In situ optimization of deposition layers in Ni-Co phosphate electrodes with ML-assisted predictive validation for...

The successive ionic layer adsorption and reaction (SILAR) methodology provides an economically viable and uncomplicated strategy for the fabrication of electrode materials intended for applications in energy storage. The present work focuses on synthesizing Ni-Co phosphate nanoparticles on nickel foam (NF) through the controlled deposition of multiple layers using the SILAR method. The amorphous phase of the Ni-Co phosphate on the electrodes was confirmed via XRD analysis. In contrast, the existence of the phosphate group was confirmed through FT-IR and EDS analysis. The XPS analysis shows that nickel and cobalt exist in varying oxidation states of Ni²⁺/Ni³⁺ and Co²⁺/Co³⁺, facilitating a reversible charge storage process, while phosphorus is present in its pentavalent state. The electrode demonstrated exceptional performance at a current density of 1 mA cm^-2, achieving a high areal capacitance of 426.47 mF cm^-2 (equivalent to 2132 F g^-1). Additionally, it exhibited remarkable rate capability at 5 mA cm^-2, retaining 76.4% of the capacitance observed at 1 mA cm^-2. Moreover, the material exhibited remarkable cyclic stability following 5000 cycles of charge-discharge at 5 mA cm^-2, maintaining 77.92% of its original capacitance in a three-electrode setup. Additionally, the trained ML model showed high predictive accuracy. The predicted capacitance of 2129 F g^-1 closely matched the experimental value of 2132 F g^-1, demonstrating the model's reliability. These findings underscore the potential of SILAR-grown Ni-Co phosphate for supercapacitor applications, further enhanced by integrating ML for performance prediction.