Encapsulation of active ruthenium species for hydrogenation of carbon dioxide to formic acid in porous hollow spheres of silica
Bibliographic record
Abstract
Abstract The work is dedicated to the encapsulation of active ruthenium species for the hydrogenation of carbon dioxide to formic acid in porous hollow spheres of silica. The porous hollow spheres were obtained via a sol–gel‐based reaction with 1‐dodecyl amine and cetyltrimethylammonium bromide as templates to form both the hollow void and nanospaces in the shells of the porous hollow sphere support. The metallic ruthenium species were encapsulated in the nanospaces of the hollow spheres through the immersion or incipient wetness impregnation method with a methanolic solution of ruthenium species, following the activation process under solvothermal conditions. The results of transmission electron microscopy (TEM) and X‐ray diffraction (XRD) measurements indicate that the highly dispersed active ruthenium species were effectively encapsulated in the nanospaces by adding an aqueous ammonia solution during the immersion process, and an improvement in the catalytic activity for formic acid yield via the hydrogenation of carbon dioxide was observed in the presence of the catalyst prepared with the appropriate amount of ammonia. The activity dramatically improved in the presence of the ruthenium‐encapsulated porous hollow sphere catalyst prepared via the incipient wetness impregnation method due to including the highly dispersed active ruthenium species on the surface and in the nanospaces of the hollow spheres.
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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".