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van der Waals Graphene Kirigami Heterostructure for Strain-Controlled Thermal Transparency

By Yuan Gao (159945) and Baoxing Xu (1544218)


Programming thermal transport across interfaces by engineering strain is of critical importance for exploring mechanical controllable and thermal manageable devices with multifunctionalities. Here, we report a van der Waals heterostructure that is composed of bilayer graphene kirigami with diverse layer cut patterns and assembly organizations and show that the thermal flow intensity across the van der Waals interfaces, named as thermal transparency, could be continuously regulated by applying an external in-plane tensile strain. The density of atomic interactions across the interfaces and the distribution of delocalized phonon modes in each graphene kirigami are elucidated to understand the underlying thermal transport mechanism and are also incorporated into a theoretical model for quantitative predictions of thermal conductance under mechanical strain. A proof-of-conceptual van der Waals graphene kirigami heterostructure by design is proposed and validated through extensive full-scale atomistic simulations on the feasibility and reliability of regulating the transparency ratio of thermal transport by mechanical strain, demonstrating its potential applications in thermal and electronic devices

Topics: Biophysics, Biochemistry, Cell Biology, Genetics, Molecular Biology, Physiology, Biotechnology, Developmental Biology, Inorganic Chemistry, Infectious Diseases, Computational Biology, Space Science, Chemical Sciences not elsewhere classified, Physical Sciences not elsewhere classified, van der Waals Graphene Kirigami Heterostructure, atomistic simulations, engineering strain, delocalized phonon modes, flow intensity, transparency ratio, device, bilayer graphene kirigami, assembly organizations, proof-of-conceptual van der Waals graphene kirigami heterostructure, transport mechanism, graphene kirigami, Transparency Programming, van der Waals interfaces, van der Waals heterostructure
Year: 2018
DOI identifier: 10.1021/acsnano.8b05868.s001
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Provided by: FigShare
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