Hydrogenolysis versus Methanolysis of First- and Second-Generation Grubbs Catalysts: Rates, Speciation, and Implications for Tandem Catalysis
Bibliographic record
Abstract
An unexpected “generation gap” is uncovered between the Grubbs catalysts RuCl 2 (L)(PCy 3 )(═CHPh) ( 1a, L = PCy 3; 1b, L = IMes, N,N ′-bis(mesityl)imidazol-2-ylidene) in their reactions with hydrogen versus methanol, in the presence of base. Treatment of the first-generation catalyst 1a with H 2 and NEt 3 (CH 2 Cl 2, 60 °C, 1000 psi H 2 ) affords RuHCl(H 2 )(PCy 3 ) 2 ( 2a ) in 75% yield within 30 min, as determined by in situ NMR analysis. Complex 2a is in turn efficiently converted (96%; 2 h) into the important hydrogenation catalyst RuHCl(CO)(PCy 3 ) 2 ( 3a ) by mild thermolysis with methanol and NEt 3 (4:1 CH 2 Cl 2 −MeOH, 60 °C), 72% net yield for the 1a − 3a transformation. In comparison, subjecting the second-generation catalyst 1b to this two-step process effects <40% net conversion to RuHCl(CO)(IMes)(PCy 3 ) ( 3b ) (hydrogenolysis of 1b: ca. 60% RuHCl(H 2 )(IMes)(PCy 3 ) ( 2b ) (1 h); carbonylation of isolated 2b: 65% 3b (2.5 h)), owing to the susceptibility of the dihydrogen derivative 2b to disproportionation and decomposition. The opposite trend in efficiency for 1a versus 1b is found for methanolysis under argon in the presence of base: 1b undergoes 83% conversion to 3b, versus <60% for the 1a − 3a transformation (4:1 CH 2 Cl 2 −MeOH, 60 °C). This difference reflects the longer duration of the methanolysis reaction (8 h for 1a vs 4 h for 1b; cf. 30 min and 1 h, respectively, for hydrogenolysis) and the lower thermal robustness of 1a . These findings highlight the importance of tailored, catalyst-specific approaches in devising efficient tandem catalysis methodologies based on the first- and second-generation Grubbs complexes. They are directly relevant to the improved synthesis of advanced polymer materials via tandem ROMP−hydrogenation and potentially relevant to RCM- and CM-functionalization processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".