Novel green synthesis of Gd-doped TiO2 nanoparticles for environmental remediation: statistical modeling and process optimization†

Abstract

The contamination of freshwater with harmful antibiotic pollutants has driven researchers to create new, efficient, and affordable water purification methods and materials. As a result, photocatalysis has emerged as a highly efficient method for addressing environmental remediation. The green synthesis of photocatalysts using plant extracts is regarded as a prominent method in materials synthesis that promotes environmental sustainability. Herein, this work presents the novel green synthesis of Gd-doped TiO₂ nanoparticles via the hydrothermal method using Piper betel leaf extract. The structural, morphological, and optical properties of the synthesized nanoparticles were studied in detail by various characterization techniques, and their photocatalytic activity in the degradation of tetracycline (TC) under visible light irradiation was tested. The response surface methodology (RSM) based on central composite design (CCD) was employed to examine the effect of experimental variables on the removal of TC. The obtained results indicate that the predicted outcomes using RSM closely matched the experimental outcomes with a correlation coefficient (R²) of 0.9746. The maximum removal efficiency for the GdT_0.8 sample was found to be 93.08% within 80 min of light irradiation as compared to TiO₂ doped with other metals and TiO₂ nanocomposites and the sample also exhibited good reusability up to four cycles under optimum conditions (catalyst dose = 22.50 mg, initial TC concentration = 18.25 ppm, and pH = 5.02). All degradation kinetics were well-fitted to the pseudo-first-order model. This enhanced photocatalytic activity was mainly due to the higher surface area, larger optical adsorption, and greater photo-induced charge carrier transfer. Additionally, the degradation pathways and degradation mechanisms of TC by Gd-doped TiO₂ were also proposed. Thus, the above findings demonstrated that the experimental design is an effective approach for variable optimization and modeling.