Quasi-SMILES-Based Mathematical Model for the Prediction of Percolation Threshold for Conductive Polymer Composites

Abstract

The traditional method for creating conductive polymer compositesConductive polymer composites (CPCs) involves mixing carbon black, metal powder, or carbon fibre into a polymer matrix. Since the polymerPolymer matrix acts as an insulator, when a thresholdThreshold filler level is achieved, the conductivity of these composites can exhibit a sharp increase. The common term generally used to describe such phenomena is called ‘percolation’. As the conductive filler content increases in the insulator polymerPolymer matrix, it creates different conductive routes, steady rise in the electrical conductivity is observed at a critical volume fraction Φ. That critical volume fraction Φ responsible for the transition of polymers from insulators to conducting is called the ‘percolation thresholdPercolation threshold’. The diverse experimental percolation thresholdPercolation threshold cured data of 45 conductive polymer compositeConductive polymer composites systems were classified into four sets: A = active training set; P = passive training set; C = calibration set; V = validation set. Systems of eclectic conditions of various processes of mixing such as dry mixing, latex technology, and melt blending employed to fabricate the conducting polymer composites with various polymerPolymer matrixes like high-density polyethylene (HDPE), low-density polyethylene (LDPE), maleic anhydride (MA), polyamide (PA) and the conducting fillers such as multi-wall carbon nanotube (MWNT), single-wall carbon nanotube (SWNT), polyaniline (PANI) are very important and crucial to have desired properties. Unique quasi-SMILES codes for different CPCs were suggested taking into consideration various systems of eclectic conditions. These quasi-SMILES codes were the basis for building mathematical models for predicting percolation thresholdPercolation threshold CPCs.