Abstract
Active electronic states in transition metal dichalcogenides are able to prompt hydrogen evolution by improving hydrogen absorption. However, the development of thermodynamically stable ...hexagonal 2H-MoS
2
as hydrogen evolution catalyst is likely to be shadowed by its limited active electronic state. Herein, the charge self-regulation effect mediated by tuning Mo−Mo bonds and S vacancies is revealed in metastable trigonal MoS
2
(1T'''-MoS
2
) structure, which is favarable for the generation of active electronic states to boost the hydrogen evolution reaction activity. The optimal 1T'''-MoS
2
sample exhibits a low overpotential of 158 mV at 10 mA cm
−2
and a Tafel slope of 74.5 mV dec
−1
in acidic conditions, which are far exceeding the 2H-MoS
2
counterpart (369 mV and 137 mV dec
−1
). Theoretical modeling indicates that the boosted performance is attributed to the formation of massive active electronic states induced by the charge self-regulation effect of Mo−Mo bonds in defective 1T'''-MoS
2
with rich S vacancies.
The 2H molybdenum disulfide (MoS2), as a stable hexagonal phase, has been one of the most studied transition metal dichalcogenides over the past decades. In the last five years, the metastable phases ...of MoS2 (1T, 1T′, 1T′′, and 1T′′′) have seen a revival of interests. Different from the edge‐sharing MoS6 trigonal prisms in the 2H MoS2 phase, these metastable phases are composed of the edge‐sharing MoS6 octahedra, in which the neighboring Mo−Mo distances differ. Due to the various crystal structures and different electronic configurations of the building MoS6 motifs, these metastable polytypes are endowed with intriguing physical properties and potential applications in diverse fields. In this Review, the recent research progress on metastable MoS2 is summarized, especially with an emphasis on the diverse synthetic approaches and the newly uncovered physical properties. The phase structures and electronic band structures are also outlined. In the end, a perspective of the future investigation on metastable MoS2 is discussed.
S‐Mo‐S three atomic layers with different Mo−S coordination and Mo−Mo bonding result in diverse polymorphs of MoS2 monolayer, the stacking order of which determines the various polytypes of bulk MoS2 crystals. In this Review, the recent research progress on metastable MoS2 is summarized, with an emphasis on the diverse synthetic approaches and the newly uncovered physical properties.
Traffic sign recognition plays an important role in autonomous vehicles as well as advanced driver assistance systems. Although various methods have been developed, it is still difficult for the ...state-of-the-art algorithms to obtain high recognition precision with low computational costs. In this paper, based on the investigation on the influence that color spaces have on the representation learning of convolutional neural network, a novel traffic sign recognition approach called DP-KELM is proposed by using a kernel-based extreme learning machine (KELM) classifier with deep perceptual features. Unlike the previous approaches, the representation learning process in DP-KELM is implemented in the perceptual Lab color space. Based on the learned deep perceptual feature, a kernel-based ELM classifier is trained with high computational efficiency and generalization performance. Through the experiments on the German traffic sign recognition benchmark, the proposed method is demonstrated to have higher precision than most of the state-of-the-art approaches. In particular, when compared with the hinge loss stochastic gradient descent method which has the highest precision, the proposed method can achieve a comparable recognition rate with significantly fewer computational costs.
We develop a facile one-step solid-state-chemistry strategy for synthesizing BiOI crystals with layered microstructure. The BiOI photocatalyst shows typical mesoporous feature with available surface ...area, considerable wide-spectrum light absorption and carriers lifetime properties, and remarkable solar to thermal effect for efficient solar-driven photocatalytic CO2 reduction to CH4. The engineered BiOI displays a superior CH4 formation performance with the space-time yield of 13.1 μmol g−1 h−1 and the selectivity of 82.3% as well as excellent photostability for CO2 photoreduction under solar light irradiation. Furthermore, the versatile BiOI can boost the photo-stimulated efficiency on clean H2 generation and pollutant degradation.
•Layered BiOI crystals were synthesized via one-step solid-state-chemistry strategy.•BiOI displays considerable wide-spectrum solar light absorption properties.•BiOI exhibits excellent performance for solar-driven CO2 reduction to useful CH4.•BiOI also shows high activities on H2 generation and pollutant degradation.
