Model for the Surface Tension of Dilute and Concentrated Binary Aqueous Mixtures as a Function of Composition and Temperature
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Bibliographic record
Abstract
Surface tension dictates fluid behavior, and predicting its magnitude is vital in many applications. Equations have previously been derived to describe how the surface tension of pure liquids changes with temperature, and other models have been derived to describe how the surface tension of mixtures changes with liquid-phase composition. However, the simultaneous dependence of surface tension on temperature and composition for liquid mixtures has been less studied. Past approaches have required extensive experimental data to which models have been fit, yielding a distinct set of fitting parameters at each temperature or composition. Herein, we propose a model that requires only three fitting procedures to predict surface tension as a function of temperature and composition. We achieve this by analyzing and extending the Shereshefsky (J. Colloid Interface Sci. 1967, 24 (3), 317-322), Li et al. (Fluid Phase Equilib. 2000, 175, 185-196), and Connors-Wright (Anal. Chem. 1989, 61 (3), 194-198) models to high temperatures for 15 aqueous systems. The best extensions of the Shereshefsky, Li et al., and Connors-Wright models achieve average relative deviations of 2.11%, 1.20%, and 0.62%, respectively, over all systems. We thus recommend the extended Connors-Wright model for predicting the surface tension of aqueous mixtures at different temperatures with the tabulated coefficients herein. An additional outcome of this study is the previously unreported equivalence of the Li et al. and Connors-Wright models in describing experimental data of surface tension as a function of composition at a single temperature.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it