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Title: Compressive Classification for TEM-EELS

Abstract

Electron energy loss spectroscopy (EELS) is typically conducted in STEM mode with a spectrometer, or in TEM mode with energy selction. These methods produce a 3D data set (x, y, energy). Some compressive sensing [1,2] and inpainting [3,4,5] approaches have been proposed for recovering a full set of spectra from compressed measurements. In many cases the final form of the spectral data is an elemental map (an image with channels corresponding to elements). This means that most of the collected data is unused or summarized. We propose a method to directly recover the elemental map with reduced dose and acquisition time. We have designed a new computational TEM sensor for compressive classification [6,7] of energy loss spectra called TEM-EELS.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1379437
Report Number(s):
PNNL-SA-124106
Journal ID: ISSN 1431-9276; applab
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Microscopy and Microanalysis; Journal Volume: 23; Journal Issue: S1
Country of Publication:
United States
Language:
English

Citation Formats

Hao, Weituo, Stevens, Andrew, Yang, Hao, Gehm, Michael, and Browning, Nigel D. Compressive Classification for TEM-EELS. United States: N. p., 2017. Web. doi:10.1017/S1431927617001222.
Hao, Weituo, Stevens, Andrew, Yang, Hao, Gehm, Michael, & Browning, Nigel D. Compressive Classification for TEM-EELS. United States. doi:10.1017/S1431927617001222.
Hao, Weituo, Stevens, Andrew, Yang, Hao, Gehm, Michael, and Browning, Nigel D. Sat . "Compressive Classification for TEM-EELS". United States. doi:10.1017/S1431927617001222.
@article{osti_1379437,
title = {Compressive Classification for TEM-EELS},
author = {Hao, Weituo and Stevens, Andrew and Yang, Hao and Gehm, Michael and Browning, Nigel D.},
abstractNote = {Electron energy loss spectroscopy (EELS) is typically conducted in STEM mode with a spectrometer, or in TEM mode with energy selction. These methods produce a 3D data set (x, y, energy). Some compressive sensing [1,2] and inpainting [3,4,5] approaches have been proposed for recovering a full set of spectra from compressed measurements. In many cases the final form of the spectral data is an elemental map (an image with channels corresponding to elements). This means that most of the collected data is unused or summarized. We propose a method to directly recover the elemental map with reduced dose and acquisition time. We have designed a new computational TEM sensor for compressive classification [6,7] of energy loss spectra called TEM-EELS.},
doi = {10.1017/S1431927617001222},
journal = {Microscopy and Microanalysis},
number = S1,
volume = 23,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}
}