Surface-Enhanced Fluorescence: Mapping Individual Hot Spots in Silica-Protected 2D Gold Nanotriangle Arrays
Bibliographic record
Abstract
Localized surface plasmon resonance (LSPR) is of particular interest to enhance the limit of detection for spectroscopic techniques such as Raman and fluorescence via a surface enhancement from metallic nanostructures and it is a key for single-molecule detection. In this study, using nanosphere lithography (NSL), a series of gold nanostructures over glass surfaces are prepared and modified to detect the position and the density of the individual confined hot spots. Once protected with an ultrathin layer of SiO 2, the gold nanostructures are investigated using scanning confocal fluorescence microscopy to detect the fluorescence from a dye solution deposited over the SiO 2 -protected gold platforms. The fluorescence originates from an assembly of confined and homogeneously distributed hot spots. This clearly demonstrates that the dimensions of particles, the interparticle distance, and the thickness of the SiO 2 protection layer are critical parameters to obtain the optimum electromagnetic enhancement. Herein, we show that the NSL-fabricated nanotriangle arrays made with particle sizes with dimensions closer to the excitation wavelength display the most intense hot spots for each bow tie assembly oriented along the polarization direction of the impinging light.
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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".