Performance of Cu/TiO<sub>2</sub>‐SiO<sub>2</sub> catalysts in hydrogenation of furfural to furfuryl alcohol
Bibliographic record
Abstract
Abstract A series of Cu/TiO2‐SiO2 catalysts were prepared by a sol‐gel hydrothermal method and characterized by BET, SEM, TEM, XRD, XPS, and H2‐TPR. The catalysts were used for selective hydrogenation of furfural to furfuryl alcohol. Results show that based on the Cu/SiO2 catalyst, introducing a part of TiO2 into the carrier while keeping very large specific surface area would promote the reduction of CuO, inhibit the growth of Cu crystal particles, and improve the dispersity of Cu particles in the catalyst. The electronegativity of Cu is enhanced in the double‐loaded Cu/TiO2‐SiO2 catalyst, compared with single‐loaded Cu/SiO2 or Cu/TiO2 catalysts. The Cu/TiO2‐SiO2 catalyst containing 0.40 g/g (40 wt%) TiO2 in the carrier shows high furfural hydrogenation performance.
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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".