Self-Diffusion and Viscosity in Electrolyte Solutions
Bibliographic record
Abstract
The effect of salt on the dynamics of water molecules follows the Hofmeister series. For some "structure-making" salts, the self-diffusion coefficient of the water molecules, D, decreases with increasing salt concentration. For other "structure-breaking" salts, D increases with increasing salt concentration. In this work, the concentration and temperature dependence of the self-diffusion of water in electrolyte solutions is studied using molecular dynamics simulations and pulsed-field-gradient NMR experiments; temperature-dependent viscosities are also independently measured. Simulations of rigid, nonpolarizable models at room temperature show that none of the many models tested can reproduce the experimentally observed trend for the concentration dependence of D; that is, the models predict that D decreases with increasing salt concentration for both structure-breaking and structure-making salts. Predictions of polarizable models are not in agreement with experiment either. These results suggest that many popular water models do not accurately describe the dynamic nature of the hydrogen bond network of water at room temperature. The simulations are in qualitative agreement, however, with experimental results for the temperature dependence of water dynamics; simulations and experiment show an Arrhenius dependence of D with temperature, T, with added salt, that is, ln D ∼ 1/T, over a range of temperatures above the freezing point of water.
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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 | 0.002 |
| 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 | 0.001 |
| 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".