Estimation of the solute diffusion coefficient of a dilute liquid alloy: static structure factor and isothermal compressibility estimates obtained using the rational function approximation of the radial distribution
Bibliographic record
Abstract
A simple method for estimating the mass diffusion coefficient of a dilute binary liquid alloy that sequentially uses experimental data for the static structure factor and isothermal susceptibility of the solvent is presented, as well as another using the static structure factor alone and a method using the isothermal susceptibility alone. A fourth method that simultaneously uses the static structure factor and isothermal susceptibility is also noted. Of significance is the fact that these methods do not require information about the interatomic potential. Stability with respect to weights in the optimization process employed has been established and is reported, as well as some indication of the upper limits on the applicable solute concentration. Comparisons are made with results from a high quality capillary experiment for Pb 1 wt% Au liquid alloy performed in microgravity, and with velocity autocorrelation estimates derived from molecular dynamics simulation. The results suggest that the capillary experiments are influenced by reverse diffusion of the solvent, and actually measure an average of the mass diffusion coefficients, D(ij), weighted by the equilibrium concentrations of the solvent, x(1), and solute, x(2), defined by [Formula: see text] The three methods are required to provide upper and lower estimates for the mixed solvent-solute diffusion coefficient, which is not directly accessible from the experimental data, and demonstrate agreement with the experiment via D(tot).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".