Insight into the Effect of Glycerol on Dielectric Relaxation and Transport Properties of Potassium-Ion-Conducting Solid Biopolymer Electrolytes for Application in Solid-State Electrochemical Double-Layer Capacitor

Abstract

The increased interest in the transition from liquid to solid polymer electrolytes (SPEs) has driven enormous research in the area polymer electrolyte technology. Solid biopolymer electrolytes (SBEs) are a special class of SPEs that are obtained from natural polymers. Recently, SBEs have been generating much attention because they are simple, inexpensive, and environmentally friendly. In this work, SBEs based on glycerol-plasticized methylcellulose/pectin/potassium phosphate (MC/PC/K3PO4) are investigated for their potential application in an electrochemical double-layer capacitor (EDLC). The structural, electrical, thermal, dielectric, and energy moduli of the SBEs were analyzed via X-ray diffractometry (XRD), Fourier transforms infrared spectroscopy (FTIR), electrochemical impedance spectroscopy (EIS), transference number measurement (TNM), and linear sweep voltammetry (LSV). The plasticizing effect of glycerol in the MC/PC/K3PO4/glycerol system was confirmed by the change in the intensity of the samples’ FTIR absorption bands. The broadening of the XRD peaks demonstrates that the amorphous component of SBEs increases with increasing glycerol concentration, while EIS plots demonstrate an increase in ionic conductivity with increasing plasticizer content owing to the formation of charge-transfer complexes and the expansion of amorphous domains in polymer electrolytes (PEs). The sample containing 50% glycerol has a maximal ionic conductivity of about 7.5 × 10−4 scm−1, a broad potential window of 3.99 V, and a cation transference number of 0.959 at room temperature. Using the cyclic voltammetry (CV) test, the EDLC constructed from the sample with the highest conductivity revealed a capacitive characteristic. At 5 mVs−1, a leaf-shaped profile with a specific capacitance of 57.14 Fg−1 was measured based on the CV data.