New route toward building active ruthenium nanoparticles on ordered mesoporous carbons with extremely high stability
Bibliographic record
Abstract
Creating highly active and stable metal catalysts is a persistent goal in the field of heterogeneous catalysis. However, a real catalyst can rarely achieve both of these qualities simultaneously due to limitations in the design of the active site and support. One method to circumvent this problem is to fabricate firmly attached metal species onto the voids of a mesoporous support formed simultaneously. In this study, we developed a new type of ruthenium catalyst that was firmly confined by ordered mesoporous carbons through the fabrication of a cubic Ia3d chitosan-ruthenium-silica mesophase before pyrolysis and silica removal. This facile method generates fine ruthenium nanoparticles (ca. 1.7 nm) that are homogeneously dispersed on a mesoporous carbonaceous framework. This ruthenium catalyst can be recycled 22 times without any loss of reactivity, showing the highest stability of any metal catalysts; this catalyst displays a high activity (23.3 mol(LA)h(-1)g(metal)(-1)) during the catalytic hydrogenation of levulinic acid (LA) when the metal loading is 6.1 wt%. Even at an ultralow loading (0.3 wt%), this catalyst still outperforms the most active known Ru/C catalyst. This work reveals new possibilities for designing and fabricating highly stable and active metal catalysts by creating metal sites and mesoporous supports simultaneously.
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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".