Rare earth oxide promoted Ru/Al2O3 dual function materials for CO2 capture and methanation: An operando DRIFTS and TGA study
Bibliographic record
Abstract
Dual-function materials (DFMs) combine sorbent and catalytic components to perform selective CO 2 capture and subsequent hydrogenation. This study explores the performance of rare-earth oxides (REOs) as CO 2 adsorption sites on Ru/Al 2 O 3 . REOs increase CO 2 uptake by upwards of +60 % by enhancing the overall catalyst surface basicity and favoring metal–support interactions. Thermogravimetric analysis during CO 2 adsorption-hydrogenation cycles exhibited significant catalytic activity and enhanced stability of Ru-REO/Al 2 O 3 at temperatures as low as 200 °C. This leads to methane production of 50–85 µmol g −1 , surpassing recently reported values obtained for alkali and alkali-earth promoted Ru-based materials operated at 250 °C. The highest performing studied DFM, RuNd 2 O 3 /Al 2 O 3 , achieved 85 % CO 2 capture efficiency and steadily produced methane in cyclic operation (+120 % CO 2 uptake relative to Ru/Al 2 O 3 ). Operando DRIFTS revealed that the dominant mechanism for methane formation is the hydrogenation of ruthenium carbonyls, which are stabilized by REOs. Upon CO 2 exposure, surface carbonates and bicarbonate species form more abundantly on DFMs than on Ru/Al 2 O 3 . This confirms that REOs enhance the adsorption and retention of carbonates, which generate additional promoter-related reaction pathways during low-temperature hydrogenation. These findings are crucial in the advancement of sustainable, wider operation range carbon capture and utilization technologies. • Rare-earth oxides (REOs) were used as sorbents for enhanced CO 2 capture in DFMs. • DFMs (Ru-REO/Al 2 O 3 ) capture CO 2 and convert it to CH 4 via cyclic operation at 200 ºC. • 120 % more CO 2 capture and CH 4 production with DFMs compared to Ru/Al 2 O 3 . • DFMs showed remarkable stability and sustained conversion over 15 cycles at 200 ºC. • Operando DRIFTS studies identified carbonates as reacting species on the DFMs.
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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 | 0.000 |
| 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".