Noncentrosymmetric MoS2 semiconductors (1H, 3R) possess not only novel electronic structures of spin–orbit coupling (SOC) and valley polarization but also remarkable nonlinear optical effects. A more ...interesting noncentrosymmetric structure, the so-called 1T‴-MoS2 layers, was predicted to be built up from MoS6 octahedral motifs by theoreticians, but the bulk 1T‴ MoS2 or its single crystal structure has not been reported yet. Here, we have successfully harvested 1T‴ MoS2 single crystals by a topochemical method. The new layered structure is determined from single-crystal X-ray diffraction. The crystal crystallizes in space group P31m with a cell of a = b = 5.580(2) Å and c = 5.957(2) Å, which is a √3a × √3a superstructure of 1T MoS2 with corner-sharing Mo3 triangular trimers observed by the STEM. 1T‴ MoS2 is verified to be semiconducting and possesses a band gap of 0.65 eV, different from metallic nature of 1T or 1T′ MoS2. More surprisingly, the 1T‴ MoS2 does show strong optical second-harmonic generation signals. This work provides the first layered noncentrosymmetric semiconductor of edge-sharing MoS6 octahedra for the research of nonlinear optics.
The key task in developing graph-based learning algorithms is constructing an informative graph to express the contextual information of a data manifold. Since traditional graph construction methods ...are sensitive to noise and less datum-adaptive to changes in density, a new method called ℓ 1 -graph was proposed recently. A graph construction needs to have two important properties: sparsity and locality. The ℓ 1 -graph has a strong sparsity property, but a weak locality property. Thus, we propose a new method of constructing an informative graph using auto-grouped sparse regularization based on the ℓ 1 -graph, which is called as Group Sparse graph (GSgraph). We also show how to efficiently construct a GS-graph in reproducing kernel Hilbert space with the kernel trick. The new methods, the GS-graph and its kernelized version (KGS-graph), have the same noise-insensitive property as that of ℓ 1 -graph and also can successively preserve the properties of sparsity and locality simultaneously. Furthermore, we integrate the proposed graph with several graph-based learning algorithms to demonstrate the effectiveness of our method. The empirical studies on benchmarks show that the proposed methods outperform the ℓ 1 -graph and other traditional graph construction methods in various learning tasks.
Cross-scale cost aggregation for stereo matching Zhang, Kang; Fang, Yuqiang; Min, Dongbo ...
IEEE transactions on circuits and systems for video technology,
05/2017, Letnik:
27, Številka:
5
Journal Article
Recenzirano
Odprti dostop
This paper proposes a generic framework that enables a multiscale interaction in the cost aggregation step of stereo matching algorithms. Inspired by the formulation of image filters, we first ...reformulate cost aggregation from a weighted least-squares (WLS) optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Our key motivation is that while the human stereo vision system processes information at both coarse and fine scales interactively for the correspondence search, state-of-the-art approaches aggregate costs at the finest scale of the input stereo images only, ignoring inter-consistency across multiple scales. This motivation leads us to introduce an inter-scale regularizer into the WLS optimization objective to enforce the consistency of the cost volume among the neighboring scales. The new optimization objective with the inter-scale regularization is convex, and thus, it is easily and analytically solved. Minimizing this new objective leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation approaches, including discrete and continuous parameterization methods, can be easily integrated into the proposed framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, Middlebury Third, KITTI, and New Tsukuba data sets.
Glioblastoma (GBM) is distinguished by a high degree of intratumoral heterogeneity, which extends to the pattern of expression and amplification of receptor tyrosine kinases (RTKs). Although most ...GBMs harbor RTK amplifications, clinical trials of small-molecule inhibitors targeting individual RTKs have been disappointing to date. Activation of multiple RTKs within individual GBMs provides a theoretical mechanism of resistance; however, the spectrum of functional RTK dependence among tumor cell subpopulations in actual tumors is unknown. We investigated the pattern of heterogeneity of RTK amplification and functional RTK dependence in GBM tumor cell subpopulations. Analysis of The Cancer Genome Atlas GBM dataset identified 34 of 463 cases showing independent focal amplification of two or more RTKs, most commonly platelet-derived growth factor receptor α (PDGFRA) and epidermal growth factor receptor (EGFR). Dual-color fluorescence in situ hybridization was performed on eight samples with EGFR and PDGFRA amplification, revealing distinct tumor cell subpopulations amplified for only one RTK; in all cases these predominated over cells amplified for both. Cell lines derived from coamplified tumors exhibited genotype selection under RTK-targeted ligand stimulation or pharmacologic inhibition in vitro. Simultaneous inhibition of both EGFR and PDGFR was necessary for abrogation of PI3 kinase pathway activity in the mixed population. DNA sequencing of isolated subpopulations establishes a common clonal origin consistent with late or ongoing divergence of RTK genotype. This phenomenon is especially common among tumors with PDGFRA amplification: overall, 43% of PDGFRA-amplified GBM were found to have amplification of EGFR or the hepatocyte growth factor receptor gene (MET) as well.
The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, ...and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.
To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and ...Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model's understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.
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