Cobalt-Based Nonprecious Metal Catalysts Derived from Metal–Organic Frameworks for High-Rate Hydrogenation of Carbon Dioxide
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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 (CO2). 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 CO2 conversion rate of 122 μmolCO2 g–1 s–1, eight times higher than that of a reference Cu/ZnO/Al2O3 catalyst. Our experimental and computational results suggest that nitrogen-doping lowers the energy barrier for the formation of formate intermediates (CO2* + H* → COOH* + *), in addition to the redox mechanism (CO2* + * → 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 CO2 conversion technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it