Effects of calcination atmosphere on the performance of the co‐precipitated <scp>Ni</scp> / <scp> ZrO <sub>2</sub> </scp> catalyst in dry reforming of methane
Bibliographic record
Abstract
Abstract The effects of the calcination atmospheres (H 2 , N 2 , and O 2 ) on the performance of Ni/ZrO 2 catalysts prepared by the conventional co‐precipitation method for dry reforming of methane (DRM) were investigated in this work. The catalyst calcined in O 2 atmosphere (NZO) showed the highest Ni dispersion (10.99%), followed by the one calcined in N 2 (NZN, 8.14%) and H 2 atmosphere (NZH, 5.97%). Additionally, NZO and NZN exhibited relatively stronger metal‐support interaction than NZH and the incorporated Ni species on NZO and NZN were capable of stabilizing the metastable tetragonal ZrO 2 , which possessed more oxygen vacancies and higher oxygen mobility than monoclinic ZrO 2 . The evaluation results revealed that the activity of the employed catalysts displayed the following sequence: NZO > NZN > NZH, which was mainly determined by their Ni dispersion. The amount of carbon deposited on NZO (2.2%) was less than that on NZN (5.0%), and the deactivation rate of NZO (0.47% per hour) was slower than that of NZN (0.53% per hour). Due to its low CH 4 conversion and the weak metal‐support interaction, NZH showed the least carbon deposition (1.1%) and the slowest deactivation rate (0.37% per hour) among the representative catalysts.
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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 | 0.001 |
| 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".