Supported metal nanostructures are the most widely used type of heterogeneous catalyst in industrial processes. The size of metal particles is a key factor in determining the performance of such catalysts. In particular, because low-coordinated metal atoms often function as the catalytically active sites, the specific activity per metal atom usually increases with decreasing size of the metal particles. However, the surface free energy of metals increases significantly with decreasing particle size, promoting aggregation of small clusters. Using an appropriate support material that strongly interacts with the metal species prevents this aggregation, creating stable, finely dispersed metal clusters with a high catalytic activity, an approach industry has used for a long time. Nevertheless, practical supported metal catalysts are inhomogeneous and usually consist of a mixture of sizes from nanoparticles to subnanometer clusters. Such heterogeneity not only reduces the metal atom efficiency but also frequently leads to undesired side reactions. It also makes it extremely difficult, if not impossible, to uniquely identify and control the active sites of interest. The ultimate small-size limit for metal particles is the single-atom catalyst (SAC), which contains isolated metal atoms singly dispersed on supports. SACs maximize the efficiency of metal atom use, which is particularly important for supported noble metal catalysts. Moreover, with well-defined and uniform single-atom dispersion, SACs offer great potential for achieving high activity and selectivity. In this Account, we highlight recent advances in preparation, characterization, and catalytic performance of SACs, with a focus on single atoms anchored to metal oxides, metal surfaces, and graphene. We discuss experimental and theoretical studies for a variety of reactions, including oxidation, water gas shift, and hydrogenation. We describe advances in understanding the spatial arrangements and electronic properties of single atoms, as well as their interactions with the support. Single metal atoms on support surfaces provide a unique opportunity to tune active sites and optimize the activity, selectivity, and stability of heterogeneous catalysts, offering the potential for applications in a variety of industrial chemical reactions.
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Metal species with different size (single atoms, nanoclusters, and nanoparticles) show different catalytic behavior for various heterogeneous catalytic reactions. It has been shown in the literature that many factors including the particle size, shape, chemical composition, metal-support interaction, and metal-reactant/solvent interaction can have significant influences on the catalytic properties of metal catalysts. The recent developments of well-controlled synthesis methodologies and advanced characterization tools allow one to correlate the relationships at the molecular level. In this Review, the electronic and geometric structures of single atoms, nanoclusters, and nanoparticles will be discussed. Furthermore, we will summarize the catalytic applications of single atoms, nanoclusters, and nanoparticles for different types of reactions, including CO oxidation, selective oxidation, selective hydrogenation, organic reactions, electrocatalytic, and photocatalytic reactions. We will compare the results obtained from different systems and try to give a picture on how different types of metal species work in different reactions and give perspectives on the future directions toward better understanding of the catalytic behavior of different metal entities (single atoms, nanoclusters, and nanoparticles) in a unifying manner.
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods.
A new Lagrangian formulation is introduced. It can be used to make molecular dynamics (MD) calculations on systems under the most general, externally applied, conditions of stress. In this formulation the MD cell shape and size can change according to dynamical equations given by this Lagrangian. This new MD technique is well suited to the study of structural transformations in solids under external stress and at finite temperature. As an example of the use of this technique we show how a single crystal of Ni behaves under uniform uniaxial compressive and tensile loads. This work confirms some of the results of static (i.e., zero temperature) calculations reported in the literature. We also show that some results regarding the stress-strain relation obtained by static calculations are invalid at finite temperature. We find that, under compressive loading, our model of Ni shows a bifurcation in its stress-strain relation; this bifurcation provides a link in configuration space between cubic and hexagonal close packing. It is suggested that such a transformation could perhaps be observed experimentally under extreme conditions of shock.
