Spoof plasmons in real metals with an arbitrary shape of perforation
Bibliographic record
Abstract
Spoof plasmons are bound electromagnetic waves (EM) at frequencies outside the plasmonic range mimicking (“spoofing”) surface plasmons (SPs), which propagate on periodically corrugated metal surfaces. In recent years, electromagnetic waves propagating at an interface between a metal and dielectric have been of significant interest. Although most plasmonic research so far has focused on the near-infrared and optical ranges of the electromagnetic spectrum (where noble metals support highly confined surface waves), there exists an increasing interest in transferring SPs-based photonics to lower frequencies. However, in these spectral ranges, noble metals behave like perfect electric conductors, whose surface charges are able to screen any external EM excitation with extreme efficiency, preventing the formation of a tightly bound SP. It has been shown that the binding of EM fields to a metal surface can be increased by its corrugation. A surface of a metal perforated with a one-dimensional periodic array of rectangular grooves has already been considered. The question that remains open is the calculation of the effective permittivities for arbitrary grooves. The number of works describing the calculation of the effective dielectric constants for the grooved surfaces is limited. Here we have obtained an analytical dispersion relation of spoof plasmons on an arbitrary perforated surface of a real metal. We have derived analytical expressions for calculation of the permittivities of arbitrary grooves. Based on those results we have determined the minimum spot size for a triangular groove structure.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".