Optimization and modeling of UV-TiO2 mediated photocatalytic degradation of golden yellow dye through response surface methodology

Fabrication of Ce doped TiO2 for efficient organic pollutants removal from wastewater

Pristine and Ce doped TiO₂ nanoparticles were fabricated for toxic pollutants removal from wastewater. Pristine, 2% Ce and 4% Ce doped TiO₂ photocatalysts were produced via hydrothermal route. 4% Ce doped TiO₂ exhibited 2.41 eV bandgap which is smaller than pure TiO_2. The morphology was also investigated and it was established that doping of Ce ions enhanced the surface roughness and reduced the particle size. The surface area was characterized through BET analysis and 4% Ce-TiO₂ possess higher surface with large pore diameter which helped the photocatalytic activity. The prepared photocatalysts were investigated on reduction of pollutants from wastewater under visible light. Higher efficiency was obtained for 4% Ce-TiO₂ photocatalyst for both model pollutants. The "k" value possessed was also higher for the doped TiO₂ catalyst. These analysis reports the optimum level of ceria doping to enhance morphology, surface area and it increased activity than bare TiO₂. 4% Ce-TiO₂ will be the potential candidate for efficient wastewater management. The 4% Ce doped TiO₂ photocatalyst provided 77% and 88% on reducing MB and RhB dyes. The dopant has developed higher surface area, morphology and good recombination rate which reduced the toxic pollutants and changed the wastewater to reuse.

Ultrasound Assisted Synthesis of Gadolinium Oxide-Zeolitic Imidazolate Framework-8 Nanocomposites and Their Optimization for Photocatalytic Degradation of Methyl Orange Using Response Surface Methodology

An ultrasound-assisted method was used to prepare gadolinium oxide (Gd2O3)-zeolitic imidazolate framework (ZIF)-8 nanocomposites. The surface morphology, particle size, and properties of the Gd2O3-ZIF-8 nanocomposites were examined using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, and ultraviolet-visible (UV-vis) spectroscopy. The synthesized Gd2O3-ZIF-8 nanocomposites were used as a catalyst to degrade methyl orange (MO) under UV light irradiation at 254 nm. The color of the aqueous MO dye solution during photocatalytic degradation was examined using color spectroscopy. Response surface methodology (RSM) using a four-factor Box-Behnken design (BBD) was used to design the experiments and optimize the photocatalytic degradation of MO. The significance of the experimental factors and their interactions were determined using analysis of variance (ANOVA). The efficiency of Gd2O3-ZIF-8 nanocomposites for the photocatalytic degradation of MO reached 98.05% within 40 min under UV irradiation at 254 nm under the experimental conditions of pH 3.3, 0.4 g/L catalyst dose, 0.0630 mM MO concentration, and 431.79 mg/L H2O2 concentration. The kinetics study showed that the MO photocatalytic degradation followed a pseudo-first-order reaction rate law.

A generalized predictive model for TiO2-Catalyzed photo-degradation rate constants of water contaminants through artificial neural network

Titanium dioxide (TiO₂) is a well-known photocatalyst in the applications of water contaminant treatment. Traditionally, the kinetics of photo-degradation rates are obtained from experiments, which consumes enormous labor and experimental investments. Here, a generalized predictive model was developed for prediction of the photo-degradation rate constants of organic contaminants in the presence of TiO₂ nanoparticles and ultraviolet irradiation in aqueous solution. This model combines an artificial neural network (ANN) with a variety of factors that affect the photo-degradation performance, i.e., ultraviolet intensity, TiO₂ dosage, organic contaminant type and initial concentration in water, and initial pH of the solution. The molecular fingerprints (MF) were used to interpret the organic contaminants as binary vectors, a format that is machine-readable in computational linguistics. A dataset of 446 data points for training and testing was collected from the literature. This predictive model shows a good accuracy with a root mean square error (RMSE) of 0.173.