Ethylene Polymerization with a Hafnocene Dichloride Catalyst Using Trioctyl Aluminum and Borate: Polymerization Kinetics and Polymer Characterization
Bibliographic record
Abstract
The polymerization of ethylene is investigated in a semibatch solution reactor using bis(n‐propylcyclopentadienyl)hafnium dichloride catalyst and tetrakis(pentafluorophenyl) borate dimethylanilinium salt ([B(C6F5)4]−[Me2NHPh]+) as the catalytic system. Trioctylaluminum (TOA) is used as impurity scavenger and alkylating agent. Ethylene pressure, polymerization temperature, TOA, borate, and catalyst concentrations are changed to investigate ethylene polymerization kinetics with this catalyst system. A 23 central composite design, augmented with extra runs to further explore the effect of some factors, is used as the statistical basis for the polymerization study. Ethylene propagation follows first‐order kinetics. Chain transfer to monomer, β‐hydride elimination, and transfer to TOA are the main chain transfer reactions. In addition to alkylating the catalyst precursor, TOA also deactivates the catalyst. The mode of reactor addition for catalyst, borate, and TOA has also been studied. When TOA and borate are added sequentially to the reactor, followed by the catalyst, the polymerization activity is lower than when the catalyst and borate are added simultaneously, suggesting that complexation with borate avoids deactivation reactions with TOA. image
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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".