ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

Degradation of leaf green food dye by heterogeneous photocatalysis with TiO₂ over a polyethylene terephthalate plate

Ramon V. S. Aquino, Ada A. Barbosa, Lucas B. Ribeiro, Ana F. B. Oliveira, Josivan P. Silva, Patrícia M. Azoubel, and Otidene R. S. Rocha

Universidade Federal de Pernambuco, Recife, Brazil

E-mail: otidene@hotmail.com

Abstract: This work presents a study of the removal of leaf green dye by photocatalysis using TiO₂ in suspension and immobilized on polyethylene terephthalate (PET) plates. SEM, diffuse reflectance and XRD analyses confirmed the presence of the catalyst on the PET support and FTIR analyses proved that the catalyst remained on the support after treatment. Preliminary tests with UV-C/TiO₂(susp.)/H₂O₂ system showed a degradation of 97% of dyes in 360 min. In the kinetic study, 98% degradation was obtained for the UV-C/TiO₂(susp.)/H₂O₂ system and 90% for the UV-C/TiO₂ (PET)/H₂O₂ system in 240 min. This result demonstrated the efficiency of employing TiO₂ (PET) in the process of the contaminant when compared with TiO₂(susp.). The reaction kinetics was fitted to a pseudo-first-order model, obtaining a kinetic constant of 0.0164 in the UV-C/TiO₂(susp.)/H₂O₂ treatment and 0.0094 min⁻¹ in the UV-C/TiO₂(PET)/H₂O₂ treatment. Kinetic data for the dye solution for the UV-C/TiO₂(susp.)/H₂O₂ and UV-C/TiO₂ (PET)/H₂O₂ systems were modeled using neural networks to predict contaminant concentration over time. In the phytotoxicity assays, the IC₅₀ of the treated samples increased in the UV-C/TiO₂(PET)/H₂O₂ and UV-C/TiO₂(susp.)/H₂O₂ systems compared to the initial dye solution, suggesting a decrease in dye toxicity. The UV-C/TiO₂(PET)/H₂O₂ system exhibited more than 70% of organic matter (COD) removal and 52% of mineralization (TOC). TiO₂ immobilization exhibited degradation rates close to those of the TiO₂ suspension system.

Keywords: Leaf green ; Polyethylene terephthalate ; TiO₂ immobilization ; Degradation kinetics ; Neural network 

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-019-00804-y

Chemical Papers 73 (10) 2501–2512 (2019)