Self‐Assembly of Nanoparticle‐Spiked Pillar Arrays for Plasmonic Biosensing

Plasmonic biosensors have demonstrated superior performance in detecting various biomolecules with high sensitivity through simple assays. Scaled‐up, reproducible chip production with a high density of hotspots in a large area has been technically challenging, limiting the commercialization and clinical translation of these biosensors. A new fabrication method for 3D plasmonic nanostructures with a high density, large volume of hotspots and therefore inherently improved detection capabilities is developed. Specifically, Au nanoparticle‐spiked Au nanopillar arrays are prepared by utilizing enhanced surface diffusion of adsorbed Au atoms on a slippery Au nanopillar arrays through a simple vacuum process. This process enables the direct formation of a high density of spherical Au nanoparticles on the 1 nm‐thick dielectric coated Au nanopillar arrays without high‐temperature annealing, which results in multiple plasmonic coupling, and thereby large effective volume of hotspots in 3D spaces. The plasmonic nanostructures show signal enhancements over 8.3 × 10⁸‐fold for surface‐enhanced Raman spectroscopy and over 2.7 × 10²‐fold for plasmon‐enhanced fluorescence. The 3D plasmonic chip is used to detect avian influenza‐associated antibodies at 100 times higher sensitivity compared with unstructured Au substrates for plasmon‐enhanced fluorescence detection. Such a simple and scalable fabrication of highly sensitive 3D plasmonic nanostructures provides new opportunities to broaden plasmon‐enhanced sensing applications.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

基于空域联合时频分解的海面微弱目标检测方法

海面目标不仅影响其位置处波浪运动,还会影响其周围的水域,其雷达回波信号分布于多个相邻的距离单元,所以目标回波信号表现出明显的空域相关性。该文根据海面目标信号的空间相关性提出了一种基于空域联合时频分解的海面微弱目标检测方法。该文提出的一种两信号互S-方法将相邻两个距离单元的回波信号变换到时频域,再利用互维格纳-威尔逆变换实现两距离单元信号的联合时频分解,最后根据分解分量的联合时频聚集性实现目标检测。实测X波段雷达海面回波的处理结果表明该文方法能够较精确地从海面回波中检测出微弱目标,并且能够显示目标的瞬时运动特性。      

激光主动成像技术应用及发展

近年来,激光主动成像技术得到快速发展,为获取高分辨率的图像信息提供了一种全新的探测手段。本文阐述了距离选通成像系统的基本原理,结合国外研究成果,对4种成像探测系统做了介绍:超分辨率距离映射成像系统、短波红外单基/双基成像系统和红外与激光双模组合成像系统。重点对各系统的构成、关键器件及技术优势进行了研究,并对实验结果进行了分析。分析了距离选通成像技术的发展趋势,对发展新型成像探测系统具有参考价值。     

Polytype target coverage scheme for heterogeneous wireless sensor networks using linear programming

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy‐efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

不同风向下城市街区风环境的模拟

利用CFD软件Fluent建立了一个三维城市街区十字路口模型,模拟了3种风向条件,在沙尘天气下街区速度场以及粒子浓度场的分布情况。结果表明,街区的建筑物布局与风向共同作用,会对街区的风环境及室内空气品质（IAQ）产生很大影响。     

基于串联式一维神经网络的毫米波雷达动态手势识别方法

现有的基于雷达传感器的手势识别方法,大多先利用雷达回波对手势的距离、多普勒和角度等信息进行参数估计,得到各种数据谱图,然后再利用卷积神经网络对这些谱图进行分类,实现过程较为复杂.该文提出一种基于串联式1维神经网络(1D-ScNN)的毫米波雷达动态手势识别方法.首先基于毫米波雷达获取动态手势的原始回波,然后利用1维卷积和...     

