Processing/Structure/Properties Relationships in Polymer Blends for the Development of Functional Polymer Foams
Bibliographic record
Abstract
In this study we present a comprehensive experimental investigation of the effect of polymer blending on the dispersed phase morphology and how the dispersed phase morphology influences the foaming behavior of the semicrystalline polymer matrix using three different material combinations: polyethylene (PE)/polypropylene (PP), PP/polyethylene terephthalate (PET) and PP/polytetrafluoroethylene (PTFE). Samples are prepared such that the dispersed phase domains exhibit either spherical or fibrillated morphologies. Measurements of the uniaxial extensional viscosity, linear viscoelastic properties and crystallization kinetics under ambient pressures and elevated pressures of carbon dioxide (CO2) are performed and the morphological features are identified with the aid of SEM. Batch foaming and lab-scale extrusion foaming experiments are performed, as a screening model for polymer processing, to show the enhancement of the foaming ability as a result of the blend morphology, taking into account the rheological behaviour and the effects of crystallization kinetics. The formation of high aspect ratio fibrils imparts unique characteristics to the semicrystalline matrix such as strain-hardening in uniaxial extensional flow, prolonged relaxation times, pronounced elastic properties and enhanced kinetics of crystallization. In contrast, the regular blends containing spherical dispersed phase domains do not exhibit such properties. Foam processing of the three blends reveals a marked broadening of the foaming window when the dispersed phase domains are fibrillated due to the concurrent increase in crystallization kinetics, improved elastic properties and strain hardening in extensional flow.
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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".