Dry Reforming of Methane with a Ni/Al<sub>2</sub>O<sub>3</sub>‐YSZ Catalyst: The Role of the Catalyst Preparation Protocol
Bibliographic record
Abstract
Abstract Many studies of Ni based ceramic supporting reforming catalysts are found in the literature. A synthesis of the reported results shows that their efficiency and durability are significantly affected by their fabrication protocol. This research has been aimed at evaluating how the conditions of 1) the ceramic support preparation and 2) the Ni deposition, through an impregnation‐drying‐calcination‐reduction protocol, affect the catalytic activity and the catalyst deactivation over time during methane dry reforming. The catalyst support used in this study was obtained by the mixing and pressing of alumina and YSZ (Yttria Stabilized Zirconia) powders, then calcining the mixtures at high temperature to form pellets of limited porosity (specific surface of 1.5‐10 m2/g), without inducing change to the crystalline phases. The results show that the surface density of the nickel particles, the catalyst activity, and its life span are highly dependent upon the catalyst preparation protocol. The initial nitrate solution concentration, the duration of the impregnation and the specific surface of the ceramic support have, all of them, a considerable influence on the size range of the deposited nickel particles. The surface density, the amount and the size of the latter highly affect the catalytic activity. It has been also shown that an increase in the ratio CH4/CO2 is detrimental to the catalytic activity of the tested formulations; a small excess of methane is enough to initiate the deactivation process of the catalyst very quickly for all of the composition tested in this study. A phenomenological deactivation kinetics model has been built and optimized. Although there are differences in deactivation rates among the different formulations tested, the model shows that the deactivation rate is highly dependent upon the reaction rate constant and that zero‐ and first‐order kinetics give statistically the same prediction error; the latter is always lower or equal to the experimental error.
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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".