Metal peroxides as potential photocatalysts for environmental remediation.

Inorganic oxide materials such as TiO2 and ZnO have been extensively studied for environmental remediation, that operates through photo generated Reactive Oxygen Species (ROS) such as H2O2, ·OH and O2 - to decontaminate waste water. However, inorganic solid oxidants such as metal peroxides capable of generating ROS in aqueous solutions have not been studied for environmental remediation. Towards this objective, we have synthesized peroxides of Zn, Mg, and Ba and characterized these by powder X-ray diffraction, Transmission Electron Microscopy, UV-visible spectroscopy, and X-ray photoelectron spectroscopy. The photocatalytic activity of these wide band gap semiconductors has also been investigated. The novelty of the work is in the use of these peroxides as chemical sources of ROS in aqueous suspensions in addition to their photochemical generation. Hence, these peroxides, in particular Ba, exhibit high photocatalytic activity, better than the well-known ZnO. The mechanisms of ROS generation and subsequent dye degradation are elucidated. ROS has been estimated and is correlated to the photocatalytic activity. This work reports for the first time BaO2 as potential photocatalyst.

Solid phase extraction based on trimethylsilyloxy silica aerogel.

Solid-phase extraction (SPE) based on trimethylsilyloxy-modified silica aerogel was developed for extraction of chemotherapeutic drugs from water. The developed method is easy and affordable, can be performed in separating funnel and does not require a vacuum and SPE manifold. The extraction and recovery of cyclophosphamide (CYP), dexamethasone (DEX), and paclitaxel (TAX) by the aerogel from water were investigated. The factors governing the extraction efficiency such as sample pH, sample volume, volume of eluent and concentration of analytes were studied. The LOD and LOQ of the developed method were calculated and linearity was found in the range of 4-100 µg L-1. The extraction efficiency of the aerogel was compared to that of other SPE cartridges, Oasis HLB, Strata-X-C, C18 and polymeric reversed phase, and the aerogel showed similar or better performance than the other commercial cartridges available on the market. The developed method was also used to extract chemotherapeutic drugs spiked in hospital wastewater.

Ethanol-activated granular aerogel as efficient adsorbent for persistent organic pollutants from real leachate and hospital wastewater.

Hydrophobic aerogels were used to remove three types of persistent organic pollutants: pharmaceutical drugs (i.e. doxorubicin [DOX], paclitaxel [TAX]), phthalates (diethyl phthalate [DEP]), and hydrophilic rhodamine dye (RhB) from synthetic and real wastewaters, using Lumira granular aerogel from Cabot activated with EtOH (ET-GAG). The hydrophobic silica aerogel was characterized by X-ray diffraction (XRD), High-Resolution Transmission Electron Microscopy (HRTEM), Brunauer-Emmet-Teller (BET) and attenuated total reflection-Fourier transform infrared spectroscopy. The pollutants were analysed by high-performance liquid chromatography (HPLC)-UV and HPLC-mass spectrometry. The adsorption process was governed by hydrophobic- hydrophobic interactions between the ET-GAG and micropollutants. The adsorption capacity of ET-GAG, examined by batch experiments, for DOX, TAX and DEP were 13.80, 14.28 and 17.54 mg/g respectively. The rate of adsorption to ET-GAG is high in the initial 40 min followed by no change in the rate due to saturation of adsorption sites. ET-GAG was able to completely remove micropollutants from real leachate and hospital wastewater, implying practical applications. Regeneration of the aerogel was studied by solvent extraction. Et-GAG adsorbent demonstrated better removal of toxic chemotherapeutic drugs and phthalates than GAC.

Keratin protein property based classification of mammals and non-mammals using machine learning techniques.

Keratin protein is ubiquitous in most vertebrates and invertebrates, and has several important cellular and extracellular functions that are related to survival and protection. Keratin function has played a significant role in the natural selection of an organism. Hence, it acts as a marker of evolution. Much information about an organism and its evolution can therefore be obtained by investigating this important protein. In the present study, Keratin sequences were extracted from public data repositories and various important sequential, structural and physicochemical properties were computed and used for preparing the dataset. The dataset containing two classes, namely mammals (Class-1) and non-mammals (Class-0), was prepared, and rigorous classification analysis was performed. To reduce the complexity of the dataset containing 56 parameters and to achieve improved accuracy, feature selection was done using the t-statistic. The 20 best features (parameters) were selected for further classification analysis using computational algorithms which included SVM, KNN, Neural Network, Logistic regression, Meta-modeling, Tree Induction, Rule Induction, Discriminant analysis and Bayesian Modeling. Statistical methods were used to evaluate the output. Logistic regression was found to be the most effective algorithm for classification, with greater than 96% accuracy using a 10-fold cross validation analysis. KNN, SVM and Rule Induction algorithms also were found to be efficacious for classification.