Optically and radio frequency (RF) transparent meta‐glass
Bibliographic record
Abstract
Abstract We propose a radio frequency (RF) and visibly transparent composite metasurface design comprising newly developed transparent multilayer conductive coatings. Detailed experimental and theoretical analysis of the RF/visible transparency of the proposed meta‐glass is provided. The proposed nature‐inspired symmetrical honeycomb‐shaped meta‐glass design, alters the electromagnetic properties of the glass substrate in the RF spectrum by utilizing visibly transparent Ag‐based conductive coatings on each side. Furthermore, the competing effect of the Ag thickness on optical and RF transparency is discussed. We show that using multilayer dielectric‐metal coatings, specifically 5‐layered spectrally selective coatings, RF transparency of the meta‐glass can be enhanced while preserving visible transparency. Herein we demonstrate high transparency meta‐glass with 83% and 78% peak RF and optical transmission at 28 GHz and 550 nm, respectively. The meta‐glass yields enhanced RF transmission by 80% and 10% when compared to low‐emissivity glass and bare glass, respectively. The meta‐glass design presented here is amenable to a variety of 5G applications including automobile radar systems. This work provides a superior alternative to the standard indium‐tin‐oxide (ITO) transparent material which is becoming scarce. Moreover, this study paves the way for the design of new visibly transparent metamaterials and artificial dielectrics.
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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".