@article{MTMT:34122654, title = {Current Progress of Experimental and Simulation Work of Mixing Processes in Particulate Systems}, url = {https://m2.mtmt.hu/api/publication/34122654}, author = {Jin, Xin and Shen, Yansong}, doi = {10.14356/kona.2024015}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, volume = {2024}, unique-id = {34122654}, issn = {0288-4534}, year = {2024}, eissn = {2187-5537}, pages = {151-171}, orcid-numbers = {Shen, Yansong/0000-0001-8472-8805} } @article{MTMT:34575563, title = {Engineering SiO2 Nanoparticles: A Perspective on Chemical Mechanical Planarization Slurry for Advanced Semiconductor Processing}, url = {https://m2.mtmt.hu/api/publication/34575563}, author = {Lee, Ganggyu and Lee, Kangchun and Sun, Seho and Song, Taeseup and Paik, Ungyu}, doi = {10.14356/kona.2025001}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, unique-id = {34575563}, issn = {0288-4534}, abstract = {Chemical mechanical polishing (CMP) is a process that uses mechanical abrasive particles and chemical interaction in slurry to remove materials from the surface of films. With advancements in semiconductor device technology applying various materials and structures, SiO2 (silica) nanoparticles are the most chosen abrasives in CMP slurries. Therefore, understanding and developing silica nanoparticles are crucial for achieving CMP performance, such as removal rates, selectivity, decreasing defects, and high uniformity and flatness. However, despite the abundance of reviews on silica nanoparticles, there is a notable gap in the literature addressing their role as abrasives in CMP slurries. This review offers an in-depth exploration of silica nanoparticle synthesis and modification methods detailing their impact on nanoparticles characteristics and CMP performance. Further, we also address the unique properties of silica nanoparticles, such as hardness, size distribution, and surface properties, and the significant contribution of silica nanoparticles to CMP results. This review is expected to interest researchers and practitioners in semiconductor manufacturing and materials science.}, keywords = {synthesis; Core-shell structure; silica nanoparticles; Surface functionalization; Modification; CMP (Chemical mechanical polishing)}, year = {2023}, eissn = {2187-5537} } @article{MTMT:34050739, title = {Measurement of Fugitive Particulate Matter Emission: Current State and Trends}, url = {https://m2.mtmt.hu/api/publication/34050739}, author = {Cai, Tianyi and Zhou, Wu}, doi = {10.14356/kona.2024008}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, volume = {41}, unique-id = {34050739}, issn = {0288-4534}, abstract = {Fugitive particulate matter (FPM) refers to a mixture of solid particles and liquid droplets that are released into the air without passing through confined flow equipment. These emissions of FPM can originate from natural 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 is difficult to monitor the FPM effectively owing to its dispersive and irregular emissions. Traditional measuring methods for FPM are based on sampling, which is a point monitoring approach and can be timeconsuming. In recent years, several new techniques based on optical principles, image-based processes and low-cost sensors have been developed and applied for FPM measurement, with the advantages of spatial and time resolutions. The current state and future development of FPM measurements are reviewed in this paper.}, keywords = {light scattering; REAL-TIME; Air quality; Low-cost sensor; Image-based measurement; Fugitive particulate matter}, year = {2023}, eissn = {2187-5537}, pages = {42-57} } @article{MTMT:33687380, title = {Performance Testing for Dry Powder Inhaler Products: Towards Clinical Relevance}, url = {https://m2.mtmt.hu/api/publication/33687380}, author = {Maloney, S.E. and Mecham, J.B. and Hickey, A.J.}, doi = {10.14356/kona.2023013}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, volume = {40}, unique-id = {33687380}, issn = {0288-4534}, year = {2023}, eissn = {2187-5537}, pages = {172-185} } @article{MTMT:33888549, title = {Evaluation of a Coating Process for SiO2/TiO2 Composite Particles by Machine Learning Techniques}, url = {https://m2.mtmt.hu/api/publication/33888549}, author = {Kimura, Taichi and Iwamoto, Riko and Yoshida, Mikio and Takahashi, Tatsuya and Sasabe, Shuji and Shirakawa, Yoshiyuki}, doi = {10.14356/kona.2023010}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, unique-id = {33888549}, issn = {0288-4534}, abstract = {In this study, in order to optimize a fabrication process for SiO2/TiO2 composite particles and control their coating ratio (CTi), regression models for the coating process