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.
This paper describes the reviews of the recent works in analysis, modeling, and simulation of the motion of a non-spherical particle. The motion of the non-spherical particles was analyzed in detail by means of a fully resolved direct numerical simulation (DNS). From the DNS data, the PDF-based drag coefficient model was proposed and applied to the particle dispersion simulation in an isotropic turbulent flow to assess the effect of the particle shape by comparing it with the motion of a spherical particle. Moreover, the model was applied to a large-eddy simulation (LES) of particle dispersion in an axial jet flow and validated by comparing it with the experimental data. Results showed that the effect of the particle shape was clearly observed in the characteristics of the particle dispersion in the isotropic turbulent flow by evaluating the deviation from the Poisson distribution (D number) and the radial distribution function (RDF). It was found that the non-spherical particle’s representative Stokes number becomes larger as the sphericity increases. Furthermore, it was also revealed that the effects of the particle size distribution and the shape observed in the experiment was precisely captured by the LES that coincided with the trend found in the isotropic turbulent flow.
This research work is a significant step toward further understanding of the β-σ two-fluid model for the simulation of fully-suspended slurry flows in pipeline systems, with the goal of enhancing its potential for scientific research and engineering applications. Particularly, the focus of the study is the characterization and handling of the two main empirical coefficients of the model, namely, β and σ, which require case-specific tuning based on a given set of experimental data. Reference is made to the relevant case of slurry transport in horizontal pipes with infinite length. The influence of β and σ on different features of the fluid dynamic solution has been extensively investigated, considering also the role played by the specific testing conditions. Based on these findings, a procedure for determining appropriate values of β and σ has been developed, which requires only two experimental measurements, namely the concentration profile from a test at moderate slurry concentration, and the hydraulic gradient from another test in which the same slurry flows at high concentration. The procedure has been satisfactorily tested against published experimental data on pipe transport of fine glass bead and sand slurry.
Higher performance is constantly required in rare earth permanent magnets, which are an indispensable component of the motors of electric vehicles. When producing sintered magnets, advanced structural control is necessary in the powder metallurgy process in order to achieve high performance. Especially in recent years, it has become important to develop processes for Sm-Fe-N magnets and metastable phase magnets as next-generation magnets to replace the Nd-Fe-B magnets. Because the crystal grain refinement of sintered magnets is most effective for improving coercivity, production methods for raw powders have evolved from the traditional pulverization to chemical synthesis approaches, and as a result, a submicron-sized Sm-Fe-N powder with huge coercivity has been developed. State-of-the-art physical synthesis methods have also been applied successfully to the synthesis of nanopowders. Since control of the grain boundary is very effective in Nd-Fe-B magnets, this approach has also been evolved to Sm-Fe-N magnets by nano coating. On the other hand, since technologies for crystalline orientation control and high-density sintering are indispensable for improvement of remanence, new low-thermal load consolidation techniques such as spark plasma sintering are being developed for Sm-Fe-N magnets and metastable phase magnets in order to overcome the inherent low thermal stability of these materials.
The aim of this theoretical investigation is to seek any similarities between the Austin model and the Kotake–Kanda (KK) model for the specific breakage rate function in the population balance model (PBM) used for tumbling ball milling and assess feasibility of the KK model for scale-up. For both models, the limiting behavior for small particle size-to-ball size ratio and the extremum behavior for a given ball size are described by “power-law.” Motivated by this similarity, specific breakage rate data were generated using the Austin model parameters obtained from the lab-scale ball milling of coal and fitted by the KK model successfully. Then, using the Austin’s scale-up methodology, the specific breakage rate was scaled-up numerically for various mill diameter scale-up ratios and ball sizes of 30–49mm and coal particle sizes of 0.0106–30mm. PBM simulations suggest that the KK model predicts identical evolution of the particle size distribution to that by the Austin model prior to scale-up. Upon scale-up, the differences are relatively small. Hence, modification of the exponents in the Austin’s scale-up methodology is not warranted for scale-up with the KK model. Overall, this study has established the similarity of both models for simulation and scale-up.
