Yoshifumi Tsuge 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（24 件）

• [2022] Design of porous metal collector via bubble template-assisted electrochemical deposition using numerical simulation
• [2022] Numerical analysis on influence of surface structures of cathode catalyst layers on performance of polymer electrolyte fuel cells
  DOI: https://doi.org/10.1002/elsa.202200003
• [2022] Evaluation of ionomer distribution on porous carbon aggregates in catalyst layers of polymer electrolyte fuel cells
  DOI: https://doi.org/10.1016/j.powera.2022.100096
• [2022] Stress Prediction of the Particle Structure of All-Solid-State Batteries by Numerical Simulation and Machine Learning
  DOI: https://doi.org/10.3389/fceng.2022.836282
• [2022] Microscale simulations of reaction and mass transport in cathode catalyst layer of polymer electrolyte fuel cell
• [2022] Simulation of the compaction of an all-solid-state battery cathode with coated particles using the discrete element method
  DOI: https://doi.org/10.1016/j.jpowsour.2022.231279
• [2022] Simulation to estimate the correlation of porous structure properties of secondary batteries determined through machine learning
  DOI: https://doi.org/10.1016/j.powera.2022.100094
• [2022] Simulation of All-Solid-State Lithium-Ion Batteries With Fastening Stress and Volume Expansion
  DOI: https://doi.org/10.1115/1.4054015
• [2022] 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning
  DOI: https://doi.org/10.1016/j.powera.2022.100084
• [2022] Microscale simulations of reaction and mass transport in cathode catalyst layer of polymer electrolyte fuel cell
  DOI: https://doi.org/10.1016/j.ijhydene.2022.02.021

科研費（0 件）

まだデータがありません（KAKEN 取り込み後に表示）。

所属学会・役職（0 件）

まだデータがありません（学会データ連携後に表示）。