In situ formation of catalytically active graphene in ethylene photo-epoxidation.

Ethylene epoxidation is used to produce 2 × 107 ton per year of ethylene oxide, a major feedstock for commodity chemicals and plastics. While high pressures and temperatures are required for the reaction, plasmonic photoexcitation of the Ag catalyst enables epoxidation at near-ambient conditions. Here, we use surface-enhanced Raman scattering to monitor the plasmon excitation-assisted reaction on individual sites of a Ag nanoparticle catalyst. We uncover an unconventional mechanism, wherein the primary step is the photosynthesis of graphene on the Ag surface. Epoxidation of ethylene is then promoted by this photogenerated graphene. Density functional theory simulations point to edge defects on the graphene as the sites for epoxidation. Guided by this insight, we synthesize a composite graphene/Ag/α-Al2O3 catalyst, which accomplishes ethylene photo-epoxidation under ambient conditions at which the conventional Ag/α-Al2O3 catalyst shows negligible activity. Our finding of in situ photogeneration of catalytically active graphene may apply to other photocatalytic hydrocarbon transformations

Generation of single skyrmions by picosecond magnetic field pulses

We numerically demonstrate an ultrafast method to create $\textit{single}$ skyrmions in a $\textit{collinear}$ ferromagnetic sample by applying a picosecond (effective) magnetic field pulse in the presence of Dzyaloshinskii-Moriya interaction. For small samples the applied magnetic field pulse could be either spatially uniform or nonuniform while for large samples a nonuniform and localized field is more effective. We examine the phase diagram of pulse width and amplitude for the nucleation. Our finding could ultimately be used to design future skyrmion-based devices.Comment: 4.5 pages+Supplemental Materia

Bioactive composites for bone tissue engineering

One of the major challenges of bone tissue engineering is the production of a suitable scaffold material. In this review the current composite materials options available are considered covering both the methods of both production and assessing the scaffolds. A range of production routes have been investigated ranging from the use of porogens to produce the porosity through to controlled deposition methods. The testing regimes have included mechanical testing of the materials produced through to in vivo testing of the scaffolds. While the ideal scaffold material has not yet been produced, progress is being made

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Robust Multi-Image HDR Reconstruction for the Modulo Camera

Photographing scenes with high dynamic range (HDR) poses great challenges to consumer cameras with their limited sensor bit depth. To address this, Zhao et al. recently proposed a novel sensor concept - the modulo camera - which captures the least significant bits of the recorded scene instead of going into saturation. Similar to conventional pipelines, HDR images can be reconstructed from multiple exposures, but significantly fewer images are needed than with a typical saturating sensor. While the concept is appealing, we show that the original reconstruction approach assumes noise-free measurements and quickly breaks down otherwise. To address this, we propose a novel reconstruction algorithm that is robust to image noise and produces significantly fewer artifacts. We theoretically analyze correctness as well as limitations, and show that our approach significantly outperforms the baseline on real data.Comment: to appear at the 39th German Conference on Pattern Recognition (GCPR) 201

Atomic Layer Deposition of Ni Thin Films and Application to Area-Selective Deposition

Ni thin films were deposited by atomic layer deposition (ALD) using bis(dimethylamino-2-methyl-2-butoxo)nickel [Ni(dmamb)(2)] as a precursor and NH3 gas as a reactant. The growth characteristics and film properties of ALD Ni were investigated. Low-resistivity films were deposited on Si and SiO2 substrates, producing high-purity Ni films with a small amount of oxygen and negligible amounts of nitrogen and carbon. Additionally, ALD Ni showed excellent conformality in nanoscale via holes. Utilizing this conformality, Ni/Si core/shell nanowires with uniform diameters were fabricated. By combining ALD Ni with octadecyltrichlorosilane (OTS) self-assembled monolayer as a blocking layer, area-selective ALD was conducted for selective deposition of Ni films. When performed on the prepatterned OTS substrate, the Ni films were selectively coated only on OTS-free regions, building up Ni line patterns with 3 mu m width. Electrical measurement results showed that all of the Ni lines were electrically isolated, also indicating the selective Ni deposition. (C) 2010 The Electrochemical Society. [DOI: 10.1149/1.3504196] All rights reserved.ope

Non-monotonic temperature dependent transport in graphene grown by Chemical Vapor Deposition

Temperature-dependent resistivity of graphene grown by chemical vapor deposition (CVD) is investigated. We observe in low mobility CVD graphene device a strong insulating behavior at low temperatures and a metallic behavior at high temperatures manifesting a non-monotonic in the temperature dependent resistivity.This feature is strongly affected by carrier density modulation. To understand this anomalous temperature dependence, we introduce thermal activation of charge carriers in electron-hole puddles induced by randomly distributed charged impurities. Observed temperature evolution of resistivity is then understood from the competition among thermal activation of charge carriers, temperature-dependent screening and phonon scattering effects. Our results imply that the transport property of transferred CVD-grown graphene is strongly influenced by the details of the environmentComment: 7 pages, 3 figure