Tunable Tensile Properties of Polypropylene (PP) and Polyethylene Terephthalate (PET) Fibrillar Blend Through Micro-/Nano-layered Extrusion Technology
Bibliographic record
Abstract
In this study, we demonstrate the use of micro-/nano-layer (MNL) extrusion technology to tune the mechanical properties of polypropylene (PP)/polyethylene terephthalate (PET) fibrillar blends. Nano-fibril-in-microfiber composites, with 3, 7, and 15 wt.% PET, are prepared using a spunbond system, and then fed into an MNL extrusion system to be subjected to strong shear and extensional flow fields in the multipliers. Preferential alignment of the PET nano-fibrils is confirmed via morphological observations using scanning electron microscopy. Additionally, increasing the mass flow rate is shown to further increase the degree of fibril orientation along the machine direction (MD). Tensile tests revealed that for each PET loading, the elastic modulus and yield strength of the composites are significantly enhanced with increasing number of multipliers and mass flow rate, while such mechanical enhancement is accompanied by a slight sacrifice in ductility. Overall, MNL extrusion is a promising technology to further enhance mechanical properties of nano-fibril composites.
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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".