Dual-Mode Comb Plasmonic Optical Fiber Sensing
Bibliographic record
Abstract
Surface plasmon (SP) excitation in metal-coated tilted fiber Bragg gratings (TFBGs) has been a focal point for highly sensitive surface biosensing. Previous efforts focused on uniform metal layer deposition around the TFBG cross section and temperature self-compensation with the Bragg mode, requiring both careful control of the core-guided light polarization and interrogation over most of the C + L bands. To circumvent these two important practical limitations, we studied and developed an original platform based on partially coated TFBGs. The partial metal layer enables the generation of dual-comb resonances, encompassing highly sensitive (TM/EH mode families) and highly insensitive (TE/HE mode families) components in unpolarized transmission spectra. The interleaved comb of insensitive modes acts as wavelength and power references within the same spectral region as the SP-active modes. Despite reduced fabrication and measurement complexity, refractometric accuracy is not compromised through statistical averaging over seven individual resonances within a narrowband window of 10 nm. Consequently, measuring spectra over 60 nm is no longer needed to compensate for small temperature or power fluctuations. This sensing platform brings the following important practical assets: (1) a simpler fabrication process, (2) no need for polarization control, (3) limited bandwidth interrogation, and (4) maintained refractometric accuracy, which makes it a true game changer in the ever-growing plasmonic sensing domain.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".