Tunable Tensile Properties of Polypropylene and Polyethylene Terephthalate Fibrillar Blends through Micro-/Nanolayered Extrusion Technology
Bibliographic record
Abstract
Fiber-reinforcement is a well-established technique to enhance the tensile properties of polymer composites, which is achieved via changing the reinforcing material concentration and orientation. However, the conventional method can be costly and may lead to poor compatibility issues. To overcome these challenges, we demonstrate the use of micro-/nanolayer (MNL) extrusion technology to tune the mechanical properties of polypropylene (PP)/polyethylene terephthalate (PET) fibrillar blends. PET nanofibers-in-PP microfiber composites, with 3, 7, and 15 wt.% PET, are first prepared using a spunbond system to induce high aspect-ratio PET nanofibers. The PP/PET fibers are then reprocessed in an MNL extrusion system and subjected to shear and extensional flow fields in the channels of the uniquely designed layer multipliers. Increasing the mass flow rate and number of multipliers is shown to orient the PET nanofibers along the machine direction (MD), as confirmed via scanning electron microscopy. Tensile tests reveal that up to a 45% and 46% enhancement in elastic modulus and yield strength are achieved owing to the highly aligned PET nanofibers along the MD under strongest processing conditions. Overall, the range of tensile properties obtained using MNL extrusion implies that the properties of fiber-reinforced composites can be further tuned by employing this processing technique.
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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".