A study of Ni‐Co/ <scp> CeO <sub>2</sub> </scp> catalyst derived from metal–organic framework for dry reforming of methane
Bibliographic record
Abstract
Abstract The efficacy of cobalt‐doped, MOF‐derived catalysts for dry reforming of methane (DRM) was examined. The focus was on the influence of varying nickel and cobalt molar ratios on catalytic performance. Three catalysts with Ni:Co ratios of 1:1, 1:0.5, and 0.5:1, were synthesized and tested with cerium oxide as a constant support. The DRM reaction was conducted at a low temperature of 700°C for 24 h. Despite the low reaction temperature, the catalyst containing an equimolar ratio of Ni and Co demonstrated the highest performance, achieving CO 2 and CH 4 conversions of 91% and 84%, respectively, with an H 2 /CO ratio of 0.96. A decrease in the loading of either nickel or cobalt reduced catalytic activity. The better performance of the Ni:Co (1:1) catalyst compared to Ni:Co (1:0.5) and Ni:Co (0.5:1) catalysts can be attributed to its smallest cobalt crystallite size of 1.6 nm, indicating better metal dispersion compared to the other catalysts. Smaller crystallite sizes enhance the availability of active sites, improve metal–support interaction, and promote efficient CO 2 activation. This improved dispersion likely contributed to the superior catalytic performance and coke resistance observed in the Ni(1)‐Co(1)‐Ce catalyst. The findings emphasize the critical role of active metal loading in achieving optimal DRM performance in the design of MOF‐derived multi‐metallic catalysts for DRM.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".