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

Journal Article (2021)
Author(s)

Laurien I. Roest (Kavli institute of nanoscience Delft)

Sabrya E. van Heijst (Kavli institute of nanoscience Delft, TU Delft - QN/Conesa-Boj Lab)

Louis Maduro (TU Delft - QN/Conesa-Boj Lab, Kavli institute of nanoscience Delft)

Juan Rojo (Vrije Universiteit Amsterdam)

Sonia Conesa Conesa-Boj (Kavli institute of nanoscience Delft, TU Delft - QN/Conesa-Boj Lab)

Research Group
QN/Conesa-Boj Lab
Copyright
© 2021 Laurien I. Roest, S.E. van Heijst, L.A. Maduro, Juan Rojo, S. Conesa Boj
DOI related publication
https://doi.org/10.1016/j.ultramic.2021.113202
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Laurien I. Roest, S.E. van Heijst, L.A. Maduro, Juan Rojo, S. Conesa Boj
Research Group
QN/Conesa-Boj Lab
Volume number
222
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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.