Statistical thermodynamics predictions of the solubility parameter
Bibliographic record
Abstract
Abstract The Simha and Somcynsky (S‐S) lattice‐hole theory has been shown to represent accurately the pressure–volume–temperature ( PVT ) surface of chain molecular melts and their mixtures. The characteristic scaling parameters, P *, T * and V *, extracted from equation of state (eos) measurements, are known for a large number of polymers. On this basis it is possible to compute the configurational internal energy density and thus the solubility parameter δ as a function of temperature and pressure, δ = δ( T , P ). In the first part of this paper it is shown that the theory leads to an energy approximately proportional to the first power of density, that is of the van der Waals type, as found for low molar mass fluids by Hildebrand. We continue with a computation of δ for a series of polymer melts at two levels of temperature, namely T = 25 °C and T = T g + 300 K. Next, the results are compared with those listed in reference publications, all at ambient pressure. The theoretical values extrapolated from the melt to 25 °C were systematically higher than those listed. However, good correlation is obtained with the high‐temperature calculations for a large variety of polymers. Arguments for this low–high temperature correlation are presented in terms of corresponding levels of molecular mobility and packing in solution and in bulk. Copyright © 2004 Society of Chemical Industry
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".