Role of stable Ni nano catalysts for dry reforming of methane
Bibliographic record
Abstract
Dry reforming of methane (DRM) offers a promising pathway towards carbon neutrality by converting the greenhouse gases methane (CH 4 ) and carbon dioxide (CO 2 ) into valuable syngas (CO + H 2 ). This sustainable process not only mitigates climate change but also contributes to a circular carbon economy by utilizing waste gases as valuable feedstocks. However, the successful industrial implementation of DRM hinges on the development of stable and efficient catalysts. This study investigated the influence of the ceria support source on the catalytic performance of Ni/CeO 2 catalysts. Three commercially available ceria supports from Germany, Canada, and the USA were employed, denoted as Ni-P, Ni-M, and Ni-C, respectively. These supports were impregnated with nickel and characterized using a suite of techniques, including XRD, FTIR, SEM, N 2 adsorption-desorption, and TGA. Catalytic activity and stability were evaluated within a temperature range of 550 to 750 °C. Our findings revealed that the catalytic performance is significantly influenced by the physicochemical properties of the catalyst. The Ni/CeO 2 (Ni-C) catalyst demonstrated superior activity and stability, exhibiting minimal carbon deposition as evidenced by TGA analysis and a low deactivation factor. This research provides valuable insights into the critical role of support materials in optimizing Ni/CeO 2 catalyst performance for DRM. The development of highly stable and active catalysts, such as the Ni/CeO 2 (Ni-C) catalyst, is crucial for the successful industrial implementation of DRM, contributing to a more sustainable and environmentally friendly energy future.
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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".