Correlation of Polymerization Conditions with Thermal and Mechanical Properties of Polyethylenes Made with Ziegler-Natta Catalysts
Bibliographic record
Abstract
In this study, the synthesis of polyethylenes has been carried out with titanium-magnesium supported Ziegler-Natta catalysts in laboratory-scale reactors. A correlation of different polymerization conditions with thermal and mechanical properties of polyethylenes has been established. It is seen that there is lowering of molecular weight (Mw), polymer yield, and catalyst activity at high hydrogen pressure and high temperature. The Mw, polymer yield, and catalyst activity are improved with the increase in ethylene pressure. Dynamic mechanical analysis (DMA) results show that the increase in temperature and hydrogen pressure decreases storage modulus. The samples with higher Mw showed high activation energy. The melting point decreases with the increase in hydrogen pressure but increases slightly with the increase in ethylene pressure. It is seen that the increase in reaction temperature, ethylene pressure, and hydrogen pressure leads to an increase in crystallinity. The tensile modulus increases with the increase in hydrogen pressure and can be correlated with the crystallinity of polymer. The Mw has a major influence on the flow activation energy and tensile strength. But the other mechanical and thermal properties depend on Mw as well as other parameters.
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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 | 0.001 |
| 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".