Facile Carbon–Fluorine Bond Activation and Subsequent Functionalisation of 1,1‐Difluoroethylene and Tetrafluoroethylene Promoted by Adjacent Metal Centres
Bibliographic record
Abstract
The bridging fluoroolefin ligands in the complexes [Ir(2)(CH(3))(CO)(2)(μ-olefin)(dppm)(2)][OTf] (olefin = tetrafluoroethylene, 1,1-difluoroethylene; dppm = μ-Ph(2)PCH(2)PPh(2); OTf(-) = CF(3)SO(3)(-)) are susceptible to facile fluoride ion abstraction. Both fluoroolefin complexes react with trimethylsilyltriflate (Me(3)SiOTf) to give the corresponding fluorovinyl products by abstraction of a single fluoride ion. Although the trifluorovinyl ligand is bound to one metal, the monofluorovinyl group is bridging, bound to one metal through carbon and to the other metal through a dative bond from fluorine. Addition of two equivalents of Me(3)SiOTf to the tetrafluoroethylene-bridged species gives the difluorovinylidene-bridged product [Ir(2)(CH(3))(OTf)(CO)(2)(μ-OTf)(μ-C=CF(2))(dppm)(2)][OTf]. The 1,1-difluoroethylene species is exceedingly reactive, reacting with water to give 2-fluoropropene and [Ir(2)(CO)(2)(μ-OH)(dppm)(2)][OTf] and with carbon monoxide to give [Ir(2)(CO)(3)(μ-κ(1):η(2)-C≡CCH(3))(dppm)(2)][OTf] together with two equivalents of HF. The trifluorovinyl product [Ir(2)(κ(1)-C(2)F(3))(OTf)(CO)(2)(μ-H)(μ-CH(2))(dppm)(2)][OTf], obtained through single C-F bond activation of the tetrafluoroethylene-bridged complex, reacts with H(2) to form trifluoroethylene, allowing the facile replacement of one fluorine in C(2)F(4) with hydrogen.
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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".