Ni-Cu/Al2O3 from Layered Double Hydroxides Hydrogenates Furfural to Alcohols
Bibliographic record
Abstract
The hydrogenation of furfural is an important process in the synthesis of bio-based chemicals. Copper-based catalysts favor the hydrogenation of furfural to alcohols. Catalytic activity and stability were higher at a Ni-to-Cu atomic ratio of 1:1 and 1.5:0.5 compared to 0.5:1.5. Here, we prepared Ni-Cu/Al2O3 hydrogenation catalysts derived from layered double hydroxides (LDHs). Catalysts calcined at 673 K and reduced at 773 K with nominal Ni/Cu atomic ratios y/x = 1.5/0.5, 1/1 and 0.5/1.5 were characterized by XRD, FESEM-EDX, H2-TPR, XPS, FAA and BET. Their activity was tested at 463 K and in a 0.05 g g−1 furfural solution in ethanol, and the space velocity in a packed-bed reactor (PBR) was 2.85 gFF gcat−1 h−1. In a slurry reactor (SSR) at 5 MPa H2 and a contact time of 4 h, conversion was complete, while it varied from 91 to 99% in the PBR. Tetrahydrofurfuryl alcohol (TFA) was the main product in the SSR, with a selectivity of 32%, 63% and 56% for Ni0.5Cu1.5Al1, Ni1Cu1Al1 and Ni1.5Cu0.5Al1, respectively. The main product in the atmospheric PBR was furfuryl alcohol (FA), with a selectivity of 57% (Ni0.5Cu1.5Al1), 61% (Ni1Cu1Al1) and 58% (Ni1.5Cu0.5Al1). Other products included furan, methylfuran, 1-butanol and 1,2-pentanediol. Ethyl tetrahydrofurfuryl ether and difurfuryl ether were also formed via the nucleophilic addition of furfural with ethanol and furfuryl alcohol.
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.
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".