Electron energy-loss spectroscopy of surface plasmon activity in wrinkled gold structures
Bibliographic record
Abstract
The surface plasmon response of a cross-sectional segment of a wrinkled gold film is studied using electron energy loss spectroscopy (EELS). EELS data demonstrate that wrinkled gold structures act as a suitable substrate for surface plasmons to propagate. The intense surface variations in these structures facilitate the resonance of a wide range of surface plasmons, leading to the broadband surface plasmon response of these geometries from the near-infrared to visible wavelengths. The metallic nanoparticle boundary element method toolbox is used to simulate plasmon eigenmodes in these structures. Eigenmode simulations show how the diverse morphology of the wrinkled structure leads to its high spectral complexity. Micron-sized structural features that do not provide interactions between segments of the wrinkle have only a small effect on the surface plasmon resonance response, whereas nanofeatures strongly affect the resonant modes of the geometry. According to eigenmode calculations, different eigenenergy shifts around the sharp folds contribute to the broadband response and infrared activity of these structures; these geometrical features also support higher energy (shorter wavelength) symmetric and anti-symmetric plasmon coupling across the two sides of the folds. It is also shown that additional plasmon eigenstates are introduced from hybridization of modes across nanogaps between structural features in close proximity to each other. All of these factors contribute to the broadband response of the wrinkled gold structures.
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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".