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Preparation of Mesoporous Silica Materials MCM-41 and SBA-15 Using a Template Extraction Method with Supercritical CO2

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Kagaku Kogaku Ronbunshu 2025年第3期51卷 58-63页

作者： Ushiki, Ikuo Oka, Haruna Hironaka, Mizuki Tsuji, Hiromu Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1‒4‒1 Kagamiyama Hiroshima Higashi Hiroshima-shi739‒8527 Japan

In this study, mesoporous materials MCM-41 and SBA-15 were prepared by template extraction using supercritical carbon dioxide (CO2). When using the supercritical CO2 method, the extraction ratio of the template agent for MCM-41 reached 97.8%, and silica with a high specific surface area, high pore volume, and uniform pore size was obtained compared to the conventional calcination method. For SBA-15, the template extraction ratio using the supercritical CO2 method was only 88%, but the supercritical CO2 method was superior to the calcination method in maintaining the uniformity of the pore size distribution. Furthermore, the pressure dependence of the template agent extraction ratio in the supercritical CO2 method was investigated, and it was found that the extraction ratio showed a maximum around 8 to 10 MPa, which could be interpreted in terms of the vapor‒liquid equilibrium relationship between CO2 and methanol. Based on the above results, it was suggested that the supercritical CO2 method is an extremely effective method for maintaining the pore structure and producing high-performance mesoporous silicas (MCM-41 and SBA-15) compared to conventional methods. © 2025 The Society of chemical Engineers, Japan.

关键词： Mesoporous materials

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Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions

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Journal of Cheminformatics 2024年第1期16卷 11页

作者： Chen, Lung-Yi Li, Yi-Pei Department of Chemical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan Taiwan International Graduate Program on Sustainable Chemical Science and Technology (TIGP-SCST) No. 128 Sec. 2 Academia Road Taipei 11529 Taiwan

In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 ^∘C of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering. Scientific contribution: The combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation. Graphical Abstract: [Figure not available: see fullt

关键词： Multi-label classification Multi-task modeling Reaction condition Recommendation system

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Biomass-Based Discrete Furan Oligomers as Materials for Electrochromic Devices

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ACS Sustainable Chemistry and engineering 2024年第1期12卷 459-469页

作者： Atayde, Eduardo C. Fong, Guan-Lun Yeh, Jyun-Yi Matsagar, Babasaheb M. Wang, Yu-Cheng Chen, De-Chian Peng, Chi-How Li, Yi-Pei Yamauchi, Yusuke Ho, Kuo-Chuan Wu, Kevin C.-W. Molecular Science and Technology Program Taiwan International Graduate Program (TIGP-MST) Academia Sinica No. 128 Sec. 2 Academia Road Taipei 11529 Taiwan Department of Chemistry National Tsing Hua University No. 101 Sec. 2 Kuang-Fu Road Hsinchu 30013 Taiwan Department of Chemical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan Sustainable Chemical Science and Technology Program Taiwan International Graduate Program (TIGP-SCST) Academia Sinica No. 128 Sec. 2 Academia Road Taipei 11529 Taiwan School of Chemical Engineering and Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensl Brisbane 4072 QLD Australia Department of Materials Process Engineering Graduate School of Engineering Nagoya University Nagoya 464-8603 Japan Institute of Polymer Science and Engineering National Taiwan University No.1 Sec. 4 Roosevelt Rd. Taipei 10617 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University No. 135 Yuandong Rd. Taoyuan 320 Taiwan

Electrochromic devices (ECDs) are materials that can undergo reversible changes in transmittance when exposed to different applied potentials. While many conjugated heterocyclic oligomers, such as oligothiophenes, have been developed for this purpose, there is no reported study on the use of discrete oligomeric furans as ECD materials. In this work, we introduce a furan trimer that can be utilized as a discrete electrochromic material, which transitions from light yellow to red and has a very high coloration efficiency of 1940 cm²/C at 507 nm. We propose that the reversible π-stack formation of the oligomer radical cations is behind this phenomenon, as evidenced by both experimental and computational results. The derivability of this oligomer from biomass and its facile synthesis make it an interesting alternative for other existing organic electrochromic materials. © 2023 American chemical Society

关键词： electrochromism furan oligomer hydroxyalkylation−alkylation reaction solvatochromism π-stacks

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3d Fluid-Particle Interaction Dynamics and Filtration Performance of Realistic Fibrous Filters Using Deep Learning and X-Ray Computed Tomography Images

