Study of the inward protection efficiency of various facial masks
Bibliographic record
Abstract
COVID-19 and other respiratory diseases largely spread through aerosols and droplets shed by an infected individual. Face masks can act as both a source control measure and a method of protection from airborne disease. In this work, the inward protection efficiencies of N95, KN95, and ASTM level 3 surgical masks worn both regularly and “tied and tucked” are evaluated for aerosol particle sizes ranging from 0.2 to 1 μm. Tests are conducted on a realistic soft-surface medium NIOSH manikin to accurately simulate the human/mask interface. For filtering facepiece respirators, the N95 mask is found to provide significantly greater protection than the KN95 masks. A marginal increase in mean inward protection efficiency is observed by improving surgical mask fit through the “tie and tuck” method. Mean inward protection efficiency of 86.5±2.5%, 61.5±6.0%, 20.4±3.9%, and 24.2±4.6% for N95, KN95, regularly worn surgical mask, and tied and tucked surgical mask, respectively, were found. Comparisons with material filtration efficiency highlight that inward protection is impacted significantly by leakage due to gaps at the mask-face interface. Minimal variation in inward protection efficiency across the tested particle size range was observed. A weighted analysis of available data for N95 and surgical masks is performed to obtain estimates for mean inward efficiency ranges for these mask types that can guide future risk assessment and modeling studies.Copyright © 2025 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 | 0.001 |
| 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) | 0.001 | 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 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".