Biochar-supported highly dispersed ultrasmall Cu/ZnO nanoparticles as a highly efficient novel catalyst for CO2 hydrogenation to methanol
Bibliographic record
Abstract
Methanol synthesis via CO2 hydrogenation is a key pathway for producing methanol. Considerable research has focused on enhancing Cu/ZnO-based catalysts for this process. In this study, biochar, a porous material derived from renewable waste, was employed to support the immobilization of Cu/ZnO nanoparticles for CO2 hydrogenation to methanol. The catalyst developed in this work exhibited exceptional performance, with a methanol space-time yield (STY) of 496.5 mgMeOH gCu-1 h-1, selectivity of 71%, and stability (maintaining catalytic activity for over 45 h). These metrics significantly outperformed those of the Cu/ZnO/Al2O3 catalyst (STY of 98.6 mgMeOH gCu-1 h-1, selectivity of 54%, with catalytic activity loss after 25 h) under identical reaction conditions (260 °C, 1 MPa). Structural characterizations revealed that the enhanced catalytic activity and improved stability of the biochar-supported Cu/ZnO nanoparticles, relative to Cu/ZnO/Al2O3, were attributed to the enrichment of Cu-Zn interfacial sites. This was facilitated by the highly efficient dispersion and formation of ultrasmall Cu/ZnO nanoparticles on the biochar surface, along with biochar’s role in enhancing H2 and CO2 adsorption and activation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".