Nanoholes As Nanochannels: Flow-through Plasmonic Sensing
Bibliographic record
Abstract
We combine nanofluidics and nanoplasmonics for surface-plasmon resonance (SPR) sensing using flow-through nanohole arrays. The role of surface plasmons on resonant transmission motivates the application of nanohole arrays as surface-based biosensors. Research to date, however, has focused on dead-ended holes, and therefore failed to harness the benefits of nanoconfined transport combined with SPR sensing. The flow-through format enables rapid transport of reactants to the active surface inside the nanoholes, with potential for significantly improved time of analysis and biomarker yield through nanohole sieving. We apply the flow-through method to monitor the formation of a monolayer and the immobilization of an ovarian cancer biomarker specific antibody on the sensing surface in real-time. The flow-through method resulted in a 6-fold improvement in response time as compared to the established flow-over method.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".