Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis
Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis
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使用机器学习辅助 XANES 分析解析“单原子”催化剂的结构
DOI：
10.1039/d1cp05513e
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发表时间：
2022
影响因子：
3.3
通讯作者：
Frenkel, Anatoly I.
中科院分区：
文献类型：
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作者：
Xiang, Shuting;Huang, Peipei;Li, Junying;Liu, Yang;Marcella, Nicholas;Routh, Prahlad K.;Li, Gonghu;Frenkel, Anatoly I.
“单原子”催化剂（SAC）在具有挑战性的化学转化（如光催化CO2还原）中表现出优异的活性和选择性。对于多相光催化SAC系统，为了理解反应机理，在原子水平上获得足够的结构信息是至关重要的。在这项工作中，SAC的制备通过接枝分子钴催化剂的光吸收的氮化碳表面。由于X射线吸收近边结构（XANES）光谱在反应条件下的Co SAC结构的细微变化的敏感性，不同的机器学习（ML）方法，包括主成分分析，K-均值聚类，和神经网络（NN），被用于原位Co XANES数据分析。因此，我们获得了SAC最近原子环境的定量结构信息，从而扩展了先前针对纳米颗粒和尺寸选择性簇所证明的NN-XANES方法。
“Single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment, thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.