Amino Acids under Hydrothermal Conditions: Apparent Molar Heat Capacities of Aqueous α-Alanine, β-Alanine, Glycine, and Proline at Temperatures from 298 to 500 K and Pressures up to 30.0 MPa
Bibliographic record
Abstract
The apparent molar heat capacities C p ° of aqueous α-alanine, β-alanine, glycine, and proline have been determined using a differential flow calorimeter and a Picker flow microcalorimeter at temperatures of 298 K ≤ T ≤ 500 K and at pressures from steam saturation to 30 MPa. Comprehensive equations to describe the standard-state properties over this range are reported. Values of the standard partial molar heat capacities C p ° for the aqueous amino acids increase with temperature and then deviate toward negative values at temperatures above about 390 K, consistent with increasing the critical temperature in the solutions relative to water, i.e., negative Krichevskii parameters. This is opposite to the behavior predicted by correlations reported in the geochemical and chemical literature. The temperature dependence of C p ° predicted using the very recent functional group additivity model of Yezdimer et al. ( Chem. Geol. 2000, 164, 259−280) is only in qualitative agreement with the experimental results. The results are consistent with a simple solvation model in which the zwitterions are represented by point dipoles.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".