Specially inactivation of SARS-CoV-2 (COVID-19) on surgical or homemade masks and medical clothing: Innovative solution attachment
Bibliographic record
Abstract
Dear Editor, As we know, SARS-CoV-2 spreads when you cough, sneeze, talk, sing, laugh, or breathe out rapidly.[1] Therefore, to protect the individuals or health-care workers, the WHO and CDC have given their standard precautions to use the different types of masks depending on the load of SARS-CoV-2 in their working area.[2,3] Furthermore, not only we should protect ourselves from SARS-CoV-2 through respiration, but also we should take a precautions not to transfer the attached virus through our clothing, e.g., surgical or homemade masks and medical clothing. For these reasons, we made a modified molecule of macrocycle (Ghanavi, United States patent; 10147951) and coated into clothing surface using spray. After its exposure to contaminated area, the attachment of virus was observed under scanning electron microscopy (SEM). As shown in [Figures 1 and 2], after using the spray with modified macrocycle, the load of SARS-CoV-2 (COVID-19) attachment increased to “64 folds” (94%) in the clothing surface and remained attached without blowing to free air. The technology can be used in any clothing materials. This means that we can stop the spread and transmission of virus from contaminated to clean area.Figure 1: Scanning electron microscopy shows the homemade mask surface sprayed with macrocycle at 1 μmFigure 2: Scanning electron microscopy shows the homemade mask surface sprayed with macrocycle at 10 μm
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".