FeCl<sub>2</sub>·4H<sub>2</sub>O-Mediated Conversion of the CF<sub>3</sub> Group into a Series of Esters: A Strategy for the Synthesis of Fe<sup>II</sup> Complexes with In Situ-Formed Ester-Containing Ligands
Bibliographic record
Abstract
A practical strategy for the preparation of a series of iron(II) complexes has been developed. This methodology features in situ esterification of the CF 3 group on the backbone of the PIP–CF 3 ligand (HPIP = 3-(pyridin-2-yl)imidazo[1,5- a ]pyridine, PIP–CF 3 = 3-(pyridin-2-yl)-1-(trifluoromethyl)imidazo[1,5- a ]pyridine) by a wide range of alcohols. Treatment of FeCl 2 ·4H 2 O with the PIP–CF 3 ligand in EtOH under solvothermal conditions leads to the formation of complexes [Fe(PIP–COOEt) 2 Cl 2 ] ( 1 ), [Fe 2 (PIP–COOEt) 2 Cl 4 ] ( 2 ), and [Fe(PIP–COOEt)Cl 2 ] ( 2′ ) (PIP–COOEt = ethyl 3-(pyridin-2-yl)imidazo[1,5- a ]pyridine-1-carboxylate). EtOH serves as a solvent and is also involved in the esterification of the CF 3 group. The esterification protocol features a broad substrate scope. The CF 3 moiety of the PIP–CF 3 ligand could be esterified by a wide range of alcohol substrates. Compounds [Fe(PIP–COO n Pr) 2 Cl 2 ] ( 3 ), [Fe 2 (PIP–COO n Pr) 2 Cl 4 ] ( 4 ), [Fe 2 (PIP–COO i Pr) 2 Cl 4 ] ( 5 ), and [Fe(PIP–CF 3 ) 2 Cl 2 ]· i PrOH ( 6 · i PrOH) were isolated, and their structures were characterized. The mechanism for the esterification of the CF 3 group was proposed by examining the conditions for the esterification transformations.
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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".