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

Abel Brokkelkamp; Jaco Ter Hoeve; Isabel Postmes; Sabrya E. Van Heijst; Louis Maduro; Albert V. Davydov; Sergiy Krylyuk; Juan Rojo; Sonia Conesa-Boj

doi:10.1021/acs.jpca.1c09566

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

Abel Brokkelkamp, Jaco Ter Hoeve, Isabel Postmes, Sabrya E. Van Heijst, Louis Maduro, Albert V. Davydov, Sergiy Krylyuk, Juan Rojo, Sonia Conesa-Boj^*

^*Corresponding author for this work

(Astro)-Particles Physics

Research output: Contribution to Journal › Article › Academic › peer-review

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 zero-loss peak background. As a proof of concept we assess the band gap and dielectric function of InSe flakes and polytypic WS₂nanoflowers 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.

Original language	English
Pages (from-to)	1255-1262
Number of pages	8
Journal	Journal of Physical Chemistry A
Volume	126
Issue number	7
Early online date	15 Feb 2022
DOIs	https://doi.org/10.1021/acs.jpca.1c09566
Publication status	Published - 24 Feb 2022

Bibliographical note

Funding Information:
A.B. and S.C.-B. acknowledge financial support from the ERC through the starting grant “TESLA”, Grant Agreement No. 805021. L.M. acknowledges support from The Netherlands Organizational for Scientific Research (NWO) through the Nanofront program. The work of J.R. has been partially supported by NWO. The work of J.t.H. is funded by NWO via an ENW-KLEIN-2 project. S.K. and A.V.D. acknowledge support through the Materials Genome Initiative funding allocated to NIST.

Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.

Funding

A.B. and S.C.-B. acknowledge financial support from the ERC through the starting grant “TESLA”, Grant Agreement No. 805021. L.M. acknowledges support from The Netherlands Organizational for Scientific Research (NWO) through the Nanofront program. The work of J.R. has been partially supported by NWO. The work of J.t.H. is funded by NWO via an ENW-KLEIN-2 project. S.K. and A.V.D. acknowledge support through the Materials Genome Initiative funding allocated to NIST.

Funders	Funder number
Netherlands Organizational for Scientific Research
National Institute of Standards and Technology
Horizon 2020 Framework Programme	805021
European Research Council
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1021/acs.jpca.1c09566

Persistent URL (handle)

Copy link address

Cite this

@article{dc5e1f13d2304061ba3f83fd15419d71,

title = "Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy",

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 zero-loss peak background. As a proof of concept we assess the band gap and dielectric function of InSe flakes and polytypic WS2nanoflowers 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.",

author = "Abel Brokkelkamp and {Ter Hoeve}, Jaco and Isabel Postmes and {Van Heijst}, {Sabrya E.} and Louis Maduro and Davydov, {Albert V.} and Sergiy Krylyuk and Juan Rojo and Sonia Conesa-Boj",

note = "Funding Information: A.B. and S.C.-B. acknowledge financial support from the ERC through the starting grant “TESLA”, Grant Agreement No. 805021. L.M. acknowledges support from The Netherlands Organizational for Scientific Research (NWO) through the Nanofront program. The work of J.R. has been partially supported by NWO. The work of J.t.H. is funded by NWO via an ENW-KLEIN-2 project. S.K. and A.V.D. acknowledge support through the Materials Genome Initiative funding allocated to NIST. Publisher Copyright: {\textcopyright} 2022 American Chemical Society. All rights reserved.",

year = "2022",

month = feb,

day = "24",

doi = "10.1021/acs.jpca.1c09566",

language = "English",

volume = "126",

pages = "1255--1262",

journal = "Journal of Physical Chemistry A",

issn = "1089-5639",

publisher = "American Chemical Society",

number = "7",

}

TY - JOUR

T1 - Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy

AU - Brokkelkamp, Abel

AU - Ter Hoeve, Jaco

AU - Postmes, Isabel

AU - Van Heijst, Sabrya E.

AU - Maduro, Louis

AU - Davydov, Albert V.

AU - Krylyuk, Sergiy

AU - Rojo, Juan

AU - Conesa-Boj, Sonia

N1 - Funding Information: A.B. and S.C.-B. acknowledge financial support from the ERC through the starting grant “TESLA”, Grant Agreement No. 805021. L.M. acknowledges support from The Netherlands Organizational for Scientific Research (NWO) through the Nanofront program. The work of J.R. has been partially supported by NWO. The work of J.t.H. is funded by NWO via an ENW-KLEIN-2 project. S.K. and A.V.D. acknowledge support through the Materials Genome Initiative funding allocated to NIST. Publisher Copyright: © 2022 American Chemical Society. All rights reserved.

PY - 2022/2/24

Y1 - 2022/2/24

N2 - 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 WS2nanoflowers 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.

AB - 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 WS2nanoflowers 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.

UR - http://www.scopus.com/inward/record.url?scp=85125115606&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85125115606&partnerID=8YFLogxK

U2 - 10.1021/acs.jpca.1c09566

DO - 10.1021/acs.jpca.1c09566

M3 - Article

C2 - 35167301

AN - SCOPUS:85125115606

SN - 1089-5639

VL - 126

SP - 1255

EP - 1262

JO - Journal of Physical Chemistry A

JF - Journal of Physical Chemistry A

IS - 7

ER -

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

Abstract

Bibliographical note

Funding

UN SDGs

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Cite this