Repository of Research and Investigative Information

Repository of Research and Investigative Information

Torbat Heydariyeh University of Medical Sciences

Capture of I(131) from medical-based wastewater using the highly effective and recyclable adsorbent of g-C3N4 assembled with Mg-Co-Al-layered double hydroxide

(2020) Capture of I(131) from medical-based wastewater using the highly effective and recyclable adsorbent of g-C3N4 assembled with Mg-Co-Al-layered double hydroxide. Journal of hazardous materials. p. 122151. ISSN 1873-3336 (Electronic) 0304-3894 (Linking)

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Official URL: http://www.ncbi.nlm.nih.gov/pubmed/32006938

Abstract

This paper reports a very high capacity and recyclable Mg-Co-Al-layered double hydroxide@ g-C3N4 nanocomposite as the new adsorbent for remediation of radioisotope-containing medical-based solutions. In this work, a convenient solvothermal method was employed to synthesize a new nano-adsorbent, whose features were determined by energy dispersive X-ray (EDS/EDX), XRD, FESEM, TEM, TGA, BET, and FT-IR spectroscopy. The as-prepared nano-adsorbent was applied to capture the radioisotope iodine-131 mainly from the medical-based wastewater under different conditions of main influential parameters, (i.e. adsorbent dose, initial I2 concentration, sonication time, and temperature). The process was evaluated by three models of RSM, CCD-ANFIS, and CCD-GRNN. Furthermore, comprehensive kinetic, isotherm, thermodynamic, reusability cycles and optimization (by GA and DF) studies were conducted to evaluate the behavior and adsorption mechanism of I2 on the surface of Mg-Co-Al-LDH@ g-C3N4 nanocomposite. High removal efficiency (95.25) of (131)I in only 30min (i.e. during 1/384 its half-life), along with an excellent capacity that has ever been reported (2200.70mg/g) and recyclability (seven times without breakthrough in the efficiency), turns the nanocomposite to a very promising option in remediation of (131)I-containing solutions. Besides, from the models studied, ANFIS described the process with the highest accuracy and reliability with R(2) > 0.999.

Item Type: Article
Keywords: LDH, Iodine, ANFIS, GRNNGenetic algorithm, Desirability function, Modeling
Page Range: p. 122151
Journal or Publication Title: Journal of hazardous materials
Journal Index: ISI, Pubmed
Volume: 389
Identification Number: 10.1016/j.jhazmat.2020.122151
ISSN: 1873-3336 (Electronic) 0304-3894 (Linking)
Depositing User: دکتر محبوبه عبداللهی
URI: http://eprints.thums.ac.ir/id/eprint/2346

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