Journal Article Antibiotic Susceptibility Testing by a Standardized Single Disk Method Get access A. W. Bauer, M.D., A. W. Bauer, M.D. Departments of Microbiology and Medicine, University of Washington, School of Medicine, Seattle, Washington 98105 Search for other works by this author on: Oxford Academic Google Scholar W. M. M. Kirby, M.D., W. M. M. Kirby, M.D. Departments of Microbiology and Medicine, University of Washington, School of Medicine, Seattle, Washington 98105 Search for other works by this author on: Oxford Academic Google Scholar J. C. Sherris, M.D., J. C. Sherris, M.D. Departments of Microbiology and Medicine, University of Washington, School of Medicine, Seattle, Washington 98105 Search for other works by this author on: Oxford Academic Google Scholar M. Turck, M.D. M. Turck, M.D. Departments of Microbiology and Medicine, University of Washington, School of Medicine, Seattle, Washington 98105 Search for other works by this author on: Oxford Academic Google Scholar American Journal of Clinical Pathology, Volume 45, Issue 4_ts, April 1966, Pages 493–496, https://doi.org/10.1093/ajcp/45.4_ts.493 Published: 01 April 1966 Article history Received: 17 August 1965 Published: 01 April 1966
MOTIVATION: Genomics has revolutionized biological research, but quality assessment of the resulting assembled sequences is complicated and remains mostly limited to technical measures like N50. RESULTS: We propose a measure for quantitative assessment of genome assembly and annotation completeness based on evolutionarily informed expectations of gene content. We implemented the assessment procedure in open-source software, with sets of Benchmarking Universal Single-Copy Orthologs, named BUSCO. AVAILABILITY AND IMPLEMENTATION: Software implemented in Python and datasets available for download from http://busco.ezlab.org. CONTACT: evgeny.zdobnov@unige.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
The results of a single-crystal structure determination when in CIF format can now be validated routinely by automatic procedures. In this way, many errors in published papers can be avoided. The validation software generates a set of ALERTS detailing issues to be addressed by the experimenter, author, referee and publication journal. Validation was pioneered by the IUCr journal Acta Crystallographica Section C and is currently standard procedure for structures submitted for publication in all IUCr journals. The implementation of validation procedures by other journals is in progress. This paper describes the concepts of validation and the classes of checks that are carried out by the program PLATON as part of the IUCr checkCIF facility. PLATON validation can be run at any stage of the structure refinement, independent of the structure determination package used, and is recommended for use as a routine tool during or at least at the completion of every structure determination. Two examples are discussed where proper validation procedures could have avoided the publication of incorrect structures that had serious consequences for the chemistry involved.
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.
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MOTIVATION: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis. RESULTS: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization. AVAILABILITY AND IMPLEMENTATION: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info CONTACT: phil.ewels@scilifelab.se.
Abstract Summary: MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252 Gbps in 44.1 and 99.6 h on a single computing node with and without a graphics processing unit, respectively. MEGAHIT assembles the data as a whole, i.e. no pre-processing like partitioning and normalization was needed. When compared with previous methods on assembling the soil data, MEGAHIT generated a three-time larger assembly, with longer contig N50 and average contig length; furthermore, 55.8% of the reads were aligned to the assembly, giving a fourfold improvement. Availability and implementation: The source code of MEGAHIT is freely available at https://github.com/voutcn/megahit under GPLv3 license. Contact: rb@l3-bioinfo.com or twlam@cs.hku.hk Supplementary information: Supplementary data are available at Bioinformatics online.
Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).
A sensitive single-beam technique for measuring both the nonlinear refractive index and nonlinear absorption coefficient for a wide variety of materials is reported. The authors describe the experimental details and present a comprehensive theoretical analysis including cases where nonlinear refraction is accompanied by nonlinear absorption. In these experiments, the transmittance of a sample is measured through a finite aperture in the far field as the sample is moved along the propagation path (z) of a focused Gaussian beam. The sign and magnitude of the nonlinear refraction are easily deduced from such a transmittance curve (Z-scan). Employing this technique, a sensitivity of better than lambda /300 wavefront distortion is achieved in n/sub 2/ measurements of BaF/sub 2/ using picosecond frequency-doubled Nd:YAG laser pulses.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Optical trapping of dielectric particles by a single-beam gradient force trap was demonstrated for the first reported time. This confirms the concept of negative light pressure due to the gradient force. Trapping was observed over the entire range of particle size from 10 μm to ~25 nm in water. Use of the new trap extends the size range of macroscopic particles accessible to optical trapping and manipulation well into the Rayleigh size regime. Application of this trapping principle to atom trapping is considered.
In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.
Arc-synthesized single-walled carbon nanotubes have been purified through preparative electrophoresis in agarose gel and glass bead matrixes. Two major impurities were isolated: fluorescent carbon and short tubular carbon. Analysis of these two classes of impurities was done. The methods described may be readily extended to the separation of other water-soluble nanoparticles. The separated fluorescent carbon and short tubule carbon species promise to be interesting nanomaterials in their own right.
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A rapid, direct method for screening single plaques of Agt recombinant phage is described. The method allows at least 10(6) clones to be screened per day and simplifies physical containment of recombinants.
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