Properties of materials considered for improvised masks
Bibliographic record
Abstract
During a pandemic in which aerosol and droplet transmission is possible, such as the COVID-19 pandemic of 2020, the demand for face masks that meet medical or workplace standards can prevent most individuals from obtaining suitable protection. Cloth masks are widely believed to impede droplet and aerosol transmission, but most are constructed from materials with unknown filtration efficiency, airflow resistance and water resistance. Here we provide data on a range of common fabrics that might be used to construct masks, complimenting existing studies by largely considering particles in the micron range (a plausible challenge size for human generated aerosols). None of the materials were suitable for N95 masks, but many could provide useful filtration (>90%) of 3 micron particles, with low pressure drop. These were: nonwoven sterile wraps, dried baby wipes and some double-knit cotton materials. Decontamination of N95 masks using isopropyl alcohol produces the expected increase in particle penetration, but for 3 micron particles, filtration efficiency is still well above 95%. Tightly woven thin fabrics, despite having the visual appearance of a good particle barrier, had remarkably low filtration efficiency and high pressure drop. The better material structures expose individual fibers to the flow while the poor materials may have small fundamental fibers but these are in tightly bundled yarns. Despite the complexity of the design of a very good mask, it is clear that for the larger aerosol particles, any mask will provide substantial protection to the wearer and those around them.Copyright © 2021 American Association for Aerosol Research
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".