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

Marcella, Nicholas; Liu, Yang; Timoshenko, Janis; Guan, Erjia; Luneau, Mathilde; Shirman, Tanya; Plonka, Anna M.; van der Hoeven, Jessi S.; Aizenberg, Joanna; Friend, Cynthia M.; Frenkel, Anatoly I.

doi:10.1039/d0cp02098b

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

Journal Article · Fri May 01 00:00:00 EDT 2020 · Physical Chemistry Chemical Physics. PCCP

DOI:https://doi.org/10.1039/d0cp02098b· OSTI ID:1677655

^[1]; Liu, Yang ^[1];

^[1]; Guan, Erjia ^[1]; Luneau, Mathilde ^[2];

^[3]; Plonka, Anna M. ^[1]; van der Hoeven, Jessi S. ^[2];

^[3];

^[2];

^[4]

Stony Brook Univ., NY (United States)
Harvard Univ., Cambridge, MA (United States)
Harvard Univ., Cambridge, MA (United States). Wyss Inst. for Biologically Inspired Engineering
Stony Brook Univ., NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)

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. In this work, 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.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States); Harvard Univ., Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0012704; SC9912573; AC02-06CH11357; AC02-76SF00515; SC0012573

OSTI ID:: 1677655

Alternate ID(s):: OSTI ID: 1618142; OSTI ID: 1845278

Report Number(s):: BNL-219947-2020-JAAM

Journal Information:: Physical Chemistry Chemical Physics. PCCP, Vol. 22, Issue 34; ISSN 1463-9076

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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