Quasinormal mode theory and modelling of electron energy loss spectroscopy for plasmonic nanostructures
Bibliographic record
Abstract
Understanding light–matter interactions using localized surface plasmons (LSPs) is of fundamental interest in classical and quantum plasmonics and has a wide range of applications. In order to understand the spatial properties of LSPs, electron energy loss spectroscopy (EELS) is a common and powerful method of spatially resolving the extreme localized fields that can be obtained with metal resonators. However, modelling EELS for general shaped resonators presents a major challenge in computational electrodynamics, requiring the full photon Green function as a function of two space points and frequency. Here we present an intuitive and computationally simple method for computing EELS maps of plasmonic resonators using a quasinormal mode (QNM) expansion technique. By separating the contribution of the QNM and the bulk material, we give closed-form analytical formulas for the plasmonic QNM contribution to the EELS maps. We exemplify our technique for a split ring resonator, a gold nanorod, and a nanorod dimer structure. The method is accurate, intuitive, and gives orders of magnitude improvements over direct dipole simulations that numerically solve the full 3D Maxwell equations. We also show how the same QNM Green function can be used to obtain the Purcell factor (and projected local density of optical states) from quantum dipole emitters or two level atoms, and we demonstrate how the spectral features differ in general to the EELS spectrum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".