Study of the Pathogen Inactivation Mechanism in Salt-Coated Filters
Bibliographic record
Abstract
As COVID-19 exemplifies, respiratory diseases transmitted through aerosols or droplets are global threats to public health, and respiratory protection measures are essential first lines of infection prevention and control. However, common face masks are single use and can cause cross-infection due to the accumulated infectious pathogens. We developed salt-based formulations to coat membrane fibers to fabricate antimicrobial filters. Here, we report a mechanistic study on salt-induced pathogen inactivation. The salt recrystallization following aerosol exposure was characterized over time on sodium chloride (NaCl), potassium sulfate (K 2 SO 4 ), and potassium chloride (KCl) powders and coatings, which revealed that NaCl and KCl start to recrystallize within 5 min and K 2 SO 4 within 15 min. The inactivation kinetics observed for the H1N1 influenza virus and Klebsiella pneumoniae matched the salt recrystallization well, which was identified as the main destabilizing mechanism. Additionally, the salt-coated filters were prepared with different methods (with and without a vacuum process), which led to salt coatings with different morphologies for diverse applications. Finally, the salt-coated filters caused a loss of pathogen viability independent of transmission mode (aerosols or droplets), against both DI water and artificial saliva suspensions. Overall, these findings increase our understanding of the salt-recrystallization-based technology to develop highly versatile antimicrobial filters.
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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) | 0.001 | 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".