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SSRN 2025年

作者： Hada, Kodai Shirzadi, Mohammadreza Fukasawa, Tomonori Fukui, Kunihiro Ishigami, Toru Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8527 Japan

This paper presents a framework for analyzing the filtration performance and fluid–particle interaction inside fibrous filters using high-resolution computational fluid dynamics (CFD), deep learning convolutional neural networks (DCNNs), and X-ray computed tomography (CT) images of realistic filter microstructures. X-ray CT images of ten filter microstructures were obtained using an advanced image processing technique, and a high-resolution three-dimensional (3D) CFD model was used to predict the distribution of velocity and pressure around filter microstructures to create a database. A 3D flow surrogate model was then trained using ground-truth CFD data based on two different DCNN models with UNet and MSNet architectures. The 3D flow surrogate model was coupled with a particle model based on the discrete phase model (DPM) to calculate the filter performance in terms of the filter pressure drop, collection efficiency, and quality factor. The accuracies of UNet-DPM and MSNet-DPM were evaluated in detail. It was shown that the 3D flow surrogate model could accurately predict the distribution of flow parameters (velocity and pressure) for different microstructures with a computational speed three orders of magnitude faster than that of the CFD model. The generalizability of the models was evaluated in detail. It was found that the coupled 3D flow surrogate models could predict the particle dynamics around the fiber with some overestimations in the prediction of the collection efficiency and quality factor. Overall, UNet-DPM showed better generalization performance. The developed framework was shown to be a useful machine learning-based tool for the prediction of realistic filters in a simple manner using only a few X-ray CT images, which makes it feasible for industrial applications for design and optimization purposes. © 2025, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Impact of Morphological and Structural Properties of Submicron Core-Shell Fe-Silica Particles on High-Current Power Converter Performance in Electronics

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ACS Omega 2025年第22期10卷 23387-23396页

作者： Ratnasari, Delyana Septiani, Eka Lutfi Nandiyanto, Asep Bayu Dani Cao, Kiet Le Anh Hirano, Tomoyuki Okuda, Nobuhiro Matsumoto, Hiroyuki Ogi, Takashi Chemical Engineering Program Department of Advanced Science and Engineering Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Hiroshima Higashi-Hiroshima 739-8527 Japan Program Studi Teknik Kimia Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam Universitas Pendidikan Indonesia Jl. Setiabudhi No. 229 West Java Bandung 40154 Indonesia Materials Research Center Technology & Intellectual Property HQ TDK Corporation 570-2 Matsugashita Minami-Hadori Chiba Narita 286-8588 Japan

As the demand for energy-efficient solutions grows in industries such as electric vehicles and renewable energy systems, the need for high-current power converters, powder core inductors, has become increasingly critical. These converters are essential for managing the flow of energy while maintaining efficiency under demanding conditions. However, achieving the necessary performance, particularly in terms of energy storage and minimizing energy losses, requires advanced materials with tailored magnetic properties. This study addresses these needs by introducing a novel class of magnetic materials, i.e., submicrometer-sized silica-coated iron (Fe@SiO₂) particles. These particles are produced through a one-step aerosol process, enabling precise control over particle morphology, size, and structural characteristics. The tuned Fe@SiO₂ particles demonstrated improved packing density and saturation current as well as reduced eddy current losses, significantly boosting the stability and efficiency of power converters to meet the demands of modern high-current electronic systems. This research highlights the importance of advanced magnetic materials in the ongoing evolution of power electronics, offering new possibilities for more efficient and reliable power conversion systems in a range of applied electronic technologies. © 2025 The Authors. Published by American chemical Society.

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Effect of SiO2 Content on Photocatalytic Activities of ZnO/Ag/SiO2 Nanocomposites Prepared by Spray Pyrolysis 3

Effect of SiO2 Content on Photocatalytic Activities of ZnO/A...

