Bibliographic record
Abstract
The emergence of nanotechnology now enables the controlled fabrication of nanometer scale structures capable of steering and confining light waves over small distances. To realize complex nanoscale light guiding structures, it will be necessary to develop methods to guide light around tight bends and corners with high efficiency. Achieving high efficiency waveguide bends, however, is generally difficult because of radiation losses at the bend. To achieve tight waveguide bends several approaches have been put forward including the use of dielectric photonic crystals and resonators. One recent and promising method to limit the amount of loss over a bend is to restrict the path of light by encasing a bend in an opaque medium, such as metal. Such a bend can be conceptualized by joining the two metal-dielectric- metal (MDM) waveguides such that their dielectric cores are connected to each other at 90° as shown in Fig. 1. When the thickness of the dielectric cores is subwavelength, only the lowest order surface plasmon polariton (SPP) mode is sustained by the bend. Further, when the metal walls are constructed from a low-loss metal such as Ag, the SPP mode can propagate over the bend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".