A framework for real-time image detection of bioaerosols
Ryne A. Juidici, Yan Ye, Francisco J. Romay, Miles Owen, Qisheng Ou, David Y. H. Pui
TL;DR
This paper introduces a framework for detecting bioaerosols in real-time using image sensors based on light scattering and fluorescence.
Contribution
A novel framework is proposed that uses image sensors to differentiate bioaerosols from abiotic particles via fluorescence and scattering.
Findings
Exposure time, particle speed, and signal-to-noise ratio are critical for detection accuracy.
A long-pass filter can isolate fluorescence emission from elastic light scattering.
Color differentiation of signals may help distinguish biotic from abiotic particles.
Abstract
Starting from the elastic light scattering and the induced fluorescence emission from airborne particles and extending to the counting and differentiation of the associated signals, a framework is proposed to describe the detection of bioaerosols using an image sensor. For validation, monodisperse NaCl particles were generated to mimic abiotic particles, while monodisperse 1% mass riboflavin particles were generated to mimic biotic particles. By challenging a prototype sensor with these particles, the exposure time, particle speed, and signal-to-noise ratio were shown to be critical parameters for detection. Additionally, it was displayed that the induced fluorescence emission can be isolated from the elastic light scattering by using a well-selected long-pass filter. Furthermore, correlating the color of the captured signals to an induced fluorescence contribution was shown to be a…
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Taxonomy
TopicsIndoor Air Quality and Microbial Exposure · Atmospheric chemistry and aerosols · Air Quality and Health Impacts