Polymeric nanocomposites-based advanced coatings for antimicrobial and antiviral applications: A comprehensive overview
Bibliographic record
Abstract
The rise in microbial infections and viral outbreaks has accelerated the development of advanced antibacterial and antiviral coating that effectively minimize infection risks. Amidst rising global health concerns due to infectious diseases, these coatings have emerged as vital solutions for surface protection across various settings. This review focuses on the recent development and in-depth analysis includes synthesis process, inactivation mechanism in photoreactive antimicrobial and antiviral bio-coatings. This review comprehensively explores the development of different nanocomposite materials, including polymers based on metal oxide nanoparticles, metal nanoparticles, graphene-based materials, and organic-inorganic composites. Their practicality and compatibility with diverse surfaces such as ceramics, glass, plastics, and metals are discussed, highlighting their broad applicability in different field. The mechanisms of action of these coatings, including the production of reactive oxygen species and physical barriers against microbes, are elucidated well. The review also includes the recent studies on polymer nanocomposite-based coatings, offering insights into their efficacy in reducing infection risks and improving public health. Apart from the materials fabrication and their practical applications this review also explores several challenges such as scalability, toxicity, cost-effectiveness and stability lifetime of the coatings. Although, the advancement in the photoreactive coating exhibits the positive scenario to disinfect several microbes and viruses. This review highlights the outlook of the highly advanced coatings which can inactive the different biological moieties under light exposure. • Reviews the latest advances in photoreactive coating for microbial inactivation. • Discusses broad applicability of nanocomposite coatings across various surfaces. • Highlights challenges in scaling production and managing coating toxicity. • Presents recent studies on improving public health via antimicrobial surfaces. • Examine future perspective for innovative antimicrobial coating technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".