Salt coatings functionalize inert membranes into high-performing filters against infectious respiratory diseases
Bibliographic record
Abstract
Respiratory protection is key in infection prevention of airborne diseases, as highlighted by the COVID-19 pandemic for instance. Conventional technologies have several drawbacks (i.e., cross-infection risk, filtration efficiency improvements limited by difficulty in breathing, and no safe reusability), which have yet to be addressed in a single device. Here, we report the development of a filter overcoming the major technical challenges of respiratory protective devices. Large-pore membranes, offering high breathability but low bacteria capture, were functionalized to have a uniform salt layer on the fibers. The salt-functionalized membranes achieved high filtration efficiency as opposed to the bare membrane, with differences of up to 48%, while maintaining high breathability (> 60% increase compared to commercial surgical masks even for the thickest salt filters tested). The salt-functionalized filters quickly killed Gram-positive and Gram-negative bacteria aerosols in vitro, with CFU reductions observed as early as within 5 min, and in vivo by causing structural damage due to salt recrystallization. The salt coatings retained the pathogen inactivation capability at harsh environmental conditions (37 °C and a relative humidity of 70%, 80% and 90%). Combination of these properties in one filter will lead to the production of an effective device, comprehensibly mitigating infection transmission globally.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".