Article Accelerated Electrocatalyst Degradation Testing by Accurate and Robust Forecasting of Multidimensional Kinetic Model with Bayesian Data-Assimilation

Miao Wang SAMURAI ORCID (Research Center for Energy and Environmental Materials (GREEN)/Hydrogen Technology Materials Field/Electrochemical Energy Conversion Team, National Institute for Materials Science) ; Akimitsu Ishii SAMURAI ORCID (International Center for Young Scientists, National Institute for Materials Science) ; Ken Sakaushi SAMURAI ORCID (Research Center for Energy and Environmental Materials (GREEN)/Hydrogen Technology Materials Field/Electrochemical Energy Conversion Team, National Institute for Materials Science)

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Miao Wang, Akimitsu Ishii, Ken Sakaushi. Accelerated Electrocatalyst Degradation Testing by Accurate and Robust Forecasting of Multidimensional Kinetic Model with Bayesian Data-Assimilation. ACS Energy Letters. 2025, 10 (), 22-29. https://doi.org/10.1021/acsenergylett.4c02868

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(abstract)

Degradation test represents a significant bottleneck in the electrochemical technology development, requiring occasionally tens of thousands of hours. Thus, a reliable degradation forecasting in short timeframe is a game-changer in accelerating the establishment of future electrochemical devices. Herein, we show a multidimensional kinetic model for electrocatalyst degradation by quantifying the relationship among potential, current, and time, applicable under various conditions.

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Keyword: Bayesian Data Assimilation, Electrocatalysts, Kinetic Model

Date published: 2025-01-10

Publisher: American Chemical Society

Journal:

  • ACS Energy Letters (ISSN: 23808195) vol. 10 p. 22-29

Funding:

  • 文部科学省 JPMXP1122712807

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1021/acsenergylett.4c02868

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Updated at: 2024-12-10 16:56:51 +0900

Published on MDR: 2025-02-17 18:32:17 +0900

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