Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning

Lee, Hye Ryoung; Liao, Lei; Xiao, Wang; Vailionis, Arturas; Ricco, Antonio J.; White, Robin; Nishi, Yoshio; Chiu, Wah; Chu, Steven; Cui, Yi

[Image: see text] The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.