I-Corps: Air Quality Monitoring Based on Digital Inline Holography

I-Corps：基于数字内嵌全息技术的空气质量监测

• 批准号: 2041968
• 负责人: Lei Feng
• 金额: $ 5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041968&HistoricalAwards=false
• 关键词: Corps; Air; Quality; Monitoring; Based

The broader impact/commercial potential of this I-Corps project is to define the requirements for the development of a new generation of indoor air quality monitors based on digital inline holography (DIH). The physical pollutant monitoring segment in the global air quality monitoring market is around $1 billion and is projected to grow rapidly amid and after the COVID-19 pandemic. The proposed DIH monitor may identify pollutant types in addition to pollutant counts and size distribution. The proposed DIH sensor also has a compact design that may be used as wearable/portable devices or that may be easily integrated into filtration systems both in home and automobiles for smart control. The proposed technology may be further expanded for monitoring and characterization of particles in a broad range of applications, including monitoring: outdoor airborne particles, particles suspended in water, particle characterization in atomization and sprays, particle processing, and high throughput cellular analysis in biomedical applications.This I-Corps project explores the translation of machine learning-based digital inline holography (DIH). A highly compact 3D imaging sensor may perform real-time, in situ measurements of size distributions, shape, concentration, and other physical properties of particles (e.g., droplets, bubbles, dusts, cells, etc.) from 200 nm to ~1 mm. The proposed technology may serve as a platform technology that may be applied to monitor air quality. The DIH sensor may be used to monitor a wide range of airborne pollutants including physical pollutants (e.g., smoke, smog, dust, etc.) and biological pollutants (e.g., pollen, dust mites, house dust, animal dander, etc.)distinguishing particulate pollutants types based on particle shapes. At the conclusion of the project, the goal is to identify the requirements for air pollutant monitoring applications and the need for this technology in the broader particle diagnostic community.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.

这个I-Corps项目更广泛的影响/商业潜力是定义基于数字同轴全息术（DIH）的新一代室内空气质量监测器的开发要求。全球空气质量监测市场的物理污染物监测分部约为10亿美元，预计将在COVID-19疫情期间及之后迅速增长。 建议的DIH监测器可以识别污染物类型以及污染物计数和粒度分布。所提出的DIH传感器还具有紧凑的设计，其可以用作可穿戴/便携式设备，或者可以容易地集成到家庭和汽车中的过滤系统中以进行智能控制。所提出的技术可以进一步扩展到广泛应用中的颗粒监测和表征，包括监测：室外空气中的颗粒，水中悬浮的颗粒，雾化和喷雾中的颗粒表征，颗粒处理以及生物医学应用中的高通量细胞分析。这个I-Corps项目探索了基于机器学习的数字内联全息（DIH）的翻译。高度紧凑的3D成像传感器可以执行颗粒的尺寸分布、形状、浓度和其他物理性质（例如，液滴、气泡、灰尘、细胞等）所提出的技术可以用作可以应用于监测空气质量的平台技术。DIH传感器可用于监测宽范围的空气传播污染物，包括物理污染物（例如，烟雾、灰尘等）和生物污染物（例如，花粉、尘螨、室内灰尘、动物皮屑等）基于颗粒形状区分颗粒污染物类型。在项目结束时，目标是确定空气污染物监测应用的要求和更广泛的颗粒诊断社区对该技术的需求。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。