Abstract: A machine learning (ML)-accelerated first-principles prediction method for a hydration structure of an acid radical anion is provided. The prediction method includes the following steps: S1: constructing and optimizing an anion hydration structure M_mH2O; S2: perturbing the optimized anion hydration structure to generate a training dataset; S3: conducting a ML force field training on the training dataset to establish ML models; S4: conducting a molecular dynamics simulation on the ML models, and identifying atomic structures with a force deviation within a preset range as candidate configurations; S5: merging a validated candidate configuration into a training set for a subsequent iteration to further refine and train the ML model until the model converges, thereby generating an accurate deep potential (DP) model; and S6: conducting a ML-accelerated deep potential molecular dynamics simulation on the DP model to ultimately acquire the hydration structure of the acid radical anion.

Abstract: A method for recycling a copper-containing wastewater from a micro-etching is provided, including: modifying a FeS material with a monomer including both carboxyl and sulfhydryl, a crosslinking agent, and a stabilizing and dispersing agent to obtain a FeS-based pH-responsive material CMC-FeS@HS #SiO2 #COOH, adding the FeS-based pH-responsive material to weakly-acidic copper-containing wastewater from the micro-etching to allow a reaction, and conducting processes such as sulfide precipitation, exchange, adsorption complexation, and flocculation precipitation to finally obtain a precipitate with CuS as a main component. This method makes full use of the pH responsiveness and abundant surface active sites of the FeS-based pH-responsive material, and can control a recovery rate of copper ions in the wastewater at 99.8% or more merely by adjusting a pH value of the copper-containing wastewater from the micro-etching.