Zeolite-Confined Nano-RuO<sub>2</sub>: A Green, Selective, and Efficient Catalyst for Aerobic Alcohol Oxidation
Bibliographic record
Abstract
The development of green, selective, and efficient catalysts, which can aerobically oxidize a variety of alcohols to their corresponding aldehydes and ketones, is of both economic and environmental significance. We report here the synthesis of a novel aerobic oxidation catalyst, a zeolite-confined nanometer-sized RuO(2) (RuO(2)-FAU), by a one-step hydrothermal method. Using the spatial constraints of the rigid zeolitic framework, we sucessfully incorporated RuO(2) nanoparticles (1.3 +/- 0.2 nm) into the supercages of faujasite zeolite. Ru K-edge X-ray absorption fine structure results indicate that the RuO(2) nanoclusters anchored in the zeolite are structurally similar to highly hydrous RuO(2); that is, there is a two-dimensional structure of independent chains, in which RuO(6) octahedra are connected together by two shared oxygen atoms. In our preliminary catalytic studies, we find that the RuO(2) nanoclusters exhibit extraordinarily high activity and selectivity in the aerobic oxidation of alcohols under mild conditions, for example, air and ambient pressure. The physically trapped RuO(2) nanoclusters cannot diffuse out of the relatively narrow channels/pores of the zeolite during the catalytic process, making the catalyst both stable and reusable.
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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) | 0.000 | 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".