Projection method for improving signal to noise ratio of localized surface plasmon resonance biosensors
Bibliographic record
Abstract
This paper presents a simple and accurate method (the projection method) to improve the signal to noise ratio of localized surface plasmon resonance (LSPR). The nanostructures presented in the paper can be readily fabricated by nanoimprint lithography. The finite difference time domain method is used to simulate the structures and generate a reference matrix for the method. The results are validated against experimental data and the proposed method is compared against several other recently published signal processing techniques. We also apply the projection method to biotin-streptavidin binding experimental data and determine the limit of detection (LoD). The method improves the signal to noise ratio (SNR) by one order of magnitude, and hence decreases the limit of detection when compared to the direct measurement of the transmission-dip. The projection method outperforms the established methods in terms of accuracy and achieves the best combination of signal to noise ratio and limit of detection.
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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 | 0.001 | 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".