Direct and transfer hydrogenation of furfural over <scp>MOF</scp> ‐derived <scp>Pd‐Cu</scp> @C catalysts
Bibliographic record
Abstract
Abstract Owing to the self‐reducing ability of palladium acetate in solutions, an ethanol solution containing Pd 0 particles was prepared and coated in‐situ into copper metal–organic framework (Cu‐MOF), forming Pd@Cu‐MOF in a coated structure. The Pd@Cu‐MOF was reduced under N 2 or H 2 to form carbon‐coated Pd‐Cu@C. The pyrolysis and carbonization of Cu‐MOF and the reduction of Cu 2+ were studied. The Cu‐MOF under either N 2 or H 2 was pyrolyzed and carbonized, but the Cu 2+ reduction mechanisms were different. The high‐temperature carbothermic reduction of Cu 2+ under N 2 produced Cu 0 , but during low‐temperature reduction under H 2 , the reducing H 2 reduced Cu 2+ to Cu 0 . Furfural hydrogenation experiments showed that compared with H 2 , the Pd‐Cu@C prepared under N 2 reduction displayed higher furfural hydrogenation activity. The catalytic activity of Pd‐Cu@C prepared from in‐situ Pd 0 coating was higher than the Pd/Cu@C prepared from the impregnation method. With i ‐propanol as the solvent, the catalytic hydrogenation of furfural under H 2 consisted of direct catalytic hydrogenation with molecular hydrogen as the hydrogen source and catalytic transfer hydrogenation with i ‐propanol as the hydrogen donor. The catalytic activity of direct catalytic hydrogenation is higher than the catalytic transfer hydrogenation.
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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".