Operando XAFS Studies on Rh(CAAC)-Catalyzed Arene Hydrogenation
Bibliographic record
Abstract
Rh K-edge X-ray absorption fine structure (XAFS) spectroscopy was used to examine the Rh-catalyzed arene hydrogenation of diphenyl ether by a combination of stoichiometric reactions of [(CAAC Cy,Dipp )Rh(COD)Cl] ( Rh–Cl ) (CAAC Cy,Dipp = cyclic alkyl amino carbene) and operando XAFS kinetics studies. Our results unequivocally show that Rh nanoparticles, generated from the single-site Rh complex Rh–Cl, catalyze the arene hydrogenation. Operando XAFS studies illuminate the role of silver cation on the precatalyst reactivity, the effect of increasing H 2 pressure on increasing the catalytic efficiency, the stabilizing influence of Ph 2 O on the relative rate of formation of active Rh nanoparticles, and the absence of soluble single-site Rh species that might leach from bulk heterogeneous Rh nanoparticles. We gained insights into the divergent deactivation pathways mediated by substoichiometric benzothiophene and excess KO t Bu toward H 2 activation, which is a key step en route to Rh nanoparticles for arene hydrogenation. Excess KO t Bu leads to the formation of a Rh–O t Bu complex that interferes with H 2 activation, precluding the formation of Rh nanoparticles. Benzothiophene does not interfere with the activation of H 2 at Rh in the CAAC Cy,Dipp complex while Rh nanoparticles are formed. Once Rh nanoparticles are formed, however, benzothiophene binds irreversibly to the Rh nanoparticles, preventing the adsorption of H 2 and diphenyl ether for arene hydrogenation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".