Label-free biosensing using silicon planar waveguide technology
Bibliographic record
Abstract
We exploit the unique properties of the silicon-on-insulator material platform to demonstrate a new series of planar waveguide evanescent field sensors for biological / chemical sensing. These sensors, combined with state-of-the-art surface functionalization chemistries, offer a sensitive, label-free means for the specific detection of biomolecules, without the need for fluorescent tags employed in conventional fluorescence-based biochips. The use of silicon photonic wire waveguide technology allows sensors with extremely small footprint and small radius of curvature to be fabricated, facilitating the development of densely packed sensor arrays for multi-parameter analysis, particularly attractive for drug discovery, pathogen detection, genomics and disease diagnostics. We show that high index contrast silicon photonic wire waveguides not only provide the above stated advantages but also offer increased sensitivity over that of evanescent field sensors constructed on other common waveguide material platforms. This results from the unique properties of the optical modes of silicon photonic wire waveguides, which exhibit very large surface electric field magnitude and strong localization near the waveguide surface. We discuss the design and fabrication of silicon-on-insulator-based Mach-Zehnder interferometer sensors and experimentally demonstrate their performance to detect bulk solution refractive index change and to monitor the specific adsorption of streptavidin to biotinylated waveguides.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".