A computational study of multiple surface-directed phase separation in polymer blends under a temperature gradient
Bibliographic record
Abstract
The surface-directed phase separation (SDPS) phenomena of a model binary polymer blend quenched into the unstable region of its binary symmetric upper critical solution temperature phase diagram is numerically investigated using a mathematical model composed of the nonlinear Cahn–Hilliard (CH) theory for phase separation along with the Flory–Huggins–de Gennes (FHdG) free energy functional. The SDPS occurs in a square domain with a linear temperature gradient along the horizontal direction and with all sides having short range surface potential h 1 . The effects of different quench depth, diffusion coefficient, surface potential, and temperature gradient were studied numerically. The numerical results indicate that there is a simultaneous competition between the four surfaces in attracting the preferred polymer. The side with a higher surface potential would win the competition against the side with a lower surface attraction in the case of a uniform quench. The numerical results also indicated a later transition time for higher values of h 1 . As surface potential increased, the transition time from complete wetting to partial wetting occurred at a later time on the surface. The impact of different temperature gradient Δ T */Δ x * values on the surface enrichment rate with fixed temperature at one surface and higher temperature at the opposite surface was studied for the first time within a multiple surface potential set up. The results showed that higher values of Δ T */Δ x * increased the growth rate of the preferred polymer on the surface adding to the thickness of the wetting layer. The transition time from complete wetting to partial wetting occurred slightly later at the lower temperature side.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".