Modulation of population density and size of silver nanoparticles embedded in bacterial cellulose via ammonia exposure: visual detection of volatile compounds in a piece of plasmonic nanopaper
Bibliographic record
Abstract
The localized surface plasmon resonance exhibited by noble metal nanoparticles can be sensitively tuned by varying their size and interparticle distances. We report that corrosive vapour (ammonia) exposure dramatically reduces the population density of silver nanoparticles (AgNPs) embedded within bacterial cellulose, leading to a larger distance between the remaining nanoparticles and a decrease in the UV-Vis absorbance associated with the AgNP plasmonic properties. We also found that the size distribution of AgNPs embedded in bacterial cellulose undergoes a reduction in the presence of volatile compounds released during food spoilage, modulating the studied nanoplasmonic properties. In fact, such a plasmonic nanopaper exhibits a change in colour from amber to light amber upon the explored corrosive vapour exposure and from amber to a grey or taupe colour upon fish or meat spoilage exposure. These phenomena are proposed as a simple visual detection of volatile compounds in a flexible, transparent, permeable and stable single-use nanoplasmonic membrane, which opens the way to innovative approaches and capabilities in gas sensing and smart packaging.
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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) | 0.000 | 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".