Mechanism of DNA trapping in nanoporous structures during asymmetric pulsed-field electrophoresis
Bibliographic record
Abstract
We investigate the trapping mechanism of individual DNA molecules in ordered nanoporous structures generated by crystalline particle arrays. Two requisites for trapping are revealed by the dynamics of single trapped DNA, fully-stretched U/J shapes and hernia formation. The experimental results show there is a stronger possibility for hernias to lead the reorientation upon switching directions of the voltage at high field strengths, where trapping occurs. Fully stretched DNA has longer unhooking times than expected by a classic rope-on-pulley model. We propose a dielectrophoretic (DEP) force reduces the mobility of segments at the apex of the U or J, where field gradients are highest, based on simulations and observations of the trapping force dependence on field strength. A modified model for unhooking time is obtained after the DEP force is introduced. The new model explains the unhooking time data by predicting an infinite trapping time when the ratio of arm length differences (of the U or J) to molecule length Δx/L < β, where β is a DEP parameter that is found to strongly increase with electric field. The DNA polarizability calculated with the DEP model and experimental value of β is of the same magnitude of reported value. The results indicate the tension at the apex of U/J shape DNA is the primary reason for DNA trapping during pulsed field separation, instead of hernias.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".