During the COVID-19 pandemic, many millions have worn masks made of woven fabric to reduce the risk of transmission of COVID-19. Masks are essentially air filters worn on the face that should filter out as many of the dangerous particles as possible. Here, the dangerous particles are the droplets containing the virus that are exhaled by an infected person. Woven fabric is unlike the material used in standard air filters. Woven fabric consists of fibers twisted together into yarns that are then woven into fabric. There are, therefore, two lengthscales: the diameters of (i) the fiber and (ii) the yarn. Standard air filters have only (i). To understand how woven fabrics filter, we have used confocal microscopy to take three-dimensional images of woven fabric. We then used the image to perform lattice Boltzmann simulations of the air flow through fabric. With this flow field, we calculated the filtration efficiency for particles a micrometer and larger in diameter. In agreement with experimental measurements by others, we found that for particles in this size range, the filtration efficiency is low. For particles with a diameter of 1.5 μm, our estimated efficiency is in the range 2.5%–10%. The low efficiency is due to most of the air flow being channeled through relatively large (tens of micrometers across) inter-yarn pores. So, we conclude that due to the hierarchical structure of woven fabrics, they are expected to filter poorly.
Modeling the filtration efficiency of a woven fabric: The role of multiple lengthscales