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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.004 | 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 teacher head, 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".