Multiscale modeling and analysis of synergistic reduction of the thermal resistance of polymer composites via hybrid carbon nanotube/graphene nanoplatelet

Synergistic effect has been experimentally demonstrated as a key phenomenon to reduce the thermal resistance and enhance thermal conductivity of polymer composites with nanofiller hybrids including carbon nanotube (CNT) and graphene nanoplatelet (GNP). But there is a lack of theory that can satisfactorily explain the related mechanisms. We present a study based on the effective medium theory to describe the correlation between the thermal conductivity of a composite and the specific information of its nanofiller hybrids, i.e., their aspect ratios, volume fractions, and various thermal resistance. A new model is established and is validated by experimental data. It helps to unveil the mechanisms of the observed synergistic effect of the CNT/GNP thermal networks. It is shown that the prominent enhancement in the effective thermal conductivity is largely due to the low CNT–GNP interfacial thermal resistance R_bd^c−g, which is one magnitude lower than CNT–CNT or GNP–GNP resistance (R_bd^c−c, R_bd^g−g) in the hybrid thermal networks. Based on the developed model, one is able to achieve the highest thermal-conductivity enhancement for the composite by optimizing the CNT/GNP volume ratio. The thermal conductivity enhancement of the composite is further investigated by considering the spatial orientation and alignment arrangement of the nanofiller hybrids.