Rh<sub>2</sub>P Nanoparticles Stabilized by Carbon Patches for Hydroformylation of Olefins
Bibliographic record
Abstract
Rh 2 P nanoparticles (NPs) have been identified as suitable mimics of [Rh I (Ph 3 P) 3 ] +, the benchmark of homogeneous catalysts in liquid-phase hydroformylation. For this reason, a fitted synthetic strategy is required to develop catalysts based exclusively on Rh 2 P NPs. To attain this, two synthetic pathways have been devised. In the first one, two separate sources of Rh and P were used. In the second one, the Wilkinson complex was employed as a unique source of Rh and P to probe the positive influence of the well-defined molecular organization on the preparation of dispersed and controlled Rh 2 P nanoparticles, stabilized by carbon patches formed during the pyrolysis treatment from PPh 3 . In addition, metallic Rh nanoparticles were also synthesized to be used as reference. All catalysts have been compared by means of: transmission electron microscopy, X-ray diffraction, and X-ray adsorption spectroscopy. The application of XAS to the study of Rh 2 P NPs is unusual and has been essential in the discussion of the results. Starting with a well-defined metal precursor leads to the exclusive formation of Rh 2 P NPs with excellent catalytic activity for the liquid-phase hydroformylation. The role of P is to modulate the particle size and the electronic configuration of Rh species, resulting in the improvement of the catalytic performance and the obtention of turnover frequencies of 5236 h –1 at 60 °C and 17,788 h –1 at 100 °C.
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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 | 0.001 |
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".