Overview of Recent Advances in the Design of Plasmonic Fiber-Optic Biosensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it