Strategies to enhance THz harmonic generation combining multilayered, gated, and metamaterial-based architectures
Bibliographic record
Abstract
Graphene has unique properties paving the way for groundbreaking future applications. Its large optical nonlinearity and ease of integration in devices notably makes it an ideal candidate to become a key component for all-optical switching and frequency conversion applications. In the terahertz (THz) region, various approaches have been independently demonstrated to optimize the nonlinear effects in graphene, addressing a critical limitation arising from the atomically thin interaction length. Here, we demonstrate sample architectures that combine strategies to enhance THz nonlinearities in graphene-based structures. We achieve this by increasing the interaction length through a multilayered design, controlling carrier density with an electrical gate, and modulating the THz field spatial distribution with a metallic metasurface substrate. Our study specifically investigates third harmonic generation (THG) using a table-top high-field THz source. We measure THG enhancement factors exceeding thirty and propose architectures capable of achieving a two-order-of-magnitude increase. These findings underscore the potential of engineered graphene-based structures in advancing THz frequency conversion technologies for signal processing and wireless communication applications.
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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".