Spectro-angular optical biosensor based on surface plasmon resonance operating in the visible spectrum
Bibliographic record
Abstract
Surface plasmon resonance (SPR) is one of the most widely used methods to implement biosensors because of its label-free and sensitive detection. Surface plasmon resonance allows the change in the refractive index of a sample to be measured accurately by the analysis of the light reflecting at a metal-dielectric interface. A way to increase the sensitivity of SPR biosensors was found in fabricating a spectro-angular SPR biosensor and using of a newly developed data processing method called the Double Projection Method. The objective of the work presented in this thesis is to improve further the detection limit of the spectro-angular biosensor by upgrading the cameras used for the data acquisition. Simulations have shown that the spatial resolution and the data precision have a significant impact on the accuracy of the refractive index change measurement. In this thesis, 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 the use of higher spatial resolution and higher data precision cameras. The new design as well as the components purchased for the experimental set-up are detailed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.004 |
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".