Preparation of a Nanostructured Ni/CaO·Al<sub>2</sub>O<sub>3</sub> Catalyst for Syngas Production via Glycerol Dry Reforming: Role of the Preparation Method
Bibliographic record
Abstract
Four nickel nanostructure catalysts supported on the CaO·Al 2 O 3 (CA) support were synthesized with different synthesis procedures (i.e., coprecipitation, evaporation-induced self-assembly, sol–gel, and autocombustion) and have been investigated in a glycerol CO 2 -reforming reaction. The nitrogen adsorption–desorption, SEM, XRD, TEM, H 2 -TPR, O 2 -TPO, TGA, and FTIR techniques were used to characterize prepared Ni/CaO·Al 2 O 3 catalysts, and their catalytic performances were evaluated at 600–750 °C, atmospheric pressure, and a CO 2 /glycerol ratio of 1. It was demonstrated that the preparation route strongly influenced the structural, textural, and chemical features of the as-prepared samples. The maximal conversion of glycerol (ca. 55% at 750 °C) was obtained over the Ni/CA sample prepared by the sol–gel technique. It also exhibited better catalytic stability during the 25 h of the dry reforming reaction. The smaller Ni crystalline size (16.7 nm) and high Ni dispersion with strong interaction with the CaO·Al 2 O 3 support for this sample can result in superior catalytic performance and stability compared with other synthesized samples. This synthesis procedure was sensitive to the solvent type, and the physicochemical property was significantly affected by changing the solvent; therefore, it should be noted as a significant parameter for this preparation method. For the investigation of this parameter, three solvents, including methanol, ethanol, and propanol, were applied in the sol–gel method, and the obtained results imply that employing ethanol as a solvent in this method resulted in achieving better structural properties as well as higher catalytic efficiency in the glycerol CO 2 -reforming reaction. As a result, the simple sol–gel technique was successful for the preparation of the Ni/CaO·Al 2 O 3 sample with high potential as a catalyst for glycerol CO 2 -reforming.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".