Mechanical Decrosslinking and Reprocessing of Crosslinked Rotomolded Polypropylene Using Cryogenic-Assisted Shear Pulverization and Compression Molding
Bibliographic record
Abstract
This paper presents a novel recycling approach for porous/foamed crosslinked rotomolded polypropylene (xPP) parts, originally designed for lightweight and thermal insulation. The method uses a cryogenic-assisted shear pulverization technique to produce parts by compression molding. The part’s final gel content and crosslink density were found to depend on their dicumyl peroxide (DCP) content (0–2.5 phr) and characterized in terms of their chemical, thermal, physical and mechanical properties. The results show that this recycling technique allows for an effective reprocessing of the crosslinked materials since partial decrosslinking occurs. For example, the crosslink density decreased by 64% (3.10 to 1.11 × 10−3 mol/cm3) and the gel content by 9% (84.4% to 71.2%) at 2.5 phr DCP. Reprocessing through compression molding led to a compact and partially crosslinked structure resulting in significant improvements in terms of tensile strength (1480%), tensile modulus (604%), elongation at break (8900%), Shore A hardness (19%) and Shore D hardness (32%) compared to xPP samples (at 2.5 phr). This study paves the way for the development of more sustainable recycling methods, especially for crosslinked polymers, by providing new opportunities to reuse the wastes/end-of-life materials in advanced materials and different applications.
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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".