Fabrication of surface plasmon interferometric sensors exploiting multimode nanoslits
Bibliographic record
Abstract
Abstract As phase-based sensors, surface plasmon interferometers offer higher sensitivity than resonant or attenuation-based plasmonic sensors. In this paper we realize surface plasmon interferometric sensors based on a multimode nanoslit used as a combiner. The phase difference in the surface plasmon waves, incident on the nanoslit, determines the resonant mode excited therein, and the radiation pattern that emerges therefrom. The device construction integrates on-chip grating couplers, gold sensing and reference surfaces, transparent claddings, sealed microfluidic channels, and a nanoslit in the gold film. The structure can be arrayed with individual microfluidic channels thereby enabling multiplexing. Nanofabrication of the devices using wafer-based processes is discussed in detail. Fabrication involves integration into a full process flow of techniques such as photolithography, electron beam lithography, focused ion beam milling, plasma etching, wafer bonding, and dicing, with several overlay and precision alignment steps. We also describe the design and realization of a test jig useful for mounting a chip under test, providing in-plane sealed microfluidic interfacing to several channels simultaneously, and enabling optical interrogation in the perpendicular direction using microscope objectives. Operation of the devices is demonstrated by refractometric (bulk) sensing experiments. The device concept is of strong interest for multiplexed biosensing applications, and the fabrication flow presented can be scaled to mass-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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".