Antimicrobial Nanomaterials and Coatings: Current Mechanisms and Future Perspectives to Control the Spread of Viruses Including SARS-CoV-2
Bibliographic record
Abstract
Abstract The global COVID-19 pandemic has attracted considerable attention toward innovative methods and technologies for suppressing the spread of viruses. Transmission via contaminated surfaces has been recognized as an important route for spreading SARS-CoV-2. Although significant efforts have been made to develop antibacterial surface coatings, the literature remains scarce for a systematic study on broad-range antiviral coatings. Here, we aim to provide a comprehensive overview of the antiviral materials and coatings that could be implemented for suppressing the spread of SARS-CoV-2 via contaminated surfaces. We discuss the mechanism of operation and effectivity of several types of inorganic and organic materials, in the bulk and nanomaterial form, and assess the possibility of implementing these as antiviral coatings. Toxicity and environmental concerns are also discussed for the presented approaches. Finally, we present future perspectives with regards to emerging antimicrobial technologies such as omniphobic surfaces and assess their potential in suppressing surface-mediated virus transfer. Although some of these emerging technologies have not yet been tested directly as antiviral coatings, they hold great potential for designing the next generation of antiviral surfaces.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".