Engineering Silver-Enriched Copper Core-Shell Electrocatalysts to Enhance the Production of Ethylene and C2+ Chemicals from Carbon Dioxide at Low Cell Potentials

Copper catalysts are widely studied for the electroreduction of carbon dioxide (CO₂) to value-added hydrocarbon products. Controlling the surface composition of copper nanomaterials may provide the electronic and structural properties necessary for carbon-carbon coupling, thus increasing the Faradaic efficiency (FE) towards ethylene and other multi-carbon (C₂₊) products. Synthesis and catalytic study of silver-coated copper nanoparticles (Cu@Ag NPs) for the reduction of CO₂ are presented. Bimetallic CuAg NPs are typically difficult to produce due to the bulk immiscibility between these two metals. Slow injection of the silver precursor, concentrations of organic capping agents, and gas environment proved critical to control the size and metal distribution of the Cu@Ag NPs. The optimized Cu@Ag electrocatalyst exhibited a very low onset cell potential of −2.25 V for ethylene formation, reaching a FE towards C₂₊ products (FE_C2+) of 43% at −2.50 V, which is 1.0 V lower than a reference Cu catalyst to reach a similar FE_C2+. The high ethylene formation at low potentials is attributed to enhanced C C coupling on the Ag enriched shell of the Cu@Ag electrocatalysts. This study offers a new catalyst design towards increasing the efficiency for the electroreduction of CO₂ to value-added chemicals.     

Unfolded coprime L-shaped arrays for two-dimensional direction of arrival estimation

Generally, a coprime L-shaped array (CLsA) is composed of two uniform L-shaped subarrays with larger spacing among inter-element to accomplish the improved direction of arrival (DOA) estimation performance. In this paper, the two subarrays are unfolded to extend the array aperture and the performance of the unfolded CLsA (UCLsA) for two-dimensional (2D) DOA estimation is investigated. In addition, an all array multiple signals classification (AA-MUSIC) algorithm is proposed for the UCLsA. By stacking the received signals of the two subarrays, the ambiguity problem can be avoided on the basis of the coprime property. Simultaneously, due to the combination of the cross-correlation and auto-correlation, the proposed AA-MUSIC algorithm can achieve the full degrees of freedom (DOFs) and obtain more accurate DOA estimates, nevertheless, the expensive total spectral search is entailed. Consequently, a reduced complexity MUSIC (RC-MUSIC) algorithm is proposed to relieve the computational burden. The Cramer-Rao Bounds (CRBs) are utilised as a theoretical benchmark for the lower bound of unbiased estimate. Furthermore, numerical simulations verify the effectiveness and superiority of the AA-MUSIC algorithm and RC-MUSIC method for the UCLsA.     

Recent Progress in MOF‐Derived,Heteroatom‐Doped Porous Carbons as Highly Efficient Electrocatalysts for Oxygen Reduction Reaction in Fuel Cells

Currently, developing nonprecious‐metal catalysts to replace Pt‐based electrocatalysts in fuel cells has become a hot topic because the oxygen reduction reaction (ORR) in fuel cells often requires platinum, a precious metal, as a catalyst, which is one of the major hurdles for commercialization of the fuel cells. Recently, the newly emerging metal‐organic frameworks (MOFs) have been widely used as self‐sacrificed precursors/templates to fabricate heteroatom‐doped porous carbons. Here, the recent progress of MOF‐derived, heteroatom‐doped porous carbon catalysts for ORR in fuel cells is systematically reviewed, and the synthesis strategies for using different MOF precursors to prepare heteroatom‐doped porous carbon catalysts, including the direct carbonization of MOFs, MOF and heteroatom source mixture carbonization, and MOF‐based composite carbonization are summarized. The emphasis is placed on the precursor design of MOF‐derived metal‐free catalysts and transition‐metal‐doped carbon catalysts because the MOF precursors often determine the microstructures of the derived porous carbon catalysts. The discussion provides a useful strategy for in situ synthesis of heteroatom‐doped carbon ORR electrocatalysts by rationally designing MOF precursors. Due to the versatility of MOF structures, MOF‐derived porous carbons not only provide chances to develop highly efficient ORR electrocatalysts, but also broaden the family of nanoporous carbons for applications in supercapacitors and batteries.