Quasi-D-Shaped Fiber Optic Plasmonic Biosensor for High-Index Analyte Detection
Bibliographic record
Abstract
In this paper, we propose a highly sensitive quasi-D-shaped fiber optic biosensor for detection of high refractive index (RI) liquid analytes via surface plasmon resonance. The main mechanism of sensing is interplay between photonic crystal fiber fundamental mode and plasmonic mode which leads to formation of different resonance peaks depending on the analyte RI. We numerically analyze the structure sensitivity to design parameters and demonstrate the sensing performance of the proposed biosensor using both spectral sensitivity and amplitude sensitivity methods. The proposed biosensor has a RI detection range of 0.15 refractive index unit (RIU) from 1.45 to 1.6. The sensor exhibits linear sensing performance with a RI spectral sensitivity of 9300 nm/RIU for analyte RI ranging from 1.45 to 1.525, 1176 nm/RIU for analyte RI ranging from 1.525 to 1.6 and in particular, 11800 nm/RIU for analyte RI between 1.475 and 1.5. Furthermore, an average RI sensitivity of 4800 nm/RIU for analyte RI ranging from 1.45 to 1.6 is demonstrated. We also study the amplitude sensitivities of the proposed sensor which show promising maximum values of 183.6 RIU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> for 785 nm excitation and 820 RIU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> for 1050 nm excitation. Due to the simple structure of the proposed biosensor, large detection range, high sensitivity and promising linear sensing performance, the proposed biosensor can be a promising candidate for detecting various high RI chemical and biochemical samples.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".