Multi‐material distributed recycling via material extrusion: <scp>recycled high density polyethylene</scp> and <scp>poly (ethylene terephthalate)</scp> mixture
Bibliographic record
Abstract
Abstract The high volume of plastic waste and the extremely low recycling rate have created a serious challenge worldwide. Local distributed recycling and additive manufacturing (DRAM) offers a solution by economically incentivizing local recycling. One DRAM technology capable of processing large quantities of plastic waste is fused granular fabrication, where solid shredded plastic waste can be reused directly as 3D printing feedstock. This study presents an experimental assessment of multi‐material recycling printability using two of the most common thermoplastics in the beverage industry, polyethylene terephthalate (PET) and high‐density polyethylene (HDPE), and the feasibility of mixing PET and HDPE to be used as a feedstock material for large‐scale 3‐D printing. After the material collection, shredding, and cleaning, the characterization and optimization of parameters for 3D printing were performed. Results showed the feasibility of printing a large object from rPET/rHDPE flakes, reducing production costs by up to 88%. Highlights Study: multi‐material recycling printability of PET‐HDPE. Large‐scale fused particle‐based 3‐D printing technically possible. Direct waste 3‐D printing rPET/rHDPE flakes, reducing production costs up to 88%.
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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".