SENSORS: Collaborative Research: Biochemical Sensors and Data Processing for Security Applications

传感器：协作研究：用于安全应用的生化传感器和数据处理

• 批准号: 0329957
• 负责人: John Ketterson
• 金额: --
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0329957&HistoricalAwards=false
• 关键词: SENSORS; Collaborative; Research; Biochemical; Sensors

ABSTRACT0329957John KettersonNorthwestern UniversityTitle: Collaborative Research -- Sensors: Biochemical Sensors and Data Processing for SecurityThis research addresses current concerns about possible biochemical attacks threatening our national security. The investigators develop sensors and data processing for detecting sources of biological agents or toxic chemicals, and managing data-bases. This technology has further applications to environmental monitoring, controlling pollution, detecting explosives, and other scenarios.The investigators develop three categories of sensors for detecting bio-target molecules that bind to substrate-attached receptor molecules. The first exploits the frequency shift of an optical resonator from a change in its dielectric environment brought on by target adsorption. The second exploits the sensitivity of optical resonators to detect Raman spectra associated with the adsorbed targets. The third involves attaching fluorescent labels to secondary recognition molecules and detecting their light emissions when they bound to agents captured by substrate receptor molecules. Arrays of probes are created to screen for multiple targets, interacting with the signal processing. Strategies for making continuous sensing are considered. The signal processing part analyzes data from chemical sensor array networks, subject to resource constraints. The researchers derive physical models for substance dispersion in subway tunnels, shopping malls, city streets, airport bag screening, etc. Flux density sensors are included. This project analyzes the algorithms performance for optimally designing the arrays. To increase the functionality, the investigators consider moving sensors (robots, autonomous vehicles) and develop motion techniques while addressing other requirements (coverage, energy minimization.) The study involves managing high data volumes and processing power requirements that may arise from a large network.

约翰·凯特森西北大学题目：合作研究--传感器：生化传感器和安全数据处理这项研究解决了当前人们对可能威胁我们国家安全的生化攻击的担忧。研究人员开发传感器和数据处理，用于检测生物制剂或有毒化学品的来源，并管理数据库。这项技术还将进一步应用于环境监测、污染控制、爆炸物检测等场景。研究人员开发了三类传感器，用于检测与底物结合的受体分子上的生物靶分子。第一种是利用光学谐振器的频移，这种频移来自于目标吸附引起的介电环境的变化。第二种是利用光学谐振器的灵敏度来检测与被吸附的目标相关的拉曼光谱。第三种方法是将荧光标记贴在二级识别分子上，并检测当它们与底物受体分子捕获的试剂结合时的光发射。创建探头阵列以筛选多个目标，与信号处理交互作用。考虑了进行连续传感的策略。信号处理部分分析来自化学传感器阵列网络的数据，受资源限制。研究人员推导出地铁隧道、购物中心、城市街道、机场行李检查等中物质分散的物理模型。其中包括流量密度传感器。本项目对优化设计阵列的算法性能进行了分析。为了增加功能，调查人员考虑移动传感器(机器人、自动驾驶车辆)并开发运动技术，同时满足其他要求(覆盖范围、能量最小化)。该研究涉及管理大型网络可能产生的高数据量和处理能力需求。