Defect-Induced Dynamic Reconstruction Boosts Oxygen Evolution Activity of Perovskite Oxides

, LNO) are promising catalysts. Oxygen vacancies have been proposed to enhance activity, yet their specific role remains unclear due to the dynamic interfacial structure during OER. Here, we employ epitaxial LNO thin films with controlled oxygen-vacancy concentrations, combining electrochemical atomic force microscopy (EC-AFM), Raman spectroscopy, and angle-resolved X-ray photoelectron spectroscopy (ARXPS) to track vacancy-induced structural and chemical evolution. Furthermore, based on the structural information from characterization, machine learning molecular dynamics (MLMD) is applied to elucidate the formation mechanism of the active phase. We reveal that oxygen vacancies trigger La leaching, inducing structural distortion and reconfiguration into a highly active phase identified as γ-NiOOH. These findings establish atomic-level structure-activity relationships and provide a rational strategy for designing next-generation OER catalysts.

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Journal
Journal of the American Chemical Society
Published
2026-05-12
DOI
https://doi.org/10.1021/jacs.6c04404
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Defect-Induced Dynamic Reconstruction Boosts Oxygen Evolution Activity of Perovskite Oxides

Deng De-hui, Bo-Ying Huang, Tian-Yi Ding, Y W Sun et al.
Journal of the American Chemical Society
Machine Learning in Materials Science
article

Defect-Induced Dynamic Reconstruction Boosts Oxygen Evolution Activity of Perovskite Oxides

Deng De-hui, Bo-Ying Huang, Tian-Yi Ding, Y W Sun, Jia-Wei Yan, Zong-Rui Zheng, Ze-Hua Zhang, Feng Wang, Jun Cheng, Kelvin H. L. Zhang
article en

Abstract

, LNO) are promising catalysts. Oxygen vacancies have been proposed to enhance activity, yet their specific role remains unclear due to the dynamic interfacial structure during OER. Here, we employ epitaxial LNO thin films with controlled oxygen-vacancy concentrations, combining electrochemical atomic force microscopy (EC-AFM), Raman spectroscopy, and angle-resolved X-ray photoelectron spectroscopy (ARXPS) to track vacancy-induced structural and chemical evolution. Furthermore, based on the structural information from characterization, machine learning molecular dynamics (MLMD) is applied to elucidate the formation mechanism of the active phase. We reveal that oxygen vacancies trigger La leaching, inducing structural distortion and reconfiguration into a highly active phase identified as γ-NiOOH. These findings establish atomic-level structure-activity relationships and provide a rational strategy for designing next-generation OER catalysts.

Journal of the American Chemical Society
Dalian Institute of Chemical Physics (CN), Xiamen University (CN), Chinese Academy of Sciences (CN), Institute of Art (PL), Tan Kah Kee Innovation Laboratory (CN), Collaborative Innovation Center of Chemistry for Energy Materials (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 11%
Machine Learning in Materials Science
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Defect-Induced Dynamic Reconstruction Boosts Oxygen Evolution Activity of Perovskite Oxides — Deng De-hui, Bo-Ying Huang, et al. · Journal of the American Chemical Society (2026) | TGRS Research Map | TGRS