DLS based Optimization of ZnS-CoS Nanoparticles with Enhanced Energy and Power Density for Supercapacitor Applications and Validation by AI Models

Abstract

Zinc cobalt sulfide (ZnS-CoS) nanoparticles have emerged as promising electrode materials for supercapacitors due to their excellent electrochemical properties. However, achieving both high energy density and power density remains a challenge due to particle agglomeration and instability. In this work, dynamic light scattering based optimization of ZnS-CoS nanoparticles is carried out by adjusting synthetic parameters including temperature, pH, reagent addition rate, and stabilizer concentration, resulting in significantly smaller particle sizes and improved stability. The optimized ZnS-CoS nanoparticles based electrodes exhibited an exceptional specific capacitance of 1156 F/g, an energy density of 194 Wh/kg, and a power density of 7260 W/kg, which are significantly higher than literature-reported values. Electrochemical impedance spectroscopy (EIS) confirmed lower charge transfer resistance (35.88 Ω), indicating faster ion transport and enhanced conductivity. Moreover, the optimized ZnS-CoS electrodes demonstrated remarkable cyclic stability, retaining 93.87% capacitance after 10,000 cycles. The charge transfer mechanism has been understood by computational studies and four different machine learning models including stacking regressor, TPOT, ANN, and RSM models are applied to verify the experimental specific capacitance of ZnS. The accuracy of models performance is best for stacked regression model followed by ANN, TPOT, and RSM models respectively. These results highlight the critical role of nanoparticle size optimization in enhancing electrochemical performance and establish DLS-optimized ZnS-CoS as a superior candidate for next-generation supercapacitors.