In the early summer of 2020 I started working with colleagues at the University of Bristol to try and better understanding how face coverings/masks filter our potentially infectious droplets. The latest preprint is on filtering by cotton fabric, is on arXiv as of October 2021. Jake Wilkins, who did a project with me in which he rendered this image (with Blender) done by Ioatzin Rios De Anda, of cotton fabric. The field of view is about 300 micrometres across and 100 micrometres deep.
You can see that cotton fabric is made of bundles (these are yarns) of fibres. Fibres are the thread-like objects you see in the rendering. As you can see, there are quite large gaps in the fabric. We find (via computer simulation) that almost all of your breath goes through these gaps and as they are large, filtration is poor. A single layer of this fabric does not make a good mask.
You can see my blog for latest thoughts on COVID-19 transmission and masks.
There is also an earlier Physics of Fluids paper mainly on models of how masks/face coverings work. The theory work was done by Josh Robinson and experiments by Ioatzin Rios de Anda, both working in the group of Paddy Royall – who was then at Bristol but is now at ESPCI in Paris. I helped with some Lattice Boltzmann simulations.
The physics of how masks/face-coverings work is a bit complex but basically they are are air filters that you wear on your face. An air filter is something that allows air through (so you can breathe) but traps particles (in this case droplets that may contain virus) inside it. This means separating out the air and the particles. How this is done depends very sensitively on size of the droplets. The smallest ones (below around 0.3 micrometres) are small enough to just diffuse into the surfaces of the fibres of the mask, and stick. Big ones (a few micrometres or larger) can’t follow the air through the mask because the particles have too much inertia. This inertia means that they crash straight into the fibres and stick.
Between the diffusion and inertial mechanisms there is gap for particles around 0.3 to 1 micrometre, which both surgical and cloth masks don’t filter effectively. The more advanced N95/FFP2 respirators worn as part of PPE rely on a third mechanism to filter particles in this size range: electrostatic interactions due to charged up fibres inside the mask. Final comment: masks are only as good as their fit — air that goes round the edges is not filtered! — so please make sure the fit is reasonably good.
Also, as of early December 2020 there is another less-physicsy preprint on medRxiv:
by Joshua F. Robinson, Ioatzin Rios de Anda, Fergus J. Moore, Florence K. A. Gregson, Jonathan P. Reid, Lewis Husain, Richard P. Sear, C. Patrick Royall
Code written by Joshua Robinson (University of Bristol) and I, that was used for the calculations in this work in these papers is freely available on Github here.
Slides from a 2020 talk
The pdf of the slides of my talk entitled Some simple physics of corona virus transmission at SOFT MATTER: THE UNSEEN SCIENCE ALL AROUND US is here I cover both simple models of person-to-person transmission of SARS-CoV-2, and of masks/face coverings.
Streamlit app for Wells-Riley model
inspired by a really nice Google Sheets by Jose-Luis Jimenez and Zhe Peng, I have done a simple app that plots the Wells-Riley prediction of the probability of becoming infected, as the function of the length of time you spend in a room. Wells-Riley is a simple model for estimating the probability that you will become infected if you spend time in a room with someone who is infected. The app is hosted by Heroku and it uses Streamlit – an easy way of doing interactive plots using Python.