Fabrication of Bloch Long Range Surface Plasmon Waveguides Integrating Counter Electrodes and Microfluidic Channels for Multimodal Biosensing
Bibliographic record
Abstract
We report the fabrication of a novel multimodal biosensor combining plasmonic and electrochemical detection. The plasmonic sensors are based on monitoring the propagation of Bloch long-range surface plasmon polaritons (LRSPPs) along thin narrow Au stripes, integrating grating couplers as input/output means. The electrochemical sensors use the same Au stripes as working electrodes with nearby Pt stripes integrated on-chip as counter electrodes. The structures are fabricated on a truncated 1D photonic crystal comprised of a 15-period stack of alternating layers of SiO2/Ta2O5. The Au and Pt stripes are fabricated using bilayer lift-off photolithography, and the gratings are fabricated using e-beam lithography. The structures are arranged into arrays targeting multichannel biosensing. The wafer is covered with CYTOP as the upper cladding with etched microfluidic channels providing access to the sensing surfaces and is wafer-bonded to a Borofloat silica wafer to encapsulate the fluidic channels and enable edge (in-plane) fluidic interfacing. The wavelength response of the grating-coupled plasmonic waveguide sensors is presented along with surface sensing results. Cyclic voltammetry measurements using the Au and Pt stripes as the working and counter electrodes are also presented. [2021-0103]
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".