Broadband Metamaterial Absorbers
Bibliographic record
Abstract
Abstract The recent rise of metamaterials opens new opportunities for absorbers due to their designed electrodynamic properties and effects, allowing the creation of materials with effective values of permittivity and permeability that are not available in naturally occurring materials. Since their first experimental demonstration in 2008, recent literature has offered great advances in metamaterial perfect absorbers (MMPAs) operating at frequencies from radio to optical. Broadband absorbers are indispensable in thermophotovoltaics, photodetection, bolometry, and manipulation of mechanical resonances. Although it is easy to obtain MMPAs with single band or multiband, achieving broadband MMPA (BMMPA) remains a challenge due to the intrinsically narrow bandwidth of surface plasmon polaritons, localized surface plasmon resonances generated on metallic surfaces at nanoscale or high Q ‐factor in GHz region. To guide future development of BMMPA, recent progress is reviewed here: the methods to create broadband absorption and their potential applications. The four mainstream methods to achieve BMMPAs are introduced, including planar and vertical element arrangements, their welding with lumped elements and the use of plasmonic nanocomposites, accompanied by the description of other, less common approaches. Following this, applications of BMMPA in solar photovoltaics, photodetection, bolometry, and manipulation of mechanical resonances are reviewed. Finally, challenges and prospects are discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".