Machine Learning a Predictive Tool for the Analysis of NiCo₂S₄/Graphene Composites for Supercapacitor

Nickel cobalt sulfide (NiCo₂S₄) has considerable potential electrode material for supercapacitors owing to its distinct physical and chemical characteristics. However, the practical applications of pristine NiCo₂S₄ have been limited by issues such as small specific surface area, agglomeration, and volume changes during cycling, leading to low specific capacitance/capacity and cyclic stability at high rates. Several efforts have been taken to address these challenges. Among those the design and development of NiCo₂S₄-graphene-based composites have been widely investigated. This review explores the effect of NiCo₂S₄ architecture and its nanocomposite with graphene on the electrochemical properties. How the various preparative parameters such as synthesis methods, precursors, experimental conditions contributed to efficiently accelerating charge transport kinetics is outlined. Finally, the effect of introduction of graphene on the electrochemical performance of NiCo₂S₄ is discussed using density functional theory (DFT). Also, machine learning (ML) models are used to analyze the specific capacitance variation with respect to different synthesis parameters, morphology, energy density, and power density. ML models identify limitations and scope of work for working on NiCo₂S₄/graphene composites. It is found that most influencing parameter is annealing time that can alter specific capacitance. The review outlines future research directions, challenges, and opportunities in NiCo₂S₄/graphene-based supercapacitor.