ZnO nanopillars have been electrodeposited epitaxially onto Au(111), Au(110) and Au(100) single-crystal substrates. The nanopillars grow with the same [0001] out-of-plane orientation on all three substrates. The in-plane orientation was probed by X-ray pole figure analysis. The pole figures had six peaks on Au(111) and twelve peaks on Au(110) and Au(100). Scanning electron microscopy revealed aligned hexagonal nanopillars of ZnO with an average grain size of 85 nm on Au(111). There were two sets of hexagonal grains with an average size of 85 nm on Au(110) and 95 nm on Au(100) that were rotated 90° with respect to each other. Rocking curve analysis showed that the ZnO on Au(100) had the smallest mosaic spread.

Proteins that interact with DNA are involved in a number of fundamental biological activities such as DNA replication, transcription, and repair. A reliable identification of DNA-binding sites in DNA-binding proteins is important for functional annotation, site-directed mutagenesis, and modeling protein-DNA interactions. We apply Support Vector Machine (SVM), a supervised pattern recognition method, to predict DNA-binding sites in DNA-binding proteins using the following features: amino acid sequence, profile of evolutionary conservation of sequence positions, and low-resolution structural information. We use a rigorous statistical approach to study the performance of predictors that utilize different combinations of features and how this performance is affected by structural and sequence properties of proteins. Our results indicate that an SVM predictor based on a properly scaled profile of evolutionary conservation in the form of a position specific scoring matrix (PSSM) significantly outperforms a PSSM-based neural network predictor. The highest accuracy is achieved by SVM predictor that combines the profile of evolutionary conservation with low-resolution structural information. Our results also show that knowledge-based predictors of DNA-binding sites perform significantly better on proteins from mainly-alpha structural class and that the performance of these predictors is significantly correlated with certain structural and sequence properties of proteins. These observations suggest that it may be possible to assign a reliability index to the overall accuracy of the prediction of DNA-binding sites in any given protein using its sequence and structural properties. A web-server implementation of the predictors is freely available online at http://lcg.rit.albany.edu/dp-bind/.

Superhydrophobic and superoleophilic graphene/polyvinylidene fluoride (G/PVDF) aerogels were prepared by solvothermal reduction of the graphene oxide and PVDF mixed dispersions. The chemical reduction of the graphene oxide component was verified by FT-IR, XRD, XPS, Raman spectroscopy and TGA. The asprepared aerogel showed high specific surface area, eminent absorption capacity for oils and organic solvents, superior water repelling ability, excellent absorption recyclability, and considerable mechanical properties. Therefore, this kind of aerogel is a promising material for oil-water separation, oil spill cleanup and recovery of organic solvents. Moreover, this work paved a facile way to fabricate superhydrophobic and superoleophilic graphene-based aerogels with graphene oxide and a hydrophobic polymer.

Graphene oxide was reduced by natural gallic acid at room temperature and upon heating. Gallic acid functioned as both a reductant and stabilizer during the reaction. The reduction of graphene oxide was verified by UV-vis, IR, Raman spectroscopy and XPS. Moreover, the reduced graphene oxide (rGO) exhibited the best dispersibility ever reported both in water and in organic solvents because of the gallic acid stabilizer. For example, the dispersibility of the rGO synthesized at room temperature is up to 1.2 mg ml À1 in water and 4 mg ml À1 in dimethylsulfoxide, respectively. This study offers a green approach to the mass preparation of rGO with excellent dispersibility.Scheme 1 Illustration of the preparation of rGO.

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