Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Urchin-like hierarchical CoZnAl-LDH/RGO/g-C3N4 hybrid as a Z-scheme photocatalyst for efficient and selective CO2 reduction

Abstract: A unique urchin-like CoZnAl-LDH/RGO/g-C3N4 (LDH/RGO/CN) Z-scheme photocatalyst, which is fabricated by the hydrothermal synthesis of CoZnAl-LDH and the in situ loading of RGO and g-C3N4, is developed for the photocatalytic conversion of CO2. The special spiny external surface and hollow inner cavity endow LDH/RGO/CN with a significantly enhanced light-harvesting capacity. The well-distributed g-C3N4 nanosheets on the CoZnAl-LDH nanoplates, combined with RGO as an electron mediator, constructs an excellent heterosystem with numerous interfaces, efficient charge separation and highly exposed catalytic active sites. The Z-scheme charge-transfer process promotes the oxidizability and reducibility of CoZnAl-LDH and g-C3N4. Furthermore, the synergistic effect among the components contributes to intense adsorption and chemical activation towards CO2, which reduces the reaction barrier for CO2 photoreduction. As a result, the optimized LDH/RGO/CN exhibits highly efficient and selective photocatalytic CO2 conversion to CO. The special 3D urchin-like architecture paves a new way for design of photocatalyst with ideal performance.

Equal channel angular pressing of magnesium alloy AZ31

Abstract: Equal channel angular pressing (ECAP) was applied to a wrought magnesium alloy AZ31 for up to 8 passes at temperatures as low as 100 °C. The application of a back pressure was critical in deforming Mg alloys at lower temperatures. Room temperature mechanical properties were obtained by tensile and hardness tests. With increasing ECAP strain, the initial coarse grained structure was transformed into a submicrometer-grained microstructure. In general, hardness increased with decreasing grain size although the changes in tensile strength and ductility were more complicated.

Deep learning with S-shaped rectified linear activation units

Abstract: Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CI-FAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.