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学者姓名：黄美榕
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Abstract ：
Engineering photoelectrodes with cocatalysts is an effective approach to enhance their photocatalytic performance. However, selecting an appropriate cocatalyst for a specific photoelectrode is challenging due to various factors within the system. Machine learning techniques are revolutionizing the catalytic field and are good at addressing multifactorial problems. In this study, several fundamental factors of the photoanode catalytic system and their specific mechanisms were systematically summarized. A comprehensive machine learning process was introduced to guide cocatalyst selection for BiVO4 photoanodes. A multi-layer perceptron neural network and several tree-based ensemble models were trained to capture intricate relationships among photoanodes, cocatalysts, and electrolytes, enabling the prediction of unstudied cases. The best-performing random forest model accurately captured the essential features of high-performance cases, achieving a generalization accuracy of 96.30% for binary classification. Furthermore, the model's built-in feature importance analysis revealed that the type and preparation method of cocatalysts were the two most significant factors affecting the catalytic system's performance. According to the Shapley additive explanations interpretation, some heuristic rules were provided to propose a class of promising cocatalyst/photoanode catalysts. Machine learning models are used to capture intricate relationships among BiVO4 photoanodes, cocatalysts, and electrolytes. Model interpretability is then performed to provide some heuristic rules to guide cocatalyst selection for BiVO4 photoanodes.
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| GB/T 7714 | Huang, Meirong , Wang, Sutong , Zhu, Hongwei . A comprehensive machine learning strategy for designing high-performance photoanode catalysts [J]. | JOURNAL OF MATERIALS CHEMISTRY A , 2023 , 11 (40) : 21619-21627 . |
| MLA | Huang, Meirong 等. "A comprehensive machine learning strategy for designing high-performance photoanode catalysts" . | JOURNAL OF MATERIALS CHEMISTRY A 11 . 40 (2023) : 21619-21627 . |
| APA | Huang, Meirong , Wang, Sutong , Zhu, Hongwei . A comprehensive machine learning strategy for designing high-performance photoanode catalysts . | JOURNAL OF MATERIALS CHEMISTRY A , 2023 , 11 (40) , 21619-21627 . |
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Abstract ：
Pt-based catalysts play a crucial role in the catalytic combustion of benzene. Previous studies have highlighted the significance of metal Pt atoms as the active phase in benzene combustion, while oxidized Pt sites exhibit lower activity. In practical catalysts synthesized through wet chemical methods, a range of metal species is present, including highly dispersed oxidized metal atoms, small oxidized metal clusters, and metal nanoparticles. This paper explores an effective strategy to enhance metal utilization and develop high-performance platinum-based catalysts for benzene catalytic combustion by introducing alkali metals. This approach transforms low-activity oxidized metal atoms and small oxidized metal clusters into active small metal nanoparticles. The introduction of K-O groups displaces Pt from oxygen vacancy trapping sites on antimony-doped tin oxide (ATO) support, enhancing Pt mobility and converting them into active metallic Pt nanoparticles, ultimately improving benzene combustion. These findings highlight the role of alkali metals in promoting catalytic activity and provide insights for designing high-performance noble-metal-based catalysts.
Keyword ：
Alkali-promoted catalysts Alkali-promoted catalysts Benzene combustion Benzene combustion Catalytic activity Catalytic activity Langmuir-Hinshelwood mechanism Langmuir-Hinshelwood mechanism Platinum/antimony-doped tin oxide (ATO) Platinum/antimony-doped tin oxide (ATO)
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| GB/T 7714 | Zhou, Qiqi , Huang, Zhiwei , Huang, Meirong et al. Revitalizing platinum: Alkali-promoted formation of active metallic nanoparticles from inert Pt entities for enhanced benzene combustion [J]. | APPLIED SURFACE SCIENCE , 2023 , 642 . |
| MLA | Zhou, Qiqi et al. "Revitalizing platinum: Alkali-promoted formation of active metallic nanoparticles from inert Pt entities for enhanced benzene combustion" . | APPLIED SURFACE SCIENCE 642 (2023) . |
| APA | Zhou, Qiqi , Huang, Zhiwei , Huang, Meirong , Chen, Wen , Ni, Jiangwei , Wu, Xiaomin et al. Revitalizing platinum: Alkali-promoted formation of active metallic nanoparticles from inert Pt entities for enhanced benzene combustion . | APPLIED SURFACE SCIENCE , 2023 , 642 . |
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