A new approach to understanding the interaction effect of salt and plasticizer on solid polymer electrolytes using statistical model and artificial intelligence algorithm

Nowadays, solid polymer electrolytes (SPEs) based on natural biopolymeric macromolecules such as cellulose and its derivatives have demonstrated great potential over their synthetic counterparts due to their natural abundance, low cost of production, biocompatibility, and biodegradability. However, the low ionic conductivity of these polymers has been a serious challenge and needs to be optimized to meet demands for practical applications. Herein, the response surface methodology (RSM) and artificial neural network (ANN) were employed to predict and optimize the performance of pectin/methylcellulose (PC/MC) based SPE complexed with potassium phosphate (K3PO4) and glycerol. RSM analysis of variance (ANOVA) revealed that the interactive behaviour of both K3PO4 and glycerol, particularly at higher potassium salt content has an enormous influence on ionic conductivity and potential window of the SPE. After numerical optimization, an optimum interaction (∼3 × 10−4 Scm−1, 4.19 V) was achieved at 60 wt.% and 41.37 wt.% of K3PO4 and glycerol respectively.