Exploring the Utility of Neutral Rhodium and Iridium κ<sup>2</sup>-<i>P</i>,<i>O</i>and κ<sup>2</sup>-P(<i>S</i>),<i>O</i>Complexes as Catalysts for Alkene Hydrogenation and Hydrosilylation
Bibliographic record
Abstract
Heating of [(COD)M(κ 2 -3- P i Pr 2 -2- N Me 2 -indene)] + X - (M = Rh or Ir; X = BF 4, PF 6; COD = η 4 -1,5-cyclooctadiene) in a mixture of water and THF (48 h, 60 °C) afforded the neutral (COD)M(κ 2 - P, O ) complexes (M = Rh, 5a, 80%; M = Ir, 5b, 74%). Similarly, thermolysis of [(COD)M(κ 2 -3-P( S ) i Pr 2 -2- N Me 2 -indene)] + BF 4 - (M = Rh or Ir) in a mixture of water and CH 2 Cl 2 (14 h, 60 °C) produced the neutral (COD)M(κ 2 -P( S ), O ) complexes (M = Rh, 13a, 91%; M = Ir, 13b, 90%). Subsequent preparation of 1-P i Pr 2 -2-indanone ( 8, 91%) enabled the non-hydrolytic synthesis of 5b in 70% isolated yield via treatment with 0.5[(COD)IrCl] 2 in the presence of NEt 3 . Lithiation of ( o -P i Pr 2 )phenol followed by quenching with 0.5[(COD)IrCl] 2 afforded the neutral (COD)Ir(κ 2 - P, O ) complex 10 (91%). Single-crystal X-ray diffraction data are provided for 5b, 10, 13a, and 13b . Whereas 5a, 13a, and 13b performed poorly as catalysts for the hydrogenation of alkenes, the known complex (COD)Ir(OPh)(PCy 3 ) 2 as well as 5b and 10 proved to be excellent catalysts for hydrogenation of mono-, di-, and trisubstituted alkene substrates under mild conditions (22 °C, ∼1 atm H 2 ). Complexes 13a,b were also shown to be competent catalysts for addition of triethylsilane to styrene.
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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".