Abstract
ABSTRACTTransition metal molybdates (AMoO4 where A = Ni, Co, Mn, Fe, and Zn) have attracted much attention as promising electrode materials for energy storage devices due to their multi‐electron redox capability, higher electrical conductivity, good chemical and thermal stability, and stable crystal structure to get superior electrochemical performance. Transition metal molybdates and their graphene‐based composites possess multidimensional morphology for supercapacitors. The morphology‐dependent supercapacitor behavior has been reviewed in the present article. The formation mechanism of AMoO4 nanostructures in the form of 1D, 2D, and 3D has been identified and respective supercapacitor behavior is outlined. The density functional theory based on the calculated electronic properties of AMoO4 has been discussed. Additionally, the application of machine learning techniques in predicting and analyzing the relationships of AMoO4 has been discussed for the first time. By leveraging ML algorithms, we identify key parameters influencing their energy storage capabilities, providing insights into the rational design of molybdate‐based composites. Integrating experimental results with ML‐driven optimization offers a novel pathway for accelerating the development of next‐generation energy storage devices. In conclusion, future perspectives and challenges have been discussed.
Published Version
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