Characterization of exhaled e-cigarette aerosols in a vape shop using a field-portable holographic on-chip microscope
Ege Cetintas, Yi Luo, Charlene Nguyen, Yuening Guo, Liqiao Li, Yifang, Zhu, Aydogan Ozcan

TL;DR
This study uses a portable holographic microscope combined with deep learning to analyze exhaled e-cigarette aerosols in a vape shop, revealing increased particle volatility due to vaping and providing a new method for aerosol characterization.
Contribution
The paper introduces a novel field-portable holographic microscopy technique with deep learning for real-time volatility analysis of e-cig aerosols in indoor environments.
Findings
Vaping increases particle volatility in indoor air.
The method enables direct, in-situ aerosol analysis.
Volatility of particles correlates with vaping activity.
Abstract
The past decade marked a drastic increase in the usage of electronic cigarettes (e-cigs). The adverse health impact of secondhand exposure due to exhaled e-cig particles has raised significant concerns, demanding further research on the characteristics of these particles. In this work, we report direct volatility measurements on exhaled e-cig aerosols using a field-portable device (termed c-Air) enabled by deep learning and lens-free holographic microscopy; for this analysis, we performed a series of field experiments in a vape shop where customers used/vaped their e-cig products. During four days of experiments, we periodically sampled the indoor air with intervals of ~15 minutes and collected the exhaled particles with c-Air. Time-lapse inline holograms of the collected particles were recorded by c-Air and reconstructed using a convolutional neural network yielding phase-recovered…
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