Electron Energy-Loss Spectroscopy of Multipolar Edge and Cavity Modes in Silver Nanosquares
Bibliographic record
Abstract
The characterization of surface plasmon resonances supported by metallic nanostructures requires high spatial and energy resolution. In the past few years, electron energy loss spectroscopy (EELS) has emerged as a very powerful tool to accomplish this task. In this work, we demonstrate the power of this technique for probing and imaging resonances of metallic nanostructures by analyzing the plasmonic response of silver nanosquares of sizes ranging from 230 nm up to 1 μm. Because of the relatively large size of these structures, we find that, despite their simple geometry, these systems can support a large variety of multipolar modes, which can only be detected and imaged thanks to the high spatial and energy resolution achieved by pushing EELS to its limits. The experimental results are supported by rigorous theoretical calculations that allow a detailed interpretation of the EELS measurements. In particular, we were able to map, with high level of detail, edge and high-order cavity modes. Furthermore, by calculating the scattering cross-section of these nanostructures, we confirm that most of the observed modes are dark and thus remain hidden in optical measurements, thus demonstrating the power of EELS as a unique tool for probing and imaging a large range and variety of plasmonic resonances of metallic nanostructures.
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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".