文献类型：期刊文献

英文题名：Prediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures

作者：Li, Zhiheng[1,2,3,4,5];Mao, Xin[6,7];Feng, Desheng[1];Li, Mengran[8];Xu, Xiaoyong[1,9];Luo, Yadan[10];Zhuang, Linzhou[11];Lin, Rijia[1];Zhu, Tianjiu[1];Liang, Fengli[1];Huang, Zi[10];Liu, Dong[5];Yan, Zifeng[5];Du, Aijun[6,7];Shao, Zongping[12];Zhu, Zhonghua[1]

机构：[1]Univ Queensland, Sch Chem Engn, Brisbane, Australia;[2]Westlake Univ, Ctr Artificial Photosynth Solar Fuels, Hangzhou, Peoples R China;[3]Westlake Univ, Sch Sci, Dept Chem, Hangzhou, Peoples R China;[4]Westlake Univ, Res Ctr Ind Future, Hangzhou, Peoples R China;[5]China Univ Petr East China, Sch Chem Engn, Qingdao, Peoples R China;[6]Queensland Univ Technol, Sch Chem & Phys, Brisbane, Australia;[7]Queensland Univ Technol, Ctr Mat Sci, Brisbane, Australia;[8]Univ Melbourne, Dept Chem Engn, Melbourne, Australia;[9]Univ Adelaide, Sch Chem Engn, Adelaide, Australia;[10]Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Australia;[11]East China Univ Sci & Technol, Sch Chem Engn, Shanghai, Peoples R China;[12]Curtin Univ, WASM Minerals Energy & Chem Engn, Perth, WA, Australia

年份：2024

卷号：15

期号：1

外文期刊名：NATURE COMMUNICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001367220500029)】；

基金：The authors acknowledge the technical assistance from the Center for Microscopy and Microanalysis (CMM) at the University of Queensland and Australian Synchrotron. Z. Zhu and A. Du would like to thank the support from the Australian Research Council (ARC) Discovery Project DP170104660. Z. Zhu also acknowledges the support from ARC DP DP200101397. Z. Li acknowledges the China Scholarship Council (CSC) for awarding a visitor's scholarship (No. 201706450223) in Australia. Y. Luo acknowledges financial support from ARC DE240100105. Y. Luo and Z. Huang acknowledge the support from CE200100025. Z. Huang appreciates the support from ARC DP230101196. We acknowledge support from ARC DE230100637 and the Australian Synchrotron Project (M15364, M15867, and M21058). The authors would like to thank Dr. Jinxuan Zhang's assistance with the scanning electron microscope.

语种：英文

摘要：Efficient catalysts are imperative to accelerate the slow oxygen reaction kinetics for the development of emerging electrochemical energy systems ranging from room-temperature alkaline water electrolysis to high-temperature ceramic fuel cells. In this work, we reveal the role of cationic inductive interactions in predetermining the oxygen vacancy concentrations of 235 cobalt-based and 200 iron-based perovskite catalysts at different temperatures, and this trend can be well predicted from machine learning techniques based on the cationic lattice environment, requiring no heavy computational and experimental inputs. Our results further show that the catalytic activity of the perovskites is strongly correlated with their oxygen vacancy concentration and operating temperatures. We then provide a machine learning-guided route for developing oxygen electrocatalysts suitable for operation at different temperatures with time efficiency and good prediction accuracy. Catalyst screening is an important process but it's usually time-consuming and labor intensive. Here the authors report the prediction of oxygen vacancy for perovskites using machine learning techniques to develop suitable oxygen electrocatalysts for solid oxide fuel cells at reduced temperatures.