High Temperature End of the So-Called “Koga Line”: Anomalies in Temperature Derivatives of Heat Capacities
Bibliographic record
Abstract
The so-called "Koga line" is a collection of the loci of anomalies in various third derivatives of the Gibbs function, G, in the temperature-mole fraction field for aqueous solutions of nonelectrolytes. This splits the H(2)O-rich region into two, in each of which the molecular organization and interactions we call it mixing scheme is qualitatively different. In this work, we attempt to locate the high temperature end of the Koga line. This is a particularly interesting range of the boundary, since its extrapolation to zero solute concentration provides information on possible temperature induced changes in the properties of pure water. To this end, we determine semi-isobaric heat capacities of aqueous 2-butoxyethanol by adiabatic calorimetry up to a maximum of 95 degrees C. The corrections due to vaporization were not applied due to the lack of required vapor pressure and thermal expansivity data. Furthermore, we measured directly the isobaric heat capacities per molar volume for aqueous 1-propanol as well as 2-butoxyethanol by differential scanning calorimetry up to 120 degrees C at 3 atm. We then took one more temperature derivative of the respective heat capacity data. The resulting third derivative quantities from the former data showed step-type anomalies, while those from the latter negative peak-type anomalies. The loci of these anomalous points seem to point to about 70 degrees C at infinite dilution.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".