Is FeEt<sub>2</sub>(2,2‘-dipyridyl)<sub>2</sub> a Ziegler Catalyst for Polymerization of the Polar Monomer Acrylonitrile?
Bibliographic record
Abstract
This paper describes a reinvestigation into the putative role of a Ziegler process during polymerization of acrylonitrile by the compound FeEt 2 (2,2‘-dipyridyl) 2 . For this very efficient polymerization initiator, acrylonitrile coordination is reported to be a precondition for polymerization, seemingly more compatible with a Ziegler than with a conventional radical process. Consistent with the previous observations, we do indeed find evidence for a sequence of events in which acrylonitrile displaces a dipy ligand of FeEt 2 (dipy) 2, coordinating via the C C bond in η 2 -fashion rather than via the nitrogen. Subsequent steps involve β-hydrogen elimination from one of the ethyl groups to give a hydrido-ethylene-ethyl intermediate, followed by concomitant reductive elimination of ethane and hydride migration to the acrylonitrile to form a 2-cyanoethyliron compound. The latter then undergoes iron−carbon bond homolysis, the resulting cyanoethyl radical initiating a conventional but possibly living radical chain polymerization process. Investigated as possible ethylene polymerization catalysts are FeEt 2 (dipy) 2 “activated” with B(C 6 F 5 ) 3 and [Ph 3 C][B(C 6 F 5 ) 4 ], and both FeCl 2 (dipy) 2 and FeCl 2 (dmby) (dmby = 6,6‘-dimethyl-2,2‘-dipyridyl) activated with AlMe 3, AlEt 3, and MAO. Some of these related, potentially catalytic systems polymerize acrylonitrile, but none initiate ethylene polymerization, probably ruling out the possibility of a Ziegler process by this system for any monomer.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".