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3rd International Conference and Exhibition on Powder Technology 2021, ICePTi 2021

作者： Riwayati, I. Winardi, S. Madhania, S. Machmudah, S. Shimada, M. Kusdianto, K. Chemical Engineering Department Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 4-1 Kagamiyama 1-chome Higashi-Hiroshima Hiroshima739-8527 Japan

Spray pyrolysis is a method for preparing nanocomposite materials in the gas phase. Due to the ease with which the temperature in the furnace can be controlled, this method can produce more uniform nanoparticle sizes. This method can be used to synthesize ZnO nanoparticles. Additionally, due to the photocatalytic properties of the synthesized particles, they are used to decompose synthetic dye waste. Due to electron-hole recombination, ZnO's photocatalytic properties are limited. A doping process with various metals was used to enhance the photocatalytic properties of ZnO nanoparticles. Ag is one of these metals. The addition of Ag metal to ZnO nanoparticles results in the formation of nanocomposites with preferable photocatalytic properties. However, the addition of Ag results in the aggregation of the obtained particles. As a result, the ZnO particles must be trapped in a matrix (for example, SiO2) to inhibit particle growth. This research aims to determine the effect of the SiO2 composition on the characteristics and photocatalytic properties of ZnO/Ag/SiO2 nanocomposite synthesized via spray pyrolysis. SEM (Scanning Electron Microscope) analysis was used to determine the morphology of the nanocomposites, XRD (X-ray Diffraction) analysis was used to determine the purity and degree of crystallization, FTIR (Fourier-transform Infrared) analysis was used to determine the functional groups, and UV-Vis Spectrophotometer analysis was used to determine the photocatalytic characteristics. According to the analysis results, the optimal photocatalytic activity was obtained at 1% SiO2, with a degradation rate of up to 47% of methylene blue and a rate constant (k) of 0.0086 L/min under UV light irradiation. © 2011 Institute of Physics Publishing. All rights reserved.

关键词： ZnO nanoparticles

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Conductive RuO₂ binders enhance mechanical stability of macroporous Nb-SnO₂ particles as cathode catalyst supports for high-performance PEFCs

RSC Applied Interfaces

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RSC Applied Interfaces 2025年第3期2卷 795-807页

作者： Ho, Thi Thanh Nguyen Hirano, Tomoyuki Takano, Aoi Miyasaka, Syu Tanabe, Eishi Maeda, Makoto Septiani, Eka Lutfi Cao, Kiet Le Anh Ogi, Takashi Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashihiroshima Hiroshima 739-8527 Japan Cataler Corporation 7800 Chihama Shizuoka Kakegawa 437-1492 Japan Hiroshima Prefectural Institute of Industrial Science and Technology 3-10-31 Kagamiyama Higashi Hiroshima Hiroshima 739-0046 Japan Natural Science Center for Basic Research and Development Hiroshima University 1-4-1 Kagamiyama Higashihiroshima739-8527 Hiroshima 739-8527 Japan

Niobium-doped tin oxide (NTO) particles with a macroporous structure have been developed as catalyst supports for enhancing the durability and performance of polymer electrolyte fuel cells (PEFCs). This macroporous architecture improves the mass transport properties of the electrode. However, their weak mechanical strength can cause structural collapse, thereby limiting single-cell performance at high current densities. In this study, we employed ruthenium oxide (RuO₂) as a binder to integrate with macroporous NTO particles (denoted as NTO/RuO₂). This approach simultaneously enhanced the electrical conductivity and mechanical strength of the catalyst supports, improving the performance of PEFCs. Incorporating RuO₂ binders effectively stabilized the macroporous structure, and the NTO/RuO₂ particles with 50 wt% RuO₂ loading maintained their structural integrity under high compression pressures of up to 40 MPa. The aggregated NTO/RuO₂ particles containing 50 wt% RuO₂ binder also exhibited higher conductivity than the NTO aggregates without RuO₂ binder, which was attributed to the conductive network formed by RuO₂. Importantly, the membrane electrode assembly (MEA) fabricated with macroporous NTO/RuO₂ particles containing 20 wt% RuO₂ binder achieved a maximum current density of 2.16 A cm⁻² at 60 °C and 100% relative humidity (RH), outperforming the MEA utilizing Carbon Vulcan as the support (2.06 A cm⁻²). Furthermore, the enhanced hydrophilic properties of the RuO₂ binder improved water retention at the catalyst layer/membrane interface, thus promoting membrane hydration and overall cell performance at a high temperature of 80 °C and a low RH of 30%. © 2025 RSC.

