Length prediction of silicon nanowires (SiNWs) prepared by the MACE method using the ANN-COA-PSO algorithm for high supercapacitor applications. (September 2021)

• Record Type: Journal Article
• Authors: Zeraati, Malihe; Chen, Tzu-Chia; Ebri, Masoumeh; Chauhan, Narendra Pal Singh; Sargazi, Ghasem
• Is Part Of: Journal of physics and chemistry of solids. Volume 156(2021)
• Publication Date: 2021-09
• Subjects: SiNWs -- Metal-assisted chemical etching -- Artificial neural networks -- Cuckoo optimization algorithm -- Particle swarm optimization -- Supercapacitor; Solids -- Periodicals; Solides -- Périodiques; Solids; Periodicals; 530.41
• DOI: 10.1016/j.jpcs.2021.110146
• Languages: English
• ISSNs: 0022-3697

Abstract

Abstract: In this study, the metal-assisted chemical etching of silicon with the help of metal is used to fabricate large-scale silicon nanowire arrays (SiNWs). This method was optimised by artificial neural networks (ANN) and the cuckoo optimization algorithm (COA) was used to optimize ANN. Finally, the particle swarm optimization (PSO) method has been used to achieve the maximum length of silicon nanowires. As a result, the nanowires produced by the 1 M NaOH electrolyte had the properties of a capacitive cloud with a capacity of 758 mF/cm 2 . It has the potential to be an excellent candidate for supercapacitor applications. Highlights: Silicon nanowire arrays (SiNWs) was prepared using metal-assisted chemical etching of silicon. An artificial neural network (ANN) has been used to optimize the MACE process. Particle Swarm Optimization was used to achieve the maximum length of silicon nanowires. SiNWs prepared using 1M NaOH has excellent specific capacitance of 758 mF/cm 2 . It could be an excellent method to fabricate SiNWs for supercapacitor applications.