Study of the Factors Influencing the Adsorption of Heavy Metal Pollutants in Water by Activated Carbon Gel Particles
Publication: Journal of Environmental Engineering
Volume 150, Issue 7
Abstract
The application of sodium alginate is limited because of its relatively small adsorption capacity, easy to release cations into water after forming gel with divalent cations, and difficult to realize mass production under the condition of controllable cost. In order to solve the aforementioned problems of sodium alginate gel, this study used activated carbon (AC) as a support material to enhance the mechanical properties of sodium alginate gel. Activated carbon-sodium alginate-oxalate gel particles (CSO) were used to increase the number of its oxygen-containing functional groups and improve the adsorption capacity. The specific surface area of the synthesized CSO particles could reach . The particle size of CSO particles was 50% in the range of 80–200 um, and 70% in the range of 40–200 um. The surface zero charge of CSO particles was determined to be 3.14. The thermal stability of CSO particles at 200°C was determined by thermal gravimetric analyzer (TGA). By studying the effects of different influencing factors on the process of removing pollutants from water by CSO, it was determined that pH has different effects for different pollutants. The equilibrium adsorption capacity () of CSO for Ni(II), Pb(II), and Cu(II) under the optimum condition of reaction temperature of 50°C was 77.14, 53.04, and , respectively. The adsorption isotherms were fitted to obtain the maximum adsorption capacity () of CSO for Ni(II), Pb(II), and Cu(II) as 75.56, 49.65, and . A combination of X-ray electron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) characterization demonstrated that the main mechanisms for the removal of divalent metal cations by CSO particles involved electrostatic adsorption and ion exchange. Overall, this work provides a good reference for the purification of heavy metal–polluted water by CSO particles.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was financially supported by the Key Research and Development Program of Shaanxi Province, China (No. 2023-ZDLSF-63), and the National Natural Science Foundation of China (No. 42261144749).
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History
Received: Oct 18, 2023
Accepted: Jan 15, 2024
Published online: Apr 26, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 26, 2024
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