Atenolol adsorption onto multi-walled carbon nanotubes modified by NaOCl and ultrasonic treatment; kinetic, isotherm, thermodynamic, and artificial neural network modeling.
Artificial neural network
Atenolol
Multi-walled carbon nanotube
Wastewater
Journal
Journal of environmental health science & engineering
ISSN: 2052-336X
Titre abrégé: J Environ Health Sci Eng
Pays: England
ID NLM: 101613643
Informations de publication
Date de publication:
Jun 2019
Jun 2019
Historique:
received:
28
08
2018
accepted:
23
01
2019
entrez:
13
7
2019
pubmed:
13
7
2019
medline:
13
7
2019
Statut:
epublish
Résumé
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
Identifiants
pubmed: 31297213
doi: 10.1007/s40201-019-00347-0
pii: 347
pmc: PMC6582043
doi:
Types de publication
Journal Article
Langues
eng
Pagination
281-293Déclaration de conflit d'intérêts
Conflict of interestThe authors declare that they have no conflict of interest.
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