Simple and Accurate Calibration of the Flory–Huggins Interaction Parameter
Bibliographic record
Abstract
This paper improves upon a standard method of determining the Flory–Huggins χ parameter, whereby experimental order–disorder transitions (ODTs) of symmetric diblock polymer melts are fit to the mean-field prediction, (χ N ) ODT = 10.495. The improvement is achieved by switching to an accurate prediction of (χ N ) ODT from Glaser et al. ( Phys. Rev. Lett. 2014, 113, 068302), supplemented with corrections for the small degrees of polydispersity and compositional asymmetry that inevitably exist in real diblock polymers. The first correction is evaluated by simulating polydisperse diblocks over a wide range of invariant polymerization indices, and the second correction is extracted from analogous simulations for compositionally asymmetric diblocks by Ghasimakbari and Morse ( Macromolecules 2020, 53, 7399). The resulting calibration method is then demonstrated on 19 different chemical pairs, using previously published experimental data. It provides a considerable increase in accuracy, but yet is nearly as simple to apply as the original version.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".