Design of hybrid Chitosan-Montmorillonite materials for water treatment: Study of the performance and stability

Chitosan-modified cotton fiber: An efficient and reusable adsorbent in removal of harmful cyanobacteria, Microcystis aeruginosa from aqueous phases

In the present study, to remove harmful cyanobacterial species Microcystis aeruginosa from aqueous phases, adsorption-based strategy was utilized. For this strategy, the surface of cotton fiber was modified using chitosan molecules to develop a highly efficient and ecofriendly adsorbent in removal of Microcystis aeruginosa from aqueous solution. The pristine cotton fiber could not remove M. aeruginosa, while the chitosan-modified cotton (CS-m-Cotton) showed the 95% of cell removal efficiency within 12 h. The surface characteristics of chitosan-modified cotton compared to the pristine cotton fiber was examined by various surface analysis methods. In addition, the pre-treatment of pristine cotton using sodium hydroxide solution was an important factor for enhancement of chitosan modification efficiency on the cotton fiber. The developed chitosan-modified cotton fiber could be reusable for M. aeruginosa cell removal after the simple desorption treatment using ultrasonication in alkaline solution. During the repeated adsorbent regeneration and reuse, the chitosan-modified cotton maintained its M. aeruginosa removal efficiencies (>90%). From the acute toxicity assessment using the chitosan-modified cotton and, the measurements of chemical oxygen demand and microcystin level changes in the M. aeruginosa treatment process using the adsorbent, the environmental safety of the adsorption strategy using the developed adsorbent could be confirmed. Based on our results, the chitosan-modified cotton fiber could be proposed as an efficient and ecofriendly solution for remediation of harmful cyanobacterial species occurring water resources.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.