Label-free silicon photonic biosensors for use in clinical diagnostics
Bibliographic record
Abstract
Silicon photonics is poised to revolutionize biosensing applications, specifically in medical diagnostics. Optical sensors can be designed to improve clinically-relevant diagnostic assays and be functionalized to capture and detect target biomarkers of interest. There are various approaches to designing these sensors - improving the devices' performance, increasing the interaction of light with the analyte, and matching the characteristics of the biomolecules by using architectures that complement the biosensing application. Using e-beam lithography and standard foundry processes, we have investigated Transverse Magnetic (TM) and Transverse Electric (TE) disk and ring resonators. TM devices hold the potential for higher sensitivity and large-particle sensing capabilities due to the increased penetration distance of light into the analyte. In addition, devices such as slot wavegguide Bragg grating sensors have shown high sensitivities and high quality factors and may present advantages for specific biosensing applications. These devices have been investigated for wavelengths around λ=1550 nm (conventional wavelength window in fiber-optic communication) and λ=1220 nm, where the water absorption is greatly decreased, offering improved limits of detection. Using reversibly bonded PDMS microfluidic flow cells, the performance and bio-detection capabilities of these devices were characterized. Comparing binding performance across these devices will help validate architectures suitable for biological applications. The most promising sensors for each application will then be identified for further study and development. This paper will discuss the sensors' comparative advantages for different applications in biosensing and provide an outlook for future work in this field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".