Graphene-based materials production and application in textile wastewater treatment: color removal and phytotoxicity using Lactuca sativa as bioindicator

Abstract

The dyes used in textile industries are usually difficult to degrade in aquatic environments, being highly toxic to micro fauna and flora. Thus, textile wastewater treatments have been developed, among them, one that stands out is adsorption process. With the rise of nanomaterials applied to adsorption, graphene oxide (GO) shows promise in the removal of dyes. This work aimed to produce a more economical and environmentally friendly GO by reducing H₂SO₄ concentration during the synthesis. Adsorption tests were performed with methylene blue (MB) and brilliant blue (BB), adsorbent regeneration tests, as well as a kinetic study using real wastewater, and toxicological assays with lettuce seeds. Results showed that the sample produced with less H₂SO₄ (GO-21) performed better for MB (99% removal) and BB (29% removal); and recycling test showed that despite the decrease in removal efficiency, it remained high in the first cycles. Kinetics showed that equilibrium was reached in 30 min, removing 67.43% of color and 90.23% of the effluent’s turbidity. Phytotoxicity assays indicated that the wastewater treated with GO-21 was the least toxic, compared to other wastewater samples analyzed. Therefore, GO has demonstrated its potential to be an effective and less toxic option to treat textile effluents.

Keywords:

• real textile wastewater
• adsorption kinetics
• graphene oxide regeneration
• color removal
• phytotoxicity assays

Acknowledgments

To the Chemical Engineering Department of the Universidade Federal de Pernambuco and the Laboratory of Mineralogy; the Center for Strategic Technologies of the Northeast (CETENE); and the Civil Police Criminalistics Institute, all located in the state of Pernambuco (Brazil). Also to Ms. Bruna Figueiredo, Ms. Amanda Belém Chaves and Professor Dr. Maria Fernanda Pimentel for all the support.

Disclosure statement

The authors declare they have no conflicts of interest to disclosure.

Additional information

Funding

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) under Grant numbers 312128/2018-5 and 311133/2015-0; the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) under Grant number APQ-1086-3.06/15; and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under Grant number 206/2018.