Microstructured and Photonic Bandgap Fibers for Applications in the Resonant Bio‐ and Chemical Sensors
Bibliographic record
Abstract
We review application of microstructured and photonic bandgap fibers for designing resonant optical sensors of changes in the value of analyte refractive index. This research subject has recently invoked much attention due to development of novel fiber types, as well as due to development of techniques for the activation of fiber microstructure with functional materials. Particularly, we consider two sensors types. The first sensor type employs hollow core photonic bandgap fibers where core guided mode is confined in the analyte filled core through resonant effect in the surrounding periodic reflector. The second sensor type employs metalized microstructured or photonic bandgap waveguides and fibers, where core guided mode is phase matched with a plasmon propagating at the fiber/analyte interface. In resonant sensors one typically employs fibers with strongly nonuniform spectral transmission characteristics that are sensitive to changes in the real part of the analyte refractive index. Moreover, if narrow absorption lines are present in the analyte transmission spectrum, due to Kramers‐Kronig relation this will also result in strong variation in the real part of the refractive index in the vicinity of an absorption line. Therefore, resonant sensors allow detection of minute changes both in the real part of the analyte refractive index (10−6–10−4 RIU), as well as in the imaginary part of the analyte refractive index in the vicinity of absorption lines. In the following we detail various resonant sensor implementations, modes of operation, as well as analysis of sensitivities for some of the common transduction mechanisms for bio‐ and chemical sensing applications. Sensor designs considered in this review span spectral operation regions from the visible to terahertz.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".