Size-Specific Filtration Performance of N95 Respirators After Decontamination by Moist Heat Incubation
Bibliographic record
Abstract
Background: Decontamination and reuse of respirators have been proposed to mitigate the shortage of respirators during pandemics. The U.S. National Institute for Occupational Safety and Health (NIOSH)'s respirator filtration efficiency (FE) test has been used to confirm that decontamination procedures maintain minimum FE above 95% for N95s and similar respirators. However, it was hypothesized that the limited range of test particle sizes may not include the most penetrating particle size (MPPS) for all respirators, especially after decontamination by moist heat incubation (MHI). Materials and Methods: A custom-designed apparatus was used to measure size-specific FE for respirators across particle size bins between aerodynamic diameter of 0.07 and 1.97 μm using an electrical low-pressure impactor. FEs were measured for two N95 respirator models before and after 10 cycles of MHI. In addition, pressure drop through the respirator materials and scanning electron microscope (SEM) images of respirator layers were obtained before and after MHI. Results: For Kimtech™ brand N95 respirators, FE was not reduced at any size after MHI. For Safe Life brand N95s, FE was below 95% before MHI and decreased significantly after MHI. The MPPS for this respirator was outside the range defined in NIOSH test protocol, and increased after MHI. There was no appreciable change to the pressure drop through the two respirator models after MHI, nor was any deterioration in fiber integrity visible in SEM images. Conclusions: Based on the results of the present study and other studies in the literature, MHI can be used to decontaminate respirators without significant decrease in FE. However, potential effects of MHI on FE need to be assessed for each respirator model. The ability to evaluate size-specific FE across a wide range of particle sizes is important in identifying the MPPS and associated FE of respirators before and after MHI.
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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".