Interrelation between melt processing conditions, formulation and properties of polypropylene / short flax and hemp fiber composites
Bibliographic record
Abstract
This work investigates the effect of extrusion parameters and formulation on the properties of polypropylene / short flax fiber composites. The parameters that were varied during the twin-screw extrusion process were screw configuration, screw rate, extrusion temperature and flow rate. The effect of the location of the feeding zone of flax fibers is also considered. Concerning the composite formulation, the effect of flax content, presence of coupling agent and of a reactive additive on composite properties are analyzed. Composites obtained with hemp fibers in the same extrusion process are also considered for comparison purposes. The materials were characterized in terms of morphological characteristics, rheological, thermal and mechanical properties. The variation of extrusion parameters shows that polypropylene / flax fibers composites have a large process window that can be considered as an advantage of PP/flax compounding. Tensile strength increased up to 50% with flax fibers content when coupling agent and reactive agent are used. Due to its high flexibility, flax fibers are less oriented in the flow direction and the part performance will be more homogeneous. Furthermore, these composites show a good recyclability by keeping the integrality of their mechanical properties after five reprocessing cycles.
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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 | 0.001 |
| 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".