Fujita et al., 2020a),
ZrO2 (Wang et al., 2020b), SiO2 (Wang et al., 2020a),
LiTaO3 (Edalati et al., 2020a), CsTaO3 (Edalati et al.,2020a) and BiVO4 (Akrami et al., 2022c) are oxides which
exhibited enhanced photocatalytic activity by oxygen
vacancy formation.
Al2O3: Al2O3 is an insulator with a large bandgap(~9 eV) and does not show photocatalytic activity. Firstprinciples calculations suggested that the formation ofoxygen vacancies can reduce the optical bandgap of Al2O3
even to the visible light region (Edalati et al., 2019b). The
application of HPT to Al2O3 resulted in the formationof oxygen vacancies, the reduction of optical bandgap
and its photocatalytic activity. The formation of oxygen
vacancies after HPT is shown in Fig. 3(a) using DSC
analysis by the appearance of a peak at 530 K. After the
HPT process, a narrow bandgap of 2.5 eV was achieved, ingood agreement with the first-principles calculations, andthe material showed photocatalytic activity for rhodamine
B dye degradation under UV irradiation with an activity
comparable to TiO2 (Fig. 3(b)).
MgO: MgO, as another ceramic insulator, also exhibitedhigh light absorbance and bandgap narrowing (from 7.8 eV
to 3.9 eV) after HPT processing (Fujita et al., 2020a).
The HPT-processed MgO showed photocatalytic activity
for methylene blue dye degradation under UV irradiation(55 % decomposition after 3 h). The formation of oxygen
vacancies in this material was evidenced by XPS analysisof Mg 2p, which showed a shift to lower energy, and sucha shift became more significant by increasing the applied
shear strain (i.e. by increasing the number of HPT turns).
ZrO2: ZrO2 is another ceramic that was processed by
HPT to introduce a high concentration of oxygen vacancies(Wang et al., 2020b). The optical bandgap of this material
decreased from 5.1 to 4.0 eV due to the formation of oxygen
vacancies after HPT. The formation of oxygen vacancies
after HPT processing at various temperatures is shownin Fig. 3(c) using the EPR analysis. The appearance of apair peak with a g factor of ~2 indicates the formation ofoxygen vacancies, while the formation of oxygen vacancies
is enhanced by increasing the HPT processing temperature.
Improvement of photocatalytic activity of ZrO2 using HPT
for hydrogen evolution is shown in Fig. 3(d), indicatingthat the photocatalytic activity is improved by increasing
the concentration of oxygen vacancies.
SiO2: SiO2 quartz sand also was examined for photocatalytic activity after processing using the HPT method

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Fig. 3 Formation of oxygen vacancies achieved by HPT processing and improvement of photocatalytic activity. (a) DSC analysis and (b) photocatalytic rhodamine B degradation for γ-Al2O3 before and after HPT processing compared with TiO2 (Edalati et al., 2019b). (c) EPR spectra and (d)corresponding photocatalytic hydrogen production for ZrO2–3 wt% Y2O3 before and after HPT processing at 300, 437, 573 and 673 K (Wang et al.,2020b). (e) Oxygen vacancy concentration and (f) photocatalytic CO2 to CO conversion for BiVO4 before and after HPT processing for N = 0.25, 1 and4 turns (Akrami et al., 2022c). Copyright: (2019) Elsevier, (2020) Royal Society of Chemistry, (2022) Elsevier.

(Wang et al., 2020a). This material with the initiallyreported bandgap of ~9 eV and negligible light absorbance
exhibited a bandgap narrowing to 2.8 eV, with a small
activity for rhodamine B dye degradation under UV lightirradiation.
LiTaO3 and CsTaO3: LiTaO3 and CsTaO3 are twotantalate perovskites that show activity for photocatalytichydrogen production. After oxygen vacancy generation
by HPT processing, both materials exhibited bandgap
narrowing (4.7 to 4.2 eV for LiTaO3 and 4.7 to 3.6 eV for
CsTaO3) and enhanced photocatalytic activity for hydrogengeneration (Edalati et al., 2020a).
BiVO4: BiVO4 is a popular photocatalyst for CO2
conversion due to its low bandgap, but it suffers from ahigh recombination rate of electrons and holes and a low
position of the conduction band. HPT could successfully
solve these two problems by simultaneously introducing
the oxygen vacancies and lattice strain (Akrami et al.,2022c). Increasing the oxygen vacancy concentration byincreasing the number of HPT turns, calculated from XPS,is shown in Fig. 3(e). These structural modifications by
HPT processing resulted in significant improvement ofphotocatalytic CO2 conversion on BiVO4, as shown in
Fig. 3(f).

4.2 Generation of nitrogen-vacancy complexes
Oxynitrides are promising photocatalysts due to their
low bandgap compared with oxide photocatalysts. It
was reported that nitrogen vacancies, which are formed
during the synthesis of these materials, can act asrecombination centers, and reduce photocatalytic activity(Maeda and Domen, 2007). However, it was observedthat HPT processing of Ga6ZnON6 led to the formation of
nitrogen-based vacancies, decreasing the electron and hole
recombination and enhancement of photocatalytic activity(Edalati et al., 2020b). Fig. 4(a) shows the EPR spectra of129

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Fig. 4 Formation of nitrogen-based vacancy complexes and suppression of recombination in oxynitride processed by HPT. (a) EPR spectraand (b) photoluminescence spectra for Ga6ZnON6 before and after
HPT processing (r: radial distance from center of HPT-processed disc)(Edalati et al., 2020b). Copyright: (2020) Elsevier.

Ga6ZnON6 before and after HPT processing. A g factor of
1.958 for powder is a piece of evidence for the formation of
nitrogen monovacancies during the synthesis of oxynitrides,
while a g factor of 2.006 indicates the presence of nitrogenbased vacancy complexes by HPT processing. Introducing
the nitrogen-based vacancy complexes to this material by
HPT, led to reducing the bandgap from 2.7 eV to 2.4 eV,enhancing the light absorbance and improvement of photocatalytic hydrogen production under UV irradiation. This
study showed that in addition to the location of vacancies(surface or bulk), their arrangement (monovacancy orvacancy complex) can affect photocatalytic activity. These
vacancy complexes can act as active sites for reactions
rather than the recombination centers for electrons andholes. Decreasing the recombination of electrons and holes
after HPT processing for this oxynitride is presented inFig. 4(b), in which the photoluminescence intensity issuppressed effectively after the HPT process.

4.3 Formation of high-pressure phases
Due to high pressure and high strain in HPT, the method

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can lead to the formation of metastable and high-pressure
phases. These phases usually form with fast kinetics atlower pressures during HPT processing compared to thetransition pressures under static conditions due to thepresence of high concentrations of nanograin boundaries
and defects. It was suggested that the formation ofthese defects and their interaction increase localized
pressure and result in the formation and stability of highpressure phases (Edalati, 2019b). BaTiO3 (tetragonal to
cubic) (Makhnev et al., 2011), TiO2 (anatase-tetragonal
to orthorhombic TiO2-II) (Razavi-Khosroshahi et al.,2016b), Y2O3 (cubic to monoclinic) (Razavi-Khosroshahi
et al., 2017a), ZnO (wurtzite-hexagonal to rocksalt-cubic)(Razavi-Khosroshahi et al., 2017b) and SiO2 (quartz
to coesite) (Wang et al., 2020a) are some oxides whichshowed transition to high-pressure phases after HPT
processing. Among these materials, the formation of highpressure phases in TiO2 and ZnO was reported to lead tothe improvement of photocatalytic activity for hydrogenproduction, CO2 conversion and dye degradation. Figs. 5(a)and 5(b) illustrate the pressure-temperature phase diagramsof TiO2 and ZnO, respectively; Figs. 5(c) and 5(d) show
the formation of TiO2-II and rocksalt-ZnO high-pressure
phases after HPT processing, respectively; and Figs. 5(c)and 5(d) show the influence of HPT processing on theenhancement of the photocatalytic activity of TiO2 andZnO, respectively. Since TiO2 and ZnO are the mostcommon photocatalysts, the reports on the applicationof HPT to these two photocatalysts are of significance as
they suggested new solutions to enhance the activity bynanostructure and polymorphic control and without dopant
or impurity addition (Razavi-Khosroshahi et al., 2016b;
Razavi-Khosroshahi et al., 2017b).
TiO2: TiO2 light absorbance improved significantly after
the formation of the high-pressure TiO2-II (columbite)
phase by HPT processing (Akrami et al., 2021b; RazaviKhosroshahi et al., 2016b). The color of TiO2 changed
from white to gray or green and its bandgap decreased
from 3.1 eV to 2.5 eV, which resulted in photocatalytic
activity of the material under visible light. To solve therecombination issue due to the presence of bulk defects
after HPT processing, the HPT-processed sample wasprocessed by annealing which resulted in a further increasein the photocatalytic activity and photocurrent generation.
It was shown that the formation of TiO2-II results in photocatalytic activity for hydrogen production under visible
light and CO2 conversion under UV light (Fig. 5(e)),
although the high activity is influenced by other factors
such as the presence of oxygen vacancies and interphaseboundaries.
ZnO: HPT processing of ZnO led to the formationof a high-pressure rocksalt phase and accordingly to theimprovement of light absorbance in the visible region
with a bandgap narrowing from 3.0 eV to 1.8 eV (Razavi-

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Fig. 5 Improved photocatalytic activity by introducing high-pressure phases. (a) Phase diagram of TiO2 (Akrami et al., 2021b), (b) phase diagramof ZnO (Razavi-Khosroshahi et al., 2017b), (c) high-resolution lattice image of TiO2 including TiO2-II high-pressure phase (Akrami et al., 2021b), (d)XRD profile of ZnO including rocksalt high-pressure phase (Razavi-Khosroshahi et al., 2017b), (e) photocatalytic CO2 conversion on TiO2 before andafter HPT processing and after annealing under UV light (Akrami et al., 2021b), and (f) photocatalytic rhodamine B dye degradation for ZnO before
and after HPT processing under visible light (Razavi-Khosroshahi et al., 2017b). Copyright: (2021) Elsevier, (2017) Royal Society of Chemistry.

Khosroshahi et al., 2017b). This bandgap narrowing
was also predicted by first-principles calculations. The
HPT-processed ZnO, containing a high-pressure phasewith a large fraction of oxygen vacancies and interphaseboundaries, showed photocatalytic rhodamine B dye
degradation under visible light, while its activity improved
with increasing the processing pressure (Fig. 5(f)).

4.4 Formation of heterojunctions
Heterojunctions are the space-charge regions that areformed by the combination of two different phases ordifferent semiconductors. Charge carriers can penetrateheterojunctions which results in the formation of anelectronic field in this region (Moniz et al., 2015).
Heterojunctions accelerate the electron and hole separationand improve their migration to the surface of the photocatalyst for the reactions. Furthermore, they significantly
suppress recombination (Cao et al., 2018; Uddin etal., 2012). As mentioned earlier, among HPT-processedmaterials, some of them exhibit phase transformation
resulting in the formation of heterojunctions between
two or more phases such as cubic-rhombohedral phases
in Al2O3 (Edalati et al., 2019b), monoclinic-tetragonalphases in ZrO2 (Wang et al., 2020b) and coesite-quartz

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phases in SiO2. (Wang et al., 2020a). There is also anapplication of HPT to TiO2–ZnO composite to generateheterojunctions for enhanced photocatalytic hydrogenproduction (Hidalgo-Jimenez et al., 2020). There is anotherattempt to generate 10 different heterojunctions in ahigh-entropy oxide TiZrNbTaWO12 for visible-light-driven
photocatalytic oxygen production (Edalati et al., 2022b). In
all these cases, the interphase boundaries formed by HPT
can act as sites to accelerate the electron-hole separation toeffectively improve the photocatalytic activity, as will be
discussed below for the TiO2–ZnO composite.
TiO2–ZnO: A recent study generated heterojunctions

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using HPT by making a composite of TiO2 and ZnO as two
typical catalysts for photocatalytic hydrogen production(Hidalgo-Jimenez et al., 2020). This work was performed
to ease electron-hole separation as shown in Fig. 6(a). In
addition to TiO2–ZnO heterojunctions, HPT processing led
to the formation of high-pressure phases and formation ofvarious kinds of heterojunctions. XRD profiles of TiO2–
ZnO composite show the presence of five different phasesof TiO2 and ZnO in Fig. 6(b). After HPT processing, thelight absorbance of the composite significantly increasedin the visible light region and a bandgap narrowing wasobserved from 3.2 eV to 2.6 eV and 1.6 eV for N = 3
and 15 HPT turns, respectively. The recombination rateof electrons and holes was also suppressed effectively
after HPT processing. Although electronic structure, lightabsorbance and recombination suppression improved byincreasing the number of HPT turns to N = 15, photocatalytic hydrogen production for N = 15 was lower
compared to the powder mixture and sample processed by
N = 3 due to the formation of very small crystals and poor
crystallinity (Fig. 6(c)). The composite processed with
N = 3 had a hydrogen production amount of 2.5 time better
than initial powders mixture under UV irradiation.

4.5 Production of high-entropy ceramics

Fig. 6 Enhanced photocatalytic hydrogen production by heterojunction formation. (a) Schematic illustration of electronic band structureof TiO2–ZnO composite including heterojunctions. (b) XRD profile and(c) photocatalytic hydrogen production on TiO2–ZnO composite before
and after HPT processing for N = 3 and 15 turns (Hidalgo-Jimenez et al.,2020). Copyright: (2020) Elsevier.

132

High-entropy ceramics are materials with at least
five principal cations and a mixing entropy higher than
1.5R (R: gas constant) (Akrami et al., 2021a; Oses et al.,2020). These materials have been employed for various
applications due to their high stability, lattice strain,
inherent defects and high entropy of mixing (Zhang andReece, 2019; Xiang et al., 2021). They have potential to
be used as Li-ion batteries (Lun et al., 2021; Nguyen etal., 2020; Wang et al., 2019b), catalysts (Albedwawi et al.,2021; Qiao et al., 2021; Xu et al., 2020), photocatalysts(Akrami et al., 2022a; Akrami et al., 2022b; Edalati et al.,2020c), supercapacitors (Jin et al., 2018; Liang et al., 2020;
Talluri et al., 2021), dielectrics (Bérardan et al., 2016; Zhou
et al., 2020; Zhou et al., 2022), thermal barrier coatings(Li F. et al., 2019; Ren et al., 2020; Zhang et al., 2022),
etc. Utilizing the high-entropy ceramics as photocatalysts,
which was performed for the first time by the authors of thecurrent work, is a new functional aspect of these materials
which is in its initial research stage. The high stability ofhigh-entropy ceramics and the formation of defects andstrain in these materials were the main motivations to
use them as photocatalysts. As will be discussed below,
the oxides TiZrHfNbTaO11 and TiZrNbTaWO12 and theoxynitride TiZrHfNbTaO6N3 are high-entropy ceramics
synthesized by HPT and used for photocatalytic hydrogenproduction, CO2 conversion and O2 evolution. Since oxides
and oxynitrides with d0 and d10 electronic configurations
show good photocatalytic activity, five d0 cations wereselected to design these photocatalysts.

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Fig. 7 High light absorbance and significant photocatalytic activity of high-entropy ceramics. (a) UV-vis spectra of TiZrHf NbTaO11,
TiZrHf NbTaO6N3 and relevant binary oxides including TiO2, ZrO2, HfO2, Nb2O5 and Ta2O5 (Edalati et al., 2021), (b) diffuse reflectance infrared
Fourier transform spectra for TiZrHf NbTaO11 and TiZrHf NbTaO3N6 compared with P25 TiO2 in which the peaks at 665 cm –1 and 2350 cm –1 represent
chemisorption and physisorption of CO2 on surface (Akrami et al., 2022a), (c) photocatalytic hydrogen production on TiZrHf NbTaO6N3 comparedwith Ga6ZnON6 (Edalati et al., 2021), and (d) photocatalytic CO2 to CO conversion on TiZrHf NbTaO11 and TiZrHf NbTaO6N3 compared with P25 TiO2
(Akrami et al., 2022a). Copyright: (2021) Royal Society of Chemistry, (2022) Elsevier.

TiZrHfNbTaO11: TiZrHfNbTaO11 high-entropy oxide(HEO) with two monoclinic and orthorhombic phases
which was synthesized by the HPT method and hightemperature oxidation, showed better light absorbancecompared to relevant binary oxides including TiO2, ZrO2,
HfO2, Nb2O5 and Ta2O5 (Fig. 7(a)) (Edalati et al., 2020c).
This material contained defects such as oxygen vacancies
and dislocations as shown in Fig. 8(a). Furthermore, it
demonstrated lower photoluminescence intensity compared
to anatase TiO2 and BiVO4 as two typical photocatalysts,
indicating the low recombination rate of electrons andholes in this HEO (Akrami et al., 2022b). Moreover,this material showed the potential to generate photocurrent, produce hydrogen and convert CO2 to CO under
UV light. The rate of hydrogen and CO production for
TiZrHfNbTaO11 was higher than anatase TiO2 and BiVO4
and almost the same as P25 TiO2 as a benchmark photocatalyst. This high photocatalytic activity was attributed
to the formation of defects, interphases and high-entropy
phases which led to the acceleration of electron-hole
separation and diminishing their recombination.

TiZrHfNbTaO6N3:
Low-bandgap
high-entropy
oxynitride (HEON) TiZrHfNbTaO6N3 was produced by
HPT processing followed by oxidation and nitriding(Edalati et al., 2021). This HEON showed significantly
higher light absorbance compared to corresponding highentropy and binary oxides (Fig. 7(a)) with a narrow
bandgap of 1.6 eV, which is lower than the bandgap of
most reported oxynitride photocatalysts in the literature.
This HOEN could successfully generate photocurrent witha significantly low recombination rate of electrons andholes compared to HEO and P25 TiO2 as a benchmark
photocatalyst (Akrami et al., 2022a). Furthermore, it
showed a high potential to adsorb the CO2 molecules on
the surface as shown in Fig. 7(b). This material producedhydrogen under UV irradiation with better activity than
Ga6ZnON6 as one of the most popular oxynitride photocatalysts (Fig. 7(c)). It also showed a higher photocatalytic
activity per surface area for CO2 conversion compared
to HEO and P25 TiO2 (Fig. 7(d)) and all reported photocatalysts in the literature. Improved photocatalytic activity
on this HEON was attributed to the high light absorbance133

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Fig. 8 Formation of nanograins, grain boundaries, interphases, and dislocation defects in high-entropy ceramics synthesized by HPT. Highresolution lattice images of (a) TiZrHf NbTaO11 including dislocations (Akrami et al., 2022b) and (b) TiZrHf NbTaO6N3 including monoclinic and FCC
phases and their heterojunctions (Edalati et al., 2021). Copyright: (2022) Elsevier, (2021) Royal Society of Chemistry.

and easy electron-hole separation, low recombination rate,and high surface activity for adsorption and conversion ofwater and CO2. The presence of two phases in this HEON
and the formation of interphases, as shown in Fig. 8(b),was also believed to contribute to its high activity.
TiZrNbTaWO12: The HEO, TiZrNbTaWO12, wasproduced by HPT and consequent high-temperatureoxidation (Edalati et al., 2022b). It had five different
phases including one orthorhombic, two monoclinic
and two tetragonal phases and ten heterojunctions. This
material showed a low bandgap of 2.3–2.8 eV and alight absorbance higher than relevant binary oxides.
Introducing the heterojunctions in this material resulted insuccessful photocatalytic oxygen production under viable
light irradiation.

5. Concluding remark and outlook
Photocatalytic production of hydrogen from water,conversion of CO2 to value-added compounds anddegradation of toxic materials in wastewater undersunlight, are clean processes that have high potential
to be used instead of conventional chemical methods.
Photocatalysts used for these applications should have anarrow bandgap, appropriate band positions to support134

the reactions, low recombination rate of electrons andholes and high surface activity. In recent years, the highpressure torsion (HPT) method was used to generate
active photocatalysts for various applications. This method
applies very high strain and pressure to material andsubsequently introduces nanocrystals, lattice defects,
lattice strain and heterojunctions. Furthermore, HPT can
synthesize high-pressure and high-entropy phases. One
main reason for the successful application of HPT todevelop highly active photocatalysts is that the method cansimultaneously introduce a few structural/microstructuralfeatures as effective strategies to enhance the activity. It
was shown that the HPT method improves photocatalytic
activity by increasing light absorbance, decreasing theoptical bandgap, optimizing the electronic band structure,accelerating the electron-hole separation and migration andreducing the recombination rate of electrons and holes.
The HPT method also contributed to the introduction ofhigh-entropy photocatalysts which show high activity
for hydrogen production, oxygen evolution and CO2
conversion.
Despite the high activity of HPT-processed photocatalysts, the mechanism for their high efficiency needs to beinvestigated further by employing theoretical calculations.

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Moreover, the catalysts produced by HPT have usually low
surface area and new methods are expected to be developed
to enhance the surface area of these catalysts. Although the
HPT method has opened a new path to employing highpressure and high-entropy phases as new photocatalysts,
other synthesis methods should be used in the future forthe large-scale production of these new catalysts. It is
expected that HPT will continue its contribution to the fieldof photocatalysis, particularly due to the significance of
establishing a carbon-neutral society in recent years.

Acknowledgments
The authors S.A. and P.E. thank Hosokawa Powder
Technology Foundation, Japan for funding HPTF21511
and HPTF21512. The author K.E. was supported in partby the MEXT, Japan through Grants-in-Aid for Scientific
Research (JP19H05176 & JP21H00150, JP22K18737).

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Authors’ Short Biographies
Saeid Akrami
Saeid Akrami obtained a M.Sc. degree in Chemical Engineering, and he is currently a Ph.D.
candidate in Applied Chemistry and Life Sciences at Nagoya Institute of Technology, Japan. His
research interest is the development of highly-strained and high-entropy ceramics for CO2 conversion
and he is the author of several papers in this field.

Parisa Edalati
Parisa Edalati is currently a Ph.D. candidate in the Advanced Ceramics Research Center, Nagoya
Institute of Technology, Japan. She was a visiting researcher at Kyushu University in 2019 before
starting her Ph.D. study. She works on the design and synthesis of functional high-entropy alloys
and ceramics for various applications including photocatalysis, biomedical application, hydrogenstorage, and batteries.

Masayoshi Fuji
Masayoshi Fuji received his Dr. Eng. in 1999 and joined the Tokyo Metropolitan University, asa research associate in 1991. He was a visiting researcher at the University of Florida, USA, from2000 to 2001. He joined the Nagoya Institute of Technology as an Associate Professor in 2002 andbecame a Professor in 2007. He is the editor-in-chief of Advanced Power Technology since 2019.
His representative awards include the award from the Minister of Education, Culture, Sports, Scienceand Technology Japan in 2013, CerSJ award for academic achievements in ceramic science andtechnology in 2014, and the science award of the Society of Inorganic Materials, Japan in 2017.

Kaveh Edalati
Kaveh Edalati received his Ph.D. degree in Materials Physics and Chemistry from Kyushu
University, Japan, in 2010. He currently serves as an Associate Professor at the International
Institute for Carbon-Neutral Energy Research, Kyushu University, Fukuoka, Japan. His interests are
the development of functional materials for carbon-neutral energy and hydrogen-related applications
such as hydrogen storage and photocatalytic hydrogen generation and CO2 conversion. He particularly
utilizes the high-pressure torsion method to develop new functional materials. He is the author ofover 150 journal papers and serves as the editor of several journals.

139

KONA Powder and Particle Journal No. 41 (2024) 140–150/https://doi.org/10.14356/kona.2024009

Review Paper

Applying Bubbling Fluidized-Bed Reactors for Strongly
Exothermic Reactions: Focus on Methanation †Philipp Riechmann and Tilman J. Schildhauer *Paul Scherrer Institute (PSI), Switzerland

1. Introduction
Many processes in the chemical industry and those converting chemical energy carriers are either strongly endoor exothermic. Most hydrogen-producing reactions areendothermic e.g. steam reforming, gasification, reverse
water gas shift, dehydrogenations, and methane dehydroaromatization to benzene. In consequence, many reactions that consume hydrogen are exothermic, e.g.
hydrogenation of double bonds or aromatic systems,
water–gas shift reaction, and important fuel syntheses suchas methanation, Fischer–Tropsch and methanol synthesis.
While endothermic reactions lead to the formation of acold spot where the further conversion of the reactant completely depends on further heat supply, strongly exothermic
reactions inherently bring the risk of thermal reactor runaway with them leading to a significant increase in temperature, formation of hot spots, often low selectivity, andpotential catalyst deactivation. For such reaction systems,
either highly temperature-stable catalysts or reactors withexceptional heat removal performance are required.
†*

Received 21 May 2022; Accepted 6 September 2022
J-STAGE Advance published online 30 June 2023
Corresponding author: Tilman J. Schildhauer;
Add: Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
E-mail: tilman.schildhauer@psi.ch
TEL: +41-56-310-2706

140

Bubble Holdup εb [-]α [W/m²K]

300.0
0.20
Strongly exothermic reactions inherently pose the risk of thermal
α R0
0.18
α R3
reactor runaway, which may lead to very high increase in temperature,
250.0
Bubble Holdup R0
0.16
hot spots and potential catalyst deactivation. For such reaction systems,
Bubble Holdup R3
0.14
200.0
reactors with excellent heat removal performance are needed. In the
0.12case of methanation of CO/CO2-rich gases, full conversion is not
0.10
150.0
possible in a single adiabatic reactor due to the equilibrium limitation,
0.08
R0
R3
and in large-scale plants, e.g. coal-to-synthetic natural gas (SNG)
100.0
0.06
plants, series of four and more reactors with intercooling have been
0.04
50.0
realized. To allow for complete conversion within one reactor, the
0.02
potential of bubbling fluidized bed (BFB) reactors with immersed heat
0.00
0.0
1.5
2
3
4
exchangers was investigated in the US and Germany from the 1960s to
u/umf
the 1980s. A Swiss consortium started to expand the concept to smalland medium-scale plants to allow the production of renewable methanefrom decentral renewable sources such as wood gasification and biogas.
During their tests, it could be shown that the catalyst particle movement does not only allow for optimal heat removal—close toisothermal operation and thus little catalyst sintering—but that the catalyst particle movement over the height of the reactor with different
concentration zones favors the chemical catalyst stability. This contribution will review the fluid-dynamic studies for BFB reactors withimmersed heat exchangers in the last decades comprising studies with pressure fluctuation probes, optical probes, X-ray tomography
studies, and particle attrition studies.
Keywords: Bubbling Fluidized Bed (BFB) reactors, methanation, immersed heat exchanger, chemical catalyst stability, heat transfer

The methanation reaction is a typical example of ahighly exothermic and equilibrium-limited reaction. The
equilibrium is generally favored by lower temperatures ofabout 100 °C, where the conversion rate is close to 100 %(Witte, 2018). However, due to the low activity of the typically used catalysts at temperatures below 200–300 °C,
catalytic methanation reactors are usually operated above
this temperature level, at temperatures between 200 °C and
500 °C (Götz et al., 2016; Mills and Steffgen, 1974). The
typical pressure range is between 3 and 30 × 105 Pa withhigher pressures leading to slightly increased methane
yields (Götz et al., 2016). While ruthenium is the most active catalyst, it is seldom used due to its high cost. Nickel ishighly active, very selective and relatively cheap. It istherefore the preeminent catalyst material for catalytic
methanation processes (Mills and Steffgen, 1974; Ghaib etal., 2016).

2. Reactor types for methanation
Commonly used reactor types for the catalytic methanation process are series of adiabatic fixed-bed reactors,
cooled fixed-bed reactors, fluidized-bed reactors, slurry reactors, and metal monolith reactors (Schildhauer andBiollaz, 2015; Schildhauer, 2016). Fig. 1 shows illustrations of some of these reactor types. The requirements forthe reactor primarily depend on the composition and flow

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Philipp Riechmann et al. / KONA Powder and Particle Journal No. 41 (2024) 140–150

Review Paper

Fig. 1 Schematic representation of typical reactor types for methanation. (a): Multi-tubular cooled fixed bed, (b): Fluidized bed, (c): Stirred slurry
bubble column.

rates of the gas sources, as they determine the requirements
for the removal of the reaction heat and the prevention of
catalyst deactivation (Kopyscinski et al., 2010; Schildhauer,
2016; Schildhauer and Biollaz, 2015).
Using fixed-bed methanation reactors is an establishedmethod for gas cleaning in the ammonia synthesis process
chain. In this application, relatively small concentrations ofcarbon oxides have to be eliminated from the hydrogenrich feed streams to avoid catalyst deactivation. No additional cooling is required in this case due to the large ratio
between the inert gas and reactant gas volumes and the resulting relatively large heat capacity of the gas stream. For
the production of Synthetic Natural Gas (SNG), however,concentrations of inert gases are very low and cooling becomes necessary. This can be achieved through intermittent
cooling of the gas stream between multiple fixed-bed reactor stages. Additionally, gas can be recirculated for cooling.
Adiabatic reactors require robust catalyst materials that canwithstand a broad temperature range without sintering or
cracking (Bartholomew, 2001). For the production of SNG
from coal gasification gas, this reactor type is wellestablished, and plants at the GW-scale have been realized(Schildhauer, 2016). It is also possible to directly cool themulti-tubular fixed-bed reactors by having cooling media
flow around the catalyst-filled tubes.
Metal monolith reactors are designed to overcome someof the drawbacks of fixed-bed reactors. They are characterized by their high specific surface area, which allows relatively compact reactor designs. Their metal framework,
usually based on aluminum or copper, increases the radial
heat transfer, while the pressure drop is reduced significantly in comparison to fixed-bed reactors (Schildhauer,
2016; Schlereth et al., 2015). In this way, heat transfer coefficients of more than 1000 W/m2 K can be reached(Boger and Heibel, 2015; Schildhauer, 2016). However, thedeposition of the catalyst material on the structured framework is technically complex, especially when the catalyst

is deactivated and the reactor has to be coated again withthe catalyst.
In three-phase reactors, also known as slurry bubble
column reactors, the catalyst is suspended in an inert liquidby the reactant gas flow. On the one hand, the liquid phaseis beneficial as it significantly facilitates the heat removal,
leading to almost isothermal conditions within the reactor
and allowing a dynamic plant operation, where the reactor
temperature can be kept stable due to the thermal inertia ofthe liquid phase. On the other hand, the liquid phase induces an additional mass transfer resistance and needs tofulfill several specifications such as high thermal stability,
chemical inertness, low evaporation rates, low viscosity,and surface tension (Götz et al., 2013; Schildhauer, 2016).
Gas-solid fluidized-bed reactors are characterized bynearly isothermal conditions resulting from intensive particle circulation. Bubbles play a crucial role in catalytic
bubbling fluidized bed (BFB) reactors as they significantly
influence key properties of the fluidized bed, such as theheat and mass transfer, the mixing of gas and solids as wellas elutriation of bed material. Rising bubbles transportparticles upwards in their wake, which causes an intensive
particle motion resulting in a uniform temperature profileof the fluidized bed. Consequently, the heat transfer between the bed and heat exchanger surfaces is intensified
through the steady replacement of the particles. Due to theeven heat distribution throughout the reactor, the entire
immersed heat exchanger surface can be used at the sametime, which is the most important difference from fixedbed reactors. In this way, local temperature peaks and any
associated thermal runaway of reactions can be effectively
avoided. Therefore, BFBs with heat exchangers are well
suited for highly exothermic reactions such as catalytic
methanation. The properties of the bubble phase, including
intensity of particle circulation, are dependent on the ratio
between the superficial gas velocity u and the minimum
fluidization velocity umf, which is referred to as the141Philipp Riechmann et al. / KONA Powder and Particle Journal No. 41 (2024) 140–150

fluidization number. By setting the u/umf, the reactor performance can be adjusted according to the requirements.
For instance, it is possible to recirculate part of the productgas stream or reduce the system pressure to increase thefluidization number and thus intensify the heat transfer ifnecessary.
A commonly stated disadvantage of fluidized-bed reactors is the attrition of the catalyst material resulting fromthe comparatively high mechanical stress exerted on theparticles through elastic collisions with other particles or
the reactor walls. Such attrition can lead to the deactivationand elutriation of the catalyst particles, which therefore
would have to be replaced periodically.
Additionally, the formation of bubbles may lead to a
bypass of reactants through the reactor within the bubbles
and thus to an incomplete conversion of the CO or CO2 in
the feed stream. Other frequently mentioned disadvantages
are the possible erosion of internals, such as heat exchanger
tubes, varying solids residence time distributions, and thefrequently complex hydrodynamics that make scaling-up
difficult (Götz et al., 2016; Yang, 2013; Yatesand Lettieri,2016). The choice of suitable bed materials and detailed
knowledge of the fluid dynamics of BFBs allows overcoming these challenges during scale-up.

3. Fluidized-bed reactors for methanation
Fluidized-bed methanation was developed since the late
1950s to convert synthesis gas from coal gasification(Kopyscinski et al., 2010). The Bureau of Mines developed
a relatively slender reactor with a 2.5 cm diameter, operated with Geldart A type catalyst particles at 20 bar andaround 400 °C. Three feed injections were used to avoid
the strong axial temperature gradients observed when fed
only from the bottom. In 1963, the Bituminous Coal Research Inc. continued in the Bigas project with a 150 mm
diameter reactor, about 2 m bed height and two injection
points that were cooled with immersed heat exchanger
tubes. At temperatures from 430 to 530 °C, pressures from
6.9 to 8.7 × 105 Pa, it was operated as a BFB for several
1000 hours (Kopyscinski et al., 2010).
Within the Comflux process, the technology was scaled
up to the 20 MW scale in the early 1980s by Didier
Engineering GmbH and Thyssengas GmbH, supported by
the Technical University of Karlsruhe, and tested up to2125 h (Friedrichs et al., 1985; Hedden et al., 1986). The
aim was a conversion process from coal to SNG with significantly lower costs than the fixed-bed methanation concepts. As in the Bigas project, very high conversion wasreached within one reactor which allowed for significantly
lower equipment and capital costs (–30 %) than a series ofadiabatic fixed beds. Because of the falling oil and gas
prices, however, the development was stopped in the mid1980s.
Since about the year 2000, the development of this tech-

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nology has been pursued again, now for the implementation of wood gasification. Seemann et al. (2006) showedthat an internal regeneration of catalysts with carbon
depositions takes place in BFB methanation reactors operated with producer gas from wood gasification. Accordingly, the fluidized-bed reactor can be divided into different
zones. At the inlet zone, the deposition of carbon takes
place due to high concentrations of CO and unsaturatedhydrocarbons (ethene, benzene). In the zone above, steam
gasification and hydrogenation of the carbon deposits ortheir precursors are predominant. Ultimately, an equilibrium between carbon deposition and the regeneration of thecatalyst can be reached in this way.
Both for the conversion of wood gasification gas and forthe upgrading of biogas by methanation of the CO2 content
with added renewable hydrogen, extensive long-duration
field studies (>1000 h) with a container-based TRL 4/5
plant were conducted showing the catalyst’s resilienceagainst coke formation (Schildhauer and Biollaz, 2016;
Witte et al., 2019). As part of a long-term field test of over
1100 h regular operation, the particle elutriation rate ofcatalyst material was measured for a reactor with a diameter of 5.2 cm and an original catalyst mass of 800 g. This is
illustrated in Fig. 2, which shows the elutriation rates andthe concentration profiles over the duration of the field test.
Throughout the campaign, only 70 g of the catalyst was
elutriated from the reactor. The elutriation rate, measured
by an in-line PCME sensor, spiked when fresh catalystmaterial was inserted, because fine particles are elutriatedat much higher rates. It was therefore concluded that attrition rates are low and the catalyst material has to be replaced only once per year (Witte et al., 2019). Thesefindings are supported by the results of the experimental
campaigns for the Comflux process development, where
the fluidized bed methanation of coal gasification gas wasdemonstrated up to 20 MW in the early 1980s (Friedrichs
et al., 1985; Hedden et al., 1986). The concentration profiles were stable throughout the campaign and showed ahigh methane yield of 85–90 mol%. Further, optimizing thegas cleaning process step was identified in these tests to beof crucial importance for the extension of the catalyst lifetime, especially limiting the number of sulfur species(Calbry-Muzyka et al., 2019; Calbry-Muzyka andSchildhauer, 2020; Struis et al., 2009).
Other groups are now also investigating this technology,
for example in the French GAYA project (Hervy et al.,2021), in Korea (Nam et al., 2021) or in China; there, however, for the conversion of coal to methane (Liu et al.,2016).

4. Hydrodynamics of fluidized-bed reactors
with vertical internals
Understanding the hydrodynamics of BFB reactors is ofgreat importance for the design, scale-up and optimization

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Philipp Riechmann et al. / KONA Powder and Particle Journal No. 41 (2024) 140–150
100

Elutriation rate (g/h)

Elutriation rate [g/h]10Predictions u/umf = 2
Predictions u/umf = 5
Predictions u/umf = 8

1

0.1

0.01

0.001

0

200

400

600

800

1000

Molar fraction [%]Operational hours [h]Operational hours [h]Fig. 2 Long-duration test at TRL 5 (10 kW) with real biogas from the biogas plant in Zürich Werdhölzli. Results of the particle elutriation measurement and (dry) gas composition downstream of the reactor, adapted from Ref. (Witte et al., 2019).

of the reactor, especially in the case of the highly exothermic methanation process, where efficient heat removal isessential. Rising bubbles and correlated hydrodynamic
phenomena strongly influence key properties of catalytic
BFB reactors, e.g. the heat transfer between the bed andheat exchanger surfaces, gas–solid contacting, mass transfer between bubbles and the bed, residence time distributions, bed expansion, solids segregation and mixing as wellas catalyst lifetime, specifically the attrition and elutriationof catalyst particles (Glicksman, 1984; Grace, 2020;
Schildhauer and Biollaz, 2016; Valenzuela and Glicksman,
1985; Werther, 2007).
The most relevant bubble properties are the diameter db,
the rise velocity of the bubble ubr, the bubble hold-up εb andultimately the shape of the bubble, which allow a good estimation of the above-mentioned phenomena. Extensive
experimental studies have been conducted over several
decades; however, the majority of the correlations to predict bubble properties are empirical or semi-empirical andoften are only valid for a specific range of conditions.
Additionally, the increase of available computational
power in recent years led to significant advancement in thefield of computational fluid dynamics (CFD), which allows
the investigation of fluidized bed hydrodynamics throughnumerical simulations. While certain properties such as the
bubble hold-up εb, the average bed expansion or the minimum fluidization velocity umf can be predicted accurately
through CFD simulations, it usually requires empiricallydetermined tuning parameters to reach an agreement between simulated and experimentally measured bubble
properties. While many aspects of fluidized beds without
internals have been researched, there are far fewer publications on the influence of vertical heat exchanger tubes.
Simulations with CFD software (Liu and Hinrichsen, 2014;
Lv et al., 2020; Verma et al., 2016) were compared to experimental results and showed significant deviations between fluidized beds with and without vertical internals
where the internals such as vertical sheets or heat143

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exchanger tubes reduce the increase of the bubble size andlead to more elongated bubbles.

4.1 Pressure fluctuation measurements

Measuring the pressure drop and the pressure fluctuations caused by bubble passing, coalescence and bubble
eruptions is a simple, usually non-intrusive method that canbe applied in experimental studies on cold-flow models,
but also hot reactors (with some adaptations). It was foundthat vertical internals can decrease the pressure drop by up
to 10 % (Taofeeq and Al-Dahhan, 2018a), shift the onset ofslugging to higher gas flow rates (Coronella et al., 1994)
and also have an influence on the transition to turbulent
flow (Taofeeq and Al-Dahhanl, 2018b) in reactors with
10 cm and 14 cm inner diameter, respectively. For themeasurements of the pressure fluctuations with fastpiezo-electric probes, spectral decomposition was applied
to retrieve the average bubble size (Rüdisüli et al., 2012a;b; c). It could be shown that the bubbles in fluidized beds
with vertical tubes not only grow more slowly, but also
have a smaller hydraulic diameter. Thus, a better masstransfer between bubbles and the dense phase can be expected (Rüdisüli, 2012). This was recently confirmed for a
bed of densely packed, small-diameter tubes where thestandard deviation of the pressure fluctuations was halvedby the presence of the vertical tubes, while the diameter ofthe bubbles at the bed surface during eruptions went down
by a factor of around four (Antonini, 2019).
Horizontal heat exchanger tubes with the necessary
packing density, on the other hand, led to gas cushions andlocal defluidization, which means that relatively poor masstransfer can be expected (Rüdisüli et al., 2012d). In addition, the transversely installed pipes should limit the vertical transport of the particles, which counteracts thelimitation of carbon deposits by internal regeneration.

4.2 Sector approach

Rüdisüli et al. (2012d) also presented a modified approach for the up-scaling of fluidized-bed reactors withvertical internals, which considers defined sectors of internals as autonomous regions. As was already assumed by
Volk et al. (1962), it was shown that the vertical pipes, witha spacing of only a few centimeters, strongly structure thefluidized bed and thus minimize the influence of the reactor

wall. Experiments with optical probes were conducted withvariation of the cross-section of a fluidized bed cold-flow
model while keeping tube diameter and pitch constant (i.e.
by increasing the number of tubes in the same arrangement). From at least two complete rows of tubes around ameasuring point, the measurement is largely representative
for all other such “elementary cells” and can be used todesign larger reactors with a correspondingly increased
number of vertical tubes (with the square of the diameter
ratio) (Maurer et al., 2014), see also Fig. 3. This significantly simplifies the construction of pilot plants and theresulting scale-up, as was confirmed by comparing thepressure fluctuations from a pilot scale (1 MW) plant inGüssing and a bench-scale cold-flow model (Rüdisüli et al.,2012d).

4.3 Optical probes incl. Monte Carlo
Optical probes are versatile tools to facilitate deeper insight into the hydrodynamics of fluidized beds. While thepressure fluctuation measurements only give informationon the average size of the rising bubbles, a vertical arrangement of two optical probes can deliver the rise velocity of abubble, which in combination with the signal duration canbe translated into a pierced chord length. The challenge is,however, that the optical probes only allow local measurements, and thus many radially and axially resolved measurements as well as several statistical corrections areneeded. Statistical analysis and a Monte Carlo study led
Rüdisüli et al. (2012e) to the conclusion that the mean
chord length measured with optical probes in freely bubbling beds is a good approximation for the mean bubble
diameter. This is due to the increased pierce probability oflarger bubbles (which lead to an increased average chord
length), which is counterbalanced by the fact that the optical probes do not necessarily pierce the bubbles at theirlongest axial expansion. This was confirmed by Maurer etal. (2015a) who, however, found that these two counteracting effects are not valid for the measurement of the mean
bubble rise velocity for which a different correction approach is required. Further measurements with opticalprobes in fluidized beds with vertical internals showedsignificantly different chord lengths and rise velocities(Taofeeq and Al-Dahhan, 2018c). It is therefore necessary
to adjust the data evaluation appropriately.

Tradi�onal
scale-upØ 14 cm

Fig. 3144

dInternals,small
dInternals,large

Sectoralscale-up= 1:3
Ø 22 cmdInternals,small
dInternals,large= 1:1

Sectorial scaling approach for fluidized bed reactors with vertical internals.

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Light path from laser source
into the reactor
Light path from reactor
to photo sensors
Details of optical probe.

u/umf [-]

ubr [m/s]

Fig. 4

Chord length [cm]Fig. 5Results (right) of optical probe measurements in a pressurized pilot scale plant (left) at a pressure of 6 bar and a measurement height of 32 cm.

While most cold-flow models reported in the literature
are operated at ambient pressure, Maurer (2015) developed
and tested a new optical probe sensor, which is able tomeasure under reactive conditions with pressures of up to
2.5 × 106 Pa and temperatures of up to 400 °C, see Fig. 4.
First cold-flow measurements in the pilot plant GanyMeth (22.4 cm inner diameter, operation up to
1–11 × 105 Pa, see Fig. 5, left) with pressurized air andalumina particles at pressures of up to 5 × 105 Pa wereconducted and proved the system (Schillinger, 2018).
Recently, a unique data set was measured with these radially movable optical probes at six heights and six radial
positions for Geldart-A and Geldart-B-type bed material,pressures of 1–11 × 105 Pa and fluidization numbers u/umf
of 2–10, see Fig. 5 (right) (Riechmann, 2022). It becomes
obvious that not a single value but rather distributions ofbubble properties (chord length, rise velocity ubr) exist foreach experimental setting. Meanwhile, data evaluation is
ongoing to obtain correlations for the distributions of bubble properties to enable improved modelling/simulation ofsuch reactors.

4.4 X-ray tomography
In cooperation with TU Delft, X-ray tomographic investigations were conducted and evaluated by an algorithm
allowing the reconstruction of the bubbles in a fluidized
bed of Geldart-B particles. This allowed determining theirproperties with regard to spatial distribution over the
cross-section, bubble size and bubble velocity (Maurer etal., 2015b). As can be seen in Fig. 6, a square or concentricarrangement of heat exchanger tubes has very similar effects: there are more bubbles compared to the configuration
without internals, and the bubbles deviate significantly
from the round shape. Further, experiments with cold-flow
models of two diameters (14 cm and 22 cm) confirmed
once again the sectorial scaling approach: the differences
between beds of different sizes are small if they are structured by vertical internals, while in fluidized beds without
internals, the bubble size increases significantly with thereactor diameter.
Compared to measurements with local samples (e.g. optical sensors), the possibility of X-ray tomography to determine the bubble size and the bubble velocity of all risingbubbles over the entire cross-section is a great advantage,

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Fig. 6 Reconstructed passing of bubbles in the measurement height (chronological order, z-axis = time) for beds without (left) and with internals(middle, right) (Schillinger, 2018).

as such measurements are less subject to statistical effects.
To derive conclusive bubble property distributions from thelocal measurement at a measurement height, measurements
must be taken at several radial positions, assuming rotational symmetry. Bubbles with a larger cross-section in the
direction of flow have a higher probability of hitting thesensor, but the bubbles are not always hit at the point of thelongest expansion of the bubble in the direction of flow as
already mentioned above.
X-ray tomography reconstructions were also used tosimulate the measurements of an ideal virtual opticalprobe. The resulting chord lengths and rise velocities werethen compared to the bubble properties derived from the
X-ray tomography reconstructions. Statistical effects resulting from the assumed shape of the bubbles, such as
chord length distributions and the reduced probability ofthe optical probe to pierce elongated bubbles in comparison
to oblate bubbles, were identified and correction factors
were proposed accordingly (Schillinger, 2018). This approach opens the door to improving the evaluation of local
optical probe data in ongoing work that is additionally
supported by projecting X-ray radiography. Neutron radiography (Ozawa et al., 2002) as well as recent X-ray radiography data (see Fig. 7) show not only bubble sizes andtheir rise velocities but also the interactions with opticalprobes. Further, the trajectories can be observed whichshow that bubbles rise vertically in between or if larger,
along the vertical tubes, while in freely bubbling beds significantly more lateral movements and therefore increased
coalescence is found (Ozawa et al., 2002).

146

4.5 Heat transfer
The importance of removing or supplying heat is themain motivation for introducing vertical heat exchanger
tubes into fluidized beds, as they allow reaching more orless isothermal operation conditions. For the proper designof the reactors, quantitative data of heat transfer are needed.
Already in 1996, Gunn and Hilal (1996) measured the totalheat transfer from electrically heated tubes to the bed ofglass beads (BALLOTINI) at ambient pressure. For
Geldart-A-type material, total heat transfer coefficientsfrom 350 W/m2K (at u/umf = 2) to 550 W/m2K (at u/
umf > 7) were found, independent of the number of tubes
and the gas distributor. For Geldart-B materials, the best
results were reached with a porous-plate-type distributor,
ranging from 250 W/m2K (at u/umf = 2) to >350 W/m2K (atu/umf > 5). Ozawa et al. (2002) measured total heat transfer
coefficients from 250 W/m2K (at u/umf = 2) to 400 W/m2K(at u/umf > 5) for Geldart-B-type alumina particles at ambient pressure.
These results are confirmed by our own measurements of
PSI at the pilot scale plant GanyMeth, heating up cold nitrogen at 4 × 105 Pa with thermo-oil of 250 °C in a bed of
Geldart-B alumina particles. At a fluidization number ofaround 1.3, the axial temperature profile from the distributor to 1.5-m height ranged from 232 °C to 243 °C, proving
the strong axial heat dispersion in these systems. The estimated total heat transfer coefficient (though not very precise due to the small temperature difference of 7 to 18 K) is
170 W/m2K, which fits exactly to the results of (Gunn and
Hilal, 1996) for Geldart-B materials and a porous plate

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u/umf = 3

u/umf = 4

u/umf = 6

Without
internals

Withinternals

Fig. 7 Recent X-ray radiography data (PSI in collaboration with TU Delft) in a 22.4 cm column at ambient pressure with Geldart-B-type material(γ-alumina particles).

H = 40 cm; Twater,in = 40°C; TAir = 20°C
300.0

0.20
α R0

0.18

α R3
Bubble Holdup R0

α [W/m²K]

200.0

0.14

Bubble Holdup R3

0.12
0.10

150.0

0.08

100.0

0.06
0.04

50.0
0.0

Fig. 8

R3

0.16

R0

Bubble Holdup εb [-]

250.0

0.02
1.5

2

u/umf

3

4

0.00

Results of locally applied optical and heat transfer probes in a cold-flow model (diameter 22.4 cm) with Geldart-B-type alumina particles.

distributor at such low fluidization.
Taofeeq et al. introduced advanced non-invasive fast response heat transfer probes in the research field and usedthem for local measurements in a 14 cm column with
Geldart-B-type glass particles for fluidization numbers
from 1.4 to 2.5 (Taofeeq and Al-Dahhan, 2018a, b, c;
Taofeeq et al., 2022). For fluidization numbers below 2,
they found that the presence of vertical internals improved
the local heat transfer by up to 20 % in regions closer to thewall, but not in the center of the column. Generally, local
heat transfer coefficients from 270 W/m2K (at u/umf = 1.4)
to 320 W/m2K (at u/umf = 2.5) were found for beds with an
aspect ratio of >1.5.

Further, they found that the local heat transfer is enhanced by the presence of bubbles; our own measurements
at PSI with a similar approach with locally applied opticaland heat transfer probes (Geldart-B-type alumina particle
in a cold-flow model) showed even a direct proportionalityof bubble hold-up and local heat transfer, see Fig. 8.

5. Conclusions and outlook

Bubbling fluidized bed (BFB) reactors with immersed
vertical heat exchanger tubes proved to be a suitable reactor type for strongly exothermic reactions such as themethanation of CO/CO2-rich gases to form synthetic natural gas. They help to avoid hotspots and the risk of thermal

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reactor runaway, potentially leading to catalyst deactivation.
Many studies in literature and our own experiments wereable to show that the catalyst particle movement does not
only allow for optimal heat removal, close to isothermal
operation and thus little catalyst sintering, but that the catalyst particle movement over the height of the reactor withdifferent concentration zones favors the chemical
catalyst stability. This contribution reviewed the fluiddynamic studies for BFB reactors with immersed heatexchangers in the last decades comprising studies withpressure fluctuation probes, optical probes, X-ray tomography and studies on heat transfer.
With the results obtained so far, it can be concluded thatimmersed vertical heat exchanger tube banks improve thefluidization and have the potential to facilitate up-scaling.
Further, important knowledge was collected to better describe and correlate the distribution of sizes and rise velocities of bubbles which improve heat transfer and particle
movement but may limit the reactor performance in thecase of too low mass transfer. With the obtained correlations, the reactor models can be significantly improved interms of mass transport predictions. Planned measurementsof temperature and concentration profiles during reactive
operation in PSI’s pilot plant will help to practically validate the methanation reactor model in the experimentallyinvestigated pressure and temperature range. This together
with the sector scaling model will allow upscaling to thedifferent applications with significantly reduced risk.

Acknowledgments
The authors would like to thank Marion Ziegler, Roham
Seif, Evert Wagner, Ruud van Ommen and the whole TCPteam at PSI for support during experiments and for scientific discussions. Further, the financial support of the Energy Systems Integration platform at PSI is acknowledged.

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Authors’ Short Biographies
Philipp Riechmann
Philipp Riechmann studied process and energy engineering at TU Berlin and is a scientist at thePaul Scherrer Institut with focus on the hydrodynamic aspects of fluidized beds. He recently defended his PhD thesis successfully.

Tilman Schildhauer
Tilman Schildhauer is Senior Scientist at Paul Scherrer Institut in Switzerland where he leads theresearch in methanation, gas cleaning and PtX processes. He studied chemical engineering in Dortmund, received a PhD from ETH Zurich and spent his postdoc time in the Reactor and Catalysis
Engineering group at TU Delft.

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Review Paper

Current Progress of Experimental and Simulation Work of
Mixing Processes in Particulate Systems †Xin Jin and Yansong Shen *School of Chemical Engineering, University of New South Wales, Australia

Particle mixing is a fundamental process used in various industries to handle powders,
granules, and pellets. Understanding particle mixing is critical for optimizing industrial
processes involving particulate systems, making it an important scientific and practicalconsideration. In this review, the current research progress of experimental and simulation
works for widely used tumbler and convective mixers is reviewed, and research gaps are
summarized for future investigations. Finally, some new development points of modern
particle mixing technologies and topics are mentioned. This paper provides a comprehensive
review of the research work of mixers in particulate systems and sheds light on future
research in the field of particle mixing.
Keywords: particle mixing, mixture homogeneity, mixer, discrete element method

1. Introduction
Granular materials, powders, and bulk solids are ubiquitous in nature, including soil, sand, and grains as well as invarious process industries, such as detergent, paint, plastics, chemical, mineral processing, metallurgical, pharmaceutical, biotechnology, and chemical industries (Duran,
2012; Forterre and Pouliquen, 2008). According to relevant
statistical data, about half of industrial products andthree-quarters of the raw materials exist in the chemicalindustry and are treated in the form of particle state (Leeand Henthorn 2012; Rhodes, 2008). Currently, the fundamental theory of motion equations of individual particles
and the force analysis of their interaction with other particles are not enough to describe the bulk particles’ behavior
in a practical industrial process because a large number ofparticles makes it difficult to predict their complex interactions accurately. Meanwhile, particles naturally own different physical properties (such as size, density and shape)(Ghadiri et al., 2020), and the segregation phenomenon isfrequently encountered while the bulk particles are treatedduring the industries, further hindering the deep understanding of the granular system. Unfortunately, due to theopaque nature of granular materials, there are few reliablemeasuring methods available to date. Furthermore, in practical industrial applications, granular systems are often incompletely fluidised, resulting in a complex systemcomprising multiple coexisting granular flow states. This†*

Received 27 March 2023; Accepted 9 May 2023
J-STAGE Advance published online 31 August 2023
Corresponding author: Yansong Shen;
Add: Via Gate 2, High Street, UNSW Sydney, NSW 2052, Australia
E-mail: ys.shen@unsw.edu.au
TEL: +61-2-9385-4448

complexity poses challenges to researchers studying granular systems. Therefore, the fundamental study of the
granular system is still a great challenge for both industry
and academia at the current stage.
For powder, granular, or pellet products in different industrial processes, particle mixing is an essential unit operation, such as the drying and storage of grains, theprocessing and packaging of pharmaceuticals, and themining and processing of coal, etc. (Bridgwater, 2012;
Ottino and Khakhar, 2000). Most of these industrial processes require a well-mixed degree of mixture for the final
or middle products to confirm consistent product quality
and high production efficiency. The development of effective techniques and technologies for controlling particle
mixing and separation is of great importance, as it can leadto energy and cost savings in processing, as well as advancements in the field of particle mechanics. Achieving
rapid and homogeneous mixing is a crucial objective.
Therefore, it is of great significance to reveal the underlying fundamentals and promote the operating performanceof particle mixing processes for modern industries.
In this review, the current research progress of experimental and simulation works for widely used tumbler andconvective mixers is reviewed, and research gaps are summarized for future investigations. Finally, some new development points of modern particle mixing technologies andtopics are mentioned (Jin, 2022).

2. Research progress summary ofexperimental work
Over the past few decades, numerous experiments havebeen conducted to examine the mixing behavior of various
mixers. This section will present a concise summary of

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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relevant experimental studies organised and reviewed
based on the categorisation of mixers.

2.1 Experimental work of tumbler mixers
Rotating drums, as versatile equipment, are extensively
employed in various industries, such as rotary kilns, dryers,
mixers, reactors, and granulators for processing granularmaterials. The rotating drum is a widely used apparatus inlaboratory studies of solid mixing phenomena due to its
simplicity in geometry and ease of operation control compared to other mixing equipment. Moreover, the rotating
drum can be designed to be transparent, closed, and relatively thin, making it suitable for observing particle motion
and studying fundamental phenomena of solid mixing. For
example, to investigate the movement of particles in athree-dimensional rotating drum at low to medium rotational speeds, Ding et al. (2001) employed a combinationof experimental and theoretical methods. The researchers
employed the non-invasive positron emission particle
tracking (PEPT) technique to monitor the trajectories andvelocities of particles. Additionally, they developed a mathematical model utilizing the thin-layer approximation tocharacterise the motion of solids within the active layer.
The experimental results were compared with the model,
and good agreement was found between the two. Santomaso
et al. (2004) conducted a study on the mixing of non-ideal
powders, specifically granular tetraacetylethylenediamine(TAED), in rotating batch cylinders that were operating in
the rolling regime. They characterised and quantified the
local mixture composition by utilizing a solidification technique in combination with computerised image analysis.
Observations showed the formation of a temporarily poorly
mixed core at low rotation speeds, and the role of convection in the mixing mechanism of non-ideal granular mate-

Review Paper

rial was identified through the identification of convective
fluxes resulting from the friction between the powder andend plates of the mixer. Zhang et al. (2018) conducted astudy utilizing the Magnetic Particle Tracking (MPT)
method to examine the particle motion in flighted rotating
drums. This was the first investigation of its kind to utilise
MPT. The study focused on the behavior of plastic balls
and soybeans in a rotating drum with a 5 % filling degree atvarious drum rotating speeds ranging from 10 to 40 rpm.
The study analysed the particle motion in a rotating drumby examining particle trajectories, distributions of translational and rotational velocity, falling time of the dilutephase, and instantaneous velocity of the dilute phase in theequatorial region. The analysis showed that the rotating
speed of the drum had a significant impact on the particle
motion, with only minor effects from particle size andshape within the studied range. The schematic of the experimental setup and the critical results can be seen in Fig. 1.
Li R. et al. (2021) conducted experiments to investigate thevelocity distribution of rice particles in a rotating drum. As
illustrated in Fig. 2, the results revealed that the flow of riceparticles often displayed multiple velocity peaks. At low
rotation speeds of 1–3 rpm, the particle flow within the rotating drum transforms into an avalanche mode. This is dueto the slope of the accumulation surface exceeding the angle of repose, causing particles to slide and cover the entire
particle bed. As a widely used apparatus in solid mixing
studies, rotating drum experiments have yielded important
insights in the past three years, as summarized in Table 1.
In contrast to the rotating drum, there exists a relatively
limited amount of experimental work on other types oftumbler mixers. The following section provides a review ofsome important experimental studies.
Brone and Muzzio (2000) performed a comparison ofFig. 1 Schematic of the experimental setup and critical experimental results, reprinted with permission from Ref. (Zhang et al., 2018). Copyright:(2018) Elsevier B.V.

