Langevin dynamcis simulations of driven polymer translocation into a cross‐linked gel
Bibliographic record
Abstract
We investigate the dynamics of driving a polyelectrolyte such as DNA through a nanopore and into a cross-linked gel. Placing the gel on the trans-side of the nanopore can increase the translocation time while not negatively affecting the capture rates. Thus, this setup combines the mechanics of gel electrophoresis with nanopore translocation. However, contrary to typical gel electrophoresis scenarios, the effect of the field is localized in the immediate vicinity of the nanopore and becomes negligible inside the gel matrix. Thus, we investigate the process by which a semiflexible polymer can be pushed into a gel matrix via a localized field and we describe how the dynamics of gel penetration depends upon the field intensity, polymer stiffness, and gel pore size. Our simulation results show that a semiflexible polymer enters the gel region with two distinct mechanisms depending upon the ratio between the bending length scale and the gel pore size. In both regimes, the gel fibers cause a net increase in the mean translocation time. Interestingly, the translocation rate is found to be constant (a potentially useful feature for many applications) during the predominant part of the translocation process when the polymer is stiff over a length scale comparable to the gel pore size.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".