Multifunctional Transparent Electromagnetic Surface Based on Solar Cell for Backscattering Reduction
Bibliographic record
Abstract
Advanced electromagnetic (EM) surfaces, namely, metasurfaces, are designed for multifunctional purposes. A glass material with a chessboard configuration is exploited to realize nonabsorbing coating for backscattering reduction. Simultaneously, a solar cell is implemented above the metallic target for light energy harvesting using the transparency feature of the coat. Therefore, two separated aims are obtained in one structure using the plexiglass material and mono solar cell. Initially, the EM wave interactions with both materials are determined. Then, by the use of the wave interactions and scattering theory, two different surfaces are analytically investigated with the aim of transparent backscattering reduction cover. Next, an optimization algorithm is employed to achieve the minimum reflection from the adjacent surfaces. To validate the design performance, a prototype is manufactured. The proposed chessboard cover with plexiglass material is numerically and experimentally analyzed and compared together, having an acceptable agreement. The experimental results indicate a 75% bandwidth for backscattering reduction. In addition, the proposed structure can give promising opportunities to enhance the scattering properties with simultaneous power harvesting, which could generate critical advantages for real-world 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".