Optical near-field mapping of plasmonic nanostructures prepared by nanosphere lithography
Bibliographic record
Abstract
We introduce a simple, fast, efficient and non-destructive method to study the optical near-field properties of plasmonic nanotriangles prepared by nanosphere lithography. Using a rectangular Fourier filter on the blurred signal together with filtering of the lower spatial frequencies to remove the far-field contribution, the pure near-field contributions of the optical images were extracted. We performed measurements using two excitation wavelengths (532.1 nm and 632.8 nm) and two different polarizations. After the processing of the optical images, the distribution of hot spots can be correlated with the topography of the structures, as indicated by the presence of brighter spots at the apexes of the nanostructures. This technique is validated by comparison of the results to numerical simulations, where agreement is obtained, thereby confirming the near-field nature of the images. Our approach does not require any advanced equipment and we suggest that it could be applied to any type of sample, while keeping the measurement times reasonably short.
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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".