Design, Modeling and Simulation of a Capacitive Size-Discriminating Particulate Matter Sensor for Personal Air Quality Monitoring

Abstract

We applied the well-established thermophoretic effect to air quality monitoring. We developed a novel method for particulate matter distribution analysis in an in-flow capacitive detection device. The proposed Multiphysics model combines fluid dynamics of particulate matter influenced by thermophoresis with electric field variations in the active volume space of a charged coplanar interdigitated electrodes. The model allows to anticipate the effect of thermophoresis in separating particles of PM10 and PM2.5 size ranges into different streams from a single particle-entrained flow and provides an estimated value of sensitivity for capacitive PM detection. The model is described through the Finite Element Method from the main equations to the simulation run using COMSOL Multiphysics and validated by comparing the results with literature. We obtained high sensor sensitivity of up to 0.48 zF/particle as far as $18~\mu \text{m}$ from coplanar electrode surface using the Computational Fluid Dynamics and Heat Transfer, Electrostatics and Particle tracing modules. We compare results of the simulations for different particle positions, electrode width and inter-electrode spacing, then we use the results to identify optimal design parameters for a novel architecture of a PM detection system with high sensitivity down to PM2.5 single particles and embedded particle size discrimination by using $10~\mu \text{m}$ electrode width and \mu \text{m}$ inter-electrode spacing