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7 March 2022 Volatility measurement of particulate matter using deep learning-based holographic microscopy
Yi Luo, Yichen Wu, Liqiao Li, Yuening Guo, Ege Çetintaş, Yifang Zhu, Aydogan Ozcan
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Proceedings Volume PC11950, Optics and Biophotonics in Low-Resource Settings VIII; PC119500E (2022) https://doi.org/10.1117/12.2608830
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
We report a mobile device based on inline holography and deep learning to directly measure the volatility of particulate matter with high-throughput. We applied this mobile device to characterize aerosols generated by electronic cigarettes (e-cigs). Our measurements revealed a negative correlation between e-cig generated particle volatility and vegetable glycerin concentration in the e-liquid. Furthermore, the addition of other chemicals, e.g., nicotine and flavoring compounds, reduced the overall volatility of e-cig generated aerosols. The presented device can monitor the dynamic behavior of e-cig aerosols in a high-throughput manner, potentially providing important information for e-cig exposure assessment via e.g., second-hand vaping.
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Yi Luo, Yichen Wu, Liqiao Li, Yuening Guo, Ege Çetintaş, Yifang Zhu, and Aydogan Ozcan "Volatility measurement of particulate matter using deep learning-based holographic microscopy", Proc. SPIE PC11950, Optics and Biophotonics in Low-Resource Settings VIII, PC119500E (7 March 2022); https://doi.org/10.1117/12.2608830
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KEYWORDS
Aerosols
Holography
Atmospheric particles
Microscopy
Liquids
Mobile devices
Neural networks
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