Machine Learning for Challenging EELS and EDS Spectral Decomposition

doi:10.1017/S1431927619001636

This content will become publicly available on August 5, 2020

Title: Machine Learning for Challenging EELS and EDS Spectral Decomposition

Abstract

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).

Authors:

^[1]; Graves, Jeffery ^[2];

^[2];

^[2]; Pan, Xiaoqing ^[3];

^[2]

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

Publication Date:: 2019-08-05

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1558474

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Microscopy and Microanalysis

Additional Journal Information:: Journal Volume: 25; Journal Issue: S2; Journal ID: ISSN 1431-9276

Publisher:: Microscopy Society of America (MSA)

Country of Publication:: United States

Language:: English

Subject:: 47 OTHER INSTRUMENTATION

Citation Formats


                    Blum, Thomas F., Graves, Jeffery, Zachman, Michael, Kannan, Ramakrishnan, Pan, Xiaoqing, and Chi, Miaofang. Machine Learning for Challenging EELS and EDS Spectral Decomposition.  United States: N. p., 2019. 
        Web.  doi:10.1017/S1431927619001636.

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                    Blum, Thomas F., Graves, Jeffery, Zachman, Michael, Kannan, Ramakrishnan, Pan, Xiaoqing, & Chi, Miaofang. Machine Learning for Challenging EELS and EDS Spectral Decomposition.  United States.  doi:10.1017/S1431927619001636.

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                    Blum, Thomas F., Graves, Jeffery, Zachman, Michael, Kannan, Ramakrishnan, Pan, Xiaoqing, and Chi, Miaofang. Mon .  
        "Machine Learning for Challenging EELS and EDS Spectral Decomposition".  United States.  doi:10.1017/S1431927619001636.

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@article{osti_1558474,

  title        = {Machine Learning for Challenging EELS and EDS Spectral Decomposition},

  author       = {Blum, Thomas F. and Graves, Jeffery and Zachman, Michael and Kannan, Ramakrishnan and Pan, Xiaoqing and Chi, Miaofang},

  abstractNote = {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).},

  doi          = {10.1017/S1431927619001636},

  journal      = {Microscopy and Microanalysis},

  number       = S2,

  volume       = 25,

  place        = {United States},

  year         = {2019},

  month        = {8}

}

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Works referenced in this record:

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

Kannan, R.; Ievlev, A. V.; Laanait, N.
Advanced Structural and Chemical Imaging, Vol. 4, Issue 1
DOI: 10.1186/s40679-018-0055-8

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Title: Machine Learning for Challenging EELS and EDS Spectral Decomposition

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Citation Formats

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