Illuminating the Electronic Properties of WS2 Polytypism with Electron Microscopy

Sabrya E. van Heijst, Masaki Mukai, Eiji Okunishi, Hiroki Hashiguchi, Laurien I. Roest, Louis Maduro, Juan Rojo, Sonia Conesa-Boj*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Tailoring the specific stacking sequence (polytypes) of layered materials represents a powerful strategy to identify and design novel physical properties. While nanostructures built upon transition-metal dichalcogenides (TMDs) with either the 2H or 3R crystalline phases have been routinely studied, knowledge of TMD nanomaterials based on mixed 2H/3R polytypes is far more limited. In this work, mixed 2H/3R free-standing WS2 nanostructures displaying a flower-like configuration are fingerprinted by means of state-of-the-art transmission electron microscopy. Their rich variety of shape-morphology configurations is correlated with relevant local electronic properties such as edge, surface, and bulk plasmons. Machine learning is deployed to establish that the 2H/3R polytype displays an indirect band gap of (Formula presented.). Further, high resolution electron energy-loss spectroscopy reveals energy-gain peaks exhibiting a gain-to-loss ratio greater than unity, a property that can be exploited for cooling strategies of atomically-thin TMD nanostructures and devices built upon them. The findings of this work represent a stepping stone towards an improved understanding of TMD nanomaterials based on mixed crystalline phases.

Original languageEnglish
Article number2000499
Pages (from-to)1-9
Number of pages9
JournalAnnalen der Physik
Volume533
Issue number3
Early online date20 Jan 2021
DOIs
Publication statusPublished - Mar 2021

Bibliographical note

Funding Information:
The authors are grateful to Emanuele R. Nocera and Jacob J. Ethier for assistance in installing an EELSfitter in the Nikhef computing cluster. They also acknowledge discussions with Javier Garcia de Abajo on energy‐gain phenomena in TMDs. S.E.v.H. 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 Topconsortia voor Kennis en Innovatie (TKI) program. The work of J.R. has been partially supported by NWO.

Publisher Copyright:
© 2021 The Authors. Annalen der Physik published by Wiley-VCH GmbH

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

The authors are grateful to Emanuele R. Nocera and Jacob J. Ethier for assistance in installing an EELSfitter in the Nikhef computing cluster. They also acknowledge discussions with Javier Garcia de Abajo on energy‐gain phenomena in TMDs. S.E.v.H. 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 Topconsortia voor Kennis en Innovatie (TKI) program. The work of J.R. has been partially supported by NWO.

FundersFunder number
Netherlands Organizational for Scientific Research
Topconsortia voor Kennis en Innovatie
Horizon 2020 Framework Programme805021
European Research Council
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

    Keywords

    • machine learning methods
    • polytypes
    • transition metal dichalcogenides

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