Binding Angle Robustness of Plasmonic Nanorod Dimer Resonances
Bibliographic record
Abstract
Abstract Narrow gaps between coupled plasmonic nano‐particles show strong optical field enhancements and spectrally adjustable resonance positions, making them attractive for surface enhanced spectroscopies. Gold nanorod dimers formed from nanorod solutions with narrow size distributions are intensely investigated in this context. However, the binding angle of rods coupled at their end faces is usually not controllable. Surprisingly, it is observed that this has only little effect on field enhancement and resonance energies. In this work, gold nanorod dimers are investigated by mapping their plasmon resonances using electron energy‐loss spectroscopy in a scanning transmission electron microscope. For a wide range of dimer orientations, a negligible influence of the angle between the two rods on the bonding and antibonding longitudinal dipole resonances is confirmed, in good agreement with numerical simulations. The results are interpreted via the predominant end‐coupling of the individual nanorod's plasmonic modes, as illustrated by an analytical charge coupling model. In addition, the simulations emphasize that conclusions from experimental data on the gap morphology on the size range of one nanometer can be ambiguous. In any case, the full understanding of the angle‐invariant resonances of nano‐rod dimers can further promote their controlled application in surface enhanced spectroscopy or ‐sensing.
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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 | 0.000 |
| 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".