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  5. Tunable van Hove singularities and correlated states in twisted monolayer-bilayer graphene

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Article
English
2021

Tunable van Hove singularities and correlated states in twisted monolayer-bilayer graphene

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English
2021

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Konstantin ‘kostya’  Novoselov
Konstantin ‘kostya’ Novoselov

The University of Manchester

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Shuigang Xu
Mohammed M. Al Ezzi
Nilanthy Balakrishnan
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Abstract

Understanding and tuning correlated states is of great interest and importance to modern condensed-matter physics. The recent discovery of unconventional superconductivity and Mott-like insulating states in magic-angle twisted bilayer graphene presents a unique platform to study correlation phenomena, in which the Coulomb energy dominates over the quenched kinetic energy as a result of hybridized flat bands. Extending this approach to the case of twisted multilayer graphene would allow even higher control over the band structure because of the reduced symmetry of the system. Here we study electronic transport properties of twisted monolayer–bilayer graphene (a bilayer on top of monolayer graphene heterostructure). We observe the formation of van Hove singularities that are highly tunable by changing either the twist angle or external electric field and can cause strong correlation effects under optimum conditions. We provide basic theoretical interpretations of the observed electronic structure. A structure of monolayer and bilayer graphene with a small twist between them shows correlated insulating states that can be tuned by changing the twist angle or applying an electric field.

How to cite this publication

Shuigang Xu, Mohammed M. Al Ezzi, Nilanthy Balakrishnan, Aitor García-Ruiz, Bonnie Tsim, Ciaran Mullan, Julien Barrier, Na Xin, B. A. Piot, Takashi Taniguchi, Kenji Watanabe, Alexandra Carvalho, Artem Mishchenko, AK Geim, Vladimir I. Fal’ko, Shaffique Adam, A. H. Castro Neto, Konstantin ‘kostya’ Novoselov, Yanmeng Shi (2021). Tunable van Hove singularities and correlated states in twisted monolayer-bilayer graphene.

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Publication Details

Type

Article

Year

2021

Authors

19

Datasets

0

Total Files

0

Language

English

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