Spectro-angular optical biosensor based on surface plasmon resonance operating in the visible spectrum
Bibliographic record
Abstract
Surface plasmon resonance (SPR) sensing is one of the most widely used methods to implement biosensors due to its sensitivity and capacity for label-free detection. Most conventional SPR sensors measure the change in reflectance at a metal-dielectric interface as a function of either angle or wavelength. However, it has recently been shown that an increase in sensitivity and a greater robustness against noise can be achieved by measuring reflectivity in both domains simultaneously, in a so-called spectro-angular SPR biosensor. This provides a surface plasmon dispersion curve captured on an image sensor that can be tracked in real time. A single value decomposition method is used to project the dispersion curve onto a basis set and allow the image obtained from an unknown refractive index sample to be compared very accurately with a pre-calculated reference set. The objective of the current work is to further improve the detection limit of the spectro-angular biosensor. Simulations have shown that the spatial resolution and numerical precision of the image sensor have a significant impact on the accuracy of the refractive index change measurement. Therefore, upgrading the cameras used for the data acquisition could significantly improve the detection limit of the SPR biosensor. In this work, simulation results are presented to justify the modifications of the experimental system and to estimate the expected improvement in the detection limit of the spectro-angular biosensor by using higher spatial resolution and higher data precision cameras. Experimental results are presented and compared with the previous design.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".