Discrete Element Method (DEM) was proposed by Cundall and Strack (1979) and gained traction in the field of powder engineering during the late 1980s. In the 1990s, various applications of DEM were conducted in the powder technology field, and the capability of DEM for predicting powder behavior was acknowledged. In the 2000s and 2010s, advancements in computing technology and the availability of general-purpose software led to the widespread adoption of numerical simulation as a prevalent tool in industrial applications. The authors have been engaged in discrete particle modeling of gas–solid two-phase flows and the development of DEM–CFD models for the numerical analysis of both dense gas–solid two-phase flows and gas–liquid–solid three-phase flows. Thus, this paper aims to provide a comprehensive description of the pioneering development of discrete particle models and simulations conducted by the authors.

The Discrete Element Method (DEM) is a widely used numerical method for studying, analyzing, and optimizing particulate processes. Based on contact models, DEM can predict the interactions of particles and particles with wall surfaces. Various contact models have been developed that consider various deformation and adhesion behaviors, particle shapes, and surface morphologies. However, accurate prediction of the real behavior requires experimental estimation and calibration of the model parameters for the studied particles. The objective of this paper is to provide a comprehensive overview of recent advancements in particle-property measurement methods, with a particular focus on the parameters of contact models. The measurement methods are classified into different categories, including static and dynamic loading and single particle and particle bed tests, with and without consideration of the surrounding liquid. A range of measurement techniques for estimating elastic and plastic properties, as well as friction and restitution coefficients in both air and liquid environments, are described, including nano- and tribo-indentation, compression, and impact tests. The applicability of these techniques was demonstrated using our own elastic–plastic model. The impacts of various influencing factors on material parameters and contact interactions are discussed. This overview can help identify suitable experimental techniques for calibrating and validating DEM models.

Specific ion effects (SIEs) play a pivotal role in governing particle suspension behavior and manipulation across diverse scientific and industrial fields. This review summarizes the underlying mechanisms of SIEs, focusing on ion accumulation at particle interfaces, Hofmeister series implications, electrokinetic properties, and their impact on coagulation, dispersion, and rheological characteristics. It examines the interplay among ion identity, concentration, and surface properties— including hydrophilicity, hydrophobicity, and surface-charge density— offering critical insights into particle–particle interactions. Particular emphasis is placed on the structural and electrostatic roles of the electrical double layer and the Stern layer. Recent advances in understanding the microscopic origins of SIEs, facilitated by innovative experimental approaches and computational modeling, are also highlighted. This review explores the influence of SIEs on aggregate formation and stability, gelation behavior, and suspension viscoelastic properties. These insights underscore the significance of accounting for SIEs across disciplines such as chemistry, industry, agriculture, environmental science, biology, and medical science, and emphasize the need for continued advances to realize innovative applications and transformative solutions.

Particle characterization underpins a wide range of fields, including mineral processing, materials science, nanotechnology, pharmaceuticals, food processing, agriculture, and environmental applications, by providing essential information on particle size, shape, surface properties, and size distribution within material systems. Particle characterization techniques have undergone a significant transformation over the last 70 years, moving from simple instruments like sieving and sedimentation to AI-driven analytics and novel imaging technologies that examine structures at the nanoscale and even atomic scale. Because they relied on fundamental physical principles and diffraction limitations, early characterization techniques in the 1950s, like gravity sedimentation and optical microscopy, provided limited results. The introduction of light scattering techniques in the 1970s, such as dynamic light scattering (DLS) and laser diffraction, significantly improved accuracy and efficiency of particle size analysis. The development of atomic force microscopy (AFM), electron microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM), X-ray diffraction (XRD), and nanoparticle tracking analysis (NTA) increased the capacity to analyze complex and ultra-small particles precisely. These innovations are driving groundbreaking applications across various domains, including drug delivery systems, sustainable materials development, and environmental monitoring. Despite these technological advancements, challenges such as scalability in industrial plants, environmental interference, and computational accuracy still pose significant hurdles that must be overcome to fully unlock the potential of next-generation characterization techniques. The ongoing convergence of nanotechnology, AI-driven analytics, and cutting-edge imaging technologies holds immense promise for the future of particle characterization and plant-scale applications. This review traces the historical evolution of particle characterization, highlights key technological milestones over different periods, and explores its future as a driving force for innovation in science and technology.

