Barrier face coverings: Impact of washing on filtration efficiency using viral model and submicron particles
Bibliographic record
Abstract
Barrier face coverings were commonly used during the SARS-CoV-2 pandemic as a solution for the shortage of medical-grade masks. Many of these devices are designed to be washable and reusable. As concerns rise about the environmental repercussions of single-use masks, the use of efficient, reusable barrier face coverings has begun to emerge as an alternative. If the ASTM standard F3502-21 governs the efficiency of inert particle filtration, it does not, however, require that devices be tested against viral aerosols. Although the impact of repeated washes on these devices’ ability to filter inert particles has been investigated, disagreements remain on whether washing barrier face coverings can significantly affect their efficiency. The goal of this study was to evaluate the impact of washes on the filtration efficiency of five barrier face coverings from a single manufacturer against submicron particles and a model virus. To that end, a wind tunnel specifically designed for the evaluation of masks’ filtration efficiency was used in conjunction with bacteriophage PhiX174, which proxied for human pathogens. The five barrier face coverings were washed and tested at different washing frequencies, determined as per the manufacturer’s indications. Results showed a significant loss of efficiency in filtering submicron particles and viruses for all five barrier face coverings. Resistance to washing varied across the tested devices. Reusable barrier face coverings that are intended to substitute for medical-grade masks should be tested after undergoing a maximum number of washes to provide an accurate assessment of their filtration efficiency at the time of use.Copyright © 2025 American Association for Aerosol Research
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".