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Fig. 2 System and particle setup: (a) the measuring system includes a
LED light source and a CCD camera; (b) the drum is half filled withthree types of particles, respectively: spherical particles, Rice particles
A with AR = 2 and Rice particles B with AR = 3, reprinted with permission from Ref. (Li R. et al., 2021). Copyright: (2021) Elsevier B.V.

the mixing performance between a conventional doublecone blender and a modified version incorporating a stationary deflector plate. The latter was designed to enhance
axial particle flow and was found to be highly effective inachieving improved mixing outcomes. The results showedthat the incline of the deflector plate relative to the mixer
plane resulted in the creation of a convective axial flow
across the centre of the blender, leading to an increase in
the mixing rate by a factor of 25. The images demonstrating the plate effect on mixing within the double cone can befound in Fig. 3. Volpato et al. (2018) examined the effectsof the unloading process on mixture quality in three distinct
types of tumbling mixers, namely symmetrical double
cones, asymmetrical double cones, and conic mixers. The
experiments utilised tetraacetylethylenediamine (TAED)
powder that was available in different size ranges and differentiated by colour, either white or blue. The results
showed that tumblers capable of unloading the material in
the mass flow regime performed better than those operatingin funnel flow. Specifically, the best mixing performance
was obtained using an asymmetrical double-cone mixer.
An intrusive investigation of mixing characteristics in a
double-cone mixer utilizing discrete pocket samplers was
conducted by Cho et al. (2012). Millimetre-sized glass
beads were used in the study. The results indicated that axial mixing was improved by 70–90 % with the implementation of dual-axis rotation, compared to single-axis rotation.
It was observed that particles of smaller size (art sand) exhibited faster mixing compared to larger particles (glass
beads) due to the presence of mild cohesive effects. Fig. 4
provides visual representations, obtained through experiments, of the mixing characteristics of glass beads ofvarying sizes during single-axis and dual-axis rotations.
Alexander et al. (2004) conducted experimental studies
to examine the stable segregation patterns in V-blenders

Review Paper

under varying vessel capacities, fill levels, and rotation
speeds. The findings indicated that the magnitudes of particle velocities determined the pattern formation. However,
the relationship between the formation of segregation patterns and the fill level of the blender could not be established due to the intricate flow patterns in the V-blender.
The photographs related to this study are depicted in Fig. 5.
Kuo et al. (2005) utilised the PEPT technique to investigate
the movement of 3 mm glass beads within a V-mixer. The
results of their experimentation exhibited a linear correlation between the axial dispersion coefficient of the particles
and the rotational speed of the V-mixer, with an increase
observed in the coefficient from 15 rpm to 60 rpm. Crouterand Briens (2015) conducted a study exploring the feasibility of utilizing passive acoustic emission monitoring as atool for monitoring the mixing process in a V-blender. The
acoustic emissions acquired during the tumbling of the
V-shell were subjected to a wavelet filter to remove the associated vibrational noise. This enabled a concentration of
information pertaining to particle movement and interactions within the V-shell. The study conducted by Crouterand Briens demonstrates that passive acoustic emissions
can be employed as a monitoring method to gain insight
into the movement and interactions of particles in a
V-blender. By analysing the measured vibrations, valuable
information about particle behavior can be obtained. As
such, the study highlights the potential of this approach formonitoring V-blender operations.

2.2 Experimental work of convective mixers
In contrast to tumbler mixers, convective mixers incorporate rotating components to increase the mixing rate andimprove mixing performance (Rahmanian N. et al., 2008).
Some typical types of ribbon mixer can be found in ourprevious work, as shown in Fig. 6. MArczuk et al. (2017)
conducted experimental studies on a horizontal ribbon
mixer in a livestock farming mechanization laboratory,
employing a personal computer, measurement and control
devices, and instruments in accordance with GOST 15.10198. The results indicated that the mixing time significantly
affected the amount of mixing components, the uniformity
coefficient of the mix, the capacity, and the specific energy
consumption. Yeow et al. (2011) conducted an experimental investigation to examine the effects of various operating
parameters and feed preparation characteristics on the homogeneity of mixing in a batch ribbon mixer. Lactose
100 M, Lactose 200 M, Ascorbic Acid, and Zinc Oxide
powders were utilised as the testing materials for the study.
The physical geometry of the batch ribbon mixer is depicted in Fig. 6. The findings indicated that improved
mixing homogeneity could be achieved by increasing therotational speed and duration of mixing, as well as throughpre-blending and using smaller feed particle sizes, whichenabled the attainment of the desired homogeneity in a

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154

Solid properties
Cork particles
6.3 mm152 kg/m3
6 and 12 mm
2500 kg/m3

Sphere, oblate, prolate
and elongated-needle
1–6 mm2500 kg/m3
Plastic particles
6 mm
1800 kg/m3
Glass beads
5 mm2500 kg/m3
Glass beads 2 mm
2500 kg/m3
Rice length = 4 mm
aspect ratio 21020 kg/m3
Acrylonitrile butadienestyrene (ABS)15, 10, 6, and 3 mm
1100 kg/m3
Glass beads
22 mm and 12 mm
2500 kg/m3

StructureD = 50 mm
L = 50 mm
Plexiglass

D = 400 mm
L = 400 mm
Plexiglass

D = 290 mm
L = 50 mm
Plexiglass

D = 122 mm
L = 75 mm
Plexiglass

D = 220 mm
L = 320 mm
Plexiglass

D = 160 mm
L = 10 mm
Plexiglass

D1 = 287 mm
D2 = 209 mm
D3 = 162 mm
L = 100 m

D = 400 mm
L = 400 mm
Plexiglass

Reference(Nijssen et al., 2022)(Miao et al., 2022)(Kumar et al., 2022)(Kato et al., 2022)(Jadidi et al., 2022)(Li R. et al., 2021)(Jiang et al., 2021)(Huang et al., 2021)

Digital camera(NIKON D5300)

60 % fill level
5 rpm

Digital camera

PIV algorithm
using the images

PhantomV10
high-speed camera

Image recognition
method using
stacked images

40 %10, 40 and 70 rpm
50 % fill level
3–5 rpm

30 % fill level
0–200 rpm

20 %–50 % filllevel
10 rpmDigital camera

Multi-camera
acquisition system

10 % and 20 % filllevel
±40 rpm2000 white2000 black
30, 50 and 70 rpm

・The presence of water leads to significant alterations in the particlewall and particle-particle interactions, resulting in significant changes
in the behaviour of the bed.

Magnetic particle
tracking (MPT)

35 % and 45 % filllevel
20, 40, 60 and80 rpmThe segregation indices, radial for short drums and axial for longdrums, can be utilised to quantify the overall level of segregation inrotating drums.

・The movement trajectories of individual and multiple particles inrotating drums were found to conform to the established similarity
criteria.

・The flow of rice particles frequently exhibits a distribution withmultiple velocity peaks.
・When the rotating drum’s speed is lowered to 1–3 rotations per
minute, the flow of rice particles transforms into an avalanche mode.

・The mixing performance was significantly impacted by both the
impeller speed and the initial loading pattern.

・A novel approach for determining the mixing curve of particles wasestablished through the use of discrete element method (DEM) simulation, which involved the examination of particle behaviour over alimited time.

・The mixing behaviour is influenced by the shape of both coarse andfine particles.
・The extent of radial segregation for coarse particles demonstrates adecreasing trend: sphere>oblate>prolate>elongated-needle. For the fineparticle, the trend follows as sphere>cube>prolate.

・The formation of alternating axial segregation bands can be observedin adjacent axial segments with similar velocity directions, while
improved overall mixing is observed under conditions where thevelocity direction is opposite.

Reported results

Operating condition Measuring method

Table 1 Some essential experimental experiences for the mixing processes in the rotating drum in the last 3 years. (Continued on next page)

Xin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171
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Solid properties
Glass beads
1.19 mm–1.41 mm
0.84 mm–1.00 mm
2500 kg/m3

Polyformaldehyde beads
3 mm and 4 mm

Polyformaldehyde
Particles
3 mm and 4 mm
1410 kg/m3
7 μm Al2O3 powders
Glass bead
6 mm
596 kg/m3

Polyformaldehyde
4 mm 1410 kg/m3
7 μm Al2O3

Polyformaldehyde
3 mm2480 kg/m3
1410 kg/m3
900 kg/m3
Glass beads
4–6 mm2500 kg/m3

StructureD = 100 mm
L = 400 mm
Plexiglass

D = 280 mm
L = 20 mm
Glass

D = 280 mm
L = 20 mm
Glass

D = 194 mm
L = 150 mm
Acrylic glass

D = 280 mm
L = 20 mm
Glass

D = 300 mm
L = 15 mm
Glass

D = 140 mm
L = 200 mm
Plexiglass

Reference(Huang et al., 2021b)(L and Huang, 2021)(Chung et al., 2021)(Zhang et al., 2020)(Liao et al., 2020)(Lia and Tsai, 2020)(Chen et al., 2020)

Image-processing
technology and aparticle-tracking
methodDigital Video (DV)
camcorder (SONYHDR-PJ820)

Area CCD camera(IDT Y7-S1)

50 % fill level
1, 2, 3, and 4 rpm

50 % fill level2, 3, and 4 rpm

10 %–50 % filllevel
0.3 rpm

Digital video
motion recorder/
analyzer(SONY
HDR-PJ820)

50 % fill level2, 3 and 4 rpm

Particle tracking
velocimetry (PTV)

Image-processing
method andparticle-tracking
technology

50 % fill level2, 3 and 4 rpm

10, 15, and 20 %fill level10, 20, and 30 rpm

・When the IS ring pair is positioned outside the end wall shearing
zone, which is defined as an area with a radius of 10 particle diameters,
it was observed to have an independent impact on local mixing throughits ring shearing mechanism.
・In a drum with 40 % of the particles present, the critical width thatmust be present for an Inner-Shear (IS) ring pair to exist is roughly 30
particle diameters or 52 % of the transverse area of the bed.

Camera (1 fps,
HDR XR-520,
Sony, Japan)

20 % fill level
20 rpm・The movement of particles resulting from an avalanche mode
induces the rearrangement of particles at the upstream region of the
granular bed.
・The magnitude of energy dissipation through particle rearrangement
increases with the degree of particle filling.

・In a binary granular mixture, the steady-state segregation index
increases proportionally with the density ratio.
・The final steady-state segregation index for multi-density granularmaterials is lower than that of binary granular mixtures.

・The inclusion of a minimal quantity of fine powder substantially
impacts the dynamic characteristics and segregation behaviour as
influenced by density.
・An increase in the fine powder content is found to result in a corresponding increase in both the average velocity and the average granulartemperature. This phenomenon is attributed to the presence of a lubrication effect between the particles.

・In relation to the rotating drum, an increase in the speed of rotation
corresponds to an increase in the holdup ratio and the discharge rangeof materials from the flight. The cascading rate of a single flight isfound to be more affected by the rotation speed during the initial andfinal discharging stages.

・The research has shown that the flow properties of granular mixtures
can be enhanced by the lubricating effect generated by an optimal
amount of fine powders.
・The degree of segregation, as measured by the segregation index, as
well as the rate of segregation, exhibit a positive correlation with thefraction of fine powder and the rotational speed of the drum.

・The state of liquid distribution significantly impacts the behaviour ofsize-based granular segregation.
・In this study, an examination of the relationship between the granulartemperature and segregation behaviour was conducted.

Reported results

Operating condition Measuring method

Table 1 Some essential experimental experiences for the mixing processes in the rotating drum in the last 3 years. (Continued from previous page)

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Fig. 3 Demonstration of the effect of the plate on mixing within the double cone. All experiments correspond to a filling level of 50 %. The standard
double cone shown at (a) the initial condition, (b) after one min and (c) after 10 min (160 revolutions). The enhanced double cone shown at (d) the
initial condition, (e) after 1 min and (f) after 10 min (160 revolutions), reprinted with permission from Ref. (Brone and Muzzio, 2000). Copyright:(2000) Elsevier B.V.

Fig. 4 Snapshots from the experiment with glass beads of different sizes for single and dual axes rotations. (a) 1 mm glass beads: corresponds to X
rotational speed = 10 rpm; no rotation about the Y axis (i.e. 0 rpm); (b) 1 mm glass beads: corresponds to X rotational speed = 10 rpm; the Y rotational
speed = 10 rpm; (c) 3 mm glass beads: corresponds to X rotational speed = 10 rpm; no rotation about the Y axis (i.e. 0 rpm); and (d) 3 mm glass beads:
corresponds to X rotational speed = 10 rpm; the Y axis rotational speed = 30 rpm. Dual rotation enhances mixing. Reprinted with permission from Ref.
(Cho et al., 2012). Copyright: (2012) Elsevier B.V.

shorter period at a lower rotational speed. Muzzio et al.
(2008) conducted a study to evaluate the impact of processing and equipment parameters in a ribbon blender on thehomogeneity of magnesium stearate. The study was carriedout by analysing the properties of the blender using a coresampling technique, which involved collecting at least one
hundred samples from throughout the blender and characterizing the entire bed. The investigation took into accountseveral parameters, such as the loading technique for the
lubricant, fill level, blade design, and blade speed, to evaluate their influence on the homogeneity of mixing in abatch ribbon mixer. The outcomes of this research canserve as a valuable reference for devising suitable blending
processes and characterisation protocols for ribbon blend-

156

ers, as presented in Fig. 7. Gijón-Arreortúa and Tecante(2015) conducted a study to evaluate the impact of helical
double-ribbon impeller parameters on the mixing time andpower consumption during the blending of normal corn
starch and icing sugar. Results indicated that the optimal
mixing performance was achieved with an impeller speedof 75 rpm and a load ratio of 0.33, resulting in the lowest
mixing time and the highest coefficient of mixing rate.
By summering the previous experimental studies of ribbon mixers, it can be observed that the mixing behavior of
ribbon mixers is influenced by various parameters, including the design and geometry of the mixer, rotational speed,
fill level, feed preparation characteristics, and loading
methods. The results of these studies suggest that an

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optimal combination of these parameters can result in efficient mixing and homogenisation of the material. Moreover, various sampling and characterisation techniques,
such as core sampling and PEPT, have allowed for a morecomprehensive understanding of the mixing behavior in

Fig. 5 The three segregation patterns found in the 1 quart V-blenderwhen run at 50 % of capacity. (a) At low rotation rates (<7.5 rpm), the‘small-out’ pattern forms; (b) intermediate speeds (7.9–19 rpm) produce‘stripes’; and (c) high rotation rates (>19.3 rpm) induce ‘left–right’ segregation, reprinted with permission from Ref. (Alexander et al., 2004).
Copyright: (2004) Elsevier B.V.

Review Paper

ribbon mixers. Despite the advancements in the understanding of mixing in ribbon mixers, there is still a need forfurther research to fully comprehend the mixing mechanisms in these mixers and optimise their design for specificindustrial applications. This is especially important for thepharmaceutical and food industries, where strict standards
are in place for homogeneity, particle size distribution andproduct quality. Furthermore, the limited number of studies
conducted in this area highlights the need for future research to bridge the gap and provide a more comprehensive
understanding of the fundamental aspects of ribbon mixer
design, performance, and optimization.
The paddle mixer is a significant type of convective
mixer that finds widespread application in industrial operations. Ebrahimi et al. (2018) conducted an experimental
study to investigate the influence of impeller design on the
mixing efficiency of mono-disperse, free-flowing sphericalparticles in a horizontal paddle mixer. Fig. 8 displays theexperimental setup. The study involved collecting 16 samples for each experiment, which can be utilised to verify
simulation results. Sixteen samples were gathered during
each experiment, which can be employed to verify the results of simulations. Shenoy et al. (2015) conducted a study
that involved the examination of binary powder mixing in
a 2 L prototype lab-scale paddle mixer using several typesof food powders with varying particle sizes and poured

Fig. 6 The geometry of ribbon impellers: (a) 2-bladed impeller spiralling in the same direction (i.e., Design I); (b) 2-bladed impeller spiralling in the opposite direction (i.e., Design II); (c) 4-bladed impeller (i.e.,
Design III), reprinted with permission from Ref. (Jin et al., 2022).
Copyright: (2022) Elsevier B.V.

Fig. 8 The experimental setup, reprinted with permission from Ref.
(Ebrahimi et al., 2018). Copyright: (2018) Elsevier B.V.

Fig. 7 Blender and fill level designations, reprinted with permission
from Ref. (Muzzio et al., 2008). Copyright: (2008) Elsevier B.V.

Fig. 9 Image of the 2 L paddle mixer, reprinted with permission from
Ref. (Shenoy et al., 2015). Copyright: (2015) Elsevier B.V.

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bulk densities (Fig. 9). The results of the study conductedby Shenoy et al. showed that a particle size ratio of up to
4.45 resulted in a well-mixed powder mixture. However,for higher ratios, there was a decrease in mixture quality
without any visual observation of segregation. The study
found that bulk density had a greater impact on mixture
quality compared to particle size. When the bulk density
ratios were elevated, complete segregation was noted, andits manifestation was primarily attributed to the irregularshapes of thyme and oregano powder particles. Berntsson
et al. (2002) proposed a technique for quantifying thechemical composition of powder mixtures in situ usingnear-infrared spectroscopy (NIRS) and a fibre-optic diffuse
reflection probe. The probe was inserted into the powder ina Nauta mixer. The application of NIRS and a fibre-optic
diffuse reflection probe for in-situ quantitative measurement of the chemical composition of powder mixtures in a
Nauta mixer was examined, and the results demonstrated
its feasibility. The findings indicated that high-speed sampling is an effective method for evaluating both the mean
composition and the distribution of the mixture composition during processing. The empirical content-over-time
trajectories were found to be consistent with a theoreticalsimulation model of blending in Nauta mixers.
Despite some progress being made, the research andunderstanding of solid mixing processes remain relatively
limited in scope. This highlights the need for further experimental studies to be conducted in this field in order to gaina more comprehensive understanding of the underlying
principles and mechanisms involved in these processes.
Most experimental studies in the field of solid mixing werelimited to a paradigm where only a few samples were collected for characterisation. Recent experimental studies
have investigated the impact of various parameters, such as
fill level, blade speed, and the number of blades, on the
mixing performance of mixers. Advanced measurement
techniques, such as non-invasive near-infrared (NIR) spectroscopy, tomography, and PEPT, have been utilised inthese investigations (Cullen et al., 2017; Page et al., 2015).
However, the experimental analysis of mixers is impededby the opaqueness of the equipment and the intricacy of theflow within the mixer. As a result, obtaining particle-scaledetails, such as particle velocity, inter-particle contact
force, and particle dispersion, is challenging. The absenceof such data limits the prospects for improving mixer performance. Furthermore, due to the high cost of experiments, the optimal design of mixers, which necessitates
extensive experimentation, has not been fully explored.

3. Research progress summary of simulation
work
The progression of computer technology and parallel
computing has facilitated the utilisation of numerical simulation as a primary approach in the advancement of solid

158

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mixing technology. This approach can potentially reducethe cost of design, operating time, and technical risks and isincreasingly being utilised to optimise mixer performance.
Additionally, numerical simulation offers the advantage of
observing the mixing process in a virtual environment, allowing for in-depth analysis and understanding of themixing mechanisms. As a result, numerical simulation has
become an indispensable tool in the field of solid mixing
technology, providing valuable information and insightsthat are otherwise difficult to obtain through experimental
methods.
The current simulation approaches used in the field ofsolid mixing can be classified into two main categories: thetwo-fluid model (TFM) and the discrete element method(DEM). TFM is a commonly used simulation method forthe study of mixing processes in industry-scale simulations. In this approach, the gas and solid phases are treatedas interpenetrating continua, and the computational load is
manageable. As an Eulerian-Eulerian method, TFM employs the kinetic theory of granular flow (KTGF) to determine the solid pressure and viscosity, providing valuable
mathematical insights into the particulate material’s microscopic and macroscopic behavior (Brone et al., 1998). The
method employs the kinetic theory of non-uniform dense
phases to model particle-particle collisions. However, themodel’s accuracy is constrained as it relies on simplified
and estimated empirical relationships for the physical properties of the solid particles, including their shape and size.
Another approach is DEM approach. In DEM, individual
particles are considered within the mixer, and the forces
acting on each particle are calculated through the specification of contact with all other particles. This approach allows for the calculation of the resulting acceleration ofeach particle through the application of the equation of
motion. The trajectory of each individual particle is determined by applying Newton’s equation of motion, which is
calculated by considering the forces acting on the particle
and its acceleration. Nonetheless, it is worth noting that theLagrangian approach for simulating solid mixing systems
demands a vast amount of computational resources (CPUand memory). Due to the importance of particle-particle
interaction details in solid mixing processes, DEM is
widely utilised for investigating such processes. Therefore,
this section will focus on a literature review of the DEM
approach.
DEM has been utilised extensively to examine the processes of particle mixing and segregation within a varietyof mixer configurations (Marigo M. and Stitt E.H., 2015;
Sakai M., 2016; Sakai M. et al., 2020). This section will
present a review of relevant DEM studies, which will besummarized based on the classification of mixers.

3.1 Simulation work of tumbler mixers
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examine the intricate particle mixing behaviors and theimpact of different parameters on the mixing efficiency inthe rotating drum. For example, (Yang et al., 2016) employed DEM to conduct a three-dimensional simulation of
the motion of solid particles in a laboratory-scale rotating
drum. The validity of the simulation results was confirmed
through comparison with experimental data. Subsequently,
the active-passive interface was identified, and particlescale information was obtained for both of these regions, as
shown in Fig. 10, utilizing DEM to perform a threedimensional simulation of the solid motion in a laboratoryscale rotating drum. The active-passive interface was
identified upon validation of the simulation results withavailable experimental data, and particle-scale information
were obtained for both regions. The investigation exploredthe influence of fill level and rotating velocity on the solid
motion. The study results revealed a higher concentrationof particles in the passive region compared to the active
region, with a more significant collision force observed in
the active region, particularly in the y-direction, comparedto the x and z directions. Yamamoto et al. (2016) investigated the mixing behavior of particles in a rotating drum
mixer (RDM) by means of DEM simulation to examine theimpact of particle density. DEM results of the study revealed the effects of fill level and rotating velocity on the
mixing behavior of stainless steel and alumina particles
within a rotating drum. The results indicated that the lowerdensity particles had greater mobility compared to higherdensity particles, leading to segregation. However, the use

Review Paperof lifters was found to control the behavior of both high andlow-density particles and improve the mixing of particles.
The mixing degree of the alumina and stainless-steel particles was evaluated, and the results were in good agreementwith the DEM simulation. Yazdani and Hashemabadi(2019) examined the effects of cohesive forces and rotating
speed on the segregation and mixing of cohesive particles
in a horizontal rotating drum. The simulation results indicated that both cohesion and rotation speed significantly
impacted the behavior of highly cohesive particles in thedrum, as illustrated in Fig. 11. Ma and Zhao (2017) conducted an investigation on the flow of granular materials
comprising ellipsoidal particles in a horizontal rotating
drum by using DEM. The study focused on examining theinfluence of the aspect ratio and rotation speed of the drumon transverse mixing, as shown in Fig. 12. The simulation
demonstrated that cohesive particles’ cohesion and rotation
speed in a horizontal rotating drum significantly impact
their behavior. The results showed that particles with lower
sphericity had a higher consistency in their orientation andthat better coating could be achieved at lower rotation
speeds in the rolling/cascading regime.
Currently, numerous DEM investigations of the rotating
drum have been conducted. Given a large number of rotating drum simulation studies, Table 2 presents a summaryof some representative recent DEM works regarding themixing processes in the rotating drum.
A limited number of simulations can be found for othertypes of tumbler mixers. Hence, the following discussion

Fig. 10 Schematic representation of the apparatus showing the instantaneous velocity distribution of the solid phase in the rotating drum, reprinted
with permission from Ref. (Yang et al., 2016). Copyright: (2016) Wiley Online Library.

Fig. 11 Snapshots of mixing of binary cohesive mixture for segregated loading pattern in the rotating drum containing 6000 cohesive particles withk ii and k jj of 100 kJ/m3 and without interparticle cohesion (k ij = k ji = 0), reprinted with permission from Ref. (Yazdani and Hashemabadi, 2019). Copyright: (2019) Elsevier B.V.

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Fig. 12 Snapshots of the flow of particles coloured by their magnitude of the translational velocity (from blue (minimum) to red (maximum)), reprinted with permission from Ref. (Ma and Zhao, 2017). Copyright: (2017) Elsevier B.V.

presents a review of selected literature on other types ofmixers to provide a representative sample. Arratia et al.
(2006) utilised DEM to examine the flow dynamics ofspherical particles within a tumbling cone blender. The
simulation results showed that the radial convection rate
was more significant than the axial dispersive transport,
leading to decreased mixing performance within this geometry. The particle velocity profiles were analysed, revealing the presence of two distinct regions in the flow: ahigh-velocity layer cascading above and a region with anearly solid body rotation, as depicted in Fig. 13. Ren et al.
(2013) employed a GPU-based DEM software to investigate the mixing and flow dynamics of granular materials ina conical tote blender. A parametric study was performed toevaluate the impact of fill level and rotation rate on the
mixing rate, productivity, and energy consumption. The
study revealed that the optimal values for fill level and rotation rate could enhance mixing effectively. Moreover, thefindings indicate that the conventional horizontal configuration of the blender leads to inadequate axial mixing.
However, tilting the blender at a specific angle can considerably enhance the mixing efficiency. Tanabe et al. (2019)
utilised DEM to assess the impact of an increase in particle
size and a reduction in blender geometry on the uniformityof the blend. The study revealed that the variation in particle size among components resulted in the segregation of
the three blends, as evidenced by the diverse standard deviation of active ingredient content. The research also identified two crucial factors that impact blending performance,
namely, the ratio of the blender container to particle size
and the total number of particles and samples in theblender. Furthermore, the study demonstrated the processof quantitative prediction of the sample BU probability
density distribution using DEM simulation, which can befound in Fig. 14.
Tahvildarian et al. (2013) employed DEM to simulate
the behavior of monodisperse, non-cohesive particles in a
V-blender. The simulation accounted for both particle–
particle and particle–boundary interactions, as depicted inFig. 15. As the filling level increased from 20 % to 46 %,160

the circulation intensity decreased, and at a filling level of
34 %, it reached its minimum value for all rotational
speeds. No significant change in circulation intensity wasobserved for filling levels greater than 34 %. The results
also depict the particle velocity in the Y–X plane when the
V-blender rotates from 0° to 360° at a fill level of 20 % androtational speed of 30 rpm. Lemieux et al., (2008) employed DEM to predict the flow dynamics of multiple particles over extended periods, with a specific emphasis on
the mixing phenomena that necessitate a considerable
amount of time to develop in such systems. To this end, theresearchers performed several extensive numerical simulations using the DEM approach, investigating the flow ofmonodisperse and bidisperse blends consisting of up to
225,000 particles for 120 seconds in a V-blender. Quantities such as particle velocity, granular temperature, mixing
system torque, RSD curves, and mixing times are examplesof variables commonly used to characterise and analyse thebehavior of granular materials during mixing processes.

3.2 Simulation work of convective mixers
In contrast to the tumbler mixer, convective mixers employ stationary outer vessels with added moving components to improve solid mixing performance.
Sarkar and Wassgren (2015) conducted a study using
DEM to examine the impact of modelled particle size on
flow and mixing in a bladed granular mixer. The findings
revealed that the macroscopic advective flow was considerably influenced by particle size, particularly in areas withsparse flow. Yaraghi et al., (2018) utilised DEM to investigate the flow patterns and mixing kinetics of non-cohesive,
monodisperse, and spherical particles in a horizontal paddle blender. The authors found that the most significant
factor affecting mixing quality was the rotational speed of
the impeller, with the quadratic effect of the impeller rotational speed and the vessel fill level having a lesser impact.
Furthermore, the empirical data obtained on granular temperature demonstrated that an increase in impeller rotational speed, from 10 to 70 rpm, resulted in a corresponding
increase in the granular temperature values. In a study

Solid properties

Particle size:
3–6 mmParticle density
2500 kg/m3
Particle number:
88379, 10105, 113630.

Particle size:
5.0 × 2.5 × 2.5 mm
4.0 × 4.0 × 2.0 mm
Particle density
1150 kg/m3
Particle number:1000

Particle size:
1.09 mm
Particle density
2445 kg/m3
Particle number:
43,200, 54,000, and63,837

Particle diameter:
4.9–10 mm
Particle density
1200 kg/m3
Aspect ratio:
1.0–6.0
Particle number:
25,027–31,764

Particle diameter:
0.1–0.3 mm
Particle density
596 kg/m3
Particle number:
3474

Particle diameter:
0.71 mm–0.84 mm
0.50 mm–0.59 mm
Particle density
2492 kg/m3

Particle diameter:
1–10 mm
Particle density
3100 kg/m3
Particle number:
29623, 24241, 9846
9928

Reference(Yu et al., 2022)(Mori and Sakai, 2022)(Silveira et al., 2022)(Li M. et al., 2021)(Zhang et al., 2021)(Huang et al., 2021a)(Deng et al., 2021)

・The experimental findings revealed that the flight configuration with aseparation distance of 15° resulted in a 24.6 % increase in the occupied
area by the particles in the active region, and a 38.5 % reduction in theheterogeneity of the solid dispersion compared to the configuration withaligned flights.
・Sphero-cylinders with different Ars can reach well-mixed states undersufficient rotation conditions.
・Both the macro- and microscopic properties change with the rotation
speed and AR; however, when AR > 3, its influence becomes less obvious.

・The number of flights varies depending on the holdup ratio and cascading rate of both single and active flights.
・The FRD exhibits a greater production of the dilute phase, which ismore stable, when configured with either 8 or 12 flights.

・When the CR value equals 0.9, the percolation mechanism is improved,
and the contracting wall has a greater impact on smaller particles, leading
to their migration towards regions with larger inner diameters, resulting in
expanded dimensions. On the other hand, when the CR value is 0.5 or 0.3,
the contracting wall imposes additional shear forces on the bed and
impedes the migration of particles of both sizes.
・Previous studies have highlighted the limitations of whole-process
mixing criteria in fully capturing the intricate nuances of subprocesses.
・The use of subprocess criteria, which combine the index of entry orderand the number of contacts, has been shown to be effective in accurately
evaluating the duration of mixing in granular beds.

Identify the optimal configurationand layout of the vanes or blades toachieve maximum dispersion anddistribution of the particles in theactive zone.
Investigate the flow behavior andmixing process of sphero-cylinders
with varying shapes at a filling levelof 40.5 % in a rotating drum.

Influence of the number of flights on
the dilute phase ratio in flighted
rotating drums

Study size segregation of binary
mixture in a rotating drum with innerdiameter variations.

The effect of slag size distributions
on mixing and segregation

Fill level
10.1 %, 12.7 %, and15 %,Rotating speed
21.3 rpm

Fill level
40.5 %
Rotating speed
10–50 rpm

Fill level
15 %Rotating speed
10–30 rpm

Fill level
20 %Rotating speed
20 rpmFill level
30–33 %
Rotating speed
10 rpm・The influence of a nonspherical shape on the progression of powder
mixing in a ribbon mixer has been demonstrated to be negligible.

The powder mixing of non-sphericalparticles in an industrial rotating
drum mixer.

Rotating speed
20, 60 rpm

・Polydisperse particles are characterised by a composite structure where
small particles serve as nuclei surrounded by larger particles.
・Monodisperse particles exhibit a relatively elevated velocity of theactive layer, whereas polydisperse particles tend to exhibit greater radial
segregation.

The flow behavior of polydisperse
particles in a rolling mode rotating
drum.

Reported results

Fill level
30 %, 40 % 45 %Rotating speed
5.4, 8.5, 11.6 rpm

Operating condition Research Descriptions

Table 2 Summary of representative DEM work for the mixing processes in the rotating drum in recent years. (Continued on next page)

Xin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171
Review Paper

161

162

Solid properties

Particle density
2500 kg/m3
Aspect ratios
0.5 and 2.0

Particle density
2500 kg/m3
Aspect ratios
0.4–2.5
volume-equivalentdiameter: 3 mm
Particle number:
400000Particle diameter:
1.5 and 3 mm
Particle density
2500 kg/m3
Particle number:
2500 for 3 mm
20,000 for 1.5 mm

Particle diameter:
1.1 and 6.3 mm
Particle density
2455 and 1160 kg/m3
Particle number:
2500 for 3 mm
20,000 for 1.5 mm

Particle diameter:
3 mm
Particle density
2500 kg/m3
Particle number:
2000

Particle diameter:
1.5 and 2 mm
Particle density
1000 kg/m3
Particle number:
14,222 for 1.5 mm
6000 for 2.0 mm

Reference(Wang et al., 2021)(He et al., 2021)(Li D. et al., 2021)(Brandao et al., 2020)(Rong et al., 2020)(Yazdani and
Hashemabadi, 2019)

・The sliding friction coefficient was identified as a crucial element indetermining both the mean power draw and mean collision energy. On theother hand, the restitution coefficient had the most pronounced impact on
the frequency of collisions.
・The influence of the sliding friction coefficient and restitution coefficient on both the collision frequency and mean collision energy wasobserved to vary across different rotational speeds.

Investigate the effects of particle
contact parameters, sliding friction
coefficient (SFC), rolling friction
coefficient (RFC) and restitution
coefficient (RC) in a rotating drum.

Fill level
35 %Rotating speed
40 rpm・When mixing cohesive and non-cohesive particles, the bed displayed atendency towards segregation, with cohesive particles gravitating towards
the outer layer of the rotating bed. Conversely, complete mixing was not
attained without interparticle cohesion between two types of cohesive
particles.
・The results indicated that the duration necessary to attain the final
mixed state increased in the case of highly cohesive particles.

・The simulations, utilising experimentally determined input parameters
obtained through the DEM, demonstrated the closest agreement with theexperimental responses, such as the repose angle and radial mixture.

Investigate the mixing/segregation
behavior of particles in an unbaffledrotary drum

Fill level
25 % and 50 %Rotating speed
0.59, 1.10 and
1.77 rad/s

Simulate the segregation and mixing
of cohesive particles in a horizontal
rotating drum・At elevated rotational speeds, the degree of mixing of binary-sizedparticles in the RED can be augmented.
・The resulting contact forces exhibit a gradual increase with the increment of rotational speed and flattening.

Forecast the flow characteristics of
two-sized particles, binary particle
systems, in a horizontal rotating
cylindrical drum and an ellipsoidal
drum, a numerical simulation
approach can be employed.

Fill level
20 %Rotating speed
40,150 and 200 rpm

Fill level
25 % and 55 %Rotating speed
6, 12 and 18 rpm

・The severity of axial segregation attains a peak at aspect ratios of 0.67
and 2.0.
・In the outer region, the migration of both ellipsoids and spheres
towards the central section of the transverse slice was observed, while in
the inner region, particles showed a propensity to migrate towards the end
walls.

・The distribution of small and large particles in the drum was found to
be concentrated in the centre and periphery, respectively, while mediumsized particles were evenly distributed throughout the system.
・As the granular system approaches a high-speed centrifugal state, thesmallest particles exhibit the highest radial velocity and tend to be locatednear the drum wall.

Report an interesting phenomenon
that for ellipsoid-sphere mixtures.

The influences of particle shape,
standard deviation of the mixture,and rotating speed on the segregation
behaviors.

Reported results

Fill level
38 %Rotating speed
40 rpmRotating speed
7.5–130 rpm

Operating condition Research Descriptions

Table 2 Summary of representative DEM work for the mixing processes in the rotating drum in recent years. (Continued from previous page)

Xin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171
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Xin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171

conducted by (Ebrahimi et al., 2018), the efficacy of mixing monodisperse spherical particles in a horizontal agitating paddle mixer was investigated using the DEM method.
The study involved simulating five different impeller
designs to determine their influence on mixing performance. The study evaluated five distinct impeller designs
through simulation. The findings demonstrated that impeller design significantly affected both the mixing performance and the granular behavior of the materials, as
demonstrated in Fig. 16. Additionally, the study analysed
(a)

(b)

Fig. 13 Example of velocity fields of a mono-disperse system at 40 %fill level. (a) Side view at 3/4 of a revolution. (b) Top view at 3/4 of arevolution, reprinted with permission from Ref. (Arratia et al., 2006).
Copyright: (2006) Elsevier B.V.

Review Paper

the effect of impeller configuration on the forces of contact
between the impeller and particles, and between the particles themselves.
Ribbon mixers are deemed appropriate for blending dry
powders and free-flowing granular materials. Literaturesuggests that a ribbon mixer can achieve improved uniformity in powder mixing due to the high shear stresses it
generates and its ability to handle a mixture of particles ofvarying sizes. Hence, the utilisation of DEM in analysing
the ribbon mixer offers valuable insights into its performance and potential optimization. Halidan et al. conducteda series of DEM studies investigating the mixing behaviors
in ribbon mixers. A survey was conducted by the researcher
to investigate the influence of impeller speed on the mixing
behavior of cohesive and non-cohesive particle mixtures ina ribbon mixer, which is characterised by a horizontal cylindrical vessel (Halidan et al., 2016; Musha et al., 2013).
The DEM simulations indicated that the mixing rate ofboth cohesive and non-cohesive mixtures in the ribbon
mixer increased as the impeller speed increased up to acertain threshold, after which a decrease was observed. For
non-cohesive particles, the mixture quality may deteriorate
at higher impeller speeds, but this was not the case for cohesive particles. Halidan et al. (2018) employed DEM to
examine the impact of impeller speed and fill level on theblending behaviors of particle mixtures with varying

Fig. 14 Quantitative prediction of the sample BU probability density distribution in reality using the DEM simulation, reprinted with permission
from Ref. (Tanabe et al., 2019). Copyright: (2019) Elsevier B.V.

163

Xin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171

Review Paper

Fig. 15 Particle velocity in the Y-X plane when the V-blender rotated from 0° to 360° at 20 % fill level and 30 rpm, reprinted with permission from
Ref. (Tahvildarian et al., 2013). Copyright: (2013) Elsevier B.V.

Fig. 16 A snapshot of particle mixing after 10 revolutions of mixing from the front view sliced at the centre of the mixer, reprinted with permission
from Ref. (Ebrahimi et al., 2018). Copyright: (2018) Elsevier B.V.

cohesion in both two-bladed and four-bladed ribbon mixers, each possessing a horizontal cylindrical vessel. Beyond
that, the mixing rate was observed to decline. In particular,
the mixing rate of cohesive particles was found to deteriorate
at higher impeller speeds in the 2-bladed mixer. The results
showed that with an increase in fill level, the flow behaviorof non-cohesive particles changed from sliding flow to recirculation flow and eventually to cascading flow. The
comparison results can be seen in Fig. 17. Chandratilleke
et al. conducted a numerical investigation of the impact of
blade-supporting spokes on the overall mixing performance in a ribbon mixer using DEM. This study analysed
the effect of several key variables, including the number of
spokes, the cohesiveness of particles, and the fill level on
the mixing performance. The results indicated that, in the164case of non-cohesive particles, increasing the number of
spokes was advantageous for improving the overall mixing
rate when the fill level was substantial. However, this improvement came with the cost of increased contact forces.
In contrast, the best overall mixing performance wasobserved with a no-spoke impeller for cohesive particles.
The critical findings are depicted in Fig. 18. Subsequently,
DEM was used to examine the impact of particle size anddensity on particle mixing in a laboratory-scale ribbon
mixer (Chandratilleke et al., 2021). The study conducted byBasinskas and Sakai (2016) employed numerical simulations to examine the impact of various factors on the degreeof mixing in a laboratory-scale ribbon mixer. The results
indicated that the mixing rate decreased as the particle size
was reduced with the fill level fixed, and the density effectXin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171

Review Paper

Fig. 17 Effect of fill level on the mixing performance of a 2-bladed mixer at different Bond numbers (shaft speed = 100 rpm), reprinted with permission from Ref. (Halidan et al., 2018). Copyright: (2018) Elsevier B.V.

was negligible for small particle sizes. An empirical equation was formulated to describe the particle-size effect,
which was also extrapolated to consider much smaller particles. The present investigation employed numericalmethods to assess the impact of powder quantity, blade
speed, and initial loading on the degree of mixing in a ribbon mixer (See Fig. 19). The degree of mixing in the axial
direction was found to be relatively less variable than thatin the perpendicular direction. The results indicated that theamount of powder and the blade speed positively influenced the mixing performance. However, the study foundthat only the initial loading and powder quantity had a significant impact on the quality of mixing in the ribbon
mixer, with only modest variations observed in the axial
direction compared to the perpendicular direction. Recently, several noteworthy DEM studies on the mixing
processes in convective mixers have been reviewed andsummarized in Table 3.

4. Current progress and future perspectives
The mixing of particles is a common practice in several
industries, including ceramics, pharmaceuticals, food, andchemicals, where a high-quality end product is crucial. The
quality of powder products, which are composed of particulate ingredients that must be thoroughly mixed, is heavily
influenced by the design of the mixer and its operating
conditions. Various types of mixers, such as rotating drums,
orbiting screw mixers, and ribbon mixers, are used for thispurpose. However, the mechanism of particle mixing inthese mixers is still not well understood due to the complex

nature of particle behavior in the mixing system. Understanding and controlling particle behavior during mixing
remains a significant challenge for improving the operationand optimization of mixers. Up to date, the design of solid
mixers is generally limited to first selecting qualitatively
the equipment that might be suitable and then arranging
tests with potential suppliers, where unified theory andcriteria are still not available due to insufficient knowledgeof the mixing mechanism. An extensive understanding ofparticle mixing is crucial both from a scientific perspective
and a practical standpoint to optimise industrial processes
related to it.
Despite the prevalence of powder mixing in various industries, there remains a lack of robust and systematic
methods for characterizing mixing quality and determining
the optimum mixing conditions. This has motivated researchers to focus on developing more advanced experimental techniques to better understand the underlying
physics and mechanics of particle mixing. By improving
our understanding of the mechanisms and principles thatgovern particle mixing, researchers aim to improve the efficiency and quality of powder processing. Therefore, there
is a growing need for improved experimental techniques
and advanced simulation methods to better understandparticle mixing behaviors and incorporate the particles’
microscopic properties into the models. This would lead toa more accurate prediction of mixing performance andcould aid in the design and optimization of industrial solid
mixing processes. Additionally, the integration of experimental data and simulation results can help to establish a

165

166

Solid properties

Particle size:
4.75–8 mm
Particle density
2837 kg/m3

Particle size:
3, 4, 5 and 6 mm
Particle density
2500 kg/m3
Particle number:
88379, 101005 and
113630Particle size:
3 mmParticle density
1414 kg/m3
Particle number:
20000, 30000, 35000
and 40000

Particle size:
6 and 12 mm
Particle density
2500 kg/m3
Particle number:
20000, 30000, 35000
and 40000

Particle size:
3 mmParticle density
2700 kg/m3
Particle number:
10000

Particle size:
3 mmParticle density
1358.8 kg/m3
Particle number:
37600

Particle size:
3 and 5 mm
Particle density
2500 kg/m3
Particle number:
153,780 for 3 mm,
38,446 for 5 mm

Particle size:
0.6 mmParticle density
2500 kg/m3
Particle number:
100,000, 200,000 and
300,000

Reference(Xiao et al., 2023)(Yu et al., 2022)(Xu et al., 2023)(Miao et al., 2022)(Liu et al., 2022)(Wu et al., 2022)(Ebrahimi et al., 2021)(Deng et al., 2021)

・Factors such as rotational speed, filling level, liquid viscosity, and paddle obliquity
can significantly impact the mixing performance.
The identification of various mixing mechanisms can be achieved through the utilization of the probability distribution function.

The mixing of bi-disperse particles in ahorizontal paddle mixer.

The effects of various operational parameterson the mixing performance.

Fill level
40 % and 60 %Rotating speed
40, 70 and 100 rpm

Rotating speed
20, 40 and 60 rpm

・rn had the most significant effect on the mixing performance.
The study’s results revealed that the mechanism of diffusion was predominant over thatof convection. Furthermore, when the values of the diffusion coefficients for 5 mm and
3 mm particles were found to be comparable, the optimal mixing performance wasachieved.

Prediction of particle mixing time in a rotary
drum by 2D DEM simulations and crosscorrelation.

Compared to machine learning approaches, the cross-correlation method has theadvantage of interpretability, expandability, and relatively high accuracy for limited
data size.

・The optimal length-to-diameter ratio of central baffles varies between 0.4 and 0.7.
・The peripheral baffles perform best at a high length-to-radius ratio of 0.9.

Effect of baffles on mixing and heat transfer indrums is studied.

Fill level
20 %Rotating speed
15 rpmFill level
50 %Rotating speed
3 rpm

Fill level
20 %Rotating speed±40 rpm・Alternating axial segregation bands can be formed in adjacent axial segmentations
with the same velocity direction, while the overall mixing degree is improved underconditions of the opposite velocity direction.

・The contact number between particles and wall is improved in LC and LLC compared to the cylinder drum.

Effect of drum structure on particle mixing
behavior.

Fill level
15 %, 23 %, 26 % and 31 %Rotating speed
5, 8, 10 and 15 rpm

Particle mixing and segregation behaviors in
the rotating drums with adjacent axial
segmentations in different speed directions.

・The behaviour of particle flow in a drum was investigated through the examinationof particle velocity, duration of residence, mixing intensity, and axial dispersion invarious regions within the drum. The objective was to reveal the flow behaviour ofpolydisperse particles.
・Propositions are put forth to enhance the rotating drum’s efficiency in the rollingmode.
・The distinction in flow behaviour between polydisperse and monodisperse particles
is presented.

The flow behavior of polydisperse particles ina rolling mode rotating drum.

Fill level
35 %, 40 % and 45 %Rotating speed
5.4, 8.5 and 11.6 rpm

・The introduction of rods into a drum has been found to enhance the mixing degree ofparticles.
・The diameter of the cylindrical rods appears to have a minimal effect on the particle
mixing degree.
・The smaller the distance between the rods, the better the mixing degree.

Fill level
45 %Rotating speed
40 rpmReported results

Research Descriptions
The mixing processes of gravel materials.

Operating condition

Table 3 Summary of representative DEM work for the mixing processes in the convective flow in recent years. (Continued on next page)

Xin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171
Review Paper

Solid properties

Particle size:
6, 10 and 20 mm
Particle density
7750 kg/m3
Particle number:
43,200, 54,000, and63,837

Particle diameter:
3 mm
Particle density
2500 kg/m3
Particle number:
120,000

Particle diameter:4.52 mm
Particle density
1000 kg/m3
Particle number:
60000

Particle diameter:
1.5 mm–12 mm
Particle density
1000 kg/m3
Particle number:
113,152

Particle diameter:
5–15 mm
Particle density
417–2500 kg/m3

Particle diameter:
4 mm
Particle density
2500 kg/m3
Particle number:
120,000 80,000

Particle density
3948 kg/m3
Particle diameter:
40, 50 and 60 mm
Particle number:
52348 and 104549

Particle diameter:
15 mm
Particle density
417 kg/m3
Particle number:
18000

Reference(Horváth et al., 2019)(Yaraghi et al., 2018)(Pasha et al., 2015)(Sarkar and Wassgren, 2015)(Chandratilleke et al., 2021)(Tsugeno et al., 2021)(Gao et al., 2019)(Halidan et al., 2016)

・The alterations in impeller configurations had minimal effect on the particle-particle
contact forces.
・The simulation with the 0° angle paddle resulted in higher granular temperature
values than the rectangular one. This observation partially explains the improved
mixing performance obtained for the former impeller compared to the latter impeller.

・It takes 251 seconds for a tracer to traverse the entire active space of the system.
・The average velocity distribution remains unaffected by an increase in the number oftracers in the paddle mixer system.

The effect of particle size on the macroscopic advective flow was found to be significant, particularly in dilute flow regions. These findings indicate that particle size should
be taken into consideration when performing DEM mixing simulations with larger
particles.

・When the fill level was kept constant, a reduction in particle size resulted in a slower
mixing rate at a given density. However, for particles with small sizes, the effect ofdensity on mixing was negligible. The particle size effect was described by an empiricalequation and was extrapolated for even smaller particle sizes.
・The blade width is identified as a critical aspect for optimizing mixing performance.
・The relationship between the swept volume and the mixing index remained consistent despite changes in the paddle design.

・The initial loading and the rotational speed of the impeller have a significant impact
on the quality of particle mixing, while the effect of the other two parameters was
relatively small.
A numerical investigation on the ribbon mixer equipped with a double U-shaped vessel
indicates that better mixing performance is achieved under top–bottom and front–backinitial loadings, whereas inferior mixing performance is observed under side–side
initial loading.
・With increased cohesion, mixing rate and uniformity deteriorate. The coordination
number increases, indicating a loss in free-flowing motion ability, and many particles
have more vital tangential velocity for bulk angular motion.

The influence of impeller configuration on the
mixing performance of free-flowing monodisperse spherical particles in a horizontal
agitating paddle mixer.

Simulate PEPT
experiments for a paddle mixer using DEM.

The influence of the modelled particle size on
flow and mixing in a bladed granular mixer.

The effects of the particle size and density on
particle mixing on a laboratory-scaleribbon mixer.
An effective parameter for better mixing in the
ribbon mixer using DEM.

The effects of the operational and geometricalparameters, including initial loading, particle
size, impeller rotational speed, and inner
blades on the mixing quality of particles witha double U-shaped vessel.

The effect of interparticle cohesion on powder
mixing in a ribbon mixer was studied bymeans of the discrete element method.

Fill level
40 %Rotating speed
40 rpm

NA

Fill level
30 %Rotating speed
30 rpmFill level
40 %Rotating speed
100 rpm

Rotating speed
100 rpm

Rotating speed
15, 25 and 35 rpm

Rotating speed
20 and 60 rpm

・The results of the study showed that the moisture content of the material and therotational speed of the mixing unit have a significant impact on the mixing power
requirement. In addition, the mixing torque requirement was found to decrease as therotational speed increased.

Create a DEM model suitable for determining
the mixing power requirements of an agitateddrum dryer pilot plant, which can be applied
to the modeling of mixing granular materialsof various moisture content.

Fill level
10 %–25 %,Rotating speed
0.63; 0.95; 1.58 r/s

Reported results

Research Descriptions

Operating condition

Table 3 Summary of representative DEM work for the mixing processes in the convective flow in recent years. (Continued from previous page)

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Review Paper

Xin Jin et al. / KONA Powder and Particle Journal No. 41 (2024) 151–171(a) Two-bladed impeller withno spokes(b) Two-bladed impeller with
6 spokes

Fig. 18 The effect of blade-supporting spokes of the impeller on overall mixing performance in a ribbon mixer, reprinted with permission from Ref.
(Chandratilleke et al., 2018). Copyright: (2018) Elsevier B.V.

Fig. 19 Mixing states at different times for different loading methods, reprinted with permission from Ref. (Basinskas and Sakai, 2016). Copyright:(2016) Elsevier B.V.

more comprehensive understanding of particle mixing,
which is crucial for the continuous improvement of thepowder processing industry. Additionally, developing moreadvanced computational models that consider the microscopic properties of particles will be critical to understanding and improving the efficiency of solid mixing processes
in the industry (Ghadiri, 2020). This, in turn, can lead tosignificant advances in the quality and consistency of awide range of powder products, as well as reduced energy
consumption, lower costs, and reduced environmental impact associated with the production of these products.
Moreover, the integration of Industry 4.0 technologies,
such as the Internet of Things (IoT) and artificial intelligence (AI), can further enhance the accuracy and efficiency

168

of measurement and control systems, thereby enabling
more effective and efficient solid mixing processes in the
future (Bowler et al., 2020). It is also worth focusing on the
recently developed soft sensors, expected to gain popularity as they provide a low-cost and simple-to-implement
method of predicting final product qualities.

Acknowledgments

We sincerely acknowledge the financial support from theAustralian Research Council (DP220100306 and
FT190100361).

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DOI: 10.1016/j.cherd.2019.10.045
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Authors’ Short Biographies
Xin Jin
Dr Jin is a Postdoctoral fellow in Prof. Yansong Shen’s lab in the Department of Chemical Engineering at the University of New South Wales. He received a PhD in Chemical Engineering in 2022
from the same university. His research interests include the numerical simulation and process optimisation of industrial mixing and segregation systems.

Yansong Shen
Yansong Shen is a full Professor (tenured) and ARC Future Fellow (A/Prof, 2019–2021, SeniorLecturer, 2016–2018, UNSW; Lecturer, 2014–2016, Monash). He is the director of a vibrant researchlab - Process Modelling and Optimisation of Reacting Flows “ProMO Lab” (www.promo.unsw.edu.
au), with applications to a range of complex reactive flow processes in traditional and emerging industries particularly resource and energy sectors, including process metallurgy, solid fuel preparation/utilisation, water treatment, recycling processes of e.g. solar panels, plastic, and renewableenergy processes e.g. solar cell/biomass/hydrogen. His research interests range from understanding
fundamentals to optimising & developing new, cleaner and more efficient technologies, powered byadvanced numerical and experimental approaches. He is a member of the editorial boards of “Advanced Powder Technology” and “Metallurgical and Materials Transactions B”.

171KONA Powder and Particle Journal No. 41 (2024) 172–182/https://doi.org/10.14356/kona.2024001

Review Paper

Recent Progress in Controlled Nanostructure of Colloidal
Nanocrystal Powders for Efficient Light Emission †Naoto Shirahata
1 Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Japan
2 Graduate School of Chemical Sciences and Engineering, Hokkaido University, Japan
3 Department of Physics, Chuo University, Japan

4 CNRS−Saint-Gobain−NIMS, IRL3629, Laboratory for Innovative Key Materials and Structures, JapanThe application of semiconductor nanocrystals containing cadmium, lead, selenium andmercury as constituent elements is strictly limited by concerns about environmental
pollution and health effects. Nanocrystals free of these toxic elements are being pushed to
the forefront of nanocrystal research because of their environmentally friendly advantages
and attractive photophysical properties on recent advances in colloidal synthesis, excellent
optical properties, optoelectronic device applications, and biological applications of theseenvironmentally friendly fluorescent nanocrystals. In this context, the first topic in this review
paper introduces group IV semiconductors. In particular, the unique light emitting properties
generated in silicon nanocrystals of diameters smaller than bulk exciton Bohr radius arehighlighted. Next the topic turns to the nanocrystals of group III–V semiconductors. After that,
attentions are paid to the lead-free perovskite nanocrystals such as tin-based halide perovskite
and double perovskite structures. Recent efforts on how to control nanostructures to enhancephotoluminescence quantum yields is highlighted for each semiconductor nanocrystal.
Finally, the remaining challenges that must be overcome to realize nontoxic optoelectronic
devices will be discussed.
Keywords: quantum dot, perovskite nanocrystals, photoluminescence, optoelectronics,
bioimaging, photothermal effect

1. Introduction
Colloidal semiconductor nanocrystals have attracted
huge interests over 30 years. The nanocrystals containhundreds to thousands of atoms and are covered withorganic molecules (i.e., ligands) to make them stabilize insolution (Calvin et al., 2022), providing high accessibility
to printable technology. They are called “quantum dots
(QDs)”, “artificial atoms” or “zero-dimensional quantum
boxes”. The effect of quantum confinement happens forthe nanocrystals of diameter smaller than the bulk exciton
Bohr radius for the bulk crystal. QDs are usually larger
than the conventional organic dyes but comparable to fluorescent proteins and other large biomolecules. Their opticalproperties are unique and can be adjusted with diameter,
chemical composition, shape, and surface state. Therefore,
the QDs as optically active sources have a strong potential
for use in a variety of optoelectronic applications including
light-emitting diodes, photodiodes and photovoltaics aswell as in biomedical detection such as fluorescence celReceived 25 July 2022; Accepted 19 October 2022
J-STAGE Advance published online 29 December 2022
1, 4 Add: 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan2
Add: Kita 13, Nishi 8, Kita-ku, Sapporo 060-0814, Japan3
Add: 1-13-27 Kasuga, Bunkyo, Tokyo 112-8551, Japan
E-mail: SHIRAHATA.Naoto@nims.go.jp
TEL: +81-29-859-2743 FAX: +81-29-859-2401†

172

lular imaging. In a typical, a single QD contains atoms ofgroup IV elements (e.g., Si, C or Ge), group II–VI elements(e.g., CdS, CdSe, CdTe, ZnS, ZnSe or ZnTe), group III–V
elements (e.g., InP, InSb or InAs), group I–III–VI2 elements (e.g., CuInS2, CuInSe2, AgInS2 or AgInSe2), group
IV–VI elements (e.g., PbS, PbSe or PbTe), ZnS–AgInS2(ZAIS) or AgInSe2–ZnSe (ZAISe). The QD as a core could
be often encapsuled with additional protective shells of
ZnS and/or ZnSe, which are well-known wide-band-gap
semiconductors, to form core/shell structures.
Metal-halide perovskite nanocrystals are optically active
materials whose chemical compositions and structural
spaces are constantly expanding for successive tuning of
optical absorption and emission spectra. Lead halide perovskite has emerged as an attractive semiconductor material due to its excellent optical and electronic properties andis expected to find applications in a wide range of optoelectronic fields, including light-emitting diodes (LEDs). Until
now, many efforts have been made to increase photoluminescence quantum yield (PLQY) to nearly 100 % in thevisible and near-infrared regions by optimizing synthesis
parameters, impurity doping, alloying and surface passivation (Chen et al., 2021a). Various high-performance optoelectronic devices with the lead perovskite nanocrystals asactive layers have been investigated by taking advantageof these excellent properties (Quan et al., 2019). Thus, theCopyright © 2024 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/).

