Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution

Summary: To achieve net-zero emissions, a particular interest has been raised in the electrochemical evolution of H2 by using catalysts. Considering the complexity of designing catalyst, we demonstrate a data-driven strategy to develop optimized catalysts for H2 evolution. This work starts by collec...

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Autores principales:	Anhui Zheng, Yuxuan Wang, Fangfei Zhang, Chunnian He, Shan Zhu, Naiqin Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Chemical reaction Catalysis Materials science Computational method in materials science Science Q
Acceso en línea:	https://doaj.org/article/72e7cc013ec844c8a363cf87bd77aede
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spelling	oai:doaj.org-article:72e7cc013ec844c8a363cf87bd77aede2021-11-26T04:37:55ZData-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution2589-004210.1016/j.isci.2021.103430https://doaj.org/article/72e7cc013ec844c8a363cf87bd77aede2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589004221014012https://doaj.org/toc/2589-0042Summary: To achieve net-zero emissions, a particular interest has been raised in the electrochemical evolution of H2 by using catalysts. Considering the complexity of designing catalyst, we demonstrate a data-driven strategy to develop optimized catalysts for H2 evolution. This work starts by collecting data of Pt/carbon catalysts, and applying machine learning to reveal the importance of ranking various features. The algorithms reveal that the Pt content and Pt size have the greatest impact on the catalyst overpotentials. Following the data-driven analysis, a space-confined method is used to fabricate the size-controllable Pt nanoclusters that anchor on nitrogen-doped (N-doped) mesoporous carbon nanosheet network. The obtained catalysts use less platinum and exhibit better catalytic activity than current commercial catalysts in alkaline electrolytes. Moreover, the data formed in this work can be used as feedback to further improve the data-driven model, thereby accelerating the development of high-performance catalysts.Anhui ZhengYuxuan WangFangfei ZhangChunnian HeShan ZhuNaiqin ZhaoElsevierarticleChemical reactionCatalysisMaterials scienceComputational method in materials scienceScienceQENiScience, Vol 24, Iss 12, Pp 103430- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical reaction Catalysis Materials science Computational method in materials science Science Q
spellingShingle	Chemical reaction Catalysis Materials science Computational method in materials science Science Q Anhui Zheng Yuxuan Wang Fangfei Zhang Chunnian He Shan Zhu Naiqin Zhao Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution
description	Summary: To achieve net-zero emissions, a particular interest has been raised in the electrochemical evolution of H2 by using catalysts. Considering the complexity of designing catalyst, we demonstrate a data-driven strategy to develop optimized catalysts for H2 evolution. This work starts by collecting data of Pt/carbon catalysts, and applying machine learning to reveal the importance of ranking various features. The algorithms reveal that the Pt content and Pt size have the greatest impact on the catalyst overpotentials. Following the data-driven analysis, a space-confined method is used to fabricate the size-controllable Pt nanoclusters that anchor on nitrogen-doped (N-doped) mesoporous carbon nanosheet network. The obtained catalysts use less platinum and exhibit better catalytic activity than current commercial catalysts in alkaline electrolytes. Moreover, the data formed in this work can be used as feedback to further improve the data-driven model, thereby accelerating the development of high-performance catalysts.
format	article
author	Anhui Zheng Yuxuan Wang Fangfei Zhang Chunnian He Shan Zhu Naiqin Zhao
author_facet	Anhui Zheng Yuxuan Wang Fangfei Zhang Chunnian He Shan Zhu Naiqin Zhao
author_sort	Anhui Zheng
title	Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution
title_short	Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution
title_full	Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution
title_fullStr	Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution
title_full_unstemmed	Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution
title_sort	data-driven design and controllable synthesis of pt/carbon electrocatalysts for h2 evolution
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/72e7cc013ec844c8a363cf87bd77aede
work_keys_str_mv	AT anhuizheng datadrivendesignandcontrollablesynthesisofptcarbonelectrocatalystsforh2evolution AT yuxuanwang datadrivendesignandcontrollablesynthesisofptcarbonelectrocatalystsforh2evolution AT fangfeizhang datadrivendesignandcontrollablesynthesisofptcarbonelectrocatalystsforh2evolution AT chunnianhe datadrivendesignandcontrollablesynthesisofptcarbonelectrocatalystsforh2evolution AT shanzhu datadrivendesignandcontrollablesynthesisofptcarbonelectrocatalystsforh2evolution AT naiqinzhao datadrivendesignandcontrollablesynthesisofptcarbonelectrocatalystsforh2evolution
_version_	1718409848415059968

Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution

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