Nanoplasmonics in near-field optics and active coupling
Bibliographic record
Abstract
Surface plasmons enable the transmission of optical information in confined geometries, inaccessible for diffraction-limited far-field light, while having high signal bandwidth and propagation speed like conventional optics. These advantages have resulted in novel applications for surface plasmons, such as offset-apertured near-field scanning optical microscope (NSOM) probes. A subwavelength aperture couples surface plasmons that illuminate the tip apex of an adjacent metal-coated tip, which results in a single-lobed probing optical spot having a full-width half maximum (FWHM) similar to the apex diameter. Since the surface plasmons converge at the apex, an offset-apertured probe promises stronger localized electric fields than an apertured NSOM having comparable FWHM. Additionally, the subwavelength aperture does not permit the passage of far-field light, reducing the background signal in comparison to apertureless NSOM probes. For other applications, the ability to selectively switch a waveguide "on" or "off" is desired. Optical-optical switching for selective surface plasmon coupling would ideally permit high-speed switching on a small scale. Two nodes are presented as means to perform switching of four planar thin surface plasmon waveguides by interfering TEM10, TEM01, and TEM00 light beams normally-incident upon a node. One node uses a flat-apexed pyramidal reflector to reflect the incident light toward the waveguides' ends. An alternative node is a simple square aperture, which couples surface plasmons through light diffraction at the aperture's edges. Individually turned-off waveguides are shown to have their coupled power attenuated by at least -10 dB.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".