Naoto Shirahata / KONA Powder and Particle Journal No. 41 (2024) 172–182

lead halide perovskite nanocrystals are expected to be thenext generation of light emitting devices, and the external
quantum efficiency of lead-based LEDs exceeds 20 % (Linet al., 2018). There are concerns of environmental pollution and health problems due to the toxicity of Pb. Recent
studies have shown that the bioavailability of lead fromlead halide perovskite is stronger than other sources of leadand that as the concentration of lead halide perovskite inplants increases, the capacity for lead uptake increases (Liet al., 2020). To overcome this short coming, lead-free perovskite nanocrystals such as tin halide perovskite or double
perovskite nanostructures have attracted attention over theyears. However, the luminescence performances of thelead-free perovskite nanocrystals currently far inferior to
the lead perovskite ones.
Controlling the fate of free-charge carriers generated in
nanocrystals of semiconductor through sequential absorption of photons in the photoexcited state determines optical
performance (Chen et al., 2021b). For example, radiative
recombination between electrons and holes should be
strengthened to increase the PLQY. Furthermore, defective
surface serving as electron trapper should be removed todecrease the nonradiative recombination rate (Ghosh et al.,2020). On the other hand, the formation of shallow energy
level is key to synthesize long persistent phosphors, which
are a class of optical batteries, allowing emission of luminescence continuously for a long time after the removalof excitation sources (Liu Q. et al., 2021a). In this review,
recent efforts on defect control to improve PLQY of typical
non-toxic nanocrystals for optical applications such as
light-emitting diodes with emission layer of QD (QLED),
namely, electrically driven luminescent (EL) devices will
be presented.

2. Group IV QDs

QDs in group IV semiconductors such as carbon, silicon

Review Paper(Si), and germanium have attracted attention in the applications of abundant and nontoxic fluorescence materials, with
an emphasis on their role in optoelectronics and nanomedicine. This chapter presents the research advances in fluorescent SiQD on controlled nanostructures giving a strong
light emission and various photonic applications such as
light-emitting diodes, biomarkers for cellular imaging andphotothermal therapy of cancer cells.

2.1 Improved PLQYs

Bulk Si crystal has an indirect band structure, which
results in very low PLQY (<10–4 %). However, the situation changes drastically when crystals are nanosized tonanoparticles of 5 nm or less in diameter. In the electronic
structure of 2–5 nm crystals, the quantum confinement
effect of photoexcited carriers relaxes the momentum conservation based on the uncertainty principle (ΔχΔρ ≥ ħ/2)
and interface scattering-induced Γ–X coupling, leading
to the zero-phonon optical interband transitions (Lee etal., 2016). In the 1.1–1.7 nm crystal size range, a diamondcubic crystalline Si can behave like a molecule in termsof energy structure (Shirahata et al., 2020). The mostconventional Si surface is terminated with hydrogen atoms(H-SiQD) as known from the wafer chemistry (Shirahata etal., 2005). Photoluminescence (PL) properties are dependent of the size of diamond cubic nanocrystal of Si. First,
the peak position of the PL spectrum depends on the sizeof the H-SiQD and can be modulated over a wide wavelength range of 530–1050 nm as shown in Fig. 1(a)–(c).
Second, the PLQYs monotonically increase with decreasein size of H-SiQD based on the quantum confinement
effect (see Fig. 1(d)). PL peak positions at 1050 nm forthe 5-nm diameter SiQD and does not shift to a longer
wavelength side anymore for the diameter range between
5 and 8 nm. The hydrogen atoms may cover 99 % of the
Si atoms in the outermost layer. In the oxidized SiQD,

Fig. 1 Optical properties of SiQD powders: (a) photographs of visible-light emitting H-SiQD powders excited at UV light (λ = 365 nm), (b) PL spectraof the H-SiQD powders listed in the Panel (a), (c) near-infrared PL spectra of the H-SiQDs excited at UV light (λ = 365 nm), (d) PLQYs of the H-SiQDs
before and after hydrosilylation of 1-decene. The PL spectral tuning could be realized by controlling diameters of SiQDs in the 1–5 nm range. Adaptedwith permission from Ref. (Shirahata et al., 2020). Copyright 2020, American Chemical Society.

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the electron-hole recombination involves O-related defectcenters (Dohnalová et al., 2013). In contrast, the hydrogentermination minimizes surface oxidation that forms SiOx.
Nevertheless, the resultant PLQYs are still less than 15 %as shown in Fig. 1(d). A significant increase in PLQY is
realized by replacement of surface hydrogen atom withalkyl monolayer through the hydrosilylation reaction (seeFig. 1(d)). The best value of PLQY is more than 73 % for
the near-infrared light emitting QD (Jurbergs et al., 2006).
Two possible mechanisms have been proposed to explain
the improved PLQY. Dohnalová et al. reported the theoretical study of the change in radiation rate and recombination
process due to the attachment of alkyl monolayers, whichswitches SiQD from an indirect-bandgap-like material toa direct-one, leading to the phonon-less character of excitonic recombination close to band edges (Poddubny andDohnalová, 2014). The increase in radiative recombination
rate resulting from the alkylation of surface Si atoms has
also been reported (Mastronardi et al., 2012), consistent
with the theoretical prediction (Lee et al., 2016). Furthermore, alkyl monolayers work to inhibit the formation of
defects as a nonradiative channel in the outermost layer
of QD. Specifically, combining scanning transmission
electron microscopic (STEM) observation with Raman
spectroscopy demonstrated that the hydrogenated surface
layer is distorted under structural reconstruction to generate an amorphous phase (Ghosh et al., 2016), whereas thepassivation with alkyl monolayers suppresses the surface
reconstruction to hold a bulk-like geometry (i.e., a diamondcubic lattice structure) in a broad range from the center

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toward the near-surface in QD (Ghosh et al., 2018a). The
amorphous region might have many dangling bonds as anonradiative relaxation channel, resulting in a low PLQY,
but there are few defects around the surface terminatedwith alkyl monolayers. This suggests that the passivation
with alkyl monolayers works to prevent any undesired
surface reconstruction, leading to high PLQY.

2.2 Optoelectronic devices

The tremendous improvement in PLQY has attracted
interests of scientists and encouraged the fabrication ofdevices such as current-driven QLEDs in which alkyl
terminated SiQD works as an active layer. The reported device architectures have a multilayer structure where SiQD
are sandwiched between hole and electron transportation
layers as shown in Fig. 2(a)–(c). The electroluminescence(EL) signals are observed under low applied voltage.
Fig. 2(d)–(f) demonstrates that the EL spectral peak shapeand broadness are similar with those of PL spectra. Besides, the photocurrent depends on applied voltage, leading
to the increase of luminance at high device current density.
The EL spectral peak position precisely tuned by diameterof SiQD. To date, the highest values of external quantum
efficiency (EQE) at present were 4.8 %, 8.6 %, 3.36 %,6.2 %, for the EL spectral peaking at 1000 nm (Watanabe
et al., 2021), 853 nm (Cheng et al., 2011), 755 nm (Yamada
et al., 2020), 720 nm (Ghosh et al., 2018), 700 nm (Liu X.
et al., 2018), respectively. The EQEs of EL spectra peaking
at the wavelength shorter than 700 nm are less than 1.2 %(Yamada et al., 2020). These values are still lower thanFig. 2 (a) Schematic representation, (b) a cross-sectional TEM image with (c) EDS maps of the area indicated in the image (b) of the Si-QLED. Scalebars are 200 nm for the image (b) and 20 nm for the image (c). (d–f) Device current-voltage and luminance-voltage characteristics for each QLED.
Inset figure shows comparison of EL and PL spectra and photograph demonstrates a representative EL emission during operation. Adapted with permission from Ref. (Yamada et al., 2020). Copyright 2020, American Chemical Society.

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Naoto Shirahata / KONA Powder and Particle Journal No. 41 (2024) 172–182

20 % which is commonly employed as a benchmark. Further improved EQE requires both improvement of carrier
mobility and charge balance (Watanabe et al., 2021).
By taking advantage of the large Stokes shift between
optical absorption and emission derived from SiQDs, awhite-light EL is realized with a superimposed spectrumof a red EL peak and a blue–green EL peak without lossof optical energy (Ghosh et al., 2014). The high PLQYs
are realized by various method such as nonthermal plasma(Kortshagen et al., 2016), annealing of Si-rich borophosphosilicate (Kano et al., 2018) and thermal disproportionation of hydrogen silsesquioxane (Dasog et al., 2016) orsilicon monoxide (Sun et al., 2015). Hydrogen silsesquioxane are commercially available from Dow Corning Corporation (trade name FOx-17) but could be synthesized byhydrolysis and condensation of triethoxysilane (Chandra etal., 2017) or trichlorosilane (Sun et al., 2016).

2.3 Bio imaging and analysis

Inherently nontoxic element such as Si is expected touse for fluorescence labeling of cells or tissues. Wang etal. reported the first ever biomedical use of SiQDs as afluorescence label for DNA (Wang et al., 2004). Since
then, many papers have been reported on modifying thesurface of QDs to give them water solubility (Xu et al.,2016). Swihart et al. further studied the labeling of cellswith SiQDs and found that SiQDs can be used for in vivo
imaging (Erogbogbo et al., 2010). Si as an element shows
cell cytotoxicity but the toxicity is strongly influenced bysurface terminal groups (Ruizendaal et al., 2009). Specifically, amine-terminated surfaces are known to have low
cell viability, while carboxyl-terminated surfaces are lesstoxic to cells than amine-terminated ones, since most living
cells are negatively charged. In many cases, amphiphilicmolecules such as Pluronic F127 or PEG are employed
to encapsulate the core of SiQDs because the PEGylated
surface is electronically neutralized due to hydroxyl groups
exposed on the QD surface (Chinnathambi et al., 2019)
resulting in high hydrophilicity and biocompatibility(Hessel et al., 2010). Such a biologically inert surface
demonstrates a potential for medical diagnosis withoutmajor conformational change of human blood plasma
proteins in the bloodstream (Chinnathambi et al., 2020).
Thus, water-soluble SiQDs have the potential to be used as
fluorescent labeling materials for cells and tissues due totheir low cytotoxicity, but there are still challenging topics
in terms of optical properties. Specifically, PL spectra aredependent of QD size in the near-infrared (NIR) wavelength range, but SiQDs exhibit low absorption efficiency
for NIR light. Therefore, it is difficult to realize NIR–NIR
excitation-emission bioimaging in a single-photon excitation environment. There are two methods to overcome
this difficulty. First one is the fluorescence imaging undermultiphoton excitation conditions (He et al., 2008). The

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first multiphoton-excited fluorescence imaging of cells
labeled with SiQD was reported in 2016 (Chandra et al.,2016). Second one takes advantage of long radiative decay
time (~µs scale) originating from energy structure of SiQD.
That is, the time-gated fluorescence imaging in the NIR
wavelength range was investigated for the observation of
biological tissues labeled with SiQDs (Sakiyama et al.,2018).
The Si surface exhibits high chemical affinity for covalent bonding with carbon, oxygen, and nitrogen, and can
produce a variety of organic derivatives hybridized at themolecular level (Shirahata, 2011). Their bonding ability
can be used for post-synthesis purification by gel permeation chromatography (Shirahata et al., 2010). During
purification, QDs can be separated according to differencesin PLQY (Watanabe et al., 2021).

2.4 Photothermal effect

The photothermal effect refers to the ability to convert
photon energy from light absorption into heat. Thermal
conversion by photoexcited nanostructures can take placeby different mechanisms depending on the nature of thematerial. Unlike metallic nanoparticles, for semiconductor
QDs, photothermal conversion is explained by excess
carriers undergoing nonradiative recombination, whichis enhanced by maximizing the nonradiative decay rates
and manipulating nonradiative channels. Controlling theheat of the QDs expands their availability for applications
including phototheranostics and photoacoustic bioimaging.
The photothermal responses of crystalline Si have been
investigated for various forms, including nanoribbons,nanowires, films, porous particles, and nanoparticles.
Multiple structural parameters such as size, surface configuration, and shape affect the photothermal response,but size is the most influential parameter. The temperature
trend for larger crystals is explained by the Mie resonances(Ishii et al., 2016). As shown in Fig. 3(a), the photothermal
response was dependent of size of QD even in the narrow
range of 2–5 nm because the presence of a relatively highdensity of defect states for nonradiative thermal dissipation
for larger QDs (Özbilgin et al., 2021). The photothermal
conversion efficiency values of SiQDs are dependent of
their sizes and estimated to be 25–37 % which are comparable to the nanoparticles of mesoporous Si (33.6 %),Au (13–21 %), CuSe (21 %) Fe/Fe3O4 (20.3 %), black
phosphorus (24.8 %), Bi2S3 (28.1 %) or CdTe (14 %),suggesting a potential of SiQD available in photothermal
applications. One example is demonstrated in the thermotherapeutic study where carboxy-terminated SiQDs internalized in the HeLa cell generates the heat to kill the cells
under laser irradiation (Özbilgin et al., 2022). Fig. 3(b)shows the fluorescence image of HeLa cells labeled withthe water-soluble red-light-emitting SiQDs, confirming thecellular internalization of the QDs. Furthermore, there is no175

Naoto Shirahata / KONA Powder and Particle Journal No. 41 (2024) 172–182

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Fig. 3 (a) Summary of temperature rise traces of SiQDs of different sizes and forms as a function of incident green-laser power. (b) HeLa cellular
uptake of QDs terminated with undecanoic acid under confocal fluorescence microscopy. (c–f) Observation of HeLa cells before and after laser irradiation: (c, d) HeLa cells without QDs and (e, f) HeLa cells with 50 μg/mL QDs. Adapted with permission from Ref. (Özbilgin et al., 2022). Copyright2022, American Chemical Society.

significant change in cell morphology. Fig. 3(c) and 3(d)show the differential interference contrast (DIC) image
where HeLa cells are observed throughout the microscope
field of view. Without QDs, the cells remained the sameeven after laser irradiation as shown in Fig. 3(d). However,there was a noticeable decrease in the number of cells observed for the irradiated cells in which the QDs with eventhe concentration as low as 50 μg/mL are internalized. As
evidenced in Fig. 3(f), the HeLa cells that take up the QDs
detached from the glass substrate. A few cells remaining
on the glass surface have also changed their morphology
to round and have lost their adhesive ability. This result
indicates that QDs can be effectively used for photothermal
therapy. Controlling the nonradiative channels allows
surgeons and medical/healthcare professions to manipulate
the photothermal heating in a relatively lower temperature
range, contributing to the development of therapeutic thermal phononics.

3. Group III–V QDsGroup III–V semiconductors are compounds formed
between group III and group V of the periodic table and176

contain InN, GaN, GaAs, InP, InAs and InSb. Group
III–V semiconductor devices are used as high electron
mobility transistors due to their high electron mobility andelectron density, which are essential for high-speed device
operation. For example, InN is a semiconductor with anarrow band gap of about 0.7 eV at room temperature.
When combined with gallium to make the ternary alloy
InGaN, the band gap is tuned in the range of 0.7–3.4 eV,covering the infrared to the UV. GaN offers a wide energy
bandgap and attract the development of nitride based blue
LEDs and ultraviolet laser diodes. Bulk crystal of InP has
a 1.35 eV of bandgap at room temperature, and its bulk
exciton Bohr radius and exciton binding energy are 11 nm
and 6 meV, respectively. Therefore, QD of InP crystal has
attracted attention for the spectral tunability of the emission
and the first exciton peak of absorption in a wide visible
wavelength range. This chapter describe recent strategies
for structural control of InP QDs to obtain high PLQY for
optoelectronic applications.

3.1 Improved PLQYs
To improve PL performance, it is necessary to eliminate

Naoto Shirahata / KONA Powder and Particle Journal No. 41 (2024) 172–182

Fig. 4

Types of Core/Shell energy structure.

surface defects, which work as nonradiative exciton
recombination centers (Coughlan et al., 2017). In a typical,
a semiconductor core/shell structure is employed to reduce
defects such as electron traps at the interface as depicted
in Fig. 4 (Reiss et al., 2009). A core QD (e.g., indium
phosphide, InP) is encapsulated by another semiconductor
shell whose band gap (Eg) is large enough for sandwichingthat of the core QD. Such a core/shell heterostructure isreferred to as a Type I band alignment. In this configuration, excitons are spatially confined to the core, minimizing
their interaction with surface states, and preventing theirdiffusion throughout the core/shell QD due to high energy
barrier of shell Eg (Tamang et al., 2016). This confinement
strength allows for enhanced radiative recombination rates
and high PLQY. A good example of Type I architecture is
InP/ZnSe/ZnS core/shell/shell structure. In the bulk state,
direct deposition of ZnS (lattice constant, aZnS = 0.541 nm)
on InP (aInP = 0.587 nm) results in a large lattice mismatch of 7.8 %, sufficient to cause defect formation at
the heterogeneous core/shell interface and relaxation of
the mismatch by lattice distortion is no longer thermodynamically feasible because of lattice mismatch relaxation.
There are three approaches to solve this problem. First is
to use the ZnSe (aZnSe = 0.566 nm) as the shell material;
the lattice mismatch (≤3.5 %) between ZnSe and InP is
reduced compared to the case of ZnS, so an energy-graded
core/shell interface is constructed, and defect generation issuppressed. As a result, PLQY is significantly enhanced up
to unity (Wu et al., 2020). Second, multielement alloying
for the core (e.g., InxZnyP) is effective to mediate the latticestrain at the heterogeneous core/shell interface, leading to
the improved PLQY. As expected, the strategy of alloying
core has inspired the concept of compositional alloying
between Se and S or Mg and Zn for shell materials (Limet al., 2013). In the InP/GaP/ZnS core/shell/shell system,
the strain energy that would have been induced at the interface between the inner shell and core is relaxed by cationexchange. During high-temperature synthesis for shellformation, Ga3+ ions diffuse into the core and are alloyedby replacing In3+ ions. This alloying QD achieves a PLQY
as high as 80 % by continuously changing the chemicalcomposition and reducing the strain energy at the interface(Srivastava et al., 2018). Third, Nemoto et al. reported aninteresting idea of using material strain to build a QD with a

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Fig. 5 Illustration and HAADF-STEM image of InP/ZnS QDs ofcoherent core/shell structure with a single lattice parameter of 0.56 nm.
Adapted with permission from Ref. (Nemoto et al., 2022). Copyright2022, Royal Society of Chemistry.

coherent core/shell structure (Nemoto et al., 2022). Specifically, the core/shell QD allowed both epitaxial growth of
the shell layer and distortion of the spherical core, changing
the intrinsic interatomic distance of each crystal for coherency, where the core crystal is elastically compressed, andthe shell crystal is stretched by lattice distortion to a lattice
constant similar with that of the core. As depicted in Fig. 5,
the coherent growth of ZnS on a InP core QD yields InP/ZnS core/shell structure with a single lattice constant of
0.563 nm. Compared to the bulk crystal of zinc-blend (cubic) InP, the lattice of the core QD is compressed by 4.1 %.
In contrast, the lattice of the shell expands by 4.1 % relative
to the bulky ZnS crystal throughout the core/shell QD. This
could be because the change in lattice constant due to stressis thermodynamically more favorable than the formationof defect trapping sites at the core/shell interface to releasestrain energy. As expected, PLQY is enhanced up to 70 %and PL spectrum is also as narrow as 36 nm.

3.2 Optoelectronic devices

The first QLED that exceeded the benchmark was reported in winter of 2014 and used CdSe as the active layerof the device, emitting red light (Dai et al., 2014). The
second QLED exhibited the green EL with 20 % EQE andhad CsPbBr3/MABr quasi-core/shell structure as the active
layer (Lin et al., 2018). InP-based QDs as optically active
layers are the most promising alternative Cd- and Pb-free
QLEDs. Since the innovative work of the first InP-QLED
in 2011, intensive efforts have produced visible light emitting QLEDs. Finally, in 2019, Jang et al. reported blue,
green, and red emitting QLEDs with 20 % of EQE using
InP/ZnSe/ZnS QDs as active layers (Won et al., 2019).
Photoelectrode is also one of the optoelectronic applications. It is expected to realize the wavelength tunability ofdetected light by taking advantage of size dependence ofthe first exciton peak in the optical absorption spectrum,
but not yet reported before due to possible difficulty indissociation of photogenerated carriers into electrons andholes. Do-Hyun Kwak et al. reported a phototransistor witha light absorbing layer of InP QD in the hybrid structure of
InP QD/black phosphorus. They replaced the oleylamine

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on the QD surface with 1,2-ethanedithiol (EDT), which
has a shorter alkyl length, by ligand exchange to improvethe carrier mobility and enhance device characteristics(Kwak et al., 2019). Under violet-light illumination, thephotogenerated electrons from the InP QD layers arequickly transferred to the BP transport layer, resulting in ahigh photo response of 109 A/W and a high detectivity of
4.5 × 1016 Jones.

4. Lead-free perovskite nanocrystals
Lead halide perovskites are increasingly gaining attention for advanced optoelectronic applications due totheir excellent optoelectronic properties, but there are
two serious concerns for commercial use. First, metallic
lead ions are readily soluble in water to cause ecologicalthreats. Second, the current lead perovskite nanocrystals
have been found to have low stability to UV irradiation,
moisture, and heat. These disadvantages have stimulated
the development of research into the colloidal synthesisof lead-free perovskite nanocrystals. This chapter presents
recent progress in synthesis methods to enhance the PLQY
of lead-free perovskite nanocrystals.

4.1 Cesium tin halide perovskite nanocrystals

Inorganic metal halide perovskites have a generalchemical formula ABX3, where A is a monovalent cation(e.g., cesium, Ce+), B is a divalent metal ion (typicallytin, germanium or copper), and X is a halide anion (Cl–,
Br–, or I–). Tin halide perovskite exhibits a band gap of
1.2–1.4 eV, high carrier mobilities, and long carrier diffusion lengths (Pitaro et al., 2022). Therefore, nanocrystalsof cesium tin halide perovskite structure (i.e., Cs2SnX3)
are expected to be a representative of Pb-free perovskite
nanocrystals. Owing to a large absorption coefficient and awide bandgap-tunability between 1.3 and 2.8 eV, Cs2SnX3
has a potential of their use in various applications such as
photovoltaics, photoelectrode and light-emitting devices(Chen J. K., 2021c). So far, the synthetic development of its
nanocrystals lags far behind lead perovskite nanocrystals.
As a result, their optical properties are also inferior to thoseof Pb-based materials; Jellicoe et al. reported that a higher
density of trapped states results in lower PLQY (Jellicoe
T. C. et al., 2016); Wong et al. pointed that Sn vacancies
as the trap states work to lower PLQY (Wong et al., 2018).
How to reduce defect density to obtain high PLQY? To
answer this conundrum, Sun et al. proposed that precisely
adjusting the ratios of starting precursors corresponding to
Cs, Sn, and X over a wide range is crucial for suppressing
the formation of lattice defects in CsSnX3 nanocrystals
because the defect formation energy is closely related tothe chemical potential of each component. Wang et al.
tuned the molar ratios of precursors in a wide range to suppress the formation of defects contributing to nonradiative
charge-carrier recombination and found that addition of the

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small amount of zwitterionic phosphatidylcholine as a surfactant in the reaction is crucial for controlling the growthrate of the nanocrystals (Wang et al., 2021). According totheir experimental study, the growth rate of CsSnX3 nanocrystals was controlled by the formation of an intermediate
complex between the zwitterionic phosphatidylcholine
and the precursor and the steric hindrance effect of the
branched fatty acid side chain of phosphatidylcholine. The
resultant nanocrystal exhibited PLQY of 12.5 %.
Great attention has more recently been paid to the colloidal synthesis guiled by theoretical study using machinelearning. In this context, Sun et al. investigated the formation energy and the thermodynamical charge-transitionlevels of typical point defects, as well as the influenceof Sn vacancy on the photophysical properties based on
density functional theory (DFT) calculations and molecular
dynamics (MD) simulations (Liu et al., 2021b). Specifically, the stability regions of different compounds vs. I
and Sn chemical potentials of CsSnI3 was calculated at theoutset (see Fig. 6(a)). The result implied that the defectformation energy of CsSnI3 is sensitive to the chemical
potentials of reactants. Then, the defect formation energies
and defect charge-transition levels were calculated to show
Fig. 6(b) and 6(c). Clearly, tin vacancies (VSn) introduced
deep trap levels. Guided by this calculation result, CsSnI3
nanocrystals were colloidally synthesized through thecareful tuning of the ratio of precursors. It was found thatsuppressing the Sn vacancy strengthens the increase ofradiative recombination rate for enhanced PLQY alongwith a narrow emission spectral peak. By intentionallyadopting a Sn-rich reaction condition, along with judicious
choice of precursors with suitable reactivity, the resulting
narrow-band-emissive CsSnI3 nanocrystals exhibit a record
PLQY of 18.4 %. The development of synthetic methods,
guided by computational science, will facilitate the studyof tin-based perovskite nanocrystals and accelerate theirapplication in high-performance optoelectronic devices
such as solar cells, light-emitting diodes, and lasers.

4.2 Double perovskite nanocrystals

Inorganic double perovskite crystals have a chemical formula, A2B(I)B(III)X6 where A is a monovalent cation (typically Ce+), B(I) is a monovalent cation (Ag+ or Na+), B(III)is trivalent cation (In3+, Bi3+ or Sb3+). Since the first reporton the synthesis of lead-free double perovskite crystals, various chemical compositions of compound A2B(I)B(III)X6
have been investigated to tune the bandgap. For example,
when Cl– (2.77 eV), Br– (1.95 eV), and I– (1.75 eV) areintroduced as X sites in the Cs2AgBiX6 structure, the band
gap decreases in that order (Creutz et al., 2018). Among
the reported lead-free double perovskite structures, a bulk
crystal of white-light emitting Cs2AgxNa1–xInyBi1–yCl6
double perovskite structure was reported to show a high
PLQY of 86 ± 5 % (Luo et al., 2018). After that, the PLQY

Naoto Shirahata / KONA Powder and Particle Journal No. 41 (2024) 172–182

of Bi3+-doped Cs2Ag0.4Na0.6InCl6 microcrystals of about
10 μm in diameter was improved up to 97.33 % (Peng K.
et al., 2022). From now on, the double perovskite crystals
become a potential material for single-emitter layer appliedin optical applications (Zhang et al., 2022). However, a dramatic decrease in PLQY resulting from nanostructuration
happens due to the formation of defective structures.
Research to increase PLQY in nanocrystals has been
intensively pursued recently. Here, a typical method tofind the underlying mechanism of the enhancement of
the PLQY is demonstrated using Cs2Ag0.65Na0.35InCl6
nanocrystal as an example. HR-TEM image shown in
Fig. 7(a) demonstrates Cs2Ag0.65Na0.35InCl6 nanocrystals

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having a square shape and an average diameter of about
10 nm. The estimated PLQY was 3 %. Improvementof PLQY was realized by impurity doping of Bi3+ ions.
Fig. 7(b) shows a typical HR-TEM image of the Bi-doped
nanocrystals. When the 1 % Bi3+ ion was doped, the
PLQY was enhanced up to 33.2 % (see Table 1). Fig. 7(c)shows PL spectra of the undoped sample measured in thetemperature range from 20 K to 300 K. As clearly seen,
the integrated PL intensity decreases with the increasing
temperature monotonously possibly due to the thermal
activation of the nonradiative channels present in the
nanocrystals. Doping of 1 % Bi3+ ions suppressed thedecreasing rate in PL intensity with temperature compared

Fig. 6 (a) Stability regions of different compounds vs I and Sn chemical potentials of CsSnI3. The shaded region indicates the equilibrium chemical
potential region. The representative points (A: Sn-poor/I-rich, B: moderate, C: Sn-rich/I-poor conditions) are chosen for the defect formation energy
calculations. (b) Calculated defect formation energies for 2 × 2 × 2 orthorhombic CsSnI3 supercell at Sn-poor/I-rich, moderate, and Sn-rich/I-poor
conditions, corresponding to the A, B, and C point in (a), respectively. (c) Defect charge-transition levels of CsSnI3. Adapted with permission from Ref.
(Liu Q. et al., 2021b). Copyright 2021, American Chemical Society.

Fig. 7 TEM images and temperature dependent PL spectra of Cs2Ag0.65Na0.35InCl6 nanocrystals (a, c) without and (b, d) with 1 % Bi3+ ions. Adaptedwith permission from Ref. (Huang et al., 2022). Copyright 2022, Royal Society of Chemistry.

179Naoto Shirahata / KONA Powder and Particle Journal No. 41 (2024) 172–182
Table 1 PL decay parameters * of the Cs2Ag0.65Na0.35InCl6 doped withand without Bi3+ ions.

Sample

PLQY (%) kr a(μs–1)

knr b(μs–1)

R2

Undoped

3.0

0.13

4.20

0.99

Doped

33.2

0.14

0.28

0.97

a k is the radiative recombination rate calculated by the formula:rkr = PLQY/τ(avg). τ(avg) means the value of average PL lifetime.
knr is the nonradiative recombination rate calculated by theformula: knr = 1/τ(avg) – kr.
* Other parameters for calculation are reported in the publishedpaper (Huang et al., 2022).
b

to undoped ones as shown in Fig. 7(d), suggesting thedecrease of nonradiative channels. As summarized inTable 1, the nonradiative recombination rate (knr) was
reduced by a factor of 15 in the doped sample compared tothe undoped sample. The possible defects as the nonradiative channel identified by investigating the temperaturedependence of PL spectral linewidth combined with chemical composition analysis were indium vacancies generated
in the crystal growth (Huang et al., 2022). The possible
mechanism of enhanced PLQY was that Bi3+ ions occupy
the indium vacancy to relax the strained lattice, yielding thedecreasing number of nonradiative channels. Thus, spectroscopic studies at low temperatures provide structural
insight into the cause of low PLQY.

5. Summary

Driven by the toxicity concerns of cadmium, lead, mercury, and selenium, research on the synthesis of colloidal
nanocrystals composed of nontoxic elements has madesignificant progress over the last decade. While previous
studies have already detailed the synthesis and excellent
properties of Cd- and Pb-based nanocrystals, non-toxic
nanocrystals, the subject of this review, have not yet been
fully described. The review was structured as follows:First, optical properties of SiQD were summarized. Dueto their low absorption coefficients, they do not work as
strong phosphors, but they find an application in the deicefield of current driven LED because carriers are given fromelectrode and does not need absorbing the light. Second,
InP was topicalized among III–V semiconductors. Metallic
In is a small amount of constituent element on earth but
does not exclude it from the discussion here. The pioneering work on red−green−blue (RGB) LEDs with beyond
20 % EQE spurs a lot of interests. Next target is narrowingof their EL spectra (<30 nm). For Pb-free perovskite nanocrystals, recent advances in colloidal chemical method
were presented here. The journey to find experimental parameters to reduce defects has just begun. Much research isstill needed to advance the field of non-toxic semiconductor
nanocrystals. Although technological advances in synthesis
methods, reports of prototype devices using light emission

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from nanocrystals, and even induced emission and nanobio
applications have progressed over the past decade, it is safe
to say that nanophotonics is still in its infancy.

Acknowledgments

This work was supported by WPI program, JSPS
KAKENHI Grant-in-Aid for Scientific Research (B) Grant
Number 21H01910 and The Murata Science Foundation.

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Author’s Short Biography
Naoto Shirahata
Naoto Shirahata is Group Leader of Nanoparticle Group of National Institute for Materials Science (NIMS) in Japan, and Guest Professors of Hokkaido University and Chuo University. He earned
his Ph.D. in materials science from Kyoto Institute of Technology in 2001. He worked as postdoc
researchers at Nagoya University, AIST, and NIMS from 2001 to 2006. Since 2006 he has beenpermanent researcher at NIMS. He was holder of JST-PRESTO in 2009–14. He was visiting scientist
at University of Toronto in 2015–16. He has received awards for his research from the Division of
Colloid and Surface Chemistry, the Chemical Society of Japan, the Ceramic Society of Japan, andJapan Society of Powder and Powder Metallurgy.

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Review Paper

Characteristics of Ultrafine Bubbles (Bulk Nanobubbles) and
Their Application to Particle-Related Technology †Keiji Yasuda
Department of Chemical Systems Engineering, Nagoya University, Japan
Colloid of gold nanoparticles
Ultrafine bubbles (bulk nanobubbles), small bubbles with a diameterwith UFB
without UFBof less than 1 μm, have attracted academic and industrial attention
Gold nanoparticles synthesized
because of their numerous advantages, including their chemical-free
without ultrafinewith UFB
bubbles (UFB)
nature and extraordinarily long lifetime. The long lifetime is related tothe much higher Brownian motion velocity compared to buoyancy. The
reason why ultrafine bubbles can endure under stable conditions is still
Immediately after synthesis
unclear, even though their inside is highly pressured. They have several
with UFB
without UFB
characteristics, such as pH-dependent surface charge and reduced
friction. They are also closely related to ultrasound. Ultrafine bubbles
are generated and removed by selecting the ultrasonic frequency.
Reaction and separation using ultrasonic cavitation and atomization,
100 nm
100 nmAfter two days
respectively, are enhanced by ultrafine bubbles. They can produce
hollow nanoparticles, enhance adsorption on activated carbon, andclean solid surfaces. This review discusses the fundamental and ultrasonic characteristics of ultrafine bubbles and their application inparticle-related technology, encompassing fine particle synthesis, adsorption, desorption, extraction, cleaning, and prevention of fouling.
Keywords: ultrafine bubbles, bulk nanobubbles, ultrasound, sonochemistry, fine particle synthesis, cleaning

1. Introduction

Submicron-sized bubbles have attracted academic andindustrial interest lately. Although these floating bubbles ina liquid have previously been referred to as “nanobubbles”
or “bulk nanobubbles,” the International Organizationfor Standards (ISO) decided in 2017 that these bubbles
less than 1 μm in diameter be termed “ultrafine bubbles”(ISO 20480-1, 2017). Therefore, this review will refer tonanobubbles and bulk nanobubbles as ultrafine bubbles.
Furthermore, bubbles between 1 and 100 μm in diameterare termed microbubbles, and ultrafine bubbles and microbubbles are collectively termed fine bubbles.
Ultrafine bubbles and microbubbles are liquid-floating
bubbles with the entire bubble interface surrounded by liquid. Microbubbles in water appear cloudy, like milk, while
ultrafine bubbles are transparent because they do not scatter
visible light. Surface nanobubbles, on the other hand, aresmall bubbles with one side of the bubble interface in contact with liquid and the other side attached to the solid wall
(Lohse and Zhang, 2015). The surface nanobubbles arespherical caps with heights ranging from a few nanometers
to tens of nanometers and widths of about 1 μm. They do
not move from a solid wall.

†

Received 17 October 2022; Accepted 19 December 2022
J-STAGE Advance published online 25 March 2023
Add: Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
E-mail: yasuda.keiji@material.nagoya-u.ac.jp
TEL: +81-52-789-3623

The rising velocity U of fine bubbles (microbubbles andultrafine bubbles) in liquid due to buoyancy depends on the
diameter of fine bubbles d and the physical properties ofliquid and is expressed by the following Stokes equation(Stokes, 1851),U

d 2  ρ L  ρG  g
18 μ

(1)

where ρL and ρG are liquid and gas densities, respectively,
μ is the liquid viscosity, and g is the gravitational accelera(1)The Stokes equation is known to accurately representtion.
the setting motion of a solid sphere in a liquid when the
Reynolds number is less than 2. Experimental results of therising velocity of microbubbles agree with those calculatedusing Eqn. (1) (Takahashi, 2005; Lee and Kim, 2005).
However, ultrafine bubbles are subject to intense Brownian
motion, like solid colloidal particles, due to collisions
caused by the thermal motion of liquid molecules. The
following Stokes–Einstein equation expresses the displacement of spherical particles using Brownian motion at low
Reynolds numbers (Einstein, 1905).
Δx 2  Δy 2  Δz 2 

2kTt
πμd

(2)

where ⟨Δx2 + Δy2 + Δz2⟩ is the mean square displacementof the fine particles, k is the Boltzmann constant, T is the(2)temperature,and t is the observation time. Fig. 1 shows thecomparison between the rising velocity using Eqn. (1) andthe average velocity of Brownian motion per 1 s and 100 s

Copyright © 2024 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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by Eqn. (2) in the range of bubbles diameters from 10 nm
to 10 μm. When the temperature is 298.15 K, the liquid andgas are considered to be water and air, respectively, and the
bubbles are expected to be perfectly spherical. In the caseof ultrafine bubbles less than 100 nm, the average velocityof Brownian motion is much greater than the rising velocity. This means that the ultrafine bubbles are dominatedby random motion in all directions compared to the rising
motion. Due to this interesting behavior, the removal of ultrafine bubbles from the liquid surface is nearly negligible.
If there is no collision between the bubbles or dissolution
into the liquid, ultrafine bubbles remain for a long time.
Thus, ultrafine bubbles have various interesting properties, such as a very long lifetime in liquid, a charged

Moving velocity [nm/s]

100000
10000
1000
100
10
1
Air, Water,
298.15 K, 0.1 MPa

0.1
0.01
10

100

1000

10000

Bubble diameter [nm]Fig. 1

Velocity comparison between Brownian motion and buoyancy.

Review Paper

surface, and controllable bubble concentration using ultrasound. Therefore, they have been applied in many fields,
including water treatment, medicine, and the food industry(English, 2022). This review explains the fundamental andultrasonic characteristics of ultrafine bubbles and describes
their applications to particle-related technologies.

2. Fundamental characteristics

2.1 Size, concentration, and zeta potential

Like colloidal particles in solution, the surface of ultrafine bubbles is charged. The size and concentration ofultrafine bubbles in a liquid are closely related to the zeta
potentials of ultrafine bubbles. They are mainly affectedby the pH of solution, concentration, and several types ofinorganic ions and surfactants in water.
Nirmalkar et al. (2018b) prepared air-filled ultrafine
bubbles from pure water at pH = 6.5 using hydrodynamic
cavitation methods and then changed the pH of ultrafine
bubbles water by HCl and NaOH. The size distribution andzeta potential of ultrafine bubbles were measured using
nanoparticle tracking analysis (Patois et al., 2012) andelectrophoresis, respectively. Fig. 2 shows the effects of
pH on (a) size distribution, (b) number density (concentration), (c) mean diameter, and (d) zeta potential of ultrafine
bubbles. When pH was higher than 4, the bubble size
distribution did not significantly change. However, at pH
below 4, the peak of the bubble size distribution decreased
sharply. This is reflected in the sharp drop in the bubble

a

b

c

d

Fig. 2 Effects of pH on (a) size distribution, (b) concentration, (c) mean diameter, and (d) zeta potential of ultrafine bubbles. Reproduced with permission from Ref. (Nirmalkar et al., 2018b). Copyright: (2018) The Royal Society of Chemistry.

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number concentration (density) as the pH decreased from4 to 3. The mean bubble diameter increased significantly
as the pH decreased below 4. The negative zeta potentialof bubbles approached 0 with decreasing pH and became
positive below the isoelectric point between pH 3 and 3.5.
This is because the electrostatic repulsive force between ultrafine bubbles becomes weaker, bubbles agglomerate, andthe bubble diameter increases, as the zeta potential is close
to the isoelectric point. At the same time, excessively large
bubbles rise and collapse at the gas–liquid interface, andthe bubble concentration decreases. Other researchers havereported similar results with isoelectric points between
pH 3 and 5 (Calgaroto et al., 2014; Azevedo et al., 2016;
Bui et al., 2019; Ma et al., 2022).
Effects of NaCl concentration on properties of ultrafine
bubbles have been widely reported (Millare and Basilia,
2018; Nirmalkar et al., 2018a, 2018b; Meegoda et al.,2018, 2019; Ke et al., 2019). Most of the data indicated thatas the NaCl concentration increased, the bubble concentration decreased, the mean diameter increased, and the zeta
potential approached zero. This is because sodium ions areadsorbed at the surface of the ultrafine bubble, and the zeta
potential is close to zero. In the case of AlCl3, a trivalentcation, the zeta potential first approached zero and thenchanged to positive with increasing AlCl3 concentration(Nirmalkar et al., 2018b). Hewage et al. (2021a) investigated the mean diameter and zeta potential of ultrafine
bubbles in aqueous solutions for several inorganic salts.
The addition of cetyltrimethylammonium bromide, acationic surfactant, changed the zeta potential from negative to positive, as in the case of AlCl3 (Nirmalkar et al.,2018b; Agarwal et al., 2022). On the other hand, adding
sodium dodecyl sulfate, an anionic surfactant, further decreased the zeta potential (Phan et al., 2021b). The additionof nonionic surfactants had a little significant effect on thezeta potential (Nirmalkar et al., 2018b). These experimental results of surfactant addition were explained using theadsorption of surfactants on ultrafine bubbles due to hydrophobic attraction. Effects of gas types inside ultrafine bubbles have been reported (Meegoda et al., 2018; Zhou et al.,a

Review Paper2021). For air, nitrogen, oxygen, and ozone gases, it was
explained that the bubble size increased with increasing
gas solubility in water and the zeta potential depended on
the ability of the gas to produce OH⁻ ions at the water–gas
interface (Meegoda et al., 2018).
The ultrafine bubbles in water were concentrated without loss and size change when water was evaporated fromultrafine bubble water under reduced pressure using a rotary evaporator (Tanaka et al., 2020). Additionally, ultrafine
bubbles in water were diluted by gently adding pure water
without loss and size change of ultrafine bubbles. A facile
membrane-based physical sieving method was developed
to make the ultrafine bubbles in targeting size (Zhang R.
et al., 2022b). They used several membranes consistingof six different materials with pore diameters of 0.22 and0.45 μm. The size distribution of the ultrafine bubbles
could be intentionally adjusted by selecting the membrane
and filtration rate. The ability to control the concentrationand size of ultrafine bubble will be important for futureapplication development.

2.2 Stability

The unique property of ultrafine bubbles is their unusually long lifetime. Ultrafine bubbles, once formed, arehighly persistent (Zimmerman et al., 2011) and stable fordays (Ohgaki et al., 2010; Ushikubo et al., 2010; Liu et al.,2013) and even months (Duval et al., 2012; Nirmalkar etal., 2018a; Kanematsu et al., 2020; Soyluoglu et al., 2021;
Tanaka et al., 2021b).
Tanaka et al. (2021b) generated air-filled ultrafine bubbles from ultrapure water using the pressurized dissolution
method and stored the ultrafine bubble water at 298 K for
74 days. The results showed little difference in the stabilityof ultrafine bubbles between 30 mL glass vials, 1 L glass
bottles, and 20 L high-density polyethylene containers.
Fig. 3 shows the effect of storage temperature on the stability of ultrafine bubbles in 30 mL glass vials. Althoughnumber concentration decreased with time, ultrafine bubbles still existed on day 74. This is because the translational velocity due to Brownian motion is considerablybFig. 3 Effect of storage temperature on the stability of ultrafine bubbles in 30 mL glass vials: a) number concentration and b) median diameter ofultrafine bubbles. Reprinted with permission from Ref. (Tanaka et al., 2021b). Copyright: (2021) John Wiley and Sons.

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higher than the rising velocity due to buoyancy, as shownin Fig. 1. The stability became higher as the temperature
decreased, and median diameter slightly increased withtime. They explained that the Brownian motion of ultrafine
bubbles became more active at higher temperatures andultrafine bubbles tended to aggregate more easily. They
expressed the stability of the ultrafine bubbles in terms ofa second-order flocculation rate equation. Kanematsu et al.
(2020) investigated the stability of ultrafine bubbles for 9
months at 298 K and reported that ultrafine bubbles werestable and zeta potential did not change significantly for 9
months.
Effects of additives on the long-term stability of oxygen
ultrafine bubbles were investigated for 60 days (Soyluoglu
et al., 2021). The bubble stability decreased with decreasing pH in the range of 3 to 9. The addition of Na+ and Ca2+
ions reduced the stability. This is because the zeta potentialof the ultrafine bubbles became close to 0, and the ultrafine
bubbles tended to aggregate. Furthermore, the stability
became low when the concentration of organic substances
was high, but the effect of chlorine was small.
The boundary layer structure of ultrafine bubbles has
been studied to elucidate the reason for their stability.
Ohgaki et al. (2010) used attenuated total reflectance infrared spectroscopy to analyze and reported that the surfaces of
the ultrafine bubbles contain hard hydrogen bonds that may
reduce the diffusivity of gases through the interfacial film.
Weijs et al. (2012) used molecular dynamics and concluded
that ultrafine bubbles within a cluster of bubbles protect
each other from diffusion by a shielding effect. Zhang X.
et al. (2016) performed quantum computations and Raman
spectrometric measurements. They proposed that an ultrafine bubble is more dynamically stable because the bubble
is surrounded by a layer of supersolid skin. Hirai et al.
(2019) studied the structure of ultrafine bubbles using
small- and wide-angle X-ray scattering and explained thatan ultrafine bubble is surrounded by a diffusive boundary,and the electron density differs between the diffusive
boundary and the bulk solution. Zhang R. et al. (2022a)
quantified the boundary layer thickness of the ultrafine
bubbles using solvent relaxation of nuclear magnetic resonance. For effective gas diameters of 243.5, 358.5, and412.8 nm, the determined boundary layer thicknesses were41.5, 44.8, and 35.9 nm, and the ratios of the boundary
layer thickness to the effective gas diameter were 0.17,
0.125, and 0.087, respectively.
The bubble was surrounded by water, which has highintermolecular forces, and the inner bubble was pressurized
because it is a gas with a sparse density compared to theliquid. The pressure difference Δp is expressed using theYoung–Laplace equation as follows (Attard, 2014),
Δp  pin  pout 

(3)
186

2σ
r

(3)

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Fig. 4 Dynamic equilibrium model. Reprinted with permission from
Ref. (Yasui et al., 2016). Copyright: (2016) American Chemical Society.

where pin and pout are the inner and outer pressure of the
bubble, respectively and σ is the interfacial surface tension,and r is the bubble radius. Assuming that the interfacial
tension at the macroscale and nanoscale is the same, theinternal pressure of a bubble increases with decreasing
bubble radius and becomes significantly high in the case ofultrafine bubbles. The inner pressure of bubbles becomes
2.98 MPa, when the bubble radius is 50 nm, the interfacial
tension of water is 72 mN/m, and the outer pressure of the
bubble is 0.1 MPa. The inner pressure of surface nanobubbles obtained using electrochemical measurement (German
et al., 2016) and that of ultrafine bubbles estimated fromthe measurement of gas content in water (Kim E. et al.,2020) were consistent with the theoretical pressure values
in Eqn. (3).
However, ultrafine bubbles are expected to dissolve immediately into the surrounding liquid because the pressureof the gas dissolved in a liquid is usually in the order of
0.1 MPa. The time for the complete dissolution of a bubble
with 100 nm in radius is only about 80 μs according to the
Epstein–Plesset theory (Epstein and Plesset, 1950; Yasui,
2015, 2018). Therefore, the lifetime of ultrafine bubbles
should theoretically be very short, but in practice, as previously mentioned, it is very long.
Several stabilization models of ultrafine bubbles havebeen proposed to explain this discrepancy. Yasui et al.
(2018) reviewed these models and supported the dynamic
equilibrium model (Yasui et al., 2015, 2016; Brenner andLohse, 2008; Petsev et al., 2013) because images of thetransmission electron microscope showed stable ultrafine
bubbles partly covered with hydrophobic materials inaqueous solutions (Sugano et al., 2017). Fig. 4 illustrates
the dynamic equilibrium model. This model shows that ahydrophobic substance adheres to the bubble surface, andthe gas concentrated on the surface of the hydrophobic substance flows into the bubble and is balanced by the amountof gas flowing out of the bubble in the rest of the bubble.
However, the high stability of ultrafine bubbles must be
investigated further, both experimentally and theoretically,
to obtain a consistent conclusion in the future.

2.3 Other characteristics

Other characteristics of ultrafine bubbles include aKeiji Yasuda / KONA Powder and Particle Journal No. 41 (2024) 183–196

reduction in solution viscosity (Phan et al., 2021a), a
reduction in friction (Ushida et al., 2012a; Nakagawa et al.,2022), and the generation of radicals.
Viscosities of apple juice concentrate and canola oil
were examined after they were injected with CO2 ultrafine
bubbles with sizes ranging from 50 to 850 nm (Phan et al.,2021a). A significant viscosity reduction was observed by
up to 18 % and 10 % in apple juice concentrate and canolaoil, respectively. The pressure drop across a micro-orifice
was measured using water and glycerol aqueous solutions
containing ultrafine bubbles (Ushida et al., 2012a). The
friction reduction was observed for orifice diameters of
50 μm or less. This was because the ultrafine bubbles adhered to the wall surface, forming a gas phase that resulted
in wall slip. The reduction of friction factor in an acrylic
pipe with glass beads increased with an increasing number
of ultrafine bubbles (Nakagawa et al., 2022). They explained that ultrafine bubbles filled more undulated spaceand made the rough surface smoother.
Free radicals, including hydroxyl radicals, were detectedin ultrafine bubble water (Minamikawa et al., 2015; Liuet al., 2016a, b; Atkinson et al., 2019). Free radicals are
believed to be formed through the collapse of ultrafine
bubbles (Takahashi et al., 2021). Soyluoglu et al. (2021)
reported that hydroxyl radicals in oxygen ultrafine bubble
solutions were detected at pH 3 but were little detected at
pH 6.5. Yasui et al. (2018) discussed the generation possibility of hydroxyl radicals from air ultrafine bubbles using
numerical simulations. They suggested that hydroxyl radicals formed due to hydrodynamic cavitation in producing
ultrafine bubbles.

3. Ultrasonic characteristics
3.1 Ultrasonic cavitation

When water is irradiated with ultrasound, fine bubbles
are generated from bubble nuclei, grow to a resonancesize through rectified diffusion (Ashokkumar et al., 2007;
Crum, 1980) and collapse by semi-adiabatic compression(Leighton, 1994; Yasui, 2015). This phenomenon is called
ultrasonic cavitation. By collapsing fine bubbles, the field
inside the fine bubbles becomes high temperature and pressure, generating various radical species, which produceschemical effects (Suslick, 1990). Simultaneously, thecollapse of fine bubbles generates jet flow and shock wave
(Lauterborn and Ohl, 1997), producing mechanical effects.
Examples of the chemical effects include the decomposition of harmful substances (Pétrier et al., 1994;
Yasuda, 2021) and the synthesis of metal nanoparticles(Gedanken, 2004; Sakai et al., 2009). However, examplesof the mechanical effects include ultrasonic cleaning(Muthukumaran et al., 2005; Mason, 2016), ultrasonicemulsification (Beal and Skauen, 1955; Yasuda et al.,2012), and ultrasonic atomization (Wood and Loomis,
1927; Sato et al., 2001). Mechanical effects have been used

Review Paper

to develop commercial products such as cleaners, homogenizers, and atomizers. Usually, plate-type transducers areused for ultrasonic cleaners and atomizers, while horn-typetransducers are used for ultrasonic homogenizers. Thus,
ultrasound is closely related to ultrafine bubbles, and manystudies using ultrasound have been reported.

3.2 Generation and removal of ultrafine bubbles

Ultrafine bubbles were generated using ultrasoniccleaner at 42 kHz (Kim J.-Y. et al., 2000) and ultrasonichomogenizer at 20 kHz (Cho et al., 2005; Nirmalkar et al.,2018a, 2019; Li et al., 2021; Lee and Kim, 2022b; Ma etal., 2022). Their generation rate increased with increased
dissolved air content in pure water (Nirmalkar et al.,2018a). Studies have used several plate-type transducers
to change ultrasonic frequencies ranging from 20 kHz to
1 MHz and to generate ultrafine bubbles in ultrapure water(Yasuda et al., 2019). Figs. 5(a) and (b) show the effectof ultrasonic frequency on the generation rate and mode
diameter of ultrafine bubbles. The generation rate increased
with decreasing frequency because cavitation collapse
became stronger at lower frequencies. However, the mode
diameters of ultrafine bubbles were 90–100 nm regardlessof the frequency. The diameter, zeta potential, and stabilityof ultrafine bubbles generated using ultrasound are almost
identical to those generated by hydrodynamic cavitation(Jadhav and Brigou, 2020), which is a commonly usedmethod. The ultrasound method has the advantage ofsmall sample volumes and the ability to generate ultrafine
bubbles in variety of liquids, including organic solvents(Nirmalkar et al., 2019), inorganic solutions (Ma et al.,2022), and surfactant solutions (Lee and Kim, 2022a).
Removing ultrafine bubbles from liquid media is important to distinguish them from nanoparticles. The freeze–
thawing method is often used to remove ultrafine bubbles
in pure water. However, this method little removes ultrafine
bubbles in the surfactant solution (Nirmalkar et al., 2018a),and it is time-consuming. Ultrasound has been irradiated atvarious frequencies to highly concentrated ultrafine bubble
water produced using the pressurized dissolution method(Yasuda et al., 2019). Fig. 5(c) shows the effect of ultrasonic frequency on the removal rate of ultrafine bubbles.
The removal rate increased with increasing frequency because the secondary Bjerknes force, an attraction between
bubbles, increased. We modeled the formation and removal
of ultrafine bubbles using ultrasound and discovered thatultrafine bubbles reach a frequency-dependent equilibrium
concentration (Fig. 5(d)), regardless of the ultrasoundintensity. Tanaka et al. (2021a) confirmed the ultrasonicremoval of ultrafine bubbles using indirect irradiation. The
ultrasonic frequency was 1.6 MHz, and ultrafine bubbles
dispersions were prepared using two different bubble generator methods: pressurized dissolution and swirling liquid
flow. Results showed that the indirect ultrasonic irradiation

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Keiji Yasuda / KONA Powder and Particle Journal No. 41 (2024) 183–196

b

×109

1.2
1.0
0.8
0.6
0.4
0.2
0.0

0

5

10

15

20

25

Number concentration [mL-1]

3.5

Ultrafine bubble water, 15 W

3.0
2.5

22 kHz43 kHz129 kHz488 kHz
1 MHz

2.0
1.5
1.0
0.5
0.0

0

5

10

15

20

25

Irradiation time [min]30

Ultrapure water

100

100

1000

Frequency [kHz]

d

×109

5 min
30 min50
1030

Irradiation time [min]c150Mode diameter [nm]22 kHz Ultrapure water, 15 W
43 kHz129 kHz488 kHz
1 MHz

Equilibrium concentration [mL-1]

Number concentration [mL-1]

a

2.0

× 109

1.5

1.0

0.5

0.0
10

100

1000

Ultrasonic Frequency [kHz]Fig. 5 Effect of ultrasonic frequency on (a) generation rate, (b) mode diameter, (c) removal rate, and (d) equilibrium concentrations of ultrafine
bubbles.

for 30 min reduced the number concentration of ultrafine
bubbles by 90 % regardless of the generation method. Feasibility studies were also conducted for ultrafine bubbles in
an aqueous surfactant solution or solid particle dispersion.
The ultrasonic method selectively destabilized ultrafine
bubbles in the solutions. Ultrafine bubble removal using
ultrasound has been adopted as an international standard
by the International Organization for Standardization (ISO24261-2, 2021) because of its simplicity and quickness ofoperation.

3.3 Sonochemical characteristics

Sonochemistry is a field in chemistry, physics, andbiology related to ultrasonic cavitation. Some reports haveshown that ultrafine bubbles promote ultrasonic cavitation(Tuziuti et al., 2020, 2021; Tsuchida et al., 2022). Sonoluminescence, which is light emission from a liquid due toultrasonic cavitation (Brenner et al., 2002; Ashokkumar,2011), was enhanced by ultrafine bubbles when ultrasoundwas applied at 54 kHz with high intensity (Tuziuti et al.,2020). This phenomenon was observed for various bubble
concentrations (Tuziuti et al., 2021). They considered thatultrafine bubbles had the potential to provide nucleationsites for ultrasonic cavitation. The sonochemical degradation of acid orange was accelerated when the microbubble
solution was irradiated with ultrasound at 45 kHz (Tsuchida
et al., 2022). They concluded that ultrafine bubbles played
an important role in improving sonochemical reactions.

188

An in-situ sediment remediation method using a combination of ultrasound and ozone ultrafine bubbles wasinvestigated to remediate both organic (p-terphenyl) andinorganic (chromium) materials in contaminated sedimentsof the lower Passaic River in United States (Batagoda etal., 2019; Hewage et al., 2020, 2021b). The ozone ultrafine
bubbles increased the solubility of ozone in water andreduced wastage. Also, the high-ozone concentration inwater also increases chromium oxidation (Batagoda etal., 2019). This proposed treatment method showed sufficient remediation success with removing these combined
contaminants, on average, 60 % and 71 % for p-terphenyl
and chromium, respectively. The chromium removal was
directly influenced by the chemical oxidation and sonication for chromium desorption from sediments. However,p-terphenyl degradation was more likely influenced by the
combined effects of chemical oxidation and ultrasoundassisted pyrolysis (Hewage et al., 2021b). Dark green Rit
dye was effectively decolored by combining ultrafine bubbles and ultrasonic irradiation (Bui and Han, 2020). The
color removal mechanisms were due to the electrostatic
attraction between the ultrafine bubble and dye and theoxidation of the dye using reactive species such as hydroxy
radicals generated through ultrasonic cavitation.
Ultrasonic atomization generates a mist, such as fine
droplets at room temperature (Wood and Loomis, 1927;
Yasuda et al., 2011). When an aqueous ethanol solution is
atomized using ultrasound, the ethanol is enriched in theKeiji Yasuda / KONA Powder and Particle Journal No. 41 (2024) 183–196

mist (Sato et al., 2001; Yasuda et al., 2003; Matsuura et al.,2007). Ethanol enrichment is attributed to the hydrophobic
interaction of ethanol molecules in water (Yasuda et al.,2014). Separation using ultrasonic atomization has also
been reported on ketones (Yasuda et al., 2004), amino acids(Suzuki et al., 2006), surfactants (Jimmy et al., 2008), fineparticles (Nii and Oka, 2014), and ion liquid (Mai et al.,2019). The advantages of ultrasonic atomization separation
include simple operation, maintenance-free, and suitability
for heat sensitive materials. Ultrafine bubbles water wasused as a solvent to improve the enrichment performanceusing ultrasonic atomization (Yasuda et al., 2020b; 2022).
The ethanol concentration in the collected mist with ultrafine bubbles was higher than that without ultrafine bubbles(Yasuda et al., 2020b). When the ethanol concentrationin the solution was low and the carrier gas velocity was
slow, the enrichment effect of ultrafine bubbles was particularly remarkable. Next, the enrichment characteristics ofamino acids were investigated using ultrasonic atomization(Yasuda et al., 2022). Fig. 6 shows the effect of the concentration of amino acids in solution on the enrichment factor
with and without ultrafine bubbles for L-phenylalanine andL-tyrosine. Here, the ratio of the solute concentration in
the mist to that in the solution is defined as the enrichment
factor. The enrichment factor increased with decreasing
solution concentration and was enhanced by adding ultrafine bubbles. The authors explained the reason for thisEnrichment factor [-]13(a) Phenylalanine

11□ with ultrafine bubbles■ without ultrafine bubbles

9
7
5
3
1
0

0.2

0.4

Enrichment factor [-]

13

0.6

0.8

1

(b) Tyrosine

11〇 with ultrafine bubbles● without ultrafine bubbles

9
7
5
3
1
0

0.02

0.04

0.06

0.08

0.1

0.12

Solute concentration in solution [mmol/L]
Fig. 6 Effect of solute concentration in solution on enrichment factor
with and without ultrafine bubbles for (a) phenylalanine and (b) tyrosine.

