Folding Dynamics of Linear ssDNA–dsDNA Heterostructures through Solid-State Nanopores
Bibliographic record
Abstract
Understanding the intricate details of the molecular mechanisms behind nanopore translocation is crucial to further the development of nanopores as single-molecule sensors. In this study, we assemble hybrid ss–dsDNA constructs with different effective persistence lengths to investigate their capture and folding behavior in solid-state nanopores. In general, we observe differences in event shapes for different flexible polymers with more flexible polymers leading to deeper, longer, and more complex events. We show that folding happens favorably in ssDNA segments along the heterogeneous polymer contour and can happen multiple times during the same event, blocking more current and generating more convoluted event shapes. By designing DNA heterostructures with various ss–dsDNA distributions, we further show that the propensity for folds at the ssDNA segments arises due to their higher flexibility allowing them to diffuse around the vicinity of a nanopore during the translocation process, as opposed to stiffer and nonrelaxed dsDNA segments through which the tension propagates during passage. These findings highlight the different translocation regimes of polymers of various rigidity, improving our understanding of the folding of polymers into nanopores which will ultimately be useful for the development of applications requiring single-file translocations, for example, for the decoding of barcoded nanostructured molecules for multiplexed biomarker detection or for molecular information storage schemes.
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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) | 0.001 | 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".