Facile Generation of Active Sites in Nodes of Ni-MFU-4l Metal–Organic Framework for Hydrogenation Reaction
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) represent a well-defined class of materials capable of incorporating catalytically active sites for gas-phase catalysis. However, the reducing conditions of hydrogenation catalysis can lead to nanoparticle formation in MOFs, which can significantly diminish the catalytic activity of single-site metals and reduce the longevity of MOF-based hydrogen solutions. Here, we present a straightforward approach to accessing catalytically active single metal sites in a robust Ni-MFU-4l MOF for gas-phase hydrogenation without the formation of Ni nanoparticles. By carefully tuning the local node chemistry through postsynthetic exchange of the terminal ligand coordinated to the Ni(II) centers in the MOF, from −Cl to −OH or −HCOO, we can readily generate Ni–H active species. We further demonstrate, using in situ pair-distribution function analysis, that these Ni–H sites are the sole catalytically active sites in the terminal ligand-exchanged counterparts, whereas nanoparticles readily form in the parent Ni-MFU-4l-Cl under otherwise identical catalytic conditions.
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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 | 0.001 | 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".