The Modified Sanchez−Lacombe Equation of State Applied to Polydisperse Polyethylene Solutions
Bibliographic record
Abstract
The modified Sanchez−Lacombe equation of state (MSL-EOS) is Neau’s version of the Sanchez−Lacombe equation of state modified to include a Péneloux-type volume translation. The purpose of this work is to report parameters for modeling the phase behavior of polyethylene solutions. The MSL equation is an empirical equation that contains four parameters to define each pure compound whether a solvent or a polydisperse polymer. The MSL equation uses conventional linear and quadratic mixing rules. A parametrization can be used to obtain the pure compound parameters from the molar mass, critical temperature, critical pressure, and acentric factor. These properties cannot be defined for polydisperse polymers, and parameters were determined from a combination of liquid density ( PVT ) data and polymer + solvent cloud point ( xPT ) data. Binary interaction parameters have been obtained for over 50 mixtures by correlating the fluid phase boundaries. Binary mixtures including ethylene, hexane, and/or cyclohexane are of particular interest to polyethylene production. A single binary interaction parameter is usually sufficient to represent vapor−liquid equilibrium, but temperature dependence is required to accurately represent liquid−liquid equilibrium. The MSL equation of state can be used to correlate the cloud points of polyethylene solutions at high pressures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".