Thermal transport across carbon nanotube-graphene covalent and van der Waals junctions

Carbon nanotubes and graphene are promising materials for thermal management applications due to their high thermal conductivities. However, their thermal properties are anisotropic, and the radial or cross-plane direction thermal conductivity is low. A 3D Carbon nanotube (CNT)-graphene structure has previously been proposed to address this limitation, and direct molecular dynamics simulations have been used to predict the associated thermal conductivity. In this work, by recognizing that thermal resistance primarily comes from CNT-graphene junctions, a simple network model of thermal transport in pillared graphene structure is developed. Using non-equilibrium molecular dynamics, the resistance across an individual CNT-graphene junction with sp covalent bonds is found to be around 6 10 11 mK/W, which is significantly lower than typical values reported for planar interfaces between dissimilar materials. In contrast, the resistance across a van der Waals junction is about 4 10 8 mK/W. Interestingly, when the CNT pillar length is small, the interfacial resistance of the sp covalent junction is found to decrease as the CNT pillar length decreases, suggesting the presence of coherence effects. To explain this intriguing trend, the junction thermal resistance is decomposed into interfacial region and boundary components, and it is found that while the boundary resistance has little dependence on the pillar length, the interfacial region resistance decreases as the pillar length decreases. This is explained by calculating the local phonon density of states (LDOS) of different regions near the boundary. The LDOS overlap between the interfacial region and the center region of CNT increases as the pillar length decreases, leading to the decrease of interfacial region resistance. The junction resistance Rj is eventually used in the network model to estimate the effective thermal conductivity, and the results agree well with direct MD simulation data, demonstrating the effectiveness of our model. VC 2015 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4927273]