Ozone Eliminates SARS-CoV-2 from Difficult-to-Clean Office Supplies and Clinical Equipment
Bibliographic record
Abstract
(1) Background: Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to cause profound health, economic, and social problems worldwide. The management and disinfection of materials used daily in health centers and common working environments have prompted concerns about the control of coronavirus disease 2019 (COVID-19) infection risk. Ozone is a powerful oxidizing agent that has been widely used in disinfection processes for decades. The aim of this study was to assess the optimal conditions of ozone treatment for the elimination of heat-inactivated SARS-CoV-2 from office supplies (personal computer monitors, keyboards, and computer mice) and clinical equipment (continuous positive airway pressure tubes and personal protective equipment) that are difficult to clean. (2) Methods: The office supplies and clinical equipment were contaminated in an area of 1 cm2 with 1 × 104 viral units of a heat-inactivated SARS-CoV-2 strain, then treated with ozone using two different ozone devices: a specifically designed ozonation chamber (for low–medium ozone concentrations over large volumes) and a clinical ozone generator (for high ozone concentrations over small volumes). SARS-CoV-2 gene detection was carried out using quantitative real-time polymerase chain reaction (RT-qPCR). (3) Results: At high ozone concentrations over small surfaces, the ozone eliminated SARS-CoV-2 RNA in short time periods—i.e., 10 min (at 4000 ppm) or less. The optimum ozone concentration over large volumes was 90 ppm for 120 min in ambient conditions (24 °C and 60–75% relative humidity). (4) Conclusions: This study showed that the appropriate ozone concentration and exposure time eliminated heat-inactivated SARS-CoV-2 RNA from the surfaces of different widely used clinical and office supplies, decreasing their risk of transmission, and improving their reutilization. Ozone may provide an additional tool to control the spread of the COVID-19 pandemic.
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 | 0.002 | 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 | 0.001 |
| 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".