4,694 research outputs found
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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
skyrmions in a 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
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Influence of error terms in Bayesian calibration of energy system models
Calibration represents a crucial step in the modelling process to obtain accurate simulation results and quantify uncertainties. We scrutinise the statistical Kennedy & O’Hagan framework, which quantifies different sources of uncertainty in the calibration process, including both model inputs and errors in the model. In specific, we evaluate the influence of error terms on the posterior predictions of calibrated model inputs. We do so by using a simulation model of a heat pump in cooling mode. While posterior values of many parameters concur with the expectations, some parameters appear not to be inferable. This is particularly true for parameters associated with model discrepancy, for which prior knowledge is typically scarce. We reveal the importance of assessing the identifiability of parameters by exploring the dependency of posteriors on the assigned prior knowledge. Analyses with random datasets show that results are overall consistent, which confirms the applicability and reliability of the framework
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)
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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
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