Hybrid silicon-tellurium-dioxide DBR resonators coated in PMMA for biological sensing
Bibliographic record
Abstract
We report on silicon waveguide distributed Bragg reflector (DBR) cavities hybridized with a tellurium dioxide (TeO 2 ) cladding and coated in plasma functionalized poly (methyl methacrylate) (PMMA) for label free biological sensors. We describe the device structure and fabrication steps, including reactive sputtering of TeO 2 and spin coating and plasma functionalization of PMMA on foundry processed Si chips, as well as the characterization of two DBR designs via thermal, water, and bovine serum albumin (BSA) protein sensing. Plasma treatment on the PMMA films was shown to decrease the water droplet contact angle from ∼70 to ∼35°, increasing hydrophilicity for liquid sensing, while adding functional groups on the surface of the sensors intended to assist with immobilization of BSA molecules. Thermal, water and protein sensing were demonstrated on two DBR designs, including waveguide-connected sidewall (SW) and waveguide-adjacent multi-piece (MP) gratings. Limits of detection of 60 and 300 × 10 −4 RIU were measured via water sensing, and thermal sensitivities of 0.11 and 0.13 nm/°C were measured from 25–50 °C for SW and MP DBR cavities, respectively. Plasma treatment was shown to enable protein immobilization and sensing of BSA molecules at a concentration of 2 µg/mL diluted in phosphate buffered saline, demonstrating a ∼1.6 nm resonance shift and subsequent full recovery to baseline after stripping the proteins with sodium dodecyl sulfate for a MP DBR device. These results are a promising step towards active and laser-based sensors using rare-earth-doped TeO 2 in silicon photonic circuits, which can be subsequently coated in PMMA and functionalized via plasma treatment for label free biological sensing.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".