Plasmonic Coupling of Multipolar Edge Modes and the Formation of Gap Modes
Bibliographic record
Abstract
The coupling of plasmonic resonances is an effective tool to tailor the optical properties of nanostructures. However, the coupling of higher order plasmonic resonances has not received much attention, with most studies focusing on the interaction of dipolar modes. Taking advantage of the high spatial and energy resolution of modern scanning transmission electron microscopes equipped with electron energy loss spectroscopy, we analyze the coupling of edge modes in planar nanostructures with emphasis on the interaction of high order modes and the formation of gap modes. We show that coupling of edge modes can be understood by a simple and intuitive scheme, with three regimes: First, a strong coupling through the edge of the structure resulting in bonding and antibonding gap edge modes; second, coupling through the corners of the structures resulting in bonding and antibonding corner edge modes; and a third behavior where the edge modes do not couple and behave independently of the rest of the structure. The formation of gap modes through the coupling of edge modes is analyzed and compared to the modes found in planar slot waveguides, finding that the properties of the symmetric and asymmetric modes on slot waveguides are equivalent to the antibonding and bonding gap edge modes, respectively. Our experimental and numerical analysis of the plasmon resonances in nanosquares and waveguides shows that our scheme of plasmonic coupling of edge modes can be generalized to other planar structures with straight edges and might inspire the design of more complex planar plasmonic devices based on the coupling of edge modes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".