Prediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures

Simple item page
dc.contributor.authorLi, Z.
dc.contributor.authorMao, X.
dc.contributor.authorFeng, D.
dc.contributor.authorLi, M.
dc.contributor.authorXu, X.
dc.contributor.authorLuo, Y.
dc.contributor.authorZhuang, L.
dc.contributor.authorLin, R.
dc.contributor.authorZhu, T.
dc.contributor.authorLiang, F.
dc.contributor.authorHuang, Z.
dc.contributor.authorLiu, D.
dc.contributor.authorYan, Z.
dc.contributor.authorDu, A.
dc.contributor.authorShao, Z.
dc.contributor.authorZhu, Z.
dc.date.issued2024
dc.description.abstractEfficient 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.
dc.description.statementofresponsibilityZhiheng Li, Xin Mao, Desheng Feng, Mengran Li, Xiaoyong Xu, Yadan Luo, Linzhou Zhuang, Rijia Lin, Tianjiu Zhu, Fengli Liang, Zi Huang, Dong Liu, Zifeng Yan, Aijun Du, Zongping Shao, Zhonghua Zhu
dc.identifier.citationNature Communications, 2024; 15(1):9318-1-9318-12
dc.identifier.doi10.1038/s41467-024-53578-7
dc.identifier.issn2041-1723
dc.identifier.issn2041-1723
dc.identifier.orcidXu, X. [0000-0002-0149-815X]
dc.identifier.urihttps://hdl.handle.net/2440/144568
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.granthttp://purl.org/au-research/grants/arc/DP170104660
dc.relation.granthttp://purl.org/au-research/grants/arc/DP200101397
dc.relation.granthttp://purl.org/au-research/grants/arc/DE240100105
dc.relation.granthttp://purl.org/au-research/grants/arc/CE200100025
dc.relation.granthttp://purl.org/au-research/grants/arc/DP230101196
dc.relation.granthttp://purl.org/au-research/grants/arc/DE230100637
dc.rights© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by-nc-nd/4.0/.
dc.source.urihttps://doi.org/10.1038/s41467-024-53578-7
dc.subjectComputational chemistry; Fuel cells
dc.titlePrediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures
dc.typeJournal article
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
hdl_144568.pdf
Size:
1.9 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Research Outputs