Properties of Wood-Plastic Composite Manufactured with High Ratio of Pine Wood Flour
Bibliographic record
Abstract
The wood supply chain generates significant amounts of residues, which can be repurposed as reinforcement in wood-plastic composites (WPCs). Despite their potential, few studies have investigated the use of recycled wood in WPC production. This study aims to evaluate the effects of incorporating high loads of recycled pine wood flour on the properties of WPCs. Composites were produced with three different wood flour-to-polypropylene ratios (25/75, 50/50, and 60/40), and their hardness, water absorption, density, and density profile were analyzed. Dynamic mechanical analysis (DMA) was conducted to assess the viscoelastic response under dynamic loading. Results revealed a positive correlation between the wood flour content and water absorption. After two hours of water immersion, the WPC with the highest wood content (60%) showed the greatest water absorption rate (1.38%), compared to 0.99% for WPC-50 and 0.55% for WPC-25. Similar trends were observed after 24 hours of immersion. Density measurements indicated significant differences between composites, with WPC-60 exhibiting the highest average density (1,137 kg/m³), followed by WPC-50 (1,093k g/m³) and WPC-25 (976 kg/m³). This represents a 16.50% density increase when comparing WPC-60 to WPC-25. The density profile revealed no significant variations along the composite length, suggesting effective homogenization of wood flour within the polypropylene matrix. DMA results demonstrated that all WPCs had higher storage moduli than pure polypropylene, highlighting the stiffening effect of the lignocellulosic material. Among the tested compositions, the 50/50 ratio provided an optimal balance of hardness and rheological properties. The production of composites with a high wood flour load shows a promising approach for industry, promoting environmental sustainability and enabling the development of materials with improved properties. However, the limit for the use of pine waste was identified at 50% in relation to the polypropylene polymeric matrix, a proportion that presented the best results in terms of hardness and rheological properties.
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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 | 0.001 | 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".