Green synthesis and machine learning driven analysis of SiO2 mixed carbon nanomaterial from agriwaste (Rice Husk) for supercapacitor applications

Abstract

In the quest for sustainable solutions, a high-performance electrode material for supercapacitors was developed from agricultural waste (AW). The study used a one-pot greener approach to create a SiO2 mixed carbon nanomaterial (SiO2@CNM) from AW at 240 degrees C. This nanomaterial, with its high surface area and porous sheet-like structure, was evaluated for supercapacitive behavior using three different electrolytes: 1 M H2SO4, 1 M KOH, and 1 M Na2SO4. The synthesized material showed exceptional specific capacitance, achieving 679.9 Fg-1 at current density of 0.5 Ag-1 with 1 M H2SO4 in a three-electrode system. When configured into a symmetrical supercapacitor in a two-electrode system, SiO2@CNM in 1MH2SO4 delivered a specific capacitance of 371.8 Fg-1 at 1 Ag-1. This electrode-electrolyte configuration achieved an energy density of 51.64 Whkg-1 and a power density of 249.97 Wkg-1 at 0.5 Ag-1. Notably, the material retained over 80 % capacitance after 5000 charge discharge cycles, making SiO2@CNM a promising candidate for energy storage applications. The performance with 1 M H2SO4 surpassed other electrolytes, highlighting SiO2@CNM's potential as an efficient material for supercapacitor applications. Furthermore, to complement these experimental findings, different machine learning (ML) regression models viz. Random Forest, Gradient Boosting, XGBoost and AdaBoost were employed to determine the importance of parameters such as scan rate and current density in estimating specific capaci-tance. Feature importance analysis identified scan rate as the most significant factor influencing specific capacitance, providing a framework for predicting and optimizing operational conditions and synthesis parameters.