| Autor: |
Ege Çetintaş, Yi Luo, Charlene Nguyen, Yuening Guo, Liqiao Li, Yifang Zhu, Aydogan Ozcan |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 12, Iss 1, Pp 1-8 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-022-07150-2 |
| Popis: |
Abstract The past decade marked a drastic increase in the usage of electronic cigarettes. 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 ~ 16 min 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 percentage of volatile and semi-volatile particles in air. The reported methodology and findings can guide further studies on volatility characterization of indoor e-cig emissions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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