A numerical conductive network model for investigating the strain-sensing response of graphene nanoplatelets-filled elastomeric strain sensors

• The piezoresistivity of graphene nanoplatelets elastomeric nanocomposite is investigated by a novel finite element approach. • The incorporation of randomly oriented graphene nanoplatelets led to a lower percolation threshold. • More electrical paths between two electrodes led to a diminished resistivity. • The increased resistance change came from the increased average distances between the platelets upon strain. Strain sensors have attracted great attentions for practical applications such as human-machine interface. It is challenging to model the electrical behavior of elastomeric strain sensors modified with nanomaterials. This study presents a finite element simulation to explore the piezoresistivity of graphene nanoplatelet (GNP)-filled elastomeric nanocomposites. The percolation network model is employed to determine the critical distance necessary for electrical percolation. The finite element percolation network model uses the Euclidian distance between GNPs distributed inside the representative volume element to calculate the resistivity. The influence of various parameters, including GNP alignment direction, aspect ratio, and volume fraction, on the resistivity changes of the nanocomposite is investigated. Results demonstrate that nearly 20% more tunneling distance is allowed for percolation of nanocomposite with 30% larger aspect ratio GNPs. Findings reveal that as the volume fraction of GNPs increases, the critical distance for the electrical percolation decreases significantly.