Stochastic Percolation Network Model for Hybrid Nanocomposites

Abstract

Agglomeration is a common occurrence in polymer nanocomposites with carbon nanotubes (CNT) and graphene nanoplatelets (GNP) as fillers and usually leads to a reduction of electrical and mechanical properties of the composites. While the conductive nanocomposites exhibit remarkable electromechanical properties and have potential applications in sensing and actuation, their performance mainly depends on the polymer matrix and the dispersed nanofillers. In this paper, we use a two-dimensional Monte Carlo percolation model for monofiller and hybrid nanocomposites to examine the effect of CNT agglomeration, GNP geometry, CNT-to-GNP volume ratio and polymer matrix properties on the strain sensing behavior of the nanocomposites. We generate microstructures of tunneling network with different size, aspect ratio, alignment and content of GNP fillers. Agglomeration parameters such as agglomerate content, morphology (equiaxed to rope-like), and agglomerate filler density are also modelled to generate realistic microstructure. The Poisson's ratio and the tunneling barrier height of the polymer matrix are also parametrized. Our results indicate that high level of agglomeration leads to a systematic decrease of piezoresistivity, while low agglomeration level with low filler density within agglomerates improves the electromechanical behavior. The addition of GNP as second filler to the CNT composites leads to a better piezoresistive behavior of the nanocomposites. Increasing the content, aspect ratio and size of the GNP also leads to an improvement of the piezoresistivity. GNPs uniformly aligned in the direction of electrical conductivity measurements lead to a significantly higher piezoresistive behavior, up to 6 times greater than that of nanocomposites based on only CNT. Higher values of the polymer matrix Poisson's ratio lead to an improved piezoresistivity.