Atomically Dispersed Fe-Co Bimetallic Catalysts for the Promoted Electroreduction of Carbon Dioxide
Bibliographic record
Abstract
Abstract The electroreduction reaction of CO2 (ECO2RR) requires high-performance catalysts to convert CO2 into useful chemicals. Transition metal-based atomically dispersed catalysts are promising for the high selectivity and activity in ECO2RR. This work presents a series of atomically dispersed Co, Fe bimetallic catalysts by carbonizing the Fe-introduced Co-zeolitic-imidazolate-framework (C–Fe–Co–ZIF) for the syngas generation from ECO2RR. The synergistic effect of the bimetallic catalyst promotes CO production. Compared to the pure C–Co–ZIF, C–Fe–Co–ZIF facilitates CO production with a CO Faradaic efficiency (FE) boost of 10%, with optimal FECO of 51.9%, FEH2 of 42.4% at − 0.55 V, and CO current density of 8.0 mA cm−2 at − 0.7 V versus reversible hydrogen electrode (RHE). The H2/CO ratio is tunable from 0.8 to 4.2 in a wide potential window of − 0.35 to − 0.8 V versus RHE. The total FECO+H2 maintains as high as 93% over 10 h. The proper adding amount of Fe could increase the number of active sites and create mild distortions for the nanoscopic environments of Co and Fe, which is essential for the enhancement of the CO production in ECO2RR. The positive impacts of Cu–Co and Ni–Co bimetallic catalysts demonstrate the versatility and potential application of the bimetallic strategy for ECO2RR.
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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".