Multimode spectroscopy using dielectric grating coupled to a surface plasmon resonance sensor
Bibliographic record
Abstract
A new platform is proposed to solve one of the main shortcomings of surface plasmon resonance biosensors, namely, the cross sensitivity to surface and bulk effects. This approach is based on multimode spectroscopy in which three different modes are excited simultaneously. The proposed design consists of an SPR sensor loaded with a dielectric grating. The design parameters (dimensions and wavelength) are optimized with a genetic algorithm. The optimized design has two resonance modes excited with TM polarized light, each sensitive to surface effects, and one TE mode mostly sensitive to variations in the bulk fluid refractive index. Numerical and analytical methods are used to justify the simulation results, which are in good agreement. Finally, it is shown that, by applying three-mode spectroscopy, decoupling the properties of the attached biomaterial from the background index variations is possible with the proposed design.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".