Modelling the filtration efficiency of a woven fabric: The role of multiple lengthscales
Ioatzin Rios de Anda, Jake W. Wilkins, Joshua F. Robinson, C. Patrick, Royall, Richard P. Sear

TL;DR
This study models how woven fabric masks filter particles, revealing that their hierarchical structure results in low filtration efficiency for virus-laden droplets, which has implications for mask design during pandemics.
Contribution
The paper introduces a novel multi-scale modeling approach combining microscopy and simulations to analyze woven fabric filtration efficiency.
Findings
Filtration efficiency for 1.5 micrometre particles is 2.5-10%.
Most airflow passes through large inter-yarn pores, reducing filtration.
Woven fabric filters are less effective due to their hierarchical structure.
Abstract
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 virus that are exhaled by an infected person. Woven fabric is unlike the material used in standard air filters. Woven fabric consists of fibres twisted together into yarns that are then woven into fabric. There are therefore two lengthscales: the diameters of: (i) the fibre 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…
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