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Title: Machine Learning for Challenging EELS and EDS Spectral Decomposition

Journal Article · · Microscopy and Microanalysis

Scanning transmission electron microscopy (STEM) is one of the primary methods of characterizing heterogenous catalysts due to its unprecedented spatial resolution and the ability to perform imaging and chemical analysis simultaneously at the atomic scale. Here the characterization of heterogenous catalysts that are composed of metal and oxide support, the atomic configurations are often probed by Z-contrast imaging while the chemical distributions can be revealed by using either electron energy loss spectroscopy (EELS) or energy dispersive X-ray spectroscopy (EDS).

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1558474
Journal Information:
Microscopy and Microanalysis, Journal Name: Microscopy and Microanalysis Journal Issue: S2 Vol. 25; ISSN 1431-9276
Publisher:
Microscopy Society of America (MSA)Copyright Statement
Country of Publication:
United States
Language:
English

References (1)

Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform journal April 2018