Behaviors of a Polymer Chain in Channels: From Zimm to Rouse Dynamics
Bibliographic record
Abstract
The effects of confinement and hydrodynamic interactions on single-chain diffusion behaviors are studied by using a combination of molecular dynamics and multiparticle collision dynamics simulations. For polymers in free space, the simulation results showed that the diffusion coefficient D ∞ for long chains scales with the chain length N as D ∞ ∼ N –ν (ν = 0.588), consistent with the Zimm dynamics, but it deviates from the Zimm dynamics for short chains. For polymers confined in channels with width H, the diffusion coefficient is found to follow two different scaling relations. The observed behaviors could be understood by introducing a microscopic hydrodynamic length ξ h, below which the overall effect of hydrodynamic interactions becomes less important. For a confined chain, the diffusion behavior exhibits a crossover from Zimm (nondraining) to Rouse (free draining) dynamics as the channel size H decreases. When H is larger than ξ h, the confinement experienced by the polymer chain is weak and the diffusion coefficient scales as D ∼ H 0.7, in accordance with the prediction of blob theory; when H is smaller than ξ h, D becomes independent of H, implying a free draining condition. A general analytical expression of D is derived by extending the partially permeable sphere model to the blob scale, which gives a quantitative description of the transition from Zimm to Rouse dynamics as H decreases.
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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 | 0.001 | 0.001 |
| 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".