Dynamical scaling exponents for polymer translocation through a nanopore
Bibliographic record
Abstract
We determine the scaling exponents of polymer translocation (PT) through a nanopore by extensive computer simulations of various microscopic models for chain lengths extending up to $N=800$ in some cases. We focus on the scaling of the average PT time $\ensuremath{\tau}\ensuremath{\sim}{N}^{\ensuremath{\alpha}}$ and the mean-square change of the PT coordinate, $⟨{s}^{2}(t)⟩\ensuremath{\sim}{t}^{\ensuremath{\beta}}$. We find $\ensuremath{\alpha}=1+2\ensuremath{\nu}$ and $\ensuremath{\beta}=2∕\ensuremath{\alpha}$ for unbiased PT in two dimensions (2D) and three dimensions (3D). The relation $\ensuremath{\alpha}\ensuremath{\beta}=2$ holds for driven PT in 2D, with a crossover from $\ensuremath{\alpha}\ensuremath{\approx}2\ensuremath{\nu}$ for short chains to $\ensuremath{\alpha}\ensuremath{\approx}1+\ensuremath{\nu}$ for long chains. This crossover is, however, absent in 3D where $\ensuremath{\alpha}=1.42\ifmmode\pm\else\textpm\fi{}0.01$ and $\ensuremath{\alpha}\ensuremath{\beta}\ensuremath{\approx}2.2$ for $N\ensuremath{\approx}40\ensuremath{-}800$.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".