A Comparative Study of the Antiviral Properties of Thermally Sprayed Coatings against Human Coronavirus HCoV-229E
Bibliographic record
Abstract
For decades, novel viral strains of respiratory tract infections have caused human pandemics and initiated widespread illnesses. The recent coronavirus disease 2019 (COVID-19) outbreak caused by the SARS-CoV-2 virus has raised an urgent need to develop novel antiviral coatings as one of the potential solutions to mitigate the transmission of viral pathogens. Titanium dioxide is considered an excellent candidate for viral disinfection under light irradiation, with the potential to be activated under visible light for indoor applications. This research assessed the antiviral performance of thermally sprayed TiO2 coatings under UVA and ambient light. We also report the antiviral performance of TiO2 composites with other oxides, such as Cu2O and Al2O3, produced by suspension plasma spray, atmospheric plasma spray, and suspension high-velocity oxygen fuel techniques. To evaluate the antiviral performance of the above coatings in a containment level-2 laboratory, a human common cold coronavirus, HCoV-229E, was initially used as a relevant surrogate for SARS-CoV-2. Coatings were also analyzed using SEM and XRD and were classified based on their surface roughness, porosity, and phase composition. Collectively, the thermally sprayed coatings showed comparable or slightly better antiviral activity compared to copper. The most significant level of activity observed was approximately 20% to 50% higher than that of a pure copper plate.
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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".