Features and In Vitro Assessment of Antiviral Activity of Organic Coatings Doped with Silver-Based Compounds Against Human Coronavirus
Bibliographic record
Abstract
Implementation of novel antiviral coatings and textiles, which can be utilised in the production of personal protective equipment, has the potential to enhance public health security against future pandemic outbreaks. Respiratory viruses, particularly SARS-CoV-2, responsible for COVID-19, have emerged as a major global concern due to their rapid transmission and high mortality rates, leading to nearly seven million deaths worldwide between 2020 and 2025. This statistic underscores the necessity for the development and implementation of advanced antiviral materials to prevent viral infections. This research focused on the in vitro evaluation of the antiviral properties of three antibacterial compounds containing silver (Ag) that were functionalized with coatings. We assessed onsite synthesised Ag powder in comparison to commercially available antibacterial additives, which included nanosilver on colloidal silica (AgSiO2) and silver sodium hydrogen zirconium phosphate (AgNaOPZr), as potential antiviral agents in coatings against human coronavirus (HCoV). Antiviral assessments revealed that coatings containing Ag at higher concentrations (2.5 and 5%) exhibited limited antiviral effectiveness, with a titer reduction in log < 2. In contrast, the functionalization of AgSiO2 on coatings significantly suppressed viral replication resulting in a notable reduction in virus titer of log ≥ 2 for all tested concentrations.
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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".