Air Quality Monitoring Using Mobile Microscopy And Machine Learning

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

Field particulate matter/air monitoring

Field-portable cost-effective platform for high-throughput quantification of particulate matter (PM) using computational lens-free microscopy and machine-learning

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.

Related Materials

Y. Wu, A. Shiledar, Y. Li, J. Wong, S. Feng, X. Chen, C. Chen, K. Jin, S. Janamian, Z. Yang, Z.S. Ballard, Z. Göröcs, A. Feizi, and A. Ozcan. Air Quality Monitoring Using Mobile Microscopy and Machine Learning. Light: Science & Applications (Nature Publishing Group). 2017.

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Tech ID/UC Case

29262/2017-513-0

Related Cases

2017-513-0

美国

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