Selectivity control in hydrogenation through adaptive catalysis using ruthenium nanoparticles on a CO2-responsive support
Bibliographic record
Abstract
Abstract With the advent of renewable carbon resources, multifunctional catalysts are becoming essential to hydrogenate selectively biomass-derived substrates and intermediates. However, the development of adaptive catalytic systems, that is, with reversibly adjustable reactivity, able to cope with the intermittence of renewable resources remains a challenge. Here, we report the preparation of a catalytic system designed to respond adaptively to feed gas composition in hydrogenation reactions. Ruthenium nanoparticles immobilized on amine-functionalized polymer-grafted silica act as active and stable catalysts for the hydrogenation of biomass-derived furfural acetone and related substrates. Hydrogenation of the carbonyl group is selectively switched on or off if pure H2 or a H2/CO2 mixture is used, respectively. The formation of alkylammonium formate species by the catalytic reaction of CO2 and H2 at the amine-functionalized support has been identified as the most likely molecular trigger for the selectivity switch. As this reaction is fully reversible, the catalyst performance responds almost in real time to the feed gas composition.
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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".