Design of an ultra-sensitive bimetallic anisotropic PCF SPR biosensor for liquid analytes sensing
Bibliographic record
Abstract
In this research work, an anisotropic photonic crystal fiber ( PCF ) biosensor working on a refractive index ( RI ) variation and based on surface plasmon resonance ( SPR ) is presented. Liquid analytes ( LA ) having a RI within the range of 1.340 to 1.380 RIU are investigated from the proposed biosensor. Spectroscopy analysis of LA having RI values of 1.340 RIU , 1.360 RIU , and 1.380 RIU is performed from the developed sensing setup for modeling an ultrasensitive biosensor. The numerical analysis of the sensing parameters for the proposed sensor presents a maximum wavelength sensitivity ( WS ) of 20000 nm / RIU for x- polarization ( x − pol .) and 18000 nm / RIU for y- polarization ( y − pol .), respectively, using the wavelength interrogation technique. Maximum amplitude sensitivity ( AS ) of 2158 RIU −1 and 3167 RIU −1 is obtained for x − pol . and y − pol ., respectively, using the amplitude interrogation technique. Maximum sensor resolution ( SR ) of 5.00 × 10 −6 RIU and 5.55 × 10 −6 RIU is obtained for x − pol . and y − pol ., respectively. The linear relationship of the resonant wavelength ( RW ) with the RI produces R 2 = 0.9972 and R 2 = 0.9978, corresponding to a degree (2) for x − pol . and y − pol ., respectively. The figure of merit ( FOM ) for x − pol . and y − pol . are 93.45 RIU −1 and 105.88 RIU −1 , respectively. The sensing parameters have obtained the maximum value for the LA having a RI value of 1.375 RIU .
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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 | 0.000 |
| 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".