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Title: Speciation of Nanocatalysts Using X-ray Absorption Spectroscopy Assisted by Machine Learning

Journal Article · · Journal of Physical Chemistry. C
ORCiD logo [1];  [2]; ORCiD logo [3]
  1. Stony Brook University, NY (United States)
  2. University of Illinois at Urbana-Champaign, IL (United States)
  3. Stony Brook University, NY (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)

Structure and morphology of supported nanoparticle catalysts play important roles in many industrial reactions. Recent progress has identified key aspects of structure-activity relationships at nanoscale and novel methods to study the local environment of the active sites. X-ray absorption fine structure (XAFS) spectroscopy, despite being a leading technique for this purpose, is hampered significantly by its ensemble-averaging nature which often leads to a bias towards a single "representative" structure. Learning heterogeneous distributions of nanostructures at the inter- and intra-particle level from the average XAFS spectrum is a formidable challenge that can be overcome in some cases, described in this Perspective. Here we also discuss emerging machine learning techniques for extracting the information about heterogeneity of metal species from XAFS data.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
SC0012704; SC0022199
OSTI ID:
1969909
Report Number(s):
BNL-224239-2023-JAAM
Journal Information:
Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 12 Vol. 127; ISSN 1932-7447
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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