Effect of Few-Layer Graphene on the Properties of Mixed Polyolefin Waste Stream
Bibliographic record
Abstract
This work demonstrates how the addition of few-layer graphene (FLG) influences the processability and mechanical properties of the mixed polyolefin waste stream (R-(PE/PP)). Three different types of compounds were investigated: (1) R-(PE/PP) with FLG; (2) blends of R-(PE/PP) with prime polyethylene (PE) or polypropylene (PP) or PP copolymer; and (3) R-(PE/PP) with both the prime polymer and FLG. The processability was assessed by measuring the torque during melt extrusion, the melt flow index (MFI), and viscosity of the compounds. Investigations of the processability and mechanical properties of the composites indicate that the presence of FLG can reinforce the composites without hindering the processability, an unusual but desired feature of rigid fillers. A maximum increase in tensile strength by 9%, flexural strength by 23%, but a reduction in impact strength were observed for the compounds containing R-(PE/PP), 4 wt.% FLG, and 9 wt.% prime PP. The addition of FLG concentrations higher than 4 wt.% in R-(PE/PP), however, resulted in higher tensile and flexural properties while preserving the impact strength. Remarkably, the addition of 10 wt.% FLG increased the impact strength of the composite by 9%. This increase in impact strength is attributed to the dominant resistance of the rigid FLG particles to crack propagation.
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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".