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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".