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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 GholizadehEmail author
  • Mohammad Miri
  • Lida Rafati
Research Article
  • 9 Downloads

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%) ((qe,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

Atenolol Wastewater Multi-walled carbon nanotube Artificial neural network 

Notes

Acknowledgements

Our deepest appreciations are expressed to the Isfahan University of Medical Sciences that supported this study financially.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

40201_2019_347_MOESM1_ESM.docx (33 kb)
ESM 1 (DOCX 32 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Student Research Committee, School of HealthIsfahan University of Medical SciencesIsfahanIran
  2. 2.Department of Environmental Health Engineering, School of HealthIsfahan University of Medical SciencesIsfahanIran
  3. 3.Environment Research Center, Research Institute for Primordial Prevention of Non-communicable DiseaseIsfahan University of Medical SciencesIsfahanIran
  4. 4.Esfarayen Faculty of Medical SciencesEsfarayenIran
  5. 5.Department of Environmental Health, School of Public HealthSabzevar University of Medical SciencesSabzevarIran
  6. 6.Deputy of HealthHamadan University of Medical SciencesHamadanIran

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