Flow-Through vs Flow-Over: Analysis of Transport and Binding in Nanohole Array Plasmonic Biosensors
Bibliographic record
Abstract
We quantify the efficacy of flow-through nanohole sensing, as compared to the established flow-over format, through scaling analysis and numerical simulation. Nanohole arrays represent a growing niche within surface plasmon resonance-based sensing methods, and employing the nanoholes as nanochannels can enhance transport and analytical response. The additional benefit offered by flow-through operation is, however, a complex function of operating parameters and application-specific binding chemistry. Compared here are flow-over sensors and flow-through nanohole array sensors with equivalent sensing area, where the nanohole array sensing area is taken as the inner-walls of the nanoholes. The footprints of the sensors are similar (e.g., a square 20 μm wide flow-over sensor has an equivalent sensing area as a square 30 μm wide array of 300 nm diameter nanoholes with 450 nm periodicity in a 100 nm thick gold film). Considering transport alone, an analysis here shows that given equivalent sensing area and flow rate the flow-through nanohole format enables greatly increased flux of analytes to the sensing surface (e.g., 40-fold for the case of Q = 10 nL/min). Including both transport and binding kinetics, a computational model, validated by experimental data, provides guidelines for performance as a function of binding time constant, analyte diffusivity, and running parameters. For common binding kinetics and analytes, flow-through nanohole arrays offer ∼10-fold improvement in response time, with a maximum of 20-fold improvement for small biomolecules with rapid kinetics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".