Assessing the impact of multiple ultraviolet disinfection cycles on N95 filtering facepiece respirator integrity
Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, N95 filtering facepiece respirators (FFRs) were recommended to protect healthcare workers when providing care to infected patients. Despite their single-use disposable nature, the need to disinfect and repurpose FFRs is paramount during this global emergency. The objectives of this study were to (1) determine if UV treatment has an observable impact on respirator integrity; (2) test the impact of UV treatment on N95 FFR user fit; and (3) test the impact of UV treatment on FFR integrity. Ultraviolet (UV) disinfection was assessed in maintaining N95 FFR integrity. Two models of FFRs were exposed to UV fluences ranging from 0 to 10,000 mJ cm−2 per side and subsequently tested for fit, respirator integrity, and airflow. Inspection of N95 FFRs before and after UV treatment via microscopy methods showed no observable or tactile abnormalities in the integrity of respirator material or straps. Tensile loading tests on UV-treated and untreated respirator straps also demonstrated no impact on breaking strength. Standardized fit test methods showed no compromise in user fit following UV treatment. Evaluation of particle penetration and airflow through N95 FFRs showed no impact on integrity, and average filtration efficiency did not fall below 95% for any of the respirator types or fluence levels. This work provides evidence that UV disinfection does not compromise N95 FFR integrity at UV fluences up to 10,000 mJ cm−2. UV disinfection is a viable treatment option to support healthcare professionals in their strategy against the spread of COVID-19.
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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) | 0.002 | 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".