Photoinduced Transport and Activation of Polymer-Embedded Silver on Rice Husk Silica Nanoparticles for a Reusable Antimicrobial Surface
Bibliographic record
Abstract
Antimicrobial materials are gaining significant interest as awareness of pathogens spread through contact becomes increasingly prevalent. While various compounds with antibacterial properties have been explored as active ingredients in such materials, many are prone to leaching, leading to undesirable risks to the environment and to human health. Herein, we develop and test a multilayered plastic film filled with silver nanoparticles, long known to be potent antibacterial agents, supported in a silica matrix. Cross-linked methacrylate layers on both sides of these nanostructures prevent leaching even after several uses, making the material essentially benign. Furthermore, we derive silica from rice husk, an abundant and affordable agricultural waste product. Our findings demonstrate that initial irradiation of the material with UVA light facilitates the photothermal migration of nanoparticles towards the material's surface, thereby significantly enhancing its antimicrobial properties. Remarkably, after just 5 min of visible light irradiation, the material exhibits over 99.999% inhibition of bacterial growth. This environmentally friendly plastic composite harnesses visible light to actively combat bacteria, providing an exciting proof-of-concept for future applications in antimicrobial coatings.
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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".