were constructed using various machine learning techniques. The composite particles with a core (SiO2)/shell (TiO2) structure were synthesized by mechanical stress under various fabrication conditions with respect to the supply volume of raw materials (V), addition ratio of TiO2 (rTi), operation time (t), rotor rotation speed (S), and temperature (T). Regression models were constructed by the least squares method (LSM), principal component regression (PCR), support vector regression (SVR), and the deep neural network (DNN) method. The accuracy of the constructed regression models was evaluated using the determination coefficients (R2) and the predictive performance was evaluated by comparing the prediction coefficients (Q2). From the perspective of the R2 and Q2 values, the DNN regression model was found to be the most suitable model for the present coating process. Moreover, the effects of the fabrication parameters on CTi were analyzed using the constructed DNN model. The results suggested that the t value was the dominant factor determining CTi of the composite particles, with the plot of CTi versus t displaying a clear maximum.}, keywords = {SIO2; Neural network; TIO2; Machine learning techniques; coated composite particles; coating ratio}, year = {2023}, eissn = {2187-5537}, pages = {236-249} } @article{MTMT:33002850, title = {Review and Further Validation of a Practical Single-Particle Breakage Model}, url = {https://m2.mtmt.hu/api/publication/33002850}, author = {Tavares, Luis Marcelo}, doi = {10.14356/kona.2022012}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, unique-id = {33002850}, issn = {0288-4534}, abstract = {Particle breakage occurs in comminution machines and, inadvertently, in other process equipment during handling as well as in geotechnical applications. For nearly a century, researchers have developed mathematical expressions to describe single-particle breakage having different levels of complexity and abilities to represent it. The work presents and analyses critically a breakage model that has been found to be suitable to describe breakage of brittle materials in association to the discrete element method, either embedded in it as part of particle replacement schemes or coupled to it in microscale population balance models. The energy-based model accounts for variability and size-dependency of fracture energy of particles, weakening when particles are stressed below this value, as well as energy and size-dependent fragment size distributions when particles are stressed beyond it, discriminating between surface and body breakage. The work then further validates the model on the basis of extensive data from impact load cell and drop weight tests. Finally, a discussion of challenges associated to fitting its parameters and on applications is presented.}, keywords = {FRACTURE; IMPACT; modelling; compression; Discrete element method; particle breakage}, year = {2022}, eissn = {2187-5537}, pages = {62-83} } @article{MTMT:32881641, title = {Progress in Multidimensional Particle Characterization}, url = {https://m2.mtmt.hu/api/publication/32881641}, author = {Frank, Uwe and Uttinger, Maximillian J. and Wawra, Simon E. and Lubbert, Christian and Peukert, Wolfgang}, doi = {10.14356/kona.2022005}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, unique-id = {32881641}, issn = {0288-4534}, abstract = {The properties of particle ensembles are defined by a complex multidimensional parameter space, namely particle size, shape, surface, structure, composition and their distributions. Macroscopic product properties are a direct result of these disperse particle properties. Therefore, the comprehensive multidimensional characterization of particle ensembles is a key task in any product design. However, the determination of complex property distributions is major challenge. We provide a broad overview of the current tools for multidimensional particle characterization. First, the mathematical handling of multidimensional (nD) property distribution is outlined as a necessary framework for the correct handling of nD particle size distributions (PSDs). Then, well-established techniques as well as recent developments with the potential to extract nD property distributions are reviewed. Ex situ imaging techniques like electron tomography or Raman spectroscopy with AFM co-localization, for instance, provide a resolution on the level of single particles but are limited in terms of sample statistics. A particular focus lies therefore on methods in the gas and the liquid phase, which provide multidimensional particle properties either directly or by a combination of one-dimensional techniques.