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Title: Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis
“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.  more » « less
Award ID(s):
2102299 2102655
PAR ID:
10345060
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Physical Chemistry Chemical Physics
Volume:
24
Issue:
8
ISSN:
1463-9076
Page Range / eLocation ID:
5116 to 5124
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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