Combining tensile test results with atomistic predictions of elastic modulus of graphene/polyamide-6,6 nanocomposites

Official URL: https://dx.doi.org/10.1016/j.mtcomm.2023.105636

In this work, we combined tensile test results with atomistic simulations to investigate the effect of filler parameters including distribution, stacking, loading and lateral graphene size on elastic moduli of graphene/PA-6,6 nanocomposites. Stacked and randomly distributed atomistic models were adapted in Molecular Dynamics (MD) simulations to establish the limits of stiffness enhancement in graphene reinforced PA-6,6 nanocomposites with loading ratios changing from 0 to 1 wt%. Experimental results showed that incorporating of 0.3–0.4 wt% graphene loading improved the elastic modulus of the neat polymer by 41.7%−43.5%. While the test sample behaved close to the computational results of the stacked atomistic model at low graphene loadings up to 0.4 wt%, it overshot the predictions of the randomly distributed model at all considered loadings up to 1 wt%. Elastic moduli of graphene-based PA-6,6 nanocomposites increased linearly with graphene loading in the stacked model, however, no such relation was detected in the randomly distributed model. The lower stiffness enhancement provided by the randomly distributed model compared to the stacked model was revealed as the small lateral size of graphene plates in PA-6,6 matrix. As the graphene size increased, the elastic modulus of the graphene dramatically increased, directly improving the elastic modulus of the nanocomposite. The developed computational approach is highly useful to estimate the boundaries of stiffness enhancement provided by graphene dispersions in macroscale nanocomposite samples.