Low-temperature catalytic CO2 methanation over nickel supported on praseodymium oxide
Bibliographic record
Abstract
Nickel (Ni)-based catalysts are widely used for CO 2 methanation due to their cost-effectiveness compared to noble metals and high efficiency. However, their catalytic performance at low temperatures remains a significant challenge, primarily due to the limited activation of CO 2 . This study reveals that the Ni supported on praseodymium oxide (PrO x ) significantly enhanced low-temperature CO 2 methanation activity. This enhancement was primarily attributed to the dual role of PrO x : promoting CO 2 activation and modifying the reducibility of Ni active sites. PrO x facilitated the formation of oxygen vacancies (O v ) through the valence state transition (Pr 3 + ↔ Pr 4+ ), providing electron donor sites for direct CO 2 dissociation (CO 2 → CO + O*). Furthermore, metal-support interaction (MSI) between Ni and PrO x enhanced the reducibility of Ni 2+ to Ni 0 , inducing a higher density of hydrogen activation sites for the hydrogenation of CO 2 . The integration of these properties induced a high efficiency of the CO 2 methanation pathway by enhancing reactant activation efficiency. These findings demonstrate that the synergistic interaction between Ni and PrO x enhances CO 2 methanation by simultaneously improving Ni site reducibility and providing abundant oxygen vacancies for CO 2 activation, indicating PrO x as a highly effective support material for low-temperature CO 2 methanation catalysts.
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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 | 0.000 | 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 | 0.001 |
| 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".