A Computational Study of the Polymerization‐Induced Phase Separation Phenomenon in Polymer Solutions under a Temperature Gradient
Bibliographic record
Abstract
Abstract This paper studied the polymerization‐induced phase separation phenomenon (spinodal decomposition) in a model binary polymer solution under a linear spatial temperature gradient for the purpose of fabricating anisotropic polymeric materials by using mathematical modeling and computer simulation. Reaction kinetics were incorporated with the non‐linear Cahn‐Hilliard theory and the Flory‐Huggins free energy expression in the model. Moreover, the slow mode theory and Rouse law were used to account for polymer diffusion. It was found that an anisotropic morphology was obtained when a temperature gradient was imposed along the polymer solution sample. The direction of the structural anisotropy, however, depended significantly on the overall phase separation time. The presence of a temperature gradient along the polymer solution sample generated a spatial variation in polymerization rate, which resulted in a spatial variation of quench depth. Consequently, at a given instant, the phase separation at different locations of the polymer solution was at different stages of spinodal decomposition. The droplet size formed along the polymer solution was therefore dependent on the polymerization rate, the quench depth and the stage of spinodal decomposition. Furthermore, the spatial temperature gradient produced a spatial variation in the process induction time, which contains the polymerization induction time and phase separation induction time. It was also found that the polymerization induction time played a significant role on the spatial variation in the overall process induction time. image
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".