Issue 34, 2020

Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy

Abstract

X-ray absorption spectroscopy is a common method for probing the local structure of nanocatalysts. One portion of the X-ray absorption spectrum, the X-ray absorption near edge structure (XANES) is a useful alternative to the commonly used extended X-ray absorption fine structure (EXAFS) for probing three-dimensional geometry around each type of atomic species, especially in those cases when the EXAFS data quality is limited by harsh reaction conditions and low metal loading. A methodology for quantitative determination of bimetallic architectures from their XANES spectra is currently lacking. We have developed a method, based on the artificial neural network, trained on ab initio site-specific XANES calculations, that enables accurate and rapid reconstruction of the structural descriptors (partial coordination numbers) from the experimental XANES data. We demonstrate the utility of this method on the example of a series of PdAu bimetallic nanoalloys. By validating the neural network-yielded metal–metal coordination numbers based on the XANES analysis by previous EXAFS characterization, we obtained new results for in situ restructuring of dilute (2.6 at% Pd in Au) PdAu nanoparticles, driven by their gas and temperature treatments.

Graphical abstract: Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy

Supplementary files

Article information

Article type
Paper
Submitted
18 avr. 2020
Accepted
01 mai 2020
First published
01 mai 2020

Phys. Chem. Chem. Phys., 2020,22, 18902-18910

Author version available

Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy

N. Marcella, Y. Liu, J. Timoshenko, E. Guan, M. Luneau, T. Shirman, A. M. Plonka, J. E. S. van der Hoeven, J. Aizenberg, C. M. Friend and A. I. Frenkel, Phys. Chem. Chem. Phys., 2020, 22, 18902 DOI: 10.1039/D0CP02098B

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