Application of a constant hole volume Sanchez–Lacombe equation of state to mixtures relevant to polymeric foaming
Bibliographic record
Abstract
A variant of the Sanchez-Lacombe equation of state is applied to several polymers, blowing agents, and saturated mixtures of interest to the polymer foaming industry. These are low-density polyethylene-carbon dioxide and polylactide-carbon dioxide saturated mixtures as well as polystyrene-carbon dioxide-dimethyl ether and polystyrene-carbon dioxide-nitrogen ternary saturated mixtures. Good agreement is achieved between theoretically predicted and experimentally determined solubilities, both for binary and ternary mixtures. Acceptable agreement with swelling ratios is found with no free parameters. Up-to-date pure component Sanchez-Lacombe characteristic parameters are provided for carbon dioxide, dimethyl ether, low-density polyethylene, nitrogen, polylactide, linear and branched polypropylene, and polystyrene. Pure fluid low-density polyethylene and nitrogen parameters exhibit more moderate success while still providing acceptable quantitative estimations. Mixture estimations are found to have more moderate success where pure components are not as well represented. The Sanchez-Lacombe equation of state is found to correctly predict the anomalous reversal of solubility temperature dependence for low critical point fluids through the observation of this behaviour in polystyrene nitrogen mixtures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".