Selectivity control in hydrogenation through adaptive catalysis using ruthenium nanoparticles on a CO2-responsive support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 H 2 or a H 2 /CO 2 mixture is used, respectively. The formation of alkylammonium formate species by the catalytic reaction of CO 2 and H 2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it