Statics and dynamics of DNA in a network of nanofluidic entropic traps
Bibliographic record
Abstract
A nanofluidic slit embedded with a lattice of square pits was used to entropically trap polymers. DNA in the system was confined to two dimensions and underwent self-assembly into discrete conformational states based on the number of occupied pits. The molecules diffused by undergoing transitions to higher or lower pit-occupancy states and relaxing to their equilibrium state. A statistical mechanical model was used to predict the mean occupancy state as a function of various geometric parameters. Experiments confirm many of the predictions of the model. Regions of parameter space over which a single state dominates were observed, indicating that entropic trapping can be used to create stable self-assembled single polymer conformations. Measurements of diffusion showed it to be geometry dependent, allowing a fine-tuning of diffusivity. The diffusion showed non-monotonic behaviour: local minima corresponding the stable equilibrium states were observed. This demonstrates that the diffusion can be fine-tuned to a local resonance using entropic trapping. The results show that polymers can self-assemble into entropically stable structures, with implications for nanotechnology and biophysics.
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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 | 0.001 |
| 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".