Activation of atom-precise clusters for catalysis
Bibliographic record
Abstract
The use of atom-precise, ligand-protected metal clusters has exceptional promise towards the fabrication of model supported-nanoparticle heterogeneous catalysts which have controlled sizes and compositions. One major challenge in the field involves the ease at which metallic clusters sinter upon removal of protected ligands, thus destroying the structural integrity of the model system. This review focuses on methods used to activate atom-precise thiolate-stabilized clusters for heterogeneous catalysis, and strategies that can be used to mitigate sintering. Thermal activation is the most commonly employed approach to activate atom-precise metal clusters, though a variety of chemical and photochemical activation strategies have also been reported. Material chemistry methods that can mitigate sintering are also explored, which include overcoating of clusters with metal oxide supports fabricated by sol-gel chemistry or atomic layer deposition of thin oxide films or encapsulating clusters within porous supports. In addition to focusing on the preservation of the size and morphology of deprotected metal clusters, the fate of the removed ligands is also explored, because detached and/or oxidized ligands can also greatly influence the overall properties of the catalyst systems. We also show that modern characterization techniques such as X-ray absorption spectroscopy and high-resolution electron microscopy have the capacity to enable careful monitoring of particle sintering upon activation of metal clusters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".