Gas-Phase Ethylene/Hexene Copolymerization with Metallocene Catalyst in a Laboratory-Scale Reactor
Bibliographic record
Abstract
Gas-phase ethylene and hexene copolymerization using a silica-supported ( n -BuCp) 2 ZrCl 2 metallocene catalyst has been investigated in a 2 L laboratory reactor. Replicate experimental runs were conducted to confirm the reproducibility of measured responses, which included polymerization rate, reactant concentrations, and copolymer properties. Comparisons of polymerization rate profiles and catalyst activity were made using a number of designed experimental runs. The experiments revealed that triisobutyl aluminum scavenger was the most important cause of low catalyst activity, and a low initial polymerization rate that was followed by a rate increase. The effects of other influencing factors, including residence time, temperature, pressure, concentration of reactants, catalyst, and cocatalyst, were also investigated. As expected, hydrogen concentration and hexene concentration had significant effects on molecular weight and short-chain branching, respectively. In addition, hexene enhanced the polymerization rate and catalyst activity, while cocatalyst and hydrogen both led to a lower polymerization rate. The results from this study provide important quantitative information that will be used for parameter estimation in fundamental models.
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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) | 0.001 | 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 | 0.001 |
| 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".