A sequential nanopore-channel device for polymer separation
Bibliographic record
Abstract
In this work, we investigated whether a series of nanopores connected by channels can be used to separate polymer mixtures by molecular size. We conducted multiscale coarse-grained simulations of semiflexible polymers driven through such a device. Polymers were modelled as chains of beads near the nanopores and as single particles in the bulk of the channels. Since polymers rarely escape back into the bulk of the channels after coming sufficiently close to the nanopores, the more computationally expensive simulations near the pores were decoupled from those in the bulk. The distribution of polymer positions after many translocations was deduced mathematically from simulations across a single nanopore-channel pair, under the reasonable assumption of identical and independent dynamics in each channel and each nanopore. Our results reveal rich polymer dynamics in the nanopore-channel device and suggest that it can indeed produce polymer separation. As expected, the mean time to translocate across a single nanopore increases with the chain length. Conversely, the mean time to cross the channels from one nanopore to the next decreases with the chain length, as smaller chains explore more of the channel volume between translocations. As such, the time between translocations is a function of the length and width of the channels. Depending on the channel dimensions, polymers are sorted by increasing length, decreasing length, or non-monotonically by length such that polymers of an intermediate size emerge first.
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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 | 0.001 | 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".