Effect of Graphene on the Mechanical Properties of Recycled High-Density and High-Molecular-Weight Polyethylene Blends
Bibliographic record
Abstract
This study uses an extrusion process to formulate blends based on recycled high-density and high-molecular-weight polyethylene (recHDPE, recHMWPE) for the manufacture of rainwater drainage pipes. The main objective of this project is to investigate the effects of incorporating graphene on the mechanical, thermal, and stress-cracking resistance properties of the recycled HDPE and HMWPE blends. Also, it aims to demonstrate that the addition of graphene may enable the use of different recycled polymers without compromising their properties. The effects of adding two amounts of graphene (0.5 and 1%) to recycled blends on the tensile and flexion properties, stress crack resistance (SCR) (using a notched crack ligament stress (NCLS) test), thermal behavior (using a differential scanning calorimeter (DSC) and a rheological plastometer) were investigated. The experimental results showed a significative enhancement when adding graphene in the SCR, some tensile properties (elongation at break and tensile strength), and flexural modulus. However, physical characterization showed that the samples containing 0.5% graphene exhibited lower crystallinity compared to the reference and, for the blend with 1% graphene, the fluidity also decreased for the blend filled with the graphene compared to the reference blend without any filler.
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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".