Highly Selective Electrocatalytic Reduction of CO<sub>2</sub> into Methane on Cu–Bi Nanoalloys
Bibliographic record
Abstract
Methane (CH 4 ), the main component of natural gas, is one of the most valuable products facilitating energy storage via electricity conversion. However, the poor selectivity and high overpotential for CH 4 formation with metallic Cu catalysts prevent realistic applications. Introducing a second element to tune the electronic state of Cu has been widely used as an effective method to improve catalytic performance, but achieving high selectivity and activity toward CH 4 remains challenging. Here, we successfully synthesized Cu–Bi NPs, which exhibit a CH 4 Faradaic efficiency (FE) as high as 70.6% at −1.2 V versus reversible hydrogen electrode (RHE). The FE of Cu–Bi NPs has increased by approximately 25-fold compared with that of Cu NPs. DFT calculations showed that alloying Cu with Bi significantly decreases the formation energy of *COH formation, the rate-determining step, which explains the improved performance. Further analysis showed that Cu that has been partially oxidized because of electron withdrawal by Bi is the most possible active site.
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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 | 0.001 |
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".