Selective biomass conversion over novel designed tandem catalyst
Bibliographic record
Abstract
Selective conversion of biomass into targeted molecules like polyols, especially, from cellulosic compounds, is being widely investigated as a sustainable process to produce biodiesel and bio-additives. The known process involves two steps, namely hydrolysis and hydrogenation. Thus, it requires two different catalytic materials or bifunctional catalysts. In this context, the present work reports a new catalytic approach based on the use of tandem catalysts, consisting of the combination of an acid solid catalyst (active for hydrolysis) and a supported metal catalyst (active for hydrogenation). Two different functionalized activated carbons and the resin Amberlyst 15 have been tested as solid acid catalysts, and Ru nanoparticles supported on the original activated carbon (SA) are the metal catalyst part of the tandem. All the tested tandem catalysts exhibited higher activity than the supported Ru catalyst did. The highest cellulose conversion and selectivity to sorbitol (70% and 86%, respectively) have been obtained over a novel tandem catalyst, which resulted from a physical mixture between a sulfuric acid modified SA carbon (SASu) and Ru loaded SA (Ru/SA), leading to a tandem catalyst (Ru/SA+SASu). This novel-designed tandem catalyst is reusable. Based on tandem catalysts with a solid-solid system combination, the adopted novel-designed catalytic approach is cost-efficient and sustainable, and can be considered promising for the green production of high-added-value chemicals.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".