Cobalt-Based Nonprecious Metal Catalysts Derived from Metal–Organic Frameworks for High-Rate Hydrogenation of Carbon Dioxide
Bibliographic record
Abstract
The development of cost-effective catalysts with both high activity and selectivity for carbon–oxygen bond activation is a major challenge and has important ramifications for making value-added chemicals from carbon dioxide (CO 2 ). Herein, we present a one-step pyrolysis of metal organic frameworks that yields highly dispersed cobalt nanoparticles embedded in a carbon matrix which shows exceptional catalytic activity in the reverse water gas shift reaction. Incorporation of nitrogen into the carbon-based supports resulted in increased reaction activity and selectivity toward carbon monoxide (CO), likely because of the formation of a Mott–Schottky interface. At 300 °C and a high space velocity of 300 000 mL g –1 h –1, the catalyst exhibited a CO 2 conversion rate of 122 μmol CO 2 g –1 s –1, eight times higher than that of a reference Cu/ZnO/Al 2 O 3 catalyst. Our experimental and computational results suggest that nitrogen-doping lowers the energy barrier for the formation of formate intermediates (CO 2 * + H* → COOH* + *), in addition to the redox mechanism (CO 2 * + * → CO* + O*). This enhancement is attributed to the efficient electron transfer at the cobalt–support interface, leading to higher hydrogenation activity and opening new avenues for the development of CO 2 conversion technology.
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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".