Simulated Recycling of Polypropylene and Maleated Polypropylene for the Fabrication of Highly-Filled Wood Plastic Composites
Bibliographic record
Abstract
In this study, polypropylene [PP] and maleic anhydride grafted polypropylene [MAPP] were reprocessed from one to three times to simulate recycling. 30 wt % of the reprocessed PP and MAPP were compounded with 70 wt % wood filler to fabricate highly filled wood plastic composites [WPCs]. The neat and composite samples were produced using a batch mixer and injection molder. The effect of recycling and maleation of PP was analyzed using light scattering, nuclear magnetic resonance, rheology, microscopy, and other physical/mechanical properties. While thermomechanical processing may lead to chain scission reactions, shown by a reduction in melt viscosity and weight-average molecular weight, the observed changes are minor and did not change the bulk properties significantly. The repeated reprocessing allowed for the incorporation of up to 70 wt % wood flour [WF] to fabricate the highly filled WPC while the maleation allowed for better interactions and the composite’s strength. Overall, the WPCs with maleic anhydride displayed appealing physical properties due to their increased tensile properties and a lack of oxidation or significant level of degradation. This indicates that the maleation of reprocessed PP could be an important and effective strategy to reduce virgin and petroleum-based MAPP while revitalizing mechanical properties for WPCs.
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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 | 0.001 |
| 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".