Generating terahertz multiple vortex beams using graphene metasurfaces
Bibliographic record
Abstract
Abstract This paper investigates the generation of orbital angular momentum vortex beams using a graphene metasurface in the terahertz frequency band. The proposed design consists of 20 × 20 unit-cell elements to operate in 1.2 THz applications. Each element is a graphene ring patch printed on a silicon dioxide substrate backed with a polysilicon ground plane of size 75 × 75 × 25 µm 3 . The graphene reconfigurable surface conductivity is used to control the beam shape, direction, and directivity radiated from the metasurface, through the application of DC biasing voltages. A parametric study on the effect of graphene chemical potential, relaxation time and temperature on the unit-cell reflection properties is introduced. The reflection magnitude varies from − 2.1 dB to -0.8 dB with a 350-degree phase variation for µ c ranging from 0.25 eV to 1.6 eV at $$\tau$$ =5 ps, and T = 300 K. The effect of graphene relaxation time from 0.3 ps to 10 ps on the reflection coefficient at µ c = 0.7 eV, and T = 300 K is investigated. The metasurface radiation characteristics are investigated under the illumination of two types of incidence sources, plane-wave, and focused-waves. A depiction of a single vortex beam in various orientations θ = 0, 30 o , 50 o , and 70 o , φ = 90 o for l = 1 is presented. The purity of the OAM single beam shows that 94% of the power is concentrated in the designed mode. A graphene metasurface can to convert linearly polarized input into multiple beams exhibiting orthogonal modes. Two/four vortex beams in different directions are demonstrated. The capacity for wireless communication in the terahertz band can be enhanced by utilizing a graphene metasurface.
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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".