DNA Capture by Nanopore Sensors under Flow
Bibliographic record
Abstract
Integrating nanopore sensors within microfluidic architectures is key to providing advanced sample processing capabilities upstream of the biosensor. When confined in a microchannel, the nanopore capture and translocation characteristics are altered when subjected to cross-flow, affecting sensor performance. Here, we study the capture rate and translocation of 1-5 kbp double-stranded DNA molecules through solid-state nanopores in the presence of tangential fluid flow over the nanopore aperture. Experiments reveal a trend of increased capture rate with cross-flow, reaching a 5-fold enhancement (dependent on DNA length) at moderate flow rates, before decreasing at higher flow rates. By modeling DNA dynamics in microchannels under the combined effect of laminar flow, Brownian motion and electrophoretic drift, it is shown that the observed trend is the result of two competing mechanisms: enhanced DNA transport by convection and reduction in the nanopore's capture volume with increased flow velocity. Moreover, it is shown that the viscous drag force exerted by flow on a translocating DNA can be exploited to tune the kinetics of DNA translocation.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".