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 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".