Plasmon-drag-assisted terahertz generation in a graphene layer incorporating an asymmetric plasmon nanostructure
Bibliographic record
Abstract
This Rapid Communication presents a structure and full theoretical analysis to exploit the photon drag effect for THz signal generation in a graphene layer integrated with a plasmonic structure. The plasmonic structure is composed of a periodic array of asymmetric nanoparticles patterned over a graphene layer. The nanoparticles are designed to accomplish two goals: field localization due to the plasmonic resonance and manipulating the phase of the near field to effectively drag the quasiparticles in graphene. Combining the asymmetry with the plasmon resonances of nanoparticles, we show that an enhancement as large as three orders of magnitude is attainable in the power of the generated THz wave. This level of unprecedented enhancement mostly stems from the phase manipulation of the near field caused by asymmetric nanoparticles. Using the achieved enhancement, it is demonstrated that an ultra-wideband THz signal carrying the power of $1\phantom{\rule{0.28em}{0ex}}\ensuremath{\mu}\mathrm{W}$ can be generated using a commercially available femtosecond pulsed laser.
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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".