Wafer-bonded surface plasmon waveguide sensors with in-plane microfluidic interfaces
Bibliographic record
Abstract
Abstract Sensors exploiting long-range surface plasmon polariton (LRSPP) waveguides comprised of Au stripes embedded in Cytop with integrated and encapsulated microfluidic channels are fabricated and demonstrated. A fabrication approach was devised where the lower cladding and recessed Au stripes are fabricated on a Si supporting substrate, and the upper cladding and microfluidic channels are fabricated on a glass substrate, followed by wafer bonding to assemble the wafers into complete sealed structures. The bond is centered over the full length of the optical path, yet no evidence of optical scattering or excess loss due to the bond could be observed, and no evidence of a bonding interface could be discerned from high-magnification cross-sectional images. We also demonstrate wafer-scale fabrication of in-plane microfluidic inlets and outlets, along with a fixture for fluidic edge coupling that provides sealed interfaces to external fluidic tubing and components. In-plane microfluidic interfaces are automatically defined along chip facets upon wafer dicing, precluding the need to drill through holes in lids. The performance of the chips was assessed by measuring the attenuation of LRSPPs on fully cladded reference waveguides, on waveguides passing through microfluidic channels, and by measuring the response of sensors to changes in refractive index produced by injecting various sensing solutions. Our fabrication approach based on wafer bonding and in-plane fluidic interfacing is compelling for low-cost high-volume manufacturing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".