Photocatalytic degradation of ciprofloxacin by synthesized TiO2 nanoparticles on montmorillonite: Effect of operation parameters and artificial neural network modeling

Aydin Hassani, Alireza Khataee*, Semra Karaca

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

193 Citations (Scopus)

Abstract

TiO2/MMT nanocomposite was synthesized and characterized by X-ray diffraction (XRD), fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), X-ray fluorescence (XRF) and Brunauer-Emmett-Teller (BET) techniques. The average size of TiO2 nanoparticles was decreased from 60-80 nm to 40-60 nm through the immobilization on MMT. The main influential factors such as the TiO2/MMT dose, ciprofloxacin (CIP) concentration, pH of the solution, UV light regions, reusability of the catalyst and electrical energy determination were studied. The addition of radical scavengers (e.g. chloride, iodide, sulfate and bicarbonate) and enhancers (e.g. hydrogen peroxide, potassium iodate and peroxydisulfate) on the degradation efficiency was studied. The predicted data from the designed artificial neural network model were found to be in a good agreement with the experimental data (R2 = 0.9864). The main intermediates of CIP degradation were determined by GC-Mass spectrometry.

Original languageEnglish
Pages (from-to)149-161
Number of pages13
JournalJournal of Molecular Catalysis A: Chemical
Volume409
DOIs
Publication statusPublished - 1 Dec 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V. All rights reserved.

Keywords

  • Ciprofloxacin
  • Nanocatalyst
  • Pharmaceuticals
  • Photocatalysis
  • TiO/MMT nanocomposite

Fingerprint

Dive into the research topics of 'Photocatalytic degradation of ciprofloxacin by synthesized TiO2 nanoparticles on montmorillonite: Effect of operation parameters and artificial neural network modeling'. Together they form a unique fingerprint.

Cite this