Decontamination of N95 and surgical masks using a treatment based on a continuous gas phase-Advanced Oxidation Process
Bibliographic record
Abstract
A gas-phase Advanced Oxidation Process (gAOP) was evaluated for decontaminating N95 and surgical masks. The continuous process was based on the generation of hydroxyl-radicals via the UV-C (254 nm) photo-degradation of hydrogen peroxide and ozone. The decontamination efficacy of the gAOP was dependent on the orientation of the N95 mask passing through the gAOP unit with those positioned horizontally enabling greater exposure to hydroxyl-radicals compared to when arranged vertically. The lethality of gAOP was independent of the applied hydrogen peroxide concentration (2-6% v/v) but was significantly (P<0.05) higher when H2O2 was introduced into the unit at 40 ml/min compared to 20 ml/min. A suitable treatment for N95 masks was identified as 3% v/v hydrogen peroxide delivered into the gAOP reactor at 40 ml/min with continuous introduction of ozone gas and a UV-C dose of 113 mJ/cm2 (30 s processing time). The treatment supported >6 log CFU decrease in Geobacillus stearothermophilus endospores, > 8 log reduction of human coronavirus 229E, and no detection of Escherichia coli K12 on the interior and exterior of masks. There was no negative effect on the N95 mask fitting or particulate efficacy after 20 passes through the gAOP system. No visual changes or hydrogen peroxide residues were detected (<1 ppm) in gAOP treated masks. The optimized gAOP treatment could also support >6 log CFU reduction of endospores inoculated on the interior or exterior of surgical masks. G. stearothermophilus Apex spore strips could be applied as a biological indicator to verify the performance of gAOP treatment. Also, a chemical indicator based on the oxidative polymerization of pyrrole was found suitable for reporting the generation of hydroxyl-radicals. In conclusion, gAOP is a verifiable treatment that can be applied to decontaminate N95 and surgical masks without any negative effects on functionality.
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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".