Microstructure analysis and image-based modelling of face masks for COVID-19 virus protection

Wenjia Du; Francesco Iacoviello; Tacson Fernandez; Rui Loureiro; Daniel J. L. Brett; Paul R. Shearing

doi:10.1038/s43246-021-00160-z

Communications Materials (Jun 2021)

Microstructure analysis and image-based modelling of face masks for COVID-19 virus protection

Wenjia Du,
Francesco Iacoviello,
Tacson Fernandez,
Rui Loureiro,
Daniel J. L. Brett,
Paul R. Shearing

Affiliations

Wenjia Du: The Electrochemical Innovation Lab, Department of Chemical Engineering, University College London
Francesco Iacoviello: The Electrochemical Innovation Lab, Department of Chemical Engineering, University College London
Tacson Fernandez: Royal National Orthopaedic Hospital
Rui Loureiro: Royal National Orthopaedic Hospital
Daniel J. L. Brett: The Electrochemical Innovation Lab, Department of Chemical Engineering, University College London
Paul R. Shearing: The Electrochemical Innovation Lab, Department of Chemical Engineering, University College London

DOI: https://doi.org/10.1038/s43246-021-00160-z
Journal volume & issue: Vol. 2, no. 1
pp. 1 – 10

Abstract

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Face masks are key for slowing the spread of COVID-19. Here, the microstructure of three common masks is determined by x-ray tomography, combined with image-based modelling of droplet permeability, revealing that N95 masks are best for droplet filtration.

Published in Communications Materials

ISSN: 2662-4443 (Online)
Publisher: Nature Portfolio
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
Website: https://www.nature.com/commsmat/

About the journal