Promotional role of methanol and CO2 in carbon dioxide-rich syngas hydrogenation over slurry reactor utilizing combustion induced Cu-based catalysts
Bibliographic record
Abstract
Converting CO 2 to methanol directly remains a hurdle due to catalyst and thermodynamic limitations. This study proposes a solution: using Cu–MgO–CeO 2 (CuMgCe) catalysts (synthesized by solvent combustion) in slurry reactors for methanol formation through methanol-assisted CO 2 -rich syngas hydrogenation. The key innovation lies in the catalyst design by focusing on CO 2 -rich syngas mixtures, we establish a crucial link between catalyst structure and its activity (structure-activity relationship). Our CuMgCe catalyst achieves a space-time yield of 646 g MeOH /kg cat -h −1 , exceeding lab-made industrial catalysts (608.5 g MeOH /kg cat -h −1 ). This yield is further boosted by 5% through an ingenious method - adding initial methanol, which promotes formate intermediates for enhanced productivity. In-depth analysis reveals CO 2 formation during CO-TPD-MS and CO-TPR-MS, generating highly active surface species (CO 2 δ− ) ideal for forming formate intermediates. In-situ DRIFTS confirms the dominance of this formate pathway on CuMgCe for selective methanol synthesis. A mechanistic study sheds light on the synergistic effect of MgO and CeO 2 in the lab-prepared CuMgCe catalyst. This synergy promotes methanol formation during CO 2 -cofed syngas conversion. This research paves the way for highly efficient and selective catalysts for CO 2 utilization in slurry reactor technology, offering a significant step towards cleaner fuel production. • The CuMgCe is more active for CO 2 -rich syngas to methanol than CuZnCe and CuZnMg. • Oxygen vacancies are responsible for CO oxidation to CO 2 . • The in-situ DRIFTS confirmed the Formate pathway for CO 2 hydrogenation to methanol. • The autocatalytic pathways increased the STY of methanol by 5%. • The CO and CO 2 adsorption are more favorable on Cu/MgO and Cu/CeO 2 , respectively.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".