Impact of the Contamination Method on the Disinfection of N95 Respirators: Drops versus Aerosols
Bibliographic record
Abstract
The recent surge in the use of filtering facepiece respirators (FFRs) during the SARS-CoV-2 pandemic triggered economic and environmental concerns with regards to their safe reuse and/or disposal. Their decontamination through ultraviolet (UV) irradiation has proven efficient in bench tests. Nevertheless, no study has yet investigated to what extent the decontamination method’s performance was impacted by the contamination method. In this study, Bacillus subtilis spores were inoculated in three suspensions used to contaminate coupons of FFRs via aerosols nebulisation or 2 µL drops deposition. The contaminated coupons were then exposed to UV irradiation in a monochromatic UVC lamp collimated beam reactor. The results revealed that contamination and decontamination were more efficient for drops (maximum 0.72 log losses and 3 log inactivation at 150 mJ cm-2) than for aerosols (maximum 2.47 log losses and 1.75 log inactivation at 150 mJ cm-2). Inactivation was greater in coupons contaminated using artificial saliva, followed by phosphate buffer solution, and finally artificial saliva with mucin which also presented the highest fraction of resistant spores, based on kinetic modeling. Disinfection was determined sensitive to the method of contamination (p < 0.001). However, the composition of the contaminating suspension was the most important performance predictor for decontamination by UV irradiation (p = 9.2 × 10-10).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".