Detection of Airborne Nanoparticles through Enhanced Light Scattering Images

Abstract

A new method is proposed in this paper to detect airborne nanoparticles, detecting the light scattering caused by both the particle and the surrounding molecules, which can surpass the limitations of conventional laser optical methods while maintaining simplicity and cost-effectiveness. This method is derived from a mathematical analysis that describes the particle light scattering phenomenon more exactly by including the influence of light scattered from surrounding gas molecules. The analysis shows that it is often too much of a simplification to consider only light scattering from the detected nanoparticle, because light scattering from the surrounding gas molecules, whether visible or invisible to the sensor, is important for nanoparticle detection. An image detection approach utilizing the light scattering from surrounding air molecules is described for the detection of airborne nanoparticles. Tests using monodisperse nanoparticles confirm that airborne particles of around 50 nm in size can even be detected using a low-cost testing device. This shows further that even when using a simple image processing code, captured particle light scattering images can be converted digitally into instantaneous particle counts or concentrations. The factors limiting conventional pulse detection are further discussed. This new method utilizes a simple static light scattering (SLS) approach to enable the development of new devices with better detection capabilities, paving the way for the further development of nanoparticle detection technology.

Keywords: aerosol; airborne nanoparticles; image processing; laser particle detector; light scattering; light scattering image.

Figure 1

Light scattering when a laser beam irradiates a nanoparticle suspended in the air. Unlike a particle in a vacuum, there are air molecules surrounding the particle, and the effect of light scattering from air molecules should be included in a light scattering model. Using an image detection approach, light scattered from air molecules can be utilized to detect nanoparticles.

Figure 2

An air-light light-tight device for detecting particle and air molecule light scattering. Low-cost components are used intentionally in the testing setup to check the feasibility for future low-cost applications.

Figure 3

A particle calibration system using monodisperse NaCl and soot particles for evaluating the ability to detect airborne nanoparticles.

Figure 4

Images of light scattered from air molecules and airborne nanoparticles captured in light scattering detection videos. (a) Image formed when a green laser (λ = 532 nm) irradiates aerosol-free air. Unlike the Tyndall effect, the image results from the light scattered from molecules instead of colloidal particles. (b) Image formed when a green laser irradiates air containing monodisperse NaCl nanoparticles (70 nm). The nanoparticles become clearly visible as bright dots with air molecule light scattering as a background. The image size difference is caused by optical distortion from the optical system. These images are clearly present when the laser and the image sensor have sufficient power and sensitivity.

Figure 5

Images of light scattering captured in light scattering detection videos when irradiating air containing light-absorbing soot nanoparticles using a blue laser (λ = 450 nm). (a–d) Brighter blue images are formed from the particle light scattering of 70, 60, 50, and 40 nm soot nanoparticles and air molecule light scattering, respectively. The lighter blue background appears due to weak light reflections from the testing device surface. As the airborne nanoparticle size decreases, the particle light scattering images change from large bright dots to tiny darker dots mixed with air molecule light scattering.

Figure 6

Results after image processing to extract quantitative information from videos recording particle light scattering. (a,b) Photos taken after image processing of videos recorded while detecting 70 and 50 nm soot particles, respectively. The original video image prior to processing is attached in the bottom left corner. Particles identified by the image processing code are highlighted using a yellow outline. The number count and concentration of identified particles are also provided.

Figure 7

A plot of instantaneous concentration of identified particles. The particle number concentration information is extracted through an image process from 150 photos in a 5 s video while detecting 50 nm soot particles in the air. The particle detection video is the same as presented in Figure 6b. The time between frames is 0.03 s.