Filtering Efficiency and Design Properties of Medical- and Non-Medical-Grade Face Masks: A Multiscale Modeling Approach

Abstract

Approved medical face masks have been shown to prevent the spread of respiratory droplets associated with coronavirus transmission in specific settings. The primary goal of this study was to develop a new strategy to assess the filtering and transmissibility properties of medical- and non-medical-grade face masks. In this study, we designed and assessed the filtering efficiency of particles through six different masks with a diverse set of fabrics, textures (woven and non-woven), fiber diameters, and porosity. The filtering and transmissibility properties of face mask layers individually and in combination have been assessed using mathematical analyses and new experimental data. The latter provided velocity profiles and filtration efficiencies for which the data were shown to be predictable. The filtration efficacy and pressure drop across each fabric have been tested using an aerosol particle spray and scanning electron microscopy. To assess clinical significance, the temperature and humidity of the masks were tested on a group of healthy volunteers spanning various age ranges (9–79 years old), utilizing an embedded temperature sensor disc. Also, a mask filter model was developed using fluid dynamic simulations (Solidworks Flow) to evaluate the aerodynamic dispersion of respiratory droplets. Overall, the FFP2 and FFP3 masks demonstrated the highest filtration efficiencies, each exceeding 90%, a feature of multi-layered masks that is consistent with simulations demonstrating higher filtering efficiencies for small particles (<5 µm). The velocity and temperature simulations of all six masks revealed a low air velocity (~1 m/s) inside the mask and a temperature variation of approximately 3 °C during the breathing cycle.