Recycled HDPE/Natural Fiber Composites Modified with Waste Tire Rubber: A Comparison between Injection and Compression Molding
Bibliographic record
Abstract
With the objective of turning wastes into added-value materials, sustainable and fully recycled wood-plastic composites were reinforced by waste tire rubber particles to show balanced properties and potentially low-cost materials. Recycled high density polyethylene (rHDPE) was compounded (melt extrusion) with flax fiber (FF) and waste regenerated tire rubber (RR) to investigate the effect of mixing ratio, coupling agent (maleated polyethylene, MAPE) and molding process (injection and compression molding) on the properties of hybrid composites. In particular, a complete set of characterization was performed including thermal stability, phase morphology and mechanical properties in terms of tension, flexion and impact, as well as hardness and density. Adding 40 wt.% of flax fibers (FF) increased the tensile (17%) and flexural (15%) modulus of rHDPE, while the impact strength decreased by 58%. Substitution of FF by waste rubber particles improved by 75% the impact strength due to the elasticity and energy absorption of the rubber phase. The effects of impact modification were more pronounced for rHDPE/(FF/RR) compatibilized with MAPE (10 wt.%) due to highly improved interfacial adhesion and compatibility. The results also suggest that, for a fixed hybrid composition (FF/RR, 25/55 wt.%), the injection molded composites have a more homogenous morphology with a uniform distribution of well embedded reinforcements in the matrix. This better morphology produced higher tensile strain at break (12%) and impact strength (9%) compared to compression molded samples.
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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".