Controlling the Hierarchical Structuring of Conductive PEBA in Ternary and Quaternary Blends
Bibliographic record
Abstract
When a conductive polymer is blended with commodity polymers such as polyolefins and/or polystyrene (PS) as a ternary blend, it has a tendency to form the core phase due to its high interfacial tension with the other components. This can limit its capacity to reduce resistivity compared to situating it at the interface. In this work, starting with a ternary low-density polyethylene/polystyrene/poly(ether- block -amide) (LDPE/PS/PEBA) blend, we examine the influence of the conductive PEBA concentration on morphology and resistivity when it exists as a core phase. Then, the hierarchical structuring of the PEBA phase will be modified through two strategies: by the addition of a fourth phase (polyethylene terephthalate (PET) or polyvinylidene fluoride (PVDF)) and by the addition of a copolymer interfacial modifier to the LDPE/PS/PEBA blend. Each of these approaches is shown to be capable of allowing the conductive PEBA to form a percolated structure assembled at the interface of two other continuous phases. The completely wet layered structuring of PEBA between PS and PVDF in the quaternary LDPE/PS/PEBA/PVDF blend leads to an exceptionally low percolation threshold of 0.37% compared to 9.7% in the initial LDPE/PS/PEBA blend where PEBA is the inside or core phase. To the best of our knowledge, this is the lowest value ever reported in the literature for a conductive polymer in melt blended systems.
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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".