Illuminating the Electronic Properties of WS2 Polytypism with Electron Microscopy. Issue 3 (20th January 2021)

• Record Type: Journal Article
• Authors: van Heijst, Sabrya E.; Mukai, Masaki; Okunishi, Eiji; Hashiguchi, Hiroki; Roest, Laurien I.; Maduro, Louis; Rojo, Juan; Conesa‐Boj, Sonia
• Is Part Of: Annalen der Physik. Volume 533:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-20
• Subjects: machine learning methods -- polytypes -- transition metal dichalcogenides; Physics -- Periodicals; Chemistry -- Periodicals; 530.05
• DOI: 10.1002/andp.202000499
• Languages: English
• ISSNs: 0003-3804

Abstract

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 E BG = 1 . 6 − 0.2 + 0.3 eV . 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. Abstract : Flower‐like WS2 nanostructures exhibiting a mixed 2H/3R polytype are fingerprinted with transmission electron microscopy. Their morphological features are correlated with local electronic properties such as plasmons using electron energy‐loss spectroscopy (EELS). Machine learning is deployed to characterize the bandgap of the 2H/3R polytype. Energy‐gain peaks in the EEL spectra, exhibiting a gain‐to‐loss ratio greater than unity, are identified.