The static and dynamic adsorptive performance of a nitrogen and sulfur functionalized 3D chitosan sponge for mercury and its machine learning evaluation

The use of chitosan-based sponge materials for Hg(II) removal has gained attention recently due to their effectiveness. However, the complex preparation, limited performance, and poor acid resistance remained major drawbacks. Herein, a nitrogen‑sulfur functionalized macroporous chitosan sponge was successfully synthesized via two mild amidation reactions and exhibited abundant interconnected mesopores. These features endowed the functionalized chitosan-based sponge with high adsorption capacity (1227.15 mg g −1 ), fast reaction rate (8.27 × 10 −3  g mg −1 ·min −1 ), broad pH adaptability (1–7), and high selectivity, even in the artificial chlor-alkali wastewater. Furthermore, the impressive saturation capacity of 1329.24 mg g −1 was achieved in various heights and injection rates in the fixed-bed column test, and the good removal efficiency (>85 %) was maintained after six dynamic regeneration cycles. The excellent performance was primarily attributed to the chemisorption of C S groups. Among the three machine learning models, the ANFIS algorithm owned the best results of the smallest RMSE (0.00315) and highest R 2 (0.9752) for predicting dynamic adsorptive behaviors. Overall, this research provided a reference for preparing a promising mesoporous sponge as an alternative recyclable and efficient candidate for industrial wastewater treatment and offered a machine learning model to predict the dynamic adsorptive performance.