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DOE OSTI · 1855095

Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis

Xiang, Shuting·Huang, Peipei·Li, Junying·Liu, Yang·Marcella, Nicholas·Routh, Prahlad K.·Li, Gonghu·Frenkel, Anatoly I.
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Abstract

We show that "single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO 2 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.

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Xiang, Shuting, Huang, Peipei, Li, Junying, Liu, Yang, Marcella, Nicholas, Routh, Prahlad K., Li, Gonghu, Frenkel, Anatoly I.. 2022-02-04. Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis. https://doi.org/10.1039/d1cp05513e

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