Efficiently Controlling the 3D Thermal Conductivity of a Polymer Nanocomposite via a Hyperelastic Double-Continuous Network of Graphene and Sponge

Graphene-reinforced polymer composites with high thermal conductivity show attractive prospects as thermal transfer materials in many applications such as intelligent robotic skin. However, for the most reported composites, precise control of the thermal conductivity is not easily achieved, and the improvement efficiency is usually low. To effectively control the 3D thermal conductivity of graphene-reinforced polymer nanocomposites, a hyperelastic double-continuous network of graphene and sponge is developed. The structure (orientation, density) and thermal conductivity (in-plane, cross-plane) of the resulting composites can be effectively controlled by adjusting the preparation and deformation parameters (unidirectional, multidirectional) of the network. Based on the experimental and theoretical simulation results, the thermal conduction mechanism is summarized as a two-stage transmission of phonons. The in-plane thermal conductivity increases from 0.175 to 1.68 W m(-1) K-1 when the directional compression ratio increases from 0% to 95%, and the corresponding enhancement efficiency exceeds 300. The 3D thermal conductivity reaches a maximum of 2.19 W m(-1) K-1 when the compression ratio is 70% in three directions, and the graphene content is 4.82 wt%. Moreover, the thermal conduction network can be largely prepared by power-driven roller equipment, making the composite an ideal candidate for sensitive robotic skin for temperature detection.