The development of novel porous composite materials for organic dye degradation and removal has received increasing attention due to water contamination problem. In this paper, hydrothermal synthesized nano zeolite A have been encapsulated with porous periodic mesoporous organosilica (PMO) through a simple modified StÖber method an organosilane-directed growth-induced etching strategy, the obtained yolk-shell structured sample was further functionalized by the impregnation of copper, realizing the composite material with hierarchical porous and catalytic properties. The morphology, porosity and metal content of the zeolite Cu/A and Cu/A@Et-PMO were fully characterized. As compared to the parent material, the composite Cu/A@Et-PMO have an efficient adsorption and catalytic degradation performance on methylene blue (MB), the removal efficiency reached as high as 95% of 60 mg/L MB with 10min. These novel structured porous composites may have great potential for adsorption and degradation application including waste effluents.
Morency, Matthew W.; Vorobyov, Sergiy A.; Leus, Geert(arXiv, 2018-05-22)[Preprint]
Source localization and spectral estimation are among the most fundamental problems in statistical and array signal processing. Methods which rely on the orthogonality of the signal and noise subspaces, such as Pisarenko's method, MUSIC, and root-MUSIC are some of the most widely used algorithms to solve these problems. As a common feature, these methods require both apriori knowledge of the number of sources, and an estimate of the noise subspace. Both requirements are complicating factors to the practical implementation of the algorithms, and when not satisfied exactly, can potentially lead to severe errors. In this paper, we propose a new localization criterion based on the algebraic structure of the noise subspace that is described for the first time to the best of our knowledge. Using this criterion and the relationship between the source localization problem and the problem of computing the greatest common divisor (GCD), or more practically approximate GCD, for polynomials, we propose two algorithms which adaptively learn the number of sources and estimate their locations. Simulation results show a significant improvement over root-MUSIC in challenging scenarios such as closely located sources, both in terms of detection of the number of sources and their localization over a broad and practical range of SNRs. Further, no performance sacrifice in simple scenarios is observed.
Polymer chains confined to a substrate show promise as a tool for nanoscale architecture, with applications ranging from anti-reflection coatings to oriented nanowires. However, the desired control of polymer orientation is hard to achieve at the nanoscale. In this Letter, we illustrate how a collective orientation can emerge among interacting polymer chains influenced by a spatially modulated substrate potential while experiencing strong thermal fluctuations. The resulting polymer orientation is tilted by an angle proportional to an integer topological invariant (the Chern number) and hence is robust against substrate disorder, as demonstrated by Langevin dynamics simulations. We establish the topological underpinning of the tilted polymeric pattern via a correspondence between equilibrium polymer configurations and Thouless pumping of a quantum Mott insulator in imaginary time. More generally, our strategy illustrates how to construct topologically protected gapped states in soft matter systems described by the diffusion equation with the addition of many-body interactions.
Low, Tony; Chen, Pai-Yen; Basov, D. N.(arXiv, 2017-12-28)[Preprint]
AB-stacked bilayer graphene with a tunable electronic bandgap in excess of the optical phonon energy presents an interesting active medium, and we consider such theoretical possibility in this work. We argue the possibility of a highly resonant optical gain in the vicinity of the asymmetry gap. Associated with this resonant gain are strongly amplified plasmons, plasmons with negative group velocity and superluminal effects, as well as directional leaky modes.
Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. After training a convolutional neural network, we successfully imaged various samples, including blood smears, histopathology tissue sections, and parasites, where the recorded images were highly compressed to ease storage and transmission for telemedicine applications. This method is applicable to other low-cost, aberrated imaging systems, and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications.
Recently the one-dimensional time-discrete blind deconvolution problem was shown to be solvable uniquely, up to a global phase, by a semi-definite program for almost any signal, provided its autocorrelation is known. We will show in this work that under a sufficient zero separation of the corresponding signal in the $z-$domain, a stable reconstruction against additive noise is possible. Moreover, the stability constant depends on the signal dimension and on the signals magnitude of the first and last coefficients. We give an analytical expression for this constant by using spectral bounds of Vandermonde matrices.
Nitrospirae spp. distantly related to thermophilic, sulfate-reducing Thermodesulfovibrio species are regularly observed in environmental surveys of anoxic marine and freshwater habitats. However, little is known about their genetic make-up and physiology. Here, we present the draft genome of Nitrospirae bacterium Nbg-4 as a representative of this clade and analyzed its in situ protein expression under sulfate-enriched and sulfate-depleted conditions in rice paddy soil. The genome of Nbg-4 was assembled from replicated metagenomes of rice paddy soil that was used to grow rice plants in the presence and absence of gypsum (CaSO4x2H2O). Nbg-4 encoded the full pathway of dissimilatory sulfate reduction and showed expression thereof in gypsum-amended anoxic bulk soil as revealed by parallel metaproteomics. In addition, Nbg-4 encoded the full pathway of dissimilatory nitrate reduction to ammonia, which was expressed in bulk soil without gypsum amendment. The relative abundance of Nbg-4-related metagenome reads was similar under both treatments indicating that it maintained stable populations while shifting its energy metabolism. Further genome reconstruction revealed the potential to utilize butyrate, formate, H2, or acetate as electron donor, with the Wood-Ljungdahl pathway being expressed under both conditions. Comparison to publicly available Nitrospirae genome bins confirmed that the pathway for dissimilatory sulfate reduction is also present in related Nitrospirae recovered from groundwater. Subsequent phylogenomics showed that such microorganisms form a novel genus within the phylum Nitrospirae, with Nbg-4 as a representative species. Based on the widespread occurrence of this novel genus, we propose for Nbg 4 the name Candidatus Sulfobium mesophilum, gen. nov., spec. nov.
The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior,
while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region
of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.
High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the zigzag and
armchair directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies.
Graphs are a useful abstraction of image content. Not only can graphs represent details about individual objects in a scene but they can capture the interactions between pairs of objects. We present a method for training a convolutional neural network such that it takes in an input image and produces a full graph. This is done end-to-end in a single stage with the use of associative embeddings. The network learns to simultaneously identify all of the elements that make up a graph and piece them together. We benchmark on the Visual Genome dataset, and report a Recall@50 of 9.7% compared to the prior state-of-the-art at 3.4%, a nearly threefold improvement on the challenging task of scene graph generation.
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