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.

Hybrid Metal Halides with Multiple Photoluminescence Centers

Abstract: Very little is known about the realm of solid-state metal halide compounds comprising two or more halometalate anions. Such compounds would be of great interest if their optical and electronic properties could be rationally designed. Herein, we report a new example of metal halide cluster-assembled compound (C9 NH20 )9 [Pb3 Br11 ](MnBr4 )2 , featuring distinctly different anionic polyhedra, namely, a rare lead halide cluster [Pb3 Br11 ]5- and [MnBr4 ]2- . In accordance with its multinary zero-dimensional (0D) structure, this compound is found to contain two distinct emission centers, 565 nm and 528 nm, resulting from the formation of self-trapped excitons and 4 T1 -6 A1 transition of Mn2+ ions, respectively. Based on the high durability of (C9 NH20 )9 [Pb3 Br11 ](MnBr4 )2 upon light and heat, as well as high photoluminescence quantum yield (PLQY) of 49.8 % under 450 nm blue light excitation, white light-emitting diodes (WLEDs) are fabricated, showcasing its potential in backlight application.

Sensing Time Optimization and Power Control for Energy Efficient Cognitive Small Cell With Imperfect Hybrid Spectrum Sensing

Abstract: Cognitive radio enabled small cell network is an emerging technology to address the exponential increase of mobile traffic demand in next generation mobile communications. Recently, many technological issues, such as resource allocation and interference mitigation pertaining to cognitive small cell network have been studied, but most studies focus on maximizing spectral efficiency. Different from the existing works, we investigate the power control and sensing time optimization problem in a cognitive small cell network, where the cross-tier interference mitigation, imperfect hybrid spectrum sensing, and energy efficiency are considered. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. We solve the proposed problem in an asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed by considering imperfect hybrid spectrum sensing, cross-tier interference mitigation, minimum data rate requirement, and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in the cognitive small cell network.