Metallocene-catalyzed olefin polymerizations using triphenylcyclopropenium tetrakis(pentafluorophenyl)borate as the activator
Bibliographic record
Abstract
Triphenylcyclopropenium (TPCP) tetrakis(pentafluorophenyl)borate activates bis(cyclopentadienyl)dimethyl titanium resulting in a highly reactive initiating system for the polymerization of styrene. In contrast to triphenylmethyl tetrakis(pentafluorophenyl)borate which is quite active in the absence of the metallocene, TPCP borate shows no activity for styrene polymerization in the absence of bis(cyclopentadienyl)dimethyl titanium. TPCP is the most efficient activator in the carbonium ion borate class. We propose, based on 1H NMR evidence that reaction of Cp2TiMe2 and TPCP borate leads to the formation of the cationic Ti complex [Cp2TiMe]+B(C6F5)4. Evidence for the latter is also provided by UVvis spectroscopy in that we found a bathochromic shift of the Cp2TiMe2 LMCT absorption band from 361 to 482 nm in CH2Cl2 and 487 nm in toluene, respectively. Thermal decomposition of the cationic complex [Cp2TiMe]+B(C6F5)4 leads to less activity. The systems are good catalysts for ethylene polymerization as well, but are less active when using propylene. A conventional ZieglerNatta coordination polymerization mechanism accounts for ethylene and propylene polymerization while a carbocationic polymerization mechanism is proposed for styrene.Key words: triphenylcyclopropenium tetrakis(pentafluorophenyl)borate, bis(cyclopentadienyl)dimethyl titanium, activator, olefin polymerization.
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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) | 0.001 | 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) | 0.002 | 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".