Electrochemical synthesis of MnO ₂ / NiO / ZnO trijunction coated stainless steel substrate as a supercapacitor electrode and cyclic voltammetry behavior modeling using artificial neural network

Abstract

Considering the limit of resources and the frequent use of energy, energy storage is nowadays the subject that everyone cares about. In the present work, we investigate trijunction metal oxides as supercapacitor electrode for energy storage application. A MnO 2 /NiO/ZnO trijunction electrode is synthesized for the first time using a successive three electrochemical deposition steps onto stainless steel (SS) substrate. This approach can effectively yield a good distribution and adhesion of all metal oxides onto the substrate with enhanced hydrophilicity and wettability. The specific capacitance of this trijunction electrode, as studied by cyclic voltammetry was higher than that of individual metal oxide electrodes. The maximum specific capacitance was estimated to be 1569.75 g À1 at a scan rate of 5 mV s À1 . The enhanced electrochemical performance of this trijunction electrode compared to single metal oxide electrodes (MnO 2 , NiO, or ZnO) is mainly due to the improvement of ion and electron transportation pathways in the combination of three metal oxides electrode. In addition, this study presents an Artificial Neural Network (ANN) model to predict cyclic voltammetry behavior of the prepared trijunction supercapacitor electrode, with a high satisfactory performance for the predicted performance with <0.05% error. The low and simply synthesized hierarchical trijunction electrode with superior electrochemical performance and the good correlation between experimental and theoretical results prove huge potential for its practical application in supercapacitors (SCs).

Freestanding carbon‐based hybrid anodes for flexible supercapacitors: Part I—An inclusive outlook on current collectors and configurations

“…The most popular conducting substrates are still metal-based, such as nickel foams (Ahmad & Shah, 2022;Qi et al, 2022;Tao, He, et al, 2022), stainless steel foils or meshes built of metals such as Al, Ti, Ni, and Cu (Alimi et al, 2022;Cho et al, 2022;D. Huang et al, 2022;Lei et al, 2019;J.-H. Lin & Du, 2021;X.…”

Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology

“…The ANN model has superior performance, as seen by its low percentage error of 0.14%. Additionally, Alimi et al [105] investigated an ANN model to achieve precise forecasting of the CV characteristics of trijunction supercapacitor electrodes composed of MnO 2 , NiO, and ZnO. The obtained R 2 value of 0.999 indicates a high level of accuracy in their predictions.…”

Correlation between the Experimental and Theoretical Photoelectrochemical Response of a WO3 Electrode for Efficient Water Splitting through the Implementation of an Artificial Neural Network

“…supercapacitor applications [21][22][23], and water splitting [24][25][26]. In this context, Wang and colleagues have successfully built a machine learning (ML) model to predict the doping effect of 17 metal dopants into hematite (Fe 2 O 3 ), a prototype photoelectrode material [27].…”