Design and development of a graphene-based reconfigurable patch antenna array for THz applications
Bibliographic record
Abstract
Abstract This paper presents a graphene-based antenna array for terahertz (THz) applications. The suggested antenna array has four radiating square shaped patches fed by a coplanar waveguide (CPW) technique. The proposed antenna array operates at the three frequencies with operational bandwidths of 1.173–1.210 THz (at 1.19 THz), 1.270–1.320 THz (at 1.3 GHz), and 1.368–1.346 THz (at 1.4 GHz). The total area of the antenna array is reported as 1000 × 1000 µm 2 , printed on a Silicon substrate with a thickness of 20 µm and a dielectric constant of ϵ r = 11.9. To enhance the structure’s performance and optimize the feeding network, a parametric analysis was performed using the FDTD technique. Furthermore, the key properties of the proposed antenna array, such as resonance frequency, peak gain, and radiation efficiency, may be changed by adjusting the chemical potentials of the graphene in the antenna array. The use of graphene’s chemical potential tuneability yields exceptional results comparing to the recent research outputs, with a peak gain and radiation efficiency of 10.45 dB and 70%, respectively. These results show the performance of the suggested design for constructing antenna arrays for use in THz applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".