Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence.

ABSTRACT: In this study, the specific capacitance characteristics of a carbon nanotube (CNT) supercapacitor was predicted using different machine learning algorithms, such as artificial neural network (ANN), random forest regression (RFR), k-nearest neighbors regression (KNN), and decision tree regression (DTR), based on experimental studies. The results of the simulation verified the accuracy of the ANN algorithm with respect to the data derived from the specific capacitance of the supercapacitor module. It was observed that there was a strong correlation between the experimental results and the predictions made by the ANN algorithm. Comparative analysis showed that the developed ANN algorithm was consistently superior over other algorithms in terms of different metrics, as indicated by the

SUBMITTER: Tawfik WZ 

PROVIDER: S-EPMC11781533 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature