Harnessing Multi-Center-2-Electron Bonds for Carbene Metal–Hydride Nanocluster Catalysis
Bibliographic record
Abstract
N-Heterocyclic carbene (NHC) ligands possess the ability to stabilize metal-based nanomaterials for a broad range of applications. With respect to metal–hydride nanomaterials, however, carbenes are rare, which is surprising if one considers the importance of metal–hydride bonds across the chemical sciences. In this study, we introduce a bottom-up approach leveraging preexisting metal–metal m-center-n-electron (mc-ne) bonds to access a highly stable cyclic(alkyl)amino carbene (CAAC) copper–hydride nanocluster, [(CAAC)6Cu14H12][OTf]2. Using electrochemical measurements and thermogravimetric analysis we showcase that this cluster exhibits superior stability compared to Stryker’s reagent, a popular commercial phosphine-based copper hydride catalyst. Density Functional Theory (DFT) calculations reveal that the enhanced stability stems from hydride-to-ligand backbonding with the π-accepting carbene. This new cluster emerges as a highly efficient and selective copper–hydride pre-catalyst across six reaction classes, thereby providing a bench-stable alternative for catalytic 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 | 0.001 |
| 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".