Dynamic Imaging and Characterization of Volatile Aerosols in E-Cigarette Emissions Using Deep Learning-Based Holographic Microscopy.

Journal: ACS sensors
Published Date: Jun 25, 2021

Abstract

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.

Authors

  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Yichen Wu
    Department of Electrical Engineering, University of California Los Angeles (UCLA), USA. ozcan@ucla.edu.
  • Liqiao Li
    Department of Environmental Health Sciences, University of California, Los Angeles, California 90095, United States.
  • Yuening Guo
    Department of Environmental Health Sciences, University of California, Los Angeles, California 90095, United States.
  • Ege Çetintaş
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.
  • Yifang Zhu
    Department of Environmental Health Sciences, University of California, Los Angeles, California 90095, United States.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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