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通过计算了解金属掺杂TaO催化剂对酸性析氧反应的催化性能。

Understanding the catalytic performances of metal-doped TaO catalysts for acidic oxygen evolution reaction with computations.

作者信息

Han Congcong, Wang Tao

机构信息

Department of Chemistry, Zhejiang University Hangzhou 310058 Zhejiang Province China.

Center of Artificial Photosynthesis for Solar Fuels and Department of Chemistry, School of Science and Research Center for Industries of the Future, Westlake University 600 Dunyu Road Hangzhou 310030 Zhejiang Province China

出版信息

Chem Sci. 2024 Aug 6;15(35):14371-8. doi: 10.1039/d4sc03554b.

DOI:10.1039/d4sc03554b
PMID:39165725
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11331345/
Abstract

The design of stable and active alternative catalysts to iridium oxide for the anodic oxygen evolution reaction (OER) has been a long pursuit in acidic water splitting. Tantalum pentoxide (TaO) has the merit of great acidic stability but poor OER performance, yet strategies to improve its intrinsic OER activity are highly desirable. Herein, by using density functional theory (DFT) calculations combined with aqueous stability assessment from surface Pourbaix diagrams, we systematically evaluated the OER activity and acidic stability of 14 different metal-doped TaO catalysts. Apart from the experimentally reported Ir-doped TaO, we computationally identified Ru- and Nb-doped TaO catalysts as another two candidates with reasonably high stability and activity in acidic OER. Our study also underscores the essence of considering stable surface states of catalysts under working conditions before a reasonable activity trend can be computationally achieved.

摘要

设计用于阳极析氧反应(OER)的稳定且活性高的氧化铱替代催化剂,一直是酸性水分解领域长期以来的追求目标。五氧化二钽(TaO)具有很强的酸性稳定性,但OER性能较差,因此非常需要提高其本征OER活性的策略。在此,通过使用密度泛函理论(DFT)计算并结合表面Pourbaix图进行的水稳定性评估,我们系统地评估了14种不同金属掺杂TaO催化剂的OER活性和酸性稳定性。除了实验报道的Ir掺杂TaO外,我们通过计算确定Ru和Nb掺杂TaO催化剂是另外两种在酸性OER中具有相当高稳定性和活性的候选催化剂。我们的研究还强调了在通过计算得出合理的活性趋势之前,考虑催化剂在工作条件下稳定表面状态的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/9ac53ab8b130/d4sc03554b-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/d13110245ae9/d4sc03554b-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/4dc834db63f8/d4sc03554b-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/b03bd6e7a0e0/d4sc03554b-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/9ac53ab8b130/d4sc03554b-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/d13110245ae9/d4sc03554b-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/4dc834db63f8/d4sc03554b-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/b03bd6e7a0e0/d4sc03554b-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ed/11389465/9ac53ab8b130/d4sc03554b-f4.jpg

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The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities.密度泛函理论和机器学习方法加速了具有高氧化还原反应活性和析氧反应催化活性的二甲基亚砜的发现。
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