Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Graphene. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Cd0.2Zn0.8S@UiO-66-NH2 nanocomposites as efficient and stable visible-light-driven photocatalyst for H2 evolution and CO2 reduction

Abstract: Metal-organic frameworks (MOFs), a new class of porous crystalline materials, have attracted great interest as fascinating materials for sustainable energy and environmental remediation. However, the functionalization and diversification of MOFs are still challenging and imperative for the development of highly active MOF-based materials. In this study, a series of Cd0.2Zn0.8S@UiO-66-NH2 nanocomposites with different UiO-66-NH2 contents were fabricated via a facile solvothermal method. The photocatalytic performances of the obtained Cd0.2Zn0.8S@UiO-66-NH2 nanocomposites were evaluated by photocatalytic H2 evolution and CO2 reduction under visible-light irradiation. The resultant hybrids exhibit significantly enhanced photocatalytic activity for hydrogen evolution and CO2 reduction as compared with pristine components, and the optimal UiO-66-NH2 content is 20 wt%. The composite can show a hydrogen evolution rate of 5846.5 μmol h−1 g−1 and a CH3OH production rate of 6.8 μmol h−1 g−1. The remarkable enhancement of the photocatalytic activity should be attributed to the efficient charge separation and transfer on the interface between Cd0.2Zn0.8S and UiO-66-NH2. Furthermore, the Cd0.2Zn0.8S@UiO-66-NH2 photocatalysts show excellent stability during photocatalytic hydrogen evolution and CO2 reduction. This work demonstrates that MOF-based composite materials hold great promise for applications in the field of energy conversion and environmental purification.

Lattice Dislocations Enhancing Thermoelectric PbTe in Addition to Band Convergence

Abstract: Phonon scattering by nanostructures and point defects has become the primary strategy for minimizing the lattice thermal conductivity (κL ) in thermoelectric materials. However, these scatterers are only effective at the extremes of the phonon spectrum. Recently, it has been demonstrated that dislocations are effective at scattering the remaining mid-frequency phonons as well. In this work, by varying the concentration of Na in Pb0.97 Eu0.03 Te, it has been determined that the dominant microstructural features are point defects, lattice dislocations, and nanostructure interfaces. This study reveals that dense lattice dislocations (≈4 × 1012 cm-2 ) are particularly effective at reducing κL . When the dislocation concentration is maximized, one of the lowest κL values reported for PbTe is achieved. Furthermore, due to the band convergence of the alloyed 3% mol. EuTe the electronic performance is enhanced, and a high thermoelectric figure of merit, zT, of ≈2.2 is achieved. This work not only demonstrates the effectiveness of dense lattice dislocations as a means of lowering κL , but also the importance of engineering both thermal and electronic transport simultaneously when designing high-performance thermoelectrics.

Fast, Robust and Accurate Digital Image Correlation Calculation Without Redundant Computations

Abstract: High-efficiency and high-accuracy deformation analysis using digital image correlation (DIC) has become increasingly important in recent years, considering the ongoing trend of using higher resolution digital cameras and common requirement of processing a large sequence of images recorded in a dynamic testing. In this work, to eliminate the redundant computations involved in conventional DIC method using forward additive matching strategy and classic Newton–Raphson (FA-NR) algorithm without sacrificing its sub-pixel registration accuracy, we proposed an equivalent but more efficient DIC method by combining inverse compositional matching strategy and Gauss-Newton (IC-GN) algorithm for fast, robust and accurate full-field displacement measurement. To this purpose, first, an efficient IC-GN algorithm, without the need of re-evaluating and inverting Hessian matrix in each iteration, is introduced to optimize the robust zero-mean normalized sum of squared difference (ZNSSD) criterion to determine the desired deformation parameters of each interrogated subset. Then, an improved reliability-guided displacement tracking strategy is employed to achieve further speed advantage by automatically providing accurate and complete initial guess of deformation for the IC-GN algorithm implemented on each calculation point. Finally, an easy-to-implement interpolation coefficient look-up table approach is employed to avoid the repeated calculation of bicubic interpolation at sub-pixel locations. With the above improvements, redundant calculations involved in various procedures (i.e. initial guess of deformation, sub-pixel displacement registration and sub-pixel intensity interpolation) of conventional DIC method are entirely eliminated. The registration accuracy and computational efficiency of the proposed DIC method are carefully tested using numerical experiments and real experimental images. Experimental results verify that the proposed DIC method using IC-GN algorithm and the existing DIC method using classic FA-NR algorithm generate similar results, but the former is about three to five times faster. The proposed reliability-guided IC-GN algorithm is expected to be a new standard full-field displacement tracking algorithm in DIC.

Multi-oriented Text Detection with Fully Convolutional Networks

Abstract: In this paper, we propose a novel approach for text detection in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine procedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Finally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple orientations, languages and fonts. The proposed method consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013.