Hierarchical structure of 3D ZnO experimentally designed to achieve high performance in the sertraline photocatalysis in natural waters

Abstract

Hierarchical nanostructures of 3D ZnO are considered great potential materials for many technological applications. In this study, we employed a chemometric approach to investigate the detailed effects of the synthetic parameters in the obtaining of ZnO. We applied the microwave-assisted solvothermal method of high control. Using Central Composite Design (CCD) and Principal Component Analysis (PCA) we identified that the proportions of solvents and physical parameters, efficiently controlled, were responsible for the formation of 3D ZnO structures in only 10 min. The physical–chemical properties of the nanostructures were characterized by XRD, Raman, HR-TEM, XPS, FTIR, MEV-EDS, FEG, DRS, Mott-Schottky and other methods, which led to the discovery of important characteristics that were treated by chemometry. As a consequence, the materials were efficient to produce •OH and O2•–, reaching high performance in sertraline (SRTL) degradation in up to five cycles of application. The transformation products (TP) were followed by mass spectrometry analysis, which allowed us to describe in detail the SRTL degradation mechanisms. The degradation efficiency after 5 cycles decreases by only 20% without any treatment on the recovered photocatalyst before reapplication. Photocatalytic studies using RhB dye reached 100% efficiency in 45 min under UV-C and 99% in 75 min under UV-A, showing that the photocatalyst has excellent performance for different classes of organic pollutants and light sources. In addition, phytotoxicity assays proved that by-products formed in the SRTL degradation process are not toxic to the Cucumis sativus species, proving the safety of using this photocatalyst for wastewater treatment. Therefore, the experimentally designed synthesis method is an alternative to obtaining hierarchical 3D ZnO structures that showed high efficiency for the SRTL degradation in environmental matrices.