Pressure Scanning Volumetry of Physically Aged Polymer Glasses, Fictive Pressure, and Memory Effect
Bibliographic record
Abstract
Physical aging of a glass decreases its volume, V, entropy, and enthalpy, H, toward the equilibrium state values. For glasses usually formed by cooling a melt, the effect is modeled in terms of non-exponential, nonlinear structural relaxation by using a plot of the heat capacity, C p = (d H /d T ) p, against T obtained from differential scanning calorimetry (DSC) cooling and heating scans. A melt becomes glass also on isothermal pressurizing and the glass formed becomes liquid on depressurizing, showing a hysteresis of the sigmoid-shape plot of −(d V /d p ) T against p, which resembles the thermal hysteresis observed in the C p against T plots. By analogy with DSC, it was named pressure scanning volumetry (PSV). Here, we use the known values of non-exponential and nonlinearity parameters β and x and volume of activation for structural relaxation time, Δ V *, of atactic poly(propylene) to investigate the effect of aging pressure, p age, of aging time, t age, and of the pressurizing rate on aging features in PSV scans. The scans show a post- p g → l feature on depressurizing before the −(d V /d p ) T overshoot peak appears. We provide quantitative plots (i) of the monotonic decrease of V and increase of fictive pressure, p f, with t age and (ii) of the memory (Kovacs) effect in V and p f of the polymer and (iii) provide generic plots of −(d V /d p ) T against p for different combinations of β, x, and Δ V *. The study is of academic significance because PSV scans show a change in the density fluctuation response. It is of technological significance in polymer-extrusion processing and it may stimulate the commercial development of computer-controlled, high-pressure equipment.
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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".