Hydrogenation of Furfural to Furfuryl Alcohol over Ru Particles Supported on Mildly Oxidized Biochar
Bibliographic record
Abstract
Catalytic hydrogenation of aldehydes is required as the stabilizing step in bio-oils conversion. Ruthenium supported on carbon was used in the present work for hydrogenation of furfural (FF) to furfuryl alcohol (FA). Converting a biochar with no surface area and low carboxyl groups surface density to an outstanding catalyst support using a very simple mild air/steam oxidation is the original contribution of this work. The mildly oxidized biochar is impregnated with a targeted loading of 2.5 wt.% Ru via ion-exchange, using Ru(NH3)6Cl2 precursor. ICP analysis shows that the mild oxidation increases Ru adsorption capacity of untreated biochar from 1.2 to 2.2 wt.%. H2 chemisorption and TEM analysis indicate that the preliminary mild oxidation leads to higher Ru dispersion. XPS analysis also shows that the treatment prevents Ru from surface segregation. The highest value of 93% FA selectivity at 53% FF conversion was obtained in a batch autoclave reactor under optimized conditions.
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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".