Ethylene Polymerization and Ethylene/1-Octene Copolymerization with <i>rac</i>-Dimethylsilylbis(indenyl)hafnium Dimethyl Using Trioctyl Aluminum and Borate: A Polymerization Kinetics Investigation
Bibliographic record
Abstract
The polymerization of ethylene and ethylene/1-octene in a semibatch solution reactor using rac -dimethylsilylbis(indenyl)hafnium dimethyl and tetrakis(pentafluorophenyl) borate dimethylanilinium salt ([B(C 6 F 5 ) 4 ] − [Me 2 NHPh] + ) as the catalytic system and trioctylaluminum (TOA) as the scavenger was investigated. Ethylene and 1-octene concentrations, polymerization temperature, borate/catalyst ratio, and TOA concentration were changed to study the ethylene polymerization kinetics with this system. The mode of addition for catalyst, borate, and TOA was also studied. When TOA and borate were added sequentially to the reactor followed by the catalyst, the polymerization activity was low and the molecular weight distribution (MWD) bimodal. Contrarily, when TOA was added first to the reactor and a mixture of catalyst and borate were added to the reactor, the polymerization rate was much higher and the MWD unimodal. An augmented 2 × 2 central composite design was used to investigate this phenomenon. Borate helped stabilize the active sites and reduce potential deactivation reactions with excess TOA.
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".