Characterization of exhaled e-cigarette aerosols in a vape shop using a field-portable holographic on-chip microscope

• Published in: arXiv.org 2021-09
• Main Authors: Ege Cetintas, Luo, Yi, Nguyen, Charlene, Guo, Yuening, Li, Liqiao, Zhu, Yifang, Ozcan, Aydogan
• Format: Article
• Language: English
• Subjects: Aerosols; Artificial neural networks; Electronic cigarettes; Emission analysis; Experiments; Holography; Image reconstruction; Particle decay; Portable equipment; Vaping; Volatility
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2109.10865

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.