Normal and anomalous diffusion in highly confined hard disk fluid mixtures
Bibliographic record
Abstract
Monte Carlo simulation is used to study binary mixtures of two-dimensional hard disks, confined to long, narrow, structureless pores with hard walls, in a regime of pore sizes where the large particles exhibit single file diffusion while the small particles diffuse normally. The dynamics of the small particles can be understood in the context of a hopping time, tau(21), that measures the time it takes for a small particle to escape the single file cage formed by its large particle neighbors, and can be linked to the long time diffusion coefficient. We find that tau(21) follows a power law as a function of the reduced pore radius for a wide range of particle size ratios with an exponent, alpha, that is independent of the size ratio, but linearly dependent on the Monte Carlo step size used in the dynamic scheme. The mean squared displacement of the small particles as a function of time exhibits two dynamic crossovers. The first, from normal to anomalous diffusion, occurs at intermediate times then the system returns to normal diffusion in the long time limit. We also find that the diffusion coefficient is related to tau(21) through a power law with exponent beta=-0.5, as predicted by theory. Finally, we show that particle separation in a binary mixture will be optimal at the pore radius that causes the large particles to undergo their transition from normal to anomalous diffusion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".