Machine Learning Tailored Anodes for Efficient Hydrogen Energy Generation in Proton-Conducting Solid Oxide Electrolysis Cells

Machine Learning Tailored Anodes for Efficient Hydrogen Energy Generation in Proton-Conducting Solid Oxide Electrolysis Cells

Highlights Machine learning technique was employed to develop anode for proton-conducting solid oxide electrolysis cells (P-SOEC). The screened high-performance La0.9Ba0.1Co0.7Ni0.3O3−δ (LBCN9173) and La0.9Ca0.1Co0.7Ni0.3O3−δ (LCCN9173) anodes achieved a synergistic enhancement of water oxidation re...

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Bibliographic Details
Main Authors: Fangyuan Zheng, Baoyin Yuan, Youfeng Cai, Huanxin Xiang, Chunmei Tang, Ling Meng, Lei Du, Xiting Zhang, Feng Jiao, Yoshitaka Aoki, Ning Wang, Siyu Ye
Format: Article
Language:English
Published: SpringerOpen 2025-05-01
Series:Nano-Micro Letters
Subjects:
Machine learning
Proton-conducting solid oxide electrolysis cells
Hydrogen energy
Anode
Online Access:https://doi.org/10.1007/s40820-025-01764-7
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