Review Paper

enhancement as follows. Amino acid bonds to the surface
of ultrafine bubbles in a solution because of their hydrophobic affinity. Under ultrasonic fields, ultrafine bubbles
containing amino acids aggregate or coalesce by secondary
Bjerknes force (Leighton, 1994), rise in the solution bybuoyancy force and burst at the solution surface. The concentration of amino acids increases at the solution surface,and many amino acids are contained in the droplets.
Freezing of foods is a complicated and time-consuming
process that requires the aggregation of molecules froma liquid to a solid crystal network (Kiani and Sun, 2011).
The influences of CO2 ultrafine bubbles and ultrasoundon the crystallization behavior of water during freezingof model sugar solutions were examined (Adhikari et al.,2019). The combination treatment facilitated in reducing
the supercooling degree, resulting in faster nucleation
during the freezing process. This method may improve thecharacteristics of frozen products such as ice cream andfrozen desserts.

4. Application to particle-related technologies
4.1 Fine particle synthesis

Synthesis of fine particles using ultrafine bubbles has
been reported in several papers. Authors (Yasuda et al.,2020a) used HAuCl4 aqueous solutions and synthesizedgold nanoparticles by ultrasonic irradiation at 500 kHz
with the aid of ultrafine bubbles without any capping andreducing agents. Figs. 7(a) and (b) show the electron micrographs and size distribution of gold nanoparticles synthesized with and without ultrafine bubbles. The particle
sizes prepared with ultrafine bubbles were much smaller
than those of the gold nanoparticles prepared without
ultrafine bubbles. The mean diameter of the spherical gold
nanoparticles synthesized with ultrafine bubbles decreased
with increasing concentration of ultrafine bubbles. This isbecause ultrafine bubbles accelerate the sonochemical reduction of gold ions and suppress the aggregation between
gold nanoparticles. Moreover, the gold nanoparticles werestable in a solution containing ultrafine bubbles (Fig. 7(c))
because gold nanoparticles were electrostatically adsorbed
on ultrafine bubbles (Fig. 7(d)), which have a very long
lifetime in water.
Nanocomposites of CaCO3 and pulp fiber were preparedby injecting ultrafine bubbles of CO2 into an aqueous
Ca(OH)2 solution containing pulp fibers (Fuchise-Fukuoka
et al., 2020). The precipitated CaCO3 nanoparticles formedon the pulp fiber surfaces were more stably attached tothe pulp fiber surfaces against shear force. Comparedwith mixing method, the specific surface area and surface
smoothness of nanosized CaCO3-containing handsheets
increased. Water containing ultrafine bubbles of hydrogenat high concentrations was used as the blending solvent for
preparing cement mortar (Kim W.-K. et al., 2021). The cement mortar had greater flexural and compressive strengths

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Keiji Yasuda / KONA Powder and Particle Journal No. 41 (2024) 183–196ab

100 nmRelative number [-]without UFB

0.2

without UFB

0.1

d
UFB

UFB

0

100 nmcwithout UFB

0.3

UFB

without UFB

UFB

0

50

[Au(I)Cl2]−
AuNP
＋

＋

AuNP
＋

AuNP
＋

UFB

[Au(I)Cl2]−＋＋

After two days

200

AuNP
Electrostatic repulsion
AuNP
AuNP
between UFB＋

Immediately after synthesis

100
150
Diameter [nm]

＋

AuNP

＋

＋

＋

AuNP
AuNP

AuNP

＋
＋

＋

UFB

Fig. 7 Effect of ultrafine bubbles (UFB) on sonochemical synthesis and colloidal stability of gold nanoparticles: (a) Electron micrograph and (b)the size distribution of particles; (c) Photograph of colloids immediately and 2 days after synthesis; (d) Stabilization mechanism by ultrafine bubbles.

than plain water-based mortars. This was because ultrafine
bubbles enhanced the collision between the solvent andcement particles, accelerating the hydration and pozzolanic
reactions.
Jadhav and Brigou (2020) formed the hollow zinc phosphate nanoparticles using ultrafine bubbles as shown in
Fig. 8. First, zinc nitrate was added to the ultrafine bubble
water to adsorb Zn2+ ions on the negatively charged surface
of ultrafine bubbles. Next, by adding diammonium phosphate, the ultrafine bubbles served as nucleation sites forthe subsequent reaction of Zn2+ with PO43– ions. Finally,
the pH of the solution was adjusted to 8.5 to precipitate zinc
phosphate on the ultrafine bubble surfaces, forming hollow
particles. The advantages of using ultrafine bubbles for fineparticle synthesis are safety, low environmental loads, andno impurities in fine particles. Ultrafine bubbles, in particular, are optimal for creating fine hollow fine particles since
there is no need to remove the core templates, which are
generally employed with solid or liquid particles.

a

b

c

4.2 Adsorption, desorption, and extraction

Studies have been reported on the use of ultrafine
bubbles to promote adsorption onto particles, as well as
desorption and extraction from particles. The effect of ultrafine bubbles on the adsorption of lead ions onto activatedcarbon was investigated with and without agitation (Kyzas
et al., 2019; 2020). In the case of agitation, the effect ofultrafine bubbles on the adsorption capacity of lead ions
was small. However, in the case without agitation, the190

Fig. 8 Images of transmission electron microscopy of hollow zinc
phosphate nanoparticles obtained by encapsulation of ultrafine bubbles
shown on different scales: (a) 1000 nm; (b) 200 nm; (c) schematic
representation of the formation of the hollow nanoparticles. Reprinted
with permission from Ref. (Jadhav and Brigou, 2020). Copyright: (2020)
American Chemical Society.

Keiji Yasuda / KONA Powder and Particle Journal No. 41 (2024) 183–196

adsorption capacity in the presence of ultrafine bubbles became much higher than that in the absence of ultrafine bubbles. Moreover, ultrafine bubbles largely accelerated theadsorption rate with and without agitation. They explainedthat the ultrafine bubbles accepted positively charged leadions onto their interface and assisted the diffusion and penetration phenomena of lead ions into the activated carbon
pores. The influence of ultrafine bubbles on the adsorptionof sodium oleate on calcite surface was examined (Wang etal., 2019). Ultrafine bubbles had a certain inhibitory effecton the adsorption of sodium oleate on the calcite surface,
and it significantly promoted the flocculation between particles of sodium oleate.
Hydrogen ultrafine bubbles were applied to soils contaminated with copper to desorb copper (Kim and Han,
2020). Ultrafine bubbles improved the desorption of copper. Additionally, when the ultrafine bubbles were used asdesorption enhancers in electrokinetic experiments, they
had better remediation effects than distilled water.
Ettoumi et al. (2022) generated ultrafine bubbles in
ion liquid using compression–decompression method andextracted polyphenols from Chinese Hickory. Compared tothe extract using ion liquid without ultrafine bubbles, theextract of this method showed significantly higher antioxidant activity and polyphenol yields. Electron micrographs
confirmed that nanojets due to ultrafine bubbles caused
morphological destruction of the husk powder. Their groupalso extracted phytochemical compounds from Camellia
Oleifera shells using water with CO2 ultrafine bubbles(Javed et al., 2022). Compared with ethanol extraction,
ultrafine bubble water led to a higher extraction yield of
total phenolic and flavonoid content. Collagen was extracted from tilapia scales by bubbling ultrafine bubbles ofthree different gases (O2, CO2, and O3) into an acetic acidsolution (Kuwahara, 2021). Using CO2 in the acetic acidsolution was the most effective method to obtain collagenin a relatively high yield. The ultrafine bubble method is a
simple, mild, cost-effective, and environmentally friendly
method for adsorption onto particles, as well as desorption
and extraction from particles.

4.3 Cleaning and prevention of fouling

The most typical application of ultrafine bubbles iscleaning. There are several reports on the use of ultrafine
bubbles to remove contaminants from solid surfaces. Minute particles with a diameter of about 50 nm were successfully removed from the wafer surfaces by impinging a jetof ultrapure water containing ultrafine bubbles (Morimatsu
et al., 2004). Removal of protein from the solid–liquidinterface was investigated using a quartz crystal microbalance to evaluate the removal amount (Liu et al., 2008; Liuand Craig, 2009). Bovine serum albumin was completely
removed from both hydrophobic and hydrophilic surfaces
using ultrafine bubbles. The cleaning efficiency compared

Review Paperto sodium dodecyl sulfate, a typical surfactant (Liu et al.,2008). Lysozyme was completely removed from hydrophobic surfaces by combing ultrafine bubbles and sodium dodecyl sulfate (Liu and Craig, 2009). Ushida et al. (2012b)
investigated the washing rate of cloth in an alternating flow
system. The ultrafine bubble water achieved a washingrate greater than that of ion-exchanged water. Moreover,the mixture with ultrafine bubbles and anionic surfactant
exhibited a washing rate higher than that of an aqueous
anionic surfactant solution without ultrafine bubbles. The
ceramic membrane completely clogged by humic acidfouling was effectively cleaned using ultrafine bubbles(Ghadimkhani et al., 2016). Dyed cotton towels were
washed without soap using oxygen-rich ultrafine bubbles
and with soap using the conventional method (Anis et al.,2022). The lowest color strength and highest fastness wereobtained after washing with ultrafine bubbles. Chemical
oxygen demand measurements of washing baths revealed
that ultrafine bubble washing was more environmentallyfriendly than conventional methods.
Terasaka (2021) investigated the cleaning process of salt
adhering to wall surfaces when ultrafine bubble water was
passed through a sample surface at a constant flow rate.
The cleaning rate was determined using the dissolved salt
concentration in water. Fig. 9(a) shows the progress of thecleaning rate when “hard deposits,” such as salts, were
washed with running water, as opposed to paste-like “softdeposits,” such as biofilm and starch. The cleaning process
consists of adhesion, dissolution, peeling, and dissolution.
The starting time of the peeling step is promoted by ultrafine bubbles, and consequently, the cleaning is completed
in a shorter time with ultrafine bubble water. Fig. 9(b)shows a hypothesis of the cleaning mechanism. It explainsthat the cleaning mechanism using ultrafine bubbles uses
the high permeability of ultrafine bubbles accompanying
water and the metastability of ultrafine bubbles that shift
to a stable state when the specific potential is exceeded. In
2011, West Nippon Expressway Company Limited started
cleaning toilets in expressway service stations and parking
areas using ultrafine bubbles. Ultrafine bubbles have beensuccessfully used to remove salts from bridge surfaces.
The necessary time for removing salt from the surface has
shortened to less than one-fourth using a water jet containing ultrafine bubbles (Yabe, 2021).
Ultrafine bubbles were found to prevent surface foulingof bovine serum albumin (Wu et al., 2008) and lysozyme(Zhu et al., 2016). Farid et al. (2022) demonstrated thebenefits of introducing ultrafine bubbles into the feed foralleviating flux reduction and membrane pore wetting
associated with mineral scaling in membrane distillation.
Introducing ultrafine bubbles reduced membrane scaling
propensity and offered excellent resistance to pore wetting
associated with scaling. This is attributed to high surface
shear forces due to flow turbulence and electrostatic191

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Keiji Yasuda / KONA Powder and Particle Journal No. 41 (2024) 183–196a(1) Adhesion(2) Dissolution(3) Peeling(4) Dissolution

100

Cleaning rate β [%]

(4)

(3)

0

(2)

(1)

0

tUFB

t0
Cleaning time t

b
Salt

Salt

Salt
Flat plate

Flat plate

Flat plate

UFB water flows over thesolid salt adhering flat plate.

UFB water permeates into
narrow gap and UFBs attachat the plate and the salt.

Local dissolved gas
concentration in the gap
is increasing.

MicrobubbleThe plate is cleaned by chain
peeling.

MicrobubbleSalt

Flat plate

Flat plate

By expanding microbubbles,
the adhered salt is lifted andpeeled off from the plate.

UFBs are growing tomicrobubbles by gas
absorption in the gap.

Fig. 9 (a) Cleaning process of salt adhering to wall surface with ultrafine bubble water and (b) hypothesis of removal mechanism of wall-attached salt
by flowing ultrafine bubble water. Reprinted with permission from Ref. (Terasaka, 2021). Copyright: (2021) Jenny Stanford Publishing.

attractions between the negatively charged ultrafine bubbles and the counterions. The potential use of ultrafine
bubbles as corrosion and scale inhibitors for geothermal
power plants was discussed (Kioka and Nakagawa, 2021).
The crystal growth rate of calcite, which causes scaling,
was found to be more retarded as the concentration of ultrafine bubbles increased (Tagomori et al., 2022). Ultrafine
bubbles inhibit the corrosion of low-carbon steels, with an
inhibition efficiency of 20–50 % in the acidic geothermal
fluid (Aikawa et al., 2021). They concluded that ultrafine
bubbles could act as a nanoscopic coating material in
mitigating corrosion, increasing the slip length on the solidinterface and preventing exposing the interface to acidic
geothermal fluids by (1) behaving as a bubble mattresscovering most of the steel surface or (2) promoting nucleation and aggregation of an insignificant quantity of silica
precipitation on the steel surface. Compared to commonlyused chemical products, ultrafine bubbles have shown to192

be a potent, chemically benign, environmentally friendly,
and inexpensive corrosion and scale inhibitor of calcium
carbonate.

5. Conclusions

This review discussed the fundamental and ultrasoniccharacteristics of ultrafine bubbles (bulk nanobubbles) andintroduced their application to particle-related technology.
The diameter of ultrafine bubbles is less than 1 µm, andthe bubble surface is highly charged. The rising velocity
due to buoyancy is much smaller than the Brownian motion velocity. The diameter and concentration are closely
related to zeta potential. The diameter, concentration,and zeta potential of ultrafine bubbles depend on pH of
solution, concentration, and several types of inorganic ionsand surfactants in water. Additionally, the concentration
can be increased using a rotary evaporator. They are stablein water for several months; however, there is no modelKeiji Yasuda / KONA Powder and Particle Journal No. 41 (2024) 183–196

to fully explain the stabilization mechanism. They formedhydroxy radicals at strong acid conditions and reduced infriction in the orifice.
Ultrafine bubbles can be generated and removed using ultrasonic irradiation at low and high frequencies,
respectively. Chemical and physical effects of ultrasoniccavitation, such as luminescence and degradation, were enhanced by ultrafine bubbles. It was considered that ultrafine
bubbles became the nucleation of cavitation. Separation
performance of ethanol and amino acids from aqueous
solutions using ultrasonic atomization was improved byultrafine bubbles.
Ultrafine bubbles have been applied to fine particle
synthesis, adsorption, desorption, extraction, cleaning, andprevention of fouling. Fine gold nanoparticles, CaCO3/pulp
fiber nanocomposites, and hollow zinc phosphate nanoparticles were synthesized by ultrafine bubbles. Adsorption oflead ions to activated carbon and desorption of copper fromsoil were enhanced by ultrafine bubbles due to electrostaticinteraction between ultrafine bubbles and metal ions. Bubbling CO2 ultrafine bubbles effectively extracted collagenfrom tilapia scales. Ultrafine bubble water without surfactant efficiently cleaned the protein, dye, and salt on solid
surfaces and prevented surface fouling. Thus, ultrafine
bubbles have been shown to be powerful, environmentallyfriendly, safe, clean, simple, and inexpensive methods.
Currently, ultrafine bubbles have many theoretically
unexplained characteristics, such as high stability andchargeability, and a great deal of academic research is still
being conducted. Clarifying the various characteristics ofultrafine bubbles is expected to lead to academic development and further social and industrial diffusion of ultrafine
bubbles through applied research, such as developing new
applications in various fields and reducing production
costs.

Acknowledgments

This work was partly supported by JSPS KAKENHI
Grant Numbers JP16H04560 and JP19H02505.

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Physics, 42 (2003) 2956–2957. DOI: 10.1143/JJAP.42.2956
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Kawase Y., The effects of the solute properties in aqueous solutions
on the separation characteristics in ultrasonic atomization, Journalof Chemical Engineering of Japan, 37 (2004) 1290–1292. DOI:
10.1252/jcej.37.1290
Yasuda K., Honma H., Xu Z., Asakura Y., Koda S., Ultrasonic atomization amount for different frequencies, Japanese Journal of Applied
Physics, 50 (2011) 07HE23. DOI: 10.1143/JJAP.50.07HE23
Yasuda K., Nakayama S., Asakura Y., Characteristics of nanoemulsion
prepared by tandem acoustic emulsification at a high frequency,
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10.1252/jcej.12we057
Yasuda K., Mochida K., Asakura Y., Koda S., Separation characteristics
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Review Paper

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Yasuda K., Hamada K., Asakura Y., Enrichment of amino acids from its
aqueous solution by ultrasonic atomization and ultrafine bubbles,
Japanese Journal of Applied Physics, 61 (2022) SG1009. DOI:
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Yasui K., Chapter 3—Dynamics of acoustic bubbles, in: Grieser F., Choi
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cocis.2011.01.010

Author’s Short Biography
Keiji Yasuda
Keiji Yasuda is currently an Associate Professor of the Department of Chemical Systems Engineering, Nagoya University. He received Doctor degree of Engineering from Nagoya University in
1997. His research interests are in the area of fundamentals of fine bubbles and sonochemistry, andtheir application to material and environmental fields.

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Review Paper

Synthesis of Anisotropic Metal Oxide Nanoparticles via
Non‑Aqueous and Non‑Hydrolytic Routes †Sherif Okeil 1,4, Julian Ungerer 2, Hermann Nirschl 2 and Georg Garnweitner 1,3*
1 Institute for Particle Technology, Technische Universität Braunschweig, Germany

2 Institute for Mechanical Process Engineering and Mechanics, Karlsruhe Institute of Technology (KIT), Germany
3 Laboratory for Emerging Nanometrology, Technische Universität Braunschweig, Germany

4 Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Ain Shams University, Egypt

Due to their low cost, high stability and low toxicity, metal oxide
nanomaterials are widely used for applications in various fields such as
electronics, cosmetics and photocatalysis. There is an increasing
demand thereby for nanoparticles with highly defined properties, inparticular a narrow particle size distribution and a well-definedmorphology. Such products can be obtained under high control via
bottom-up synthesis approaches. Although aqueous processes arelargely found in literature, they often lead to particles with low
crystallinity and broad size distribution. Thus, there has been a growing
trend towards the use of non-aqueous and non-hydrolytic synthesis
routes. Through variation of the reaction medium and the use of
adequate additives, such non-aqueous systems can be tuned to adapt the
product properties, and especially to yield anisotropic nanoparticles
with peculiar shapes and even complex architectures. Anisotropic
particle growth enables the exposure of specific facets of the oxide nanocrystal, leading to extraordinary properties such as enhanced
catalytic activity. Thus, there is an increasing demand for anisotropic nanoparticles with tailored morphologies. In this review, the nonaqueous and non-hydrolytic synthesis of anisotropic metal oxide nanoparticles is presented, with a particular focus on the different
parameters resulting in anisotropic growth to enable the rational design of specific morphologies. Furthermore, secondary phenomena
occurring during anisotropic particle growth, such as oriented attachment mechanisms, will be discussed.
Keywords: non-aqueous synthesis, non-hydrolytic synthesis, anisotropic nanoparticles, metal oxides, oriented attachment

1. Introduction
Metal oxides emerged as an indispensable class of materials due to the wide range of properties they can exhibit
according to their composition, degree of crystallinity andmorphology. Thus, they are employed in various technological fields such as catalysis (Védrine, 2017), electronics,
including transistors (Yu et al., 2016) and gas sensors(Arafat et al., 2012; Chavali and Nikolova, 2019; Yu et al.,2022), as well as energy storage (Ellis et al., 2014;
Fleischmann et al., 2022; Su et al., 2016) and solar cells(Chen et al., 2012). These diverse applications of metaloxides often require the use of particles with defined properties. In particular, due to their advantages such as highspecific surface area providing high surface activity or
small size providing a high degree of homogeneity,
nanoparticles of metal oxides are desired. Hence, synthesis
†

*
1
3

Received 7 September 2022; Accepted 4 May 2023
J-STAGE Advance published online 31 August 2023
Corresponding author: Georg Garnweitner;
Add: Volkmaroder Str. 5, 38104 Braunschweig, Germany
Add: Langer Kamp 6A, 38106 Braunschweig, Germany
E-mail: g.garnweitner@tu-braunschweig.de
TEL: +49 (531) 391-65371 FAX: +49 (531) 391-9633

routes which enable the synthesis of metal oxides with tailored properties and which at the same time are reliable,
cost-effective, versatile and scalable are important prerequisites for many of these applications.
In the past decades, scientists have always been working
on different synthesis techniques to access metal oxide
nanomaterials. Therefore, various synthesis methods exist,
ranging from top-down methods such as ball milling andlaser ablation to bottom-up methods like gas-phase synthesis and solution-based methods (Chavali and Nikolova,
2019; Parashar et al., 2020). While gas-phase synthesis isoften expensive and requires large instrumentation, thesolution-based synthesis of metal oxide nanoparticles is
simple, comparatively less expensive, easily scalable andstill enables precise control of the formed nanoparticles. To
date, the solution-based synthesis of metal oxides is carriedout mainly via aqueous synthesis, although non-aqueousand non-hydrolytic synthesis routes offer interesting advantages for nanoparticle synthesis.
In the case of aqueous synthesis, the precursor compound—which often is a metal alkoxide or other reactive
metal compound—reacts with water directly in a hydrolysis step. This usually results in very high reaction rates,

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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which decreases the homogeneity of the resulting productand leads to difficulty in adjusting or controlling the product properties (Chang and Doong, 2006). Many reaction
parameters such as the rate of addition of precursor or water, the type and intensity of mixing, as well as solution
parameters like pH, ionic strength, the type and concentration of chelating agents must be precisely controlled toobtain reproducible results. Concerning crystallinity of theresulting nanoparticles, the aqueous sol-gel synthesis at
low temperatures usually results in amorphous or lowcrystallinity oxides, and requires further heat treatment of
the obtained product to obtain crystallinity (Wu et al.,2007a; Zhang et al., 2005). However, calcination at hightemperatures results in an increase of crystallite size, neckformation and thus aggregate formation, and eventuallyeven decomposition or phase transformation (Satapathy etal., 2014; Wang et al., 2002). In contrast, in the nonaqueous or non-hydrolytic synthesis, highly crystalline
metal oxides are obtained directly in many cases (Wang etal., 2005). Moreover, the crystal phase can even be tuned
by varying the reaction conditions of the non-aqueous
synthesis (Wu et al., 2007a). This makes the non-aqueousand non-hydrolytic synthesis an attractive pathway for thesimplified production of certain nanomaterials (Buonsantiet al., 2008; Niederberger and Garnweitner, 2006).
In non-aqueous synthesis routes, no water is added to thereaction mixture. Usually, an organic solvent is employed
as a reaction medium but it can also act as a stabilizing
agent, which results in a simpler reaction procedure andenables the synthesis of nanomaterials with uniform properties and a lower amount of strongly bound organics dueto the absence of surfactants in the case of solvent-directedsyntheses (Mutin and Vioux, 2013; Niederberger and
Pinna, 2009). In contrast, the synthesis of nano-sized particles in aqueous media often requires the addition of various
surfactants and capping agents that limit the growth of the
inorganic particles and stabilize the resulting dispersion.
While enabling a precise control over the resulting shapeand particle size distribution, this also leads to coverage ofthe surface of the resulting particles by a large amount of
surface organics (Niederberger and Pinna, 2009). In
non-aqueous synthesis, alcohols are often used as reaction
media, and water may be formed in situ during the synthesis process. On the other hand, non-hydrolytic synthesis
proceeds in the absence of hydroxyl groups and thus requires the use of an aprotic medium.
In the past few decades, there was an immense increaseof interest in crystal engineering, where a huge spectrum of
anisotropic particle shapes has been reported for nanoparticles of different materials (Görke and Garnweitner, 2021).
A general classification of nanomaterials can be made according to their dimensionality into one-dimensional nanostructures, like nanorods and nanowires, two-dimensional
nanostructures such as nanoplatelets and sheet-like struc-198

Review Papertures, and finally more complex three-dimensional nanostructures such as nanocubes, nanopyramids, nanostars,
tetrapods or multipods. Small nanoparticles, in contrast, areoften referred to as zero-dimensional nanodots. Each ofthese morphologies can lead to distinct anisotropic properties which are in most cases unique and make them suitable
for specific applications rather than spherical nanoparticles
which lack the direction-dependent properties (Burrows etal., 2016; Sajanlal et al., 2011). For example, the exposureof specific crystal planes was found to have a drastic effecton the photocatalytic efficiency of metal oxide nanoparticles (Chang and Waclawik, 2012). Anisotropic iron oxide
nanoparticles show magnetic behavior which is highly dependent on its shape anisotropy, resulting in enhanced performance in different biomedical applications (Andrade etal., 2020). Metal oxide nanotube structures expose outerand inner surfaces as well as edges, which results in an increased surface area that proved to be highly beneficial forenergy storage applications and can be used to embed othermaterials to form nanocomposites (Ellis et al., 2014; Patzke
et al., 2002). All this shows the importance of anisotropic
metal oxide nanoparticles. Generally, anisotropic shapes
can be formed either through seed-mediated growth whichusually results in monocrystalline nanoparticles, throughoriented attachment or controlled aggregation (Burrows etal., 2016). Interestingly, non-aqueous synthesis routes canfeature chemical species formed in situ that can direct
anisotropic growth (Djerdj et al., 2008a) or even form
inorganic-organic hybrid nanostructures through selfassembly (Pinna and Niederberger, 2008). Thus, thenon-aqueous or non-hydrolytic synthesis routes show ahigh potential in obtaining anisotropic shapes in a simple
manner.
Understanding the formation mechanisms of metal oxide
nanoparticles is crucial for predicting anisotropic growthand enabling the rational design of new non-aqueous synthesis routes capable of producing such anisotropic shapes.
There are several review articles (Deshmukh andNiederberger, 2017; Jun et al., 2006; Mutin and Vioux,2013, 2009; Niederberger and Garnweitner, 2006;
Niederberger and Pinna, 2009; Pinna and Niederberger,
2008; Styskalik et al., 2017) presenting the different
non-aqueous or non-hydrolytic synthesis routes for metaloxides and discussing the reaction mechanisms leading toparticle formation. Additionally, several general reviews
have been published on the wet chemical synthesis of metaloxide nanoparticles (Jun et al., 2006; Nikam et al., 2018) or
their applications (Chavali and Nikolova, 2019; Yu et al.,2016). In contrast to these, this contribution will be limited
to the synthesis of anisotropic metal oxides in non-aqueousand non-hydrolytic systems. In this way, we aim to provide
a deeper insight into the formation of anisotropic metaloxide nanoparticles or nanostructures, and discuss the different factors and mechanisms involved in the emergenceSherif Okeil et al. / KONA Powder and Particle Journal No. 41 (2024) 197–220

of anisotropic particle shapes in the different non-aqueous
and/or non-hydrolytic reaction systems. This should help inthe design of new tailored non-aqueous synthesis routes foranisotropic metal oxide nanoparticles.
In the following paragraphs, an overview of the synthesis of anisotropic particles of the most important metal oxides via non-aqueous and/or non-hydrolytic synthesis
routes will be presented. Each oxide is discussed separately
as the interaction of the different solvents and surfactants
with the nanocrystal surface differs according to the crystal
properties of the metal oxide. This will be followed by adiscussion of the unique mechanisms, especially oriented
attachment, encountered in the formation of anisotropic
metal oxides in non-aqueous and non-hydrolytic synthesis
routes. Then, the formation of anisotropic metal oxides insome special solvents, like benzyl alcohol is reviewed. The
review is finally concluded by an outlook on possible future directions.

2. Titanium dioxide
The synthesis of titanium dioxide as the most-used photocatalytic material has been the focus of research for decades (Liu et al., 2014). Many studies were devoted to thesynthesis of TiO2 nanoparticles with distinct morphologies
due to the increased surface-to-volume ratio where the increased interface permits enhanced catalytic activity(Fattakhova-Rohlfing et al., 2014). Table S1 in the supplementary material provides a list of non-aqueous andnon-hydrolytic synthesis approaches resulting in different
anisotropic TiO2 nanocrystal morphologies in chronological order of their publication. The main factors affecting theanisotropic growth in non-aqueous systems will be discussed in the following text. Although the main factors arepresented, it must be considered that interconnections between the different factors exist, which makes it hard toisolate the effect of single factors completely.

2.1 Effect of solvent
Non-aqueous sol-gel synthesis routes were shown as
being capable of controlling the size and shape, thusachieving different anisotropic TiO2 structures with simple
one-pot synthesis by simply changing the solvent used. The
first non-aqueous synthesis procedure to well-defined
anisotropic titania particles was reported by Wang et al.,using titanium tetrachloride (TiCl4) as the precursor in asolvent mixture of absolute ethanol and acetic acid (Wanget al., 2001). In this work, different organic solvent mixtures were tested for the synthesis of titania nanoparticles,with only the combination of absolute ethanol with acetic
acid yielding anisotropic rutile titania structures (tenuous
fiber and rod-like structures, Table S1). Ethylene glycol or
glycerol as alternatives for ethanol yielded spherical
nanoparticles, which was evident from the corresponding
TEM and XRD analysis. The authors suggested that the ti-

Review Paper

tania formation in these systems mainly proceeds via a hydrolytic mechanism due to the water formed in the systemfrom the esterification reaction between ethanol and acetic
acid. The use of solely ethanol as a solvent also resulted inisotropic nanoparticles (Wang et al., 2001; 2002). The difference in the obtained morphologies by changing the alcohol type was rationalized by the preferential surface
binding of the ethanol molecules to specific lattice planes,
slowing down their growth (Doxsee et al., 1998). The investigation of a series of other alcohols as solvents for the
non-aqueous synthesis of titania from TiCl4 at a low temperature showed that fibrous structures could also be obtained in the case of using isopropanol and n-butyl alcohol
as solvents (Wang et al., 2002).

2.2 Effect of precursor concentration (precursorto‑solvent ratio)

Investigating the influence of the precursor concentration, it was found that the TiCl4/butanol ratio plays an important role in determining the phase and morphology ofthe formed TiO2 nanocrystals. Only a low TiCl4/butanol
ratio resulted in the formation of rutile-phase nanorods,
while higher ratios resulted in isotropic particles (Cao etal., 2011). While the sol-gel reaction of titanium tetrachloride and benzyl alcohol normally yields spherical particles(Niederberger et al., 2002a), nanorod particles have beenobtained by Abazović et al. (Abazović et al., 2006; 2008)
using the same reaction system, which underlines the importance of the applied reaction conditions. The formationof anatase TiO2 nanorods in this reaction system has been
attributed to the prolonged aging time at an elevated temperature (75 °C for 3 days) , which is longer than that used
for spherical particles (24 h only at 75 °C). Rutile-phase
TiO2 nanofibers were also synthesized in a non-aqueous
system from TiCl4 in acetone at 110 °C, where the ratio of
the precursor to acetone was found to be crucial for tuning
the phase and morphology of the synthesized TiO2. A 1:10
ratio of precursor to acetone was required to obtain rutile-phase nanofibers, while lower precursor concentrations
resulted in anatase-phase isotropic and irregular-shaped
particles (Wu et al., 2007a; 2007b). The same behavior was
also observed with other ketones like butanone, 4methylpentanone, and acetophenone (Wu et al., 2007b). In
contrast, the reaction of titanium isopropoxide in ketones
generally results in spherical anatase nanoparticles(Garnweitner et al., 2005).

2.3 Effect of surfactants
While it is evident that changing the solvent can control
the morphology of the resulting metal oxide, the addition ofa surfactant can further tune and direct the nanoparticle
growth and can be used to obtain new particle shapes.
Surfactants can preferentially bind to certain facets of acrystal changing its surface-free energy and thus changing

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the growth rate of the different facets resulting in anisotropic
growth (Bakshi, 2016). Alivisatos et al. used two different
surfactants, namely lauric acid (LA), which selectively
binds to the anatase {001} facets, and trioctylphosphineoxide (TOPO), which nonselectively coordinates to theTiO2 surface. By variation of the ratio of these surfactants,
it is possible to obtain different anisotropic TiO2 particles
ranging from bullet-shaped and rod-shaped nanocrystals tomore complex branched nanocrystal shapes (Jun et al.,2003). At low LA concentrations, anisotropic crystal
growth along the [001] direction takes place, eliminatingthe {001} faces and exposing {101} faces forming bulletshaped (Fig. 1(a)) and diamond-shaped (Fig. 1(b)) nanocrystals. Increasing the amount of the LA surfactant slows
down the growth along the [001] direction, leading to theformation of facetted nanorods (Fig. 1(c) and (d)). At very
high LA concentrations, further inhibition of the growth in
the [001] direction favors the growth of the {101} crystal
faces and results in the branching-out of the {101} faces,
yielding branched nanorod structures (Fig. 1(e)) (Jun et al.,2003).
The use of oleic acid as a surfactant in the non-aqueous
titania synthesis in anhydrous toluene yielded dumbbellshaped nanorods at 250 °C only when the amount of titanium isopropoxide (TIP) precursor was high, using a constant surfactant/precursor ratio of 3:1. At lower precursor
concentrations, an increase of the surfactant/precursor ratio
above 3:1 was necessary to obtain the dumbbell-shaped
nanorods (Kim et al., 2003). This shows that not only thesurfactant/precursor ratio is crucial for obtaining anisotropic structures but also the relative amount of precursor to
the non-aqueous solvent used.

a

Review Paper

A reaction of TIP with oleic acid at lower temperatures(80 °C) results in the derivatization of the titanium precursor forming a titanium oleate complex. This can be used as
an alternative precursor for the TiO2 synthesis due to its
lower reactivity, which can enable better control of thenanoparticle formation. Adapting the chemical reactivity ofthe precursor, further control of the nanoparticle synthesis
can be achieved. The injection of oleylamine into thenon-coordinating solvent 1-octadecene at 260 °C starts thedecomposition of the titanium oleate complex throughaminolysis, resulting in the formation of TiO2 which iscapped by carboxylate ions. The higher reactivity of the(001) planes leads to the facilitated aminolysis of the capping oleate at these planes, which enables anisotropic
growth of the titania nanocrystals in the [001] direction.
Thus, titania nanorods are formed whose length can beeasily tuned through variation of the injected oleylamine
amount. An increase in the oleylamine amount from 1 to
2 mmol increases the titania nanorod length in this system.
A further increase was found to suppress anisotropic
growth where 3 mmol oleylamine leads to a decrease in the
resulting nanorod length; finally for 4 mmol oleylamine,
isotropic titania nanoparticles were achieved. The observed
suppression of anisotropic growth could be due to the accelerated aminolysis and increased consumption of
surface-coordinated oleate by the high oleylamine concentration. Even the diameter of the titania nanorods formed
could be tuned in this system through the use of the more
weakly binding surfactant cetyltrimethylammonium bromide (CTAB) (Zhang et al., 2005). Thus, variation of theoleylamine/oleic acid ratio represents a powerful tool totune the morphology of titania nanocrystals.

b
c

d

e

Fig. 1 High-resolution TEM images showing the evolution of different shapes of TiO2 with varying relative ratio of LA and TOPO surfactants: (a)bullet-shaped, (b) diamond-shaped, (c) short nanorod, (d) long nanorod and (e) branched nanorod structures (Scale bar = 3 nm). Adapted with permission from Ref. (Jun et al., 2003). Copyright 2003 American Chemical Society.

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Through variation of the oleylamine/oleic acid ratio it isalso feasible to finely control the shape of the obtained nanorods. Buonsanti et al. observed that at a lower oleylamine/oleic acid ratio of 26, brookite-phase TiO2 nanorods
with tapered tips were obtained, whereas at much higher
ratios of about 104, rectangular endings of the TiO2 nanorods were observed (Buonsanti et al., 2008). It has to be
noted that the precursor used is TiCl4 instead of TIP and aseeded-growth technique has been adopted for the synthesis.
A similar system was used by Hyeon et al. for the largescale non-aqueous synthesis of TiO2 nanorods, where theTIP precursor was allowed to react with oleic acid at
270 °C for 2 hours, assuming that an ester elimination reaction results in the formation of the titania nanorods growing
along the [001] direction (Fig. 2(a)). The addition of controlled amounts of 1-hexadecylamine (HDA) enabled control of the resulting nanorod diameter as, according to theauthors, HDA strongly coordinates to the (101) anatasefaces slowing down its growth. Thus, an increase of the
HDA amount leads to a decrease in the titania nanorod diameter from 3.4 nm to 2.0 nm (Joo et al., 2005b). Although
it was noted by Zhang et al. that the use of oleic acid oroleylamine alone did not result in particle formation(Zhang et al., 2005), the reaction system of Joo et al. enabled the formation of particles by solely reacting TIP witholeic acid (Joo et al., 2005b).
It was also feasible to obtain ultrathin TiO2 nanosheets
upon heating of TiCl4 precursor in oleylamine alone, whichin this case serves as both the solvent and surfactant(Fig. 2(b)) (Yang et al., 2015). This shows that the concentration of the precursor and surfactant plays an importantrole in the particle formation, along with the reaction temperature and synthesis time. It is noteworthy that the synthesis of TiO2 in oleic acid results in preferential growth in
the [001] direction, resulting in nanorods (Fig. 2(a)) (Joo etal., 2005b), while the synthesis in oleylamine results in an
inhibition of the growth in the [001] direction (c-axis)
where the preferred growth along the a- and b-axis yields inabFig. 2 TEM images of (a) TiO2 nanorods synthesized in oleic acid(adapted with permission from Ref. (Joo et al., 2005b). Copyright 2005
American Chemical Society) and (b) ultrathin TiO2 nanosheets synthesized in oleylamine (adapted with permission from Ref. (Yang et al.,2015). Copyright 2015 The Royal Society of Chemistry).

Review Paper

the formation of nanosheets (Fig. 2(b)) (Yang et al., 2015),
which could indicate the different roles of these surfactants.
Detailed investigations of the parameters leading to theanisotropic growth of TiO2 nanocrystals using a solvothermal synthesis in cyclohexane with titanium butoxide as aprecursor and linoleic acid as a surfactant at a low temperature (150 °C for 48 h) were performed by Li et al. The influence of surfactant amount, reaction temperature, reaction
time and type of surfactant were studied, where an increasein surfactant amount promoted the anisotropic growth. The
nanorods formed also showed spherical tips similar to thedumbbell-shaped nanorods previously obtained in the workby Kim et al. (2003). GC-MS analysis determined the sideproducts formed during the solvothermal synthesis, whichindicated an ester elimination reaction mechanism. Checking the effect of carboxylic acid surfactants revealed thatester elimination alone is not sufficient for particle formation; a long-chain organic acid is also crucial for TiO2 particle formation (Li et al., 2006).

2.4 Effect of doping and precursor addition rate
Alkyl halide elimination for the synthesis of Zr-doped
TiO2 nanocrystals, using a non-hydrolytic, non-aqueous
synthesis route, was adopted by using a mixture of a metal
halide and a metal alkoxide. TOPO was used as the coordinating solvent. The formation of anisotropic facetted Zrdoped TiO2 nanorods was only possible at high temperatures(400 °C), with an increase in Zr-doping leading to a subsequent enlargement of the nanorods (Chang and Doong,2006). The same titania precursors also yielded anisotropic
structures at a lower temperature of 300 °C in the noncoordinating solvent 1-octadecene when using oleylamine
as the surfactant. The morphology and even the phase of
the evolving TiO2 nanostructures could be precisely controlled through the slow addition of the precursors to hot
oleylamine via a syringe pump. A comparably high injection rate of 30 mL/h of the precursors yielded anatase TiO2
nanorods of 6 nm in diameter and 50 nm in length(Fig. 3(a)). Lowering the precursor addition rate to
2.5 mL/h enables the formation of larger anatase nanorodsof 9 nm in diameter and 100 nm in length together withrutile-phase star-shaped nanostructures (25 × 200 nm indimension) (Fig. 3(b)). Reducing the flow rate to 1.25 mL/hyields pure rutile star-shaped TiO2 of 25 nm × 450 nm insize (Fig. 3(c)). Consequently, a slower precursor addition
rate enabled the formation of fewer nuclei which continuously grew with continuous precursor addition. In contrast,
one-shot injection of these precursors into the hot oleylamine resulted in the formation of a mixture of polydisperse
nanoparticles and nanorods, which underlines the importance of the addition rate of the precursors in controlling
particle morphology (Koo et al., 2006).

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3. Zinc oxide
Zinc oxide, as a relatively cheap and non-toxic
semiconductor with a wide range of functional properties,
has attracted broad attention in various application fields
such as gas sensing, piezoelectric devices, photocatalysis,
optoelectronic devices and even biomedicine (Bharat et al.,2019; Kolodziejczak-Radzimska and Jesionowski, 2014;
Zhang et al., 2013). In all of these applications, shape control can open the door for better integration of ZnO nanostructures into the selected application to reach the desired
outcome. Using non-aqueous and non-hydrolytic synthesis
routes, unique morphologies such as teardrop-like shape(Zhong and Knoll, 2005), hexagonal pyramid shape (Choi
S.H. et al., 2005) and nanowires (Yuhas et al., 2006) wereachieved. Supplementary Table S2 lists the different ZnO
particle shapes reported to be obtained from non-aqueousand non-hydrolytic synthesis routes in chronological orderof their publication. In the following paragraphs, the effects
of different factors on ZnO particle morphology will be
discussed in more detail.

3.1 Effect of solvent

Through the use of solvents with different coordinatingab

powers, it was possible to control the shape of evolving
ZnO nanoparticles. Thin zinc oxide nanorods (2 nm in diameter) were obtained in trioctylamine (TOA) with oleic
acid as a surfactant. The self-assembly of the synthesized
nanorods in stacks was observed in this system, with thenanorods being arranged parallel to each other (Fig. 4(a))(Andelman et al., 2005; Yin et al., 2004). The use of the lesscoordinating solvent 1-hexadecanol (HD) resulted in theformation of less elongated ZnO nanoparticles in the formof nanotriangles (Fig. 4(b)). Using the non-coordinating
solvent 1-octadecene (OD) results in the loss of anisotropic
growth, yielding spherical ZnO nanoparticles (Fig. 4(c))(Andelman et al., 2005). Thus, the choice of solvent, in
combination with the surfactant, played an important rolein stabilizing the different crystal planes and determining
the resulting nanoparticle morphology.
Zhang et al. also tested the influence of different noncoordinating and coordinating solvents on the morphologyof the evolving ZnO nanoparticles formed from zinc carboxylate precursors using oleylamine as reagent and surfactant. It is interesting to note that in this work, whenemploying oleylamine as both a reagent and surfactant, theuse of non-coordinating solvents such as dioctyl ether,cFig. 3 TEM images of (a) anatase TiO2 nanorods formed at 30 mL/h, (b) mixture of anatase nanorods and rutile star-shaped TiO2 structures formed
at 2.5 mL/h and (c) pure star-shaped rutile TiO2 formed at 1.25 mL/h precursor addition rate. Adapted with permission from Ref. (Koo et al., 2006).
Copyright 2006 American Chemical Society.

a

b

c

Fig. 4 TEM images with the corresponding crystal structures of (a) ultrathin ZnO nanorods self-assembled in stacks, (b) ZnO nanotriangles and (c)spherical ZnO nanoparticles formed in solvents with different coordinating power. Adapted with permission from Ref. (Andelman et al., 2005). Copyright 2005 American Chemical Society.

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diphenyl ether and OD yielded ZnO nanorods (Zhang Z. etal., 2006), which shows that the control of anisotropic
growth is a complex interplay between different factors,
including the combination of solvent and surfactant used.
In this particular system, the use of the coordinating solvent
trioctylphosphine (TOP) led to the formation of nanotetrahedrons, similar to the nanotriangles obtained in the workof Andelman et al. (Andelman et al., 2005; Zhang Z. et al.,2006). Increasing the coordinating power by using TOPO
as a solvent results in the formation of irregularly shaped
particles, which was attributed to the very strong coordination of TOPO to the zinc acetate precursor and the stericeffect of the bulky alkyl chains hindering the access foroleylamine to the nanocrystal surface, thus drastically decreasing the kinetics of the aminolytic reaction essential forparticle growth in this system (Zhang Z. et al., 2006).
It also has to be mentioned that the role of solvent andsurfactant cannot be separated as the solvent itself usuallyshows some coordination and thus surface activity in covering certain facets of the evolving nanocrystal.
A study of the effect of solvent alone was conducted by
Kunjara Na Ayudhya et al., where the influence of different
glycols, alcohols, n-alkanes and aromatic solvents on themorphology of ZnO nanoparticles was investigated(Kunjara Na Ayudhya et al., 2006). The formation of ZnOfrom the zinc acetate precursor in glycols and alcohols wasachieved at 250 °C, while in non-polar and noncoordinating solvents (n-alkanes and aromatic solvents),
the precursor decomposition temperature increased to
300 °C. The decomposition temperature of the zinc acetate
precursor in the corresponding solvent was related to thedielectric constant of the latter. When the highly polar glycols were used as a solvent, low anisotropic particles ofpolyhedral shape were obtained, while alcohols yielded
nanorods of moderate aspect ratio. The use of n-alkanes
and aromatic solvents resulted in nanorods with a highaspect ratio (Kunjara Na Ayudhya et al., 2006). Thus, theaspect ratio of the ZnO nanorods formed was found to be
tunable depending on the polarity of the solvent used: thehigher the polarity of the solvent, the less the preferential
growth of ZnO in the [001] direction. Thus, alcohols withincreasing chain length resulted in an increase of the aspectratio of the ZnO nanorods formed (Cheng and Samulski,
2004; Kunjara Na Ayudhya et al., 2006; Rai et al., 2013;
Tonto et al., 2008). Similar observations were made when
benzyl alcohol was replaced by the non-polar anisole as asolvent in ZnO synthesis from zinc acetate, where a substantial increase in the nanorod aspect ratio could be observed (Clavel et al., 2007). Other studies reported similar
but still deviating behavior using a different precursor anddifferent reaction conditions (Ramya et al., 2019; Šarić etal., 2019). The synthesis of ZnO nanoparticles through a
precipitation reaction from zinc acetate and sodium hydroxide in a series of alcohols at low temperatures resulted

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Fig. 5 TEM image of a ZnO nanostar formed from zinc acetate in a
mixture of ethanol and ethylene glycol at 80 °C. Adapted with permission from Ref. (Wang et al., 2005). Copyright 2005 Elsevier.

in isotropic nanoparticles in methanol, mixed nanoparticles
and short nanorods in ethanol and nanorods with increasing
aspect ratio from butanol to octanol. In octanol, isotropic
particles were also observed together with the highaspect-ratio nanorods. Finally, isotropic particles were obtained again in decanol. In this system, the growth behavior
was found to be related to the zeta potential of the formed
colloids, which results in different aggregation rates wherea lower zeta potential increases the aggregation rate of thecolloid and leads to an increased growth rate (Šarić et al.,2019).
Solvent mixtures are also capable of producing interesting anisotropic structures through selectively enhancing or
retarding the growth of specific crystal planes. For example, a mixture of ethanol and ethylene glycol resulted in theformation of ZnO nanostars from a zinc acetate precursor
at a low temperature (80 °C) without the need for a surfactant, as can be seen in Fig. 5 (Wang et al., 2005). In thiscase, the anisotropic structure formed could be an aggregate of smaller primary particles which possibly resulted ina hierarchical structure, which, however, was not investigated in this work.

3.2 Effect of surfactant and reaction temperature
The ester elimination reaction, similar to that used forthe large-scale synthesis of TiO2 nanorods, has also been
applied for the synthesis of different ZnO nanoparticles.
The Hyeon Group demonstrated thereby that the use ofdifferent surfactants resulted in strong changes in morphology. TOPO, OA, HDA and TDPA in dioctyl ether as solvents resulted in the formation of wedge-shaped ZnOnanocones self-assembled in spherical nanostructures(Fig. 6(a)), cone-shaped ZnO nanocrystals (Fig. 6(b)),
hexagonal cone-shaped ZnO nanocrystals (Fig. 6(c)) andZnO nanorods (Fig. 6(d)), respectively (Joo et al., 2005a).
Changing the alcohol used to 1,2-dodecanediol and thesolvent to the higher-boiling-point-solvent benzyl ether toperform the synthesis at higher temperatures while using
TOPO as the surfactant yielded similarly pyramidal orcone-shaped particles (Kachynski et al., 2008).

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a

c

b

d

Fig. 6 TEM micrographs of (a) spherical hierarchical structures composed of wedge-shaped ZnO nanocones synthesized using TOPO, (b)ZnO nanocones synthesized using oleic acid, (c) hexagonal ZnO nanocones synthesized using HAD and (d) ZnO nanorods synthesized using
TDPA as surfactant. Adapted with permission from Ref. (Joo et al.,2005a). Copyright 2005 Wiley-VCH.

Increasing the concentration of the surfactant also has an
influence on the particle shape and can even result in the
self-assembly of anisotropic particles to various complex
nanostructures. For example, a rise in the concentration ofoleylamine used in the synthesis of ZnO in TOP led to theformation of nanotetrahedrons that self-assembled into socalled nanofans. A further increase in the oleylamine concentration resulted in larger dumbbell-shaped nanostructures(Zhang Z. et al., 2006).
The decomposition of zinc acetylacetonate (Zn(acac)2)
in oleylamine, which in this case had the role of solvent andsurfactant, yielded different particle shapes by varying theoleylamine/precursor molar ratio together with the reaction
time and temperature. A ratio of 10:1 at a reaction temperature of 205 °C for 1 h yielded short nanorods arranged inbelt-like structures. A lower precursor concentration (ratio
100:1 of oleylamine/precursor) resulted in heart-shaped
nanoparticles at 205 °C for 24 h (Liu et al., 2007).
Similarly, the influence of oleylamine on the particle
morphology was also investigated by Zhang et al., however, using zinc acetate as the precursor. In this system, theamine was able to attack the precursor in an amide elimination reaction or aminolysis yielding ZnO. In that case, it
was found that increasing the molar ratio of oleylamine toprecursor decreases the anisotropic growth along the [001]direction, leading to the formation of nanorods with a lower
aspect ratio and finally nanoprisms in the case of ratios
greater than 3 (Fig. 7). A further increase of the oleylamine/precursor ratio up to 24:1 had no significant effect on the
resulting morphology (Zhang et al., 2007). It has to be
noted that in this work, the precursor used is different,

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Fig. 7 Diagram showing the relation between the molar ratio of oleylamine to zinc acetate used and the resulting aspect ratio as well as thediameter of the ZnO nanocrystals. Above, for each morphology in the
diagram (long nanorods, short nanorods, bullet-shaped nanocrystals andnanoprisms), the corresponding TEM image proving the formation ofsuch ZnO nanomaterial is shown (scale bar: 100 nm). Adapted withpermission from Ref. (Zhang et al., 2007). Copyright 2007 Wiley-VCH.

which affects the rate of monomer generation and partly
explains the difference to the shapes obtained in the work
by Liu et al. (2007). Nevertheless, both works showed adecrease in anisotropic growth with an increasing amountof oleylamine (Liu et al., 2007; Zhang et al., 2007), which
was related to an increased reaction rate, leading to rapid
nucleation and leaving fewer monomers available for further particle growth. On the other hand, a low amine-toprecursor ratio resulted in a low reaction rate, leading to theformation of fewer initial nuclei and leaving more monomers for further particle growth, where the growth offaster-growing planes becomes apparent, forming highly
anisotropic nanocrystals (Zhang et al., 2007).
The testing of other amines (hexadecylamine, dioctylamine and dodecylamine) showed that the longer the alkyl
chain of the amine used, the thinner and longer are the ZnO
nanowires or nanorods produced when all other reaction
parameters are kept constant (Zhang et al., 2007). Correlating these results with other studies where the effect of thesolvent was investigated (Clavel et al., 2007; Kunjara Na
Ayudhya et al., 2006), it can be deduced that the amines
with longer-chain lengths result in higher aspect ratios dueto their lower polarity. Using other amines like benzylamine as a reagent to initiate the aminolysis reaction of zincacetate in benzyl ether also resulted in different anisotropic
shapes such as nanocones, nanobullets, nanorods and nanoplates depending on the molar ratio of benzylamine to thezinc precursor as well as the reaction temperature used
(Ahmad et al., 2013; Chang and Waclawik, 2012). By

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adjusting the amount of benzylamine and the reaction temperature, the reaction rate was controlled which resulted indifferent nucleation and growth mechanisms. It was foundthat zinc acetate does not decompose in pure benzyl ether
at temperatures below 210 °C unless a reagent initiating anaminolysis reaction is added. Thus, performing a reaction
at 170 °C using a benzylamine/Zn ratio of 10 would be apure aminolysis reaction resulting in the formation of ZnO
nanocones. On the other hand, keeping the same amineto-Zn ratio while using a higher reaction temperature of
210 °C would result in a mixed thermal decomposition andaminolysis reaction, together with an increased reaction
rate due to the increased temperature. Thus, the reaction at
210 °C resulted in the formation of ZnO nanobullets where
the top cone shape of the nanobullets was explained by thecapping effect of the excess benzylamine. This underlines
the dual role of the amine reagent, where on the one hand it
is a reagent for the aminolysis reaction, and on the other
hand, any excess not involved in the aminolysis reaction
acts as a capping agent for the polar ZnO facets. This isfurther proved by decreasing the benzylamine/Zn ratio to 1
while keeping the reaction temperature at 210 °C, resulting
in the formation of nanorods without the cone-shaped tip
due to the absence of excess benzylamine, which would
take the function of a capping agent. An increase of the ratio to 40 resulted in nanoplate formation due to the strong
coordination of excess benzylamine to the polar Zn2+ surface, inhibiting growth along the [001] direction. It has tobe mentioned that the heating rate was also found to have apronounced effect on the resultant morphology in this system, where rapid heating resulted in the ZnO nanoplates,
while slow heating (~ 5 °C/min) to the same reaction temperature led to the formation of “uncompleted” ZnO nanocones (Chang and Waclawik, 2012).
While oleylamine has been widely used as a surfactant
and morphology-controlling agent in the synthesis of ZnOnanostructures, oleic acid would be another possible interesting surfactant for morphology control due to its strong
coordination ability with the ZnO surface. Usually, however, OA was used in combination with amines (Choi S.H.
et al., 2005; Yin et al., 2004) during the synthesis of ZnO,abwhich makes it hard to determine the role of each in theformation of the anisotropic structure. Zhong et al. used a
mixture of OA and oleyl alcohol to synthesize ZnO nanostructures from zinc acetate by the well-known ester elimination reaction. In this system, the alcohol acts as a solvent
and reagent for the formation of ZnO from zinc acetate
through an “esterification alcoholysis reaction”, while the
OA acts as a surfactant and structure-directing agent. By
adjusting the relative amount of oleic acid to oleyl alcohol,
while keeping the total volume of the mixture constant, it
was possible to obtain a range of complex nanostructures
such as spiked ZnO nanostructures (Fig. 8(a)) and ZnO
tetrapods of various dimensions (Fig. 8(b) and (c)). By increasing the relative amount of oleyl alcohol and decreasing the relative amount of oleic acid, smaller-sized ZnO
tetrapods were obtained due to the increased reaction rateof the alcohol with the precursor, which results in a large
number of initial nuclei in a short time (Zhong et al., 2007).
Similarly, Zhang et al. also obtained Mn-doped ZnO tetrapods and multipods from bulk ZnO using OA and oleyl alcohol in the non-coordinating solvent 1-octadecene (Zhang
and Li, 2009). The use of other alcohols like 1,12dodecanediol and 1-hexadecanol with oleic acid and zincacetate yielded ZnO nanocones (Joo et al., 2005a) andnanotriangles (Andelman et al., 2005), respectively.
Oleic acid was also used as a capping agent without anyother additives in the non-polar solvent n-octadecene. In
this medium, previously prepared zinc oleate was used as aprecursor with further addition of oleic acid as the surfactant, and the mixture reacted under reflux. Interestingly,
defined triangular and hexagonal two-dimensional platelike structures were formed with prolonged reflux time
(Fig. 9(a)). The formation of these two-dimensional thinplate-like ZnO structures of about 10 nm in thickness is
attributed to the strong binding affinity of oleic acid to thechemically active Zn2+ terminated (0001) surface, which
prevents further growth in this direction (c-direction) (Chiuet al., 2008). While a longer synthesis time yielded moredefined shapes, it was also observed that a thinning of thecentral area of the ZnO platelets took place (Fig. 9(b)),
which was correlated to an etching activity of the boundcFig. 8 TEM images of (a) ZnO spiked clusters formed using a volume ratio of 1:2 oleic acid to oleyl alcohol, (b) large ZnO tetrapods formed using a
volume ratio of 1:3.5 oleic acid to oleyl alcohol and (c) small ZnO tetrapods formed using a volume ratio of 1:8 oleic acid to oleylamine while keeping
the total volume constant. Adapted with permission from Ref. (Zhong et al., 2007). Copyright 2007 Wiley-VCH.

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Sherif Okeil et al. / KONA Powder and Particle Journal No. 41 (2024) 197–220abFig. 9 TEM images of two-dimensional ZnO platelets (a) showing thedifferent triangular and hexagonal platelet structures formed after
90 min synthesis time and (b) an individual ZnO platelet formed after
120 min synthesis time showing a thinning effect in the center with increased synthesis time. Adapted with permission from Ref. (Chiu et al.,2008). Copyright 2008 Elsevier.

oleic acid. Thus, it was predicted that even longer reflux
times could yield ring-like ZnO structures (Chiu et al.,2008).
The use of ethylene diamine as a short-chain diamine
was reported to yield micrometer-long ZnO nanorods inalcohols (Panchakarla et al., 2007). Hexamethylenetetramine (HMTA) was also used as a surfactant for the synthesis of ZnO nanorods from Zn(acac)2 in the coordinating
solvent trioctylamine at a low temperature, where an increase in the surfactant-to-precursor ratio resulted inwell-defined nanorods with a higher aspect ratio. However,increasing the reaction temperature from 70 °C to 140 °C
while maintaining the HMTA to Zn(acac)2 constant resulted in a decrease in the aspect ratio, forming less anisotropic structures (Devarepally et al., 2012). Alternatively,
polyvinylpyrrolidone (PVP) in ethanol was reported to result in nanorods at a low temperature (80 °C) with good
crystallinity (Wang et al., 2005), showing that strongly coordinating ligands such as amines are not necessary fornanorod growth.
The formation of ZnO from zinc acetate and a hydroxide
is a typical precipitation reaction that can also be performed
in organic media, and as no water was added to the system,
this can still be interpreted as a non-aqueous synthesis. Alcohols like methanol and ethanol have been mainly used assolvents, where usually an alcoholic solution of NaOH or
KOH is added to a zinc salt dissolved in the same alcohol.
While ZnO nanorods have been mainly obtained at higher
temperatures (Cheng and Samulski, 2004; Pacholski et al.,
2002; Rai et al., 2013), interwoven ZnO nanosheets wereobtained when the reaction was performed at room temperature (Khokhra et al., 2015) which shows that the reaction temperature is also a crucial factor for tuning the shapeof the nanostructures obtained.

