Highly Dispersed ZnO Sites in a ZnO/ZrO <sub>2</sub> Catalyst Promote Carbon Dioxide‐to‐Methanol Conversion
Bibliographic record
Abstract
Abstract ZnO/ZrO 2 catalysts have shown better activity in the CO 2 hydrogenation to methanol compared with single component counterparts, but the interaction between ZnO and ZrO 2 is still poorly understood. In particular, the effect of the ZrO 2 support phase (tetragonal vs. monoclinic) was not systematically explored. Here, we have synthesized ZnO/ZrO 2 catalysts supported on tetragonal ZrO 2 (ZnO/ZrO 2 ‐t) and monoclinic ZrO 2 (ZnO/ZrO 2 ‐m), which resulted in the formation of different ZnO x species, consisting of sub‐nanometer ZnO moieties and large‐sized ZnO particles, respectively. ZnO/ZrO 2 ‐t exhibited a higher methanol selectivity (81 vs. 39 %) and methanol yield (1.25 vs. 0.67 mmol g −1 h −1 ) compared with ZnO/ZrO 2 ‐m. The difference in performance was attributed to the redox state and degree of dispersion of Zn, based on spectroscopy and microscopy results. ZnO/ZrO 2 ‐t had a high density of ZnO x ‐ZrO y sites, which favored the formation of active HCOO* species and enhanced the yield and selectivity of methanol along the formate pathway. Such ZnO clusters were further dispersed on ZrO 2 ‐t during catalysis, while larger ZnO particles on ZnO/ZrO 2 ‐m remained stable throughout the reaction. This study shows that the phase of ZrO 2 supports can be used to control the dispersion of ZnO and the catalyst surface chemistry, and lead to enhanced catalytic performance.
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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".