Shiro Yoshikawa 研究室

AI 要約（直近 5 年の研究成果）

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

研究成果（26 件）

• [2026] Quantitative mapping of catalyst ink rheological regimes governing agglomerate structural dynamics in a shear flow field
• [2026] Quantitative mapping of catalyst ink rheological regimes governing agglomerate structural dynamics in a shear flow field
  DOI: https://doi.org/10.1016/j.colsurfa.2026.139952
• [2026] Quantitative mapping of catalyst ink rheological regimes governing agglomerate structural dynamics in a shear flow field
• [2025] Dynamic analysis for prediction of flow patterns in an oscillatory baffled reactor using machine learning
  DOI: https://doi.org/10.69997/sct.101931
• [2024] Flow pattern analysis for design of gas-liquid-solid three-phase hydrogenation process using oscillating flow
• [2022] Swiss-roll electrodes for efficient degradation of carbofuran by electrochemical advanced oxidation processes
  DOI: https://doi.org/10.1016/j.jece.2022.108801
• [2022] Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency
  DOI: https://doi.org/10.1016/j.cej.2022.139540
• [2022] Framework for discovering porous materials: Structural hybridization and Bayesian optimization of conditional generative adversarial network
  DOI: https://doi.org/10.1016/j.dche.2022.100058
• [2022] Analysis of Dynamic Behavior of Gas Bubbles and Solid Particles in an Oscillatory Baffled Reactor and Its Application to Design of Hydrogenation Proces
  DOI: https://doi.org/10.3303/cet2294161
• [2022] Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency

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