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Atomic-scale finite element modelling of mechanical behaviour of graphene nanoribbons

  • D. A. Damasceno
  • E. Mesquita
  • R. K. N. D. Rajapakse
  • R. Pavanello
Article
  • 118 Downloads

Abstract

Experimental characterization of Graphene NanoRibbons (GNRs) is still an expensive task and computational simulations are therefore seen as a practical option to study the properties and mechanical response of GNRs. Design of GNR elements in various nanotechnology devices can be approached through molecular dynamics simulations. This study demonstrates that the atomic-scale finite element method (AFEM) based on the second generation REBO potential is an efficient and accurate alternative to the molecular dynamics simulation of GNRs. Special atomic finite elements are proposed to model graphene edges. Extensive comparisons are presented with MD solutions to establish the accuracy of AFEM. It is also shown that the Tersoff potential is not accurate for GNR modeling. The study demonstrates the influence of chirality and size on design parameters such as tensile strength and stiffness. Graphene is stronger and stiffer in the zigzag direction compared to the armchair direction. Armchair GNRs shows a minor dependence of tensile strength and elastic modulus on size whereas in the case of zigzag GNRs both modulus and strength show a significant size dependency. The size-dependency trend noted in the present study is different from the previously reported MD solutions for GNRs but qualitatively agrees with experimental results. Based on the present study, AFEM can be considered a highly efficient computational tool for analysis and design of GNRs.

Keywords

Atomistic simulation Elastic modulus Graphene Nanoribbons Tensile strength 

Notes

Acknowledgements

The study was funded by the São Paulo Research Foundation (Fapesp) through Grants 2012/17948-4, 2013/23085-1, 2015/00209-2 and 2013/08293-7 (CEPID). Support from CAPES and CNPQ is also acknowledged.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computational MechanicsUniversity of CampinasCampinasBrazil
  2. 2.School of Engineering ScienceSimon Fraser UniversityBurnabyCanada

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