Multi‐material distributed recycling via material extrusion: <scp>recycled high density polyethylene</scp> and <scp>poly (ethylene terephthalate)</scp> mixture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it