A Pseudo Equation-of-State Approach for the Estimation of Solubility Parameters of Polyethylene by Inverse Gas Chromatography
Bibliographic record
Abstract
Hildebrand solubility parameters ( δ ) of a series of linear low-density polyethylenes (LLDPE) were measured at several elevated temperatures by the inverse gas chromatography (IGC) method of DiPaola-Baranyi and Guillet [ Macromolecules 1978, 11, 228−235] with the incorporation of a compressible regular solution model, recently proposed by Ruzette and Mayes [ Macromolecules 2001, 34, 1894−1907], for data analysis. It was found that, with the inclusion of the pressure−volume−temperature (PVT) properties of the pure components in the data analysis, the measured δ agreed well with those obtained from PVT measurements on similar systems. This suggests that the model of Ruzette and Mayes, even though it is mathematically less sophisticated than the conventional equation-of-state (EOS) theories, it is useful for deriving solubility properties of polymers. The results were consistent with the prediction of conventional EOS theories that, in addition to the type and strength of intermolecular interaction, PVT properties of the individual components comprising polymer solutions and blends also play a significant role in determining their phase behavior, especially at elevated temperatures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".