Overview of Recent Advances in the Design of Plasmonic Fiber-Optic Biosensors
Bibliographic record
Abstract
Plasmonic fiber-optic biosensors combine the flexibility and compactness of optical fibers and high sensitivity of nanomaterials to their surrounding medium, to detect biological species such as cells, proteins, and DNA. Due to their small size, accuracy, low cost, and possibility of remote and distributed sensing, plasmonic fiber-optic biosensors are promising alternatives to traditional methods for biomolecule detection, and can result in significant advances in clinical diagnostics, drug discovery, food process control, disease, and environmental monitoring. In this review article, we overview the key plasmonic fiber-optic biosensing design concepts, including geometries based on conventional optical fibers like unclad, side-polished, tapered, and U-shaped fiber designs, and geometries based on specialty optical fibers, such as photonic crystal fibers and tilted fiber Bragg gratings. The review will be of benefit to both engineers in the field of optical fiber technology and scientists in the fields of biosensing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".