Study of Filtration in Polyethylene Fibers used in Masks for Protection Against SARS-CoV-2 via Luminescent Aerosolized Silicon Nanoparticles
Bibliographic record
Abstract
Abstract Commercial polyethylene (PE) fiber-based masks are currently used as personal filters for protection against various microorganisms. Due to the coronavirus (SARS-CoV-2) pandemic of 2020, the use of masks has become the critical mechanism in reducing the spread. The PE mask filter uses a sieve (geometry) in a spider web fashion to filter out microorganisms using Van der Waals atomic forces. However, the non-geometrical part of the filtration process is not fully understood. In this work, we utilized luminescent ultra-small silicon nanoparticles, which are Si-H or/and Si-OH terminated to examine how the filter operates at a chemical level. The particles were sprayed onto the fiber network by an atomizer and we used scanning electron microscopy (SEM), optical microscope and fluorescence spectroscopy under UV radiation. The images and measurements clearly showed that the Si nanoparticles bonded to the PE fiber network. The results were analyzed in terms of chemical bonding between Si nanoparticle and fiber. Our findings suggest that the PE fibers could act as a chemical filter via hydrogen or hydrolysis–based bonding or via Si-C bonding, which is complementary to their physical filtration ability via the geometric sieve process. Moreover, the results indicate that the filter would be effective against the novel coronavirus, not only by the geometrical sieve but can be additionally enhanced as a chemical filter by allowing the amine, carboxyl and hydroxyl function groups of the virus to attach to fibers via C-N and C-O bonds.
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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 | 0.000 |
| 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) | 0.000 | 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 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".