Development of reusable cloth mask with nanoparticle filtration efficiency greater than 95%
Bibliographic record
Abstract
After the rapid spread of SARS-Cov-2 virus, the use of masks was suggested by the world health organization (WHO) to reduce the virus transmission, whose primary mode of transmission was suggested to be through respiratory droplets. The recommended face coverings were single use surgical and respirator masks made of non-woven materials. With the increased demand for masks worldwide, the environmental impacts of mask disposal and the pollution caused by microplastic fibers of the non-woven materials were presented. This challenge necessitates the need for the development of a novel reusable mask reducing the environmental effects, while providing the necessary personal protective properties. Based on the ASTM F2299 standard test method, the performance, i.e., particle-size dependent filtration efficiency and pressure drop were studied for 20 samples with multilayer knit fabrics of natural and synthetic fibers (inner layer of pure cotton, cotton-nylon and cotton-polyester, middle layer of Lycra, and outer layer of superhydrophobic polyester). The results show that all the samples had an efficiency of >94% and 87–99% for large (250 nm–1 μm) and small (100–250 nm) particles, respectively. The best performing structure has a material composition of 41% superhydrophobic polyester, 26% natural cotton, 24% nylon and 9% Lycra. The filtration efficiency, pressure drop, and quality factor for this sample are 97.8% (for 100 nm particles), 4.04 mmH2O/cm2 and 4.77 kPa−1, respectively. It was also demonstrated that the developed mask maintains its performance after 50 wash/dry cycles, verifying its reusability. It should be noted that charge neutralizer was not used in the experimental setup of this study which might have led to enhanced results for the filtration efficiency of small (100–250 nm) particles due to the dominance of electrostatic attraction. However, several samples were tested by the third-party company who uses a certified testing equipment based on ASTM F2299, and similar results were obtained.
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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 | 0.001 |
| 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) | 0.000 | 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".