Evidence for Iron Nanoparticles Catalyzing the Rapid Dehydrogenation of Ammonia-Borane
Bibliographic record
Abstract
A series of precatalysts of the general formula [Fe(NCMe)(L)(PPh 2 C 6 H 4 CH═NCHR-) 2 ][BF 4 ] 2 (where L = CO or NCMe, and R = Ph or H) were tested for the dehydrogenation of amine-boranes. They have already been used in our lab for the transfer hydrogenation or direct hydrogenation of ketones and the oxidative kinetic resolution of alcohols. We compared a series of sterically- (R = H or Ph) and electronically- (L = NCMe or CO) varied precatalysts in both protic and aprotic solvents for the release of hydrogen from ammonia-borane (AB) and studied the products by NMR. At room temperature in tetrahydrofuran (THF) we optimized our systems, and achieved maximum turnover frequencies (TOF) of up to 3.66 H 2 /sec and 1.8 total H 2 equivalents, and in isopropanol we were able to release a maximum of 2.9 equiv of H 2 and reuse some of our catalytic systems. In previous mechanistic studies we provided strong evidence that the active species during transfer hydrogenation (TH) and oxidation catalysis are zerovalent iron nanoparticles formed by the reduction of the Fe-PNNP precatalysts with base. To probe the dehydrogenation active species we successfully show comparable activity between preformed catalysts, and those generated in situ using commercially available Fe 2+ sources and substoichiometric amounts of PNNP ligand. This result, when paired with transmission electron microscope images of ∼4 nm iron nanoparticles of reaction solutions provide evidence that the highly active systems studied are heterogeneous in nature. This would be the first report of iron nanoparticles catalyzing H 2 evolution from AB in nonprotic solvents. We also report the evolution of hydrogen from dimethylamine-borane and the resultant product mixtures using the same catalyst series.
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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".