Photonic Crystal Fiber and Waveguide-Based Surface Plasmon Resonance Sensors for Application in the Visible and Near-IR
Bibliographic record
Abstract
In the proposed photonic crystal waveguide-based surface plasmon resonance (SPR) sensor, a plasmon wave on the surface of a thin metal film is excited by a Gaussian-like leaky mode of an effectively single-mode photonic crystal waveguide. By judicious design of a photonic crystal waveguide, the effective refractive index of a core mode can be made considerably smaller than that of the core material, thus enabling efficient phase-matching with a plasmon, high sensitivity, and high coupling efficiency from an external Gaussian source, at any wavelength of choice from the visible to near infrared (IR), which is, to our knowledge, not achievable by any other design. Moreover, unlike the case of total internal reflection (TIR) waveguide-based sensors, a wide variety of material combinations can be used to design photonic crystal waveguide-based sensors as there is no limitation on the value of the waveguide core refractive index. This sensor design concept was implemented using planar multilayer photonic crystal waveguides, solid and hollow core Bragg fibers, as well as microstructured photonic crystal fibers. Amplitude and spectral-based methodologies for the detection of changes in the analyte refractive index were devised. Sensor resolution as low as 8.3·10−6 refractive-index unit (RIU) was found for aqueous analyte.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".