AFM-based model of percolation in graphene-based polymer nanocomposites

Here, we show that a prediction of conductivity in composites can be improved by replacing fitting parameters of the percolation models by information on composite’s microstructure. The methodology was demonstrated on the modified McCullough’s structure-oriented model combined with current maps obtained by Conductive Atomic Force Microscopy (CA-AFM). The approach was tested on nanocomposites with graphene nanoplatelets (GNPs/PS) and proved to be coherent with experimental conductivity measurements and able to predict a percolation threshold. For the composite GNPs/PS both experimental and calculated percolation thresholds are approximately equal to 0.9 wt.% of GNPs. The model can be used for a prediction of conductivity of different kinds of conductive–dielectric composites.