Origin of third harmonic generation in plasmonic nanoantennas
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Bibliographic record
Abstract
Plasmonic nanoantennas have been recently proposed to boost nonlinear optical processes. In a metal dipole nanoantenna with a dielectric nanoparticle placed in the gap, the linear field enhancement can be exploited to enhance third harmonic emission. Since both metals and dielectrics exhibit nonlinearity, the nonlinear far-field contains contributions from each, and the impossibility of measuring these contributions separately has led to seemingly contradictory interpretations about the origin of the nonlinear emission. We determine that the origin of the third harmonic from metal-dielectric dipole nanoantennas depends on nanoantenna design, and in particular, the width. We find that the emission from gold dominates in thin threadlike nanoantennas, whereas the emission from the gap material dominates in wider nanoantennas. We also find that monopole nanoantennas perform better than dipoles having the same width, and due to their simplicity should be preferred in many applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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.
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