Nanofluidics Meets Plasmonics: Flow-Through Surface-Based Sensing
Bibliographic record
Abstract
Nanostructures exhibit both nanofluidic and nanophotonic phenomena that can be exploited in sensing applications. In the case of nanohole arrays, the role of surface plasmons on resonant transmission motivates their application as surface-based biosensors. Research to date, however, has focused on dead-ended (or ‘blind’) holes, and therefore failed to harness the benefits of nanoconfined transport combined with plasmonic sensing. A flow-through nanohole array format presented here enables biomarker sieving and rapid transport of reactants to the sensing surface. Proof of concept operation is demonstrated and compared with previous methods. The various transport mechanisms are characterized with the aim to utilize the metallic plasmonic nanostructure as an active element in concentrating as well as detecting analytes. The invited presentation will provide an overview of all our experimental, computational and analytical work in this area. This paper is focused on the analysis and evaluation of flow-through nanohole arrays for analyte 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".