Lithium-ion batteries (LIBs) provide the largest source of electrical energy storage today. This paper covers the use of comminution processes and, thus, crushers and mills for particle breakage and dispersing, as well as classifiers for particle separation within the process chain, from the raw material to the final lithium battery cell and its recycling at end of life. First of all, the raw materials for the active material production have to be produced either by processing primary raw materials, or by recycling the spent lithium batteries. The end-of-life battery cells have to be shredded, the materials separated and then milled in order to achieve the so-called black mass, which provides a secondary material source with very valuable components. Using these materials for the synthesis of the cathode active materials, milling has to be applied in different stages. The natural graphite, increasingly used as anode material, has to be designed in mills and classifiers for achieving targeted properties. Nanosized silicon is produced by nanomilling using stirred media mills as a primary option. Conductive additives for LIBs, like carbon black, have to be dispersed in a solvent with machines like planetary mixers, extruders or stirred media mills. In the future, mechanochemical synthesis of solid electrolytes will especially require additional application of comminution processes.
Photocatalytic H2O2 production based on graphitic carbon nitride (g-C3N4) materials has been attracting increasing attention. However, it is difficult to reveal the inner relationships among the structure, properties and performance of a g-C3N4-based photocatalyst by simply summarizing preparation methods, properties and performances in previous works. In this review, the three most important issues for improving H2O2 generation based on the band diagram and physicochemical properties of pristine g-C3N4 are proposed. Improvement of the charge separation, promotion of the light absorption and introduction of active sites for 2e– oxygen reduction reaction to suppress side reactions are the most three attractive strategies for enhancing the activities. Following discussion of these strategies, representative functionalization methods are summarized on the basis of the most desired properties for improving the photocatalytic activities for H2O2 production. Other influence factors for improving H2O2 production such as addition of electron donors and adjustment of pH value of the solution are also discussed. Future challenges for photocatalytic H2O2 based on g-C3N4 materials are also summarized to provide future directions in this field.
To create advanced materials with minimal energy consumption and environmental impacts, a green and sustainable powder processing technology is essential. The authors have developed this technique based on powder grinding technology. In this paper, the authors will explain the recent progress of the smart powder processing, and its applications. Firstly, particle bonding process, and novel one-pot processing methods to synthesize nanoparticles, to create nanostructured composite granules and to form nano-porous films on substrates in dry phase will be discussed. Their applications on the advanced material fabrications contributing to the sustainable economy will also be explained. Then, the use of grinding technology in wet processing to synthesize nanoparticles and control their morphology will be explained. Smart powder processing can be a foundation to move forward material development technologies and create many more high-quality advanced materials in the future.
Particulate and surfactant systems are an integral part either in processing or product lines in essentially every major industry, including Energy and Minerals, Pharmaceutical, Agriculture & Food, Microelectronics, Healthcare, Cosmetics, Consumer Products, and Analytical Instrumentation & Services. In most applications, product and process specifications depend on the synergistic or competitive interactions between the particles and reagent schemes. The primary goal of our research efforts has been to generate the structure-property-performance correlations-based knowledge and technology platforms for industry to develop more sustainable products and processes. Engineering the physicochemical/mechanical properties of surfaces, particles, and self-assembling surfactant systems enables their enhanced performance in industrial applications. Specifically, understanding and control of the nano and atomic-scale forces between particles and synthesis of functionalized particles form the foundation for targeted contributions in biomedical, advanced materials and minerals, sensor, and coating technologies. A synoptic overview of selected projects is presented in this review. Additional details can be found in the topic-specific references listed at the end of this manuscript.
Advances in nanotechnology have changed conventional concepts in materials science. This has aloso strongly influenced natural biomass products with hierarchically built-up structures. In general, hierarchical structures in bio-based materials are built up by molecular self-assembly, followed by nanoassembly to form higher-level structures. Key to each step is the formation of interactions at each individual scale. Nature usually achieves such fabrication through a bottom-up process. However, fabrication can also be achieved through a top-down process, with various such downsizing methods now in development. This review article aims to describe trends in nanofiber technology among downsizing processes applied to cellulose as a representative biomass, ranging from fundamentals to recent techniques. The advantages of our recently developed technique, nanopulverization by aqueous counter collision, are also discussed. This method successfully decomposes interactions selectively without damaging the molecular structure, finally liberating components of various sizes into water to provide a transparent and homogeneous component–water system. As nanocellulose research is a broad area involving various fields, the cited references are limited to the scope of the author’s knowledge.