Parametric Optimization for Enhancing Surface Electrical Conductivity of Hybrid Functionalized Woven Natural Fiber Composites With <scp>rGO</scp> and <scp>PANI</scp>

ABSTRACT The present work aims to enhance the surface electrical conductivity of pineapple fiber (PA) by functionalizing reduced graphene oxide (rGO) and polyaniline (PANI). The hybrid rGO/PANI functionalized woven pineapple fiber composite (HFPAC) is prepared using electroless deposition of rGO onto alkaline treated PA followed by in situ polymerization of PANI. A statistical model, Response surface methodology (RSM) is employed to determine the optimal concentrations of rGO, initial concentrations of aniline, and molar ratio (MR) to attain maximum surface electrical conductivity in HFPAC. An artificial neural networks (ANN) model is developed to predict the conductivity of HFPAC and it achieves 94% prediction accuracy, which represents a significant improvement over RSM. The numerical models predicted that HFPAC exhibited the highest surface electrical conductivity (ANN‐19.22 &amp; RSM‐19.36 S/cm), with optimal combinations of 8.75% (rGO), 17.5% (Aniline) initial concentration, and 0.75 (MR). The above data is experimentally validated with an average electrical conductivity of 19.29 S/cm. The optimal combinations represent a significant enhancement compared to rGO functionalized woven pineapple fiber composite (rFPAC) and PANI functionalized woven pineapple fiber composite (PFPAC), which individually provide electrical conductivities of 16.58 and 9.81 S/cm, respectively. The change in microstructure and functional groups due to the synthesis of rGO and PANI is studied through Field emission scanning electron microscope (FE‐SEM) and Fourier transforms infrared thermography (FTIR) respectively.