Metal Heptafluoroisopropyl (M-hfip) Complexes for Use as hfip Transfer Agents
Bibliographic record
Abstract
New coinage-metal heptafluoroisopropyl (L n M-hfip) complexes are synthesized from the metal fluoride and inexpensive hexafluoropropene (M = Ag, Cu; L = PPh 3, 2,2,6,6-tetramethylpiperidine (Htmp)). Reaction of the silver Htmp complex with a Ni dibromide complex led to efficient hfip transfer to afford L 2 NiBr(hfip) (L = 2-ethylpyridine). Treatment of the Ni-hfip complex with ZnPh 2 gave the corresponding L 2 NiPh(hfip) complexes, which were investigated for reductive elimination of PhCF(CF 3 ) 2 . Although the desired reductive elimination proved unsuccessful, addition of carbon monoxide to L 2 NiPh(hfip) effected an efficient heptafluoroisopropyl carbonylative cross-coupling. Further, while the silver complex does not undergo hfip transfer to organic electrophiles, the copper complex (phen)(PPh 3 )Cu(hfip) ( 3b ) effectively transfers the hfip unit to various substrates. We investigated the scope of 3b with acid chlorides toward the synthesis of perfluoroisopropyl aryl ketones. Additionally, reaction conditions for hfip transfer to p -fluorobenzyl bromide and p -fluorobenzaldehyde were identified. As a bonus, 3b was easily generated on a gram scale using commercially available copper hydride by taking advantage of a rapid hydrodefluorination to generate “Cu–F” in situ. Aspects of the observed reactivity are supported by DFT calculations.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".