Estimating polystyrene equation of state (<scp>EOS</scp>) parameters using the cloud and critical points of polystyrene + hydrocarbon mixtures*
Bibliographic record
Abstract
Abstract Three polystyrene parameters for the modified Sanchez‐Lacombe equation of state were estimated by performing a correlation of the cloud and critical point data for six well defined polystyrene + hydrocarbon mixtures. The volume shift, a fourth parameter, was adjusted to the polystyrene pressure‐volume‐temperature (PVT) data. A parameterization of the modified Sanchez‐Lacombe equation of state is used to estimate the hydrocarbon parameters given their critical temperature, critical pressure, and acentric factor, but these properties are not available for the polymer. The original polystyrene parameters estimated solely on the pure component PVT data results have difficulty representing the slope and curvature of the cloud points of binary polystyrene + hydrocarbon mixtures. A previous technique for adjusting one of the polystyrene parameters to match the cloud points with the remainder fit to the PVT data results in an improved match over the pure component PVT data alone. Adjusting three polystyrene parameters can better match the cloud points of a binary polystyrene + hydrocarbon mixture, while only increasing the absolute average deviation in pure polystyrene density by 0.07%. The polystyrene parameters obtained by fitting the cloud points were used to correlate the bubble points of polystyrene in other solvents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".