Computational analysis of short‐range surface‐directed polymerization‐induced phase separation
Bibliographic record
Abstract
Abstract The conventional off‐critical polymerization‐induced phase separation (PIPS) of binary mixtures leads to the fabrication of droplet‐type structure. However, the presence of a surface preferably attracting one of the components while phase separation by spinodal decomposition is occurring can change the configuration of surface energy and result in the formation of wetting layer(s) adjacent to the wall. Farther from the wall, droplets can still form. The method is called surface‐directed spinodal decomposition. The method is applicable in the fabrication of materials with layered morphology and may lead to the enhancement of the surface physical characteristics of mixtures. The current paper theoretically studied short‐range surface‐directed PIPS of a solvent/monomer mixture. The results showed the average diameter of particles in the bulk in the intermediate stage of phase separation grew with time by power‐law function <dave > ∝ t*α, and the exponent (αave ≈ 0.3) was found close to the Lifshitz‐Slyozov growth exponent (1/3) for the diffusivities studied in this paper. The time evolution of the thickness of the wetting layer was found to be logarithmic. The effects of the parameters such as diffusion coefficient, surface potential, and temperature gradient on the morphology development by the short‐range surface‐directed PIPS technique are discussed in this paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.005 | 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".