Bibliographic record
Abstract
Abstract The pressure-volume-temperature (PVT) melt behavior of 12 polyethylene resins was evaluated at pressures up to 200 MPa, using both isothermal and isobaric measurements in a GNOMIX high pressure dilatometer. The resins included high density polyethylene (HDPE), low density polyethylene (LDPE) and linear low density polyethylene (LLDPE). They were produced using a variety of catalysts, including Ziegler-Natta (ZN) and metallocene catalysts. The PVT data were used to evaluate two empirical equations of state (the Tate and Inverse Volume equations) in predicting PVT behavior of the melt, the isothermal compressibility, and the thermal expansion coefficient. The dependence of the melting and crystallization temperatures on pressure was also evaluated, and compared to existing equations. It was not possible to identify structural effects on the PVT melt behavior, as well as the isothermal compressibility and thermal expansion coefficient. However, some slight dependence on density was observed for the parameters of the equations of state. The isobaric experiments revealed that the pressure effect on the melting and crystallization temperatures was similar for all resins. While the melting and crystallization temperatures varied widely for the various resins, the pressure coefficients of the melting and crystallization temperatures were approximately equal (0.25°C/MPa.) The main differences in the behavior of the resins were in the transitional region during melting and crystallization.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".