文献类型：期刊文献

英文题名：Fabrication of a Congo red/Chitosan@Melamine sponge composite for efficient U(VI) removal: adsorption behavior, mechanistic insights, and machine learning-assisted performance modeling

作者：Lu, Lihong[1];Yang, Fang[1];Zhong, Xingyu[1];Shi, Hongfa[1];Zhang, Junran[1];Liu, Xin[1];Zhang, Wenqing[1];Zhang, Lingfan[1,2]

机构：[1]East China Univ Sci & Technol, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Res Ctr Anal & Test, Shanghai 200237, Peoples R China

年份：2026

卷号：637

外文期刊名：DESALINATION

收录：;EI(收录号：20262020726123);WOS:【SCI-EXPANDED(收录号:WOS:001773305300001)】；

基金：Funding: This work was supported by the National Natural Science Foundation of China [grant number 21407050] . We also thank the Research Center of Analysis and Test (ECUST) for help on data and characterization.

语种：英文

外文关键词：Uranium; Congo red; Chitosan; Machine learning

摘要：In the present work, a novel Congo red/Chitosan@Melamine Sponge composite (CRCS@MS) was successfully fabricated via a facile crosslinking strategy, adhering to the "waste control by waste" concept, for the efficient elimination of U(VI) from aqueous systems. Systematic physicochemical analyses confirmed the successful grafting of amino and sulfonic acid groups within the preserved three-dimensional porous skeleton of the material. Batch adsorption tests demonstrated that U(VI) adsorption onto CRCS@MS complied with the Sips model, indicating a monolayer-dominated adsorption process. A removal efficiency of 98.58% was achieved under optimized conditions. The composite maintained over 90% removal after four consecutive cycles, which indicated that it exhibited a certain degree of reusability, and it exhibits stable adsorption performance in real seawater and river water environments with certain uranium concentrations. Mechanistic studies revealed that the adsorption process was primarily dominated by electrostatic interactions between sulfonic acid groups and U (VI), with amino and hydroxyl groups synergistically enhancing the adsorption through hydrogen bonding or coordination interactions, thereby achieving certain U(VI) uptake. Furthermore, machine learning techniques were employed to model the adsorption behavior, with seven models systematically evaluated including Support Vector Regression (SVR) with various kernels, Random Forest (RF), and Artificial Neural Network (ANN). Results demonstrated that the RF model outperformed others, achieving a test set R2 of 0.9422 with the lowest prediction errors, validating the effectiveness of machine learning in effectively capturing the intricate relationships within adsorption processes. This work provides methodological support for the development of sustainable and efficient adsorbents for radionuclide remediation.