The Low-Attenuation Endfire Leaky-Wave State on an Optically Transparent Lossy Film
Bibliographic record
Abstract
The development of high-performance optically transparent radio frequency (RF) radiators is limited by the intrinsic loss issue of transparent conductive films (TCFs). Instead of pursuing expensive endeavors to improve the TCFs’ electrical properties, this study introduces an innovative approach that leverages leaky-wave mode manipulation to mitigate the TCFs’ attenuating effect and maximize the RF radiation. Our finding reveals that the precise control of the mode confinement on glass-coated TCFs can create a low-attenuation window for leaky-wave propagation, where the total attenuation caused by TCF dissipation and wave leakage is effectively reduced. The observed low-attenuation leaky-wave state on lossy TCFs originates from the delicate balance between wave leakage and TCF dissipation, attained at a particular glass cladding thickness. By leveraging the substantially extended radiation aperture achieved under suppressed wave attenuation, this study develops an optically transparent antenna with an enhanced endfire realized gain exceeding 15 dBi and a radiation efficiency of 66%, which is validated to offer competitive transmission performance for advancing ubiquitous wireless communication and sensing 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 | 0.000 |
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".