On the use of pressure‐volume‐temperature data of polyethylene liquids for the determination of their solubility and interaction parameters
Bibliographic record
Abstract
Abstract Specific volumes of high‐density and low‐density polyethylene liquids at several elevated temperatures and pressures were measured. The measured specific volumes were then used to estimate the thermal expansion coefficients \documentclass{article}\pagestyle{empty}\begin{document}$\left( {{\rm \alpha = }\frac{{\rm 1}}{v}\left( {\frac{{\partial v}}{{\partial T}}} \right)_P } \right)$\end{document} and isothermal compressibility \documentclass{article}\pagestyle{empty}\begin{document}$\left( {{\rm \beta = } - \frac{{\rm 1}}{v}\left( {\frac{{\partial v}}{{\partial P}}} \right)_T } \right)$\end{document} of the polymers. Two different approaches were used in which one was simply to fit the raw data by second order polynomials to obtain (∂ v /∂ T ) P and (∂ v /∂ P ) T , while the other by the Sanchez‐Lacombe (S‐L) equation of state. It was found that the resultant α and β obtained from the above methods differ significantly, indicating that the S‐L equation of state may not be suitable for determining α and β at elevated temperatures. When these two sets of α and β were used to calculate the corresponding solubility parameters and then the Flory‐Huggins interaction parameters (χ) of the polymers, the results also differ considerably. Nonetheless, χ obtained from the first method agrees well with the results obtained from small angle neutron scattering measurements while the S‐L equation of state method does not. The current results suggest that solubility and interaction parameters obtained from pressure‐volume‐temperature experiments depend critically on the manner by which the data analysis is performed. Polym. Eng. Sci. 44:853–860, 2004. © 2004 Society of Plastics Engineers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".