Nickel-Based Ceria, Zirconia, and Ceria–Zirconia Catalytic Systems for Low-Temperature Carbon Dioxide Reforming of Methane
Bibliographic record
Abstract
Carbon dioxide reforming of methane (CDRM) was studied over a variety of ZrO 2 -, ceria-doped ZrO 2 -, and CeO 2 –ZrO 2 -supported Ni catalysts. Different techniques were used to prepare supports material having different physicochemical properties, and a correlation was established to show the importance of a robust support material. Various characterization of the catalyst further established that the coking behavior of the catalyst depends on the support preparation techniques. Compared to zirconia and ceria-doped zirconia, the use of ceria–zirconia (Ce x Zr 1– x O 2 ) solid solution as a support prepared by using a surfactant was found to be the most stable for low-temperature CDRM. It seems the inhibition of reactions leading to carbon deposition is prominent in systems having ZrO 2 . Temperature-programmed oxidation (TPO) experiments indicated excellent resistance toward carbon formation for Ni supported on Ce x Zr 1– x O 2 compared with other catalysts studied. H 2 -TPR (temperature-programmed reduction) analyses also showed that the stability of Ce x Zr 1– x O 2 solid solution is a function of its enhanced reducibility at lower temperatures as compared to either pure ceria or ceria-doped ZrO 2 . Based on all the catalysts studied, 5% Ni Ce 0.6 Zr 0.4 O 2 was found to be the best catalyst as activity was stable for up to 100 h at 650 and 700 °C, while at 800 °C the catalyst activity remained stable for more than 200 h.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".