Preparation and characterization of ZnO/MMT nanocomposite for photocatalytic ozonation of a disperse dye


Khataee A., Kiransan M., KARACA S., Arefi-Oskoui S.

TURKISH JOURNAL OF CHEMISTRY, cilt.40, sa.4, ss.546-564, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 4
  • Basım Tarihi: 2016
  • Doi Numarası: 10.3906/kim-1507-77
  • Dergi Adı: TURKISH JOURNAL OF CHEMISTRY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.546-564
  • Anahtar Kelimeler: ZnO/MMT nanocomposite, Disperse Red 54, photocatalytic ozonation, artificial neural network, PHOTOELECTRO-FENTON PROCESS, VISIBLE-LIGHT IRRADIATION, AZO-DYE, CATALYTIC OZONATION, ENHANCED OZONATION, AQUEOUS-SOLUTION, CARBON NANOTUBE, TEXTILE DYE, DEGRADATION, NANOPARTICLES
  • Atatürk Üniversitesi Adresli: Evet

Özet

ZnO was immobilized on the montmorillonite (MMT) to synthesize ZnO/MMT nanocomposite. Physicochemical properties of the as-synthesized nanocomposite were determined using X-ray diffraction, scanning electron microscopy, transmission electron microscope, Fourier transform infrared spectroscopy, N-2 adsorption/desorption, and point of zero charge pH (pH,) analysis. The performance of the prepared ZnO/MMT nanocomposite was examined for the photocatalytic ozonation of Disperse Red 54 (DR54) and the highest decolorization efficiency (88.75% after 60 min of reaction time) was the result for the mentioned process compared to adsorption, single ozonation, catalytic ozonation, and photolysis. The influence of various operational parameters including initial dye concentration, catalyst concentration, pH value, inlet gas concentration, and type of irradiation source was investigated on the efficiency of the photocatalytic ozonation removal of DR54. Various inorganic and organic reactive oxygen species (ROS) scavengers were applied to investigate the mechanism of photocatalytic ozonation. In addition, a three-layer perceptron neural network was developed for modeling the relationship between the operational parameters and decolorization efficiency of the dye. High R-2 values were obtained for both the training and test data.