Effects of Polyethylene Molecular Weight Distribution on Phase Morphology Development in Poly(<i>p</i>-phenylene ether) and Polyethylene Blends
Bibliographic record
Abstract
Bimodal molecular weight distribution (MWD) in a polymer can potentially deliver mechanical toughness from the high molecular weight (MW) components without compromising its processability due to the presence of the low MW components. The influence of MW bimodality on the morphology development in a heterophase polymer blend system, however, remains unclear. In this study, high-density polyethylenes (HDPEs) of high unimodal, low unimodal, and bimodal MWDs, namely HDPE-H, HDPE-L, and HDPE-B, were synthesized and then blended with PPE to study the effect of the MWD on morphology development. After blending, the low MW components in HDPE-B are found to populate the HDPE/PPE interface, and the morphological and continuity behaviors of PPE in HDPE-B/PPE are virtually identical to those in the unimodal HDPE-L/PPE blend. Despite the high interfacial tension between HDPE and PPE, all three HDPEs form fiber-like dispersions in HDPE/PPE blends when PPE is the majority phase. This leads to an early HDPE phase continuity onset and an extremely broad cocontinuity region from 10 to 60 wt % of HDPE in all the HDPE/PPE blend systems. These fiber-like HDPE dispersions in the PPE matrix are likely a consequence of the extremely high PPE viscosity. The study opens up the concept/potential of using biomodal polymers in such a way that the higher molecular weight component could be used to control mechanical properties while the lower molecular weight component controls the viscous/morphological characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".