Highly active and stable Ru‐promoted Ni/<scp>CeO<sub>2</sub></scp> catalysts for <scp>CO<sub>2</sub></scp> methanation reaction
Bibliographic record
Abstract
Abstract Biogas is not only a promising renewable source of energy that can be integrated into existing infrastructures, but biogas could also be a CO2 source for green methane synthesis by hydrogenation with renewable energy‐derived hydrogen. Since biogas is a mixture of CO2 and CH4, we targeted to accomplish direct methanation of CO2 in CH4‐rich gases that are supposed to be a simulated biogas without any processes for CH4 and CO2 separation in biogas. This direct CO2 methanation in biogas can be a more sustainable and environmentally friendly approach. Methane production from biogas‐derived CO2 by conventional methods has some difficulties, including (1) the vast energy cost for separation process of CO2 and CH4 and (2) the slow rate of the reaction in biochemical CO2 methanation. In this paper, we developed active and durable catalysts in direct methanation of CO2 contained in biogas. The focus was on the effect of coexisting CH4 and steam in biogas on CO2 methanation activity and durability of CeO2 supported Ni catalysts. Among all prepared catalysts, a 1 wt.% Ru‐10 wt.% Ni/CeO2 catalyst exhibited 90% CO2 conversion and 100% CH4 selectivity at 275°C reaction temperature. Adding steam to the standard reaction gas mixture (CO2/H2/N2 = 1/4/5) did not impact CO2 conversion in the temperature range investigated, whereas adding CH4 to the standard reaction gas mixture had only a minute decrease (i.e., 86.9% CO2 conversion and 100% CH4 selectivity at 275°C temperature). Furthermore, the 1 wt.% Ru‐10 wt.% Ni/CeO2 catalyst demonstrated remarkable stability in the CO2 methanation reaction.
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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".