STTR Phase II: Microsensors for In-Situ, Real-Time Detection and Characterization of Toxic Oganic Substances

这个小企业技术转让研究（STTR）第二阶段项目的主要重点是开发和商业化一种新型的微传感器，用于现场，实时检测有毒有机化学品。拟议的微传感器将能够在实地条件下工作，具有足够的灵敏度，以允许高检测率，并具有足够的选择性，以防止高误报率。使用革命性的光热概念，探测器将以高化学选择性和低于十亿分之一的灵敏度运行。提出的探测器（CalSpec）的技术概念赢得了1998年R D 100奖。通过简单地在积分化学检测模式下操作，化学灵敏度可以显著增强到小于万亿分之一的水平。本研究的目的是展示高特异性，灵敏度和选择性检测的有机化合物，并开发一种多化学检测器，可以检测浓度从千分之几到万亿分之几的有毒有机物，灵敏的监测和检测是一个领域的持续重要性，美国环保署，国防部，能源部和其他联邦机构。CalSpec探测器可用于各种应用，包括过程监测和控制、环境合规性（包括排放监测）、环境空气监测、机场安全、有毒气体或金属蒸气的个人剂量计以及烟雾和火灾成分检测。

This Small Business Technology Transfer Research (STTR) Phase II project has as the primary focus the development and commercialization of a novel microsensor for the in-situ, real-time detection of toxic organic chemicals. The proposed microsensor will be capable of operating under field conditions, with sufficient sensitivity to permit high detection rates, and with sufficient selectivity to prevent high false alarm rates. Using a revolutionary photo-thermal concept, the detector will operate with both high chemical selectivity and a less than parts per billion sensitivity. The technological concept of the proposed detector (CalSpec) won the 1998R&D 100 award. The chemical sensitivity can be substantially enhanced to a less than parts per trillion level by simply operating in an integrating chemical detection mode. The objective of this research is to demonstrate highly specific, sensitive and selective detection of organic chemical compounds and to develop a multichemical detector which can detect toxic organics with concentrations varying from a few parts per thousand to a few parts per trillion.Sensitive monitoring and detection is an area of continuing importance to EPA, DOD, DOE and other federal agencies. The CalSpec detector could be used in a variety of applications, including process monitoring and control, environmental compliance (including emissions monitoring), ambient air monitoring, airport security, personal dosimeters for toxic gases or metal vapor, and smoke and fire constituent detection.