Particle motion affects microscopic momentum, heat/mass transfers, and macroscopic mixing/separation performances in engineering processes ranging from those involving granular flows to multiphase systems. The primary interests of researchers seeking to improve the performances of such processes are finding the best ways to understand particle motion. Several methods have been developed to understand particle motion, among which radiation-based techniques have shown particular advantages because they can non-invasively investigate dynamic particle motion. This article reviews four prominent radiation-based techniques for studying particle motion, including Radioactive Particle Tracking (RPT), Positron Emission Particle Tracking (PEPT), X-ray-based methods and Magnetic Resonance Imaging (MRI). The principles and characteristics of these techniques are explained, and recent advances and applications are reviewed.

Dry powder coating, characterised by the blending of poorly flowing powders with finer coating powders to optimise flowability, represents a sophisticated and evolving approach to powder processing. The optimisation of this method involves precise formulation, carefully combining powders with different particle properties to achieve a desirable blend aimed at enhancing the flow characteristics during the application process. Over the last decade, this field has witnessed increasing activity, focusing on key mixing parameters, such as mixer type and mixing power, as well as understanding the influence of constituent powder characteristics, including size ratio, density, and cohesion. Various techniques have been used to assess the flowability improvement or quantify the degree of coating. This review aims to provide a comprehensive exploration of the literature on powder coating research, highlighting its significance in both academic research and industrial applications. This paper discusses current coating analysis techniques using state-of-the-art equipment and reviews recent findings, particularly the nascent attempts to establish regime maps for dry powder coating.

As an extension of Particle Technology (PT), “nanoparticle technology (NPT)” has emerged as a transformative research endeavor in many countries, including Thailand. NPT has delivered significant strides in research, development, and applications. This review paper explores the advancements and contributions of NPT within Thailand’s scientific community and industrial sectors by analyzing original technical publications on Thailand’s contributions during 2008–2024. Also, a brief mention of the establishment of the Center of Excellence in Particle Technology (CEPT) at Chulalongkorn University and the National Nanotechnology Center (NANOTEC) under the National Science and Technology Development Agency (NSTDA) is given. The key NPT research issues, namely, synthesis methods, characterization techniques, and applications across various disciplines, including medicine, agriculture, and the environment, are selectively reviewed. The paper also discusses collaborative efforts, challenges, and future directions, highlighting Thailand’s roles in the global landscape.

Dose accuracy and precision for pulmonary drug delivery have been core elements of therapy for asthma for 70 years. As the technology has developed, its application has spread to various diseases. For many inhaled products, solid-state chemistry, the nature of the drug particles, and their relationship to other particles in the formulation underpin success in disease treatment. Methods of manufacturing yield unique particle systems whose properties support the range of doses required to treat diseases with low- and high-potency drugs requiring high and low doses, respectively. To ensure the quality of these particulate products, which correlates with safety and efficacy, comprehensive characterization of their physicochemical properties and aerosol performance is required. The delivered dose and aerodynamic particle size distribution are key characteristics related to lung exposure required in clinical efficacy trials for non-communicable, genetic, environmental, and communicable (i.e., infectious) diseases. The breadth of inhaled therapy has increased significantly since the introduction of the initial products in the last century. The desire to treat genetic diseases, such as cystic fibrosis, and the emergence of new approaches to lung therapy during the COVID-19 pandemic are opening up new opportunities in inhaled biologicals that are anticipated to lead to future developments.

