Z-Contrast Enhancement in Au-Pt Nanocatalysts by Correlative X-ray Absorption Spectroscopy and Electron Microscopy: Implications for Composition Determination

Yang Liu; Maichong Xie; Nicholas Marcella; Alexandre C. Foucher; Eric A. Stach; Marc R. Knecht; Anatoly I. Frenkel

doi:10.1021/acsanm.2c00393

Z-Contrast Enhancement in Au-Pt Nanocatalysts by Correlative X-ray Absorption Spectroscopy and Electron Microscopy: Implications for Composition Determination

Yang Liu, Maichong Xie, Nicholas Marcella, Alexandre C. Foucher, Eric A. Stach, Marc R. Knecht, Anatoly I. Frenkel

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

The properties of bimetallic nanoparticles (BNPs) vary widely as a function of their composition and size distributions. X-ray absorption fine structure analysis is commonly used to characterize their structure, but its application to elements that are close to each other in the periodic table is hampered by poor Z-contrast. We trained an artificial neural network to recognize the partial coordination numbers in AuPt NPs synthesized via peptide templating using their X-ray absorption near-edge structure spectra. This approach, combined with scanning transmission electron microscopy analysis, revealed unique details of this prototype catalytic system that has different forms of heterogeneities at both the intra- and inter-particle levels. Our method based on the enhancement of Z-contrast of metal species will have implications for compositional studies of BNPs.

Original language	English
Pages (from-to)	8775-8782
Number of pages	8
Journal	ACS Applied Nano Materials
Volume	5
Issue number	7
DOIs	https://doi.org/10.1021/acsanm.2c00393
State	Published - Jul 22 2022
Externally published	Yes

Funding

This contribution has been primarily supported by the National Science Foundation under grants 1903576 (A.I.F.) and 1903649 (M.R.K.). Electron microscopy analysis by A.C.F. and E.A.S. was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences under award #DE-SC0012573. This research used beamline 8-ID of the National Synchrotron Light Source II, a U.S. DOE Office of Science User Facility operated for the DOE Office of Science by the Brookhaven National Laboratory under contract no. DE-SC0012704. This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at the Brookhaven National Laboratory under contract no. DE-SC0012704. This work was carried out in part at the Singh Center for Nanotechnology at the University of Pennsylvania, which is supported by the NSF National Nanotechnology Coordinated Infrastructure Program under grant NNCI-2025608. Additional support to the Nanoscale Characterization Facility at the Singh Center has been provided by the Laboratory for Research on the Structure of Matter (MRSEC) supported by the National Science Foundation (DMR-1720530). We thank E. Stavitski and D. Leshchev for synchrotron measurements.

Funders	Funder number
Laboratory for Research on the Structure of Matter
National Science Foundation	1903649, 1903576, NNCI-2025608
National Science Foundation
U.S. Department of Energy
Office of Science
Basic Energy Sciences	-SC0012573
Basic Energy Sciences
Brookhaven National Laboratory	DE-SC0012704
Brookhaven National Laboratory
Materials Research Science and Engineering Center, Harvard University	DMR-1720530
Materials Research Science and Engineering Center, Harvard University

Keywords

machine learning
nanoparticles
neural network
peptides
STEM
XANES

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10.1021/acsanm.2c00393

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AU - Foucher, Alexandre C.

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Z-Contrast Enhancement in Au-Pt Nanocatalysts by Correlative X-ray Absorption Spectroscopy and Electron Microscopy: Implications for Composition Determination

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Keywords

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