From Waste to Function: Compatibilized r-PET/r-HDPE Blends for Pellet Extrusion 3D Printing
Bibliographic record
Abstract
The increasing accumulation of plastic waste-especially from packaging and post-consumer sources-calls for the development of sustainable recycling strategies. Due to the challenges associated with sorting mixed waste, directly processing waste streams offers a practical approach. Polyethylene terephthalate (PET) and high-density polyethylene (HDPE) are common consumer plastics, but they are difficult to recycle together due to immiscibility and degradation. In mixed waste, recycled HDPE (r-HDPE) often contaminates the recycled PET (r-PET) stream. Additive manufacturing (AM) offers a promising solution to upcycle these mixed polymers into functional products with minimal waste. This study investigates the processing and characterization of r-PET/r-HDPE blends for AM, focusing on the role of compatibilizers in enhancing their properties. Blends were melt-compounded using a twin-screw extruder to improve dispersion, followed by direct pellet-based 3D printing. A compatibilizer (0-7 php) was incorporated to improve miscibility. Rheological testing showed that the 5 php compatibilizer optimized viscosity and elasticity, ensuring smoother extrusion. Thermal analysis revealed a 30 °C increase in crystallization temperature and a shift in decomposition temperature from 370 °C to 400 °C, indicating improved thermal stability. Mechanical testing showed a tensile strength of 35 MPa and 17% elongation at break at optimal loading. Scanning electron microscopy (SEM) confirmed reduced phase separation and improved morphology. This work demonstrates that properly compatibilized r-PET/r-HDPE blends enable sustainable 3D printing without requiring polymer separation. The results highlight a viable path for the conversion of plastic waste into high-value, customizable components, contributing to landfill reduction and advancing circular economy practices in polymer manufacturing.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".