3.3 Effect of precursor type
The type of metal oxide precursor used also has a significant influence on the nanostructure obtained. Heating different zinc alkylcarboxylate precursors in TOP using

206

a

c

b

d

Fig. 10 TEM micrographs of ZnO nanostructures formed from different zinc carboxylate precursors: (a, b) multiarmed ZnO nanostructures
formed of nanotetrahedrons obtained from (a) zinc acetate and (b) zinc
hexanoate, (c, d) squamous ZnO nanostructures produced from (c) zincoctanoate and (d) zinc oleate. The inset of (d) shows a schematic of thestructure of the squamous nanocrystals. Adapted with permission from
Ref. (Zhang Z. et al., 2006). Copyright 2006 American Chemical Society.

oleylamine as a surfactant results in various types of nanostructures. Zinc salts of short alkyl chain carboxylic acids,
such as zinc acetate and zinc hexanoate, resulted in theformation of multiarmed ZnO nanostructures formed of
nanotetrahedrons, while precursors containing longer-chain
carboxylates like zinc octanoate and zinc oleate produced“squama-like” (fish scale-like) nanostructures (Fig. 10)(Zhang Z. et al., 2006). In these non-aqueous synthesis
methods, the oleylamine attacks the carboxylates in anaminolytic reaction resulting in amide elimination and ZnO
formation. The change in morphology with precursor typewas attributed to the difference in the reactivity of the precursor to the oleylamine, thus modulating the rate of theaminolytic reaction. With an increasing chain length of the
carboxylate in the precursor, the steric hindrance increased,
resulting in a decrease of the aminolysis rate at the coordinated {110} planes but leaving the growth in the [001] direction unhindered. Thus, precursors containing
shorter-chain carboxylates formed nanotetrahedrons, while
the longer-chain carboxylate precursors formed squamouslike nanocrystals, where the strong binding affinity andsteric effects of the longer-chain carboxylates such as
oleate to the (110) plane leads to further suppression of thegrowth in the [110] direction, forming very thin “nanosquamas” (Zhang Z. et al., 2006).
In a separate study by Xu et al., different precursors also
resulted in a good variation of particle morphology whenused in tetrahydrofuran (THF). While zinc acetate resulted
in hexagonal-based pyramidal particles, using Zn(acac)2

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instead led to isotropic particles arranged in a cauliflowerlike morphology (Xu et al., 2009). The decomposition
temperature of each precursor in the same solvents alreadygives a clue about the different behavior of different precursors. Kunjara Na Ayudha et al. found that the solvothermal
synthesis of ZnO from zinc acetate in the non-polar solvents decane and toluene was not feasible at 250 °C, andthe synthesis temperature needed to be increased to 300 °C
to obtain ZnO nanorods (Kunjara Na Ayudhya et al., 2006).
On the other hand, it was possible to obtain ZnO from
Zn(acac)2 in decane and toluene at the much lower temperature of 120 °C. In that case, completely different morphologies were obtained where the synthesis in decane
resulted in truncated hexagonal pyramidal particles, while
in toluene hourglass-like particles were obtained (Xu et al.,2009).

3.4 Effect of precursor concentration

It is generally known from different reaction systems
that nanocrystal growth is highly dependent on the monomer concentration. A low monomer concentration usually
results in isotropic growth while a high monomer concentration results in an anisotropic growth profile due to thedifferent growth rates of the diverse crystal facets (Manna
et al., 2000; Peng and Peng, 2002). In the synthesis of ZnO
nanoparticles in a basic methanolic solution of zinc acetate
dihydrate, it was observed that by simply increasing the Zn
precursor concentration (10-fold compared to the concentration yielding spherical particles), anisotropic ZnO nanorods could be obtained (Pacholski et al., 2002). Similar
observations were also made for the microwave-assisted
synthesis of ZnO from zinc acetylacetonate in 1-butanol,
where an increase of the precursor concentration increased
the aspect ratio of the formed nanoparticles, resulting in
ZnO nanorods (Ambrožič et al., 2011).
While in some systems, no effect of the precursor concentration on the resulting morphology of the nanoparticles
was detected—although an increase of the overall particle
size was still observed (Ambrožič et al., 2010; Tonto et al.,2008)—the influence of precursor concentration was more
pronounced in other systems (Ambrožič et al., 2011;
Pacholski et al., 2002), which underlines the fact that it isan interplay of the precursor type used and overall synthesis conditions used. In this context, Ambrožič et al. observed a concentration-dependent morphology change
when using microwave irradiation, whilst a concentrationindependent behavior was observed for the same reaction
system using reflux conditions with conventional heating(Ambrožič et al., 2010; 2011).

3.5 Effect of doping

It was also observed that doping could affect the anisotropic growth of metal oxide nanoparticles. Co- andMn-doping of ZnO nanorods synthesized in benzyl alcohol

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was found to decrease the aspect ratio of the resulting ZnOnanorods, with the effect of Co-doping on the nanorod aspect ratio being more pronounced (Djerdj et al., 2008b).

3.6 Other factors

There are different non-trivial factors that can significantly affect the growth behavior of nanocrystals in the reaction system studied but they are usually overlooked.
While the effects of solvents, ligands or surfactants, typesof metal oxide precursors, solvent/surfactant/precursor ratios, reaction temperature and synthesis time are themost-studied factors, some other factors can still significantly affect anisotropic crystal growth. Zhao et al. investigated the influence of the mixing time of the precursor,
dicyclohexyl zinc, with the ligand, dodecylamine, on theanisotropic growth of ZnO particles (Zhao et al., 2021). A
mixing time of more than 6 hours before the start of theactual hydrolysis reaction was found to inhibit the anisotropic particle growth, which was ascribed to the increasing
viscosity of the reaction mixture with increasing mixing
time, subsequently hindering the anisotropic growth ofZnO through the oriented attachment mechanism (Zhao etal., 2021). Thus, whilst many different factors have been
identified that facilitate the synthesis of ZnO nanostructures with anisotropic morphologies, a particular reaction
system must always be understood in detail to enableproper control of the particle properties.

4. Iron oxide
Iron oxide is one of the most important magnetic materials, and iron oxide nanocrystals with defined properties areused in manifold applications such as magnetic storage
media and magnetic resonance imaging (MRI) (Ajinkya etal., 2020; Teja and Koh, 2009). Anisotropy can be used tomodulate the magnetic properties of such nanoparticles
where it was found that anisotropic iron oxide nanoparticles possess improved magnetic properties (Zhang et al.,2009). Tuning the shape of the iron oxide nanoparticles results in the exposure of different crystal facets that could be
used to vary the T1 and T2 proton relaxation time shortening
effects, which in turn is important for the design of efficient
MRI contrast agents (Zhou et al., 2015). Table S3 in the
supplementary material summarizes the state of the literature on the synthesis of anisotropic iron oxide nanocrystals
in non-aqueous media in chronological order.
Whilst typically, magnetite (Fe3O4) is the desired material, maghemite (γ-Fe2O3) shows similar ferromagnetic
behavior and is obtained under more oxidative conditions.
As magnetite particles may oxidize during extended storage, particularly in aqueous media, the direct synthesis ofmaghemite might thus provide a more stable product(Grabs et al., 2012; Masthoff et al., 2014). In non-aqueous
reaction systems for the synthesis of iron oxide, surfactants
are typically required to obtain particle shapes other than207

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spheres. The use of long-chain amines such as dodecylamine was found to result in the formation of large
hexagon-shaped γ-Fe2O3 particles from iron pentacarbonyl(Fe(CO)5) at a surfactant-to-precursor ratio of at least 10.
Lower ratios resulted in a mixture of spherical, triangularand diamond-shaped nanocrystals (Cheon et al., 2004).
Unique star-shaped Fe3O4 nanocrystals were obtained by
the use of the bulky 1-adamantanecarboxylic acid insteadof the linear oleic acid as the surfactant together witholeylamine (Fig. 11) (Zhang L. et al., 2006). It was proposed that the growth mechanism resulting in the anisotropic star shape is based on the bulkiness of the
1-adamantanecarboxylic acid surfactant molecules. Dueto their bulkiness, some sites were not covered by thesurfactant molecules and led to their further growth,which resulted in the observed spikes of the star-shaped
nanocrystals. Further substitution of oleylamine with thebulky 1-adamantaneamine so that the surfactant system
is composed of 1-adamantanecarboxylic acid and 1adamantaneamine instead of the traditional oleic acid/oleylamine surfactant system resulted in a certain anisotropy where irregular-shaped particles were obtained forming flower-like aggregates (Zhang L. et al., 2006). With aternary surfactant mixture of oleic acid, oleylamine andhexadecane-1,2-diol in a ratio of 3:3:5, it was even possible
to produce tetrapod-shaped maghemite nanocrystals from
Fe(CO)5 (Fig. 12) (Cozzoli et al., 2006). Increasing the
precursor concentration of resulted in an increase in anisotropic growth that led to longer tetrapod arms. It was also
found that a significant deviation from the optimized 3:3:5
ratio of the three-component surfactant system—or omitting any of these surfactants from the reaction system—
resulted in a loss of the tetrapod geometry, which shows theimportant role of all surfactants in this specified ratio in
forming the unique tetrapod geometry (Cozzoli et al.,2006). Analogous tripods, or shamrock-shaped particles, ofnickel manganese ferrite were interestingly obtained in amuch more facile synthesis without surfactants via an ori-aented attachment mechanism (Masthoff et al., 2015).
Hofmann et al. also investigated the influence of the surfactant ratio of the surfactant system of trioctylamine and oleic
acid on the morphology of iron oxide nanoparticles andfound that the nanocrystal shape is significantly influenced
by the surfactant ratio used. Increasing the trioctylamine tooleic acid ratio resulted in the formation of cubic wüstiteiron oxide nanocrystals with concave faces which were referred to as nanotetracubes (Hofmann et al., 2008).
Fatty acid surfactants such as oleic acid were used inseveral studies, often in the form of its oleate anion as partof the iron oxide precursor. Typically, iron(III) oleate is first
synthesized in a separate step and the purified precursor isthen mixed with the solvent and heated for the actual synthesis. In this case the oleate already present in the precursor can act as a surfactant and structure-directing agent.
Heating the iron oleate complex in 1-octadencene at
320 °C for one hour resulted in cubic iron oxide nanocrystals (Kwon et al., 2007). In another study, the controlled
decomposition of the iron oleate precursor at a low temperature of 150 °C in the same solvent formed iron oxide
nanowhiskers. At temperatures higher than 300 °C, spherical particles were formed, as commonly reported in the literature. Thermogravimetric analysis (TGA) and density
functional theory (DFT) calculations revealed the dissociation of two of the three oleate ligands from the complex at
150 °C, and the third bound oleate ligand was assumed toact as a structure-directing agent for the formation of the
one-dimensional nanowhiskers (Palchoudhury et al.,2011a). The addition of the weakly binding surfactant
TOPO to a system of iron oleate and oleic acid at a ratio of
6.3:1 (TOPO/OA) resulted in the formation of iron oxide
nanoworms, which was attributed to the controlled aggregation of spherical nanoparticles (Palchoudhury et al.,2011b).
A plethora of different iron oxide nanocrystal shapes
were accessed by the use of oleate salts instead of oleic acidas the surfactant. Although the oleate ligands in thebFig. 11 (a) TEM image of iron oxide nanostars obtained using thebulky 1-adamantanecarboxylic acid as a surfactant. The inset shows the
HRTEM image of a single iron oxide nanostar. (b) Schematic illustration showing the proposed formation mechanism of iron oxide nanostars. Adapted with permission from Ref. (Zhang L. et al., 2006).
Copyright 2006 Elsevier.

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Fig. 12 TEM image of tetrapod maghemite nanocrystals formed
through a 3:3:5 surfactant ratio of oleic acid, oleylamine and hexadecane-1,2-diol. The inset shows a HRTEM image of a single tetrapod
nanocrystal viewed along the <111> zone axis. Adapted with permission
from Ref. (Cozzoli et al., 2006). Copyright 2006 American Chemical
Society.

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precursor can act as a surfactant, further addition of oleic
acid and sodium oleate to the reaction mixture has a significant influence on the shape evolution of the iron oxide
nanocrystals. Kovalenko et al. observed that replacing thesurfactant oleic acid with its sodium or potassium salt in areaction system containing iron oleate as a precursor resultsin the formation of cubic instead of spherical iron oxide
nanocrystals. Decreasing the sodium oleate-to-iron oleate
precursor ratio even resulted in bipyramidal nanocrystals.
From conductivity measurements, it was inferred that theoleate salts in octadecene dissociate at temperatures above
220–230 °C, resulting in ionic oleate species, which is not
the case for oleic acid; this difference was related to thedifferent shape evolution (Kovalenko et al., 2007). Similarly, Bao et al. synthesized iron oxide nanorods from ironoleate in benzyl ether by replacing oleic acid with sodium
oleate as surfactant, whereby the aspect ratio of the productcould be carefully tuned by varying the synthesis temperature (Bao et al., 2012).
A broad range of different iron oxide nanocrystal shapes
was also synthesized by Gao’s group through variation of
the sodium oleate-to-iron oleate molar ratio and using
1-octadecene or trioctylamine as a solvent (Fig. 13) (Zhouet al., 2015). Increasing this ratio in 1-octadecene resulted
in an increased exposure of the {111} facets, which led tothe shape evolving from spherical particles (in the absenceof sodium oleate) to hexagonal plates, truncated octahedrons, and finally to tetrahedrons (Fig. 13(b)–(d)). Accordingly, it was assumed that the oleate ions preferentiallyattached to the {111} facets, thus stabilizing them and retarding their growth, which resulted in an increased exposure of the {111} facets with an increasing sodium oleate
amount. Using trioctylamine as the solvent, in whichhigher synthesis temperatures are possible, iron oxide
nanocubes (Fig. 13(e)) were obtained at low sodium
oleate-to-iron oleate ratios (NaOL/FeOL ≤ 1:10), while at aratio of 2:10, spikes started to emerge at the cube corners
forming what the authors called “concave” nanocrystals(Fig. 13(f)). A further increase of the sodium oleate amount(NaOL/FeOL = 5:10) resulted in assembled nanostructures
which were supposedly formed through the oriented attachment of smaller building blocks (Fig. 13(g)). Using
low NaOL to FeOL ratios (NaOL/FeOL = 1:10) that previously resulted in nanocubes formed multibranched ironoxide nanoparticles when higher dilutions were used (i.e.
lower concentrations of NaOL and FeOL in the system)(Fig. 13(h)) (Zhou et al., 2015).
In addition, the use of long-chain quaternary ammonium
salts such as tetraoctylammonium bromide was found to
induce anisotropic iron oxide nanocrystal growth where
nanooctahedra were obtained from the iron oleate precursor. In this system, the authors also addressed the fact thatchemical species formed in situ during the synthesis canplay an important role in determining the shape of the synthesized nanoparticles (Shavel et al., 2009).
Besides the amount of surfactant or the surfactant-toprecursor ratio, some other non-trivial factors were described to influence the anisotropic growth during the
non-aqueous and non-hydrolytic synthesis. Recently, theeffect of nitrogen purging on the anisotropic growth of ironoxide nanoparticles was investigated by AbuTalib et al.
(2021). Through testing different configurations for nitrogen flow, it was found that bubbling the reaction mixture
with nitrogen induced anisotropic growth in their system,
while applying nitrogen flow over the reaction mixture reduced the anisotropic growth, and finally the use of only a

a

b

c

d

e

f

g

h

Fig. 13 (a) Overview of the different iron oxide particle morphologies obtained using iron oleate as a precursor and sodium oleate as a surfactant;
TEM images showing iron oxide (b) nanoplates, (c) truncated octahedrons, (d) tetrahedrons obtained in 1-octadecene and (e) nanocubes, (f) concave
particles, (g) assembled structures and (h) multibranched nanocrystals obtained in trioctylamine using different sodium oleate-to-iron oleate ratios.
Adapted with permission from Ref. (Zhou et al., 2015). Copyright 2015 American Chemical Society.

209Sherif Okeil et al. / KONA Powder and Particle Journal No. 41 (2024) 197–220“nitrogen blanket” (i.e. positive pressure of nitrogen in thesystem without steady flow) favored isotropic crystal
growth. The nitrogen flow rate did not play a significant
role in the extent of anisotropic growth. It is assumed thatthe reason for this anisotropy observed in the nitrogenbubbling system arises from increased turbulence, the bubbles acting as nucleation sites and inducing aggregation, or
the improved removal of volatile by-products from thesystem which otherwise influence the particle morphology(AbuTalib et al., 2021). Additionally, a significant influenceof the amount of the oleylamine surfactant relative to theFe(acac)3 precursor on the morphology of the product was
found in this system. While at very high oleylamine-toprecursor ratios (above 40 equivalents), isotropic particles
were observed, decreasing the oleylamine amount to 25
equivalents resulted in anisotropic nuclei which formed
iron oxide nanocrystals with two branches (bipods). A further decrease in the oleylamine amount to 17 equivalents
resulted in multiply branched nanocrystals and finallybranched nanocrystals with a flower-like appearance at 15
equivalents of oleylamine. An investigation of the evolution of the branched iron oxide nanocrystals over time revealed the formation of small anisotropic nuclei in mostcases, which further aggregate in the initial stages of thesynthesis forming polycrystalline nanocrystals. The defects
and misalignments in the formed aggregates are then removed through rearrangement and ordering over the courseof the reaction, and finally monocrystalline branched ironoxide nanocrystals are obtained.
Prolongation of the synthesis time also has an influenceon the product morphology. While the decomposition ofiron oleate in 1-octadecane at 320 °C results in the formation of monodisperse spherical nanocrystals for a synthesistime up to 30 minutes (Park et al., 2004a), an increase of
the synthesis time to one hour resulted in monodisperse
cubic nanocrystals (Kwon et al., 2007). The spherical ironoxide nanocrystals are converted to cubic nanocrystals
upon prolonged aging at 320 °C, forming the thermodynamically most stable shape for the iron oxide spinel structure with only {100} planes exposed (Kwon et al., 2007). Achange of shape with reaction time was also observed
during the synthesis of iron oxide from iron(II) acetate intrioctylamine using oleic acid as the surfactant at 255 °C.
While at shorter synthesis times (10–25 min) cubic nanocrystals were formed, the particles transformed into truncated octahedrons when the reaction time was prolonged
further (Redl et al., 2004).
Two-dimensional iron oxide nanocrystals obtained from
non-aqueous and non-hydrolytic synthesis are still rare andthe parameters and conditions required for their synthesis
are still not well understood. Examples of such twodimensional iron oxide nanocrystals were presented by
Casula et al., where circular nanodisks were obtained fromiron pentacarbonyl in octyl ether using tridecanoic acid as

210

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the surfactant (Casula et al., 2006). It is important to notethat the disk form of the obtained nanoparticles has been
confirmed through AFM height measurements. In general,
even if TEM investigations reveal a spherical nanocrystal
appearance, it is important to investigate the true height of
the nanoparticles to prove their isotropy or anisotropy, but
this is usually not performed in most of the publishedworks so far. In another work, hexagonal and triangulariron oxide nanoplates were obtained in diethylene glycol
from iron(III) chloride without the use of any surfactant(Zhang et al., 2009).

5. Other metal oxides
A number of other metal oxides and mixed-metal oxides
have been synthesized as anisotropic nanocrystals via
non-aqueous synthesis routes. As there are only a few reports about each, they are discussed in this section in combined form. Table S4 in the supplementary material
provides an overview of the materials and morphologies
obtained and the respective synthesis conditions. In thefollowing paragraphs, some more insight will be given forsome of these reaction systems.
Zirconia nanocrystals synthesized in benzyl alcohol using sodium lauryl sulfate as a surfactant showed onedimensional growth when ZrOCl2∙8H2O was used as the
precursor, while Zr(SO4)2·H2O yielded only isotropic
nanoparticles (Siddiqui et al., 2012). The reaction of zirconium n-propoxide in benzyl alcohol was reported to lead tofractal nanostructures, in particular at lower reaction temperatures and extended reaction times (Stolzenburg et al.,2016).
The synthesis of V2O3 from VOCl3 in benzyl alcohol led
to ellipsoidal-shaped nanorods (Niederberger et al., 2002b;
Ohayon and Gedanken, 2010), whereby doping with therare earth metals Gd and Nd resulted in an alteration of themorphology to nanoflakes and nanocubes, respectively(Venkatesan et al., 2015).
Due to the great potential of tungsten oxide in different
fields, such as for batteries, gas sensing, as well as photocatalytic and electrochromic applications, different
non-aqueous synthesis procedures for tungsten oxide nanorods or nanowires have been developed (Choi H.G. etal., 2005; Lee et al., 2003; Polleux et al., 2006; Woo et al.,2005). Woo et al. conducted a systematic study which revealed that the aspect ratio of tungsten oxide nanorods
could be tuned through the coordinating power of the surfactant system used. Increasing the coordinating power was
thereby found to decrease the tungsten oxide nanorod
length, which was attributed to the increased inhibition of
growth in the [010] direction resulting from the stronger
ligand binding (Woo et al., 2005). In a different approach,
Choi et al. were able to tune the aspect ratio of tungsten
oxide nanorods by adjusting the precursor concentration inan ethanolic solution, without the need for any surfactant.

Sherif Okeil et al. / KONA Powder and Particle Journal No. 41 (2024) 197–220

Using lower WCl6 precursor concentrations resulted inhigher aspect ratios and even in the growth of tungsten oxide nanowires (Choi H.G. et al., 2005). To obtain more
complex structures, Zhao et al. added urea to the ethanolic
WCl5 precursor solution to act as a ligand and structuredirecting agent due to its strong hydrogen bonding ability.
This enabled the formation of tungsten oxide nanotubes
using a template-free non-aqueous synthesis for the firsttime, whereby the as-synthesized product was identified astungstic acid hydrate (H2W1.5O5.5·H2O) and was transformed to WO3 through calcination without loss of thehollow structure (Zhao and Miyauchi, 2008). Variation of
the synthesis parameters, such as the urea amount and reaction time, yielded further hollow tungstic acid hydrate
nanostructures (hollow spheres and boxes), which wereconverted to tungsten trioxide upon calcination. Interestingly, a broad range of morphologies was obtained thereby,
which was attributed to different self-assembly processesof smaller nanoparticles and nanorods due to the presenceof urea in the reaction system (Zhao and Miyauchi, 2009).
There has also been an interest in synthesizing manganese oxide due to its magnetic properties, especially at thenanoscale (Park et al., 2004b). Uniform MnO nanorods
were obtained by Park et al. through injecting a previously
prepared manganese oleylamine complex into trioctylphosphine or triphenylphosphine at high temperatures (330 °C)(Park et al., 2004b). Zitoun et al. were able to prepare MnO
multipods via the decomposition of Mn(oleate)2 in ntrioctylamine in the presence of oleic acid at 320 °C, where
the arms showed an arrow-like ending and a zigzag structure which indicated an oriented attachment process of theinitially formed cube-shaped or truncated octahedral nuclei(Zitoun et al., 2005). Similar anisotropic MnO nanoparticle
shapes termed as “dumbbell-shaped” MnO nanocrystals
were obtained by Zhong et al. through the use of manganese formate hydrate as a precursor (Zhong et al., 2006).
Anisotropic shapes for manganese cobalt oxide (MnCoOx)
ternary mixed-metal oxides were obtained through the decomposition of Mn(oleate)2 and Co(oleate)2 precursors in
1-octadecene in the presence of oleic acid (Gliech et al.,2020). Interestingly, the use of a single metal oxide precursor in this system yielded cubic or octahedral shapes for
manganese oxide and aggregated octahedral shapes forcobalt oxide, while for the mixed-metal oxide, rod-, T-,cross- and hexapod-shaped nanocrystals were obtainedwith decreasing cobalt concentrations. A new solutionsolid-solid (SSS) oxide mechanism was suggested for the
anisotropic growth of the mixed-metal oxide nanoparticles,
which is a good example of the influence of other metal
ions on the anisotropic growth of metal oxides. In the suggested SSS growth mechanism, first MnO nuclei areformed, followed by the deposition of Co monomers toform Co-rich regions which serve as sites for outward
growth in the <100> directions, thus forming the arms. At

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low Co concentration, the Co has time to accumulate on
different facets of the MnO nuclei, thus forming several
starting points for the branching, resulting in multiarmed
structures such as hexapods. Increasing the Co concentration decreases the cobalt accumulation time and lessbranched structures occur, thus leading to nanorod structures at high concentrations (Gliech et al., 2020).
Even more exotic metal oxides such as actinide oxides
have been synthesized using the non-aqueous and nonhydrolytic synthesis route. In several cases, the use of certain surfactants and certain synthesis conditions yielded
anisotropic shapes for those oxides. For example, anisotropic thorium oxide (ThO2) has been obtained from thorium
acetylacetonate in benzyl ether using a ternary surfactant
mixture of TOPO, OA and trioctylamine at 280 °C (Hudry
et al., 2012).

6. Oriented attachment mechanisms
resulting in anisotropic structures

Different mechanisms have been reported to potentiallylead to the formation of anisotropic nanoparticles or nanostructures such as controlled aggregation (for example via
oriented attachment), etching and branching (AbuTalib etal., 2021). According to the classical crystallization model,
anisotropic growth in metal oxides can be mainly achieved
via kinetic control through the use of surfactants or by theselective formation of nuclei of specific crystal phases featuring different surface energies (Jun et al., 2006; Sajanlal
et al., 2011). Besides the classical crystallization theory,oriented attachment has been proven to represent an important pathway to result in anisotropic metal oxide nanocrystals.
The oriented attachment mechanism is based on thecontrolled aggregation of nanoparticle building blocks
showing an aligned crystal orientation towards each otherand thus, in principle, leads to one larger crystal. Thereby,
isotropic metal oxide nanoparticles can also be used asbuilding blocks for larger anisotropic and more complex
three-dimensional structures. This process can be observedin the absence of organic surfactants which delimits thisprocess from usual self-assembly phenomena that occur
due to organic building blocks. This phenomenon was
identified to represent an important non-classical crystal
growth mechanism and was found in many systems including non-aqueous and/or non-hydrolytic metal oxide synthesis (Chen et al., 2022; Pacholski et al., 2002; Xue et al.,2014). Kinetic models describing the oriented attachment
process have been developed by Ribeiro et al. (2005;
2006).
The general mechanism for the formation of anisotropic
nanoparticles or nanostructures from smaller (usually isotropic) nanoparticles was identified to consist of the following steps: 1) Collisions of nanoparticles leading to theirloose attachment where sometimes the crystal planes are

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Sherif Okeil et al. / KONA Powder and Particle Journal No. 41 (2024) 197–220

already aligned, 2) Rotation of the attached nanoparticles
takes place to eliminate some misorientation, 3) Smoothening of the surface occurs due to further monomer additionand eventually Ostwald ripening (Fig. 14) (Lee et al., 2005;
Xue et al., 2014). Interestingly, this process can occur insome systems even at room temperature without the addition of specific ligands (Leite et al., 2003), while higher
temperatures usually enhance the particle coalescence andthus the oriented attachment process (Lee et al., 2005). The
presence of some crystal defects or misalignments such as
twinning, stacking faults or dislocations in the final productcan give a hint on the crystal evolution through the oriented
attachment mechanism (Chen et al., 2022). The oriented
attachment process can even give rise to anisotropic growth
directions that are generally not accessible through tradi-tional monomer deposition or classical crystal growth (Leeet al., 2005; Leite et al., 2003). The use of selective surfactants can facilitate and direct the oriented attachment process and inhibit Ostwald ripening (Xue et al., 2014). This
makes the oriented attachment process a very effective
mechanism for achieving novel and even more complex
anisotropic structures.
A good example showing the formation of anisotropic
zinc oxide nanorods from quasi-spherical ZnO nanoparticles via oriented attachment in non-aqueous systems waspresented by Pacholski et al. (2002). Thereby, a sol of theZnO nanoparticles previously prepared from a dilute basic
methanolic solution of zinc acetate dihydrate (Fig. 15(a))
was first concentrated through solvent evaporation. Refluxing of the resulting sol resulted in ZnO nanorods where thedimensions of the nanorods increased with increasing reflux time, yielding excellent monocrystalline ZnO nanorods after one day (Fig. 15(b)). Sampling at different
time intervals showed the arrangement of the quasispherical particles into chains where neck formation andcoalescence with excellent alignment of the particle crystal
planes was observed in high-resolution TEM images(Fig. 15(c)–(g)) (Pacholski et al., 2002). Similar observations were made by Peukert et al. through an in-depth investigation of ZnO nanorod formation kinetics in the same

Fig. 14 Schematic showing the collision, attachment and rotation steps
involved in the oriented attachment process. Adapted with permission
from Ref. (Leite et al., 2003). Copyright 2003 American Institute ofPhysics.

a

c

b

d

e

f

g

Fig. 15 TEM images of (a) quasi-spherical ZnO nanoparticles used as starting sol for the synthesis of ZnO nanorods, (b) final monocrystalline ZnO
nanorods formed by oriented attachment of the quasi-spherical ZnO particles, (c) sample of the reaction mixture after 2 h showing some ZnO nanorods
and quasi-spherical ZnO particles arranged in shorter and longer chains, (d–g) high-resolution TEM micrographs of differently attached particles
showing neck formation and coalescence with aligned crystal planes. Adapted with permission from Ref. (Pacholski et al., 2002). Copyright 2002
Wiley-VCH.

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Fig. 16 Schematic showing the steps involved in the temporal evolution of ZnO fan- and bouquet-like nanostructures in benzyl alcohol. Adapted withpermission from Ref. (Ludi et al., 2012). Copyright 2012 The Royal Society of Chemistry.

system (Voigt et al., 2010). Buha et al. observed the formation of hexagonal ZnO nanoplatelets from Zn(acac)2 in
acetonitrile, which was identified in high-resolution TEM
to be a mesocrystal formed of perfectly aligned crystal domains, so that the final hexagonal nanocrystal behaves as asingle crystal (Buha et al., 2007). Similarly, the formationof hexagonal Al-doped ZnO (AZO) in benzylamine wasobserved and investigated via small-angle X-ray scattering(SAXS), where time-resolved analysis revealed oriented
aggregation forming hexagonal mesocrystals (Ungerer etal., 2019; 2020). Oriented attachment was also observed
for the formation of TiO2 nanorods when the reaction system of Joo et al. (2005b) (synthesis of TiO2 from TIP using
oleic acid as a surfactant, Table S1) was reinvestigated byDalmaschio and Leite, where even the detachment ofwell-faceted nanoparticles was observed for longer reaction times and this was interpreted as Rayleigh instabilityof the TiO2 originally formed through oriented attachment(Dalmaschio and Leite, 2012). Although Joo et al. (2005b)
already observed the presence of spherical nanoparticles
along with the synthesized TiO2 nanorods, the reason behind their presence in the product was not further investigated.
Oriented attachment can also be the reason for the formation of ordered aggregates as was observed in the formation of ZnO nanorod bundles (Zhang et al., 2007). Morecomplex geometries could even be obtained due to oriented-attachment mechanisms such as hollow hexagonal ZnO
mesocrystals which form via the oriented attachment oftrapezoidal ZnO nanosheets (Wang et al., 2013), interwoven ZnO nanosheets (Khokhra et al., 2015) and fan- andbouquet-like ZnO nanostructures (Ludi et al., 2012).
Khokhra et al. proposed that the aggregation of ZnOnanosheets to form an interwoven structure was induced by
the interaction of the solvent used with the adsorbed hy-

droxide and acetate ions on the surface of the ZnO nanocrystals via hydrogen bonds (Khokhra et al., 2015). In adetailed study, the temporal evolution of the fan- and bouquet-like ZnO nanostructures was followed from platelet-like ZnO nanoparticles formed in benzyl alcohol(Fig. 16). Overlapping stages of classical nucleation, oriented attachment followed by further growth and surface
smoothening could be observed in this reaction system(Ludi et al., 2012).

7. Benzyl alcohol route for anisotropic
particle growth
The “benzyl alcohol route” has emerged as a unique
non-aqueous synthesis route for multiple metal oxides. The
advantage of this route lies in the fact that it is mainly asolvent-directed route which makes it possible to synthesize metal oxides and control their morphology through thevariation of different experimental parameters without theneed of any surfactants. This is achieved via the coordinating ability of benzyl alcohol to the metal oxide surface,
however, compared to surfactants, a relatively low amountof surface organics results, rendering a “clean surface” thatis accessible for small molecules, which is important for
catalytic or sensing applications (Clavel et al., 2007; Pinna
and Niederberger, 2008).
Benzyl alcohol can act as a solvent, reducing agent andstructure-directing agent for many metal oxides (Pinna etal., 2011; Qamar et al., 2017). The fact that it can also act
as a reducing agent was successfully applied to synthesize
several non-stoichiometric metal oxide nanoparticles,
which is very useful for catalytic applications where these
defects can act as catalytic centers (Qamar et al., 2017).
The structure-directing properties of benzyl alcohol are revealed in the formation of different anisotropic metal oxides. Qamar et al. synthesized a series of non-stoichiometric213

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metal oxides (TiOx, ZnOx, SnOx, CeOx, In2Ox and Ga2Ox) ata high temperature of 245 °C, where for several metal oxides, a square or rectangular plate-like morphology was
obtained (Qamar et al., 2017). ZnO nanorods were successfully synthesized in benzyl alcohol from Zn(acac)2 hydrate
without the need of any surfactant (Djerdj et al., 2008b).
Moreover, Al-doped ZnO formed with a rod-like shape inbenzyl alcohol, in contrast to the synthesis in benzylamine
where no elongated growth was observed (Zellmer et al.,2015). Similarly, TiO2 nanorods were obtained in benzyl
alcohol from TiCl4 at a temperature as low as 70 °C(Abazović et al., 2008; 2006). While shorter synthesistimes of some hours up to one day yielded spherical anatase
TiO2 nanoparticles (Garnweitner and Grote, 2009;
Niederberger et al., 2002a), longer synthesis times of threedays resulted in the formation of nanorods (Abazović et al.,2008; Jia et al., 2009). The use of TIP as a precursor together with minor amounts of silanes in the benzyl alcoholroute yielded platelet-like TiO2 particles (Koziej et al.,2009), while the use of OLA as a surfactant even resulted in
two-dimensional TiO2 nanosheets (Wu et al., 2008). Interestingly, the analogous synthesis in benzylamine also results in nanosheets that are evenly stacked, proving thetendency of amines to guide the reaction towards the formation of 2-D structures (Garnweitner et al., 2008).
Zirconia nanocrystals synthesized in benzyl alcohol using sodium lauryl sulfate as a surfactant showed onedimensional growth when ZrOCl2∙8H2O was used as the
precursor while Zr(SO4)2∙H2O yielded only isotropic
nanoparticles (Siddiqui et al., 2012). The reaction of zirconium n-propoxide in benzyl alcohol was reported to lead tofractal nanostructures, in particular at lower reaction temperatures and extended reaction times (Stolzenburg et al.,2016).
In several cases, the synthesis of metal oxides via the
benzyl alcohol route yielded nanoparticles of narrow size
distribution together with a surprising tendency of forming
well-defined aggregates, sometimes even with twodimensional or complex three-dimensional anisotropic
morphologies. In situ formed organics were often judged toexert a pronounced effect on the nanoparticle and aggregate
morphology formed. An in-depth analysis of the organics
present on the particle surface as well as organic byproducts in the reaction mixture revealed the in-situ formation of benzoate species that adsorb on the nanocrystal
surface and thus act as a capping and structure-directing
agent (Pinna et al., 2005; Pucci et al., 2012). In anotherstudy, the formation of WO3 nanowire bundles was attributed to the partial oxidation of benzyl alcohol to benzaldehyde which contributed to the formation of nanowire
bundles as an inorganic-organic hybrid nanostructure(Polleux et al., 2005; 2006).
The use of additives other than surfactants could also
have a significant effect on the formed nanostructures in the214

Review Paper“benzyl alcohol route”, giving access to novel shapes. For
example, the addition of HCl to Ti(OBu)4 in benzyl alcohol
enabled the formation of walnut-like aggregate structures
composed of rutile-phase nanorods as building blocks. An
increase in the amount of HCl added allowed tuning thesize of the nanorods and thus, of the formed aggregate
structure. It was proposed that the presence of chloride ions
supports the anisotropic growth of rutile TiO2 through selective adsorption to the (110) plane of rutile TiO2 suppressing its growth, thus favoring the growth in the [001]direction (Kim et al., 2013). In the synthesis of tungsten
oxide from WCl6 in benzyl alcohol, the formation of tungstite (WO3∙H2O) nanoplatelets was observed. Upon the
addition of the siderophore deferoxamine mesylate as anadditive, a fundamental change in the morphology of theresulting tungsten oxide was observed. Tungsten oxide
nanowires were obtained, which were arranged in bundles
to form inorganic-organic hybrid superstructures (Polleux
et al., 2005).

8. Summary and outlook

In this article, the different non-aqueous and/or nonhydrolytic reaction systems used for the synthesis of simple
and complex anisotropic metal oxide nanocrystals andnanostructures were summarized. Many examples for themost common metal oxides, in particular titania, zinc andiron oxide as well as for more specialized systems weresummarized, showing that there are many parameters controlling the resultant morphology. Moreover, the common
theories resulting in anisotropic crystal growth in these
non-aqueous systems were elaborated. While some generalmechanisms and concepts have been developed for the
anisotropic crystal growth in non-aqueous media, some reaction systems are still found to possess some uniquefeatures and mechanisms for anisotropic nanostructureformation, such as unique aggregation behavior, and need
to be investigated in detail for each system on its own.
To date, the synthesis of anisotropic metal oxides has
still largely focused on low-dimensional nanoparticles, andmore complex anisotropic structures remain relatively uncommon and need further investigation of the reaction parameters that can yield such structures in a single step or
through multi-step approaches. It should be noted that
anisotropic metal oxide nanoparticles or nanostructures canbe formed directly through either the “truly” anisotropic
growth of a single crystal or via an oriented-attachment orcontrolled-aggregation process, usually leading to polycrystalline anisotropic structures or mesocrystals with certain misalignments and defects. A deeper understanding of
the factors governing the controlled aggregation and oriented attachment of nanoparticles in non-aqueous media
would help to engineer more complex anisotropic metaloxide nanostructures formed from smaller isotropic or
anisotropic building blocks.

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Different parameters have been identified to play an important role in anisotropic growth, where surfactants represent a cornerstone in regulating anisotropic particle growth.
However, the solvent can also fulfill this function and additionally act as a structure-directing agent. Moreover, theprecursor type and its ratio to the solvent, as well as possibly the surfactant, can influence the growth processes andinduce the formation of anisotropic structures. This can be
attributed to specific precursor decomposition mechanisms, as well as the presence of certain counter ions or ligands from the precursor in the system. The precursor may
also contain water from crystallization which can also influence the nanoparticle formation process in various ways.
It has to also be taken into consideration that side products
formed during the non-aqueous synthesis can coordinate to
the nanoparticle surface, resulting in strong modification of
the nanoparticle growth and triggering aggregation, eventhough they might only be formed in very small amounts.
The reaction temperature is another substantial factor that
affects nanocrystal nucleation and the subsequent reaction
rate, which can result in different crystal growth mechanisms. While most of the studies concentrate on factors
such as the surfactant used, surfactant concentration, reaction solvent and metal oxide precursor, attention should
also be paid to several uncommon factors which need morein-depth investigation in non-aqueous systems such as theheating rate, cooling rate, mixing of reaction components,
rate and order of reagent addition, hot-injection of the precursor, etc.
As presented in this contribution, the synthesis of metaloxides in organic media has been intensively studied in theliterature, while the preparation of other metal oxides has
been only rarely investigated in non-aqueous media. It is
important to note that different metal oxide precursors exhibit different reactivity and reaction kinetics, even in similar reaction media, which makes it important to study theshape evolution of each metal oxide in non-aqueous media
separately. There is still a high potential for the development of non-aqueous and non-hydrolytic synthesis routes
capable of synthesizing various anisotropic metal oxide
nanostructures because of the almost infinite possibilities
of combining different organic reaction media, metal oxide
precursors and surfactants, as well as varying the corresponding reaction parameters.

Supplementary information
The online version contains supplementary material
available at https://doi.org/10.14356/kona.2024014.

Acknowledgments
The authors acknowledge funding by the German Research Foundation (Deutsche Forschungsgemeinschaft,
DFG), grants GA 1492/9-2 and NI 414/24-2.

Review Paper

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Review Paper

Authors’ Short Biographies
Sherif Okeil
Sherif Okeil is currently working as a competence team leader for nanomaterial synthesis in Georg
Garnweitner’s group at the Institute for Particle Technology, Technical University of Braunschweig.
He received his bachelor’s degree in Pharmacy at Ain Shams University in Cairo, Egypt in 2009. In
2013, he obtained his masters in Pharmaceutical Analytical Chemistry from the same university. In
2014, he was awarded a scholarship from the German Academic Exchange Service (DAAD) throughwhich he obtained his Ph.D. in Inorganic Chemistry at the Technical University of Darmstadt in2020. His current research interests are inorganic nanomaterial synthesis and their electronic applications.

Julian Ungerer
Julian Ungerer obtained his bachelor’s degree in applied mechanics in 2013 and his master’s degree in the process engineering machines group of Prof. Dr.-Ing Hermann Nirschl at the Karlsruhe
Institute of Technology in 2016, where he also continued his academic research in the field of functional nanomaterials for renewable energy systems before receiving his Ph.D. in 2022.

Hermann Nirschl
Hermann Nirschl received his Ph.D. in Fluid Mechanics from the Technical University of Munich
in 1994. For his habilitation in 1997 he worked on the numerical simulation of the particle-laden
flows. He entered the 3M company in the dental division as the head of process engineering in theyears between 1997 and 2002 where he worked as a project manager for different projects in Munich
and St. Paul/Minnesota. Since 2003 he is Professor for Mechanical Process Engineering at the KIT
in Karlsruhe. The focus of the research is on particle technology with a special emphasis on separation processes, numerical simulations and the development of particle analysis technologies.

Georg GarnweitnerGeorg Garnweitner received a Diploma in Technical Chemistry at Vienna University of Technology, Austria, in 2003 and then moved to the Max Planck Institute of Colloids and Interfaces, Potsdam, Germany, where he obtained his Ph.D. in Colloids Chemistry in 2005. He was appointed as
Professor at Technische Universität Braunschweig, Germany, in 2007. His research is centered on thesynthesis of inorganic nanoparticles and hybrid materials, in particular via nonaqueous approaches.
In addition, the surface modification and functionalization of nanomaterials are studied in his group,targeting diverse application fields such as solid-state and lithium-sulfur batteries, lightweight composites and drug delivery.

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KONA Powder and Particle Journal No. 41 (2024) 221–228/https://doi.org/10.14356/kona.2024002

Original Research Paper

Particle Size Measurement Using a Phase Retrieval Holography
System with a GPU-Equipped SBC †Yohsuke Tanaka 1* and Dai Nakai 2
1 Faculty of Mechanical Engineering, Kyoto Institute of Technology, Japan

2 Graduate School of Mechanical Engineering, Kyoto Institute of Technology, Japan

We have developed a phase retrieval holography system using a single-board
computer (SBC) with a graphics processing unit (GPU) for particle size measurement.
The system comprises two cameras connected to the SBC with a GPU (Jetson
NanoTM, NVIDIA®), a diode-pumped solid-state green laser, and a beam splitter.
The GPU enables us to reconstruct holograms in real-time and measure particle
size. The system can record the shapes and positions of particles falling in a static
flow in a three-dimensional volume as two holograms generating an interference
pattern. Two holograms solve the twin image problem that arises because of the lackof phase information using phase retrieval holography. We also present the requirement of this system for experimentally recording andnumerically reconstructing holograms of particles. Finally, we compare the particle size distribution obtained by the system to that ofconventional two-dimensional image measurement.
Keywords: particle size measurement, phase retrieval holography, single-board computer, graphics processing unit

1. Introduction
Digital transformation (Stolterman and Fors, 2004) has
changed society as a whole, as evidenced by the spread oftelework and other online activities due to the COVID-19
pandemic. The use of Internet of Things (IoT) (Mattern and
Floerkemeier, 2010) has also been advancing. In particular,
Malik et al. (2021) reported that the use of Industrial IoT
in industry has brought significant improvements and profitability in process visualization using the Raspberry Pi,
an inexpensive single-board computers (SBC). In powder
production, big data aggregated from each process are used
for trouble detection, quality control, and determination of
production conditions through artificial intelligence technology (Fujita et al., 2020).
As the processing speed of computers has improved, theamount of information obtained by optical measurement,
which is used in powder production, has increased frompoint measurement with phase Doppler anemometry (Durstet al., 1981) to image measurement (Lichti and Bart, 2018).
In addition, holography, invented by Gabor (1948), hasbeen used for measuring particles in three-dimensional
space. Gabor holography can be realized easily with a lightsource and a complementary metal oxide semiconductor(CMOS) camera and is insensitive to vibration because thereference and object waves are on the same optical axis.
†*

Received 20 August 2022; Accepted 19 October 2022
J-STAGE Advance published online 28 January 2023
Corresponding author: Yohsuke Tanaka;
Add: Matsugasaki, Sakyo-ku, Kyoto 606-8585, Japan
E-mail: tyohsuke@kit.ac.jp
TEL: +81-75-724-7355 FAX: +81-75-724-7355

Gabor holography is a three-dimensional microparticle
measurement with a deep focal depth, involving size
(Nayak et al., 2021), position (Memmolo et al., 2015), andvelocity (Katz and Sheng, 2010). However, only the lightintensity of the complex amplitude is recorded by the camera, which causes the twin image problem that degrades
the contrast of reconstructed particles (Latychevskaia andFink, 2007). To solve this problem, phase retrieval holography using two or more cameras has been proposed (Liuand Scott, 1987; Zhang et al., 2003). The present authors
have used this method to measure microparticles (Tanaka
et al., 2016), microbubbles (Kubonishi et al., 2018), andmicrodroplets (Nakatani et al., 2019; Tanaka et al., 2019b).
However, practical usage of Gabor holography and phaseretrieval holography is limited by the huge execution timefor volumetric reconstruction.
Parallel processing by a graphics processing unit (GPU)accelerates holography processing (Tian et al., 2010) to bemuch faster than that by a central processing unit (CPU).
In particle measurements, the CPU to GPU execution time
ratio has been shown to be higher than 100 times (Tanaka
et al., 2019a). Since 2014, NVIDIA® has released GPUequipped SBCs for AI and IoT applications, and in 2019 it
released the Jetson NanoTM as an inexpensive SBC.
This paper proposes particle size measurement using aphase retrieval holography system with a GPU-equipped
SBC with theoretical and fabrication considerations. We
also confirm the effectiveness of the system by comparing
particle size distributions measured by it and conventional
two-dimensional image measurement.

Copyright © 2024 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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Fig. 1

Recording hologram in particle measurement.

where dp is the diameter of the particle, xpi and ypi are its
center position.
Because this aperture has circular symmetry, the intensity distribution is derived by performing the integration2 πd r  πd p r  
J1  p 2  2 J1 222 πr   λz p i   πd p  πd p λz p i  
I z pi  1 
sin 




πd p r
λz p i λz p i  πd p r
 4 λz p i  
λz p iλz p i2 πd r pFig. 2 Hologram intensity of a particle from Eqn. (3) for zp = 50 mm, J1 πr 2   λz p i πd p 2
dp = 62 μm, and λ = 532 nm.

I z pi  1 sin 
λz p i λz p i 

πd(3)
pr
λz p i2. Recording and reconstruction ofholograms in particle measurement πd p r   λz p i  
πd p r
λz p i2  2 J1  πd p 2   4 λz p i  



(3)

where J1 represents the first-order Bessel function. The
particle information is contained mostly in the first lobe
(Vikram, 1992) as shown in Fig. 2. The recording distanceof a particle is limited to the range in which the lobe can be
resolved, that is,First, we focus on the principles of recording and recon(3) space using Gaborstructing particles in three-dimensional
holography. In particular, we discuss hologram recording
conditions, the twin image problem, and particle elongationin the optical direction. Moreover, we explain the principleof phase retrieval holography used in the proposed system
to suppress the twin image problem.

dp2

 z1 

λd pW /2(4)

1.22 λwhere W/2 is the half width of the CMOS elements. Additionally, the pixel pitch ∆x of the elements is also limitedby(4)the relation,

2.1 Recording holograms using Gabor holography
We consider the case in which particles illuminated by aplane wave of wavelength λ as shown in Fig. 1. The complex amplitude Ψz at a distance z is formulated as

dp

(5)
2πz 
2.44

exp  j λ   1  b  exp j π x  ξ  2   y  η 2 dxdy
Ψz 
∬ i

jλz
λz 
2.2 Reconstruction of holograms using Gabor(5)holographyπ22
We reconstruct holograms explained in sec. 2.1 by(1)
1  bi  exp j x  ξ    y  η  dxdy
λz
introducing the convolution kernel defined as (Onural and(1)
Scott, 1987)2
where j = –1 is the imaginary unit. The diffracting aperture

x 









bi for the ith particle is defined as the transmittance function, namely,1 ( x  x ) 2   y  y  2  d / 2
pi
pi
p

bi  xpi , y pi , z pi   (2)
0 ( x  xpi ) 2   y  y pi  2  d p / 2

222(2)




hz 

1
2π 2
exp  jx  y 2 
λz
 λz


(6)

Eqn. (1) can be expressed compactly using the twodimensional convolution ** as(6)2π Ψzi  exp  jzi  1  bi  **hzi
(7)
 λ 

(7)

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The hologram intensity is recorded on a hologram as(8)

I zi  Ψzi  1  bi ***h * zi  bi **hzi

where a superscript * denotes complex conjugate, and thecomplexamplitude of particles are reconstructed as(8)(9)Ψz pi  I zi **h zpi  1  bi ***h2 zpi  bi **hz pi

The second and third terms on the right-hand side ofEqn.
(9) (9) lead to the virtual and real particle images. The
virtual particle image causes the out-of-focus particle, as
shown in the intensity line profile of Fig. 3. This is known
as the twin image problem (Latychevskaia and Fink, 2007)
due to the loss of phase information when the complex amplitude of the hologram is recorded by the CMOS camera.

2.3 Suppression of twin image problem using
phase retrieval holography
We use phase retrieval holography, which requires twoholograms separated by a distance Δz to obtain the phase
information as shown in Fig. 4 (Liu and Scott, 1987; Zhang
et al., 2003). This iterative phase retrieval algorithm isbased on the Gerchberg–Saxton algorithm (Gerchberg and
Saxton, 1972) using two holograms as intensity constraint

Original Research Paperconditions. The distance Δz between the two hologramswith good convergence of the iterative process is given by
Tanaka et al. (2016)

Δz 

z1
m  1, 2,, m  Nm

(10)

This method has an iterative process with four steps asshown in Fig. 5(a).
(10)
Step 1. Reconstruct the complex amplitude of the first
hologram to the second hologram with the distance:
Ψz 2k  exp  jΔzΨ z1k **hΔz  Ψz 2k exp  j 2 (11)

Step 2. Replace the absolute value of Ψz 2k with

I 2:

(11)
Ψz 2k  I 2 exp  j 2 

(12)
(52)
(51)
Step 3. Back-reconstruct the amplitude of step 2 to thefirsthologram:(12)Ψz1k  exp  jΔzΨz 2k **hΔz  Ψ z1k exp  j 2 (13)

Step 4. Replace the absolute value of Ψz1k with I1 :
(13)
(14)
Ψz1k  I1 exp  j 1
(54)
(53)

(14)

Fig. 3

Reconstruction of a hologram in particle measurement.

Fig. 4Phase retrieval holography using two holograms.

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Fig. 5Phase retrieval holography: (a) iterative process and (b) reconstruction of a hologram with retrieved phase information.

where ϕ1 and ϕ2 are the retrieved phases at the first and second hologram, respectively. The iterative loop exits whenthe mode of the reconstructed image at zp3 becomes the
maximum value (Tanaka et al., 2016). The virtual particle
image has been removed by phase retrieval holography, as
shown in the intensity line profile of Fig. 5(b) comparedwith Fig. 3.

2.4 Particle elongation in the optical axis direction
In addition to the twin image problem, another problem
is that the reconstructed particle is elongated in the opticalaxis direction. To understand this problem, the complex
amplitude reconstructed from the hologram formed by one
particle at (xp, yp) = (0,0) on the z axis can be considered asa circular aperture on a screen (Yang et al., 2006), that is,ψ z a 0, 0, z  1  exp  j (15)where ϕ is πrp2/(λz). On the other hand, the amplitude ψzoof (15)an opaque disk is derived by using Babinet’s principle(ψza + ψzo = 0) (Born et al., 1970) as
ψ z o 0, 0, z  1  cos    jsin  

whereupon we obtain the intensity as2(16) πrp 2 
 πrp 2 
I z  ψ z o  1  cos  sin 2 
 λz 
 λz 

(16)(17)

Fig. 6 shows that the intensity oscillates, with the frequency increasing toward the position of the particle. In(17) the oscillations are limited owing to the finite
practice,
sensor size, as shown in the numerical plots in Fig. 6.
Particle elongation is defined by a zero-valued position

224

that oscillates symmetrically about z = 0 in the following
equation (Nakatani et al., 2019):

ΔL 

dp2
n  1, 2, , n  N 
2 2n  1 λ(18)Particle elongation can be expressed as dp2/(6λ) by substituting n = 2 into Eqn. (18) under the conditions of Fig. 2.
(18)
Furthermore, taking this elongation into account, the recording range of Eqn. (4) can be extended as
dp2
6λ z1 

d pW /2(19)

1.22 λ3. Holographic particle size measurement(19)
system using a GPU-equipped singleboard computer
In this section, we illustrate the details of the holographic
particle size measurement system using a GPU-equipped
SBC (Jetson Nano, NVIDIA). Fig. 7 shows the opticalsetup for the phase retrieval holography system with twoholograms. The laser beam (532 nm, 5 mW, Edmund®) ismagnified using an objective lens (40/0.65) and converted
to a collimated beam using a beam expander (TS Scorpii®,
8×, Edmund). After passing through the measurement
volume (6.9 mm × 6.9 mm × 40 mm) where the particles(SP20SS, Mitsubishi Chemical Corporation) fall at theirterminal velocity, the collimated light is split by a beam
splitter and recorded by two CMOS cameras (BFS-U331S4C-BD2, FLIR, processing rates: 0.4 fps (Gabor holography) and 0.5 fps (phase retrieval holography), shutter
speed: 200 μs, image size: 512 pixels × 512 pixels, cell
pitch: 3.45 μm) fitted with telecentric lenses (VS-TCT0565/S, 0.5×, WD: 65.7 mm, VS Technology®). Two cameras

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Yohsuke Tanaka et al. / KONA Powder and Particle Journal No. 41 (2024) 221–228

Table 1 Comparison of execution time comparison between CPU andGPU for Gabor and phase retrieval holography.

[s]Fig. 6 Theoretical and numerical distribution of light intensity ofFig. 2 through the center of the particle.

[Ratio]CPUGPU

CPU/GPU

Gabor

82.5

2.0

41.2

Phase retrieval

101.7

2.6

39.1

used image measurement to determine the sizes of particles stuck to a glass plate, as shown in Fig. 8(a). We used
image analysis software (Fiji 2.3.0: Schindelin et al., 2012)
to apply dynamic thresholding (Prewitt et al., 1966) to
the recorded image to obtain a binarized image, and wemeasured the particle size as the Feret diameter, as shownin Fig. 8(b).

4. Results

Fig. 7 Optical setup for particle size measurement using phaseretrieval holography system with a GPU-equipped SBC: (a) laser; (b)objective lens; (c) collimator lens; (d) ND filter; (e) observation box; (f)beam splitter; (e) telecentric lens; (h1) CMOS camera 1; (h2) CMOS
camera 2; (i) NVIDIA Jetson Nano as GPU-equipped SBC.

are installed at positions z1 = 10 mm and z2 = 20 mm under
the condition of m = 1. The misalignment of a rotation anda shift in the xy plane between the two cameras was calibrated numerically within one pixel by bundle adjustmentof the image distortion correction (Russ and Neal, 2016)using a calibration plate printed with random dots.
The holograms recorded by the two cameras are processed by the NVIDIA Jetson Nano with JetPack® SDK
4.6.1. Camera control and image acquisition are conductedwith the Spinnaker® SDK C++ v2.5.0.80 library. The
CUDA C/C++ 10.2 library and Thrust API are used forimage processing and hologram reconstruction calculation
using the GPU. The textbook by Shimobaba and Ito (2019)is informative, detailing GPU programming of hologram
recording and reconstruction. OpenCV 4.5.4 is used todisplay images in GUI windows during processing. The
application for bundle adjustment of the two cameras is
implemented in Julia® 1.7.2.
Table 1 gives the CPU and GPU execution times forreconstruction processes using Gabor and phase retrieval
holography. The CPU to GPU execution time ratio indicates that a GPU-equipped SBC is essential for early-timeprocessing.
For comparison with holographic measurement, we

In this section, we report use of the fabricated system
to measure particle size distributions in three-dimensional
space using Gabor holography and phase retrieval holography. The effectiveness of the system is confirmed bycomparing its measured distributions with that measured
conventionally by two-dimensional image measurement.
Reconstructed volumes (6.9 mm × 6.9 mm × 40 mm)
of Gabor and phase retrieval holography are shown in
Figs. 9(a) and 10(a), respectively. Particles are elongatedalong the optical axis, as shown in Eqn. (18) and Fig. 6.
Although particle size can be measured from the elongation(Nakatani et al., 2019), to reduce the computational load
for real-time processing, we measure the size by using thebinarized image with the minimum value in the optical axis
direction in the volume.
The obtained minimum-intensity image, binarized image, and intensity histogram are shown in Figs. 9(b) and9(c) and Figs. 10(b) and 10(c), respectively. As comparedin Figs. 3 and 5, phase retrieval holography, which suppresses the twin image problem, has a histogram peakat the value of zero light intensity as focused particles.
In contrast, Gabor holography, which is affected by thetwin image problem, has no peak in the histogram at thezero value. Consequently, Gabor holography has a larger
threshold value than that of phase retrieval holography,
and its binarized particle size is smaller than that of phaseretrieval holography.
The Feret diameter is measured from the binarized
images as in the image measurement of Fig. 8(b), and theparticle size distributions of Gabor and phase retrieval holography are shown in Figs. 11(a) and 11(b), respectively.
Gabor and phase retrieval holography have peaks of similar
size. However, as mentioned above, Gabor holography has
more particles below 40 μm than does phase retrieval holography because of the smaller size due to the twin image
problem.

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Original Research Paper

Fig. 8 Particle size distribution by image measurement: (a) observation of particles with telecentric lens; (b) histogram of particle size distribution(number of particles Np = 1147).

