Surface Migration of Conductive PEBA in Ternary Polymer Blend Films with Different Wetting Morphologies
Bibliographic record
Abstract
It will be shown that ternary blends comprising a low-density polyethylene (LDPE) matrix and dispersed poly(ether- block -amide)/poly(butylene-adipate- co -terephthalate) (PEBA/PBAT) or PEBA/polyvinylidene fluoride (PVDF) can result in different wetting morphologies which can significantly enhance or fully restrain the surface migration of a conductive PEBA copolymer in the blend films. In the LDPE/PEBA/PBAT blends, the minor PEBA and PBAT phases combine to form a unique highly associated dispersed phase morphology demonstrating some weak partial wetting characteristics (i.e., three-phase contact), but neither PEBA nor PBAT can be regarded as the middle phase partially wetting the other component. This unique morphology is also a result of the particularly low interfacial tension between PEBA and PBAT. The use of a low-viscosity PBAT results in an increase in the surface composition of PEBA of up to 4 times as compared to that of the binary LDPE/PEBA blends. This is due to the enhancement of the combined PEBA/PBAT continuity, the low interfacial tension between PEBA and PBAT, and the high migration velocity of PBAT in the ternary blend. On the other hand, the LDPE/PEBA/PVDF blend system generates a completely wet morphology where PEBA is confined as a layer phase at the LDPE and PVDF interface. In this latter system, PEBA/PVDF also has a low interfacial tension, and even under conditions of similar viscosity and continuity to the partially wet LDPE/PEBA/PBAT system, the PEBA surface migration in LDPE/PEBA/PVDF can be fully suppressed. With these different PEBA surface migration characteristics in LDPE/PBAT/PEBA and LDPE/PEBA/PVDF, either a hydrophilic or a hydrophobic film surface can be generated at various surface resistivities.
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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".