Machine Learning-Based Prediction of Cyclic Voltammetry Behavior of Substitution of Zinc and Cobalt in BiFeO/BiFeO for Supercapacitor Applications.

Artificial intelligence and machine learning have become indispensable tools across various disciplines in the present century. In that way, the role of artificial intelligence and machine learning in energy storage devices was investigated. As a preliminary study, the data derived from electrochemical studies were used for the prediction.

One-pot synthesis of TEA functionalized and NiSe embedded rGO nanocomposites for supercapacitor application.

NiSe and NG-NiSe (triethanolamine functionalized and NiSe embedded rGO), as electrode materials for supercapacitor application, were prepared by a hydrothermal technique. XRD confirmed the formation of pure NiSe and NG-NiSe nanocomposites, which showed a hexagonal crystalline structure of NiSe. The structural morphology and particle size of NiSe and NG-NiSe were measured using FESEM and HRTEM analysis, respectively.