Simulation tool for predicting and optimizing the performance of nanoparticle based strain sensors

Abstract

In this work a Monte-Carlo tool simulating platinum nanoparticle (NP) based strain-sensors, on flexible substrates, is presented. The tool begins by randomly placing the NPs on the simulation area, with the ability to tune the NP surface coverage. After the calculation of the conductive paths that were generated in the previous step, the whole system is represented with an equivalent circuit; the NPs and the NP clusters act as nodes and the inter-particle gaps as resistances. The effective resistance is then calculated with the use of a Laplacian Matrix, which has proven extremely effective in significantly reducing the overall computational time. The simulation results are then benchmarked with experimental measurements from actual strain-sensing devices. The software is capable of predicting the strain-sensitivity for different NP sizes as well as surface coverages, emerging as a powerful computational tool for design-optimization of NP based devices in polymeric substrates, while it could well be extended to other nanocomposite materials used in flexible or stretchable electronic applications.