Removal of Hazardous Pollutants from Wastewaters: Applications of TiO 2

The direct release of untreated wastewaters from various industries and households results in the release of toxic pollutants to the aquatic environment. Advanced oxidation processes (AOP) have gained wide attention owing to the prospect of complete mineralization of nonbiodegradable organic substances to environmentally innocuous products by chemical oxidation. In particular, heterogeneous photocatalysis has been demonstrated to have tremendous promise in water purification and treatment of several pollutant materials that include naturally occurring toxins, pesticides, and other deleterious contaminants. In this work, we have reviewed the different removal techniques that have been employed for water purification. In particular, the application of TiO2-SiO2 binary mixed oxide materials for wastewater treatment is explained herein, and it is evident from the literature survey that these mixed oxide materials have enhanced abilities to remove a wide variety of pollutants

Prediction of optoelectronic properties of Cu2O using neural network potential

Neural network potentials (NNPs) trained against density functional theory (DFT) are capable of reproducing the potential energy surface at a fraction of the computational cost. However, most NNP implementations focus on energy and forces. In this work, we modified the NNP model introduced by Behler and Parrinello to predict Fermi energy, band edges, and partial density of states of Cu2O. Our NNP can reproduce the DFT potential energy surface and properties at a fraction of the computational cost. We used our NNP to perform molecular dynamics (MD) simulations and validated the predicted properties against DFT calculations. Our model achieved a root mean squared error of 16 meV for the energy prediction. Furthermore, we show that the standard deviation of the energies predicted by the ensemble of training snapshots can be used to estimate the uncertainty in the predictions. This allows us to switch from the NNP to DFT on-the-fly during the MD simulation to evaluate the forces when the uncertainty is high

Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network

The use of machine learning in chemistry is on the rise for the prediction of chemical properties. The input feature representation or descriptor in these applications is an important factor that affects the accuracy as well as the extent of the explored chemical space. Here, we present the periodic table tensor descriptor that combines features from Behler–Parrinello’s symmetry functions and a periodic table representation. Using our descriptor and a convolutional neural network model, we achieved 2.2 kcal/mol and 94 meV/atom mean absolute error for the prediction of the atomization energy of organic molecules in the QM9 data set and the formation energy of materials from Materials Project data set, respectively. We also show that structures optimized with a force field derived from this model can be used as input to predict the atomization energies of molecules at density functional theory level. Our approach extends the application of Behler–Parrinello’s symmetry functions without a limitation on the number of elements, which is highly promising for universal property calculators in large chemical spaces

A Kinetic Study of Photocatalytic Degradation of Phenol over Titania–Silica Mixed Oxide Materials under UV Illumination

A set of titania–silica mixed oxide materials were prepared by a cosolvent-induced gelation method using ethanol and toluene as solvent and cosolvent, respectively. These materials were extensively characterized by utilizing several characterization techniques and assessed for phenol degradation under UV illumination. The degradation of phenol follows first-order kinetics, and fragmented products formed during the phenol degradation were qualitatively identified by using high performance liquid Chromatographic (HPLC) and atomic pressure chemical ionization mass spectroscopic (APCI-MS) techniques. The complete mineralization of phenol was further evidenced by the measurement of the total organic contents that remained in the solution after irradiation. The pore diameter of the materials was found to be the key factor for phenol degradation, whereas surface area and pore volume play a role among the mixed oxide materials. In addition, in the mixed oxide system there was an inverse correlation obtained with the particle size of the materials and the degradation efficiency. The smaller particle size of titania in the mixed oxide material was found to be a requirement for an effective degradation of phenol

Nanocasting of Periodic Mesoporous Materials as an Effective Strategy to Prepare Mixed Phases of Titania

Mesoporous titanium dioxide materials were prepared using a nanocasting technique involving silica SBA-15 as the hard-template. At an optimal loading of titanium precursor, the hexagonal periodic array of pores in SBA-15 was retained. The phases of titanium dioxide could be easily varied by the number of impregnation cycles and the nature of titanium alkoxide employed. Low number of impregnation cycles produced mixed phases of anatase and TiO2(B). The mesoporous TiO2 materials were tested for solar hydrogen production, and the material consisting of 98% anatase and 2% TiO2(B) exhibited the highest yield of hydrogen from the photocatalytic splitting of water. The periodicity of the pores was an important factor that influenced the photocatalytic activity. This study indicates that mixed phases of titania containing ordered array of pores can be prepared by using the nanocasting strategy

Aging dependent phase transformation of mesostructured titanium dioxide nanomaterials prepared by evaporation-induced self-assembly process: Implications for solar hydrogen production

Mesostructured titanium dioxide materials were prepared by Evaporation-Induced Self-Assembly (EISA) method using titanium isopropoxide and a cationic surfactant. The titania phase could be tuned by simply varying the aging time. As the aging time increased, hierarchically structured mesoporous materials with mixed phases of titania were obtained. The rutile content was found to generally increase with length in aging time. The mesostructured materials were evaluated for hydrogen production, and a mixed phase consisting of 95% anatase and 5% rutile showed the highest activity. This study indicates that the aging time is an important parameter for the preparation of mesostructured materials with hierarchical porosities and mixed phase(s) of titania