Velocity fluctuation and force scaling during driven polymer transport through a nanopore
Bibliographic record
Abstract
Inspired by its central role in many biological processes, the transport of biopolymers across nanoscale pores is at the heart of a single-molecule sensing technology aimed at nucleic acid and protein sequencing, as well as biomarker detection. When electrophoretically driven through a pore by an electric potential gradient, a translocating polymer hinders the flow of ions, producing a transient current blockage signature that can be mapped to physicochemical properties of the polymer. Although investigated theoretically and by simulations, few experimental studies have attempted to validate the predicted transport properties, mainly due to the complex nature of the non-equilibrium translocation process. Here, we elucidate these fundamental concepts by constructing a patterned DNA nanostructure whose current signatures allow measurement of the instantaneous velocity throughout the translocation process. With simple physical insights from polymer and fluid dynamics, we show how the resulting molecular velocity profiles can be used to investigate the nanoscale forces at play and their dependence on experimental parameters such as polymer length, pore size and voltage. These results allow testing of theoretical models and outline their limitations. In addition to bridging experiment and theory, knowledge of the velocity fluctuation and force scaling during passage can assist researchers in designing nanopore experiments with optimized sensing performance.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".