Issue 20, 2023, Issue in Progress

Using deep eutectic solvent dissolved low-value cotton linter based efficient magnetic adsorbents for heavy metal removal

Abstract

In this study, a novel magnetic bio-adsorbent was synthesized by modifying cotton linter (CL) cellulose with deep eutectic solvents (DESs) and Fe3O4 magnetic nanoparticles. The adsorption capacity of CL, Fe3O4/CL, Fe3O4/CL-oxidation, and Fe3O4/CL-DES for Cu2+ was 11.0, 66.1, 85.7, and 93.1 mg g−1, respectively, under the optimal adsorption conditions of an initial pH value of 6.0, stirring rate of 300 rpm, and a temperature of 30 °C. The presence of Fe3O4 nanoparticles increased the proportion of hydroxyl groups and thus improved the ion-exchange ability of Cu2+. The dissolution of DES significantly decreased fiber crystallinity and increased the number of hydroxyl group (amorphous regions increased), thus improving the chelation reaction of Cu2+, which was favorable for surface adsorption. In addition, we used the Langmuir and Freundlich isothermal models to simulate the adsorption behavior of Fe3O4/CL-DES, and the results indicated that Cu2+ follows a Freundlich isotherm model of multilayer adsorption. The fitting of the adsorption kinetics model indicated that the adsorption process involves multiple adsorption mechanisms and can be described by a quasi-second-order model. These results provide a potential method for the preparation of high-efficiency adsorbents from low-value cotton linter, which has broad application prospects in wastewater treatment.

Graphical abstract: Using deep eutectic solvent dissolved low-value cotton linter based efficient magnetic adsorbents for heavy metal removal

Article information

Article type
Paper
Submitted
24 Feb 2023
Accepted
18 Apr 2023
First published
03 May 2023
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2023,13, 13592-13603

Using deep eutectic solvent dissolved low-value cotton linter based efficient magnetic adsorbents for heavy metal removal

S. Ye, M. Xu, H. Sun, Y. Ni, R. Wang, R. Ye, L. Wan, F. Liu, X. Deng and J. Wu, RSC Adv., 2023, 13, 13592 DOI: 10.1039/D3RA01248D

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