DLS-based optimization of ZnS–CoS nanoparticles with enhanced energy and power density for supercapacitor applications and its 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 nanoparticle-based electrode exhibited an exceptional specific capacitance of 1156 F g⁻¹, an energy density of 194 Wh kg⁻¹, and a power density of 7260 W kg⁻¹, which are significantly higher than the values reported in the literature. Electrochemical impedance spectroscopy (EIS) results confirmed lower charge transfer resistance (35.88 Ω), indicating faster ion transport and enhanced conductivity. Moreover, the optimized ZnS–CoS electrode demonstrated remarkable cycling stability, retaining 93.87% capacitance after 10 000 cycles. The charge transfer mechanism was understood by computational studies and four different machine learning models, namely, stacking regressor, TPOT, ANN, and RSM models, which were applied to verify the experimental specific capacitance of ZnS. The accuracy of performance is best for the stacking regression model, followed by ANN, TPOT, and RSM models. These results highlight the critical role of nanoparticle size optimization in enhancing the electrochemical performance and demonstrate DLS-optimized ZnS–CoS as a superior candidate for next-generation supercapacitors.