Model for the Surface Tension of Dilute and Concentrated Binary Aqueous Mixtures as a Function of Composition and Temperature
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".