Effective Thermal Conductivity of Hybrid AlN/Graphene Polymer Matrix Composites: A Hierarchical Micromechanical Model

The demand for lightweight polymer composites with enhanced thermal conductivity is increasing in advanced thermal-management applications. This study develops a hierarchical micromechanical model to predict the effective thermal conductivity of hybrid polymer composites reinforced with aluminum nitride (AlN) particles and graphene nanoplatelets (GNPs). The modeling procedure is performed in two successive stages. First, the effective thermal conductivity of the GNP/polymer matrix is determined by considering the graphene volume fraction, length, thickness, and flatness ratio as well as the interfacial thermal resistance. The resulting graphene/polymer phase is then treated as an effective matrix for AlN micro-reinforcement. An analytical micromechanics formulation on the basis of the representative volume element (RVE) is used to determine the thermal conductivity of the final hybrid composite. The results show that adding graphene considerably enhances the thermal conductivity of the AlN/polymer composite. Larger graphene length also improves the heat-transfer characteristics of the hybrid polymer composite. In contrast, increasing graphene thickness reduces the thermal conductivity enhancement. It is found that the AlN content and aspect ratio strongly affect the AlN/GNP/polymer composite thermal conductivity. The proposed model provides useful guidance for designing thermally conductive polymer composite structures.