Decolorization and degradation of acid dye with immobilized titania nanoparticles

This paper addresses the decolorization and degradation of acid dye by a heterogeneous photocatalytic process using immobilized nano-sized TiO2 particles as the photocatalyst. Sackcloth fiber was used as a support to immobilize the nano-sized TiO2 photocatalyst. The structural properties of the immobilized photocatalyst were characterized by XRD, SEM and EDX. UV–Vis absorption spectroscopy and the measurement of the chemical oxygen demand (COD) were also used for the process performance studies. The XRD results did not show significant changes in the structure of P25 as a consequence of the immobilization procedure. The formation of titania crystallites in the sackcloth fiber was confirmed by SEM/EDX. The photocatalytic activities of TiO2-coated sackcloth fiber catalyst were evaluated using Acid Black 26 as a model organic contaminant and using UV-A radiation. Experimental results showed that after 60 min, the degradation of Acid Black 26 with the immobilized TiO2 particles was higher than that with plain TiO2. Based on the COD results, after 3 h, the TiO2-coated sackcloth fiber effectively decomposed all of the organic compounds present in dye solution under the studied experimental conditions. The effects of the oxidant H2O2, initial dye concentration and pH on the photocatalytic degradation were also investigated. The presence of CO32− as a dissolved inorganic anion had the highest inhibitory effect on the decolorization of the dye, when compared with the other anions investigated. Kinetics analysis indicates that the photocatalytic decolorization rate of Acid Black 26 can be described by a pseudo-first-order model.

► Nano-sized TiO2 particles were immobilized onto Sackcloth fiber.
► The photocatalytic activity of immobilized TiO2 nanoparticle was evaluated using acid dye.
► The degradation of acid dye using immobilized TiO2 nanoparticle was higher than that of plain TiO2.
► The photocatalytic decolorization kinetics of acid dye followed with pseudo-first order model.