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

ABSTRACT: 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 ?m 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.

SUBMITTER: Lee HR

PROVIDER: S-EPMC7737533 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Lee Hye Ryoung HR Liao Lei L Xiao Wang W Vailionis Arturas A Ricco Antonio J AJ White Robin R Nishi Yoshio Y Chiu Wah W Chu Steven S Cui Yi Y

Nano letters 20201207 1

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 to ...[more]

PMID: 33283521

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Project description:The COVID-19 pandemic has caused a high demand for respiratory protection among health care workers in hospitals, especially surgical N95 filtering facepiece respirators (FFRs). To aid in alleviating that demand, a survey of commercially available filter media was conducted to determine whether any could serve as a substitute for an N95 FFR while held in a 3D-printed mask (Stopgap Surgical Face Mask from the NIH 3D Print Exchange). Fourteen filter media types and eight combinations were evaluated for filtration efficiency, breathing resistance (pressure drop), and liquid penetration. Additional testing was conducted to evaluate two filter media disinfection methods in the event that the filters were reused in a hospital setting. Efficiency testing was conducted in accordance with the procedures established for approving an N95 FFR. One apparatus used a filter-holding device and another apparatus employed a manikin head to which the 3D-printed mask could be sealed. The filter media and combinations exhibited collection efficiencies varied between 3.9% and 98.8% when tested with a face velocity comparable to that of a standard N95 FFR at the 85 L min-1 used in the approval procedure. Breathing resistance varied between 10.8 to >637 Pa (1.1 to > 65 mm H2O). When applied to the 3D-printed mask efficiency decreased by an average of 13% and breathing resistance increased 4-fold as a result of the smaller surface area of the filter media when held in that mask compared to that of an N95 FFR. Disinfection by dry heat, even after 25 cycles, did not significantly affect filter efficiency and reduced viral infectivity by > 99.9%. However, 10 cycles of 59% vaporized H2O2 significantly (p < 0.001) reduced filter efficiency of the media tested. Several commercially available filter media were found to be potential replacements for the media used to construct the typical cup-like N95 FFR. However, their use in the 3D-printed mask demonstrated reduced efficiency and increased breathing resistance at 85 L min-1.

Dataset Information

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

Publications

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

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