University of Science and Technology Beijing

About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.

A Dual‐Salt Gel Polymer Electrolyte with 3D Cross‐Linked Polymer Network for Dendrite‐Free Lithium Metal Batteries

Abstract: Lithium metal batteries show great potential in energy storage because of their high energy density. Nevertheless, building a stable solid electrolyte interphase (SEI) and restraining the dendrite growth are difficult to realize with traditional liquid electrolytes. Solid and gel electrolytes are considered promising candidates to restrain the dendrites growth, while they are still limited by low ionic conductivity and incompatible interphases. Herein, a dual-salt (LiTFSI-LiPF6) gel polymer electrolyte (GPE) with 3D cross-linked polymer network is designed to address these issues. By introducing a dual salt in 3D structure fabricated using an in situ polymerization method, the 3D-GPE exhibits a high ionic conductivity (0.56 mS cm-1 at room temperature) and builds a robust and conductive SEI on the lithium metal surface. Consequently, the Li metal batteries using 3D-GPE can markedly reduce the dendrite growth and achieve 87.93% capacity retention after cycling for 300 cycles. This work demonstrates a promising method to design electrolytes for lithium metal batteries.

Enhanced Performance of CdS/CdSe Quantum Dot Cosensitized Solar Cells via Homogeneous Distribution of Quantum Dots in TiO2 Film

Abstract: The thickness and porosity of TiO2 mesoporous film were optimized for better distribution of quantum dots to enhance the performance of CdS/CdSe quantum dot cosensitized solar cells. The CdS and CdSe quantum dots were prepared on TiO2 mesoporous film through a successive ion layer absorption and reaction (SILAR) method and a chemical bath deposition (CBD) method, respectively. It was found that the distribution of quantum dots was inhomogeneous from the surface to the interior of the TiO2 film, being mainly concentrated at the upper layer of the TiO2 film. As a result, simply increasing film thickness did not make significant contribution to improving solar cell efficiency since only a small portion of quantum dots might access the interior of the film, leading to an exposure of TiO2 nanoparticles in electrolyte and thus reducing the electron lifetime due to increased charge recombination rate. Our study revealed that the efficiency could reach its maximum, ∼4.62%, with the TiO2 film, the thickness of whi...

Fog Computing Based Face Identification and Resolution Scheme in Internet of Things

Abstract: The identification and resolution technology are the prerequisite for realizing identity consistency of physical–cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.

Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics

Abstract: In this paper, the infinite-horizon robust optimal control problem for a class of continuous-time uncertain nonlinear systems is investigated by using data-based adaptive critic designs. The neural network identification scheme is combined with the traditional adaptive critic technique, in order to design the nonlinear robust optimal control under uncertain environment. First, the robust optimal controller of the original uncertain system with a specified cost function is established by adding a feedback gain to the optimal controller of the nominal system. Then, a neural network identifier is employed to reconstruct the unknown dynamics of the nominal system with stability analysis. Hence, the data-based adaptive critic designs can be developed to solve the Hamilton–Jacobi–Bellman equation corresponding to the transformed optimal control problem. The uniform ultimate boundedness of the closed-loop system is also proved by using the Lyapunov approach. Finally, two simulation examples are presented to illustrate the effectiveness of the developed control strategy.