STTR Phase II: Novel real-time particulate matter (PM) sensor for air quality measurements

该 STTR 项目更广泛的影响/商业潜力在于通过主动响应新型传感器的数据来减少医疗保健相关感染 (HAI)。 此外，该项目正在开发的技术还可以帮助测量其他环境中的空气颗粒浓度。 该提案的成功完成将通过减少住院时间来改善公共卫生，并通过降低住院费用来为医院和患者带来商业利益。 该项目支持的医院传感器的开发和部署将帮助我们的初创公司建立一个关键数据集来训练分析模型，以近乎实时地识别存在病原体问题的医院空间。该 STTR 第 2 阶段项目建议开发一种新型传感器，用于综合测量空气质量 (AQ) 和空气中的病原体负荷，并将这些测量结果结合起来创建一个预警系统，以识别医院等关键设施中的污染区域。 医疗保健相关感染 (HAI) 是一个价值 200 亿美元的问题，导致美国超过 10 万人死亡。 我们的低成本、强大、可联网且易于操作的传感器将为关键的公共卫生问题提供数据驱动的解决方案。 我们的传感器系统将实时检测空气颗粒的新技术与最先进的病原体分析相结合，将提供数据来开发分析模型，该模型将在造成昂贵的感染之前预测存在病原体问题的医院空间。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this STTR project is in the reduction of healthcare-associated infections (HAIs) by proactively responding to data from novel sensors. In addition, the technology being developed in this project can also help measure airborne particle concentrations in other environments. The successful completion of this proposal will result in improved public health from reduced hospital stay and commercially benefit both hospitals and patients by reducing hospitalization costs. The development and deployment of sensors in a hospital, enabled by this project, will help our startup build a critical dataset to train analytical models for near real-time identification of hospital spaces with a pathogen problem.This STTR Phase 2 project proposes to develop a novel sensor for integrated measurement of air quality (AQ) and pathogen loads in the air and combine these measurements to create a an early warning system to identify contaminated areas in critical facilities like hospitals. Healthcare-associated infections (HAIs) are $20 billion problem and are responsible for over 100,000 deaths in the US. Our low-cost, robust, networkable, and easy-to-operate sensors will provide a data driven solution to the critical public health problem. Combining novel techniques for real-time detection of airborne particles with state-of-the-art pathogen analysis, our sensor system will provide data to develop analytical models that will predict hospital spaces with pathogen problems before they cause expensive infections.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.