Investigation of Intra-Cavity SPR Sensor Based on Erbium-Doped Fiber Laser
Bibliographic record
Abstract
In this paper, an intra-cavity SPR sensor based on erbium-doped fiber (EDF) laser is proposed to achieve higher sensitivity for detecting analyte refractive indices. A liquid-core fiber-based SPR sensor is first designed to match the operating wavelength of 1530 nm in the fiber laser intra-cavity, and then placed into the cavity of the EDF laser to constitute the all-fiber intra-cavity SPR sensor. We theoretical investigate the influence of pump power, cavity loss and EDF length on the sensitivity of the proposed intra-cavity SPR sensor. The results indicate that the sensitivity of the intra-cavity SPR sensor can reach 1.051 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> RIU<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> (refractive index unit), which is enhanced 4 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> times compared with the sensitivity of 253 RIU<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> obtained by the SPR sensor without fiber laser cavity. In addition, the sensitivity of the proposed intra-cavity SPR sensor can be flexibly tuned by changing pump power, cavity loss or EDF length. This work demonstrates that as an alternative approach, the combination of the fiber laser intra-cavity spectroscopy and the fiber-based SPR sensor can achieve a remarkable enhancement in sensitivity.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".