Published August 7, 2024 | Version Published
Journal Article Open

Length and torsion dependence of thermal conductivity in twisted graphene nanoribbons

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Federal University of Pernambuco

Abstract

Research on the physical properties of materials at the nanoscale is crucial for the development of breakthrough nanotechnologies. One of the key properties to consider is the ability to conduct heat, i.e., its thermal conductivity. Graphene is a remarkable nanostructure with exceptional physical properties, including one of the highest thermal conductivities (TCs) ever measured. Graphene nanoribbons (GNRs) share most fundamental properties with graphene, with the added benefit of having a controllable electronic bandgap. One method to achieve such control is by twisting the GNR, which can tailor its electronic properties, as well as change their TCs. Here, we revisit the dependence of the TC of twisted GNRs (TGNRs) on the number of applied turns to the GNR by calculating more precise and mathematically well defined geometric parameters related to the TGNR shape, namely, its twist and writhe. We show that the dependence of the TC on twist is not a simple function of the number of turns initially applied to a straight GNR. In fact, we show that the TC of TGNRs requires at least two parameters to be properly described. Our conclusions are supported by atomistic molecular dynamics simulations to obtain the TC of suspended TGNRs prepared under different values of initially applied turns and different sizes of their suspended part. Among possible choices of parameter pairs, we show that TC can be appropriately described by the initial number of turns and the initial twist density of the TGNRs.

Copyright and License

©2024 American Physical Society

Acknowledgement

This work was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)–Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), São Paulo Research Foundation (FAPESP), Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE), and Financiadora de Estudos e Projetos (FINEP). A.F.F. acknowledges Grant No. 303284/2021-8 from CNPq, and Grants No. 2020/02044-9 and No. 2023/02651-0 from FAPESP. L.F.C.P. acknowledges Grant No. 0041/2022 from CAPES; Grants No. 436859/2018, No. 313462/2020, No. 200296/2023-0, and No. 371610/2023-0 INCT Materials Informatics from CNPq, Grant No. APQ-1117-1.05/22 from FACEPE, Grant No. 0165/21 from FINEP, and the visiting professors program at Sapienza. This work used computational resources provided by “Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD-SP)” (project proj937), and by the John David Rogers Computing Center (CCJDR) in the Gleb Wataghin Institute of Physics, University of Campinas.

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Additional details

Funding

Coordenação de Aperfeicoamento de Pessoal de Nível Superior
001
Coordenação de Aperfeicoamento de Pessoal de Nível Superior
0041/2022
National Council for Scientific and Technological Development
303284/2021-8
National Council for Scientific and Technological Development
436859/2018
National Council for Scientific and Technological Development
313462/2020
National Council for Scientific and Technological Development
200296/2023-0
National Council for Scientific and Technological Development
371610/2023-0
Fundação de Amparo à Pesquisa do Estado de São Paulo
2020/02044-9
Fundação de Amparo à Pesquisa do Estado de São Paulo
2023/02651-0
Financiadora de Estudos e Projetos
0165/21
Fundação de Amparo à Ciência e Tecnologia de Pernambuco
APQ-1117-1.05/22

Dates

Accepted
2024-07-24
Accepted paper

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Publication Status
Published