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. 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.
- Authors:
-
[1];
[1];
[3];
[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)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States); Harvard Univ., Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1677655
- Alternate Identifier(s):
- OSTI ID: 1618142; OSTI ID: 1845278
- Report Number(s):
- BNL-219947-2020-JAAM
Journal ID: ISSN 1463-9076
- Grant/Contract Number:
- SC0012704; SC9912573; AC02-06CH11357; AC02-76SF00515; SC0012573
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Chemistry Chemical Physics. PCCP
- Additional Journal Information:
- Journal Volume: 22; Journal Issue: 34; Journal ID: ISSN 1463-9076
- Publisher:
- Royal Society of Chemistry
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY
Citation Formats
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., and Frenkel, Anatoly I. Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy. United States: N. p., 2020.
Web. doi:10.1039/d0cp02098b.
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. Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy. United States. https://doi.org/10.1039/d0cp02098b
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., and Frenkel, Anatoly I. Fri .
"Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy". United States. https://doi.org/10.1039/d0cp02098b. https://www.osti.gov/servlets/purl/1677655.
@article{osti_1677655,
title = {Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy},
author = {Marcella, Nicholas and Liu, Yang and Timoshenko, Janis and Guan, Erjia and Luneau, Mathilde and Shirman, Tanya and Plonka, Anna M. and van der Hoeven, Jessi S. and Aizenberg, Joanna and Friend, Cynthia M. and Frenkel, Anatoly I.},
abstractNote = {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.},
doi = {10.1039/d0cp02098b},
journal = {Physical Chemistry Chemical Physics. PCCP},
number = 34,
volume = 22,
place = {United States},
year = {Fri May 01 00:00:00 EDT 2020},
month = {Fri May 01 00:00:00 EDT 2020}
}
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