Fabrication of an asymmetric supercapacitor using a novel electrode design and introduce a robust machine learning model for its performance evaluation

Abstract

Attaining high performance supercapacitors requires comprehensive investigations into electrode and electrolyte properties, along with the recognition of operational parameters. The present research focuses on fabricating an asymmetric two-electrode supercapacitor (ASC) using Co3O4/VS4-rGO@NF and conductive carbon cloth (CC). This work evaluates the ASC performance using experimental and machine learning techniques. Experimental analyses are performed to investigate the influence of morphology, structure, specific surface area, and surface charge on electrochemical performance. The ASC exhibits a Csp of 238.8 F g−1 at 0.5 Ag-1 and demonstrates capacity retention after 8500 charge-discharge cycles, showcasing efficient electrochemical processes. Moreover, the ASC device shows an energy density of 74.6 Wh kg−1 at a power density of 1500 W kg−1. A data-driven machine learning approach based on artificial neural networks (ANN) predicates ASC's capacity with accuracy. The developed model accurately predicts Csp values for different architectures, ranging from 208.2 to 214.0 Fg-1 at 0.5 Ag-1. When comparing experimental and predicted values using the optimized architecture, the relative error averages 5.8 %. The successful results obtained from this study not only introduce a novel electrode design but also propose a robust machine learning model that facilitates accurate decision-making in experimental research.