Tailoring the strength and ductility of graphene/metal composites with percolation network

The graphene network architecture is a promising structure for improving both strength and ductility of graphene/metal composites that has attracted widespread attention. A key issue in designing and optimizing networked graphene/metal composites is the development of theoretical model that can quantitatively predict their overall mechanical properties. Herein, we present a novel computational framework to investigate the effect of graphene percolation network on the strength-ductility of graphene/metal composites utilizing the Cauchy's probabilistic model, the field fluctuation method and the irreversible thermodynamics principle. Simultaneously, the accuracy of multiscale model is demonstrated by the reported experimental data. To detect how the strength and ductility of networked graphene/metal composites are elevated synchronously, the influences of the primary microstructural parameters including percolation threshold, graphene concentration and aspect ratio on the strength-ductility are quantitatively assessed. Overall, the computational framework can provide theoretical guidance for the architecture optimization of networked graphene/metal composites.

Automated summary

The study presents a novel computational framework to investigate the effect of graphene percolation network on the strength-ductility of graphene/metal composites. It utilizes the Cauchy's probabilistic model, the field fluctuation method, and the irreversible thermodynamics principle.