ArticleRSC advances2025
Predictive modeling of pulse-electrodeposited Cu-Zn alloy and dealloying for porous electrode fabrication.
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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2 authors.
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Abstract
Porous metals (PMs) have attracted significant attention in recent years due to their unique structural and functional properties, holding potential for a wide range of applications in catalysis, sensing, energy storage, and filtration. Among these, porous copper (PC), which is produced by dealloying copper-zinc (Cu-Zn) alloys has evolved as a particularly valuable material. In this study, a Cu-Zn alloy is electrochemically deposited onto a Cu wire in a sulphate-based electrolyte containing tri-sodium citrate as a complexing agent. To produce PC, the alloy has been subjected to chemical dealloying to dissolve the less noble element. We have implemented machine learning algorithms such as adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN), and response surface methodology (RSM) to model the interaction of process parameters and responses. Statistical modeling has been carried out to investigate the influence of operating parameters, including precursor reagent quantities (0.002-0.2 M), electrodeposition time (15-45 min), and dealloying time (16-24 h), on Zn content, dealloyed weight, and change in grain size. The test results confirm that both models fit the experimental data well, with the ANN model achieving high accuracy (
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