Poisoning of Heterogeneous, Late Transition Metal Dehydrocoupling Catalysts by Boranes and Other Group 13 Hydrides
Bibliographic record
Abstract
Borane reagents are widely used as reductants for the generation of colloidal metals. When treated with a variety of heterogeneous catalysts such as colloidal Rh, Rh/Al2O3, and Rh(0) black, BH3.THF (THF = tetrahydrofuran) was found to generate H2 gas with the concomitant formation of a passivating boron layer on the surface of the Rh metal, thereby acting as a poison and rendering the catalyst inactive toward the dehydrocoupling of Me2NH.BH3. Analogous poisoning effects were also detected for (i) colloidal Rh treated with other species containing B-H bonds such as [HB-NH]3, or Ga-H bonds such as those present in GaH3.OEt2, (ii) colloidal Rh that was generated from Rh(I) and Rh(III) salts using borane or borohydrides as reductants, and (iii) for other metals such as Ru and Pd. In contrast, analogous poisoning effects were not detected for the catalytic hydrogenation of cyclohexene using Rh/Al2O3 or the Pd-catalyzed Suzuki cross-coupling of PhB(OH)2 and PhI. These results suggest that although this poisoning behavior is not a universal phenomenon, the observation that such boron layers are formed and surface passivation may exist needs to be carefully considered when borane reagents are used for the generation of metal colloids for catalytic or materials science applications.
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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".