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Charting the low-loss region in electron energy loss spectroscopy with machine learning.

Authors
  • Roest, Laurien I1
  • van Heijst, Sabrya E2
  • Maduro, Louis2
  • Rojo, Juan3
  • Conesa-Boj, Sonia4
  • 1 Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands; Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands. , (Netherlands)
  • 2 Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands. , (Netherlands)
  • 3 Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands; Department of Physics and Astronomy, VU, 1081 HV Amsterdam, The Netherlands. , (Netherlands)
  • 4 Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands. Electronic address: [email protected] , (Netherlands)
Type
Published Article
Journal
Ultramicroscopy
Publication Date
Jan 09, 2021
Volume
222
Pages
113202–113202
Identifiers
DOI: 10.1016/j.ultramic.2021.113202
PMID: 33453606
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS2 nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS2, finding EBG=1.6-0.2+0.3eV with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source Python package dubbed EELSfitter. Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

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