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.
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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) | 0.000 | 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 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".