Multi‐Functional Metasurface: Visibly and RF Transparent, NIR Control and Low Thermal Emissivity
Bibliographic record
Abstract
Abstract Rapid advances in metamaterial technology are enabling the engineering of wave–matter interactions heretofore not realized and functionalities with potentially far‐reaching implications for major challenges in the fields of energy conservation and radio frequency (RF) communication. A visibly and RF transparent composite metasurface utilizing dielectric–metal spectrally selective coatings with high near‐infrared control and low thermal emissivity is proposed, thus achieving a multi‐functional metasurface capable of enhancing fifth‐generation (5G) communication efficiency and exhibiting energy conservation features. The proposed meta‐glass yields 92% peak RF transmission at 30 GHz which corresponds to 20% and 90% enhancement when compared to plain glass and low‐emissive glass substrates. This meta‐glass possesses 86% peak optical transparency at λ = 550 nm, >60% near‐infrared reflection, and >80% mid‐infrared reflection which corresponds to ≈0.2 thermal emissivity. The proposed metasurface design is highly flexible and can be tuned to operate over different frequency ranges owing to its frequency scalability. This study provides a better alternative using earth‐abundant materials compared to low‐emissive glass based on indium tin oxide while boosting the efficiency of 5G communication amenable to window systems demanding simultaneous functionalities for emergent smart/energy‐efficient buildings/cities and autonomous transportation 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".