Metal-enhanced fluorescence and FRET on nanohole arrays excited at angled incidence
Bibliographic record
Abstract
The influence of experimental parameters on the performance of plasmonic sensors is of great importance in analytical sciences. The plasmon coupling conditions (angle of incidence, metal composition, laser frequency and excitation/emission properties of fluorophores) were thus investigated for surface plasmon-enhanced fluorescence on metallic nanohole arrays. Optimal fluorescence enhancements were achieved when the plasmon resonance, the excitation laser and the fluorophore's excitation wavelengths were matched. The enhancement of the acceptor emission of a rhodamine 6G(Rh6G)-Quasar670™ FRET pair was achieved on the nanohole arrays by tuning the plasmon wavelength with the maximal overlap of the donor's emission and acceptor excitation. Silver nanohole arrays achieved larger fluorescence enhancement than gold nanohole arrays at 532 nm, while gold nanohole arrays led to larger fluorescence enhancement at 635 nm. These results demonstrate the importance of tuning the plasmon coupling conditions for surface plasmon-enhanced fluorescence sensing.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".