Upcycling of recycled polyethylene for rotomolding applications via dicumyl peroxide crosslinking
Bibliographic record
Abstract
Abstract Polyethylene (PE), including high‐density polyethylene (HDPE) and low‐density polyethylene (LDPE), makes up a significant part of post‐consumer plastics in municipal solid waste, presenting challenges for traditional recycling methods due to a wide range of melt flow properties and poor interfacial adhesion between the different resin, which often leads to low quality products (downcycling). In this study, a method is proposed to modify the molecular structure of post‐consumer PE (rHDPE and rLDPE) and their blends by using a straightforward organic peroxide crosslinking technique with 1 phr of dicumyl peroxide (DCP). Different rHDPE/rLDPE blend weight ratios (0/100, 20/80, 40/60, 50/50, 60/40, 80/20, and 100/0) were prepared using a combination of co‐rotating twin‐screw extrusion and pulverization. The final parts were produced via rotomolding where both the forming and crosslinking processes occurred concurrently. Subsequently, the materials were characterized in terms of chemical, thermal, and mechanical properties. It was found that the tensile strength (228%), tensile modulus (345%), flexural strength (145%), and flexural modulus (251%) increased by crosslinking the 80% wt. rHDPE (x‐rHDPE). Conversely, the gel content increased by 17%, thermal resistance by 37.2%, and the impact strength by 93% with 80% wt. rLDPE (x‐rLDPE). It can be concluded that a balance between the properties occurs as the addition of DCP improved both the interfacial adhesion and melt properties of rHDPE/rLDPE blends. This innovative approach represents a simple and straightforward method to upcycle mixed plastics (PE) streams, especially for rotomolding applications. It also offers promising avenues for sustainable waste management and material reuse.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".