Probing the Plasmonic Properties of Heterometallic Nanoprisms with Near-Field Fluorescence Microscopy
Bibliographic record
Abstract
In an effort to optimize both the optical properties and chemical stability of plasmonic platforms for surface-enhanced fluorescence spectroscopy, a series of multilayered heterometallic structures were prepared. Gold–silver multilayered array of prisms were designed, where the gold layers acts as protective layers for the silver and extends the spectral range of the resulting localized surface plasmon resonances. Herein, we report an experimental study of the enhanced near-field fluorescence from quantum dots deposited at the surface of these platforms. This was performed using near-field scanning optical microscopy yielding simultaneous measurements of topographical features and near-field fluorescence of the prismatic arrays. Hence, the individual localized fluorescent hot-spots could be imaged and spatially correlated with the geometry of the nanostructures for distinct polarization configuration of the excitation light with respect to the orientation of the structures. Furthermore, the fluorescence enhancement was evaluated spatially and temporally for the series of the nanoprism platforms. In particular, the quenching of the near-field fluorescence due to the metal structures was carefully evaluated for the metal–fluorophore hybrid systems.
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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".