期刊
LIGHT-SCIENCE & APPLICATIONS
卷 6, 期 -, 页码 -出版社
CHINESE ACAD SCIENCES, CHANGCHUN INST OPTICS FINE MECHANICS AND PHYSICS
DOI: 10.1038/lsa.2017.46
关键词
air-quality monitoring; holography; machine learning; particulate matter
类别
资金
- Presidential Early Career Award for Scientists and Engineers (PECASE)
- Army Research Office (ARO) [W911NF-13-1-0419, W911NF-13-1-0197]
- ARO Life Sciences Division
- National Science Foundation (NSF) CBET Division Biophotonics Program
- NSF Emerging Frontiers in Research and Innovation (EFRI) Award
- NSF EAGER Award
- NSF INSPIRE Award
- NSF Partnerships for Innovation: Building Innovation Capacity (PFI: BIC) Program
- Office of Naval Research (ONR)
- National Institutes of Health (NIH)
- Howard Hughes Medical Institute (HHMI)
- Vodafone Americas Foundation
- Mary Kay Foundation
- Steven & Alexandra Cohen Foundation
- KAUST
- National Science Foundation [0963183]
- Directorate For Engineering
- Div Of Industrial Innovation & Partnersh [1533983] Funding Source: National Science Foundation
Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of <= 2.5 alpha m have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of similar to 93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency-approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at 47 km away from the airport, especially along the direction of landing flights. With its machinelearning- based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.
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