Surface-Enhanced Spectra on D-Gluconic Acid Coated Silver Nanoparticles
Bibliographic record
Abstract
Coated silver (Ag) colloids synthesized with D-glucose permit the observation of surface-enhanced fluorescence (SEF) and surface-enhanced resonance Raman scattering (SERRS) of the rhodamine B (RhB) molecule. The organic coating formed during the synthesis of the Ag nanostructures was identified by its surface-enhanced Raman scattering (SERS) spectrum as D-gluconic acid. The RhB molecule is used to exemplify the distance dependence of SEF and SERRS on the coated Ag nanostructures. The fluorescence enhancement factor for RhB on D-gluconic acid coated silver nanoparticles was determined experimentally and estimated using a simple model. Further support for the plasmon enhancement is obtained from the fact that the measured fluorescence lifetime of RhB on the silver coated with D-gluconic acid is shorter than that found on a glass surface. A very modest enhancement factor is obtained, as expected for very short distance between RhB and the metal surface. Given the very thin metal-fluorophore separation, estimated from the size of the D-gluconic acid, the energy transfer or fluorescence quenching is still efficient and the SEF enhancement is just overcoming the energy transfer. Therefore, both SEF and SERRS are observed. Notably, the aggregation of coated nanoparticles also increases the enhancement factor for SEF.
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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) | 0.002 | 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".