Machine Learning-Assisted Development of Platinum-Free RuNiCo Nanocages for Electrocatalytic Hydrogen Oxidation Reaction in Acidic Media

Tadgell, Colin A; Kato, Masaru; Dieb, Sae et al.

ACS Applied Energy Materials, 2025, 8(19), 14052-14057

Permalink : https://hdl.handle.net/2115/98091

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論文情報

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アクセス権
open access
URI
https://hdl.handle.net/2115/98091
タイトル
Machine Learning-Assisted Development of Platinum-Free RuNiCo Nanocages for Electrocatalytic Hydrogen Oxidation Reaction in Acidic Media
著者
Tadgell, Colin A ; Kato, Masaru ; Dieb, Sae ; Sodeyama, Keitaro ; Hoshi, Takahito ; Suzuki, Koshiro ; Du, Bohao ; Watanabe, Takeshi ; Yagi, Ichizo
著者名
Tadgell, Colin A
e-Rad_Researcher
70709633
著者名
Kato, Masaru
著者名
Dieb, Sae
e-Rad_Researcher
40386610
著者名
Sodeyama, Keitaro
著者名
Hoshi, Takahito
著者名
Suzuki, Koshiro
著者名
Du, Bohao
e-Rad_Researcher
00750382
著者名
Watanabe, Takeshi
e-Rad_Researcher
40292776
著者名
Yagi, Ichizo
言語
英語
発行日
2025-09-29
出版者
American Chemical Society
収録物名
ACS Applied Energy Materials
巻(号)
8(19)
ページ
14052-14057
抄録
Through data-driven synthesis involving 24 experiments from 16,384 possible combinations (<1%), we developed two optimal electrocatalysts for the hydrogen oxidation reaction (HOR) in acidic media: PtIrRuNiCo nanowires and Pt-free RuNiCo nanocages. The objective function obtained in the Bayesian optimization (BO) process is highly correlated with the experimental current density for the HOR. The RuNiCo nanocages exhibited higher HOR activity than Ru because of the suppression of the surface oxide formation. Our BO-assisted approach accelerates the development of highly active electrocatalysts without requiring high-throughput synthesis.
資源タイプ
学術雑誌論文
出版タイプ
Accepted Manuscript
権利情報
This document is the Accepted Manuscript version of a Published Article that appeared in final form in ACS applied energy materials, copyright © 2025 American Chemical Society. To access the final published article, see ACS Articles on Request.
PISSN
2574-0962
関連情報 (isVersionOf)
出版社版DOI
https://doi.org/10.1021/acsaem.5c02113