Modelling mechanical percolation in graphene-reinforced elastomer nanocomposites

TL;DR

This paper develops a new micromechanical model to describe the mechanical percolation and reinforcement mechanisms in graphene-reinforced elastomer nanocomposites, aligning well with experimental data and explaining the limits of graphene's reinforcing potential.

Contribution

A novel set of equations modeling mechanical percolation in graphene-elastomer nanocomposites, integrating shear-lag theory and rule-of-mixtures for below and above percolation thresholds.

Findings

Reinforcement below Vp is due to stress transfer by individual GNPs.

Above Vp, nanocomposites stiffen rapidly as flakes come closer.

Model aligns with experimental data and explains graphene's reinforcement limits.

Abstract

Graphene is considered an ideal filler for the production of multifunctional nanocomposites; as a result, considerable efforts have been focused on the evaluation and modeling of its reinforcement characteristics. In this work, we modelled successfully the mechanical percolation phenomenon, observed on a thermoplastic elastomer (TPE) reinforced by graphene nanoplatelets (GNPs), by designing a new set of equations for filler contents below and above the percolation threshold volume fraction (Vp). The proposed micromechanical model is based on a combination of the well-established shear-lag theory and the rule-of-mixtures and was introduced to analyse the different stages and mechanisms of mechanical reinforcement. It was found that when the GNPs content is below Vp, reinforcement originates from the inherent ability of individual GNPs flakes to transfer stress efficiently. Furthermore, at higher filler contents and above Vp, the nanocomposite materials displayed accelerated stiffening due to the reduction of the distance between adjacent flakes. The model derived herein, was consistent with the experimental data and the reasons why the superlative properties of graphene cannot be fully utilized in this type of composites, were discussed in depth.