  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 microscopic
images of the particles. Volumetric decay of individual particles due to
evaporation was used as an indicator of the volatility of each aerosol.
Volatility dynamics quantified through c-Air experiments showed that indoor
vaping increased the volatility of particles as well as the percentage of
volatile and semi-volatile particles in air. The reported methodology and
findings can guide further studies on volatility characterization of e-cig
emission and regulations on indoor vaping.
