Experimental Advancements in Graphene-Based Antenna Design and Performance Analysis
Bibliographic record
Abstract
This research focuses on the development of terahertz (THz) graphene holographic impedance surface antennas that demonstrate significant advancements. Unlike traditional impedance surface antennas, which manipulate the surface impedance by altering the patch size within each unit cell, the proposed antennas in this study achieve control over the surface impedance through the application of a tunable DC bias to individual graphene patch cells. The underlying principle depends on the certainty that the conductivity of graphene can be modified by adjusting the applied voltage, enabling precise control over the impedance of the surface on an antenna. The proposed antenna design features graphene patches with uniform size and equal spacing, simplifying the modeling process and enhancing efficiency. Additionally, the antenna allows for beam scanning by simply adjusting the DC supply, enabling the desired beam direction in terahertz (THz) applications. Moreover, leveraging the exceptional mechanical properties of graphene, the THz graphene holographic antenna can be designed to conform to various platforms. It can successfully achieve beam steering capabilities and radiation efficiency. To validate these concepts, the research team designed and simulated three antenna variations: a linearly polarized antenna with beam scanning capability, a circularized antenna, and a conformal antenna. The simulation outputs obtained using the HFSS full-wave commercial software demonstrated a perfect agreement with the design theories show that the antennas achieve beam scanning over ±30 with minimal variation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".