A comprehensive study of plasmonic mode hybridization in gold nanoparticle‐over‐mirror (NPoM) arrays
Bibliographic record
Abstract
Abstract Hybrid plasmonic systems that combine localized and propagative surface plasmons offer new opportunities for tunable light–matter interactions at the nanoscale. This paper provides the most comprehensive study to date of hybridization between gap localized surface plasmons (gap LSP) and diffraction‐mediated propagative surface plasmon polaritons (SPP) in arrays of gold nanodisks over a mirror, part of the larger class of nanoparticle‐over‐mirror (NPoM) devices. By systematically mapping the hybrid mode dispersion as a function of array geometry over a large parameter space, we extract the coupling strength via a coupled oscillator model and reveal its dependence on key structural parameters, with gap thickness identified as the primary tuning factor. The resulting hybrid modes enhance the optical quality factor by nearly fivefold compared to classical LSP while maintaining strong near‐field confinement, combining the advantages of their constituent modes. Dephasing times were measured with interferometric time‐resolved photoemission electron microscopy (ITR‐PEEM). Using a scalable lithography‐compatible NPoM architecture that minimizes the optical index mismatch between the dielectric between the nanodisks and the gap material (Al 2 O 3 ), we achieved the highest coupling strength (123 meV) and dephasing time range (23–50 fs) to date in NPoM arrays.
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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".