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来自新型传感器的数据来减少医疗相关感染（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。 这项建议的成功完成将导致改善公众健康，减少住院时间，并通过减少住院费用使医院和病人都获得商业利益。 该项目使传感器在医院的开发和部署成为可能，将帮助我们的创业公司建立一个关键的数据集，以训练分析模型，用于近实时识别具有病原体问题的医院空间。这个STTR第2阶段项目提出开发一种新型传感器，用于综合测量空气质量（AQ）和空气中的病原体含量，并将这些测量结果联合收割机结合起来，创建一个早期预警系统，以确定医院等关键设施中的污染区域。 卫生保健相关感染（HAI）是200亿美元的问题，在美国造成超过10万人死亡。 我们的低成本、强大、可联网和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中颗粒物的新技术与最先进的病原体分析相结合，将为开发分析模型提供数据，该模型将在病原体问题导致昂贵的感染之前预测医院空间。该奖项反映了NSF的法定使命，并通过使用基金会的知识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.