Machine Learning for Challenging EELS and EDS Spectral Decomposition

Journal Article · 04 August 2019 · Microscopy and Microanalysis

DOI: https://doi.org/10.1017/S1431927619001636 · OSTI ID:1558474

Blum, Thomas F.  ^[1]; Graves, Jeffery ^[2]; Zachman, Michael  ^[2]; Kannan, Ramakrishnan  ^[2]; Pan, Xiaoqing ^[3]; Chi, Miaofang  ^[2]

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1. Univ. of California, Irvine, CA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
3. Univ. of California, Irvine, CA (United States)

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