No abstract

The electrically conductive polymer composites (CPCs) have attracted intensive attention for several decades due to their flexibility and unique electrical properties. CPCs are potentially used in many applications such as flexible electrodes, batteries, and strain sensors. The percolated conductive pathways are formed by conductive filler in polymer matrix which is a major effect on the electrical behavior of CPCs. Computational simulations have been used to study the percolation phenomena of CPCs. The simulation algorithms need to be developed and optimized for reducing the simulation time-consuming. In this study, the in-house Monte Carlo simulation that used to estimate percolation threshold is optimized. To simulate in the large-scale system, cut-off distance will be defined to avoid unnecessary complex calculations. The calculation sequence within the code has been rearranged to omit the unnecessary calculation processes. Results show that the optimized software takes less processing time than the previous version around 5 times. Therefore, we can perform the large system to investigate the percolation phenomenon with less lattice confinement effect.

Conductive polymer nanocomposites material (PNCs) is one type of alternative polymeric materials to replace high-cost intrinsically conductive polymers (ICPs). PNC is a composite of electrical insulative polymer matrix and electrically conductive filler in which it is made by less complicated synthesis protocols but has similar quantitative conductive properties to existing conducting polymers. Therefore, PNC is a candidate for many applications, such as, light-emitting diodes, flexible electrodes, batteries and strain sensors [1]. In this study, an in-house Monte-Carlo simulation was used to investigate the percolation paths of the 3D model of nanorod filler network in the polymer lattice and estimate the nanorod concentration at percolation threshold [2]. The model also includes the nanorod filler orientation angles. We then focused on the effects of stretching lattice on the percolation threshold. The dimension of lattice length was varied with constant volume for each simulation system (incompressible material). Results of simulations showed that the percolation thresholds decreased when increasing the lattice stretching and the nanorod orientation angles have been confined by finite lattice dimension which shows there is the effect of orientations angle on the percolation threshold. Our finding will be a useful guideline for designing polymer nanocomposite as a switching sensor.