Ga‐doped <scp>CeO</scp><sub>2</sub> solid solutions as promoters for Cu‐based catalysts: Enhancing <scp>CO</scp><sub>2</sub> hydrogenation to methanol via optimized metal–support interaction
Bibliographic record
Abstract
Abstract The low methanol selectivity of Cu/CeO₂ in CO₂ hydrogenation stems from weak metal–support interaction (MSI) and dominant reverse water gas shift (RWGS) pathways. Ga‐doped CeO₂ solid solutions are proposed to enhance MSI and oxygen vacancies, addressing insufficient CO₂ activation and H₂ dissociation in conventional Cu‐based catalysts. CuO‐CeGaₓOₓ (y = 0–0.3) catalysts were synthesized via co‐precipitation. Structural and catalytic properties were analyzed by X‐ray diffraction (XRD), X‐ray photoelectron spectroscopy (XPS), temperature‐programmed H2 reduction (H₂‐TPR), temperature‐programmed desorption of adsorbed CO2 (CO₂‐TPD), and transmission electron microscopy (TEM). Ga doping (y = 0.2) optimized CeGaOₓ solid solution formation, achieving the highest specific surface area (142 m2/g), and Cu0 content (73.4%). At 260°C, CuO‐CeGa₀.₂Oₓ showed 12.6% XCO₂, 57% SCH₃OH, and STY = 308.8 gMeOH Kgcat−1 h−1. Enhanced oxygen vacancies and moderate basic sites suppressed RWGS, favouring methanol pathways. Stability tests confirmed 180 h performance retention without structural degradation. Ga doping strengthens MSI via CeGaOₓ solid solutions, promoting oxygen vacancies and Cu0 dispersion. This dual optimization enhances CO₂ adsorption, H₂ dissociation, and methanol selectivity while suppressing CO byproducts. The CuO‐CeGa₀.₂Oₓ catalyst demonstrates industrial potential, offering a design strategy for high‐performance CO₂ hydrogenation catalysts.
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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".