Effect of surface modification and fiber content on the mechanical performance of compression molded polyethylene‐maple composites
Bibliographic record
Abstract
Abstract With the objective of producing more sustainable materials, wood‐plastic composites were produced using linear low density polyethylene and a wide range of maple wood fiber content (up to 80% wt). This was possible by using a simple dry‐blending of the component in a powder form and compression molding. In particular, the effect of different surface treatments (mercerization, maleated polyethylene [MAPE], and their combination) on the morphology and mechanical performance of these composites was studied. The results show that all the surface treatments investigated were able to improve the fiber‐matrix adhesion, leading to better composite homogeneity, and higher mechanical properties. Furthermore, it was possible to increase the amount of wood that can be introduced in the composites compared to untreated fibers. In our case, up to 80% wt of maple fibers were easily processed generating significant improvements in moduli (367%) and strength (50%), especially when a combination of alkali‐MAPE treatment was performed. This simple processing of the composites is interesting to produce different parts size and geometry with limited degradation since no melt compounding is performed. The work also represents a way to produce sustainable, economic, and lightweight composites with improved properties using a high content of renewable filler.
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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".