}, keywords = {NANOPARTICLES; Particle characterization; Particle technology; particle properties; multidimensional measurement; particle property distribution}, year = {2022}, eissn = {2187-5537}, pages = {3-28} } @article{MTMT:32790833, title = {From quasi-static to intermediate regimes in shear cell devices: Theory and characterisation}, url = {https://m2.mtmt.hu/api/publication/32790833}, author = {Francia, V. and Yahia, L.A.A. and Ocone, R. and Ozel, A.}, doi = {10.14356/kona.2021018}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, volume = {38}, unique-id = {32790833}, issn = {0288-4534}, year = {2021}, eissn = {2187-5537}, pages = {3-25} } @article{MTMT:31444564, title = {Rheology and Sedimentation of Aqueous Suspension of Na-montmorillonite in the Very Dilute Domain}, url = {https://m2.mtmt.hu/api/publication/31444564}, author = {Adachi, Yasuhisa and Kawashima, Yoko Tsujimoto and Bin Ghazali, Muhamad Ezral}, doi = {10.14356/kona.2020019}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, unique-id = {31444564}, issn = {0288-4534}, abstract = {The scheme of DLVO theory and the concept of fractal structure of flocs applied to the suspension of montmorillonite have revealed out the unique nature of this clay dispersion. in this context, two major regimes are recognized. The first is the electrostatically dispersed regime. And the second is the coagulated regime. In the former, the formation of a diffusive electric double layer (EDL) characterized by reciprocal Debye length measured from the surface of the particle is distinctively important. Intrinsic viscosity with electroviscous effects and yield stress are interpreted by the steric presence of EDL in the latter, the unit of transportation is a coagulated floc with finite cohesive strength. Sedimentation process reflecting these factors is carefully observed to recognize the turbulence generation by the formation of large flocs at the moment of gel collapse. Waiting time prior to gel collapse was found to be determined reflecting the pH-dependent charging behavior. By taking into account the effect pH-dependent charge, the DLVO based two regimes are further categorized into five. The developed tools can be extensively used for the system involved with different ionic species, pH, volume fraction and organic substances.}, keywords = {Na-montmorillonite; Debye length; floc; Electroviscous effect; cohesive strength; sedimentation turbulence}, year = {2020}, eissn = {2187-5537}, pages = {145-165} } @article{MTMT:31413076, title = {Integrating Particle Microstructure, Surface and Mechanical Characterization with Bulk Powder Processing}, url = {https://m2.mtmt.hu/api/publication/31413076}, author = {Pinal, Rodolfo and Carvajal, M. Teresa}, doi = {10.14356/kona.2020008}, journal-iso = {KONA POWDER PART J}, journal = {KONA POWDER AND PARTICLE JOURNAL}, unique-id = {31413076}, issn = {0288-4534}, abstract = {Multiple industrial applications, including pharmaceuticals, rely on the processing of powders. The current powder characterization framework is fragmented into two general areas. One deals with understanding powders from the standpoint of its constituting agents-particles. The other deals with understanding based on the bulk- the collective behavior of particles. While complementary, the two aspects provide distinct pieces of information. Whenever possible, experimental techniques should be used to predict powder behavior. However, it is equally important to recognize that because of the natural complexity of powders, existing predictive approaches will continue to be of limited success for predicting the collective behavior of particles. This article discusses the understanding of powder properties from two perspectives. One is the effect of surface energy at the bulk level (large collections of particles), which controls interactions between powders. This aspect is most useful if studied at the bulk-powder level, not at the single-particle level. Another perspective deals with the physico-mechanical properties of individual particles, responsible for the observed behavior of powders when subjected to mechanical stress from unit operations such as milling. This aspect, which controls the failure mechanism of powders subjected to milling, is most useful if assessed at the single-particle, not at the bulk level. Therefore, in order to fully understand, and eventually predict, or at least effectively model powder behavior, a good-judgement-based combination of microscopic and bulk-level analytical methods is necessary.}, keywords = {particle; NANOINDENTATION; powder; Flowability; Surface composition; Surface energetics}, year = {2020}, eissn = {2187-5537}, pages = {195-213} }