Effectiveness of antiviral metal and metal oxide thin-film coatings against human coronavirus 229E
Bibliographic record
Abstract
Virucidal thin-film coatings have the potential to inactivate pathogens on surfaces, preventing or slowing their spread. Six potential nanoscale antiviral coatings, Cu, Cu2O, Ag, ZnO, zinc tin oxide (ZTO), and TiO2, are deposited on glass, and their ability to inactivate the HCoV-229E human coronavirus is assessed using two methods. In one method, droplets containing HCoV-229E are deposited on thin-film coatings and then collected after various stages of desiccation. In the second method, the thin-film coatings are soaked in the virus supernatant for 24 h. The Cu and Cu2O coatings demonstrate clear virucidal behavior, and it is shown that controlled delamination and dissolution of the coating can enhance the virucidal effect. Cu is found to produce a faster and stronger virucidal effect than Cu2O in the droplet tests (3 log reduction in the viral titer after 1 h of exposure), which is attributed, in part, to the differences in film adhesion that result in delamination of the Cu film from the glass and accelerated dissolution in the droplet. Despite Ag, ZnO, and TiO2 being frequently cited antimicrobial materials, exposure to the Ag, ZnO, ZTO, and TiO2 coatings results in no discernible change to the infectivity of the coronavirus under the conditions tested. Thin-film Cu coatings are also applied to the polypropylene fabrics of N95 respirators, and droplet tests are performed. The Cu fabric coating reduces the infectivity of the virus; it results in a 1 order-of-magnitude reduction in the viral titer within 15 min with a 2 order-of-magnitude reduction after 1 h.
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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".