Adding a Water-Repellent Barrier to Low-Cost or Home-Made Masks and Respirators Using Aerosols
Bibliographic record
Abstract
In connection with the COVID-19 pandemic in the spring of 2020, an unprecedented situation has developed with masks and respirators protecting respiratory organs.The purpose of the mask (respirator) is to protect the person from drops of liquid that the COVID-19 carrier spreads around themselves when coughing/sneezing.Unfortunately, low-cost, non-professional and home-made masks and respirators let in a significant amount of droplets.An ideal solution would be to treat a cheap or even a makeshift mask or respirator to make it water-repellent (like a rubber respirator), but to still allow the wearer of the mask the ability to breathe and talk freely.Fortunately, this dilemma was long and successfully solved by manufacturers of products to protect clothes and furniture from spills and liquids.This article describes experiments that tested the "Tana® Style 16® Protector" aerosol manufactured by JOHNSON & SON, INC for the installation of a water-repellent barrier on household water-permeable respirators and cotton fabric.The methodology and photographs of the passage of a jet of water through a household mask and cotton fabric are given before and after "Tana® Style 16® Protector" aerosol treatment.Aerosols have been shown to be highly effective for installing a water-repellent barrier on water-permeable respirators and cotton fabric.It is understood that other countries may have similar aerosols under different names.The breathing through a waterrepellent barrier, as well as possible long-term effects, are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".