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Research Article

Direct identification of monolayer rhenium diselenide by an individual diffraction pattern

Zhen Fei1Bo Wang2Ching-Hwa Ho3Fang Lin4Jun Yuan1,5Ze Zhang1Chuanhong Jin1( )
State Key Laboratory of Silicon MaterialsSchool of Materials Science and Engineering, Zhejiang UniversityHangzhou310027China
School of Physical Science and TechnologyLanzhou UniversityLanzhou730000China
Graduate Institute of Applied Science and TechnologyTaiwan University of Science and Technology, Taipei106TaiwanChina
College of Electronic EngineeringSouth China Agricultural UniversityGuangzhou510642China
Department of PhysicsUniversity of York, HeslingtonYork YO105DDUK
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Abstract

In the current extensive studies of layered two-dimensional (2D) materials, compared to hexagonal structures such as graphene, hBN, and MoS2, low- symmetry 2D materials have shown great potential for applications in anisotropic devices. Rhenium diselenide (ReSe2) possesses the bulk space group P1 and belongs to the triclinic crystal system with a deformed cadmium-iodide-type structure. Here, we propose an electron diffraction-based method to distinguish the monolayer ReSe2 membrane from multilayer ReSe2 and its two different vertical orientations. Our method is also applicable to other low-symmetry crystal systems, including both triclinic and monoclinic lattices, as long as their third unit-cell basis vectors are not perpendicular to the basal plane. Our experimental results are well explained by kinematical electron diffraction theory and the corresponding simulations. Generalization of our method to other 2D materials, such as graphene, is also discussed.

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Nano Research
Pages 2535-2544

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Cite this article:
Fei Z, Wang B, Ho C-H, et al. Direct identification of monolayer rhenium diselenide by an individual diffraction pattern. Nano Research, 2017, 10(7): 2535-2544. https://doi.org/10.1007/s12274-017-1639-7

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Received: 27 October 2016
Revised: 14 April 2017
Accepted: 18 April 2017
Published: 19 May 2017
© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2017