Multiscale Modeling of High Thermal Conductivity in Graphene Copper Composites

Graphene-copper (Gr-Cu) composites have emerged as promising materials for high-efficiency thermal management systems due to graphene's exceptional intrinsic thermal conductivity and copper's superior heat transfer capabilities. This study presents an integrated multiscale modeling approach-combining molecular dynamics (MD), finite element analysis (FEA), and experimental validation-to investigate and optimize the thermal transport properties of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Gr}-\text{Cu}$</tex> composites. Various parameters such as graphene volume fraction, dispersion, interfacial bonding, and orientation are analyzed for their impact on effective thermal conductivity. Experimentally synthesized composites using spark plasma sintering (SPS) are characterized via laser flash analysis (LFA) and SEM imaging. The results demonstrate that proper alignment and interface engineering lead to a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.8 x$</tex> enhancement in thermal conductivity compared to pure copper. This synergistic modeling-experimental strategy offers a robust framework for designing next-generation thermal interface materials for aerospace, electronics, and energy applications.