Machine Learning-Based Prediction of Cyclic Voltammetry Behavior of Substitution of Zinc and Cobalt in BiFeO₃/Bi₂₅FeO₄₀ for Supercapacitor Applications.

ABSTRACT: 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. The prediction of current from cyclic voltammetry (CV) studies was undertaken for bismuth ferrite (BFO), substitution of zinc in BFO (BFZO), and substitution of cobalt in BFO composite (BFCO). CV is a vital electrochemical technique used for studying the electrochemical behavior of any material. The electrochemical study provides insights into the energy storage behavior of the material through the specific capacitance. The machine learning models, such

SUBMITTER: Ravichandran A 

PROVIDER: S-EPMC11307299 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature