Characterization of High-Entropy Spinel Cobalt/Nickel Ferrite Nanomaterial for High-Performance Energy Storage Applications

Abstract

This study presents an integrated methodology combining machine learning, atomic-scale design, and experimental validation to develop high-entropy spinel ferrite nanomaterials (Co_0.5Ni_0.5Fe₂O₄) for advanced energy storage applications. Electrochemical evaluations revealed hybrid charge-storage behavior merging capacitive and diffusion-controlled mechanisms, with cyclic voltammetry showing a significant increase in current density at higher scan rates, confirming large surface area and quasi-reversible redox reactions. Galvanostatic charge-discharge tests exhibited distinct voltage plateaus characteristic of Faradaic processes, with a specific capacitance strongly dependent on current density; Coulombic efficiency reached 95% at 1 A/g but declined to 55% at 10 A/g due to irreversible side reactions and ion trapping. Electrochemical impedance spectroscopy demonstrated a low charge-transfer resistance (~4 Ω) and a Warburg diffusion line with a 35° slope, indicating a porous, rough surface and battery-type behavior rather than ideal supercapacitor performance. The absence of a second semicircle confirmed electrical homogeneity and high surface activity. Overall, the nanomaterial exhibited excellent electronic conductivity facilitated by cobalt and nickel ions within the spinel lattice, making it a promising positive electrode candidate for next-generation batteries and supercapacitors. This work underscores that merging machine learning-driven predictions with precise experimental synthesis offers an efficient pathway to accelerate the discovery of advanced energy storage materials by correlating atomic-scale structural distortions with macroscopic electrochemical performance.