Relaxation strength of localized motions in D-sorbitol and mimicry of glass-softening thermodynamics
Bibliographic record
Abstract
The dielectric relaxation strength, ΔεJG, the relaxation rate, fm,JG and the distribution parameter, αJG, of the faster relaxation process in D-sorbitol have been studied as a function of temperature and the cooling rate. Amongst these, fm,JG and αJG of the glass and the supercooled liquid change smoothly with the temperature, T, but ΔεJG of the glassy state increases slowly on heating until the glass-softening range is reached and thereafter it increases rapidly at T above the glass-softening temperature, Tg. Thus its plot against T has an elbow-shape, remarkably similar to that observed for the volume, enthalpy and entropy. The derivative (dΔεJG/dT) increases relatively abruptly at Tg like the thermal expansion coefficient and the heat capacity of a glass. Thus ΔεJG is a function of the state’s entropy and volume. The distribution of relaxation times became narrower as T was increased, and fm,JG increased according to the Arrhenius equation, fm,JG=2.992×1014 exp[−5.312×104/RT], where R=8.314 J (K mol)−1. It is deduced that fm,JG increases on structural relaxation of D-sorbitol. The results indicate that the relaxation mechanism involves motions of segments of the D-sorbitol molecules or of the whole molecule in local regions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".