Silicon-nitride ceramic substrates exhibit high thermal conductivity, high electrical insulation, high mechanical strength, low expansion. The Cu-clad ceramic substrate is the key material for the encapsulation of insulated gate bipolar transistor modules. In this review, we divide the research of high thermal conductivity silicon nitride ceramics into four stages: 1) exploration of thermal conductivity with different sintering additives; 2) using the anisotropy of silicon nitride grains to improve the thermal conductivity, through the addition of silicon nitride grain seeds combined with tape casting technology, sintered to obtain a thermal conductivity of 155 W/(m·K) silicon nitride, and with the development of the strong magnetic field alignment technology in the later stage, the thermal conductivity can be increased to 176 W/(m·K); 3) the use of sintered reaction-bonded silicon nitride technology to improve thermal conductivity, thermal conductivity can be increased to about 180 W/(m·K); 4) the application of high thermal conductivity silicon nitride ceramic substrate. Finally, future preparation methods and applications of high thermal conductivity silicon nitride ceramics are envisioned.

Members of the American Editorial Board of the KONA Powder and Particle Journal recently convened to consider current and future perspectives in the fields of particle science and technology. This brief overview presents their observations regarding current and emerging areas of interest in selected domains of particle and powder technologies. The major areas of practical significance identified were particle characterization, milling, granular material handling, scale-up considerations, and particle design strategies for environmental and pharmaceutical applications. An attempt has been made to highlight the role of artificial intelligence (AI)/machine learning (ML) in generating robust modeling and simulations for designing particle and powder systems for future process and product innovations.

A comprehensive literature review was conducted to summarize and analyze the mechanisms and applications of air ionization for aerosol removal and inactivation of airborne microorganisms in confined spaces. This review focuses on engineered ionization systems (ionizers) that generate negative ions through corona discharge. Numerous studies have proven that air ionization is effective in removing aerosols and inactivating airborne microorganisms in confined spaces. Multiple physical, chemical, and biological processes may be involved in air ionization, including corona discharge and ion generation, attachment of ions to aerosol particles, transport of ions and aerosols in the air, electrostatic drift, deposition of aerosol particles on surfaces, and inactivation of biological agents if air ionization is used to prevent the spread of airborne pathogens. Each of these processes, as well as their interactions, is extremely complex, and only a limited number of studies have explored the interplays of these processes or attempted to integrate them into models that quantify the fundamental behavior of air ionization.

The valorization of Sargassum into valuable carbon materials is not only driven by the need to manage its massive accumulation on beaches every year, posing ecological, economic, and health challenges, but also by its unique composition rich in carbohydrates and bioactive materials, making it an ideal precursor for carbon-rich materials. Among various thermal transformation methods, hydrothermal carbonization, slow pyrolysis, and microwave-assisted methods (microwave-assisted hydrothermal carbonization and microwave-assisted pyrolysis) have been employed to convert Sargassum into solid carbon-based materials, such as hydrochar, biochar, carbon quantum dots (CQDs), and activated carbon. These materials have demonstrated a significant role in diverse applications, including water purification, soil enhancement, energy storage, catalysis, and biomedical fields. This review discusses these synthesis methods, essential pretreatment processes to prepare Sargassum before the synthesis processes, and posttreatment processes aimed at enhancing the carbon material properties. Finally, the work also discusses the challenges of Sargassum valorization and presents future directions. Given the scale of Sargassum overgrowth affecting coastlines in the Caribbean, Gulf of Mexico, and Atlantic Ocean, this review is significant in highlighting strategies for transforming marine biomass into high-value carbon materials and provides a comprehensive analysis of the existing studies on the conversion of Sargassum into carbon materials.

In powder-based Additive Manufacturing (AM) the precise control of process parameters plays a significant role in the quality and efficiency of the printing process. Among these, the effect of temperature has received less attention in the literature, although it is a significant factor that influences the inter-particle forces and, consequently the powder flow and spreading behaviour of powders. In selective laser sintering (SLS) or selective laser melting (SLM), pre-heating the chamber and powder bed is a required step prior to sintering, hence, the temperature can significantly influence the layer adhesion and spread quality. In this context, the present study explores the effect of elevated temperature on the flow and spreading behaviours of AlSi10Mg powders. The flow properties of two different grades of aluminium alloy powders are characterised using the Carney and Hall flow tests, angle of repose and shear test techniques at different temperatures and correlated with the spreading behaviour at elevated temperatures, measured using the spreading rig with a heated bed developed at the University of Leeds. This study revealed that at elevated temperatures the spreadability of AlSi10Mg powders worsens because of changes in interparticle forces and particle surface interactions.

