Simulation insights into thermally conductive graphene-based nanocomposites

Abstract

Dispersing nanoparticles in a polymer can enhance both mechanical and transport properties. Nanocomposites with high thermal conductivity could be obtained by using thermally conductive nanoparticles. Carbon-based nanoparticles are extremely promising, although high resistances to heat transfer from the nanoparticles to the polymer matrix could cause significant limitations. This work focuses on graphene sheets (GS) dispersed within n-octane. Although pristine GS agglomerate, equilibrium molecular dynamic simulations suggest that when the GS are functionalized with short branched hydrocarbons along the GS edges, they remain well dispersed. Results are reported from equilibrium and non-equilibrium molecular dynamics simulations to assess the effective interactions between dispersed GS, the self-assembly of GS, and the heat transfer through the GS–octane nanocomposite. Tools are designed to understand the effect of GS size, solvent molecular weight and molecular architecture on GS dispersability and GS–octane thermal conductivity. Evidence is provided for the formation of nematic phases when the GS volume fraction increases within octane. The atomic-level results are input for a coarse-grained Monte Carlo simulation that predicts anisotropic thermal conductivity for GS-based composites when the GS show nematic phases.

Keywords:

• multi-scale simulations
• graphene
• thermal conductivity

Acknowledgements

This work was supported in part by the DoE-funded Center for Applications of Single-Walled Carbon Nanotubes – (Award Register#: ER64239 0012293). DVP and AS acknowledge partial financial support by DOD-EPSCOR: FA9550-10-1-0031. DK and AS acknowledge additional support provided by DOE: DE-SC0001902. The computations were carried out at the OU Supercomputing Center for Education and Research (OSCER); at the National Center for Supercomputing Applications at the University of Illinois (NCSA) under TeraGrid allocations TG-CTS-090017 and TG-CTS080042; and at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkley National Laboratory.