Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning
Hao Jiang, Keith Kolaczyk, Barry K Lavine · 2024
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Description
Hao Jiang, Keith Kolaczyk, and Barry Lavine build a sensor that weighs vape particles by size in this two thousand twenty-four paper. Health risk from e-cigarette clouds depends on how deep particles settle in the lungs. Size decides that depth. Their device shines three colors of light through the aerosol and reads how much each dims. A pressure sensor logs the puff itself.
A learning model turns those signals into particle mass in three size bands. A lab vaping machine and a reference sizer supplied the truth for training. Real-time, size-sorted readings could standardize vape testing. Regulators and product testers gain a cheap instrument. Aerosol scientists are also in the audience.
Catalog record
| CREATOR | Hao Jiang, Keith Kolaczyk, Barry K Lavine |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |