Focal shift of silicon microlens array in mid-infrared regime

采用严格数值算法对中红外硅微透镜阵列进行了模拟,该微透镜阵列特征尺寸小于波长工作波长.研究发现该微透镜阵列存在一个显著的离焦效应,其离焦量达到0; .4左右,超出了现有的传统理论模型预测范围.对微透镜阵列进行了制作和焦距测试,发现测试结果跟数值模拟基本吻合.微纳衍射光学集成系统中透镜离焦量是; 系统集成非常重要的一个参数,该研究结果为硅微透镜阵列和中红外探测器光学集成提供有效参考.In this study rigorous numerical model was utilized to characterize the focal shift of the diffractive mid infrared (MIR) silicon microlens arrays (MLAs) with the critical size smaller than the working wavelength. We found a more pronounced focal shift in mid-infrared wavelength which is out of the range predicted by existing models. Focal properties of the MLAs were also measured experimentally. The results agrees well with the simulation results. Our results provide a reference point in understanding the focal shift in MIR regime, which is important in terms of deciding the focal length of micro-nano lens systems, especially when dealing with the integration of diffractive devices in infrared optical system.Special Project on the Integration of Industry, Education and Research; of Aviation Industry Corporation of China [CXY2011XD24

An Organic–Inorganic Hybrid Cathode Based on S–Se Dynamic Covalent Bonds

A diphenyl trisulfide–selenium nanowire (DPTS‐Se) organic–inorganic hybrid cathode material is presented for rechargeable lithium batteries. During discharge, three voltage plateaus associated with three lithiation processes are observed. During recharge, the combination of the radicals formed upon delithiation leads to several new phenyl sulfoselenide compounds which are confirmed by HPLC‐QTof‐MS. The hybrid cathode exhibits superior cycling stability over pristine Se or DPTS as cathode alone. The first discharge shows a capacity of 96.5 % of the theoretical specific capacity and the cell retains 69.2 % of the initial capacity over 250 cycles. The hybrid cathode also shows a high Coulombic efficiency of over 99 % after 250 cycles. This study demonstrates that the combination of organic polysulfide and selenium can not only improve the utilization of active materials but also enhance the cycling performance

Atlastin regulates store-operated calcium entry for nerve growth factor-induced neurite outgrowth

Homotypic membrane fusion of the endoplasmic reticulum (ER) is mediated by a class of dynamin-like GTPases known as atlastin (ATL). Depletion of or mutations in ATL cause an unbranched ER morphology and hereditary spastic paraplegia (HSP), a neurodegenerative disease characterized by axon shortening in corticospinal motor neurons and progressive spasticity of the lower limbs. How ER shaping is linked to neuronal defects is poorly understood. Here, we show that dominant-negative mutants of ATL1 in PC-12 cells inhibit nerve growth factor (NGF)-induced neurite outgrowth. Overexpression of wild-type or mutant ATL1 or depletion of ATLs alters ER morphology and affects store-operated calcium entry (SOCE) by decreasing STIM1 puncta formation near the plasma membrane upon calcium depletion of the ER. In addition, blockage of the STIM1-Orai pathway effectively abolishes neurite outgrowth of PC-12 cells stimulated by NGF. These results suggest that SOCE plays an important role in neuronal regeneration, and mutations in ATL1 may cause HSP, partly by undermining SOCE

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints

Research on the dynamic characteristics of the electromagnetic repulsion mechanism of a self-driving fault current limiter

A fault self-driven current limiter is proposed in the paper, which uses a special fault current direct-driven electromagnetic repulsion mechanism to realize the first half-wave of the fault current over the zero point into the current-limiting reactance. The paper analyzes the working principle of the self-driven electromagnetic repulsion mechanism, establishes the equivalent model of the mechanism, and simulates the dynamic characteristics of the electromagnetic repulsion mechanism through the calculation of the double-layer iterative algorithm in time and space. The LC oscillation loop test platform is built, and the stroke–time curve of the prototype is measured. In the test, the prototype is driven by a 3 kA current, and the first half-wave stroke (FHWS) is 3.55 mm past the zero point, which is consistent with the simulation and test results. The effects of structural parameters such as the radius, thickness, and number of turns of the self-driven electromagnetic repulsion mechanism on the dynamic characteristics of the electromagnetic repulsion mechanism are investigated, and it is found that the first half-wave stroke can be significantly improved by increasing the number of turns and outer diameter of the coil. The optimum height of the dynamic repulsion coil is approximately 3 mm