Fig. 9 Gabor holography: (a) reconstructed particles in measurement volume; (b) imposed volume and binarized image (threshold number: 23); (c)intensity histogram.

The cumulative frequency distribution functions are
generated from the particle size distribution of image
measurement, phase retrieval, and Gabor holography. The
distributions determine the sizes corresponding to the 10th,
50th, 90th percentiles, and the span, labeled d10, d50, d90,
and dspan respectively. From the values in Table 2, unlike
Gabor holography, phase retrieval holography enables
the measurement of almost the same distribution levels as
those of image measurement.

5. Conclusion
We have illustrated a phase retrieval holography systemwith a GPU-equipped SBC with theoretical and fabrication

226

considerations. This system enables real-time measurements of particle size distributions in a three-dimensional
space with almost the same accuracy as that of conventional two-dimensional image measurement. The results
show that the fabricated system has reached the stage ofpractical particle-size measurements in three-dimensional
space.

Data Availability Statement
The data on particle distributions by image measurement, Gabor holography, and phase retrieval holography is available publicly in J-STAGE Data (https://doi.
org/10.50931/data.kona.22002518).

Yohsuke Tanaka et al. / KONA Powder and Particle Journal No. 41 (2024) 221–228

Original Research Paper

Fig. 10 Phase retrieval holography: (a) reconstructed particles in measurement volume; (b) imposed volume and binarized image (threshold number:8); (c) intensity histogram.

Fig. 11 Particle size distributions by (a) Gabor holography (number of particles Np = 1390) and (b) Phase retrieval holography (number of particles
Np = 1837).
Table 2 Particle size distributions of Feret diameter.

[μm]

[-]

d10

d50

d90

dspan*

Image measurement

53.9

65.4

74.3

0.3

Phase retrieval

44.1

61.7

74.3

0.5

Gabor

24.8

52.5

65.4

0.8

* dspan = (d90 – d10)/d50

Acknowledgments
Our research on the phase retrieval holography system
for measuring particle size distribution was supported by

the Hosokawa Powder Technology Foundation (Grant
Number HPTF20108).

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Authors’ Short Biographies
Yohsuke Tanaka
Yohsuke Tanaka is an Associate Professor at the Faculty of Mechanical Engineering, Kyoto
Institute of Technology. He worked for 3 years as a field engineer at Kyocera Corporation after receiving his Master’s Degree in 2002 from the Kyoto Institute of Technology. He received his Ph.D. in
Engineering from Osaka University in 2008. He received fellowships (DC2) from the Japan Society
for Promotion of Science from 2007 to 2008. His research interests include optical, flow, and soundmeasurements.

Dai Nakai
Dai Nakai received his Bachelor of Engineering from the Kyoto Institute of Technology in 2022.
He has since been researching particle measurement using holography as a Master’s student at the
Kyoto Institute of Technology. He is currently researching development of a machine learning-based
holographic collision detection system to observe microdroplet collision in precipitation processes.

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KONA Powder and Particle Journal No. 41 (2024) 229–241/https://doi.org/10.14356/kona.2024016

Original Research Paper

Effects of DEM Parameters and Operating Conditions on
Particle Dynamics in a Laboratory Scale Rotating Disc †Rondinelli M. Lima, Gisele M. Souza, Rodolfo J. Brandão,Claudio R. Duarte and Marcos A.S. Barrozo *School of Chemical Engineering, Federal University of Uberlândia, Brazil

Rotating discs are usually used as granulators in many industrial processes. The efficiency of the
granulation process in this device is directly related to the particle motion behavior in different
flow regimes. In this work, the granular flow in a rotating disc was investigated experimentallyand numerically. The Discrete Element Method (DEM) was used in the simulations, while
Central Composite Designs (CCD) were employed to quantify the effects of DEM input
parameters and operating conditions (filling degree (FD), angle of inclination (AI), and rotational
speed) on the contacts between particles. The results showed that the particle–wall static friction
coefficient had the most significant impact on the studied response. Additionally, the effect ofoperating variables on the collision force between particles, the angle of departure and particle
velocities were successfully investigated, with corresponding DEM simulation predictions. It wasalso verified that the simulations performed with experimentally measured DEM input parametervalues were able to reproduce the flow regimes in the rotating disc.
Keywords: rotating disc, flow regimes, DEM, DEM parameters, number of contacts

1. Introduction

The granulation process is widely employed in different
industries, such as fertilizer, pharmaceutical, food, chemical, steel, and ceramics. This particle size enlargement
technique allows the production of useful structural forms,
modifies the material rheology, increases its flowability,
reduces material loss and dust emissions, and controls porosity (Capes, 1980; Litster and Ennis, 2004).
Industrial granulators promote agglomeration throughparticle mixing and collision. Compared to other types ofequipment, rotating discs stand out due to their pronounced
segregation ability and flexibility to operate different typesof materials (Litster and Ennis, 2004). Moreover, their geometry is relatively simple, consisting of a disc with a certain diameter (D), an angle of inclination (θ) and a rim (L)that rotates around its axis (Fig. 1).
Although rotating discs have been widely used as a granulator (Azrar et al., 2016; Ball, 1959; Chadwick andBridgwater, 1997), only qualitative descriptions of solids
flow have been usually made, with little quantitative information on particle dynamics (Chadwick and Bridgwater,
1997). The effects of different operating variables, such as
rotational speed, filling degree (FD) and angle of inclina†*

Received 30 March 2023; Accepted 1 May 2023
J-STAGE Advance published online 31 August 2023
Corresponding author: Marcos A.S. Barrozo;
Add: Bloco K, Campus Santa Mônica, 38400-902, Uberlandia, MG,
BrazilE-mail: masbarrozo@ufu.br
FAX: +55-34-32394188

tion (AI), on the motion and contacts of particles are still
not fully understood (Lima et al., 2022). Furthermore, as
granulation is a dynamic process, investigating both the
responses that are directly related to the granule formation(i.e., particle velocity, and number and force of contacts
between particles) and flow regimes can also be a potential
tool to optimize the granulation process in rotating discs(Gladky et al., 2021).
Depending on the operating conditions, different regimes of solids motion can be identified in a rotating disc:slipping, rolling, cascading, cataracting, and centrifuging,each with a specific flow behavior. Detailed descriptions ofthese flow regimes can be found in Mellmann (2001).
In the rolling regime, the granular bed can be divided
into two distinct regions: the passive layer, where the particles are dragged up by the disc wall as a rigid body, and theactive layer, where the particles roll over the bed surface
downwards. For higher velocities, a kidney-shaped curve
develops on the bed surface and a larger area of the disc isused, corresponding to the cascading regime. Regarding
the granulation process, the rolling and cascading regimes
in particular have the advantage of promoting more effective contacts between particles than the others (Azrar et al.,2016; Salman et al., 2006).
In addition to experimental studies, numerical simulations have become a complementary tool in granular flow
analysis. Introduced by Cundall and Strack (1979), theDiscrete Element Method (DEM) has been extensively applied to investigate granular dynamics in different types ofequipment. In this method, each particle is described

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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Table 1 Nonlinear Hertz–Mindlin model equations (Cundall and Strack,
1979; Di Maio and Di Renzo, 2005; Tsuji et al., 1992).

Normal forceDω

Damping normal forceθ

Equivalent contact radiusL
Equivalent mass

Fig. 1

Schematic diagram of a rotating disc.

Fn 

3

(3)


5
β S n m* vnrel
6(4)

4 * * 2
E R δn3

Fnd  2

(3)

1
1
1


*
R
Ri R j

(5)

1
1
1


*
m
mi m j

(6)

(4)

(5)
individually through the application of a balance of forces
and contact laws. The precision of the DEM model for
predicting the granular flow relies on the selected contact
model and the input parameters, which depend on both theparticle properties and the particle-contact properties.
DEM simulations provide microscopic results at theparticle level, such as trajectories of particles and forcesthat act on them, making it possible to obtain important
information about particle dynamics. In this context, theknowledge of the solids flow in rotating discs is crucial for
their design and applicability. However, there are a limited
number of studies on the fluid dynamics of rotating discs
using numerical analysis in conjunction with experimental
results. Thus, through the use of DEM simulations and experimental data the present work aims to investigate how
numerical parameters and different operating conditions
influence particle dynamics in a rotating disc. The flow regimes and particle velocity distributions were also studied.

2. Discrete element method
DEM uses a time-stepping algorithm where the modeling is repeated in each time interval. Each particle istracked contact by contact and its positions are updated
based on the results of the previous time step (Nascimento
et al., 2022; O’Sullivan, 2011). During each time step,
Newton’s second law is applied to determine the particle
trajectory, while the particle–particle and particle–wallcontact forces are evaluated based on the force–displacement law (Brandao et al., 2020; Cundall and Strack, 1979;
Potyondy and Cundall, 2004). The motion of individual
particles is determined by Eqns. (1) and (2):d vi  Fij
dt
j

(1)

dωi
Ii
  τ ij
(1) d tj(2)

miwhere t, mi, Ii, vi, and ωi are the time, mass, moment of inertia,(2) linear velocity, and angular velocity of particle i, respectively, and Fij and τij are the force and torque between

230

Equivalent Young’s modulus

11  υi2 1  υ 2j


*
E
Ei
Ej

Normal stiffness(6)

Damping coefficient(7)

S n  2E * R*δn
ln e

(8)

(9)

β

(8)
Tangential force(7)

ln 2 e  π 2(10)Ft  δt S t(9)Tangential damping force

Ftd  2

(10)
Tangential stiffness

Equivalent shear modulus5
β S t m* vtrel
6

S t  8G * R *δn(11)
1
2  υi 2  υ j


*
G
Gi
Gj
(12)

(11)

(12)

(13)

particles i and j, respectively.(13)The contact model used herein was the nonlinear Hertz–
Mindlin. The main equations of this model are summarizedin Table 1.
The equations in the Table 1 involve normal and tangential overlaps(δn, δt), normal and tangential relative veloci ties vnrel , v trel  , restitution coefficient (e), and Poisson’s ratio(υ).

3. Materials and methods[31]
Laboratory
tests and numerical simulations were conducted using a disc granulator made of stainless steel witha smooth surface and diameter of 35 cm, length of 20 cm,
and variable angle of inclination. The granular material
used was glass beads with a diameter of 6.35 ± 0.01 mm,
density of 2455 ± 24 kg/m3, and sphericity of 0.99 ± 0.01.
The DEM simulations were performed on the commercial EDEM® software. To ensure the numerical stability ofthe simulation, a time step of 4 × 10–5 s, which is equivalent to 20 % of the Rayleigh time, was adopted. The simulation conditions are listed in Table 2.

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3.1 Measurement of DEM input parameters

The restitution coefficient (e), which quantifies the energy conserved in shocks between solid bodies, can beexperimentally measured by the ratio between the relative
velocities after (V1) and before (V0) the impact of two colliding bodies (Lima et al., 2021; Machado et al., 2017;
Marinack Jr. et al., 2013), as shown in Eqn. (14):the launching device (Fig. 2) at a certain height (h0), andthen allowed to travel through the entire device. The distance traveled by the particle after leaving the launching
device until its resting point (Dr) was subsequently measured. The rolling friction coefficient was calculated using
Eqn. (16):
μR 

V
e 1
V0

(14)

To determine the velocity before and after collision, theexperimental apparatus described in previous studies was(14)
employed(Bharadwaj et al., 2010; Hastie, 2013; Marinack
Jr. et al., 2013). It consists of a vacuum pump responsible
for a suction pressure that holds particles at a height of
50 mm from the plate. In this procedure, the pump is
switched off to allow particles to fall, while their trajectoryis filmed by a high-speed video camera (Fastec IL5—up to
44,000 frames/s).
In order to measure the static friction coefficient (μS), theinclined plane method was applied (ASTM G115-10,2013). In this method, the plane is gradually inclined andthe angle of inclination (Ф) from which the particles start toslide is used to calculate the μS (Eqn. (15)).
(15)

μS  tan ΦTo replicate the particle–wall interactions, the surface of
the inclined plane was covered with a stainless steel plate.
(15)
The particle–particle interaction behavior was studied bycovering the surface of the plane with a fixed granular bedof glass beads (Brandao et al., 2020; Lima et al., 2021).
To calculate the rolling friction coefficient (μR), a measurement procedure based on that described in Lima et al.
(2022) was adopted. The particle was placed on the top of

Table 2 DEM simulation conditions.

Model

Hertz–MindlinTime step (s)4 × 10–5

h0
Dr

(16)

The experimental parameter values were used herein as
inputs to the DEM model. Brandao et al. (2020) showed
(16)
that
the simulations performed with experimentally measured parameters were in good agreement with the experimental responses.

3.2 Sensitivity analysis of DEM parameters

In order to quantify the effect of DEM parameters (i.e.,
restitution, static, and rolling friction coefficients) on thecontact between particles in the rotating disc, a fractional
Central Composite Design (CCD) (26-1, with 2x6 axial
points and one central point) was used for the DEM calibration procedure. The CCD levels are shown in Table 3.
The CCD data was used to better understand the influence of the DEM parameters on the rotating disc simulation. Therefore, the CCD can help in both the calibration
process and the development of new disc designs.
This CCD was applied to angles of inclination of 40°,
50° and 60°, and the rotating disc was filled with glass
beads (2 % per volume). The rotational speed was set at
10 rpm. The dependent variable (response) was the average
number of contacts between particles (NCpp), since it is arelevant parameter in the granulation process. The greater
the contact between particles, the more effective the granulation process.
For each CCD run, the real-time simulation was 30 s.
The NCpp average was obtained in the last 10s of simulation, since the rotating disc was operating in steady state.
The steady-state condition was considered when the granular bed reached a constant height.

Poisson’s ratio (-)
Glass beads

0.22

Table 3 CCD levels used in the sensitivity analysis of DEM parameters.

Stainless steel

0.30

Factor coding

epp, epw, μSpp, μSpw

μRpp, μRpw

–α (–1.78)

0.054

0.005

Shear modulus (Pa)Glass beads

1 × 106

–1

0.250

0.025

Stainless steel

7 × 1010

0

0.500

0.050

1

0.750

0.075

+α (+1.78)

0.946

0.095

Dr
h0
Fig. 2

Rolling friction coefficient apparatus.

*epp, epw, μSpp, μSpw, μRpp, μRpw correspond to the restitution, static,
and rolling friction coefficients, respectively. The subscripts pp
and pw indicate the particle–particle and particle–wall interactions, respectively.

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Table 4 CCD levels used in the sensitivity analysis of operating conditions.

Coded Factor

FD (%)AI (°)RS (rpm)–α (–1.47)

0.59

35.29

5.29

–1

2.00

40.00

10.00

0

5.00

50.00

20.00

+1

8.00

60.00

30.00

+α (+1.47)

9.41

64.71

34.71

*FD: filling degree, AI: angle of inclination, RS: rotational speed.

(a) Rolling(b)(b) Cascading

α(c) Cataracting(a)α(d) Centrifugingr
Reference line
Fig. 4 Determination of particle velocity distribution: (a) crosssection representation parallel to the base of the rotating disc; and (b)schematic representation for particle velocity measurement.

α

3.5 Simulation of particle velocity distributions
Fig. 3

Analyzed flow regimes and angle of departure measurement.

3.3 Sensitivity analysis of operating conditions
Another CCD was applied to evaluate the sensitivity ofoperating conditions on the number of contacts between
particles. For these simulations the DEM parameters were
kept constant following the values defined in Section 3.1.
The analyzed operating conditions and the corresponding
CCD levels are listed in Table 4.

3.4 Simulation of flow regimes

The flow regimes (i.e., rolling, cascading, cataracting,and centrifuging) were numerically investigated and compared with experimental results of our previous work
(Lima et al., 2022). The angle of departure (α) was definedas the angle at which the particles detached from the equipment wall; it was measured using the Meazure software(Fig. 3).
The analyses were carried out at filling degrees of 2 %,5 % and 8 % and angles of inclination of 40°, 50° and 60°.
The flow regimes were simulated using experimental values of DEM parameters.

232

In order to determine the passive and active layers of therolling regime, the particle velocities were measured. Dueto the inclination of the rotating disc, these layers were located in the inner part of the granular bed. Therefore, it wasnecessary to perform a cross-sectional cut parallel to thebase of the rotating disc, as illustrated in Fig. 4(a).
The particle velocity was determined along the referenceline drawn in the middle of the granular bed (Fig. 4(b))
under different conditions (Delele et al., 2016; Resende etal., 2017; Santos et al., 2013, 2015). The reference line wasdiscretized and normalized at evenly distributed points, andat each point the velocity of five different particles was determined.
The conditions used to investigate the velocity profilesof glass beads in the rotating disc were filling degrees of
2 % and 5 %, angles of inclination of 50° and 60°, and rotational speeds of 10 rpm, 15 rpm and 20 rpm.

4. Results and discussions

4.1 Sensitivity analysis of DEM parameters

A CCD (see levels in Table 3) was employed to evaluate
the influence of DEM parameters on the number of contacts between particles (NCpp) in a rotating disc. To this end,a total of 45 simulations were performed for the rotating
disc operating at angles of inclination of 40°, 50°, and 60°,
a filling degree of 2 %, and a rotational speed of 10 rpm.

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Original Research Paper

The obtained results are depicted in Appendix A (Supplementary material: Table A1).
From these results, it was possible to quantify such effects for each angle of inclination by regression techniques.
The prediction equations (Eqns. (17)–(19)) resulted incorrelation coefficients (R2) of 0.94, 0.95 and 0.95 for angles of inclination of 40°, 50° and 60°, respectively.

to determine such values (Cunha et al., 2016; El Kassem etal., 2021; Marigo and Stitt, 2015; Nascimento et al., 2022;
Santos et al., 2016). One of the disadvantages of the calibration process is the high number of simulations required
to find the parameter values. Therefore, the analysis of theeffects of DEM parameters becomes essential to quantify
the influence of each parameter on the rotating disc. As
μ 2 has a muchgreater influence than the other parameN Cpp-40º 
1.17  0.05x1  0.05x12  0.05x22  0.09 x3  0.36 x4  0.16Spwx  0.06 x52  0.05x62
ters,4 an alternative
is to only calibrate this parameter, thusx12  0.05x22  0.09 x3  0.36 x4  0.16 x42  0.06 x52  0.05x62
reducingthenumberof simulations.
(17)
Another factor to be analyzed is that μSpw has a negative(17)
effect, i.e., the number of contacts between particles deN Cpp-50º 
1.18  0.05x1  0.06 x12  0.06 x22  0.12 x3  0.06 x32  0.40 x4  0.17 x42  0.07 x52  0.06 x62  0.04 x1x4
creases as the coefficient value increases. This information
2
2
2
2
2
2
is necessary for the design and optimization of rotating
1  0.06 x 2  0.12 x3  0.06 x3  0.40 x 4  0.17 x 4  0.07 x5  0.06 x6  0.04 x1 x 4
discs, because depending on the interaction between the
(18)
(18)
granular material and the equipment wall, the contact between particles can decrease, thus hindering the granula22
2
N Cpp-60º 
1.19  0.05x1  0.06 x1  0.06 x2  0.13x3  0.06 x3  0.41x4  0.18 x42  0.07 x52  0.06 x62  0.03x1x4
tion process.
x12  0.06 x22  0.13x3  0.06 x32  0.41x4  0.18 x42  0.07 x52  0.06 x62  0.03x1x4
Rong et al. (2020) used a 3k factorial design to evaluate
the sensitivity of particle–particle DEM parameters in a
(19)
(19)
rotating drum. The authors found that the sliding friction
The DEM parameters of these equations are presented incoefficient, when compared with the rolling friction coefficoded form as follows:
cient and the restitution coefficient, was the determining
parameter on the mean power draw of the rotating drum.
epp  0.50
epw  0.50
With the increase in the sliding friction coefficient, more
x1 
x2 
0.25
0.25
power was required due to the increased resistance of parμSpw  0.50
μSpp  0.50
ticles when they come into contact with other particles.
x4 
x3 
0.25
0.25
[32]
[33]
μRpp  0.050
μ Rpw  0.050
x5 
x6 
0.025
0.025
[35]
[34]
Table 5 CCD used to analyze the influence of operating conditions on
where x1 = particle–particle restitution coefficient, x2 =NCpp.
particle–wall restitution coefficient, x3 = particle–particle
[36]
[37]
static friction coefficient, x4 = particle–wall static frictionRun
FD (%)AI (°)RS (rpm)NCppcoefficient, x5 = particle–particle rolling friction coeffi1
2.00
40.00
10.00
1.31
cient, and x6 = particle–wall rolling friction coefficient, all
2
2.00
40.00
30.00
0.79
in coded form.
3
2.00
60.00
10.00
1.34
In Eqns. (17)–(19), only the statistically significant parameters were included. It was possible to observe that all
4
2.00
60.00
30.00
0.86
DEM parameters had a significant effect on NCpp at a signif5
8.00
40.00
10.00
1.59
icance level of 5 %.
6
8.00
40.00
30.00
1.15
The coefficients of Eqns. (17)–(19) show that epw (x2 incoded form), μRpp (x5 in coded form), and μRpw (x6 in coded
7
8.00
60.00
10.00
1.65
form) were only influenced by the quadratic term. On the
8
8.00
60.00
30.00
1.26
other hand, μSpw (x4 in coded form) was the parameter that
9
0.59
50.00
20.00
0.71
most influenced NCpp, with an effect four times greater thanthat of μSpp (x3 in coded form)—the second most relevant
10
9.41
50.00
20.00
1.42
parameter—and eight times greater than that of epp (x1 in
11
5.00
35.29
20.00
1.19
coded form). In addition, the change in the angle of inclina12
5.00
64.71
20.00
1.28
tion of the rotating disc practically did not influence theintensity of the parameter effects.
13
5.00
50.00
5.29
1.73
In order to obtain accurate simulations based on the
14
5.00
50.00
34.71
1.03
DEM approach, a correct set of input parameter values is
15
5.00
50.00
20.00
1.29
required. Several studies have used the calibration process233

Rondinelli M. Lima et al. / KONA Powder and Particle Journal No. 41 (2024) 229–241

4.2 Analysis of the effects of operating conditions

Table 5 shows the results of the CCD used to analyze theinfluence of filling degree (FD), angle of inclination (AI)and rotational speed (RS) on NCpp (see levels in Table 4).
The data in Table 5 were subjected to regression analysis to quantify the effects of each independent variable andits corresponding interactions on the response (NCpp). The
variables FD, AI and RS were coded as x7, x8, and x9, respectively. The obtained equation (Eqn. (20)) has a correlation coefficient (R2) equal to 0.97.
N Cpp 1.27  0.19 x7  0.09 x72  0.03x8  0.23x9  0.06 x92(20)
FD  5%AI  50
where:; x8 
;(20) x7 
3%10RS  20 rpm
and x9 
10 rpm[38][39]analyzed had a significant effect
All operating conditions

on NCpp at a significance level of 5 %. It is noteworthy that
there[40]was no interaction between the variables, i.e., eachvariable alone had a direct influence on NCpp.
As it can be seen in Eqn. (20), the effect of FD was
positive, that is, the number of contacts between particles
increased as a function of the number of particles in therotating disc. An opposite effect was observed for rotational
speed, i.e., the RS increased with decreasing NCpp. According to Capes (1980), during the granulation process theparticle residence time can be lengthened by increasing therotational speed. However, at high rotational speeds therotating disc tends to operate in the cataracting regime,
which can result in agglomerate degradation and breakage
depending on the agitation intensity. A strict control of therotational speed is then necessary for the rotating disc tooperate in the rolling and cascading regimes, consequently
improving the efficiency of the granulation process(Pietsch, 1997; Salman et al., 2006).
The change in the disc inclination had little influence on
NCpp when compared to the other variables. This result
confirms what was observed in the sensitivity analysis of
DEM parameters.
Other variables such as size distribution of feed, liquid
content in the feed powder, and liquid surface tension can
Table 6 DEM parameter values obtained experimentally.

DEM parameters

Values

epp

0.900

epw

0.690

μSpp

0.620

μSpw

0.330

μRpp

0.014

μRpw

0.013

234

Original Research Paper

also influence the granulation process (Capes, 1980). Nevertheless, Eqn. (20) can elucidate the granulation process,assisting in the operation, design and optimization of rotating discs.

4.3 Simulation of flow regimes
DEM simulations were also used to predict the behaviorof flow regimes in a rotating disc. In these simulations, theinput parameters obtained from the experimental proceduredescribed in Section 3.1 were employed. Table 6 shows
the values of DEM parameters obtained by the respective
experimental measurement. To verify the validity of these
DEM input parameters, the numerical and experimental
results were compared. Two analyses were carried out: aqualitative one, where images of the flow regimes wereconfronted, and a quantitative one, where the angle of departure values were compared.
Figs. 5 to 7 compare the images of the experimental andsimulated results for the different flow regimes in the rotating disc, operating at different angles of inclination (AI). As
it can be seen, the DEM simulations accurately predictedeach flow regime.
Regarding the quantitative analysis, Fig. 8 compares thenumerical and experimental angles of departure for thestudied disc inclinations. As observed, the experimental
and simulated results were very similar for the entire rangeof flow regimes (i.e., rolling, cascading, cataracting, andcentrifuging). The average percentage deviation of the angle of departure was less than 2 %. Therefore, both qualitative (Figs. 5 to 7) and quantitative (Fig. 8) analyses
confirmed that the direct experimental measurements of the
DEM input parameters were able to reproduce the bulk
behavior of particles in the rotating disc. For this reason, it
was not necessary to perform the calibration of DEM parameters, allowing the preservation of their physical meanings.
Barrios et al. (2013) verified that experimental measurements of DEM parameters for a single particle proved to bea viable alternative to estimate the parameter values of ironore pellets. According to the authors, the computational
effort is reduced since the parameter values are obtained
directly, without requiring a calibration process.
Brandao et al. (2020) used DEM Simulations to study
the mixing and segregation of materials in a rotating drum.
The authors measured the DEM parameters experimentallyand performed a calibration approach. They verified thatthe parameter values obtained by the calibration process
were similar to the experimental ones.
From the validation of the DEM model it can be verified
that the DEM parameters successfully represent the evaluated experimental results. Therefore, it is possible to analyze the influence of the experimental operating conditions
on the maximum collision force (FC,max). To determine themaximum collision force, all particles were tracked along

Rondinelli M. Lima et al. / KONA Powder and Particle Journal No. 41 (2024) 229–241

AI (°)
FD (%)

Original Research Paper2
10 rpm40
510 rpm8
10 rpm25 rpm20 rpm15 rpm40 rpm35 rpm35 rpm90 rpm65 rpm65 rpmRolling

CascadingCataracting

Centrifuging

Fig. 5AI (°)
FD (%)

Experimental and numerical results of the qualitative analysis of flow regimes for an angle of inclination of 40°.

2
10 rpm50
510 rpm8
10 rpm30 rpm25 rpm20 rpm40 rpm35 rpm30 rpm130 rpm

100 rpm

90 rpmRolling

CascadingCataracting

Centrifuging

Fig. 6

Experimental and numerical results of the qualitative analysis of flow regimes for an angle of inclination of 50°.

235

Rondinelli M. Lima et al. / KONA Powder and Particle Journal No. 41 (2024) 229–241

AI (°)
FD (%)

Original Research Paper2
10 rpm60
510 rpm8
10 rpm45 rpm40 rpm35 rpm70 rpm45 rpm40 rpm165 rpm

125 rpm

105 rpm

Rolling

CascadingCataracting

Centrifuging

Fig. 7

Experimental and numerical results of the qualitative analysis of flow regimes for an angle of inclination of 60°.

(a)

(b)

(c)

Fig. 8236

Experimental and numerical results of the quantitative analysis of angles of departure for angles of inclination of (a) 40°, (b) 50° and (c) 60°.

Original Research Paper

Rondinelli M. Lima et al. / KONA Powder and Particle Journal No. 41 (2024) 229–241

the simulation time.
A 3k factorial design was used to evaluate the effects ofthe following operating conditions on FC,max: filling degree(FD), angle of inclination (AI) and rotational speed (RS).
The levels used for the independent variables (FD, AI, andRS) were the same as the experimental ones. This analysis
can be used to help prevent agglomerate degradation andbreakage.
Table 7 shows the conditions and results (FC,max) of the
3k factorial design.
The effects of FD, AI and RS on FC,max were quantified
by regression techniques using the experimental data in
Table 7. From Eqn. (21), which has a correlation coefficient (R2) equal to 0.82, it is possible to predict the maxiTable 7 3k factorial design used to analyze the influence of operating
conditions of a rotating disc on FC,max.

Run

FD (%)

12

2

2

mum collision force between particles (FC,max) as a functionof the operational variables and quantify its respective effects. The independent variables FD, AI, and RS are presented in coded forms (x10, x11 and x12, respectively).
All operating conditions had a positive effect on FC,max at
a significance level of 5 %. FC,max increased as a function ofboth FD (x10 in coded form) and RS (x12 in coded form),with the latter affecting this variable three times more than
the former. This analysis highlights the care that must be
taken when operating rotating discs at high rotational
speeds, as this can result in degradation of the agglomerates
formed, thus impairing the granulation process. On theother hand, AI (x11 in coded form) had a negative effect on
FC,max, and although it did not affect linearly the collision
force, it interacted with other variables.

2  0.33x  0.12 x x  0.19 x x  0.09 x
FC,max  1.61  0.10 x10  0.10 x11
12
10 11
11 12

2  0.33x  0.12 x x  0.19 x x  0.09 x x 2  0.10 x 2 x
FC,maxx10  0.10x11AI (°) 1.61 
RS0.10(rpm)
FC,max(N) 12
10 11
11 12
11 12
11 12

40

10

1.10

40

30

1.52

(21)

(21)
FD  5%AI  50
where: x10 ; x11 
;
3%
10

(21)
2

40

50

1.98

4

2

50

10

1.21

5

2

50

30

1.33

6

2

50

50

1.62

7

2

60

10

1.18

8

2

60

30

1.72

9

2

60

50

1.93

10

5

40

10

1.24

11

5

40

30

1.46

12

5

40

50

2.31

13

5

50

10

1.21

14

5

50

30

1.43

15

5

50

50

1.80

16

5

60

10

1.45

17

5

60

30

1.91

18

5

60

50

1.72

19

8

40

10

1.35

20

8

40

30

1.74

21

8

40

50

2.91

22

8

50

10

1.55

23

8

50

30

1.49

24

8

50

50

1.68

25

8

60

10

1.52

26

8

60

30

1.50

27

8

60

50

1.75

RS  30 rpm
20 rpm[41][42]
The effect of operating conditions on FC,max was similar
to that observed for the angle of departure and this result is[43]

and x12 

(a)

V

3

r

V

(b)

r

Fig. 9 Velocity profiles of glass beads for an angle of inclination of
50°: (a) 2 % and (b) 5 %.

237

Original Research Paper

Rondinelli M. Lima et al. / KONA Powder and Particle Journal No. 41 (2024) 229–241(a)Table 8 Analysis of active and passive layer thicknesses.

Filling
degree[%]Layer thickness [m]Passive(P)

Active
(A)

50°

2

0.040

0.029

0.714

5

0.063

0.046

0.727

2

0.034

0.023

0.667

5

0.057

0.034

0.600

V

Angle ofinclination

60°

r

V

(b)

r

Fig. 10 Velocity profiles of glass beads for an angle of inclination of
60°: (a) 2 % and (b) 5 %.

in agreement with the behavior observed by Lima et al.
(2022).

4.4 Simulation of particle velocity distributions
Due to the rotating disc inclination, the active and passive layers of the rolling regime were found to be located in
the inner regions of the granular bed. Therefore, they could
only be observed through simulations.
Figs. 9 and 10 display the particle velocity distributions,where Vp is the particle velocity and r is the particle position along the reference line (Fig. 4(b)). The variable r
starts from the disc wall and ends at the granular bed
surface. In this work, only the cases in which the rotating
disc was operating in the rolling regime were considered,i.e., at angles of inclination of 50 (Fig. 9) and 60° (Fig. 10),filling degrees of 2 and 5 %, and velocities of 10, 15 and20 rpm.
The two different bed structure regions visualized in theFigs. 9 and 10 demonstrate that as the value of r increased,
the particle velocities decreased until reaching a minimum
value (close to zero), evidencing the interface between thepassive and active layers. This behavior is typical of thepassive layer in the rolling regime, in which the particles
move as a solid body and are dragged up by the disc wall.
From the interface, the solids velocities started to increase
along r, characterizing the active layer. In this region, the

238

Ratio
(A/P)

particles had a higher dynamics, moving in the opposite
direction to the passive layer. As observed, the highest velocity values were found to be near the granular bed surface.
It can also be seen that higher rotational speeds resulted
in greater velocities in both layers, but without changing
the interface position. When the rotational speed was kept
constant and the filling degree was increased, the particle
velocities also increased in both layers, and such effect was
higher in the active layer. Regarding the angle of inclination, it can be observed that the disc operating at 50°
achieved higher particle velocities in the active layer. In
this case, the granular bed reached a higher angle of departure, increasing the potential energy of the particles andtherefore their dynamics.
In contrast to the rotational speed, changes in the filling
degree and angle of inclination modified the interface position, suggesting the significant impact of these variables on
the active layer thickness. The correct prediction of the active and passive layers for a rotating disc operating in therolling regime is very important for the granulation process. It is worth mentioning that the best particle interactions, mixing and segregation, as well as the highest ratesof heat and mass transfer, occur in the active layer. Table 8
presents the thicknesses of the active layers and their ratio
to the passive layer thicknesses for angles of inclination of
50° and 60°.
For constant filling degrees, the change in the angle ofinclination from 50° to 60° resulted in a reduced ratio between the layers of 6.58 % and 17.47 % for filling degreesof 2 % and 5 %, respectively, evidencing the movement ofparticles to the passive layer region.
Santos et al. (2015) found similar behavior, when studied, numerically and experimentally, the velocity profile ofsoybean particles in a rotating drum operating in the rolling
regime. They observed that, keeping the filling degree
constant and increasing the rotational speed, there was anincrease in the velocity of the particles in both layers (passive and active), and the point where a reverse of the flow
takes place was kept constant. On the other hand, when therotational speed was kept constant and the filling degree
was changed from 18.81 % to 31.40 %, the inflection point

Rondinelli M. Lima et al. / KONA Powder and Particle Journal No. 41 (2024) 229–241

Original Research Paperchanged from 4 cm to 5.5 cm, respectively.

E

Young’s modulus (Pa)5. Conclusions

E*equivalent Young’s modulus (Pa)F

force (N)G

shear modulus (Pa)G*

equivalent shear modulus (Pa)h0device height (m)Imoment of inertia (m s–2)L

rim height of disc granulator (m)m*equivalent mass (kg)mmass (kg)Ncnumber of contact (-)

R

radius (m)R*equivalent contact radius (m)S

stiffness (Pa m)ttime (s)vlinear velocity (ms–1)

V0

velocity before impact (m s–1)

V1

velocity after impact (m s–1)

Vp

particle velocity (m s–1)

In this work, DEM simulations were performed to study
the influence of DEM parameters and operating conditions
on particle dynamics in a rotating disc.
The methodology used herein allowed to analyze andquantify the effect of DEM parameters on the number ofcontacts between particles, an important variable in the
granulation process. All DEM parameters had a significant
influence on the analyzed response, with μSpw being theparameter with the greatest effect.
DEM simulations were also used to predict the behaviorof flow regimes in a rotating disc. In these simulations, theinput parameters obtained from the experimental procedure
were used and successfully predicted the experimental behavior of all flow regimes.
With respect to the effects of the operating conditions of
the rotating disc (i.e., filling degree, angle of inclination
and rotational speed) on NCpp and FC,max, it was observedthat the increase in the filling degree had a positive effecton both responses. In contrast, the increase in the rotational
speed led to a reduction in NCpp, but an increase in FC,max,which can impair the efficiency of the granulation process.
Lastly, in relation to the disc inclination, the change in thisparameter had little influence on NCpp. Additionally it wasverified that greater angles of inclination led to a reductionin the intensity of FC,max.
In this study, it is also possible to verify the effect of theoperating conditions of the rotating disc on the particle velocity distributions in the rolling regime. As the filling degree and rotational speed increased, the particle velocity
was also raised in both layers, whilst the change in the angle of inclination affected the active layer. The disc operating at 50° showed the highest ratio between the active andpassive layer thicknesses.

Greek Symbol
αangle of departure (°)β

damping coefficient (kg s–1)

δoverlap distance of two particles (m)θangle of inclination of disc granulator (°)μR

rolling friction coefficient (-)μS

static friction coefficient (-)τ

torque (N m–1)

υ

Poisson ratio (-)Φ

angle of inclination used in the friction coefficient measurement (-)ω

angular velocity (s–1)

Supplementary information

Indexes

The online version contains supplementary material
available at https://doi.org/10.14356/kona.2024016.

C

contactddampingi and jparticle identification indexnnormal directionppparticle–particle interaction

pw

particle–wall interactionttangential direction

Acknowledgments
The authors are grateful to the Minas Gerais Research
Funding Foundation (FAPEMIG), the Brazilian National
Council for Scientific and Technological Development(CNPq), and the Coordination for the Improvement ofHigher Education Personnel (CAPES) for their financial
support.

NomenclatureD

diameter of disc granulator (m)Drdistance traveled by the particle (m)erestitution coefficient (-)

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Rondinelli M. Lima et al. / KONA Powder and Particle Journal No. 41 (2024) 229–241

Original Research Paper

Authors’ Short Biographies
Rondinelli Moulin Lima
Dr. Rondinelli Moulin Lima received his Ph. D. in Chemical Engineering from the Federal University of Uberlândia in 2021. He has experience in Chemical Engineering, with emphasis on Computational Fluid Dynamics and Discrete Element Method in the area of particulate systems andseparation processes. He is currently a researcher at the Institutional Training Program at the Center
for Mineral Technology (CETEM), with the development of agglomerated stones.

Gisele Márcia de Souza
MSc. Gisele Márcia de Souza received her master’s degree in Metallurgical and Mining Engineering from the Federal University of Minas Gerais. Currently, she is a Ph. D. candidate at the Federal
University of Uberlândia in the area of particulate systems and separation processes. She has experience in Chemical Engineering, with emphasis on Transport Phenomena, Unit Operations, Computational Fluid Dynamics and Discrete Element Method.

Rodolfo Junqueira Brandão
Dr. Rodolfo Junqueira Brandão is Adjunct Professor I at the Technology Center at the Federal
University of Alagoas. He received his Ph. D. in Chemical Engineering from the Federal Universityof Uberlândia in 2019. He is currently Coordinator of the Technology Center and Tutor of the Special
Engineering Training Program. He works mainly in the area of particulate systems, separation processes and numerical simulation.

Claudio Roberto Duarte
Dr. Claudio Roberto Duarte is a Full Professor at the Federal University of Uberlândia in Brazil.
He received his Ph. D. in Chemical Engineering from the Federal University of Uberlândia in 2006.
He served as the Coordinator of the Postgraduate Program in Chemical Engineering at the Federal
University of Uberlândia from 2019 to 2022. He works mainly on the following topics: granulation,
rotating drums, mills, drying, pyrolysis, solid-liquid separation, spouted bed, modelling, computational fluid dynamics, and cyclone.

Marcos Antonio de Souza Barrozo
Dr. Marcos Antonio de Souza Barrozo is a Full Professor at the Federal University of Uberlândia
in Brazil. He received his Ph. D. in Chemical Engineering from the Federal University of São Carlos
in 1995. He is an A1 researcher at the National Council for Scientific and Technological Development (CNPq) and a member of the Standing Committee of the Brazilian Congress of Particulate
Systems. He has experience in Particulate Systems, working mainly on the following topics: fluid
dynamics of particulate systems, solid-fluid separation, drying, coating, granulation, flotation, pyrolysis, and hydrothermal carbonization.

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KONA Powder and Particle Journal No. 41 (2024) 242–253/https://doi.org/10.14356/kona.2024006

Original Research Paper

Simulation of the Classification of Manufactured Sands
in the Throat Air Classifier †Horacio A. Petit* and Edgardo Fabián Irassar
The Center for Research in Physics and Engineering of the Center of the Province of Buenos Aires (CIFICEN), Argentina

The scarcity of natural sands has triggered a considerable increase in the
consumption of manufactured sands for concrete production. In this regard,particle flaking and excessive fines are the main problems that should be
addressed when utilizing manufactured sands. The throat classifier is an
air classifier designed for the elimination of fine particles (smaller than 75
micrometers) from manufactured sands. The main features of the classifier havebeen presented in the literature, but the mechanism that drives the classification
has not been studied in detail. Therefore, this work explores the mechanismof classification of the throat classifier by using CFD-DPM and CFD-DEM
simulations. The accuracy and limitations of the methodologies were evaluatedby comparing the results against experimental data obtained at the pilot scale.
The simulations presented fair results in the representation of the airflow andthe particle classification inside the throat classifier. Differences between thepredictions using CFD-DPM and CFD-DEM methodologies under the simulated
conditions were found to be negligible. The results of the simulations allowed
for a more detailed understanding of the classification mechanism that occurs
inside the device and the influence of operational variables on the equipment
performance.
Keywords: manufactured sands, air classification, CFD-DEM, stone dust, aggregates

1. Introduction
The worldwide growth of population and wealth has triggered an increase in the construction of housing, facilities,
service cities, and infrastructure. The new urban building
environment (drinking water and sanitation, public transport, roads, etc.) is demanding large volumes of building
materials such as aggregates, cement, ceramics, lime, gypsum, glass, wood, steel, aluminum, and plastic. The demand
for such enormous volumes of natural resources is affecting
the sustainability of these industrial sectors (OECD, 2019).
Among the building materials, concrete and mortar are themost widely used, both consisting of cement, aggregates,
and water. Cement production represents about 5 % of the
global CO2 emissions worldwide. Therefore, the cement
and concrete industries are developing new approaches toincrease production with a lower carbon footprint and lessusage of natural resources. The main goal is to achieve net
zero emission by 2050 (GCCA, 2022). Despite cement,
aggregates also play an essential role in the eco-efficiency
of concrete; nearly 75 % of the concrete volume is repre†*

Received 14 September 2022; Accepted 25 October 2022
J-STAGE Advance published online 3 June 2023
Corresponding author: Horacio A. Petit;
Add: Pinto 399, 7000, Tandil, Argentina
E-mail: hpetit@fio.unicen.edu.ar
TEL: +54-2284-451055

242

sented by coarse and fine aggregates (Tatari and Kucukvar,2012). Aggregates can be natural (gravel or river sand) or
manufactured (crushed stone or manufactured sand). At
the beginning of this century, governments and academic
communities were on alert about the depletion of natural
sources of sand and its increasing scarcity (Torres et al.,2017; UNEP, 2014). Manufactured sands had been foundto be an alternative for river sands (Donza et al., 2002;
Goncalves et al., 2007); thus, the replacement of natural
sands by manufactured sands became an intense researchsubject ever since.
There are two main shortcomings for the application of
manufactured sands in concrete: the particle shape and thehigh content of dust. Manufactured sands are produced bycrushing and sieving during coarse aggregate production.
The angular shape (sometimes flaky) of manufactured
sand particles causes a low packing density, increasing thewater demand and affecting the rheology of the mortar or
concrete (Cepuritis et al., 2016; 2017). The content of dust
is defined as the percentage of particles smaller than 75 μm
in the sands. It increases the water demand for mortar andconcrete and enhances other problems such as particulatematter emissions during handling (Petit et al., 2021) and inhomogeneity of the stock due to humidity. The performanceof mortar and concrete constituted by manufactured sands
can be improved by reducing the content of dust and the

Copyright © 2024 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/).

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

flakiness of the sand particles (Ramos et al., 2013). Waterwashers are generally used for the elimination of dust.
Although water-washers are effective, they have several
problems such as unwanted sewage in the quarries, unsellable wastes, final wet products, and a large water footprint(Aasly et al., 2014).
New dry processes, such as magnetic (André et al.,2019) or air classification (Peng et al., 2022), can avoid theuse of water for dedusting manufactured sands. A novel
classifier, the throat classifier, was explicitly developed
for the dedusting of manufactured sands (Petit and Irassar, 2021). Its design focuses on simplicity of operation,
reduction of energy consumption, and low maintenance.
In this machine, the manufactured sand is fed using a
conveyor belt and classified into three products with different gradations. The advantage of this classifier is thatthe dust extracted from the manufactured sand remains dry
and can be stored in a silo for later use as filler in cement,
mortar, and concrete (Di Salvo Barsi et al., 2020), or otherindustrial applications such as filler in the plastic industry(Passaretti et al., 2019; Tawfik et al., 2022). The throatclassifier was tested at pilot scale as a replacement forwater-washers, giving excellent results (Petit et al., 2018a).
However, detailed knowledge about the airflow and particle classification occurring inside the device is needed
to improve its design. In this regard, Computational Fluid
Dynamics (CFD) and the Discrete Phase Method (DPM)
can be used to simulate the airflow and classification mechanism inside the chamber (Petit et al., 2020; Stone et al.,2019). However, this approach has limitations for systems
where the concentration of solids is high and particle–
particle interactions are of critical importance. The Discrete
Element Method (DEM) is similar to the DPM method but
takes into account the particle–particle interactions at the
micro level, thus representing the bulk behavior of the
granular flow (da Cunha et al., 2013).
In this paper, the airflow and particle classification inside
the throat classifier were simulated using the CFD-DPM
approach. The accuracy and limitations of the simulation
methodology were evaluated. Elements of the DEM
method were incorporated into the CFD-DPM methodology to compare the results and assess the necessity of modeling particle–particle interactions. The airflow behavior
and classification mechanisms inside the throat classifier
were studied and visualized by analyzing the simulation
results, improving the understanding of the influence ofthe main operational variables on the classification mechanisms and complementing the knowledge obtained byprevious experimental studies.

2. Materials and methods
2.1 Experimental set-up

A scheme of the throat classifier is presented in Fig. 1(Petit, 2018; Petit and Irassar, 2021). The classifier pres-

Original Research Paper

ents two classification chambers separated by an inclined
plate and a vertical deflector. The right-side chamber isthe primary chamber, while the left-side chamber is thesecondary chamber. The space between the vertical deflector and the edge of the inclined plate is called the throat
and is the region where the primary classification takes
place. The primary classification at the throat is performedby an air stream that classifies particles according to its
aerodynamic diameter. The airflow that passes throughthe throat is composed of a main airflow and an auxiliary
airflow. The main airflow enters the classifier through theprimary inlet at the top of the primary chamber, while theauxiliary airflow enters the classifier through the auxiliary
air inlet located at the bottom of the primary chamber.
Both, the main and auxiliary airflows, merge at the throat
and flow towards the secondary classification chamber. The
auxiliary airflow is used to improve the performance of theclassifier and its use is optional. Therefore, the auxiliary
air inlet can be closed using the adaptable hopper. The
manufactured sand enters the classifier through the main
air inlet at the top. Sand particles fall by gravity and impact
against the inclined plate. Coarse particles bounce after the
impact, while fine particles slide over the inclined plate. All
particles reach the edge of the inclined plate with different
inertia, depending on the segregation and percolation thattakes place on the inclined plate. Particles are then classified by the airflow at the throat. Coarse particles are lesssensitive to the drag force and fall to the bottom of the
primary chamber, while fine particles are dragged by the
airflow to the secondary chamber. If the auxiliary airflow
is used, coarse particles exit the classifier as the primary
product through the primary product outlet. Otherwise, theprimary product is collected in the adaptable hopper. The
fine particles that were dragged to the secondary chamber
experience a secondary classification. The airflow reduces
its velocity in the secondary chamber, causing some ofthe particles to settle at the bottom and to be collectedin the secondary hopper as the secondary product of theclassification. Particles that did not settle in the secondary
chamber exit the classifier with the air and are collectedin an auxiliary equipment such as a cyclone classifier ora bag filter. These particles represent the tertiary productof the classification. The performance of the classifier iscontrolled by adjusting the velocity of the airflow and theposition of the vertical deflector. The position of the deflector is characterized by a set of coordinates (d and D) set
on the edge of the inclined plate with the y-axis direction
pointing downwards, as shown in Fig. 1.

2.2 Case studies

Two experimental studies are presented in this work. The
first study was performed to characterize the airflow inside
the equipment and to obtain validation data to be comparedto the results of the CFD simulations. In this regard, the243Original Research Paper

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

Fig. 1 Scheme of the throat classifier (Petit et al., 2019), and illustration of its main classification mechanism. Stations 1–4 locate the measurementsof the air velocity profiles.

main variables of interest are the pressure loss and the air
velocity at specific positions inside the equipment. The
pressure loss was measured with a differential manometer;
the first measurement point was set at the exit tube of theclassifier and the second at an arbitrary point outside theequipment. Hence, the pressure loss accounts for the loss ofenergy inside the equipment and the loss due to the acceleration of the atmospheric air while entering the classifier.
The velocity inside the equipment was measured using a
Pitot tube. Velocity profiles for the air were measured atfour different stations, as presented in Fig. 1. Station 1 was
set to measure the velocity profile inside the exit tube. Stations 2 and 3 were set at the inlet of the classifier. Station 4
was set perpendicular to the inclined plate, near the throat,in order to study the velocity profile near the classification
zone. The measurement of the velocity profile at the throat
was not possible due to the relatively large dimensions of
the Pitot tube, so only the mean air velocity was measured
at the throat.
The second study was carried out to characterize theclassification of sand particles using different set-ups forthe operational variables of the classifier. The obtained data
was then used to validate the results of the CFD-DPM and
CFD-DEM simulations. The classification study consistedof three classification cases that differed in the position of

244

Table 1 Cases of study for the classification experiences.

Case

Position (P)uth [m/s]

Aux. Inlet (A)

1

P1

3.80

closed

2

P1

3.20

open

3

P2

7.75

closedthe vertical deflector (P), the velocity at the throat (uth), andthe utilization of the auxiliary air inlet (A), as presented inTable 1. Two different positions were used for the verticaldeflector. Position P = 1 is determined by the coordinates
D = 0.08 m and d = 0.02 m and position P = 2 by the coordinates D = 0.02 m and d = –0.02 m (Fig. 1). Case 1 and 2
differ in the utilization of the auxiliary air inlet, A = closed
for case 1, while A = open for case 2. Granitic manufactured sand was used for the study, presenting a solid densityof 2630 kg/m3, a dust content of 14.59 %, and a moisture
content of 0.24 %. Compared to the standard limits for fineaggregates in concrete (ASTM C33, 2018) (Fig. 2), themanufactured sand presents an excess of material passing
the 300 μm. For each case of study, 1 kg of granitic manufactured sand was fed to the classifier at a constant rate of
80 kg/h. After the classification, the primary and secondary
products of the classification were characterized by sieving

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253(µm)
Fig. 2 PSD of the granitic manufactured sand and comparison against
the standard limits (ASTM C33, 2018).

analysis. The partition curve and the cut sizes were computed by comparing the particle size distribution (PSD) of
the products and the feed. These cases were selected froma more complete study that the reader may find in previous
works by the authors (Petit, 2018; Petit and Irassar, 2021).

2.3 Numerical set-up
The numerical method used for the simulation has beenused previously by the authors in the simulation of similar
air classifiers (Petit and Barbosa, 2017; Petit et al., 2018b,
2020). An overall description of the simulation methodology is presented here, but the reader may find the fulldescription of the model in the supplementary materials.
The air was treated as a continuum phase, and the turbulent
flow was simulated by solving the Unsteady Reynolds Averaged Navier-Stokes equations. These equations requirea closure model for the computation of turbulence, so two
different turbulence models were assessed in this work.
The first model is the realizable k – ε model which uses
conservation equations for the kinetic turbulent energy (k)and the turbulent dissipation rate (ε). The second model isthe Reynolds Stress Model, which is defined by a transport
equation for the Reynolds stress tensor.
Particles were modeled as a discrete phase using a
Lagrangian reference frame. Particles were grouped toform what is known as parcels, groups of particles withthe same size. The trajectory of each parcel was computed
in a similar manner as that of a single particle, using thecommon particle size of the parcel. The positions of allparticles that form the parcel were updated based on thistrajectory, thus reducing computation time. The mass flowrate of the parcel is given by the number of particles times
the mass of the component particles. The trajectories of theparcels were predicted by integrating the force balance,accounting for the gravity and drag forces. Manufactured
sand particles present irregular and flaky particles (Rolnyet al., 2015; Johansson and Evertsson, 2014); therefore, theOriginal Research Paper

drag coefficient was computed using the Haider and Levenspiel model (Haider and Levenspiel, 1989). The shapefactor (ratio between the surface area of a sphere having
the same volume as the particle and the actual surface areaof the particle) was computed using an optical microscope,presenting the mean value of ϕ = 0.7. The effect of turbulence on the particle trajectories was taken into accountby using the Random Walk–Eddy Lifetime model. The
discrete phase exchanges momentum with the continuum
phase through a momentum source term in the momentum
equations of the fluid phase.
Air classifiers are generally operated at low solid concentrations. However, in the case of the throat air classifier,the concentration of particles is high near the inclined plate
and the throat. Particle–particle interactions may present
an additional influence on the classification mechanism.
If relevant, a proper model for the particle–particle interaction should be used. One way to assess the relevanceof the particle–particle interactions was proposed by
Elghobashi (1994) and then used by Greifzu et al. (2016).
The assessment is carried out by defining the volume fraction occupied by the particles (αP) and the ratio between
the particle reaction time (τP) to the Kolmogorov (τκ).
However, the particle-laden flow inside the throat classifier
cannot be easily labeled based on these parameters. The
concentration of particles is high in the incline plate wheremost particle–particle collision occurs. The concentrationof solids diminishes away from the throat and becomes
negligible further away from this region. Moreover, thesizes of the manufactured sand particles range from a few
microns to a few millimeters, changing the values of theparticle reaction time by four orders of magnitude. The
reader may find more details about this assessment in the
supplementary materials or in Petit (2018). To summarize:a DEM model was used in order to account for particle–
particle interactions and to assess the necessity of a fourway coupled simulation under the tested conditions.
Collisions between particles were computed based on thework of Cundall and Strack (1979). The normal force acting on a particle experiencing a collision was represented
by a spring-dashpot model, which is defined by a spring
constant (K) and a damping coefficient (γ). The force acting
on the second particle was computed using the Newton’s
third law. On the other hand, the tangential forces werecomputed by using the Coulomb law of friction, defined bya friction coefficient (μf). Collisions between the particles
and the geometry were modeled by assuming a coefficientof restitution for the impact, which was decomposed into
a normal (CN) and tangential contribution (CT). The methodology proposed for the modeling of the particle–particle
interactions is an extension of the DPM method that was
solved using the built-in capabilities of the CFD solver
and presents several limitations such as the lack of particle
rotation and rolling friction effects. However, these effects

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can be considered secondary when particles are inside ahigh-velocity flow. Moreover, the calibration of the DEM
parameters is not as simple as the calibration of a full DEM
method due to the influence of the continuum phase. Therefore, the values of the DEM parameters (Table 2) were
estimated based on reported values for similar systems(Chu and Yu, 2008; Traoré et al., 2015; Zhou et al., 2014;
Boemer and Ponthot, 2016). These values were used in thesimulations of different air classifiers, giving fairly realistic
results (Petit and Barbosa, 2017; Petit et al., 2018b, 2020).
A mesh independence study was carried out using acoarse, a medium and a fine mesh. Each structured meshconsisted of approximately 3 × 105, 5 × 105, and 7 × 105
elements, respectively. Mesh refinement was done progressively, paying special attention to the region near the throat
where the largest gradients of velocity and pressure arepresent. The pressure-inlet condition was used at the inlet,
the air outside the classifier was set to an arbitrary relative
static pressure of PR = 0. The same boundary condition
was applied at the auxiliary air inlet when it was open. The
pressure-outlet boundary condition was used on the outlet
face where a negative static pressure was set according to
the measured pressure loss of the current case study. The
no-slip boundary condition and standard wall functions
were used at the walls of the classifier. All simulations
were carried out by following the same procedure. First,
the CFD simulation of the airflow was performed using asecond-order transient solver. The time step used for the
fluid equations was ∆t = 2 × 10–4 s. The simulations were
continued until the airflow inside the classifier reached asteady state condition. Secondly, particles were injected
into the fluid using the DPM or DEM method. The simulations were resumed, and the trajectories of the particles
were updated for every fluid time step using the trapezoidal
rule for the temporal integration. In the case of the DEM
method, the particle time step was set two orders of magnitude smaller than the fluid time step. The simulations were
continued until all particles in the domain escaped or settled in the hoppers. Finally, the positions of the remaining
particles were used to calculate the classification efficiency.

Table 2 DEM parameters used in the present work.

