Engineering of Numerous Moiré Superlattices in Twisted Multilayer Graphene for Twistronics and Straintronics Applications
Because of their unique atomic structure, 2D materials are able to create an up-to-date paradigm in fundamental science and technology on the way to engineering the band structure and electronic properties of materials on the nanoscale. One of the simplest methods along this path is the superpositio...
Gespeichert in:
Veröffentlicht in: | ACS nano 2021-07, Vol.15 (7), p.12358-12366 |
---|---|
Hauptverfasser: | , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Because of their unique atomic structure, 2D materials are able to create an up-to-date paradigm in fundamental science and technology on the way to engineering the band structure and electronic properties of materials on the nanoscale. One of the simplest methods along this path is the superposition of several 2D nanomaterials while simultaneously specifying the twist angle between adjacent layers (θ), which leads to the emergence of Moiré superlattices. The key challenge in 2D nanoelectronics is to obtain a nanomaterial with numerous Moiré superlattices in addition to a high carrier mobility in a stable and easy-to-fabricate material. Here, we demonstrate the possibility of synthesizing twisted multilayer graphene (tMLG) with a number of monolayers N L = 40–250 and predefined narrow ranges of θ = 3–8°, θ = 11–15°, and θ = 26–30°. A 2D nature of the electron transport is observed in the tMLG, and its carrier mobilities are close to those of twisted bilayer graphene (tBLG) (with θ = 30°) between h-BN layers. We demonstrate an undoubtful presence of numerous Moiré superlattices simultaneously throughout the entire tMLG thickness, while the periods of these superlattices are rather close to each other. This offers a challenge of producing a next generation of devices for nanoelectronics, twistronics, and neuromorphic computing for large data applications. |
---|---|
ISSN: | 1936-0851 1936-086X |
DOI: | 10.1021/acsnano.1c04286 |