Closely Packed Conductive Droplets with Polygon-Like Patterns Confined at the Interface in Ternary Polymer Blends
Bibliographic record
Abstract
This work reports on the formation of closely packed conductive droplets demonstrating polygon-like patterns at the interface in partially wetted ternary polymer systems prepared by melt blending and annealing treatment. The low-density polyethylene/poly(ether- block -amide)/poly(butylene-adipate- co -terephthalate) (LDPE/PEBA/PBAT) blend showed an intermediate partial wetting tendency where the interfacially localized conductive PEBA phase developed connected structure after blending but transformed into dispersed droplets upon annealing. The coalescence of the PEBA droplets appeared to be initiated by the Rayleigh-type instability in the thin PBAT film separating PEBA. However, the intrinsic coalescence rate of the PEBA droplets was very low due to the low interfacial tension of PEBA/PBAT. This slow coalescence of PEBA combined with the fast reduction in the interfacial area during annealing and the intermediate partial wetting state of the LDPE/PEBA/PBAT system resulted in a unique morphology of closely packed PEBA droplets with polygon-like patterns at a volume fraction of 50/10/40. Two other representative ternary polymer blends, LDPE/PEBA/polypropylene (PP) and compatibilized LDPE/PEBA/polystyrene (PS), with strong and weak partial wetting morphologies were also examined to highlight the mechanism for the morphology development in the LDPE/PEBA/PBAT blend.
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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) | 0.001 | 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".