Parameter

Units

Value

Spring constant (K)N/m

1 × 10–5

Damping coefficient (γ)—

0.9Friction coefficient (μf)—

0.3Normal restitution (CN)—0.3

Tangential restitution (CT)

—

0.7

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Original Research Paper

3. Results and discussion
3.1 CFD results

The results of the CFD simulations for case 1 are presented in Fig. 3, and compared against the measured velocity profiles measured at the four stations. The overall resultspresent a fair agreement with the experimental values. It canbe observed that the velocity profile of Station 1 is underpredicted, the utilization of a more refined mesh or adifferent turbulence model did not improve the results.
Compared to the values of Station 1, the profiles obtainedin Station 2 and 3 present a better agreement with theexperimental values and a better estimation of the velocity.
However, the value of the velocity near the walls presents
differences with the experimental values, which can be
attributed to the limitations of the Pitot tube used forthe measurements for such low velocities. In the case of
Station 3, the positive values of the velocity near the walls
could not be measured with the available measurement device. Similarly to Station 1, the utilization of a refined meshor the RSM turbulence model did not improve the results in
Station 2 and 3. The results for Station 4 present the worst
agreement between simulations and experiments. The velocity near the inclined plate is severely underpredicted andthe same occurs for distances from the plate (ℓ⊥) greaterthan 0.03 m. A slight improvement in the results is obtainedby using a refined mesh but is not sufficient to compensate
for the increase in the computational effort. This region of
the classifier is of great importance for an accurate modeling of the classification mechanism. The results show thatthe boundary layer of the flow near the throat, at the end of
the inclined plate, is not accurately modeled by using the
proposed approach. In addition, the utilization of a more
complex turbulence model does not correct this major
limitation of the simulation set-up. Other alternatives suchas the utilization of different coupling algorithms (PISO
instead of SIMPLEC), or different discretization schemes(QUICK scheme instead of second-order upwind scheme)
were tested without any improvements (Petit, 2018). A
more accurate approach may be to switch the first cellheight from y+ ≈ 30 to y+ ≈ 1 and to resolve the boundary
layer instead of modeling it by using wall functions. However, this approach would demand a computational effort
that could not be matched by the available computational
power at the time of the study. The same stands for the utilization of more complex turbulence models such as LES.
Therefore, the limitations of the current methodology wereassumed and further simulations were performed using thecoarse mesh and the realizable k – ε model.
The results of the CFD simulations, in terms of thepressure loss for the classifier, are presented in Fig. 4.
The simulations were performed using the two different
positions of the vertical deflector, with and without theauxiliary airflow. For each condition, the simulations wererun using: –200, –115, –70, –35 and –10 Pa for the outlet

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Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253◦

Fig. 3 Comparison between the experimental data ( ) and the CFD predictions for case 1, using the coarse (•), medium (•), and fine (•) meshes andthe realizable k – ε turbulence model. Values obtained with the Reynolds stress model and the fine mesh (×) are also presented. Error bars represent
minimum and maximum values while ℓ⊥ and u‖ represent the normal distance to the wall and the velocity parallel to the wall, respectively.

thFig. 4thComparison between the experimental data and the CFD predictions for the pressure loss for different conditions.

boundary condition. Then, the velocity at the throat wascomputed for comparison against the experimental values.
The simulations results present a fair agreement with theexperimental values. Both the experimental and numericalresults predict a higher pressure loss when the auxiliary air
inlet is closed. The pressure loss obtained by simulations
is overpredicted in almost all conditions. For P = 1, thepressure loss predicted by CFD is considerably higher

than that observed experimentally. These differences arehigher when the auxiliary air inlet is closed with errors as
high as 20 %. For the same position, the error committed
when the auxiliary air inlet is open is smaller, being as highas 14 %. For position P = 2, the error is smaller than thatobserved for position P = 1, as high as 5 %. In this position,
the simulations overestimate the pressure drop when theauxiliary air inlet is closed and underestimate the loss when247

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the auxiliary air inlet is open. Fig. 3 shows that the simulation cannot represent in full extent the acceleration of
the flow near the throat, which impacts on the conversionof mechanical energy in the flow and the computation of
the energy losses. Other sources of error can be attributed
to the numerical diffusion, the unresolved boundary layer
near the wall and the effect of adverse pressure gradients.
Despite the limitations of the methodology, the utilization of the CFD simulations can be used to gain valuable
insights about the behavior of the flow field inside theclassifier. Contours of velocity magnitude for different
conditions for the airflow inside the classifier are presented
in Fig. 5. The results are presented for a pressure boundary
condition of –200 Pa, and velocity contours for differentpressure conditions presented a self-similar behavior.
The trajectory of the airflow inside the classifier can beclearly identified. It can be observed that the behavior of
the airflow is different depending on the position of thevertical deflector and the utilization of the auxiliary airflow.
Therefore, differences in the classification of particles are
expected due to the different flow behavior. For position
P = 1 and the auxiliary air inlet closed, the airflow entersat the primary inlet and accelerates towards the throat. The
airflow reaches the end of the inclined plate and changes
its direction almost 180°, describing a U-shaped trajectoryand entering the secondary chamber. Secondary flows canbe observed near the main flow on both sides of the verticaldeflector, which are stronger in the secondary chamber andmay affect the secondary classification. For position P = 2,
the velocity patterns are similar to those observed for position P = 1. However, the change of direction at the throat
for position P = 2 is more abrupt. The air still changes its
direction about 180° but the radius of the swirl is smaller
than that observed for position P = 1. The flow area ofthe throat is smaller for position P = 2; therefore, the air
P
A

P

P
A

P

velocity is higher.
The utilization of the auxiliary airflow modifies the flow
pattern, as the contribution of the ascending stream of air
in the primary chamber decreases the contribution of the
primary airflow entering at the top. Both streams merge
at the throat as expected. However, the trajectory of the
airflow entering the secondary chamber changes with thepresence of the auxiliary airflow. In the case of position
P = 1, the air entering the secondary chamber describes asmoother trajectory when the auxiliary airflow is present.
In addition, the presence of secondary flows in the secondary chamber is reduced, thus expecting a cleaner secondaryclassification. These changes are not as clear for position
P = 2; nonetheless, the influence of the auxiliary airflow
on the velocity pattern at the throat can still be observed.
The upward auxiliary airflow at the bottom of the primarychamber suggests that particles that could not be classified
at the throat may experience a second counter-current classification in this region; therefore, improving the classifier
performance.
The velocity contours show that the main pressure loss
is due to the strangulation of the airflow at the throat.
Therefore, position P = 2, with a smaller flow area presents
higher pressure drops than that observed for position P = 1.
The utilization of the auxiliary airflow reduces the swirl
intensity of the flow at the throat; therefore, cases with theauxiliary air inlet open present less energy dissipation andlower pressure losses. In this regard, the conclusions drawn
from the observation of the velocity contours match thetrends in the experimental values presented in Fig. 4.

3.2 CFD-DPM and CFD-DEM results

Snapshots of the position of the particles, obtained by
CFD-DPM simulations, for the three case studies are presented in Fig. 6. It can be observed that the feed (sand) falls
P
A

P

P
A

P

Fig. 5 Contours of velocity magnitude for the throat classifier for two different positions of the vertical deflector (P = 1 and P = 2), with and without
the utilization of the secondary air inlet (A = open and A = closed, respectively).

248

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

Fig. 6

Original Research Paper

Snapshots of the particle positions inside the classifier at different times after the injection.

through the inlet creating a curtain of particles. The behavior of the feed varies depending on the flow conditions.
It can be observed in cases 1 and 3 that after entering theclassifier, fine particles are dragged by the airflow and reachthe end of the inclined plate before coarse particles. Thisbehavior is observed in cases 1 and 3 where the auxiliary
air inlet is closed and the primary airflow moves downward
with high velocity. On the other hand, the auxiliary air inlet
is open in case 2 and the contribution of the primary air
inlet is less intense. Thus, the segregation of fine particles
due to the high velocity of the primary airflow in case 2 is
not as clear as in cases 1 and 3.
After falling, particles bounce against the inclined plateand change the direction of their trajectories. It was foundexperimentally that the trajectory after the impact depends
on the particle size and shape. Coarse particles present
more inertia than fine particles and are less affected by thedrag force (Petit and Irassar, 2021; Petit, 2018). Moreover,fine particles present more flaky shapes, increasing thedrag force and forcing them to follow the airflow. Thisbehavior is only partially represented in the simulations.
The main issue regarding the current simulation set-up isthat particles of different sizes share a single coefficient ofrestitution and shape factor. However, the effect of the dragon the particle is modeled sufficiently well to recreate thedifferent trajectories after the impact. The differences in249

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

the trajectories for different particle sizes are more clearlyobserved in case 2, where the auxiliary air inlet is open.
The classification of particles at the throat can beobserved in the three simulated cases. Fine particles near
the throat are dragged by the air and pass to the secondary
chamber. Particles that cannot follow the air impact against
the vertical deflector, which agrees with the large amountof particles found in the deflector after the experimental
runs (Petit, 2018). Cases 1 and 3, where only the primary
airflow is used, present a more abrupt change in the flow
direction, and fewer particles are able to pass to the secondary chamber. On the other hand, the presence of theauxiliary airflow modifies the flow at the throat (as seen in
Fig. 5), reducing the intensity of the swirl and forcing moreparticles to pass to the secondary chamber. Therefore, theutilization of the auxiliary air inlet increases the cut size ofthe primary classification. After passing to the secondary
chamber, some particles settle into the secondary hopper,
while others exit the classifier. As expected from the flow
visualizations presented in Fig. 5, the auxiliary airflow
allows a more ordered classification in the secondary
chamber. On the other hand, the strong secondary flows incases 1 and 3 disperse the particles, making the settlement
more difficult. The effect of the auxiliary airflow can also
be observed at the bottom of the primary chamber in case 2.
The auxiliary airflow enters the classifier at the bottom and(µm)

Fig. 7flows upwards, dragging fine particles that were kept in thebulk of the coarser particles and could not pass through thethroat to the secondary chamber.
The prediction of the CFD-DPM and CFD-DEM simulations in terms of the particle size distribution of the
products is presented in Fig. 7, where the standard limits
are presented in the plot for reference. The yield of the
products and the values of the cut sizes for the three casesof study are presented in Fig. 8 and Fig. 9, respectively.
It can be observed that the PSD of the primary product isaccurately estimated by the simulations, with a slight under
prediction observed in case 2. On the contrary, the PSD of
the secondary and tertiary products is not as well describedas for the primary products. An exception occurs in case 2
where the overall results for the PSD are in fair agreementwith experimental values for all products. The results indicate that the simulation methodologies are able to describe
the main classification mechanism that takes place in the
primary chamber but not the secondary classification in thesecondary chamber. The results in Fig. 8 support this affirmation, the yield of the primary product is well estimatedin all the three cases, while errors appear for the yields ofthe secondary and tertiary products. The same can be observed in Fig. 9, where the primary cut size (x50) is in better
agreement with the experimental values than that observed
for the secondary cut size (y50).

(µm)

(µm)

PSDs of the primary (PP), secondary (SP), and tertiary (PP) products obtained experimentally or by simulations.

Fig. 8250

Original Research Paper

Yield of the products of classification and comparison against the CFD-DPM and CFD-DEM results.

Original Research Paper

(µm)

(µm)

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

Fig. 9 Cut sizes of the classification and comparison against the CFD-DPM and CFD-DEM results. Variables x50 and y50 represents the primary andsecondary cut sizes, respectively.

The limitations of the CFD-DPM methodology were
tested by fine-tuning some parameters such as the coefficient of restitution (CN), the shape factor (ϕ), and the
Lagrangian time coefficient (CL) of the particles. The
results of the procedure are omitted here for brevity but
can be found in Petit (2018). In summary, reducing thevalues of the coefficient of restitution and increasing thevalue of the Lagrangian time coefficient slightly improved
the results. However, it was found that the increase in theaccuracy of the representation of the secondary classification deprecated the representation of the primary classification. This shows that one of the major limitations of themethodology is the impossibility of using size-dependent
parameters. In addition, the utilization of more classes torepresent the PSD of the feed was tested without any significant changes with the exception of more computational
effort. The influence of the Lagrangian coefficient, which
dominates the particle dispersion due to turbulent effects,
suggests that the turbulent dispersion of the particles is notcorrectly modeled. The results support this assumption, ascase 2 presents the best agreement with the experimental
values, being the case that was found to present a lessturbulent airflow in the secondary chamber (Fig. 5) and amore ordered secondary classification (Fig. 6).
The utilization of the CFD-DEM methodology does notpresent any improvements over the CFD-DPM methodology. Only minor differences in the PSD of the products,
the cut sizes, and the yield of the products were observed
between the two methodologies. In the case of the cut
sizes, all predictions using the CFD-DEM method present
a coarser cut size, for both the primary and secondary classifications. In any case, the increase in the computational
demand for the utilization of the DEM method does notbring an increase in the accuracy of the simulations for theconcentrations of solid used in this study. It is clear thatthe limitations of the simulation are associated with themodeling of the fluid–particle interaction and not with theparticle–particle interactions.
Despite the limitations, the simulations gave a good
description of the primary classification in the different

cases and offered predictions of the influence of the position of the deflector and the auxiliary airflow. Regarding
the auxiliary airflow, the simulations showed that the
upstream airflow at the bottom of the primary chamber incase 2 (Fig. 5) enables a better classification of the particles(Fig. 6), resulting in a coarser primary product (Fig. 7) anda greater primary cut size (Fig. 9). Regarding the positionof the vertical deflector, the CFD simulation described thecomplexity of the swirl that occurs in the throat in case 3(Fig. 5) and the finer classification that occurs due to themore abrupt change of direction in the airflow (Fig. 6).
Thus, resulting in finer particles in the primary product(Fig. 7) and a lower primary cut size (Fig. 9).

4. Conclusions

The airflow inside the throat classifier was simulated byusing the CFD methodology. The overall results showedthat the simulation provided fair predictions of the airflow
behavior with some limitations regarding the descriptionof velocity profiles in some locations. Changing the turbulence models or numerical schemes did not improve theresults.
Simulations of particle classification were performed
using both CFD-DPM and CFD-DEM methodologies. The
simulations showed reasonable results in the representationof the overall performance of the classifier and excellent
results in describing the primary classification products.
The utilization of the DEM method did not bring any improvements in the accuracy of the simulations. The major
limitations of the simulation methodology, for the concentration of solids used in this study, reside in the modeling of
the fluid–particle interactions.
Despite these limitations, the simulation methodology
allowed for an explanation of the previously collected
experimental results. Valuable information was gatheredregarding the description of the airflow inside the classifier
which explains the mechanisms of particle classificationthat occur inside the device. In addition, the utilization of
the simulations allowed to study in detail the influence of
operational variables on the performance of the classifier,251

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

such as the position of the vertical deflector and the utilization of the auxiliary air inlet.

Nomenclature
CFD

Computational Fluid Dynamics

DEM

Discrete Element Method

DPM

Discrete Phase Method

PP

Primary product

PSD

Particle size distribution

SP

Secondary product

TP

Tertiary productA

Opening of the auxiliary air inlet (-)

CL

Lagrangian time constant (-)

CN

Normal coef. of restitution (-)

CT

Tangential coef. of restitution (-)

D

Vertical position of deflector (m)dHorizontal position of deflector (m)K

Spring constant (N/m)

kTurbulent kinetic energy (m2/s2)ℓ⊥Normal distance to the wall (m)P

Position of the vertical deflector (-)

PR

Relative static pressure (Pa)ui

Velocity components (m/s)

uth

Fluid velocity at the throat (m/s)

u‖Velocity parallel to the wall (m/s)

x, y, zCartesian coordinates (m)x50

Primary cut size (m, μm)y+Wall dimensionless distance (-)

y50

Secondary cut size (m, μm)

αpVolume fraction of particles (-)∆tTime step (s)∆PPressure loss (Pa)γ

Damping coefficient (kg/s)

εTurbulent dissipation rate (m2/s3)

ϕParticle shape factor (-)τ

Particle relaxation time (s)τPParticle reaction time (s)τκKolmogorov time scale (s)Data Availability Statement

The data from the comparison between experimental andnumerical investigation of the throat air classifier is available publicly in J-STAGE Data (https://doi.org/10.50931/
data.kona.23272763).

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Authors’ Short Biographies
Horacio A. Petit
Horacio Andrés Petit is a Chemical Engineer and obtained a Ph.D. in Engineering at the Universidad Nacional del Centro de la Provincia de Buenos Aires. Worked as a postdoc research assistantat the Universidade Federal de Rio de Janeiro (UFRJ). Now works as a researcher at the CIFICEN
research unit (UNCPBA - CICPBA - CONICET). His research interests are the design and simulationof mineral processing units. His works focuses on the utilization of CFD and DEM methods for thesimulation of air classifiers and other mineral processing equipment.

Edgardo Fabián Irassar
Edgardo F Irassar is a Full Professor of Civil Engineering Department at the Universidad Nacionaldel Centro de la Provincia de Buenos Aires, Olavarria, Argentina. He is Head of the INMAT Research Group of the Engineering Faculty and Vice Chair of the CIFICEN Research Unit (UNCPBA- CICPBA - CONICET). His research interests are the cementitious building materials (Concrete,mortar, cement) for sustainable concrete including durability, new supplementary cementing materials, aggregates beneficiation, recarbonatation and LCA evaluation.

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KONA Powder and Particle Journal No. 41 (2024) 254–264/https://doi.org/10.14356/kona.2024007

Original Research Paper

The Mechanism behind Vibration Assisted Fluidization of
Cohesive Micro-Silica †Rens Kamphorst *, P. Christian van der Sande, Kaiqiao Wu *, Evert C. Wagner,
M. Kristen David, Gabrie M.H. Meesters and J. Ruud van Ommen
Department of Chemical Engineering, Delft University of Technology, The Netherlands

Vibro-assisted fluidization of cohesive micro-silica has been studied by means of X-ray
imaging, pressure drop measurements, and off-line determination of the agglomerate size.
Pressure drop and bed height development could be explained by observable phenomena taking
place in the bed; slugging, channeling, fluidization or densification. It was observed thatchanneling is the main cause of poor fluidization of the micro-silica, resulting in poor gas-solid
contact and little internal mixing. Improvement in fluidization upon starting the mechanicalvibration was achieved by disrupting the channels. Agglomerate sizes were found to notsignificantly change during experiments.
Keywords: fluidized bed, X-ray imaging, agglomeration, assistance methods, cohesive
powder

1. Introduction

Fluidization of cohesive powders has seen significant
interest in the last decades, fueled by the increase in applications of powders with small particle sizes. Cohesive
micro- and nanopowders find their use in medical applications (Prabhu and Poulose, 2012; Guo et al., 2013), catalysis (Li et al., 2003) and coatings (Mahato and Cho, 2016)
for example.
Due to their small particle size, the attractive van der
Waals forces between these particles are dominant, in theabsence of moisture, making them hard to fluidize(Kamphorst et al., 2023). Two main complications areseen: channeling and agglomeration. Channeling is thephenomenon where a gas bypass, or channel, is formed
through the bed in the vertical direction upon the introduction of gas. Gas mainly flowing through a channel has poorinteraction with the powder bed, meaning less drag is created and no fluidization takes place. Agglomeration is theformation of particle clusters as a result of dominant cohesive forces. Whereas agglomerates can sometimes be fluidized without assistance (Morooka et al., 1988), gas-solid
contact is reduced due to the surface area of particles being
inside the agglomerate and gas mostly passing outside of it,with diffusion into the agglomerates pores. A phenomenon
that is also sometimes observed, at startup, for cohesive
powder is slugging or plugging, here the entire bed is†*Received 4 November 2022; Accepted 24 February 2023
J-STAGE Advance published online 17 June 2023
Corresponding authors: Rens Kamphorst and Kaiqiao Wu;
Add: van der Maasweg 9, Delft, The Netherlands
E-mail: R.Kamphorst@tudelft.nl (R.K.); K.Wu-3@tudelft.nl (K.W.)
TEL: +31 (0)15 27 89806 (R.K.); +31 (0)15 27 89806 (K.W.)

254

pushed up through the fluidization column. While one
could argue this to be a third complication of cohesive
powder fluidization, we consider this an extreme form of
agglomeration, where the entire bed acts as one large cluster. Slugging is typically not seen in industrial-scale beds.
In order to fluidize cohesive powders, assistance methods can be employed, which introduce an additional manipulation. For most assistance methods it was concluded,
by one or more studies, mostly done on nano-particles, thatthe mechanism by which fluidization was improved is bybreaking up agglomerates; mechanical vibration (Namet al., 2004; Barletta and Poletto, 2012), acoustic vibration(Guo et al., 2006), magnetic field (Zhou et al., 2013), stirring (Zhang et al., 2021), micro-jet (Quevedo and Pfeffer,
2010; Nasri Lari et al., 2017) and pulsed flow (Al-Ghurabi
et al., 2020; Ali et al., 2016; Ali and Asif, 2012). This conclusion is based on smaller agglomerate sizes being measured in beds that have been fluidized, using an assistancemethod, than one that could not be fluidized. However, themechanism of agglomerate breakage should be taken into
account. First of all, they can break as a result of collisions,either with other agglomerates or with the column wall.
However, in an unfluidized bed, agglomerates will be(mostly) static, meaning no collisions take place. Anotherway for agglomerates to decrease in size is due to shearwith the fluidizing gas, though simulations for nanoparticle
fluidization have shown this effect to be minor, even at highgas velocities (van Ommen et al., 2010). For this to happen
at a significant scale, there should be significant gas-solid
contact, which is also absent before fluidization is initiated.
Based on this, we hypothesize that agglomerates breakas a result of initiated fluidization, but this agglomerate

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Rens Kamphorst et al. / KONA Powder and Particle Journal No. 41 (2024) 254–264

breakage is not the cause of the improvement. Rather than
agglomeration, we propose channeling is the major hurdle
to overcome to achieve fluidization of cohesive powders.
By introducing additional manipulation, from an assistancemethod, the channels can be destabilized and will collapse.
Note that mechanical vibration, acoustic vibration, magnetic fields, pulsed flow, stirring, and a (micro-)jet all introduce additional motion of gas, column or internals into thebed, providing a mechanism by which the channeling canbe disrupted. In the absence of channels, all fluidizing gas
has to flow through the bed, creating drag. When sufficiently large drag is created, the particles or agglomerates
will be fluidized. This reasoning leaves one to conclude
that the best assistance methods are not the ones that are
best at breaking agglomerates, but those which most effectively destroy channels. For further development and optimization of assistance methods, it is essential to have acomplete understanding of the mechanism by which thefluidization is improved.
Characterization of the fluidization quality is often doneby measuring bed height expansion and pressure drop
(Nam et al., 2004; Barletta and Poletto, 2012; Mawatari
et al., 2003). These two measurements alone are insufficient when it comes to analyzing the cause of poor fluidization. Visualization of the temporal evolution ofhydrodynamics, going from poor fluidization with channeling to bubbling fluidization under the influence of an assistance method, could provide a better understanding of themechanism. Simple optical techniques that are typicallyused to visualize hydrodynamics in two-phase systems are,however, limited to acquiring information close to the wallregion, because the large solid fraction of the dispersion
phase makes the dense flow opaque to visible light. X-ray
imaging, on the other hand, has been demonstrated to be avaluable technique to study dynamics in opaque systems,
such as fluidized beds with nanoparticles (GómezHernández et al., 2017), irregular particles (Mema et al.,2020), sintering (Macrì et al., 2020) internals (Maurer etal., 2015; Helmi et al., 2017), and spouts (Barthel et al.,2015).
This study aims to characterize the hydrodynamics of agas-solid fluidized bed, consisting of cohesive micro-silica,
with and without vibration applied to it. This is done bymeans of X-ray imaging, pressure drop, and bed height
extension measurements. Channel formation and disruption are shown and agglomerate sizes are measured to elucidate the mechanism behind the employed assistancemethod.

Original Research Paper

drop was measured from the surface of the distributor plateto the breakout box, using OMEGA PX409-10WG5V pressure transducers. The static pressure of the powder was
2.3 mbar (2.3 × 102 Pa) for all experiments. The column
was mounted on a vibration table, a frequency of 30 Hz anda vertical amplitude of 1 mm was used for all experimentswith vibration. For this study, two gas velocities were used,
1.8 and 3.1 cm s–1. During the X-ray experiments, a remotely controlled camera was used to capture images of
the column from which the bed height was measured.
The used powder, KÖstropur 050818, consists of SiO2
particles with a volume-averaged primary particle size of
9.7 μm (as measured by laser diffraction); particle size
distribution is shown in Fig. 1. The powder was sieved beforehand (mesh size 1.25 mm). Note that this is orders ofmagnitude larger than the primary particle size, meaning it
did not affect the size distribution of the powder, but wasdone to establish an equal state of aeration at the start ofeach experiment, ensuring reproducibility. The bulk density after sieving was measured to be 134 kg m–3.
After each experiment, powder samples were taken fromthe plane center of the collapsed bed at heights 2.5, 6.5, and13.5 cm from the distributor plate. A Zeiss Colibri 7 Axio
Observer microscope, equipped with Orca Flash 4.0
Hamamatsu V2 camera was used for imaging. The images
were analyzed using ImageJ. The equivalent circle diameter of the agglomerates was calculated from the projectedarea of the agglomerates on the image. To avoid including
primary particles too, which will drastically shift the
number-based distribution, agglomerates with a diametersmaller than 35 μm were excluded. A minimum of 800 agglomerates was measured for each sample.
To be able to distinguish between the effect of 1) agglomerate breaking and 2) disrupting of channels on thefluidization quality, three sets of experiments were conducted. One where no vibration was used, one where vibration was used for 8 min, and finally one where vibration
was used for 5 s before the inlet gas and vibration were
switched off, all at the two aforementioned gas velocities.

2. Experimental and methodology
2.1 Fluidization procedure

A Perspex column with an inner diameter of 9 cm andsample ports at 2.5, 6.5, and 13.5 cm from the distributor
plate was used as the fluidized bed column. The pressure

Fig. 1 Volume based size distribution of micro silica powder acquiredwith the Coulter LS230 particle size analyzer.

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2.2 X-ray imaging setup

The gas-solids distribution inside the fluidized bed
column was measured using an in-house fast X-ray analysis setup. The complete setup consists of three standard
industrial-type X-ray sources (Yxlon International GmbH)with a maximum energy of 150 keV working in cone beammode. The sources are placed at 120° around a common
center, which forms the field of view. Three 2D detectors(Teledyne Dalsa Xineos) are placed opposite of the sources.
The sources and detectors are attached to a hexagonal support frame that enables translation of the detectors. Bychanging the distance between the source and detector, or
the relative position of the column, the magnification of theimage of the column can be adjusted. In this study the experiments were performed with one X-ray source-detector
pair to obtain a projected 2D output from the 3D column asshown in Fig. 2. The column was fixed to a vibration table
which was positioned in the center of the setup. The distance between the source and the detector was 128.8 cm.
The experimental procedure was controlled from aworkstation outside the setup room, guaranteeing a safe
working environment. The workstation was used to trigger
the X-ray source and read out the signals from the detector
plate. The theoretical spatial resolution of the X-ray detector was 0.15 mm per pixel. During all experiments the
X-ray images were recorded with a frequency of 50 Hz fora measurement time of 200 s for the channel-breaking experiments or 600 s for the full experiment, which corresponded to the acquisition of respectively 10,000 and
30,000 images per experiment. The measured data wasstored for further digital image analysis.

2.3 X-ray image processing

For each experiment, the attenuation of the X-ray beam
was measured at the detector plate to obtain the time-

Fig. 2

256

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resolved projected 2D intensity map of the reactor. The raw
images acquired were corrected for dead-pixels, originating from the construction of the detector plate, by means ofinterpolation to give pre-processed images.
In order to convert the measured X-ray attenuation to asolids concentration, a two points calibration protocol was
performed. First, a reference image was acquired of theempty column. Then, the column was completely filledwith the powder and vibrated for 10 minutes to approach
the maximum packing density. After packing, the full reference image was acquired. The X-ray measurement principle is based on the attenuation of X-rays that travel througha material in a straight line from an X-ray source to a scintillation detector. The law of Lambert-Beer describes thetransmission of a monochromatic beam of high energy
photons with initial intensity I0 through a material of constant density:
I(x) = I0 exp(–μx)(1)

where I(x) represents the intensity measured at the detector,
μ the attenuation coefficient, and x the thickness. In the caseof a varying attenuation, the measured intensity is the integral effect of the local attenuation with the local attenuation
coefficient. By applying the Lambert-Beer law the measurement gas-fraction map was obtained from the measurement intensity map by using the empty and full reference.
The normalized solids fraction α was calculated as follows:
α=1–log(Imeasured / Ifull)log(Iempty / Ifull)(2)

Note the range of normalized solid fraction used in thiswork is between 0 to 1, with 0 representing pure gas and 1
representing close packing of the solids. For the solid
fraction map, low attenuation of X-rays is represented bylight colors and indicates low solid concentrations. By

Experimental X-ray imaging setup showing the X-ray source and detector plate, fluidized bed column and the vibration table.

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contrast, high attenuation of X-rays is represented by thedark colors and indicates a high solids concentration. The
acquired images obtain time-resolved, or dynamic, information of the gas-solids distribution in the column.

2.4 Pressure data processing

Pressure drop over the whole fluidized bed, or over partof it, is commonly used to evaluate fluidization. Whenmechanical vibration is applied, the periodic oscillation ofmass and gas induced is significantly greater than thatcaused by rising bubbles and suspension of solids. These
driven oscillations affect the measured time series of bed
pressure drop, greatly complicating the analysis of fluidization state. For example, Fig. 3(a) plots the time series of
bed pressure drop sampled over 600 s. The vibration was
introduced at around 120 s, after which the measured signal(black) fluctuates largely with an amplitude of up to triple
the theoretical bed pressure drop. To further determine thecomponents of such oscillations, one could analyze thesignal in the frequency domain (Wu et al., 2023). In
Fig. 3(b) the power spectrum for the signal sampled between 120–600 s is demonstrated. The dominant peaks occur at 30 Hz, corresponding to the vibration frequency
adopted in the experiment, as well as its sub-harmonic frequencies with reduced intensity. As a result, in the following sections, the 10 s time-averaged pressure drop is used
to assess fluidization quality during vibration, such as the
red lines in Fig. 3(a), while its standard deviation mainly
associated with the vibration is therefore neglected. For the

Fig. 3 (a) Pressure drop series and (b) power spectrum of the vibrated
bed at 1.8 cm s –1.

Original Research Paper

experiments where vibration was only used for 5 s, averages over 1 s were taken.

3. Results and discussion

3.1 Channel breakage by vibration
3.1.1 Pressure drop and X-ray images

In Fig. 4 the pressure drop and X-ray images of the experiments where vibration was used are shown. Experiment 1 and 2 (under the same conditions) were terminatedafter 5 s of vibration. The third one was vibrated for 8 minafter vibration started, in order to demonstrate the longterm fluidization behavior. The initial pressure drop fluctuations, seen in all pressure graphs, are caused by slugging
which is discussed in detail in Sections 3.2.1 and 3.2.3. For
each experiment, it can be seen that a semi-stable pressure
drop was achieved after roughly 75 s. The ΔP/ΔP0, the ratioof measured pressure drop over theoretical pressure drop(the static pressure of the powder; 2.3 mbar (2.3 × 102 Pa)
for all experiments) of full fluidization, after 90 s, shown in
Fig. 4(b)-1, Fig. 4(d)-1 and Fig. 4(f)-1, was found to below. The channeling, causing the low pressure drops, canclearly be seen. Comparing these images to Fig. 4(b)-2,
Fig. 4(d)-2 and Fig. 4(f)-2 it can also be seen that thesechannels were stable with time, as the images with sublabels 1 and 2 were taken 30 s apart. The relatively highand unstable pressure drop seen from 50 s to 120 s in
Fig. 4(e) is explained by the collapse and reforming of unstable channels, in Fig. 4(f)-2 the channel structure can beseen to have changed compared to Fig. 4(f)-1. We consider
this occurs simply as a matter of chance, since up to 120 s
there is no difference in operation between the three experiments. In the following images the effect of vibration,
within the first seconds of initiation, can be seen. For all
experiments, two types of channeling were observed: ratholes and vein-like channels. Where rat-holes run almost
vertically from bottom to top of the bed, creating a largegas bypass, vein-like channels branch out in several directions, resulting in a network of thinner interconnectedchannels. Not enough data was acquired to reliably assess
what causes either type of channeling to occur. It was consistently observed that the vein-like channels disappear bymerging together, due to the powder barriers, initially separating them, crumbling. The rat-holes, persist longer andare destroyed by powder falling into them, disrupting thegas flow and creating bubble-like gas pockets. In Fig. 4(a),Fig. 4(c) and Fig. 4(e) it can be seen that the ΔP/ΔP0
spiked to 0.89, 0.79 and 0.89 after 1 s, and kept increasing
to 0.91, 0.88 and 0.94 after 5 s, of vibration respectively.
Note that this increase over the first 5 s corresponds tomore and more channels breaking, as seen in the X-ray
images. The increase in the fluidized fraction of powder, as
measured by pressure drop, is in all experiments clearlycorrelated to the breaking of channels.

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Fig. 4 Images corresponding to channel breakage experiments at 1.8 cm s –1 gas velocity, in all images: ①= 90 s, ②= 120 s, ③= 120.5 s, ④= 121 s,⑤= 121.5 s, ⑥= 122 s, ⑦= 123 s and ⑧= 130 s into the experiment. (a) Pressure drop of experiment 1; (b) X-ray images of experiment 1; (c) Pressure
drop of experiment 2; (d) X-ray images of experiment 2; (e) Pressure drop of experiment 3; (f) X-ray images of experiment 3.

3.1.2 Agglomerate sizes

For the experiments displayed in Fig. 4, the agglomerate
sizes were measured after ending the experiment for the
top, middle and bottom of the bed (as described in Section
2.1). The resulting number-based cumulative percentiles,
d10, d50 and d90, are shown in Fig. 5. First comparing thetwo runs in which vibration only lasted for 5 s, labeled 1
and 2 in Fig. 5, no significant difference between them canbe seen, showing the data is reproducible. Also, comparing
the sizes in both of these experiments to the un-fluidized
powder, no significant change is observed. Looking at thesizes found after 8 min of vibro-fluidizing, labeled 3 inFig. 5, no big change in agglomerate sizes was found compared to the un-fluidized powder or compared to the otherexperiments.
It was hypothesized that channel disruption is the cause
of initiated fluidization upon the introduction of vibration.

258

Fig. 5 Agglomerate sizes after vibration all with gas velocity of
1.8 cm s –1; 1) vibrated for 5 s; 2) vibrated for 5 s; 3) vibrated for 8 min;▶ = d10 ● = d50 ◀ = d90, number based.

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Original Research Paper

It was expected to find a decreased agglomerate size after
8 min of vibro-fluidization compared to 5 s, where virtually
no decrease was expected. While no significant change inagglomerate sizes was found for any of the experiments, it
can still be concluded that the break-up of agglomerates
does not play a role in initiating fluidization of the cohesive
powder.

3.2 Influence of vibration and gas velocity
3.2.1 No vibration, 1.8 cm s–1

In Fig. 6 the bed height, pressure drop and X-ray images
from the experiment without vibration and 1.8 cm s–1 gas
velocity are shown. It can be seen that the bed was initiallylifted as a plug, which corresponded to a large pressure
drop over the column. The plug eventually broke and fell
down in pieces, as seen in Fig. 6(c)-3, which lead to heavypressure swings in the system. Note that in this image mostof the powder bed is still out of frame, higher up in thecolumn. Right after, some fluidization took place, as seenby the pressure drop, significant bed height expansion anddilute X-ray image. Within 45 s after the plug fell down,
stable channels had formed (Fig. 6(c)-5), that did not significantly change over time anymore. The pressure drop
remained constant over this time with an average ΔP/ΔP0
of 0.57. Bed height was decreasing slowly due to the bed
compacting from its initial fluidized state. Still, an expansion ratio of 1.2 was maintained, caused by the added volume of the channels now present in the bed and the looser
packing state of the powder. This experiment shows all the
expected behavior of a cohesive powder upon the introduction of a gas flow.

3.2.2 Vibration, 1.8 cm s–1

The results shown in Fig. 7 are of the same experimentshown in Fig. 4(e) and Fig. 4(f), but extended to the fullexperimental time to assess the effect of vibration overtime. The bed initially rose as a plug again, the X-ray images corresponding to this were omitted since they are virtually identical to the experiment previously described in
Section 3.2.1 and the focus here is on the effect of vibration, which started at 120 s. In Fig. 7(c)-1 to Fig. 7(c)-5,
the initial stable channel collapsing and the initiation of
fluidization are shown. Over time, compaction and partial
de-fluidization took place. It can be seen that the bed heightdecreased, coupled with a decrease in ΔP/ΔP0 from 0.93 in
the first minute after vibration started to 0.87 in the lastminute of the run. From the X-ray images, it could also be
observed that the bed was densifying during this time, witha compacted region being present, dominantly at the bottom of the bed.

3.2.3 No vibration, 3.1 cm s–1

The obtained results for the 3.1 cm s–1 experiment without vibration are shown in Fig. 8. As in the other experi-

Fig. 6 Results of fluidization experiments, 1.8 cm s –1 without vibration, in all images: ①= 0 s, ②= 15 s, ③= 45 s, ④= 60 s, ⑤= 90 s,⑥= 120 s, ⑦= 300 s and ⑧= 540 s into the experiment. (a) Bed height;(b) Pressure drop; (c) X-ray images.

ments, the bed initially formed a plug. In Fig. 8(c)-3 thefragments of the broken plug can be seen falling down,corresponding with the pressure swing seen at 30 s in
Fig. 8(b). This was followed by some degree of fluidization, giving a bed height expansion ratio of 1.5 and a ΔP/
ΔP0 of 0.89 at the peak, 115 s. The dip in bed height andpressure drop that followed is explained by the formationof two rat-holes, seen in Fig. 8(c)-5, which collapsed later
leading to vein-like channel formation and some spouting259

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Original Research Paper

Fig. 7 Results of fluidization experiments, 1.8 cm s –1 with vibration,in all images: ①= 90 s, ②= 120 s, ③= 121 s, ④= 125 s, ⑤= 130 s,⑥= 180 s, ⑦= 300 s and ⑧= 540 s into the experiment. (a) Bed height;(b) Pressure drop; (c) X-ray images.

Fig. 8 Results of fluidization experiments, 3.1 cm s –1 without vibration, in all images: ①= 0 s, ②= 15 s, ③= 30 s, ④= 120 s, ⑤= 180 s,⑥= 240 s, ⑦= 300 s and ⑧= 540 s into the experiment. (a) Bed height;(b) Pressure drop; (c) X-ray images.

and bubbling in top of the bed, coupled with stable ΔP/ΔP0
of 0.77.

term structures could be observed anymore and the bed was
homogeneously fluidized. In the remaining time that theexperiment was running, densification was observed,
mainly at the bottom of the bed, as when 1.8 cm s–1 gas
velocity was used. The densifying resulted in a lower bed
height expansion ratio, eventually even lower than when no
vibration was used at 3.1 cm s–1, and a slight reduction in
ΔP/ΔP0 from 0.91 in the first minute after vibration started,
to 0.87 after 8 min.

3.2.4 Vibration, 3.1 cm s–1

Fig. 9 shows the data from the experiment at 3.1 cm s–1
with vibration. As for all experiments, slugging occurred
initially, followed by some degree of fluidization before
stable channels formed, which are seen in Fig. 9(c)-1 andFig. 9(c)-2. After 10 s of vibration (Fig. 9(c)-5), no long-260

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Original Research Paper

Fig. 10 Agglomerate sizes for experiments ran at 1.8 and 3.1 cm s –1,
with and without vibration for 8 min; ▶ = d10 ● = d50 ◀ = d90, number
based.

densification of the bottom bed—as observed after several
minutes of vibration—is not reflected by the agglomerate
size.

3.3 Channel formation

Channeling took place in all the experiments conducted,
either without vibration or before vibration was switchedon. From the X-ray images shown in the previous sections,the variety of the formed channels across different experiments is obvious. The diversity which is seen, even underidentical conditions, underlines the random nature of channel formation. It also shows no bias from the used experimental method, since no preferential place for the channels
to form is observed. The results also suggest that even theformation of rat-hole like or vein-like channeling is random
and not affected by gas velocity within the range used forthis study.

4. Conclusions

Fig. 9 Results of fluidization experiments, 3.1 cm s –1 with vibration, in
all images: ①= 90 s, ②= 120 s, ③= 121 s, ④= 125 s, ⑤= 130 s,⑥= 180 s, ⑦= 300 s and ⑧= 540 s into the experiment. (a) Bed height;(b) Pressure drop; (c) X-ray images.

3.2.5 Agglomerate sizes

For each 10 min experiment, the agglomerate sizes (d10,d50 and d90, number-based), at all sample heights, areshown in Fig. 10. Comparing the results, across gas velocities and with or without vibration to fresh powder, thedifferences are minor. The influence of vibration and gas
velocities, within the ranges used in this study, on agglomerate sizes is therefore concluded to be insignificant. The

In this work, the mechanism behind the initiation of fluidization of cohesive micro-sized powder, by vibrations,
was studied by means of X-ray imaging, pressure drop andbed height measurements. Agglomerate sizes were measured to decouple the phenomena of channel disruption andagglomerate breakage. When no vibration was used, thepowder could not be properly fluidized at either 1.8 or
3.1 cm s–1 and severe channeling was observed, which was
not always clearly reflected in the pressure drop or bed
height, showcasing the limitations of these techniques
when it comes to assessing the dynamics of complex systems. Gas velocity, vibration and vibration time were notfound to have any significant effect on agglomerate sizes,
within the parameter ranges applied in this work. From
this, it is concluded that vibrations initiate fluidization notby breaking agglomerates, but by disrupting channels.
Furthermore, it was found that narrow, vein-like channels
are disrupted significantly faster than the larger rat-holes.
Finally, when vibration was employed, bed compaction
was observed over time, as a dense phase formed on top of261

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the distributor plate, causing a decrease in bed height andpressure drop. The study was carried out using a single
micro-silica powder, meaning material- and size-specificinfluences, which might influence results, were not investigated. While variations are deemed likely when using otherpowders, based on the described mechanism by which assistance methods work and the absence of a mechanism bywhich agglomerates can be broken before fluidization is
initiated, it is expected that the disruption of channels is thecause of initiated fluidization for other cohesive powders
too. This study is a first step towards a better understandingof the mechanism behind vibro-assisted fluidization of cohesive powder and may contribute to the further development and optimization of assistance methods.

Supplementary information

The online version contains supplementary material
available at https://doi.org/10.14356/kona.2024007.

Acknowledgments

We want to thank Stefan ten Hagen for his contribution
to designing and manufacturing the column used in thisstudy.
This work is part of the Advanced Research Center for
Chemical Building Blocks, ARC CBBC, which is cofounded and co-financed by the Dutch Research Council(NWO) and the Netherlands Ministry of Economic Affairs
and Climate Policy. The work has also received funding
from the NWO under the grant number ENPPS.
LIFT.019.027.

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Authors’ Short Biographies
Rens Kamphorst
MSc Rens Kamphorst is a PhD researcher in the department of Chemical Engineering at Delft
University of Technology (The Netherlands). In 2020 he completed his MSc in Chemical Engineering, also at this university. His research is focused on atomic layer deposition in a fluidized bed
consisting of cohesive powders.

P. Christian van der Sande
MSc Christian van der Sande is a PhD researcher in the department of Chemical Engineering at
Delft University of Technology (The Netherlands). He completed his MSc in Chemical Engineeringwith a specialization in process engineering at the same university. His research activities involvehydrodynamic studies with radiation-based imaging methods to study gas hold up, flow patterns andflow velocities inside multi-phase reactors. He has supervised over 10 BSc and MSc students for their
thesis work.

Kaiqiao Wu
Dr. Kaiqiao Wu obtained his PhD from University College London in 2020. He has publishedseveral works of gas-solid fluidization in international journals, and has presented at conferences. He
has won prizes for best talk in sessions, and placed second in the IChemE Young Researcher Awards
at UK-China International PTF VII, 2019, and also received the Springer Theses Award for recognizing outstanding PhD research in 2021. He is currently a postdoctoral researcher at TU Delft.

Evert C. Wagner
Ing. Evert Wagner is a research assistant at Delft University of Technology, The Netherlands. He
obtained a BSc degree in Applied Physics at the Technische Hogeschool, Rijswijk. Starting at TU
Delft in 2006, he has been involved in most of the development of the X-ray setup used in this paper.
He’s a co-author of over 30 papers using this experimental technique.

Kristen David
Ing. Kristen David is a lab manager at Delft University of Technology (The Netherlands). She
obtained her BSc degree in Chemical Engineering at De Haagse Hogeschool in Den Haag in 2019.
She started at TU Delft on the same year and has been involved in managing the ML-1 labs of the
Product and Process Engineering group of the Chemical Engineering department.

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Original Research Paper

Authors’ Short Biographies
Gabrie M.H. Meesters
Dr. Gabrie Meesters has 27 years of industrial formulations experience at Gistbrocades, Genencor
International and DSM. He has 23 years of experience as a Part-Time Professor at TU Delft in particle technology and product design. From January 2019, he became a fulltime Assistant Professor at
the TU Delft, Faculty of Applied Sciences, Product and Process Engineering. He is a contributor toseveral books on formulation. He is the editor of three books on product design and solids processing.
He published over 70 refereed papers, has more than 20 patents and supervised more than 100 BSc,
MSc and PhDs. He is a regular speaker at conferences and workshops. He was the organizer of the2010 Partec and the 2010 World Congress on Particle Technology in Nuremberg, Germany.

J. Ruud van Ommen
Professor of Chemical Engineering at Delft University of Technology (The Netherlands). He obtained his PhD degree at Delft in 2001. He has been Visiting Professor at Chalmers University ofTechnology (Gothenburg, Sweden) and the University of Colorado (Boulder, USA). In recent years,
he expanded his research from chemical reactor engineering to the scalable production of advanced,
nanostructured materials. In 2011, he started an ambitious program (funded by an ERC Starting
Grant) to investigate the interplay between agglomeration and coating of nanoparticles in the gas
phase. He has published about 200 journal papers, of which a considerable part on powder technology.

264

Original Research Paper

KONA Powder and Particle Journal No. 41 (2024) 265–279/https://doi.org/10.14356/kona.2024005

Lipooligosaccharide Ligands from Respiratory Bacterial
Pathogens Enhance Cellular Uptake of Nanoparticles †Mai H. Tu 1, Timothy M. Brenza 1, Margaret R. Ketterer 2, Morgan Timm 3, Benjamin M. King 4,
Michael A. Apicella 2 and Jennifer Fiegel 1,4*
1 Department of Pharmaceutical Sciences and Experimental Therapeutics, The University of Iowa, USA
2 Department of Microbiology and Immunology, The University of Iowa, USA

3 Department of Biochemistry and Molecular Biology, Gustavus Adolphus College, USA
4 Department of Chemical and Biochemical Engineering, The University of Iowa, USA% Cells associated with particles

Several bacterial pathogens contain membrane ligands that40
facilitate their binding and internalization into human tissues.
LOS-coated NPs
30
Cell
In this study, lipooligosaccharides (LOS) from the respiratory
pathogen non-typeable Haemophilus influenzae (NTHi) were20
isolated from the bacterial surface and evaluated as a nanoparticle
PAFR
LOS
coating material to facilitate uptake into the respiratory epithelium.
10
NTHi clinical isolates were screened to select a strain with high0binding potential due to their elevated phosphorylcholine content.
0
30
10
20
Incubation time (hours)
The association of particles with human bronchial epithelial
cells was investigated as a function of particle surface chemistry
and incubation time, and the uptake mechanism was evaluated
via chemical inhibitor and receptor activation studies. A morethan two-fold enhancement in particle uptake was achieved by coating the particles with LOS compared to uncoated or gelatin-coatedparticles, which was further increased by activating the platelet-activating factor receptor (PAFR). These findings demonstrate thatbacterial-derived LOS ligands can enhance the targeting and binding of nanoparticles to lung epithelial cells.
Keywords: endocytosis, platelet-activating factor receptor, lipooligosaccharides, non-typeable Haemophilus influenzae,
nanoparticle, bronchial epithelial cells

1. Introduction
Targeted drug delivery is a powerful strategy to treat
diseases that affect human tissues, offering high drugconcentrations at the target site with limited exposure ofother tissues. Efficient delivery can be achieved using drugloaded nanoparticles with chemical and physical properties
tuned to avoid host defense mechanisms and inhibit nonspecific distribution (Fenton et al., 2018; Patra et al., 2018).
Active targeting can further enhance delivery to specifictarget organs or cells within the body. With appropriate
surface functionality, such as through the attachment ofligands that recognize receptors on cellular surfaces, particulate carriers show improved efficiency in penetrating cells
in vitro. One challenge in achieving active targeting has
been the identification of cellular targets, which is limited
by our knowledge of disease-specific cell receptors andtheir ligands. A potentially useful class of ligands is produced and used by human pathogenic organisms to infectthe respiratory tract.
Received 28 May 2022; Accepted 29 August 2022
J-STAGE Advance published online 25 March 2023
* Corresponding author: Jennifer Fiegel;
1,4 Add: Iowa City, IA, 52242, USA
E-mail: jennifer-fiegel@uiowa.edu
TEL: +1-319-335-5162 FAX: +1-319-335-1415†

Haemophilus influenzae (H. influenzae), a Gramnegative bacterium that colonizes the nasopharynx of up
to 80 % of humans, is a leading cause for bacterial meningitis and other widespread bacteremic diseases (Cochi
and Broome, 1986; Foxwell et al., 1998). While a polysaccharide conjugate vaccine is available for and is effective
against H. influenzae serotype b, the vaccine is not effective
against the subtype that lacks a capsule, called non-typeable H. influenzae (NTHi) ( St. Geme, 1996). NTHi causes
localized opportunistic infections including otitis media,
sinusitis, conjunctivitis, bronchitis and pneumonia (Turk,
1984). It can infect cystic fibrosis (CF) patients early inchildhood and prime the lungs for later infection with thebacterium Pseudomonas aeruginosa, which is correlatedwith higher morbidity and mortality in CF patients (Starner
et al., 2006).
Diseases due to NTHi often start with colonization of therespiratory epithelium (Turk, 1984). The surface of NTHi isdecorated with lipooligosaccharides (LOS) which contain alipid structure that embeds in the outer leaf of the bacterial
outer membrane and an oligosaccharide structure thatfacilitates bacterial pathogenesis. By expressing specificcarbohydrates, such as phosphorylcholine (ChoP), as aterminal structure on the LOS, the bacteria are imbued with
two important defenses. First, ChoP expression reduces the

Copyright © 2024 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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ability of LOS-specific or bactericidal antibodies to bind tothe bacteria through molecular mimicry of host structures,
enabling the bacteria to evade the host immune system andlimit their pulmonary clearance (Clark et al., 2012; Pang etal., 2008; Weiser et al., 1998). Second, the presence of ChoPon the NTHi surface enhances bacterial adherence to hostcells by binding to the platelet-activating factor receptor(PAFR), effectively reducing mucociliary clearance of thebacteria and enhancing bacterial invasion of the respiratory
epithelium (Swords et al., 2000, 2001; Turk, 1984). The effectiveness of OS in inducing cellular uptake is dependent
on the specific LOS glycoform, with LOS containing higher
ChoP content resulting in enhanced NTHi colonizationand persistence in the lungs (Swords et al., 2000; Weiser
et al., 1998). These findings suggest that isolated ChoPcontaining LOS could be a potential targeting ligand for
drug delivery particles to mimic bacterial entry into lungcells and thus deliver drugs directly to infected lung cells.
We hypothesized that LOS ligands naturally expressedon bacterial cell surfaces from NTHi could be utilized
as targeting ligands to facilitate particle attachment anduptake into lung epithelial cells. The expression of ChoPon several clinical isolates of NTHi was measured andan NTHi strain with high ChoP content chosen for LOS
isolation. Nanoparticle uptake was evaluated in 16HBEobronchial epithelial cells in the absence and presence ofendocytosis pathway inhibitors and activators of PAFR.

2. Experimental

2.1 Culture of NTHi bacteria

NTHi clinical strains 2019 and 3198, isolated frompatients with chronic obstructive pulmonary disease(Campagnari et al., 1987), and strain 956 were reconstituted from frozen glycerol stocks and propagated on
chocolate agar (a non-selective, enriched growth medium)or brain heart infusion (BHI) agar (selective medium for
NTHi) supplemented with 10 μg/mL hemin and 1 μg/mL
nicotinamide adenine dinucleotide (NAD) at 37 °C and
5 % CO2. All culture chemicals were obtained from Sigma
Aldrich.

2.2 Detection of ChoP on NTHi cells via ELISA
and dot immunoblot
The three NTHi strains were screened by enzyme-linked
immunosorbent assay (ELISA) to identify bacterial strains
and growth conditions that result in bacteria with high ChoP
activity. An NTHi cell suspension (8.0 × 107 cells/mL) was
added to each well of a 96-well plate (Corning Inc. product3590; clear, flat-bottom polystyrene, high-binding). The
plate was dried in an oven uncovered overnight at 40 °C.
The wells were then washed three times with 200 μL
ELISA wash solution (0.98 % w/v sodium acetate, 0.9 %w/v NaCl, 0.05 % v/v Tween 20 in purified water from
NanoPure® Infinity UV Barnstead system). The primary

266

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monoclonal antibody 12D9, which was raised directlyagainst ChoP, was diluted 1:25 and added to each well. The
plate was incubated overnight at room temperature, thenwashed. Alkaline phosphatase-conjugated goat anti-mouse
IgG AffiniPure F(ab’)2 (Jackson ImmunoResearch Inc.)
diluted 1:2000 in ELISA buffer was added to each well andincubated for 1 h, after which the wells were again washed.
The substrate, nitrophenyl phosphate bis (cyclohexylammonium) (pNPP), was prepared at 1 mg/mL in diethanolamine buffer (10 % w/v diethanolamine and 0.01 % w/v
MgCL2 in purified water, pH 9.8) immediately before use,
added to all wells, and incubated in the dark. The substrate
was then hydrolyzed by alkaline phosphatase to develop ayellow water-soluble product, p-nitrophenol. The relative
absorbance of p-nitrophenol at 405 nm was read using a
SpectraMax® M2 spectrophotometer (Molecular Devices)
after 2 h incubation. Wells with no cells, no primary antibody, or no secondary antibody served as negative controls(n = 4). Dot immunoblot confirmed the relative high ChoP
expression on NTHi 3198 cell surfaces.

2.3 Proteinase K/hot phenol method to isolate
LOS from NTHi bacterial cells
Due to its relatively high ChoP expression, LOS was
isolated from NTHi strain 3198 using the proteinase K
digest, phenol-water extraction method (Apicella, 2008;
Galanos et al., 1969). NTHi was cultured on supplemented
BHI agar overnight, harvested, digested in ELISA buffer
containing 1 % w/v sodium dodecyl sulfate (SDS, SigmaAldrich®) and 50 μg/mL proteinase K at 65 °C for 1 h,and incubated at 37 °C overnight to lyse the cells anddigest the proteins. The mixture was washed three times
via precipitation in 3 M sodium acetate and cold absoluteethanol overnight at –20 °C, then centrifuged at 3200 × g
at 4 °C (Eppendorf centrifuge I810R). The precipitate
was washed with ethanol to remove residual SDS. Afterthe final precipitation, the pellets were air-dried at room
temperature for 2 h and then suspended in 15 mL of DNase
I buffer (10 mM Tris-HCl, 2.5 mM MgCl2, 0.5 mM CaCl2
in purified water, pH 7.6). Solutions of 10 μg/mL DNase(Roche) and 10 μg/mL RNase (Qiagen) were added andthe solution incubated at 37 °C in a water bath for 2 h todigest residual nucleic acids. Finally, a phenol extraction of
the LOS nuclease-treated mixture removed residual protein
contamination. The LOS mixture was isolated from phenol
soluble components using an equal volume of pre-heatedphenol and incubated at 65 °C for 20 min, then on ice for
1 h. Upon cooling, the suspension was separated into aphenol phase containing proteins, an interphase containing
cell-wall material, and an aqueous phase enriched in LOS.
The top aqueous layer was collected and washed threetimes before lyophilization and storage at 4 °C in a desiccant box. The activity of the isolated LOS was confirmed
using the colorimetric ELISA described above, except theMai H. Tu et al. / KONA Powder and Particle Journal No. 41 (2024) 265–279

primary antibody 12D9 was prepared at 1:50 dilution in
ELISA buffer, the secondary antibody was added at 1:2000
dilution in ELISA buffer, and the time for p-nitrophenol todevelop was 3 h.

2.4 Preparation of LOS-functionalized particles

Polystyrene particles (0.2 μm plain white Polybead®
or Fluoresbrite® polystyrene particles, Polysciences Inc.)
were prepared for functionalization by washing twice withcarbonate buffer (15.9 mg Na2CO3 and 25.9 mg NaHCO3
in 100 mL purified water, pH 9.6) an centrifuging at
12,000 × g for 30 min, followed by resuspension at a concentration of 2 × 109 particles/mL. Particles with a nominal
size of 0.2 μm were chosen to match the mean width of an
NTHi bacterium.
To achieve particle functionalization, polystyrene particles were added to a solution containing NTHi 3198 LOS ateither a low (5 ng/mL) or high (5 μg/mL) ligand concentration, mixed on a rotator for 2 h, washed twice with carbonate buffer, and resuspended in carbonate buffer. A subset
of uncoated and high ligand coated polystyrene particles
were exposed to a protein mixture of 0.1 % gelatin (Type B
from bovine skin derived from lime-cured tissue, average
MW 50,000–100,000 g/mol; Sigma G9382) solution incarbonate buffer. Particles were either stored in carbonate
buffer at a final concentration of 2 × 109 particles/mL
at 4 °C (cell association studies) or lyophilized overnightin a Labconco® FreeZone® 4.5-liter freeze dry system at achamber pressure <0.02 mbar and collector temperature ator below –50 °C.

2.5 Particle characterization
Lyophilized polystyrene particles were prepared for size
and zeta potential measurements by dispersion in water orserum-free medium (pH 7.4) via three cycles of vortexing
for 90 s and sonication for 10 min in cold water bath at
4 °C. Particle suspensions were placed into folded capillarycells (DTS 1060C cuvettes, Malvern Instruments) and theaverage particle hydrodynamic diameter and zeta potential(n = 3) measured using a Zetasizer® Nano ZS (Malvern
Panalytical LTD.). Particle size and morphology were confirmed via scanning electron microscopy using an S-4800
Field Emission Scanning Electron Microscope (FE-SEM).
Lyophilized particles were tapped onto SEM stubs coatedwith double-sided black carbon tape and sputter coatedwith Au-Pl at 10 mA and pressure above 7 × 10–2 mbar for
3 min (K550 Emitech® sputter coater).
To prepare particles for surface chemical analysis,
washed particles were dispersed in water, and 20 μL of
the suspension was added and dried on clean silicon wafers. Chemical analysis was carried out using a Kratos®
Axis Ultra X-ray photoelectron spectrometer (XPS) withconcentric hemispherical electron energy analyzers combined with an established delay-line detector. The incident

Original Research Paper

radiation from monochromatic Al Kα X-rays (1486.6 eV)
at 150 W (accelerating voltage 15 kV, emission current
10 mA) was projected 45° to the sample surface and thephotoelectron data was collected at a takeoff angle ofθ = 90°. The base pressure in the analysis chamber wasmaintained at 1.0 × 10–9 torr. Survey scans were taken ata pass energy of 160 eV and carried out over a 1200 eV ~–5 eV binding energy range with 1.0 eV steps and a dwell
time of 200 ms. High resolution scans of C 1s, O 1s, N
1s, P 2p, and S 2p were taken at a pass energy of 20 eVwith 0.1 eV steps and a dwell time of 2000 ms. Spectral
analysis was conducted using CasaXPS software (version
2.3.17dev6.4k). Spectra were calibrated using the adventitious carbon C 1s peak at 285.0 eV.

2.6 Isothermal titration calorimetry (ITC)
ITC measures the heat released or absorbed during a biomolecular binding event, enabling accurate determinationof the enthalpy of binding and the stoichiometry of eachbinding event at different concentrations of ligand, including proteins on nanoparticles. The GE MicroCal® iTC200(GE Healthcare) was used in this study. The accuracy of the
instrument was verified by titrating a 5 mM standard CaCl2
solution into a 0.4 mM standard EDTA solution, both provided by the manufacturer. LOS and nanoparticle solutions
were incubated in a 25 °C water bath for 15 min prior to
use to ensure temperature equilibration. LOS solution ata concentration of either 20, 30, 50, or 100 μg/mL wasloaded into the syringe and 625 μg/mL nanoparticle solution (the mass of nanoparticles in the original particle suspension was provided by the manufacturer) was loaded into
the sample cell, then the system was equilibrated to 25 °C.
Experimental parameters were set as follows: total numberof injections = 20 (one 0.4 or 0.5 μL injection followedby nineteen 2 μL injections); cell temperature = 25 °C;
initial delay = 60–200 s; syringe concentration = variable;
mixing speed = 700 rpm. Accounting for the dilution in the
ITC cell, the 30 and 50 μg/mL injections were equivalent toa final LOS concentration of about 4 and 7 μg/mL, respectively, interacting with the nanoparticles. Controls included
carbonate buffer titrated into 625 μg/mL of nanoparticles
and 50 μg/mL LOS titrated into carbonate buffer (to determine heat of dilution). Origin 7.0 software was used to convert all raw ITC data into DeltaH plots, and its regressionfunction was used to calculate the stoichiometry, enthalpy,
entropy and dissociation constant (Kd) of each titration.

