The effect of chemical crosslinking on the properties of Rotomolded high density polyethylene
Bibliographic record
Abstract
Abstract In this study, high density polyethylene (HDPE) was combined with dicumyl peroxide (DCP) to produce crosslinked parts via rotational molding. The effect of DCP content (0.1–2.5 phr) on the crosslinking degree was investigated to determine its effect on the chemical, mechanical, physical, and thermal properties of HDPE. The gel content and crosslink density was found to increase with DCP content. These trends led to a reduction in the degree of crystallinity, melting, and crystallization temperature. Thermogravimetric analysis (TGA) showed that crosslinked HDPE (xHDPE) has higher thermal stability than the neat matrix in both air and nitrogen atmosphere. In addition, a direct relationship was observed between improved thermal resistance and higher impact strength. Finally, relationships between the tensile properties of xHDPE and the degree of crystallinity were observed, which were all controlled by the level of crosslinking. These results have the potential to advance the manufacturing of high performance materials suitable for a wide range of applications such as automotive parts, agricultural products, chemical storage tanks, large waste containers, and fuel tanks in general.
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".