Collective diffusion in a non-homogeneous interacting lattice gas
Bibliographic record
Abstract
Collective diffusion in an interacting adsorbate on a non-homogeneous one-dimensional substrate is investigated within the framework of a variational approximation. The substrate inhomogeneity, appropriate to a periodically stepped adsorbate, is represented by a Schwoebel barrier at the step edge and a modified binding at the step site. An elementary cell of a periodic substrate consists of n identical terrace sites and one step site, i.e. it contains n + 1 sites. The adsorbed particles are allowed to interact with each other, both in equilibrium as well as during the transit of a hopping particle over the potential energy barrier separating the initial and the target adsorption site. The interactions modify the rates of particle jumps between the adsorption sites. Cases n = 1, 3 and 4 are investigated in considerable detail and, where appropriate, a comparison with the available computer simulation results in the literature for an analogous two-dimensional system is made. It is shown that preferential geometrical arrangements at several adsorbate densities (coverages) induced by repulsive intra-adsorbate interactions lead to features on the diffusion coefficient versus the coverage curves which can be consistently interpreted. The origin of these features and their relation to intra-adsorbate correlations are examined and discussed. Where possible, the variational theoretical results are confronted with the results of our own computer simulation studies of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".