220–325 GHz all-photopolymer Bragg horn antennas towards eco-friendly terahertz applications
Bibliographic record
Abstract
Abstract This paper presents the development of the world’s first high-gain, all-photopolymer Bragg horn antennas explicitly designed for the WR-3.4 band (220–325 GHz), marking a groundbreaking advancement in terahertz (THz) antenna technology. Unlike conventional metallic horn antennas, which suffer from conductor losses and manufacturing complexity, this innovative design utilizes eco-friendly photopolymer materials and additive manufacturing, achieving a fractional bandwidth of 38.5% that fully covers the WR-3.4 band. The proposed antenna achieves a measured peak gain of 28.98 dBi at 300 GHz, with a return loss better than − 20dB across the band and a consistent half-power beamwidth (HPBW) of ~ 5°, ensuring precise directivity and minimal sidelobe interference. By employing a novel horn-type adapter for seamless mode conversion from TE 10 to the fundamental HE 11 mode, the design significantly enhances coupling efficiency and reduces signal loss. Additionally, fabrication costs can be reduced by over 50% compared to traditional metallic designs, while maintaining repeatability and enabling rapid prototyping. As the first demonstration of photopolymer-based antennas achieving such high gains in the 220–325 GHz THz spectrum, this work establishes a new benchmark in THz antenna technology, providing an eco-friendly, cost-effective, and high-performance solution for high-speed communication, medical diagnostics, security imaging, and spectroscopy 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) | 0.001 | 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".