Air Quality Monitoring Using Mobile Microscopy And Machine Learning
- 技術優勢
- Field-portable/ mobile solution Cost-effective platform High-throughput quantification of particulate matter (air) Uses computational lens-free microscopy and machine-learning High accuracy Easy to use
- 技術應用
- Field particulate matter/air monitoring
- 詳細技術說明
- Field-portable cost-effective platform for high-throughput quantification of particulate matter (PM) using computational lens-free microscopy and machine-learning
- *Abstract
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UCLA researchers have developed a novel method to monitor air quality using mobile microscopy and machine learning.
- *Principal Investigation
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Name: Steve Feng
Department:
Name: Aydogan Ozcan
Department:
Name: Yichen Wu
Department:
- 其他
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State Of Development
The invention was demonstrated on 2/1/2015
Background
Air quality is an increasing concern in the industrialized world. Particulate matter (PM) is a mixture of solid and liquid particles in air and forms a significant form of air pollution. PM comes in a range of sizes which can cause serious health problems by entering the lings and bloodstream. Some PM has even been linked to be carcinogenic. Monitoring PM air quality as a function of space and time is critical for understanding the effects of industrial activities, studying atmospheric models, and providing regulatory and advisory guidelines for transportation, residents, and industries. There is a need for a low-cost, accurate, easy to use, mobile method to sample and analyze particulate matter in the field. Current solutions, such as conventional microscope-based screening of aerosols, cannot be conducted in the field and are cumbersome, heavy, expensive, and require specialized skills to operate.
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Tech ID/UC Case
29262/2017-513-0
Related Cases
2017-513-0
- 國家/地區
- 美國
