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.
Authors
- Deng De-hui
- Bo-Ying Huang
- Tian-Yi Ding
- Y W Sun (ORCID: https://orcid.org/0000-0002-4420-5708)
- Jia-Wei Yan (ORCID: https://orcid.org/0000-0002-0045-6169)
- Zong-Rui Zheng
- Ze-Hua Zhang
- Feng Wang
- Jun Cheng (ORCID: https://orcid.org/0000-0001-6971-0797)
- Kelvin H. L. Zhang (ORCID: https://orcid.org/0000-0001-9352-6236)
Institutions
- 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)
Publication Details
- 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
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities