High Density Polyethylene Degradation Followed by Closed-loop Recycling
Bibliographic record
Abstract
The objective of this work is to investigate the loss of performance undergone by a polymer during a long-term closed-loop recycling process. High density polyethylene (HDPE) is subjected up to 50 extrusion cycles under constant processing conditions. The effect of recycling is then determined by following its degradation with increasing number of generation. After selected cycles, the material is characterized in terms of physical (density, GPC), thermal (DSC, TGA), and mechanical (tension, flexion) properties. No significant change are observed in density, DSC, and TGA tests. But for GPC, the weight average molecular weight (M w ) is found to decrease while the number average molecular weight (M n ) do not change significantly, thus leading to a decreasing polydispersity index. Intrinsic viscosity also decreases, while melt flow index (MFI) increases. From the tensile stress-strain curves, recycling seems to have no significant effect on Young's modulus (E y ), but a moderate increase of the strain at yield is observed followed by a slight decrease, while the stress at yield decreases. For the break-up conditions, stress and energy at break are found to increase significantly. Finally, three-point bending tests show that the flexural modulus (E b ) decreases with recycling. Overall, the recycling process leads to an important modification of the polymer's mechanical properties mainly due to chain scission.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".