Contributions to the entropy of a glass and liquid, and the dielectric relaxation time
Bibliographic record
Abstract
An analysis of the heat capacity data of 21 materials shows that a glass loses 17%–80% of its entropy on cooling from its Tg to 0 K, and that the entropy difference between a glass and crystal phase at Tg, ΔS(Tg), is 1.2 to 4.9 times the entropy difference at 0 K. This is contrary to the premise that the vibrational entropy of a glass is the same as the entropy of its crystal phase, or that ΔS(Tg) is equal to Sconf(Tg), the configurational entropy at Tg. The excess entropy of a glass over the crystal phase is attributed to (i) the relatively lower frequency and greater anharmonicity of lattice vibrations which contribute to their vibrational entropy, (ii) the kinetically unfrozen modes corresponding to the tail of the distribution of the α-relaxation times, which contribute to the configurational entropy, and (iii) localized relaxations of molecular groups which also contribute to the configurational entropy. These contributions vanish or become negligible at 0 K. Therefore, ΔS(Tg) cannot be used in place of Sconf(Tg) in the Adam and Gibbs equation. The finding puts into question the basis for the recent inferences [J. Chem. Phys. 108, 9016 (1998)] on molecular dynamics of supercooled liquids. An upper bound Sconf may be estimated at Tg by extrapolation of the vibrational entropy of a glass and used in the Adam and Gibbs equation to estimate roughly Sconf of a supercooled liquid from the dielectric relaxation time data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".