Modeling and Optimization of Electrodeposition Process for Copper Nanoparticle Synthesis Using ANN and Nature-Inspired Algorithms

Abstract

Due to its outstanding physical, chemical, and thermal properties, an increasing consideration has been paid to produce copper (Cu) nanoparticles (NPs). Various methods are accessible for producing Cu NPs by conceiving the top–down and bottom–up approaches. Electrodeposition is a bottom–up method to synthesize high-quality Cu NPs at a low cost. The attributes of Cu NPs rely on their way of deduction and electrochemical process parameters. This work aims to deduce the mean size of Cu NPs. Artificial neural networks (ANN) and nature-inspired algorithms, namely genetic algorithm (GA), firefly algorithm (FA), and cuckoo search (CS) algorithm were used to predict and optimize the electrochemical parameters. The results obtained from ANN prediction agreed with data from the electrodeposition process. All nature-inspired algorithms reveal similar operating conditions as optimal parameters. The minimum NP size of 20 nm was obtained for the process parameters of 4 g·l−1 of CuSO4 concentration, electrode distance of 3 cm, and a potential difference of 27 V. The synthesized NP size was in line with the anticipated NP size. The scanning electron microscope and X-ray diffractometer (XRD) were performed to analyze the nanoparticle size and morphology.