Enhancing Evanescent Wave Coupling of Near-Surface Waveguides with Plasmonic Nanoparticles
Bibliographic record
Abstract
Evanescent field excitation is a powerful means to achieve a high surface-to-bulk signal ratio for bioimaging and sensing applications. However, standard evanescent wave techniques such as TIRF and SNOM require complex microscopy setups. Additionally, the precise positioning of the source relative to the analytes of interest is required, as the evanescent wave is critically distance-dependent. In this work, we present a detailed investigation of evanescent field excitation of near-surface waveguides written using femtosecond laser in glass. We studied the waveguide-to-surface distance and refractive index change to attain a high coupling efficiency between evanescent waves and organic fluorophores. First, our study demonstrated a reduction in sensing efficiency for waveguides written at their minimum distance to the surface without ablation as the refractive index contrast of the waveguide increased. While this result was anticipated, it had not been previously demonstrated in the literature. Moreover, we found that fluorescence excitation by waveguides can be enhanced using plasmonic silver nanoparticles. The nanoparticles were also organized in linear assemblies, perpendicular to the waveguide, with a wrinkled PDMS stamp technique, which resulted in an excitation enhancement of over 20 times compared to the setup without nanoparticles.
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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".