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

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Nickel cobalt sulfide (NiCo2S4) has attracted considerable interest as 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. The 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. We outlined how the various preparative parameters such as synthesis methods, precursors, experimental conditions contributed to efficiently accelerating the charge transport kinetics. Finally, the effect of introduction of graphene on the electrochemical performance of NiCo2S4 is discussed using density functional theory. Also, machine learning models are used to analyze the specific capacitance variation with respect to different synthesis parameters, morphology, energy density and power density. Machine learning models identified limitations and scope of work for working on NiCo2S4/Graphene Composites. We found that the most influencing parameter is annealing time which can alter the specific capacitance. The review outlines future research directions, challenges, and opportunities in NiCo2S4/graphene-based supercapacitor.