Atenolol adsorption onto multi-walled carbon nanotubes modified by NaOCl and ultrasonic treatment; kinetic, isotherm, thermodynamic, and artificial neural network modeling

Bahare Dehdashti; Mohammad Mehdi Amin; Abdolmajid Gholizadeh; Mohammad Miri; Lida Rafati

doi:10.1007/s40201-019-00347-0

Atenolol adsorption onto multi-walled carbon nanotubes modified by NaOCl and ultrasonic treatment; kinetic, isotherm, thermodynamic, and artificial neural network modeling

J Environ Health Sci Eng. 2019 Feb 6;17(1):281-293. doi: 10.1007/s40201-019-00347-0. eCollection 2019 Jun.

Authors

Bahare Dehdashti^{1

2}, Mohammad Mehdi Amin^{2

3}, Abdolmajid Gholizadeh⁴, Mohammad Miri⁵, Lida Rafati⁶

Affiliations

¹ 1Student Research Committee, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
² 2Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
³ 3Environment Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran.
⁴ Esfarayen Faculty of Medical Sciences, Esfarayen, Iran.
⁵ 5Department of Environmental Health, School of Public Health, Sabzevar University of Medical Sciences, Sabzevar, Iran.
⁶ 6Deputy of Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Abstract

The removal of pharmaceutical pollutants from the aqueous environment is a great environmental concern, mainly due to their diversity, high consumption, and sustainability. In the current study, we aimed to investigate the ability of multi-walled carbon nanotubes (MWCNTs) modified by sodium hypochlorite (NaOCl) and ultrasonic treatment in refining wastewaters contaminated with Atenolol β-blocker drug (ATN). The physical and structural characteristics of the raw MWCNTs and modified MWCNTs (M-MWCNTs) were analyzed using SEM, TEM, Raman spectroscopy, TGA, and FT-IR techniques. The effects of different parameters, including pH, initial concentration, contact time, and temperature were studied and optimized. Subsequently, the adsorption data were analyzed by several kinetic and equilibrium isotherm equations and modeled by artificial neural network (ANN). Highest ATN removal (87.89%) ((q_e,exp = 46.03 mg g^-1)) occurred on the adsorbent activated within 10 s of ultrasonication time and NaOCl 30%. Moreover, adsorbent modification significantly improved the ATN removal, so that the removal rate on the raw MWCNTs was about 58%, but in the same conditions, M-MWCNTs removed more than 92% of the adsorbate. The adsorption process reached equilibrium after 90 min under the optimized pH of 6. According to ANN modeling, approximately the whole values dispersed around the 45°line, indicating a good compatibility between the trial results and ANN-predicted data. The modification of MWCNTs in proper ultrasonic power via appropriate concentration of NaOCl solution removed many of the impurities and significantly improved the adsorption performance of MWCNTs.

Keywords: Artificial neural network; Atenolol; Multi-walled carbon nanotube; Wastewater.