Development of nano fiber MnO2 thin film electrode and cyclic voltammetry behavior modeling using artificial neural network for supercapacitor application. (August 2015)

• Record Type: Journal Article
• Authors: Dongale, T.D.; Jadhav, P.R.; Navathe, G.J.; Kim, J.H.; Karanjkar, M.M.; Patil, P.S.
• Is Part Of: Materials science in semiconductor processing. Volume 36(2015:Aug.)
• Page Start: 43
• Page End: 48
• Publication Date: 2015-08
• Subjects: MnO2 -- Supercapacitor -- Artificial neural network (ANN) -- Thin films; Semiconductors -- Periodicals; Integrated circuits -- Materials -- Periodicals; Semiconducteurs -- Périodiques; Circuits intégrés -- Matériaux -- Périodiques; Electronic journals; 621.38152
• DOI: 10.1016/j.mssp.2015.02.084
• Languages: English
• ISSNs: 1369-8001

Abstract

Abstract: This paper deals with the fabrication of MnO2 supercapacitor and modeling of cyclic voltammetery using Artificial Neural Network (ANN). Nano fiber MnO2 (NF-MnO2 ) films synthesized on stainless steel substrate by adopting potentiodynamic technique from an aqueous manganous sulfate monohydrate (MnSO4 · H2 O) and surface morphology have characterized using field emission scanning electron microscope (FE-SEM). The electrochemical characterizations have studied with the help of cyclic voltammogram, from which the maximum specific capacitance was estimated to be 392 F g −1 . The present investigation further involves modeling of supercapacitor performance using artificial neural network (ANN) approach. The said ANN model based on the multilayer perceptron concept and model is scripted in MATLAB. The Levenberg–Marquart back propagation algorithm (BP) and the sigmoid activation function are used to improve the performance of ANNs. The intelligent model shows the satisfactory performance with the error rate 1.24%, 1.03%, 0.87%, 1.17%, and 1.28% for electrodeposited MnO2 samples.