Adaptive Catalysts for the Selective Hydrogenation of Bicyclic Heteroaromatics using Ruthenium Nanoparticles on a CO<sub>2</sub>‐Responsive Support
Bibliographic record
Abstract
Abstract Ruthenium nanoparticles (NPs) immobilized on an amine‐functionalized polymer‐grafted silica support act as adaptive catalysts for the hydrogenation of bicyclic heteroaromatics. Whereas full hydrogenation of benzofuran and quinoline derivatives is achieved under pure H 2 , introducing CO 2 into the H 2 gas phase leads to an effective shutdown of the arene hydrogenation while preserving the activity for the hydrogenation of the heteroaromatic part. The selectivity switch originates from the generation of ammonium formate species on the surface of the materials by catalytic hydrogenation of CO 2 . The CO 2 hydrogenation is fully reversible, resulting in a robust and rapid switch between the two states of the catalyst adapting its performance in response to the feed gas composition. A variety of benzofuran and quinoline derivatives were hydrogenated to fully or partially saturated products in high selectivity and yields simply by altering the composition of the feed gas from H 2 to H 2 /CO 2 . The adaptive catalytic system thus provides controlled access to valuable products using a single catalyst rather than two specific and distinct catalysts with static reactivity.
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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".