SBIR Phase I: A Real Time, High Sensitivity Atmospheric Benzene Vapor Monitor

这个小型企业创新研究（BSIR）第一阶段项目将解决环境空气中痕量苯蒸汽传感器的需求。苯是一种致癌物质和重要的移动的源空气有毒物质（MSAT），在化石燃料的不完全燃烧过程中释放，通常从机动车辆和许多工业场所排放。Entanglement Technologies提出，基于腔衰荡光谱（CRDS）和扩散飞行时间（DiTOF）的组合，确定开发在30分钟内灵敏度优于100 pptv的苯蒸气传感器的可行性。这种组合允许使用极其灵敏的CRDS技术来检测单独使用CRDS无法在光谱上区分的大分子。第一阶段项目将开发一个原型探测器，以评估和演示关键技术。这项研究的更广泛/商业影响是开发一种传感器，能够检测苯并绘制其在环境中的扩散情况。这将直接影响许多行业的健康危害缓解、责任暴露减少和法规合规成本降低。传感器灵敏度的提高和成本的降低表明市场对传感器的需求强劲。该技术平台将很容易扩展到各种其他空气有毒物质，包括甲苯，乙苯和二甲苯。从长远来看，这项技术将应用于生物医学科学、工业过程监测、环境补救和爆炸物检测。基于扩散的选择性将允许分离和量化人类呼吸中的许多烃气体组分，这对于疾病的非侵入性诊断是有用的。

This Small Business Innovation Research (BSIR) Phase I project will address the need for a trace benzene vapor sensor for ambient air. Benzene is a carcinogen and significant Mobile Source Air Toxic (MSAT) released during incomplete combustion of fossil fuels, and is commonly emitted from motor vehicles and many industrial sites. Entanglement Technologies proposes to determine the feasibility of developing a benzene vapor sensor with sensitivity better than 100 pptv in 30 minutes based on the combination of cavity ring-down spectroscopy (CRDS) and diffusion time-of-flight (DiTOF). This combination allows the use of the extremely sensitive CRDS technique to detect large molecules that cannot be distinguished spectrally using CRDS alone. The Phase I project will develop a prototype detector to evaluate and demonstrate the critical technologies. The broader/commercial impacts of this research are the development of a sensor with the ability to detect benzene and map its dispersal into the environment. This will directly impact health hazard mitigation, liability exposure reduction, and reduced regulatory compliance costs in numerous industries. The improved sensitivity and reduced cost of the sensor indicate strong market demand for the sensor. The technology platform will be readily extended to a wide variety of other air toxics including toluene, ethyl-benzene, and xylenes. In the long term, this technology will be applied to biomedical science, industrial process monitoring, environmental remediation and explosives detection. Diffusion-based selectivity will allow separation and quantification of the many hydrocarbon gas components in human breath, that are useful for non-invasive diagnosis of disease.