Nucleation of Poly(lactide) Partially Wet Droplets in Ternary Blends with Poly(butylene succinate) and Poly(ε-caprolactone)
Bibliographic record
Abstract
This work presents the first investigation on the crystallization behavior of partially wet droplets in immiscible ternary blends. Poly(lactide), poly(ε-caprolactone), and poly(butylene succinate) (PLA, PCL, and PBS, respectively) were melt blended in a 10/45/45 weight ratio to produce a "partial wetting" morphology with droplets of the PLA minor phase located at the interface between the other two major components. The crystallization process of the higher melting PLA droplets was studied by polarized light optical microscopy, while the other components remain in the molten state. We found that neighboring partially wet droplets nucleate in close sequence. This is unexpected since partially wet droplets display points of three-phase contact and, hence, should not touch each other. Moreover, the onset of poly(lactide) crystallization is frequently observed at the interface with molten PCL or PBS, with a significant preference for the former polymer. The observed sequential droplet-to-droplet crystallization is attributed to the weak partial wetting behavior of the PCL/PLA/PBS ternary system. In fact, the contact between the interfacially confined droplets during crystallization due to their mobility can lead to a transition from a partial to a completely wet state, with the formation of thin continuous layers bridging larger partially wet droplets. This allows crystallization to spread sequentially between neighboring domains. Using a simple heterogeneous nucleation model, it is shown that the nucleation of PLA on either PCL or PBS melts is energetically feasible. This study establishes a clear relationship between the unique partial wetting morphology of ternary blends and the nucleation of the minor component, paving the way to the understanding and control of crystallization in multiphasic polymer blends for advanced applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".