2.7 Cultivation of immortalized bronchial
epithelial cells
The 16HBE14o- cell line, generated by transformationof normal bronchial cells obtained from a heart-lungtransplant patient, was generously provided by Professor
Gruenert, Department of Otolaryngology, University ofCalifornia, San Francisco (Gruenert et al., 1988). Cells

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were propagated in cell culture medium composed ofEagle’s minimum essential medium with Earle’s salts(Life Technologies Co.) supplemented with 10 % fetal
bovine serum (FBS, Atlanta Biologics Inc.), 2 mM Lglutamine (Life Technologies Co.), 100 μg/mL streptomycin and 100 units/mL penicillin G (Life Technologies Co.)in T-75 flasks (Greiner Bio-One). The cell culture medium
was replaced every other day. At 80 % cell coverage (4–6days), the cells were passaged by washing three times withbiological buffer (prepared as a 10-times concentrated
solution containing 3.795 g NaCl, 0.35 g Na2HPO4, 0.20 g
KCl, 0.99 g glucose, and 1.265 g HEPES in 1 L of purified
water, pH 7.4), detachment by treatment with Trypsin/
EDTA (Life Technologies Co.) for 5–10 min at 37 °C and
5 % CO2 with gentle agitation, and addition of the cellsuspension to new flasks with 12 mL of media per flask.
Passages 20 to 40 were used.

2.8 Immunofluorescent detection of PAFR on
16HBEo- cells
The expression of PAFR on the surface of 16HBEocells was determined using flow cytometry and confocal
microscopy. Cells were prepared for flow cytometry bysuspending cells in biological buffer containing 0.2 %Tween 20, centrifuging at 300 × g for 10 min, then incubating in cold methanol (–20 °C) for 10 min at –20 °C. The
fixed cells were washed with biological buffer containing
0.2 % Tween 20 (Sigma) and incubated with 10 % normal
goat serum in biological buffer containing 0.2 % Tween 20
at 4 °C overnight. Cells were then centrifuged, redispersed
in biological buffer at a concentration of 1 × 107 cells/mL.
To stain for PAFR expression, cells were incubated with
50 μL of 8 μg/mL solution of IgG2a PAFR (human)monoclonal antibody raised in mouse (product 160600,
Cayman Chemical) in biological buffer for 30 min at room
temperature. The controls were no stain, secondary antibody control, and isotype control using 8 μg/mL solutionof mouse IgG2ak clone eBM2a (eBiosciencesTM) in biological buffer. Cells were then washed six times with cold
biological buffer containing 0.2 % Tween 20 and incubatedwith 50 μL of either 6 % normal goat serum in biologicalbuffer (no stain control) or 0.1 μg/mL solution of Alexa
Fluor 647 Goat Anti-Mouse IgG (H+L) antibody (LifeTechnologies Co.) in biological buffer for 15 min. Cells
were again washed six times with cold biological buffer
containing 0.2 % Tween 20. After antibody treatment, cellsuspensions were analyzed immediately using an LSR II
flow cytometer (BDTM Biosciences).
To confirm and visualize PAFR expression, cells were
cultured onto collagen-coated cover slips in 24-well plates
and incubated overnight. The cells were washed threetimes with biological buffer containing 0.2 % Tween 20.
Immunostaining of the cell membranes was conducted as
described above, except 100 μL of each antibody solution

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was added to the cells and the secondary antibody Alexa
Fluor 568 Goat Anti-Mouse IgG (H+L) antibody (LifeTechnologies Co.) was added at a concentration of 4 μg/mL.
After cell washing, the glass coverslip containing the cell
membrane was mounted to a microscope slide using antifade reagent with the nuclear stain DAPI, then kept at 4 °C
until imaged. Cell layers were imaged using a Zeiss LSM
710 confocal microscope with 40× oil immersion objectivelens.

2.9 Particle association with bronchial epithelialcells
To prepare cells for particle binding and uptake studies,
16HBEo- cells were propagated on collagen-coated glass
coverslips. A volume of 100 μL of cells was dispensed, at
2 × 105 cells/well, onto dried collagen-coated coverslips
and incubated one day until reaching 80 % confluency, as
determined by visual inspection. Suspensions of fluorescent yellow particles with or without coating (0.22 μg/mL,
100 μL/well) were added to cells and incubated for 0.5, 2,
4, 18, or 24 h (n = 4 wells of cells per treatment) to quantify
uptake with time. In each case, after incubation the cells
were washed three times to remove unbound particles.
The number of cells associated with particles was quantified using an LSR II flow cytometer (BD Biosciences)with a 488 nm excitation source. Cells were processed forflow cytometry by incubating the cells with enzyme-freecell dissociation buffer (Life Technologies Co.) for 0.5–1 h
at room temperature, centrifuging at 300 × g for 10 min at
4 °C, then dispersing in cold biological buffer by gentle
pipetting to obtain a single cell suspension. Raw data wasprocessed to gate the live cell population(s) based on cellsize (forward scatter) and cell granularity (side scatter),effectively removing dead cells, cell debris and particle
agglomerates. Gating was confirmed with controls, i.e.
cells not treated with particles, particle alone, and cells andparticles mixed briefly together. The population of cells
containing particles were identified based on fluorescenceintensity using the Probability Binning (PB) Chi(T) value(Roederer et al., 2001). The percentage of cells in the population was quantified using the Enhanced Normalization
Subtraction (ENS) method in FlowJo® (Bagwell, 1996).
Results were collected for at least 10,000 cells (n = 4).
Confocal microscopy was used to assess the locationof particles within the cell cultures. After particle treatment, cell membranes were washed three times to remove
unbound particles and secretions produced during the
incubation period. Cell membranes were then stained with
1 μg/mL DiI D-282 (Life Technologies Co.), a lipophilic
indocarbocyanine fluorescent red dye (549 nm/565 nm),in cell culture medium for 30 min, washed with biological buffer three times to remove excess dye, fixed with
2 % w/v paraformaldehyde for 30 min, blocked with
50 mM ammonia chloride for 10 min, and washed with

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3. Results

3.1 Selection and characterization of NTHi
bacteria
Since ChoP content is vital for epithelial cell binding,

2.0*

Chocolate agar
Brain heart infusion agar

1.5

*

1.0
0.5

31

98

0.0
6

Significance (p < 0.05) of differences was determined by
one-way ANOVA with Holm-Sidak’s multiple comparison
tests using GraphPad Prism software (version 6.05).

Electron microscopy images of the uncoated polystyrene
particles confirmed that particles were spherical, withsmooth surfaces (Fig. 2(a)). Upon particle exposure tocarbonate buffer, salt crystals were observed attached tothe particle surfaces (Fig. 2(b)). Coating the polystyrene
particles with 5 ng/mL LOS did not change their appearance compared to the carbonate buffer-washed particles(Fig. 2(c)), as salt crystals remained attached to the particle
surface; however, upon coating with the higher concentration of LOS (5 μg/mL), salt crystals were no longer95

2.11 Statistical analysis

3.2 Physical and chemical characterization of
uncoated and functionalized particles

19

The toxicity of inhibitors to 16HBEo- cells was first
determined using the MTS assay (CellTiter 96® Aqueous
One Solution Cell Proliferation Assay; Promega Co.).
Cells were seeded onto 96-well plates at 3 × 104 cells/
well, incubated for 24 h, then treated for 0–5 h with oneof the following inhibitors: 0.1 % NaN3 with 50 mM 2deoxyglucose, 5 μg/mL cytochalasin D, 80 μM dynasore,
200 μM genistein, 10 μg/mL chlorpromazine, or 5 mM
methyl β-cyclodextrin. Negative and positive control were
cells treated with biological buffer (absorbance normalized
to 100 % cell viability) and cells treated with 2 % w/v SDS,
respectively. Based on toxicity results, uptake studies in
the presence of inhibitors were limited to time points lessthan 3 h, and studies with inhibitors of clathrin-mediatedendocytosis were not conducted as cell viability with these
inhibitors was 40–95 % at all time points.
To determine the mechanisms by which particles weretaken up by cells, 16HBEo- cells were pre-treated with oneof the following inhibitors one hour prior to particle treatment: 0.1 % NaN3 with 50 mM 2-deoxyglucose, 5 μg/mL
cytochalasin D, 80 μM Dynasore, 200 μM genistein, or
5 mM methyl β-cyclodextrin. The receptor PAFR was
activated by pre-treating cells with PAF (10 μM) for 2 h.
The treatment solutions were removed then replaced withthe particle suspension containing fresh inhibitor or PAF
and incubated. After incubation, cells were washed threetimes to remove unbound particles and secretions produced
during the incubation period. Particle–cell association wasmeasured by flow cytometry.

20

2.10 Cell uptake inhibitor and activator studies

LOS with high ChoP content may enhance association withcells containing PAFR. Three NTHi strains were screened
to select for the strain with a high activity of ChoP. NTHi
strains 956 and 3198 expressed significantly higher ChoP
activity, as measured by absorbance of p-nitrophenol, thanthe 2019 strain (Fig. 1). The higher level of ChoP activity
on NTHi 3198 compared to NTHi 2019 was confirmedby dot immunoblot, with strain 3198 expressing more
ChoP on its surface at shorter exposure times (1 s) thanstrain 2019 (30 s), as observed in the higher intensity andpositive stained area of dark spots. This is in agreementwith published literature that reports 78 % of strain 3198
expresses ChoP with high reactivity, compared to 9 % ofstrain 2019 (Swords et al., 2000; Weiser et al., 1997). Further, the expression of ChoP on strains 2019 and 3198 was
higher when bacteria were cultured on BHI agar than on
chocolate agar; in contrast, no statistical difference in ChoP
expression was observed for strain 956 grown on the two
substrates (Fig. 1). Strain 3198 cultured on supplemented
BHI agar medium was chosen for LOS isolation and coating onto particles.

Absorbances of p-nitrophenol

biological buffer once to remove excess reagents. Triton
X-100 prepared at 0.05 % w/v in biological buffer wasused to permeabilize the cell membrane for 10 min. The
membranes were washed, the cover glass containing themembranes mounted to a microscope slide, and the cells
exposed to ProLong® Gold antifade reagent with theblue-fluorescent nuclear 4’,6-diamidino-2-phenylindole(DAPI 358 nm/461 nm; Life Technologies Co.) to stain
the cell nuclei. The cells were kept at 4 °C until imaged.
Z-stacks of cell layers were obtained using a LSM 710
upright confocal microscopy (Zeiss). A 40× oil immersion
objective lens was used to take images with step size of
200 nm. Images were rendered in MATLAB.

NTHi strain
Fig. 1 Relative absorbance of p-nitrophenol at 405 nm from colorimetric ELISA indicating the expression of ChoP on NTHi bacterial strains
2019, 256, and 3198 in either chocolate agar or brain heart infusion agar
growth medium (n = 3, mean ± SD). *indicates statistical difference
between NTHi grown in different agar media (p < 0.05).

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observed, suggesting LOS displaced the salt on the particle
surface (Fig. 2(d)). Similarly, exposing uncoated particles
with a protein solution (gelatin) resulted in visible residue
on the particle surface (Fig. 2(f)); however, this residue
was not observed on particles that were pre-coated with
5 μg/mL LOS prior to gelatin coating (Fig. 2(e)).
When suspended in water, uncoated particles exhibited
low polydispersity with a hydrodynamic diameter of~255 nm, and a zeta potential of about –50 mV (Table 1).
When placed in carbonate buffer, the particles agglomerated
slightly. Agglomeration was maintained with LOS coating
and was intensified with gelatin coating. When placed inserum-free media for cell studies, particles generally deagglomerated, except for the 5 μg/mL LOS coated particles
which exhibited slightly higher agglomeration. In addition,
particles suspended in media exhibited more neutral zeta
potentials than the same particles in water. Serum-free

media contains various salts that likely compressed theelectrical double layer surrounding the particles, resulting
in a reduction in the zeta potential. Particles coated with
5 μg/mL LOS exhibited the highest zeta potential in media
at about –25 mV, suggesting significant surface coveragewith the negatively charged LOS. LOS molecules have
COOH groups on their carbohydrate structures which arenegatively charged at physiologic pH, in addition to the
ChoP moieties which are zwitterionic at physiologic pH.
The surface chemistry of uncoated and functionalized
particles was investigated by XPS (Table 2). To confirm
the existence of a coating on the functionalized particle
surfaces, the raw coating materials were also analyzed.
LOS contained mostly carbon (49.6 %) and oxygen(35.2 %), with the remaining composed of 9.3 % nitrogen,
5.6 % phosphate, and 0.3 % sulphur. Gelatin contained nophosphate, but was comprised of 65.8 % carbon, 18.7 %

a

b

c

d

e

f

Fig. 2 Scanning electron microscopy images of (a) uncoated polystyrene particles, (b) particles washed with carbonate buffer, (c) particles coatedwith 5 ng/mL LOS, (d) particles coated with 5 μg/mL LOS, (e) particles coated with 5 μg/mL LOS and exposed to gelatin, (f) uncoated particles
exposed to gelatin. Scale bar = 500 nm (a) or 1 μm (b–f).
Table 1 Characterization of geometric size and zeta potential of polystyrene particles (200 nm nominal diameter) with various surface coatings in waterand serum-free media. Values are reported as mean ± SD (n = 3).

Suspension in water
Particle coating

Suspension in serum-free media

Particle diameter (nm)

Zeta potential (mV)

Particle diameter (nm)

Zeta potential (mV)

Uncoated

255.15 ± 6.6–50.0 ± 5.3——

Carbonate buffer

381.0 ± 14.2

–40.9 ± 0.7

157.7 ± 9.9–10.4 ± 1.2

5 ng/mL LOS

295.2 ± 6.4–46.8 ± 2.2

183.7 ± 7.4–8.8 ± 0.55 μg/mL LOS

351.6 ± 14.8

–47.8 ± 2.1

461.1 ± 7.8–24.5 ± 2.2

5 μg/mL LOS and gelatin

555.4 ± 254.6–39.5 ± 1.1

187.7 ± 13.8

–7.6 ± 0.9

Gelatin1053.8 ± 372.5–36.4 ± 4.9

165.7 ± 3.2–7.3 ± 0.9

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Table 2 Surface elemental analysis of LOS, gelatin, and polystyrene particles with various surface coatings via X-ray photoelectron spectroscopy.

% Atomic concentration

Material

C/O ratio

C 1s

O 1s

N 1s

P 2p

S 2p

LOS

49.6

35.2

9.3

5.6

0.3

1.4

Gelatin

65.8

18.7

15.3

0.0

0.2

3.5

Uncoated

19.5

54.3

0.0

6.7

19.5

0.36

5 ng/mL LOS

38.6

40.8

1.0

5.0

14.6

0.9

5 μg/mL LOS

57.7

29.2

0.5

3.1

9.6

2.0

5 μg/mL LOS and gelatin

62.7

24.9

4.1

2.4

5.9

2.5

Gelatin

75.9

16.7

4.5

0.6

2.3

4.5

Particles

oxygen, 15 % nitrogen and 0.2 % sulfur. Gelatin also
had a higher C/O ratio at 3.5, compared to LOS at 1.4. In
contrast, the uncoated particle surface contained carbon,
oxygen, and sulfur since the polystyrene particles used inthis study had a polystyrene backbone composed of carbon
and oxygen, and sulfate function groups. The polystyrene
particles had the smallest C/O ratio at 0.36. Some residual
phosphate (7 %) was also observed on the uncoated particle surface, likely due to contamination from the water
washes. Given these differences among the raw materials,
the phosphate, nitrogen, and C/O ratio were used to confirm functionalization of the particles.
Particles coated with a 5 ng/mL LOS solution exhibited
an elemental surface indicative of a mostly uncoatedpolystyrene surface with a small amount of LOS adsorbed(C/O ratio of 0.9, between that of LOS and polystyrene;
with existence of N indicating some LOS adsorption).
This suggests that LOS was on the particle surface but did
not completely coat the entire surface. With a higher LOS
coating solution concentration (5 μg/mL), the C/O ratio
increased to 2.0, suggesting a higher concentration of LOS
on the surface. In addition, the phosphorus and sulphur
concentrations at the surface decreased to 3.1 % and 9.6 %,respectively, suggesting that these moieties were buried
beneath the carbon and oxygen backbone of the LOS.
When uncoated particles or LOS-coated particles were
coated with gelatin, the nitrogen concentration and C/Oratio increased significantly compared to their uncoatedcounterparts, to values closer to that of pure gelatin. Thissuggests that gelatin partially masked the surface of theparticles, even in the presence of LOS.
The LOS coating process was further analyzed by ITC to
determine the stoichiometry of LOS binding to the polystyrene particles. ITC experiments were carried out using four
different concentrations of LOS (Fig. 3). The regression
lines for the 20 and 100 μg/mL titrations exhibited negligible
slope and therefore not analyzed. For the 30 and 50 μg/mL
titrations, the amount of LOS bound to the particle surface
increased with an increase in LOS concentration resulting

in an estimated 17.7 LOS binding sites per particle (titration with 30 μg/mL LOS) and 33.9 LOS binding sites per
particle (titration with 50 μg/mL LOS). Enthalpies of binding were large and negative for all titrations (–2 kcal/mol
to –10 kcal/mol), indicating strong binding of LOS to theparticle surface.

3.3 Uptake of particles in 16HBEo- lung epithelial
cells

PAFR expression in 16HBE14o- cells was confirmed
via immunostaining (Fig. 4). The secondary and isotype
controls exhibited low levels of staining due to non-specificreceptor interactions (Figs. 4(b) and 4(c), respectively),
which was quantified by flow cytometry via fluorescenceintensity (Fig. 4(e)). PAFR expression on the cell surface
was observed as diffuse staining across the entire cell surface (Fig. 4(d)). Flow cytometry confirmed that the entire
population of 16HBEo- cells stained for PAFR exhibited
higher intensity of fluorescence than controls, confirming
the cells expressed PAFR (Fig. 4(e)).
Next, lung epithelial cells were exposed to uncoated,gelatin-coated, or functionalized particles for up to 24 hand the percentage of cells associated with particles quantified by flow cytometry. Uncoated particles and particles
coated with 5 ng/mL LOS behaved similarly, with little
uptake from 0–19 h (<10 % of cells were associated withparticles) followed by a small increase at 24 h to 10 %(Fig. 5(a)). Gelatin coated particles were associated with at
most 12 % of cells at 4 h, which was not statistically different than uncoated particles. However, particles coated with
5 μg/mL LOS were associated with about 12 % of cells attime 0 and this rose steadily to about 35 % at 24 h. Confocal microscopy confirmed that the 5 μg/mL LOS-coatedparticles were internalized into the 16HBEo- cells and notjust attached to the cell surface (Fig. 5(b)).
The energy dependence of particle uptake in 16HBEocells was quantified after cell incubation with NaN3/
2-deoxyglucose, which inhibits all energy-dependent
pathways (Fig. 6). Incubation of cells with NaN3/2-

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aLOS:nanoparticle molar ratio
5

10

15

20

25

b
0

-2

-2

-4
-6

Trial 1
Trial 2

-10

LOS:nanoparticle molar ratio

40

-4
-6

20

40

60

d

Trial 1
Trial 2

LOS:nanoparticle molar ratio
0
0

-2

-2

-4
-6
Trial 1
Trial 2
Trial 3-12

ΔH (kcal/mol)

0

-10

30

-12

0

-8

20

-10

-12

c

10

-8

-8

ΔH (kcal/mol)

LOS:nanoparticle molar ratio00

ΔH (kcal/mol)

ΔH (kcal/mol)

0

25

50

75

100

125

-4
-6
-8
-10

Trial 1
Trial 2

-12

Fig. 3 Isothermal titration calorimetry (ITC) plots of ΔH versus LOS–nanoparticle molar ratio with single injections of (a) 20 μg/mL, (b) 30 μg/mL,(c) 50 μg/mL, or (d) 100 μg/mL of LOS. Colored symbols represent unique replicates. Lines represent regression curves originated by Origin software.
A buffer–nanoparticle titration was used as a reference and subtracted from all data sets.

deoxyglucose resulted in complete loss of uptake of the
5 μg/mL LOS-coated particles, suggesting these particles
are taken up by endocytosis. No other particle type experienced inhibited cell uptake after cell exposure to NaN3/
2-deoxyglucose and thus may be taken up by both energydependent and energy-independent pathways.
Further studies were conducted with specific pathway
inhibitors to help elucidate specific uptake pathway for different particle types. Inhibitors of micropinocytosis (cytochalasin D) and caveolin-mediated endocytosis (genistein)
did not inhibit the uptake of any particle type. Instead, particle uptake was sometimes enhanced with cell exposure tothese inhibitors, suggesting that particles were taken up by
multiple pathways. The inhibitor of cholesterol- dependent
pathways (methyl β-cyclodextrin), which inhibits both
caveolin-mediated and clathrin-mediated endocytosis, significantly reduced cell uptake of the 5 μg/mL LOS-coatedparticles. This suggest that the LOS-coated particles wereprimarily taken up by receptor-mediated endocytosis.
However, due to the significant cell toxicity exhibited by
the inhibitors of clathrin-mediated endocytosis, this could
not be confirmed via chemical pathway inhibitors.
To prove that receptor-mediated endocytosis was theroute by which LOS-coated particles were taken up into
cells, we investigated whether PAFR-mediated endocytosis could be altered by the cognate ligand of the receptor(PAF). Upon cell incubation with PAF, the 5 μg/mL
LOS-coated particles exhibited a 5- to 10-fold increase in272

uptake (Fig. 7). No significant change in the associationof particles with lung cells was observed with the otherparticle types.

4. Discussion
Lipopolysaccharides (LPS), also known as endotoxins,
are large lipid- and polysaccharide-containing molecules
constituting the major component of the outer membraneof Gram-negative bacteria. They induce a strong immune
response in animals and have been implicated in various
pathogenic functions (Alexander and Rietschel, 2001;
Simpson and Trent, 2019). Key studies have shown thatisolating LPS from the bacterial surface and attaching it toa particle surface may not alter its functionality, enabling
the particles to mimic some bacterial properties (Piazza
et al., 2011). For example, titanium dioxide particles
coated with LPS from E-coli induced pro-inflammatory
signaling in primary human mononuclear phagocytes(Ashwood et al., 2007). LPS associated with polymericand metallic components of orthopedic implants stimulate
toll-like receptor (TLR) activation, which contributed to an
inflammatory response (Greenfield et al., 2010; Hold andBryant, 2011). However, the activation of the inflammatory
response by LPS-coated particles was not desired for thecurrent application.
Lipooligosaccharides (LOS) share a similar lipid A
structure to LPS, but are lower in molecular weight,
lack the O-antigens that allow serotyping, and have

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Mai H. Tu et al. / KONA Powder and Particle Journal No. 41 (2024) 265–279

c

a

b

% Cells associated with particles

ad

Uncoated 0.2 µm particles
LOS-coated 0.005 µg/ml 0.2 µm particles (no gelatin)
LOS-coated 5 µg/ml 0.2 µm particles (no gelatin)
Gelatin-coated 0.2 µm particles
LOS-coated 5 µg/ml 0.2 µm particles (with gelatin)

40
30
20
10
0

0

10
20
Incubation time (hours)

30

b

20 µm

e

% Cells

PAFR
Isotype control
Secondary control
No stainFluorescence intensity
Fig. 4 (a–d) Confocal micrographs of 16HBE14o- cells with nuclei
stained using DAPI (blue) and incubated with the (b) secondary antibody (secondary control, red stain), (c) fluorescent antibody IgG2ak to
confirm the specificity of primary antibody binding (isotype control, red
stain), or (d) the primary and secondary antibodies (red stain for PAFR).
Scale bar = 20 μm. (e) Percent fluorescent cells as a function of fluorescence intensity for cells not stained (far left curve) or incubated with thesecondary antibody (secondary control, second curve from left), IgG2ak(isotype control, third curve from left), or the primary and secondary
antibodies (PAFR staining, far right curve).

significantly lower toxicity profiles (Preston et al., 1996).
LOS is the major glycolipid expressed on mucosal Gramnegative bacteria, including members of the genera
Neisseria, Haemophilus, Bordetella, and Branhamella.
It is a heat stable, amphipathic glycolipid complex thatcovers up to 75 % of the outer membrane (Silipo et al.,2011), and is crucial for the viability and survival of Gramnegative bacteria as it helps maintain proper outer membrane structure. It also contributes to host-bacterium interactions related to adherence, colonization, internalization,and ultimately survival in host cells.
The oligosaccharide region of LOS is highly heterogeneous within and between bacterial strains, containing bothFig. 5 (a) Percentage of 16HBE14o- cells associated with uncoatedand functionalized particles as a function of incubation time at 37 °C(n = 4, mean ± SD). (b) Confocal microscopy image of 16HBEo- cells
exposed to 5 μg/mL LOS-coated particles. Cell membranes were labeled by DiI (red fluorescence) and cellular nuclei were labeled by DAPI
(blue fluorescence), and particles fluoresce green. Two representative
cross-sections of the cell layer are displayed at top and right side of the
micrograph, with green fluorescing particles denoted by the arrows.

the recognition structures for host cells and componentsof the immune system, as well as carbohydrate moietiesthat may camouflage the bacterial surface from the hostthrough molecular mimicry of host structures (Mandrelland Apicella, 1993; Masoud et al., 1997; Moran et al.,
1996; Murphy and Apicella, 1987; Risberg et al., 1999a,
1999b; Silipo et al., 2011). In some bacterial species,
these carbohydrate structures further facilitate bacterial
adherence to the respiratory epithelium (Clark et al., 2012;
Jalalvand and Riesbeck, 2014; Swords et al., 2000, 2001).
Phosphorylcholine (ChoP) expressed as a terminal structure of the OS region facilitates adherence of multiple bacterial pathogens, including NTHi, to the platelet-activating
factor receptor (PAFR) on bronchial cells by molecularly
mimicking the structure of the cognate ligand plateletactivating factor (PAF) (Cundell et al., 1995; Gillespie etal., 1996; Harvey et al., 2001; Lysenko et al., 2000; Rose-273

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Normalized % cells associated with particles

Mai H. Tu et al. / KONA Powder and Particle Journal No. 41 (2024) 265–279

1000

Uncoated 0.2 µm polystyrene particles
LOS-coated 0.005 µg/ml 0.2 µm polystyrene particles (no gelatin)
LOS-coated 5 µg/ml 0.2 µm polystyrene particles
Gelatin-coated 0.2 µm polystyrene particles
LOS-coated 5 µg/ml 0.2 µm polystyrene particles (with gelatin)

800
600

*
*

*
*

400

*

*

200
0

Control

NaN3 /2-deoxyglucose

Cytochalasin D

Genistein*
Methyl β-cyclodextrin

1000

*

800
600
400
200

PA
F

0
C
on
tr
ol

Normalized % cells associated with particles

Fig. 6 Percentage of cells, as determined by flow cytometry, associated with 16HBEo- cells before (control) and after exposure to various inhibitorsfor 2 h. Data is reported as mean ± SD (n = 4). * indicates statistically significant differences compared to the absorbance of untreated cells.

Uncoated 0.2 µm particles
LOS-coated 0.005 µg/ml 0.2 µm particles (no gelatin)
LOS-coated 5 µg/ml 0.2 µm particles (no gelatin)
Gelatin-coated 0.2 µm particles
LOS-coated 5 µg/ml 0.2 µm particles (with gelatin)

Fig. 7 Normalized percentage of 16HBE14o- cells associated with
uncoated and functionalized polystyrene particles before (control) or
after activation with PAF (n = 8, mean ± SD).

now et al., 1997; Schweda et al., 2000; Weiser et al., 1997,1998). As a potential targeting ligand for particles, our previous studies indicated that coupling ChoP-containing LOS
from NTHi (strain 2019) to the surface of 1 μm particles facilitated significantly more binding to the human bronchial
epithelial surface compared to particles coated with gelatinor coated with truncated LOS lacking the oligosaccharide
chains that contain ChoP (Swords et al., 2000). Here, we
aimed to evaluate the ability of ChoP-containing LOS tofacilitate the uptake of smaller nanoparticles into respira-274

tory epithelial cells.
Given the heterogeneity of the OS expression between
bacterial strains, a screening of clinically isolated NTHi
strains was performed to select a strain with high ChoP
content to facilitate particle adherence and uptake into lungcells. Two of the three NTHi clinical isolates tested (956
and 3198) exhibited high ChoP activity. While the ChoP
content of strain 956 had not been previously reported, theobserved differences between strains 3198 and 2019 agreed
with published literature, which has reported that 78 % ofstrain 3198 expressed ChoP with high reactivity, compared
to 9 % of strain 2019 (Swords et al., 2000; Weiser et al.,1997). Based on these results and the known ability of
NTHi 3198 to associate with and invade primary lung epithelial cells (Ketterer et al., 1999), LOS from NTHi 3198
was chosen as the ligand for further studies.
LOS is a large molecule containing a lipid structurethat embeds in the bacterial outer membrane and an
oligosaccharide structure that is exposed on the surface.
The polystyrene particles used in this study exhibit a hydrophobic, negatively charged surface containing sulfate
ester functional groups. Due to the hydrophobicity of theparticles and the lipophilicity of the LOS lipid tails, we
anticipated that the lipid tails of the LOS molecules would
readily adsorb to the hydrophobic particle surface. In thiscase, the carbohydrate heads on the LOS structure containing ChoP could be available for cell targeting. Basedon analysis of the zeta potential and XPS data, LOS wasindeed coated onto the surface of polystyrene particles,
particularly with the more highly concentrated LOS
coating solution. However, the XPS data also suggested
that the phosphate groups were buried within a mostly
carbon-oxygen LOS backbone. This would be undesirable
if the ChoP functional groups were not available to function
as a targeting ligand on the particle surface. As XPS studies

Mai H. Tu et al. / KONA Powder and Particle Journal No. 41 (2024) 265–279

were conducted on dry particles, when the carbohydrate
chains become hydrated, the LOS structure could open andgive access to the ChoP moieties.
ITC further confirmed the ability of LOS to strongly
adsorb to the nanoparticle surface. Typical enthalpies ofbinding for proteins to polystyrene nanoparticle surfaces
has been reported to be on the order of 10 s to 100 s ofkcal/mol (Porzeller et al., 2019), similar to the values
obtained in this study. Using titration concentrations of 30
and 50 μg/mL, we estimated that there are about 34 LOS
binding sites per particle. The titration concentration of
50 μg/mL (~7 μg/mL final concentration in the cell after titration), resulted in slightly more adsorbed LOS molecules
than the 30 μg/mL titration concentration (~4 μg/mL final
concentration in the cell after titration). This suggests thathigher LOS concentrations enhance LOS adsorption to theparticle surface. Previous work by Swords et al. reported
a dose-response in which increasing the concentration of
LOS coating resulted in more particle adherence to respiratory epithelium (Swords et al., 2000). LOS concentration
is limited, though, as it readily forms micelles in aqueous
solution at low concentrations due to its amphiphilic nature(Steimle et al., 2016). Thus, a higher LOS coating might beachieved by using a co-solvent to better solubilize the LOS.
The ability of LOS to form micelles, as well as the complex
structure of LOS, are potential reasons for the relatively
high variability in the ITC data.
If the ChoP moieties on the LOS molecules remained
accessible on the particle surface, we would expect theparticles to exhibit better binding to and internalization into
the respiratory epithelium. PAFR is expressed on humanlung tissue membranes (Hwang et al., 1985; Shukla et al.,2016) and various lung epithelial cell lines (Chen et al.,2015). In the current study we confirmed PAFR expressionin 16HBEo- bronchial epithelial cell monolayers via immuno-fluorescent staining and used these cells to evaluate
the uptake of uncoated and coated particles. The 5 μg/mL
LOS-coated particles exhibited the highest cell uptake ofnanoparticles, with about 35 % uptake at the 24 h timepoint. All other particle types were taken up by less than
12 % of cells at any time. Confocal microscopy confirmedthat particles were not just bound to the surface, but wereinternalized into the cell layers. While the ability of PAFR
to mediate particle attachment to the respiratory epithelium
had been previously established (Swords et al., 2000), thisis the first time to our knowledge that bacterial-derived
LOS has been used as a targeting ligand to facilitate particle uptake into cells.
We further investigated the uptake mechanism by blocking specific endocytic pathways using chemical inhibitors.
NTHi bacteria have been shown to internalize into humanlung epithelial cells by macropinocytosis and receptormediated endocytosis, so these pathways were likely candidates for LOS-coated nanoparticle uptake (Clementi and

Original Research Paper

Murphy, 2011; Ketterer et al., 1999; Swords et al., 2000, 2001).
Pre-incubation of the lung cells with NaN3-2-deoxyglucose
completely knocked out their ability to take up the 5 μg/mL
LOS-coated particles, suggesting an energy-dependent
pathway for these particles, but had no impact on the otherparticle types. We further saw no evidence of the LOScoated particles being taken up by macropinocytosis. Due
to significant cell toxicity upon cell incubation with inhibitors of the clathrin-mediated pathway, we were unable toconfirm this pathway using chemical inhibitors of endocytic pathways. Thus, a different approach was chosen inwhich the impact of a PAFR activator on the cellular uptakeof uncoated and functionalized particles was investigated.
A549 cells were pre-treated with PAF, the natural substratefor PAFR (Chao and Olson, 1993). Prior studies haveshown that PAFR stimulation is involved in bacterial entry
and could assist the delivery of siRNA to airway epithelia(Krishnamurthy et al., 2014; Zhang et al., 2000). In the current study, PAF pre-treatment significantly enhanced cell
uptake of the 5 μg/mL LOS-coated particles, suggestingthat PAF activation aided their uptake. Activation of PAFR
via PAF exhibited no significant change on the associationof any other particle type, demonstrating receptor targetingwith the LOS coating.
In summary, the functionalization of polystyrene nanoparticles with LOS bacterial ligands resulted in better cellular uptake in PAFR-expressing lung cells. This strategy
could be extended to drug-containing nanoparticles toenhance the therapeutic efficacy of drug compounds that
require cellular internalization. Further, receptor-ligand
interactions could be enhanced by activating the cell
receptor, suggesting that enhanced cellular uptake could
be achieved in disease states where the receptor is alreadyactivated, such as in the instance of infection, COPD,
asthma, and cancer.

Acknowledgments
Funding for this work was provided by the National
Institutes of Health (R21HL113876 and P30 DK054759).
M. Timm was supported through an NSF-REU programin Nanoscience and Nanotechnology (NSF 1359063). We
thank the University of Iowa Central Microscopy Research
Facility, a core resource supported by the Vice President
for Research & Economic Development, the Holden
Comprehensive Cancer Center, and the Carver College ofMedicine, for access to and training on the confocal andelectron microscopes. Flow cytometry data presented wereobtained at the Flow Cytometry Facility, which is a Carver
College of Medicine / Holden Comprehensive Cancer
Center core research facility at the University of Iowa. The
Facility is funded through user fees and the generous financial support of the Carver College of Medicine, Holden
Comprehensive Cancer Center, and Iowa City Veteran’s
Administration Medical Center. Part of the study has been275

Mai H. Tu et al. / KONA Powder and Particle Journal No. 41 (2024) 265–279

published in the PhD thesis of author Mai Tu (Tu, 2015).

Nomenclature
BHI

Brain heart infusion

BSA

Bovine serum albumin

ChoP

Phosphorylcholine

ELISA

Enzyme-linked immunosorbent assay

ITC

Isothermal titration calorimetry

LOS

Lipooligosaccharide

LPS

Lipopolysaccharide

NAD

Nicotinamide adenine dinucleotide

NTHi

Non-typeable Haemophilus influenzae

PAF

Platelet-activating factor

PAFR

Platelet-activating factor receptor

pNPP

Nitrophenyl phosphate bis (cyclohexyl ammonium)

SDS

Sodium dodecyl sulfate

SEM

Scanning electron microscope

XPS

X-ray photoelectron spectrometer

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Original Research Paper

Authors’ Short Biographies
Mai H. Tu
Mai H. Tu earned a Ph.D. in pharmaceutics from the University of Iowa, USA under the guidanceof Dr. Jennifer Fiegel. Her doctoral work focused on developing lipooligosaccharide-modified polymeric particles for targeted pulmonary drug delivery.

Timothy M. Brenza
Timothy M. Brenza completed a postdoctoral fellowship under the guidance of Dr. Jennifer Fiegel
from 2008–2010. He is currently an Assistant Professor of chemical and biological engineering at
South Dakota School of Mines and Technology.

Margaret R. Ketterer
Margaret R. Ketterer has worked at the University of Iowa Department of Microbiology and
Immunology, in the laboratory of Dr. Michael Apicella, since 1993. Her research efforts have concentrated on the role of bacterial lipo-oligosaccharides in infections of human cells by Neisseria species
and by Haemophilus influenzae.

Morgan Timm
Morgan Timm received a B.S. degree in biochemistry and molecular biology from Gustavus
Adolphus College, USA and is currently an MD/PhD student at Washington University, USA. She
conducted a summer research project in the laboratory of Dr. Jennifer Fiegel as part of the Nano NSF
REU program at the University of Iowa.

Benjamin M. King
Benjamin M. King earned a Ph.D. in chemical and biochemical engineering from the Universityof Iowa, USA in 2018 under the guidance of Dr. Jennifer Fiegel. His doctoral work focused on interactions of environmental and therapeutics particles with the airway microenvironment.

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Mai H. Tu et al. / KONA Powder and Particle Journal No. 41 (2024) 265–279

Original Research Paper

Authors’ Short Biographies
Michael A. Apicella
Michael A. Apicella is a Professor Emeritus of microbiology and immunology at the Universityof Iowa. His research focused on understanding the factors involved in human infections caused byGram-negative bacterial pathogens.

Jennifer Fiegel
Jennifer Fiegel is a Professor of chemical and biochemical engineering at the University of Iowa
(Iowa City, Iowa), with a secondary appointment in pharmaceutical sciences and experimental therapeutics. Her research expertise is in the design of improved therapeutic drug delivery strategies forthe treatment and prevention of infections.

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Information Article

The 55th Symposium on Powder Technology
The 55th Symposium on Powder Technology organized by the Hosokawa Powder Technology Foundation took place atthe Hotel Tokyo Garden Palace in Japan on Monday, September 4, 2023. This event was held by the planning of the Councilof Powder Technology, Japan and with the sponsorship of Hosokawa Micron Corporation. The theme of the symposium thisyear was “Powder Technology Contributing to a Sustainable Society”. There were seven lectures, including three KONA
Award commemorative lectures, from basic and applied perspectives. This year’s symposium was held in a hybrid style,allowing both on-site and online participation.
At the beginning, Mr. Yoshio Hosokawa, the president of the Foundation, delivered the opening address mentioning that
this symposium has been held almost annually in Osaka or Tokyo since 1968. However, due to the coronavirus pandemic,
it was canceled for two years. Last year, a special event was held to commemorate the 30th anniversary of the foundation’s
establishment in Osaka, and consequently, this event in Tokyo was for the first time in five years.
Prior to the lectures, the KONA Award presentation ceremony was held, and the plaques of the KONA Award werehanded from the president to the two 2022 KONA Awardees; namely, Professor Hidehiro Kamiya, Director and Vice
President of Tokyo University of Agriculture and Technology, and Professor Toshitsugu Tanaka of Osaka University. The
contents of the symposium are shown below.
In the morning session, Prof. Kamiya and Prof. Tanaka gave the KONA Award 2022 commemorative lectures, in which
they introduced their research results on particle adhesion and aggregation behavior in relation with the characterizationand control, as well as on the development of discrete particle modeling and simulations of gas–solid two-phase flows,
respectively.
The afternoon session began with the commemorative lecture for the KONA Award 2021 by Prof. Shuji Matsusaka of
Kyoto University on advanced characterization of fine particles and the development of novel powder handling systems.
Then, Prof. Toru Wakihara of the University of Tokyo and Prof. Tohru Sekino of Osaka University discussed a new preparation method for zeolite nanoparticles and its application, along with the creation of photo-physical-chemical functions bycontrolling the structure of the oxide nanoparticles, respectively, focusing on specific powder materials.
In the final session, Mr. Daisuke Ito of Murata Manufacturing Co., Ltd. and Dr. Aiko Sasai of Hosokawa Micron Corporation talked about the applications of powder technology and nanoparticle processing technology to the development of
noteworthy products such as next-generation batteries, and regenerative medicine/cosmetics, respectively. After the end of
the lectures, Prof. Hiroyuki Kage, Chairman of the Council of Powder Technology, Japan, gave the closing address, and thesymposium concluded successfully.
Subsequently, in the reception hall, a social gathering was held for the first time in four years, allowing the lecturers andattendees to engage in meaningful exchanges in a relaxed atmosphere from beginning to end. The number of participantsat this year’s venue was over 100, with nearly 240 in total, including online attendees. The following 56th Symposium isscheduled to be held in Osaka next year.

Opening address by President Yoshio Hosokawa.

280

For the memory of KONA Award presentation ceremony. (From left:
2022 KONA Awardee, Prof. Hidehiro Kamiya; President Yoshio
Hosokawa; 2022 KONA Awardee, Prof. Toshitsugu Tanaka).

The 55th Symposium on Powder Technology
Theme: “Powder Technology Contributing to a Sustainable Society”
Date: Monday, September 4, 2023
Place: Hotel Tokyo Garden Palace
Opening address M
 r. Yoshio Hosokawa (President of Hosokawa Powder Technology Foundation,
Chairman of Hosokawa Micron Corporation)
・2022 KONA Award presentation ceremony
Session 1 Chaired by Prof. Masayoshi Fuji (Nagoya Institute of Technology, Japan)
・Lecture 1 (Special lecture by the 2022 KONA Awardee)“Control of Nano and Fine Particles Adhesion and Aggregation Behaviors Based on Characterization
and Analysis of Particle Interactions”
Prof. Hidehiro Kamiya (Graduate School of Engineering, Tokyo University of Agriculture and Technology, Japan)
・Lecture 2 (Special lecture by the 2022 KONA Awardee)“Development of Discrete Element Model and Simulation Method to Predict Granular Flows”
Prof. Toshitsugu Tanaka (Graduate School of Engineering, Osaka University, Japan)
Session 2 Chaired by Emeritus Prof. Kikuo Okuyama (Hiroshima University, Japan)
・Lecture 3“Advancements in Powder Characterization Methods and Developments in Advanced Powder Handling Technology”
Prof. Shuji Matsusaka (Graduate School of Engineering, Kyoto University, Japan)
・Lecture 4“Novel Preparation Method of Nano-Sized Zeolite Using Pulverization Technology and Its Application”
Prof. Toru Wakihara (School of Engineering, The University of Tokyo, Japan)
・Lecture 5“Creation of Photo-physical-chemical Functions of Nanostructured Materials via Various Structure
Tuning”
Prof. Tohru Sekino (SANKEN, Osaka University, Japan)
Session 3 Chaired by Honorary Research Advisor, Dr. Hisao Makino (CRIEPI, Japan)
・Lecture 6“Designs of Ceramic Powders: Toward Next-Generation Batteries”
Mr. Daisuke Ito (Principal Researcher, Battery Development Department, Device Center, Murata Manufacturing Co., Ltd., Japan)
・Lecture 7“Development and Practical Use of Integrating Regenerative Medicine and Nanotechnology”
Dr. Aiko Sasai (Pharmaceutical & Beauty Science Research Center, Material Business Division,
Hosokawa Micron Corporation, Japan)
Closing remarks Emeritus Prof. Hiroyuki Kage (Kyushu Institute of Technology, President of Council of Powder
Technology, Japan)

Symposium on Powder Technology.

Get-acquainted Party.

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Information Article

4th International Hosokawa Powder Technology Symposium
Held in Germany
On September 14th, 2023, the 4th International Hosokawa Powder Technology Symposium took place at the Dorint An
der Kongresshalle Hotel in Augsburg, Germany. The symposium was organized by the Hosokawa Powder Technology
Foundation, in conjunction with KONA Europe e.V., the European / African Editorial Board of the academic journal
KONA, and was co-hosted and sponsored by Hosokawa Alpine AG.
The International Hosokawa Powder Technology Symposium was first held in 2014 at Hosokawa Alpine AG to celebrate
the Foundation’s 20th anniversary. Subsequent symposia took place in the United States in 2017 and China in 2019, making
this the fourth edition of the event.
The symposium began with a welcome speech by Dr. Kohei Hosokawa, Director of Hosokawa Powder Technology
Foundation and President of Hosokawa Micron Corporation. The theme of the symposium was “Sustainable Production of
Functional Particles,” and it featured eight presentations highlighting collaborations between various companies, as well as
the overviews by the academic researchers.
In the presentations, hot topics such as the future prospects of cultured meat production using continuous production
facilities for cell culture media, dry and wet technology for efficient extraction of proteins from soybean, recycling technology and equipment for lithium-ion batteries, the relationship between processing techniques for rare earth materials
and the performance of neodymium magnets, and the introduction of new spheronization equipment for graphite, a criticalsecondary battery material, were discussed.
Additionally, Dr. Akira Watanabe, General Manager of the Technology Development Department of Hosokawa Micron
Corporation in Japan, shared insights into the differences in geopolitical and cultural perspectives between Japan andGermany, and the evolution of secondary battery material manufacturing equipment in Japan. At the end of the symposium,
Dr. Fernández, President of Hosokawa Alpine AG, gave the closing remarks.
Participants came from various countries, including Germany, the host country, the Netherlands, and Lithuania. A welcome ceremony was held the day before the symposium. At the welcome ceremony, Dr. Steffen Sander, Secretary Generalof KONA Europe e.V. and head of the R&D center of Hosokawa Alpine AG, first explained about the company tour. Participants were then divided into small teams for a tour of the factory and the R&D center.
The guide explained that the company’s factory has production lines segmented by application, such as minerals, chemicals, and films, allowing the company to produce products of the high quality for each application. After the factory tour, thegroup was guided through the R&D center from the top floor to the first floor. The R&D center is equipped not only with lab
machines and small test machines, but also with test machines that are the same size as actual production facilities, whichis expected to reduce the risk of scaling up test machines when users set up actual production facilities. A separate testingmachine for pharmaceutical products is also available. With these well-equipped testing facilities, the company is able torespond to a variety of customer requests. After the company’s tour, the group was divided into two teams: one team went on
a tour of Augsburg, the famous walled city on the Romantic Road, while the other team toured the beer factory facilities. At
the end of the tour, all the teams gathered once again to enjoy delicious German beer in a beer hall adjacent to the brewery,and international exchange was promoted.

Welcome speech by President Kohei Hosokawa.

282

Lecture scenery.

4th International Hosokawa Powder Technology Symposium
Date: Tuesday, September 14, 2023
Place: Dorint Hotel An der Kongresshalle Augsburg, Germany
Theme: “Sustainable Production of Functional Particles”
Organizer: Hosokawa Powder Technology Foundation
Co-organizer: KONA Europe e.V., Hosokawa Alpine AG➢Welcome address D r. Kohei Hosokawa, Acting President of Hosokawa Powder Technology Foundation,
President of Hosokawa Micron Corporation, Japan➢Lecture 1: Current trends in particle technology
Ass. Prof. Gabrie Meesters, Department of Chemical Engineering, Delft University of Technology, The Netherlands➢Lecture 2: Leveraging AI in process engineering: Overview of multifaceted applications and synergetic
integration opportunities
Mr. Christoph Thon, Institute for Particle Technology (iPAT), Technische Universität Braunschweig, Germany➢Lecture 3: The future of cultivated meat and the continuous production of cell culture media
Mr. Appachu Kodira, KCell Biosciences / JSBiosciences; Mr. Hermann Schmidt, Hosokawa Alpine AG,
Germany➢Lecture 4: Deep Dive Pulses Protein: Combination of dry and wet extraction
Mr. Remy Kriech, Bühler Technologies GmbH, Germany; Mr. Eric Emmert, Hosokawa Alpine AG, Germany➢Lecture 5: The difference between Germany and Japan from the viewpoint of the powder-related industry,and SDGs
Dr. Akira Watanabe, Powder Technology Research Institute, Hosokawa Micron Corporation, Japan➢Lecture 6: Processing techniques for rare earth materials and the effect on magnetic materials performance
Mr. Konrad Opelt, Department of Magnetic Materials, Fraunhofer IWKS, Germany➢Lecture 7: Comminution and classification as important processes for the circular production of lithium
batteries
Prof. Carsten Schilde, Institute for Particle Technology (iPAT), Technische Universität Braunschweig, Germany➢Lecture 8: Spheronization of natural graphite—background and novel process
Dr. Marilena Mancini, Accumulators Materials Research (ECM) Department of ZSW, Germany; Mr. Benjamin
Biber, Hosokawa Alpine AG, Germany➢Closing remarks

Dr. Antonio Fernández, President of Hosokawa Alpine AG., Germany

Lecture by Dr. Akira Watanabe, General Manager of Technical Development Dept.

Recess scenery.

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Information Article

The KONA Award 2022
The KONA Award has been presented to researchers who have greatly contributed to research and development as wellas education in the field of Powder and Particle Science and Technology since 1990. It was originally given by Hosokawa
Micron Corporation, but now is presented to 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 the nominationof the awardee. It needs to be finally approved at the Board of Directors’ meeting of the Foundation. The KONA Award ispresented at a ceremony in or outside Japan with a plaque and a prize of one million yen. The KONA Award 2022 has beenpresented to two researchers this year, namely Prof. Hidehiro Kamiya and Prof. Toshitsugu Tanaka from Japan. The KONA
Award plaques were presented to the awardees at the 55th Symposium on Powder Technology on September 4, 2023.
Dr. H. Kamiya has realized various achievements in powder technology, particularly those related to fine and nanoparticles, and has widely disseminated his activities overseas. Based on the characterization of the surface interaction andstructure of nanoparticles, he investigated the fundamentals associated with the aggregation behavior of nanoparticles
in liquid. He discovered an organic ligand structure with universal dispersibility of nanoparticles into various polar andnonpolar organic solvents. By synthesizing various ligands with different molecular structures, he discovered the optimum
ligand molecular structure for universal dispersibility. Such surface-treated nanoparticles were applied to prepare new freestanding, rollable, and transparent silicone polymer films.
Utilizing fine particles and microcapsules measuring larger
than 100 nm in diameter, he investigated the original molecular
design of polymer structures on particles and capsules to characterize and control surface interaction via colloid probe AFM. In
the gas phase, the adhesion of fine ash particles at high temperatures hindered the stable operation of various energy systems.
Hence, he developed original systems for characterizing theparticle adhesion force and shear strength of single ash at hightemperatures based on a split-type tensile strength tester. Since
real ash includes various chemical elements, he developed amodel of ash particles prepared from pure silica as well as othermetal oxide fine particles, with the addition of alkali metal or
phosphorus. Based on the fundamental characterization method
and ash model, he investigated the mechanism of increasingash particles generated from different plant and fuel sources. To
control the adhesion behavior of ash, he proposed the addition ofalumina and other inorganic nanoparticles. The characterization,

At the KONA Award presentation ceremony,
President Yoshio Hosokawa (Left) and 2022 KONA AwardeeProf. Hidehiro Kamiya (Tokyo Univ. of Agriculture and Technology, Japan).

Application 1

Application 2

Application 3

Nano- and fine particles powder
process design

Energy and environment
Ash behavior control at hightemperature, dust collection coal,biomass, waste power
generation plant/ PM 2.5, Nanoparticle emission,
ISO standard/ Nano risk, Nano toxicity

Pharmaceutical / DDS, Inhalation
Oral administration using microcapsule
Orally disintegrating tablet
Life and other application/ Cosmetic powder/ Toner for dry copy, ink jet
Food and Energy system
Agriculture + Engineering

OH

HO OH
for
HO P
O
OP
O
/ Fine ceramics O/ Composite, nanoparticles
dispersed polymers/ Lithium ion battery electrode

O
O

3
3

Fundamentals :Characterization and control of adhesion, aggregation / dispersion, andpacking behavior by using interface molecular and nanometer scaled structure design/ Development of new characterization method, such as colloidal probe AFM method and NMR,
/ Molecular design of ligand for nanoparticle dispersion, nanoparticles coating on fine particles.

Particle adhesion and aggregation behavior characterization and control.
Selected research achievements for the KONA Award 2022 (Prof. Hidehiro Kamiya): Particle adhesion and aggregation behavior characterization and control.

284

simulation and modeling, and control of adhesion and aggregation have been expanded to various industrial fields, including pharmaceuticals, cosmetics, and pigments.
The results above have been published in 243 original papers (182 WoS papers) and 76 review papers. He has filed 23
patents, published 55 books, and presented 37 keynote lectures at international conferences. Additionally, he has contributed
to the activities of the “Association of Powder and Particle Industry and Engineering in Japan, APPIE” as a coordinator of
the “Fine Powder Nanotechnology” group from 2001 to 2021. Furthermore, he has served as a vice chair of the committeeof “Nanoparticle Safety” and an editor for the book, “Safe Use of Nanoparticles.”Dr. T. Tanaka has been conducting research at Osaka University, focusing on fluid engineering and powder technology,
particularly with regard to discrete particle modeling and simulations of gas–solid flows and granular flows, for 38 years.
He began his career at the dawn of discrete particle simulation. When he started his numerical research, only the Lagrangian
numerical simulation neglecting particle–particle collision or contact and the two-fluid-model simulation were available.
He made pioneering works for modeling both collision-dominant flows and contact-dominant flows.
First, he proposed a deterministic method for calculating of particle–particle collision to study the effect of particle–
particle collision on the diffusion of particles in dilute gas–solid flows, and to study the effect of particle–particle collisionon the spatial structure of particles in gas–solid turbulent flow. To reduce the calculation cost, he proposed the utilizationof DSMC method to gas–solid flows, and successfully predicted the particle cluster formation observed in the riser ofcirculating fluidized bed.
His most brilliant achievements were the developments of the DEM-CFD model and simulations. He made novel discrete
simulation models and methods to reproduce a plug flow in a horizontal pipe and fluidized beds. Since then, his model andsimulation method have been widely used for basic studies of dense gas–solid flows and applied for many industrial applications. The total number of citations of his pioneering papers on the DEM-CFD model is over 3400, demonstrating the excellence of his pioneering achievements. He extended his work to thelarge-scale DEM-CFD simulation of fluidized behavior in threedimensional fluidized bed, and to a reduced spring constantmodel for cohesive particles.
For the above achievements, Dr. Tanaka was awarded theBest Paper Award of the Society of Powder Technology, Japan
in 2012, JSME Fellow from JSME in 2011, and DistinguishedAchievement Award of the Information Center of Particle Technology, Japan in 2004, among other awards. Dr. Tanaka is an
acknowledged world leader, especially in the field of numericalmodeling and simulations of gas-solid flows and granular flows,
and has made significant contributions to the development of
powder science and technologies worldwide

At the KONA Award presentation ceremony,
President Yoshio Hosokawa (Left) and 2022 KONA AwardeeProf. Toshitsugu Tanaka (Osaka University, Japan).

Discrete Particle Modeling and Simulation of Granular Flow
─ Pioneering development of numerical prediction of granular flow and gas-solid flow ─
DEM + CFD

Transport

Collision(Hard-particle model)

Contact(Soft-particle model)

Turbulence
Difusion
modification

Clustering

Deterministic
Stochastic (DSMC)
Linear
Hertzian

Soft springmodeling

Fluidization
Heat exchange

MixingSegregation
Aggregation

From analysis to prediction
Selected research achievements for the KONA Award 2022 (Prof. Toshitsugu Tanaka): Development
of discrete particle modeling and simulations of gas-solid flows and granular flows.

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Information Article

General Information
History of the Journal
The KONA journal (currently called “KONA Powder andParticle Journal”) was first published as an open-accessed scientificjournal in 1983 by the Council of Powder Technology, Japan (CPT),
which was established in 1969 by Hosokawa Micron Corporation
as a nonprofit organization to promote powder technology, in order
to introduce excellent Japanese papers to the world. From issue
No.8, the CPT changed its editorial policy to internationalize the
KONA journal and to incorporate papers by authors throughout theworld. In response to this change, three editorial blocks have beenorganized worldwide: Asian–Oceanian, American and European–
African. Issues from No.1 (1983) to No.12 (1994) of the KONA
journal were published by the CPT, and issues from No.13 (1995)
and onwards by the Hosokawa Powder Technology Foundation.
The policy and system have not changed even after the Hosokawa
Foundation took over the publication from the CPT. From issue
No.38 (2021), the KONA journal primarily publishes review articles
by leading researchers, including established and recently
Ph.D.-awarded young researchers in the field.
Aims and Scope
KONA publishes review and original research papers in a broad
field of powder and particle science and technology, ranging fromfundamental principles to practical applications. Papers on criticalreviews of existing knowledge in special areas are particularly 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
To submit your paper, please use the online Editorial Manager®
for KONA Online Paper Submission and Peer Review System. For
further information, please visit the journal’s website at https://www.kona.or.jp/jp/journal/info.html
Publication in KONA is free of charge.
Publication Schedule
KONA is an annual publication with an expected release datearound January 10th.
Subscription
KONA is distributed for free to senior researchers at universities
and laboratories, as well as to institutions and libraries in the field
throughout the world.
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 professional English editing services toproofread their manuscripts.

(2) Data Repository Information

In order to promote open data discoverability and use of research
outputs, this journal encourages authors to submit data files supporting
their manuscript work, which can be deposited in the journal’s
J-STAGE Data site <https://jstagedata.jst.go.jp/kona> after acceptanceof 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.
For questions or more information, please contact the KONA
Editorial Office (contact_zainq@hmc.hosokawa.com).

(3) Manuscript format• Electric files should be submitted to the Editorial Secretariat
via Online Submission System. Author’s short biography withless than 100 words per person 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, (data availability statement), (acknowledgement), (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 research 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 (https://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 et al., 1995; Tsuji, 1992a, 1992b,
1993). Mori and Fukumoto (2002) have recently 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 https://www.kona.or.jp286

(HPTF)
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1. Granting the research projects at the universities in Japan
Funding research projects Supporting Ph.D. students  
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on Powder Technology

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Granting andAwarding

1. KONA Powder and Particle Journal   
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