Dry Reforming of Methane Using Ni Catalyst Supported on ZrO2: The Effect of Different Sources of Zirconia
Bibliographic record
Abstract
Dry reforming of methane (DRM) has a substantial potential to provide a cost-effective process and in reducing greenhouse gases. Its application has been hindered by carbon deposition and instability problems. The use of an appropriate catalyst is influenced by the support type. The objective of this investigation is to elucidate the effect of different sources of ZrO2 support. Four kinds of ZrO2, namely RC-100 and Z-3215, MKnano, and ELTN were acquired from Japan, Canada, and China, respectively. The catalyst samples were analyzed by BET, XRD, TPR, TPD, TEM, TGA, TPO, FT-IR, and Raman. The analysis of the structural properties displayed that all Ni-supported catalysts, regardless of their source, are mesoporous and that 5Ni-RC-100 possessed the highest BET surface area of 17.7 m2/g and 5Ni-MKnano had the lowest value of BET 3.16 m2/g. In the TPD and TEM analysis, the 5Ni-RC-100 catalyst presented the highest intensity of basicity and the minimum average particle size of 3.35 nm, respectively. The 5Ni-RC-100 catalyst outperformed 5Ni-ELTN by exhibiting 44% higher CH4 conversion; however, 5Ni-RC-100 gave the highest weight loss in the TGA analysis of 66%.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".