Predicting the Diffusion Coefficients of Concentrated Mixed Electrolyte Solutions from Binary Solution Data. NaCl + MgCl<sub>2</sub>+ H<sub>2</sub>O and NaCl + SrCl<sub>2</sub>+ H<sub>2</sub>O at 25 °C
Bibliographic record
Abstract
The model used by Stokes to interpret binary mutual diffusion in concentrated electrolyte solutions can be generalized to estimate mixed electrolyte diffusion coefficients (including cross-coefficients for coupled diffusion) from binary solution activities, viscosities, and diffusion coefficients which are available for many aqueous electrolytes. To test the accuracy of this estimation procedure, ternary D ik coefficients are predicted for NaCl (1) + MgCl 2 (2) + H 2 O solutions and compared with accurate measured values over a wide composition range. At high NaCl concentrations the cross-coefficient D 12 is larger than the main coefficients D 11 and D 22 . This behavior is correctly predicted together with the crossover of D 11 and D 22 observed at low NaCl concentrations. The strong composition dependence of the D ik coefficients is caused by changes in the viscosity and the thermodynamic driving forces for diffusion. For NaCl + MgCl 2 + H 2 O solutions the average value of | D ik (measured) − D ik (predicted)| is (0.04 × 10 - 5 ) cm 2 s - 1 at ionic strengths from 0.015 to 9 mol dm - 3 . Similar agreement is obtained for the extensive ternary diffusion data available for NaCl + SrCl 2 + H 2 O solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".