  Various volatile aerosols have been associated with adverse health effects;
however, characterization of these aerosols is challenging due to their dynamic
nature. Here we present a method that directly measures the volatility of
particulate matter (PM) using computational microscopy and deep learning. This
method was applied to aerosols generated by electronic cigarettes (e-cigs),
which vaporize a liquid mixture (e-liquid) that mainly consists of propylene
glycol (PG), vegetable glycerin (VG), nicotine, and flavoring compounds. E-cig
generated aerosols were recorded by a field-portable computational microscope,
using an impaction-based air sampler. A lensless digital holographic microscope
inside this mobile device continuously records the inline holograms of the
collected particles. A deep learning-based algorithm is used to automatically
reconstruct the microscopic images of e-cig generated particles from their
holograms, and rapidly quantify their volatility. To evaluate the effects of
e-liquid composition on aerosol dynamics, we measured the volatility of the
particles generated by flavorless, nicotine-free e-liquids with various PG/VG
volumetric ratios, revealing a negative correlation between the particles'
volatility and the volumetric ratio of VG in the e-liquid. For a given PG/VG
composition, the addition of nicotine dominated the evaporation dynamics of the
e-cig aerosol and the aforementioned negative correlation was no longer
observed. We also revealed that flavoring additives in e-liquids significantly
decrease the volatility of e-cig aerosol. The presented holographic volatility
measurement technique and the associated mobile device might provide new
insights on the volatility of e-cig generated particles and can be applied to
characterize various volatile PM.
