共 48 条
Photocatalysis of a dye solution using immobilized ZnO nanoparticles combined with photoelectrochemical process
被引:82
作者:
Khataee, A. R.
[1
]
Zarei, M.
[1
]
机构:
[1] Univ Tabriz, Fac Chem, Dept Appl Chem, Tabriz, Iran
关键词:
Carbon nanotube;
Zinc oxide nanoparticles;
Photocatalysis;
TOC;
Neural network;
INDIRECT ELECTROCHEMICAL TREATMENT;
ADVANCED OXIDATION PROCESSES;
PEROXI-COAGULATION METHOD;
ELECTRO-FENTON;
AQUEOUS-MEDIUM;
AZO-DYE;
CHLOROPHENOXY HERBICIDES;
HYDROGEN-PEROXIDE;
WASTE-WATER;
DEGRADATION;
D O I:
10.1016/j.desal.2011.01.066
中图分类号:
TQ [化学工业];
学科分类号:
0817 ;
摘要:
The decolorization of a dye solution containing C.I. Direct Yellow 12 (DY12) was performed by photoelectro-Fenton (PEF) combined with photocatalytic process. Carbon nanotube-polytetrafluoroethylene (CNT-PTFE) electrode was used as cathode. The investigated photocatalyst was ZnO nanoparticles having specific surface area (BET) 32.23 m(2)/g, and mean crystal size of 15 nm immobilized on glass plates. A comparison of electro-Fenton (EF), UV/ZnO, PEF and PEF/ZnO processes for decolorization of DY12 solution was performed. Results showed that color removal follows the decreasing order: PEF/ZnO>PEF>EF>UV/ZnO. The influence of the basic operational parameters such as initial pH of the solution, initial dye concentration, applied current, kind of ultraviolet (UV) light and initial Fe3+ concentration on the decolorization efficiency of DY12 was studied. The mineralization of the dye was investigated by total organic carbon (TOC) measurements that showed 96.7% mineralization of 50 mg/l dye at 6 h using PEF/ZnO process. An artificial neural network (ANN) model was developed to predict the decolorization of DY12 solution. The findings indicated that artificial neural network provided reasonable predictive performance (R-2 = 0.980). (C) 2011 Elsevier B.V. All rights reserved.
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页码:453 / 460
页数:8
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