Volatility measurement of particulate matter using deep learning-based holographic microscopy

Optics and Biophotonics in Low-Resource Settings VIII(2022)

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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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Digital Holographic Microscopy
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