Machine Learning for Challenging EELS and EDS Spectral Decomposition

Blum, Thomas F.; Graves, Jeffery; Zachman, Michael; Kannan, Ramakrishnan; Pan, Xiaoqing; Chi, Miaofang

doi:10.1017/S1431927619001636

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:: Mon Aug 05 00:00:00 EDT 2019

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.

Copy to clipboard


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

Copy to clipboard


                    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.  https://doi.org/10.1017/S1431927619001636.  https://www.osti.gov/servlets/purl/1558474.

Copy to clipboard


                    
@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         = {Mon Aug 05 00:00:00 EDT 2019},

  month        = {Mon Aug 05 00:00:00 EDT 2019}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1017/S1431927619001636

Other availability

Search WorldCat to find libraries that may hold this journal

Figures / Tables:

Figure 1: Z-contrast STEM image and component maps of palladium on carbon from Joint-NMF. From left to right: High angle annular darkfield (HAADF)-STEM signal, lacey carbon support, palladium component, carbon contamination. The length of the scale bar is 2 nm.

All figures and tables (2 total)

Save / Share:

Export Metadata

Save to My Library

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

Figures / Tables found in this record:

Figure 1 (p. 3)

Figure 2 (p. 3)

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

Similar Records in DOE PAGES and OSTI.GOV collections:

Probing individual single atom electrocatalyst sites by advanced analytical scanning transmission electron microscopy

Journal Article Zachman, Michael J. ; Serov, Alexey ; Lyu, Xiang ; ... - Electrochimica Acta

Single atom electrocatalysts (SAEs) are promising next-generation materials for promoting a variety of important reactions, such as the oxygen reduction, nitrogen reduction, and CO2 reduction reactions. While bulk characterization techniques such as X-ray absorption spectroscopy and Mössbauer spectroscopy have significantly enhanced our understanding of these catalysts, direct probing of individual single metal atom sites at the atomic scale is necessary to understand local variations in the properties of these sites and accelerate design and synthesis of improved SAEs. Aberration-corrected scanning transmission electron microscopy (STEM) has become a powerful tool for providing this type of atomic-scale information about SAE metal sites.more »« less
https://doi.org/10.1016/j.electacta.2023.143205
Direct Characterization of Atomically Dispersed Catalysts: Nitrogen-Coordinated Ni Sites in Carbon-Based Materials for CO₂ Electroreduction

Journal Article David, Koshy ; Landers, Alan T. ; Cullen, David A. ; ... - Advanced Energy Materials

Abstract Metal, nitrogen‐doped carbon materials have attracted interest as heterogenous catalysts that contain MN _x active sites that are analogous to molecular catalysts. Of particular interest is Ni,N‐doped carbon, a catalyst that is active for the electrochemical reduction of CO ₂ to CO. Critical to the understanding of these materials is proof of single atomic sites and characterization of the environment surrounding the metal atom; however, directly probing this coordination remains challenging. This challenge is addressed by combining scanning transmission electron microscopy (STEM), single atom electron energy loss spectroscopy (EELS), and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Through STEM imaging,more »« less
https://doi.org/10.1002/aenm.202001836

Full Text Available
Atomic-Scale Characterization of Oxide Interfaces and Superlattices Using Scanning Transmission Electron Microscopy

Book Spurgeon, Steven R. ; Chambers, Scott A.

Scanning transmission electron microscopy (STEM) has become one of the fundamental tools to characterize oxide interfaces and superlattices. Atomic-scale structure, chemistry, and composition mapping can now be conducted on a wide variety of materials systems thanks to the development of aberration-correctors and advanced detectors. STEM imaging and diffraction, coupled with electron energy loss (EELS) and energy-dispersive X-ray (EDS) spectroscopies, offer unparalleled, high-resolution analysis of structure-property relationships. In this chapter we highlight investigations into key phenomena, including interfacial conductivity in oxide superlattices, charge screening effects in magnetoelectric heterostructures, the design of high-quality iron oxide interfaces, and the complex physics governing atomic-scalemore »« less
https://doi.org/10.1016/B978-0-12-409547-2.12877-X
Direct atomic-scale imaging of ceramic interfaces

Journal Article Dickey, E C ; Fan, X ; Pennycook, S J - Acta Materialia

Understanding the atomic structure and chemistry of internal interfaces is often critical to developing interface structure-property relationships. Results are presented from several studies in which Z-contrast scanning transmission electron microscopy (STEM) and electron energy loss spectroscopy (EELS) have been employed to solve the atomic structures of oxide interfaces. The Z-contrast imaging technique directly reveals the projected cation sublattices constituting the interface, while EELS provides chemical and local electronic structure information. Because Z-contrast imaging and EELS can be performed simultaneously, direct correlations between structure and chemistry can be made at the atomic scale. The utility of Z-contrast imaging and EELS ismore »« less
https://doi.org/10.1016/S1359-6454(99)00266-9
Materials Characterization in the Aberration-Corrected Scanning Transmission Electron Microscope

Journal Article Varela del Arco, Maria ; Lupini, Andrew R ; van Benthem, Klaus ; ... - Annual Review of Materials Research

In the nanoscience era, the properties of many exciting new materials and devices will depend on the details of their composition down to the level of single atoms. Thus the characterization of the structure and electronic properties of matter at the atomic scale is becoming ever more vital for economic and technological as well as for scientific reasons. The combination of atomic-resolution Z-contrast scanning transmission electron microscopy (STEM) and electron energy loss spectroscopy (EELS) represents a powerful method to link the atomic and electronic structure to macroscopic properties, allowing materials, nanoscale systems, and interfaces to be probed in unprecedented detail.more »« less
https://doi.org/10.1146/annurev.matsci.35.102103.090513

Similar Records

Title: Machine Learning for Challenging EELS and EDS Spectral Decomposition

Abstract

Citation Formats

Figures / Tables:

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

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