Investigating the Photothermal Disinfecting Properties of Light-Activated Silver Nanoparticles
Bibliographic record
Abstract
The medicinal applications of noble metal nanomaterials are of considerable interest due to the unique properties they possess. The following contribution will showcase one of these properties, the photothermal effect produced by silver nanoparticles (AgNPs), and show how AgNPs can serve as potential viricidal materials for PPE (i.e., personal protective equipment) disinfection. In the context of the COVID-19 global pandemic, PPE disinfection is critical for addressing the shortfalls in the supply chains that ensure rapid and reliable sources of PPE for both medical professionals and the public. Two AgNP composites were prepared using both photochemical and pulsed laser ablation in liquid techniques. Then, to improve the durability of the NPs’ coating, they were mixed in a commercially available tent protectant solution or with a chitosan suspension. Transmission electron microscopy and spectroscopic analyses of the AgNP/protectant composites present mostly monodisperse, spherical particles of ∼5–10 nm in diameter, dependent on the route of nanoparticle synthesis. Blue light-emitting diodes (LEDs) were utilized to activate the surface plasmon resonance effects of the AgNP-coated materials at ∼400 nm. The resultant photothermal activation produced elevated surface temperatures on the fabrics, up to 150 °C, approaching the ignition temperature of the fabric samples, and well above the threshold required to deactivate the SARS-CoV-2 virus responsible for the COVID-19 pandemic. Photothermal heating was found to be largely dependent on both proximity of the coated sample surface to the LED source and illumination intensity. Furthermore, the rate of temperature increase was determined to be considerably faster than traditional or photothermal heating studies conducted on aqueous nanoparticle colloids, illustrating the efficiency of this methodology. Finally, durability studies of the AgNP/protectant coatings were examined and were found to maintain photothermal activity over long durations of exposure and after a 3-month storage timeframe. A marginal decrease in heating can be noticed following laundering with traditional detergent cleaners, mainly during the first washing cycle. The coating resistance to washing could be improved by the cross linkage of a biodegradable polymer, chitosan.
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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".