Hyperbolic metamaterial structures based on graphene for THz super-resolution imaging applications
Bibliographic record
Abstract
The diffraction limit of light due to the loss of evanescent waves that carry high spatial frequency information in the far field restricts the practical applications of terahertz imaging technology. In order to break the diffraction limit, we investigate the super-resolution capability of different types of multilayered graphene-dielectric hyperbolic metamaterials. A super-resolution of λ/10 is achieved for both the cylindrical and planar structures. A prominent advantage of graphene-dielectric hyperbolic structures is the dynamic tunability of the dispersion and super-resolution performance by adjusting the chemical potential of graphene through conveniently changing the gate voltage without modifying the geometry of the hyperbolic structures. Furthermore, we have investigated the influence of bilayer thickness variations on the super-resolution performance. Finally, we apply the planar hyperbolic structures for the super-resolution imaging and a roughly five-fold lateral resolution enhancement is realized in our approach. Due to many prominent advantages including super-resolution over broad spectral range, dynamic tunability, good stability and robustness, we believe this work could contribute to the improvement of the resolutions of terahertz imaging systems and the development of hyperbolic metamaterial modulation devices in the terahertz band.
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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".