Blending Recycled High-Density Polyethylene HDPE (rHDPE) with Virgin (vHDPE) as an Effective Approach to Improve the Mechanical Properties
Bibliographic record
Abstract
The mechanical properties of virgin/recycled high-density polyethylene (HDPE) blends over the complete concentration range was thoroughly investigated in this work. In particular, a focus was made on the long-term properties via mechanical fatigue. Two different mixing methods, namely powder mixing (dry blending) and extrusion mixing (melt blending), were used to determine the effect of processing conditions on the tensile and fatigue behavior of the blends after compression molding. It was found that both tensile (modulus, ultimate strength) and fatigue performances were improved with increasing vHDPE content. Based on the obtained data, a correlation between the blends composition and mechanical properties is reported. Moreover, it was observed that increasing the vHDPE content led to slower crack propagation rate, probably due to less defects (contamination) in the blends. Finally, a negligible difference in mechanical properties (fatigue resistance) between both mixing approaches was observed, but samples produced via powder mixing showed less viscous dissipation (heat generation) as the vHDPE content increased, leading to lower surface temperature rise which can be an advantage for specific applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".