Natural Sunlight‐Driven CO<sub>2</sub> Hydrogenation into Light Olefins at Ambient Pressure over Bifunctional Cu‐Promoted CoFe Alloy Catalyst
Bibliographic record
Abstract
Abstract The natural sunlight‐driven conversion of CO2 into valuable C2+ products is urgently being pursued at ambient pressure, yet it poses a substantial challenge. Herein, a bifunctional Cu‐promoted CoFe alloy catalyst is designed for the natural light‐driven CO2 hydrogenation into light olefins. Under a weak solar‐irradiation intensity of 0.45 kW m−2 (0.45 sun), the optimal catalyst exhibits excellent activity and selectivity, with an impressive 73.7% selectivity for C2+ hydrocarbons and an outstanding 56.5% selectivity for C2‐4 olefins, which is the best catalyst for C2+ hydrocarbons photosynthesis from natural sunlight to date. The bifunctional design of the catalyst combines the advantages of both metallic Cu and CoFe alloy components, providing a synergistic effect that enhances the CO2 hydrogenation performance. The Cu promoter plays a crucial role in enhancing the adsorption of CO2 and hydrogen spillover, while the CoFe alloy provides a stable coupling site of the C1 intermediate for promoting the C2‐4 olefins. This study provides new insights into the design of bifunctional catalysts for CO2 hydrogenation and opens up new possibilities for sustainable production of light olefins from renewable resources.
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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".