Microfluidic and nanofluidic integration of plasmonic substrates for biosensing
Bibliographic record
Abstract
Metallic nanohole arrays support surface electromagnetic waves that enable enhanced optical transmission and may be exploited for sensing. Our group has been active in the application of enhanced optical transmission to chemical and biological sensing, and in the optofluidic integration nanohole arrays. Recent work in this area is described here. Recent work using a blocking layer to limit the exposed metal surface to the in-hole region resulted in effective sensing in a much smaller, nanoconfined volume. This result motivates the use of through nanoholes, (i.e. nanoholes as nanochannels) to directly address the sensing area. A flow-through nanohole array based sensing format is presented that leads to enhanced transport of reactants to the active area and a solution sieving action that is unique among surfacebased sensing methods. The pertinent fluid and solid mechanics aspects of the flow-through nanohole array sensing are discussed and recent flow-through sensing results are presented. The application of dielectrophoresis to influence particle transport in flow-through nanohole arrays is also discussed. Specifically, simulations indicate that equivalent dielectrophoretic forces are compatible with drag forces for flow rates in the range already defined in the context of biomarker transport and membrane strength considerations. Importantly, these results indicate that dielectrophoretic trapping is viable in these systems. The confinement of particles in the nanoholes opens opportunities for analyte concentration and surface enhanced Raman scattering in flow-through nanohole array based fluidic systems.
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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 | 0.001 |
| 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".