Surface Organometallic Chemistry of Supported Iridium(III) as a Probe for Organotransition Metal–Support Interactions in C–H Activation
Bibliographic record
Abstract
Systematic study of the interactions between organometallic catalysts and metal oxide support materials is essential for the realization of rational design in heterogeneous catalysis. Herein we describe the stoichiometric and catalytic chemistry of a [Cp*(PMe 3 )Ir(III)] complex chemisorbed on a variety of acidic metal oxides as a multifaceted probe for stereoelectronic communication between the support and organometallic center. Electrophilic bond activation was explored in the context of stoichiometric hydrogenolysis as well as catalytic H/D exchange. Further information was obtained from the observation of processes related to dynamic exchange between grafted organometallic species and those in solution. The supported organometallic species were characterized by a variety of spectroscopic techniques including dynamic nuclear polarization-enhanced solid-state NMR spectroscopy, diffuse reflectance infrared Fourier transform spectroscopy, and X-ray absorption spectroscopy. Strongly acidic modified metal oxides such as sulfated zirconia engender high levels of activity toward electrophilic bond activation of both sp 2 and sp 3 C–H bonds, including the rapid deuteration of methane at room temperature; however, the global trend for the supports studied here does not suggest a direct correlation between activity and surface Brønsted acidity.
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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".