Enhanced Fluorescence from Arrays of Nanoholes in a Gold Film
Bibliographic record
Abstract
Arrays of sub-wavelength holes (nanoholes) in gold films were used as a substrate for enhanced fluorescence spectroscopy. Seven arrays of nanoholes with distinct periodicities (distances between the holes) were fabricated. The arrays were then spin-coated with polystyrene films containing different concentrations of the fluorescent dye oxazine 720. The dye was excited via resonant extraordinary transmission of the laser source through the nanoholes. Enhanced fluorescence was observed when the geometric characteristics of the arrays allowed for an enhancement in the transmitted excitation. This enhancement occurred via surface plasmon excitation by the laser and a consequential increase in the local electromagnetic field in a sub-wavelength region at the metal-film interface. It was demonstrated that the sensitivity of the fluorescence measurement (change in signal vs change in dye concentration in the polymer film) is significantly larger at the surface plasmon resonance conditions than that obtained from equivalent films on glass substrates. Enhancement factors for the fluorescence emission were calculated for each array, with a maximum enhancement of close to 2 orders of magnitude as compared to the emission of films on glass. The results presented here indicate that arrays of nanoholes are interesting substrates for the development of fluorescence sensors based on surface plasmon resonance, as they provide a platform that allows both spatial confinement and enhancement of excitation light. Moreover, the collinear characteristics of the present optical setup, due to the resonant extraordinary transmission through the nanohole arrays, are more conducive to miniaturization and chip integration than more traditional experimental geometries.
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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".