Theory and simulation of polymerization-induced phase separation in polymeric media
Bibliographic record
Abstract
A rigorous model of polymerization-induced phase separation (PIPS), based on the non-linear Cahn-Hilliard (C-H) and Flory-Huggins (F-H) theories combined with a second-order polymerization reaction equation, has been formulated and its solutions characterized. The model describes phase separation in system consisting of a non-reactive polymer and a monomer that undergoes condensation polymerization. The model consists of a balance equation for the low molecular weight polymerization regime and another balance equation for the high molecular weight entangled regime. The model equations are solved, and the solutions are characterized to identify the dynamical and morphological phenomena of the PIPS process. The extent of phase separation increases significantly with time during the early stage of phase separation, and slows down in the intermediate stage. The various types of phase-separated morphologies are fully characterized using a novel morphological characterization techniques, known as the intensity and scale of segregation. Both the dynamical and morphological features of the PIPS method are sensitive to the magnitudes of the dimensionless diffusion coefficient D* and the dimensionless reaction rate constant K*. The scale of segregation and the droplet size decreases as D* and K* increase. On the other hand, the intensity of segregation increases with K*, but decreases with D*. The present results extend the present knowledge of the PIPS process by taking into account the effects arising from the presence of a non-reactive polymer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".