Bibliographic record
Abstract
Surface plasmon photodetectors are of vigorous current interest. Such detectors typically combine a metallic structure that supports surface plasmons with a photodetection structure based on internal photoemission or electron‐hole pair creation. Detector architectures are highly varied, involving surface plasmons on planar metal waveguides, on metal gratings, on nano‐particles, ‐islands, or ‐antennas, or involving plasmon‐mediated transmission through one or many sub‐wavelength holes in a metal film. Properties inherent to surface plasmons, such as sub‐wavelength confinement and their ability to resonate on tiny metallic structures, are exploited to convey useful characteristics to detectors in addressing applications such as low‐noise high‐speed detection, single‐plasmon detection, near‐ and mid‐infrared imaging, photovoltaic solar energy conversion, and (bio)chemical sensing. The operating principles behind surface plasmon detectors are reviewed, the literature on the topic is surveyed, and avenues that appear promising are highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".