Thermodynamic properties of water in the water-poor region of binary water + alcohol mixtures
Bibliographic record
Abstract
In our previous thermodynamic studies, we suggested that alcohol molecules in water-poor water + alcohol mixtures exist as alcohol clusters in a form similar to the pure alcohols. Here, we use calorimetry and densitometry to investigate how H2O interacts with alcohol clusters in water-poor binary aqueous mixtures of 12 different alcohols. The composition dependence of the measured excess partial molar enthalpy and volume of water (HEW and VEW), along with entropy data calculated from HEW and literature data for excess chemical potentials, showed that in water-poor solutions of small alcohols such as methanol, ethanol, and 1-propanol, mutual waterwater interactions are endothermic, but entropically favorable. Conversely, in long-chain solvents such as 1-octanol and 1-decanol, the interaction is exothermic and entropically unfavorable. We suggest that these observations reflect wateralcohol hydrogen bonding in short-chain solvents and water clustering with more hydrogen bonding than in pure water or "dewetting" in mixtures of the longer alcohols, respectively. The composition dependence of HEW was also used to locate anomalies that specify the boundary between the mixing schemes characterizing the intermediate and the water-poor regions of alcohol + water mixtures.Key words: aqueous alkane-mono-ols, excess partial molar enthalpy, entropy and volume, mixing schemes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".