Ozone treatment effectively eliminates SARS-CoV-2 from infected face masks
Bibliographic record
Abstract
The current COVID-19 pandemic is causing profound health, economic, and social problems worldwide. The global shortage of medical and personal protective equipment (PPE) in specialized centers during the outbreak demonstrated the need for efficient methods to disinfect and recycle them in times of emergency. We have previously described that high ozone concentrations destroyed viral RNA in an inactivated SARS-CoV-2 strain within a few minutes. However, the efficient ozone dosages for active SARS-CoV-2 are still unknown. The present study aimed to evaluate the systematic effects of ozone exposure on face masks from hospitalized patients infected with SARS-CoV-2. Face masks from COVID-19 patients were collected and treated with a clinical ozone generator at high ozone concentrations in small volumes for short periods. The study focused on SARS-CoV-2 gene detection (assessed by real-time quantitative polymerase chain reaction (RT-qPCR)) and on the virus inactivation by in vitro studies. We assessed the effects of different high ozone concentrations and exposure times on decontamination efficiency. We showed that high ozone concentrations (10,000, 2,000, and 4,000 ppm) and short exposure times (10, 10, and 2 minutes, respectively), inactivated both the original strain and the B.1.1.7 strain of SARS-CoV-2 from 24 contaminated face masks from COVID-19 patients. The validation results showed that the best condition for SARS-CoV-2 inactivation was a treatment of 4,000 ppm of ozone for 2 minutes. Further studies are in progress to advance the potential applications of these findings.
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 | 0.000 | 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 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".