Metal oxide nanoparticles (NPs) are extensively employed in the biomedical, environmental, and industrial domains due to their unique physicochemical properties. However, concerns regarding their potential cytotoxicity require the development of accurate predictive models to assess nanoparticle safety. In this study, we present a machine learning-based framework for predicting the toxicity of metal oxide NPs using curated physicochemical descriptors. Data were systematically extracted and structured from 140 peer-reviewed publications, focusing on four representative metal oxide nanoparticles (ZnO, AgO, CuO, SiO₂). To ensure accessibility and consistency, the dataset was structured using a Large Language Model (LLM) API and designed to be well-balanced and minimally correlated. The maintenance of a low correlation between features (average Pearson correlation = 0.19) was prioritized to reduce redundancy and improve the interpretability of the model results. Feature selection and Principal Component Analysis (PCA) confirmed that a subset of physical descriptors effectively captured toxicity-related trends. The optimized Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) models achieved predictive accuracies of 77 % and 81 %, respectively, without overfitting. In addition, a synthetic dataset was generated to investigate the joint effects of core size and exposure dosage on toxicity probability. Overall, this study aims to provide a predictive approach framework for nanotoxicity assessment that might offer guidance for the rational design of safer nanoparticles.

Uniaxial or one-dimensional compression is widely used in granular material processing to produce granular compacts and is a key experimental method for evaluating the compressibility of granular materials. In this study, the granular micromechanics approach (GMA) was applied to simulate the one-dimensional uniaxial compression behavior of granular packing. GMA offers a continuum modeling paradigm that connects the macroscale response to the grain-scale mechanisms. To describe the stiffening behavior that elasto-frictional granular materials exhibit in confined compression, the elastic energy functional and dissipation potential were modified from previous versions of GMA-based models. The elastic energy of a grain pair was formulated to include a higher-order dependence on the normal relative displacement of the grain pair. The dissipation potential was also modified by incorporating coupling between plastic parameters that characterize plastic deformation accumulation as the grain-pair experiences compression and/or tension during loading process. The modified model, derived in the framework of geometrically nonlinear deformations, was then applied to replicate experimentally measured 1D compression behavior of granular materials undergoing large compression. The results reveal nearly universal scaling with respect to the model parameters for predicting the behavior of granular materials with different particle types or initial densities. A parametric study was conducted to determine the evolution of the microscale stored elastic energy and dissipation parameters in terms of grain-pair directions. In addition, this paper provides a brief literature review of experimental observations and modeling approaches related to powder compaction behavior.

This study explores an efficient approach to modelling the flow of particles in wet granular systems using the Discrete Element Method (DEM). Typically, when particles move in a viscous fluid, DEM is coupled with Computational Fluid Dynamics (CFD) or Smooth Particle Hydrodynamics (SPH) methods to capture both particle and fluid motion. However, the computational expense and time required for one- or two-way coupled simulations, such as DEM–CFD or DEM–SPH, can be significant. In this research, a lubrication approximation is introduced to address fluid viscosity within DEM, which is particularly suitable for dense granular systems where viscous forces play a dominant role. DEM simulations incorporating the lubrication approximation were compared with in situ data obtained from Positron Emission Particle Tracking (PEPT) experiments. These experiments involve a cylindrical setup of radius R = 230 mm and length L = 200 mm, filled with 10 mm spherical glass beads at a 50 % fill fraction, and various mixtures of water and glycerol (60 %, 75 %, and 90 % by weight) as the fluid phase. Simulations are conducted within the LIGGGHTS–DEM framework, and detailed comparisons with PEPT data assess the suitability of the lubrication approximation for rotating drum systems. The analysis of tangential velocity profiles across different Stokes numbers reveals the applicability of the same constitutive equation for modelling both the PEPT and DEM data within a specified viscosity range, with the exception of the highest viscosity. This understanding is crucial for interpreting the flowing layer dynamics and optimising the simulation parameters for accurate predictions.

