Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution
| Autor: | Naiqin Zhao, Fangfei Zhang, Chunnian He, Anhui Zheng, Shan Zhu, Yuxuan Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | iScience iScience, Vol 24, Iss 12, Pp 103430-(2021) |
| ISSN: | 2589-0042 |
| Popis: | 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. Graphical abstract Highlights • Built a database of Pt/C catalysts for H2 evolution • Provided quantitative guidance for catalyst design by machine learning • Developed a space-confined strategy to control the features of Pt/C composites • Formed a closed-loop from data-driven design to catalyst evaluation Chemical reaction; Catalysis; Materials science; Computational method in materials science |
| Databáze: | OpenAIRE |
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