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Challenges in Developing MOF-Based Membranes for Gas Separation

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Langmuir 2023年第8期39卷 2871-2880页

作者： Kang, Dun-Yen Lee, Jong Suk Department of Chemical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Center of Atomic Initiative for New Materials National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Department of Chemical and Biomolecular Engineering Sogang University Baekbeom-ro 35 Seoul4107 Korea Republic of Institute of Emergent Materials Sogang University 35 Baekbeom-ro Seoul4107 Korea Republic of

Metal-organic frameworks (MOFs) are promising candidates for membrane gas separation. MOF-based membranes include pure MOF membranes and MOF-based mixed matrix membranes (MMMs). This Perspective discusses the challenges for the next stage of the development of MOF-based membranes based on research conducted in the past decade. We focused on three major issues associated with pure MOF membranes. First, some MOF compounds have been overstudied, despite the availability of numerous MOFs. Second, gas adsorption and diffusion in MOFs are often independently investigated. The correlation between adsorption and diffusion has seldom been discussed. Third, we identify the importance of characterizing the gas distribution in MOFs to understand the structure-property relationships for gas adsorption and diffusion in MOF membranes. For MOF-based MMMs, engineering the MOF-polymer interface is essential for achieving the desired separation performance. Various approaches to modify the MOF surface or polymer molecular structure have been proposed to improve the MOF-polymer interface. Herein, we present defect engineering as a facile and efficient approach for engineering the MOF-polymer interfacial morphology and its extended application for various gas separations. © 2023 American chemical Society.

关键词： Organometallics

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Open-Air Plasma Deposition of Polymer-Supported Silica-Based Membranes for Gas Separation

SSRN

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SSRN 2022年

作者： Kawasaki, Mitsugu Nagasawa, Hiroki Kanezashi, Masakoto Tsuru, Toshinori Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-hiroshima Hiroshima739-8527 Japan

In this study, we developed an open-air atmospheric-pressure plasma-enhanced chemical vapor deposition (AP-PECVD) as a scalable technique to fabricate polymer-supported silica-based membranes for gas separation. Using the open-air AP-PECVD, we achieved a low-temperature deposition of silica-based molecular sieve membranes onto porous polymeric support in a system that can operate under open-air conditions. We investigated the effect of deposition conditions, such as precursor concentration and deposition temperature, on the membrane structure and permeation property. It was revealed that precursor concentration plays a vital role in determining membrane properties. The membranes prepared with low precursor concentration had an inorganic structure with granular morphology, resulting in poor permselective performance. In contrast, deposition at high precursor concentration led to the formation of a continuous layer with a silicone-like structure. By optimizing the precursor concentration, a membrane having a He permeance of 7.25 ×10-8 mol/m2/s/Pa with a He/SF6 permeance ratio of 171 can be fabricated at room temperature. The deposition at elevated temperature was also effective in improving the permselective performance. By increasing the deposition temperature to 150°C, the permselective performance of the membrane was improved to a He permeance of 7.7×10-8 mol/m2/s/Pa and He/SF6 of 434. The present study demonstrates that open-air AP-PECVD is promising as a scalable technique to fabricate polymer-supported silica-based membranes with high productivity. © 2022, The Authors. All rights reserved.

关键词： Silica

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Application of Deep Learning Neural Networks for the Analysis of Fluid-Particle Dynamics in Fibrous Filters

SSRN

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SSRN 2022年

作者： Shirzadi, Mohammadreza Fukasawa, Tomonori Fukui, Kunihiro Ishigami, Toru Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi Hiroshima Hiroshima739-8527 Japan

A high-resolution surrogate model based on a deep convolutional neural network (CNN) coupled with a Lagrangian particle tracking method based on the discrete phase model (DPM) was developed to predict the performance of fibrous filters under hydrodynamic and Brownian forces. A group of nonuniformly distributed 27 fibers with independent dimensions and orientations was considered to mimic the fibrous filter cross-section. For CNN training, a dataset of approximately 1000 samples was created using a two-dimensional computational fluid dynamics (CFD) model for laminar incompressible flow. A simulation speedup of three orders of magnitude was obtained by CNN for predicting the velocity components and static pressure distribution within an error range of . As a novel aspect, the physical importance of the uncertainties of the CNN-based flow surrogate model on particle dynamics, specifically in low-velocity regions near the fibers, was quantitatively and qualitatively investigated. The collection efficiency by CNN-DPM for small particle diameters with was overestimated by approximately 3%, while agreeing well with CFD-DPM for larger particles. The overprediction of collection efficiency by CNN-DPM was because of the uncertainty of flow velocity predictions near the fibers, which caused more randomness in the particle velocity. This additional randomness along with the random Brownian motion of particles increases the probability of the particle hit number, consequently increasing the collection efficiency for smaller particles. The overall acceptable accuracy of the CNN-DPM was confirmed, which is suitable for fibrous filter design and optimization purposes that require thousands of simulations. © 2022, The Authors. All rights reserved.

关键词： Convolution

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