Fictive Temperature, Structural Relaxation, and Reality of Residual Entropy
Bibliographic record
Abstract
By determining the fictive temperature, Tf, in two ways from the same Cp data, we investigate whether the residual entropy, Sres, of a glass could be an artifact of using the Cp d ln(T) integral in the glass-liquid temperature range. Although the integral gives only the upper and lower limits of the real entropy change, it is still useful and is distinguished as Delta(sigma). We determine Tf(sigma) from Delta(sigma) and the usual TfH from the Cp dT integral for two metal alloy glasses, a basalt composition glass and a spray-quenched propylene glycol glass from the available data, and find that Tf9sigma is about the same as TfH within errors. To substantiate it, we report a differential scanning calorimetry study performed during cooling of the Mg65Cu25Tb10 and Pd40Ni10Cu30P20 melts and on heating their glassy states at the same rates. In addition, we simulate Cp-T plots from a known model for nonexponential, nonlinear relaxation and analyze the data. The quantity Delta(sigma) on cooling the liquid and heating the glass differs negligibly; that is, net change in a temperature cycle between glass and its melt is close to zero, a characteristic of a nearly reversible change. We conclude that spontaneous enthalpy release has little effect on the entropy change determined from the Cp d ln(T) integral and, contrary to recent suggestions, Sres is real.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".