Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy.

Autor:	Brokkelkamp A; Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands., Ter Hoeve J; Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands.; Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands., Postmes I; Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands., van Heijst SE; Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands., Maduro L; Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands., Davydov AV; Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States., Krylyuk S; Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States., Rojo J; Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands.; Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands., Conesa-Boj S; Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands.
Jazyk:	angličtina
Zdroj:	The journal of physical chemistry. A [J Phys Chem A] 2022 Feb 24; Vol. 126 (7), pp. 1255-1262. Date of Electronic Publication: 2022 Feb 15.
DOI:	10.1021/acs.jpca.1c09566
Abstrakt:	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 zero-loss peak background. As a proof of concept we assess the band gap and dielectric function of InSe flakes and polytypic WS 2 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.
Databáze:	MEDLINE
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