Effect of moist heat reprocessing of N95 respirators on SARS-CoV-2 inactivation and respirator function
Bibliographic record
Abstract
BACKGROUND: Unprecedented demand for N95 respirators during the coronavirus disease 2019 (COVID-19) pandemic has led to a global shortage of these masks. We validated a rapidly applicable, low-cost decontamination protocol in compliance with regulatory standards to enable the safe reuse of N95 respirators. METHODS: We inoculated 4 common models of N95 respirators with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and evaluated viral inactivation after disinfection for 60 minutes at 70°C and 0% relative humidity. Similarly, we evaluated thermal disinfection at 0% to 70% relative humidity for masks inoculated with Escherichia coli. We assessed masks subjected to multiple cycles of thermal disinfection for structural integrity using scanning electron microscopy and for protective functions using standards of the United States National Institute for Occupational Safety and Health for particle filtration efficiency, breathing resistance and respirator fit. RESULTS: A single heat treatment rendered SARS-CoV-2 undetectable in all mask samples. Compared with untreated inoculated control masks, E. coli cultures at 24 hours were virtually undetectable from masks treated at 70°C and 50% relative humidity (optical density at 600 nm wavelength, 0.02 ± 0.02 v. 2.77 ± 0.09, p < 0.001), but contamination persisted for masks treated at lower relative humidity. After 10 disinfection cycles, masks maintained fibre diameters similar to untreated masks and continued to meet standards for fit, filtration efficiency and breathing resistance. INTERPRETATION: Thermal disinfection successfully decontaminated N95 respirators without impairing structural integrity or function. This process could be used in hospitals and long-term care facilities with commonly available equipment to mitigate the depletion of N95 masks.
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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 | 0.000 | 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) | 0.001 | 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".