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Recently, there has been a lot of interest in the synthesis and application of nanoparticles with anisotropic morphologies. The focus of this study has been to use a biologically mediated, low temperature approach for the synthesis of zinc oxide nanoflowers. “Green” methods have a number of advantages over conventional approaches; these include the use of environmentally benign reactants and its economic feasibility. The cell free extract of Chlamydomonas reinhardtii, a fresh water microalga was used to synthesize the nanoflowers. The nanoflowers were composed of individual nanorods that assembled to form flower‐like structures. The nanorods measured 330 nm in length and these nanorods self‐assembled to form porous nanosheets that were found to measure 55–80 nm. Particle size analysis revealed that the larger porous nanoflowers approximately measured 4 µm. Powder X‐ray diffraction studies revealed that the zinc oxide nanoflowers had a hexagonal wurtzite crystal structure. Fourier transform infrared spectroscopy analysis suggested that algal biomolecules were responsible for the synthesis and stabilization of zinc oxide nanostructures. These nanoflowers demonstrate enhanced photocatalytic activity against methyl orange (MO) under natural sunlight. The effects of dye concentration and catalyst dosage on photocatalysis were also studied. The present approach represents a novel, eco‐friendly method to synthesize zinc oxide nanoflowers that have potential applications in water treatment and dye degradation. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1020–1026, 2016

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

This study describes an eco-friendly, economical method to synthesize semiconductor zinc sulfide (ZnS) nanoparticles using the cell free extract of

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