No abstract

2D layers of metal dichalcogenides are of considerable interest for high‐performance electronic devices for their unique electronic properties and atomically thin geometry. 2D SnS2 nanosheets with a bandgap of ≈2.6 eV have been attracting intensive attention as one potential candidate for modern electrocatalysis, electronic, and/or optoelectronic fields. However, the controllable growth of large‐size and high‐quality SnS2 atomic layers still remains a challenge. Herein, a salt‐assisted chemical vapor deposition method is provided to synthesize atomic‐layer SnS2 with a large crystal size up to 410 µm and good uniformity. Particularly, the as‐fabricated SnS2 nanosheet‐based field‐effect transistors (FETs) show high mobility (2.58 cm2 V−1 s−1) and high on/off ratio (≈108), which is superior to other reported SnS2‐based FETs. Additionally, the effects of temperature on the electrical properties are systematically investigated. It is shown that the scattering mechanism transforms from charged impurities scattering to electron–phonon scattering with the temperature. Moreover, SnS2 can serve as an ideal material for energy storage and catalyst support. The high performance together with controllable growth of SnS2 endow it with great potential for future applications in electrocatalysis, electronics, and optoelectronics.

Many van der Waals layered 2D materials, such as h‐BN, transition metal dichalcogenides (TMDs), and group‐III monochalcogenides, have been predicted to possess piezoelectric and mechanically flexible natures, which greatly motivates potential applications in piezotronic devices and nanogenerators. However, only intrinsic in‐plane piezoelectricity exists in these 2D materials and the piezoelectric effect is confined in odd‐layers of TMDs. The present work is intent on combining the free‐standing design and piezoresponse force microscopy techniques to obtain and directly quantify the effective out‐of‐plane electromechanical coupling induced by strain gradient on atomically thin MoS2 and InSe flakes. Conspicuous piezoresponse and the measured piezoelectric coefficient with respect to the number of layers or thickness are systematically illustrated for both MoS2 and InSe flakes. Note that the promising effective piezoelectric coefficient (deff33) of about 21.9 pm V−1 is observed on few‐layered InSe. The out‐of‐plane piezoresponse arises from the net dipole moment along the normal direction of the curvature membrane induced by strain gradient. This work not only provides a feasible and flexible method to acquire and quantify the out‐of‐plane electromechanical coupling on van der Waals layered materials, but also paves the way to understand and tune the flexoelectric effect of 2D systems.

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named ) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named ) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.

This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics. While the goals and techniques used for them were considered to be different previously, we show that by making use of the intermediate results of the two modules, their performance can be enhanced at the same time. Our framework is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information.We conduct experiments on various datasets, and prove that our framework works effectively on improving the precision and robustness of the two tasks and outperforms existing localization and segmentation algorithms.