Highly sensitive surface plasmon resonance temperature sensor based on a hollow core fiber multilayer structure
Bibliographic record
Abstract
A surface plasmon resonance (SPR) temperature sensor based on a hollow core fiber (HCF) is designed in this paper. The sensor is composed of a multi-mode fiber (MMF)-HCF-MMF structure, and the self-made HCF is deposited successively with a thin layer of Au film (50 nm in thickness), gold nanoparticles (10 nm in diameter) and polydimethylsiloxane (PDMS). A series of theoretical and experimental investiagtions are conducted, and the results are as follows: the proposed sensing structure only with Au film can effectively excite the SPR effect, with a sensitivity of (2200 ± 100) nm / RIU in the refractive index (RI) range of 1.3334-1.3811; after adding AuNPS, the sensitivity of the sensor is effectively improved, the sensitivity can be increased to (3100 ± 100) nm / RIU, and after the PDMS coating, temperature sensing can be realized due to its unique temperature-sensitive characteristics, a linear sensitivity of (-2.1 ± 0.1) nm / °C is realized in the temperature range of 25 °C to 100 °C. The sensor has the advantages of simple structure, wide application, large measurement range, high sensitivity, good stability and repeatability. Meanwhile, the internal air hole of HCF leaves a preparation channel for dual-parameter measurement. It has broad application prospect in medical treatment, environmental monitoring and manufacturing industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".