A catalytic sites contiguity study on atomically‐dispersed <scp>ZnO</scp>‐Cu/<scp>SiO<sub>2</sub></scp> catalysts to improve methanol formation from <scp>CO<sub>2</sub></scp> hydrogenation
Bibliographic record
Abstract
Abstract Cu‐ZnO‐based catalysts are commonly used in research on catalytic carbon dioxide (CO 2 ) hydrogenation for methanol (MeOH) synthesis. This work studied the catalytic sites contiguity, for example, the surface orientation or arrangement of the CO 2 and H 2 activating sites and their capability to facilitate the interaction between the adsorbed species, and its effects on the catalytic performance of MeOH formation. Cu/SiO 2 precursor was prepared by impregnating copper nitrate solution on a commercial SiO 2 gel. Controlling the exposure time and cycle numbers in atomic layer deposition (ALD), atomic‐level dispersion of ZnO (ADZn) was formed on the uncalcined and calcined Cu/SiO 2 precursors as well as on the SiO 2 gel. Characterizations allowed for identification of Cu + –Cu 0 and ADZn 2+ –Cu + /Cu 0 sites contiguity on the reduced catalyst surface. Catalytic performance tests showed that the ALD ZnO‐Cu/SiO 2 ‐C catalyst facilitated the MeOH space–time yield to 33.1 g/(kg catal ∙ h) at 240°C, three times the yield of its Cu‐only counterpart. The property–performance correlation indicated that two types of ZnO–Cu sites contiguity were responsible for MeOH and CO formation from CO 2 hydrogenation with the ADZn 2+ –Cu + /Cu 0 favouring more MeOH formation. The various contiguity of ADZn 2+ –Cu + /Cu 0 sites also influence the MeOH formation from CO 2 hydrogenation.
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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".