Improvements in the Hydrogenation of Nitrile Rubber Using Wilkinson's Catalyst
Bibliographic record
Abstract
Abstract RhCl(PPh3)3 is an efficient catalyst precursor for the selective hydrogenation of C=C in acrylonitrile butadiene rubber(“nitrile rubber”, NBR). The established technology for the process using RhCl(PPh3)3 is to carry out reaction in the presence of a large excess of triphenylphosphine (PPh3), with monochlorobenzene (MCB) as solvent. In parallel with the hydrogenation of unsaturation in the rubber there is a side reaction involving the MCB, which produces benzene. This likely occurs via oxidative addition of the C-Cl bond in the monochlorobenzene to a Rh intermediate in the catalytic cycle for hydrogenation, followed by reductive elimination of benzene in conjunction with H2 addition to the Rh centre. This leads to formation of less active Rh intermediates which lead to rapid deterioration of catalytic activity in the absence of excess PPh3. It was postulated that some of the PPh3 in solution acts as a base that “mops up” excess HCl formed as a by product of the catalytic cycle. Supporting evidence comes from a novel improvement of the hydrogenation process, where the deactivation of catalyst, can be offset by the presence of bases, such as amines and metal oxides (as an alternative to adding a large excess of PPh3). This modification can improve catalyst activity with respect to levels of Rh used, or could be used to minimize the level of added co-catalysts needed to maintain useful rates of 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 | 0.000 |
| 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".