Influence of temperature, molecular weight, and molecular weight dispersity on the surface tension of polystyrene, polypropylene, and polyethylene. II. Theoretical
Bibliographic record
Abstract
Abstract Experimental data for the surface tension of polystyrenes of different molecular weights (3400–200,000) and different molecular weight dispersities (1–3) and of different polyolefins are compared with the predictions of the Patterson–Rastogi and Dee–Sauer cell theories, which infer the surface tension from pressure–volume–temperature (PVT) data. PVT data for these polymers were obtained from the literature and experimentally and are fitted to the Flory–Orwoll–Vrij equation of state. Both theories predict that the surface tension will decrease linearly with increasing temperature and increase with molecular weight, thereby corroborating the experimental data. However, both theories underestimate the entropy change in the surface formation per unit area at a constant volume for low molecular weight and polydisperse systems and underestimate the effect of molecular weight dispersity on surface tension. Both theories feature two parameters, m and b , that quantify the enthalpic and entropic contributions to surface tension. The theoretical predictions are fitted to the experimental data for monodisperse polystyrene (with a molecular weight above the molecular weight of entanglement), polypropylene, and linear low‐density polyethylene to quantify the enthalpic contribution to surface tension. b is then evaluated as a function of molecular weight and molecular weight dispersity and is found to decrease with increasing molecular weight and to increase with increasing molecular weight dispersity, showing that end‐group excess at the surface has some effect on surface tension. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 83: 2201–2212, 2002
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".