Properties of Recycled LDPE/Birch Fibre Composites
Bibliographic record
Abstract
This study investigates how the properties of a wood polymer composite are modified by recycling, especially how the presence of fibres modify polymer degradation. To this end, low density polyethylene was selected as the matrix and yellow birch fibres as the reinforcement. The effect of recycling was simulated via closed-loop reprocessing of the material up to ten times under constant extrusion conditions. For each generation, thermal, rheological and morphological measurements were combined with macromolecular investigations including the complete molecular weight distribution of the polymer. The results revealed that polymer crystallinity increased with the number of composite regeneration, while the zero-shear viscosity decreased with recycling. Elongational rheology also revealed that the behaviour of the polymer changed from strain hardening to strain softening for the composite. From the morphological analysis, a degradation severity coefficient was defined to characterize the impact of operating conditions on fibre length. Finally, macromolecular investigations showed that the number-average molecular weight (Mn) of the polymer was more affected than the weight-average molecular weight (Mw). The relative branching factor and the branching frequency were also modified by the effect of reprocessing with and without the presence of wood fibre.
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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".