High-Accuracy and High-Resolution Calorimetry Revealing New Correlations of Phase Change Enthalpy, Entropy, and Number of Carbon Atoms n in n-Alkanes
Bibliographic record
Abstract
Correlations between material properties are useful in engineering, and in addition, the underlying common mechanisms allow for a better understanding of the origins of the properties. Properties related to phase changes are an example, being important, e.g., in thermodynamic applications. For n-alkanes used, e.g., as phase change materials for thermal energy storage, linear correlations between enthalpy changes and entropy changes in phase transitions, as well as the number of carbon atoms n, have been observed and described by many researchers. Different correlations for odd and even n were found, though still with significant outliers. In this work, data from high-accuracy and high-resolution calorimetry were used for the analysis of enthalpy and entropy changes for alkanes with n = 14 to 30. The analysis shows more, and different, correlations than described in the previously published literature. Specifically, the ‘outliers’ have a physical and chemical origin, rooted in the phase transitions present in the specific n-alkanes, not just in whether n is odd or even. These detailed findings promise a better understanding of the thermodynamics of phase transitions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".