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Spatially-resolved bandgap and dielectric function in two-dimensional materials from Electron Energy Loss Spectroscopy

Published

Author(s)

Abel Brokkelkamp, Jaco ter Hoeve, Isabel Postmes, Sabrya van Heijst, Luigi Maduro, Albert Davydov, Sergiy Krylyuk, Juan Rojo, Sonia Conesa Boj

Abstract

The electronic properties of two-dimensional (2D) materials depend sensitively on the underlying atomic arrangement down to the monolayer level. Here we present a novel strategy for the determination of the band gap and complex dielectric function in 2D materials achieving a spatial resolution down to a few nanometers. This approach is based on machine learning techniques developed in particle physics and makes possible the automated processing and interpretation of spectral images from electron energy loss spectroscopy (EELS). Individual spectra are classified as a function of the thickness with K-means clustering, and then used to train a deep-learning model of the zeroloss peak background. As a proof of concept we assess the band gap and dielectric function of InSe flakes and polytypic WS2 nanoflowers and correlate these electrical properties with the local thickness. Our flexible approach is generalizable to other nanostructured materials and to higher-dimensional spectroscopies and is made available as a new release of the open-source EELSfitter framework.
Citation
Journal of Physical Chemistry A
Volume
126

Keywords

Machine learning, EELS, bandgap

Citation

Brokkelkamp, A. , ter Hoeve, J. , Postmes, I. , van Heijst, S. , Maduro, L. , Davydov, A. , Krylyuk, S. , Rojo, J. and Boj, S. (2022), Spatially-resolved bandgap and dielectric function in two-dimensional materials from Electron Energy Loss Spectroscopy, Journal of Physical Chemistry A, [online], https://doi.org/10.1021/acs.jpca.1c09566, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933548 (Accessed August 18, 2022)
Created February 15, 2022, Updated April 27, 2022