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Enhanced Supercapacitance of Co3O4 Nanoneedle Clusters Electrodes through Redox-Additive Electrolyte and ANN-based Prediction
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DOI：10.1016/j.colsurfa.2026.140744.png)
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Abstract
En 中文
Redox-active additive electrolytes have emerged as an effective approach to enable reversible Faradaic reactions, thereby markedly improving the energy storage capacity of supercapacitors. In this work, we have successfully grown Co3O4 nanoneedle clusters on Ni foam via a hydrothermal technique and analyzed their charge storage performance using a potassium ferricyanide [K3[Fe(CN)6] redox additive to the electrolyte separately in an aqueous KOH. The Co3O4 exhibits a maximum areal capacitance of 3661 mF cm-2 (2816 F g-1) at a 2.5 mA cm-2 in K3[Fe (CN)6] Redox-active additive electrolyte, which is approximately 9-fold higher than that obtained in bare KOH electrolyte (422 mF cm-2 or 324 F g-1). According to the CV and cyclic stability analysis, Co3O4 nanoneedle clusters displays 87% capacitance retention after 5000 cycles, in the cell containing K3[Fe (CN)6] Redox-active additive electrolyte. Furthermore, the solid-state symmetric supercapacitor (SSC) fabricated with identical Co3O4 electrodes as cathode and anode in redox-active additive electrolyte stored a maximum specific capacity of 42.6 Wh kg-1 at a specific power of 500 W kg-1. Additionally, it was found that an Artificial Neural Network (ANN) model, when trained on the electrochemical dataset from the experiments, was able to achieve R2 values higher than 0.98 for all four key performance indicators, namely specific capacitance, areal capacitance, energy density, and power density, thereby indicating that such a model can predict Co3O4 supercapacitor performance within the studied electrolyte composite. This improved performance of Co3O4 nanoneedle clusters in Redox-active additive electrolyte offers an effective strategy to enhance the specific energy and power of energy storage devices that can be used in wearable and smart electronic devices.
Keywords:
Co3O4 nanoneedle clusters
Redox-active additive electrolyte
Supercapacitor
Areal capacitance
Artificial Neural Network
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