Over the past decades, the prevalence of machine learning (ML) methods has made the development of ML-based constitutive models for granular materials undoubtedly a popular subject. Numerous studies have been made to feature the loading path or history-dependent stress–strain response of granular media using neural networks. In this work, a novel finite element method (FEM)–ML multiscale approach was developed by incorporating internal variables to improve the simulation accuracy of 3D history-dependent granular materials for the first time. To this end, a surrogate constitutive model based on the single-step-based multi-layer perceptron (MLP) neural network was used to replace representative volume element (RVE) simulations conducted by the discrete element method (DEM) in the multiscale FEM–DEM approach. Although the prediction principle of the MLP aligns with the FEM algorithm, artificially added internal variables are required to differentiate the loading history. To address this issue, history variables associated with the Frobenius norm are proposed to be fed into the MLP coupled with the strain tensor to extract the history-dependent behaviour of granular assemblies. The developed FEM–ML approach was demonstrated in 3D conventional triaxial compression (CTC) simulations. Compared to the multiscale FEM–DEM approach, the proposed FEM–ML method exhibits a significantly improved computational efficiency.

Silos are widely used structures designed with hoppers or as flat bottoms to store different types of particulate materials (pellets, grains, powders, etc.). Although they have been used in industry for a long time, further research is needed. This article provides design recommendations that prevent structural failures and ensure efficient discharge. The discrete element method (DEM) has been commonly used in recent decades in silo/bin research to simulate the behavior of stored materials. In this study, DEM was used to investigate the relationship between the slot width and particle size on the discharge behavior in a flat-bottom silo using particle shape configurations with different elongations and aspect ratios. Sixteen DEM models were developed using the multi-sphere approach to obtain the particle configurations, ranging from single spheres to 4 × 4 × 4 clusters of spheres arranged along orthogonal axes (x, y, and z). The simulations involved between 459 and 29,383 particles with a total mass of 40 kg in each model. The established comparisons include information on the bulk density, velocity profiles, residual masses, and discharge rates.

Aluminum powder is an attractive fuel, especially for micropropulsion, due to its wide availability, high volumetric and gravimetric energy densities, and environmental friendliness. For safety reasons, the powder is preferably stored and supplied to a combustion chamber separately from the oxidizer. However, powder supply systems for micropropulsion applications are technologically immature. To improve this situation, a powder supply system was developed, and the impacts of important design factors on the supply characteristics were studied. This included operating modes, carrier gas pressures, gas line conductances, and powder line lengths. The results showed that, when compared to continuous operation, pulsed operation achieved 1) significantly higher powder flow rates and 2) significantly higher ratios of powder to carrier gas mass flow rates, φ. Furthermore, higher carrier gas pressures can increase the powder mass flow rates, but this reduces φ. The gas line conductance had a lesser impact on the powder flow rates than the pressure, but increasing the former also increased the powder flow rates without significant impact on φ. The powder line length had a negative impact on the powder flow rate, but no significant impact on φ. These results will help the future development of micropropulsion systems that use powder fuels.

After 43 years of pioneering publication, the KONA Powder and Particle Journal will conclude with Issue No. 43 in 2026. Launched in 1983 as “KONA Powder Science and Technology in Japan”, it became the first English-language powder technology journal published in Japan, bridging Japanese innovation with global research communities. Throughout its remarkable journey, KONA evolved from a Japan-focused publication to an internationally recognized Diamond Open Access journal, adapting to digital transformation while maintaining its core mission of advancing powder and particle science. This retrospective chronicles the major milestones, innovations, and collaborative efforts that established KONA’s distinguished legacy in scientific publishing.

The cover image illustrates the concept of “Mountain Range of Powder Technologies”, originally propounded by Mr. Masuo Hosokawa, then President of Hosokawa Micron Corporation, who launched the publication of this KONA journal in 1983 to promote powder science and technology worldwide. The image was created using the latest AI technology, with pixels composed of various powder-related machines and equipment. The mountains symbolize individual powder unit operations—such as grinding, classification, mixing, drying, and characterization—interconnected through system engineering technologies. These operations are scaled up for practical applications across a broad spectrum of industrial sectors, environmental solutions, and related fields.