Quasi-Covalently Coupled Ni–Cu Atomic Pair for Synergistic Electroreduction of CO<sub>2</sub>
Bibliographic record
Abstract
Developing highly active, selective, and stable electrocatalysts for the carbon dioxide reduction reaction (CO 2 RR) is crucial to establish a CO 2 conversion system for industrial implementation and, therefore, to realize an artificially closed carbon loop. This can only be achieved through the rational material design based upon the knowledge of the operational active site at the molecular scale. Enlightened by theoretical screening, herein, we for the first time manipulate a novel Ni–Cu atomic pair configuration toward improved CO 2 RR performance. Systematic characterizations and theoretical modeling reveal that the secondary Cu metal incorporation positively shifts the Ni 3d orbital energy to the Fermi level and thus accelerates the rate-determining step, *COOH formation. In addition, the intrinsic inactivity of Cu toward the competing hydrogen evolution reaction causes a considerable reaction barrier for water dissociation on the Ni–Cu moiety. Due to these attributes, the as-developed Ni/Cu–N–C catalyst exhibits excellent catalytic activity and selectivity, with a record-high turnover frequency of 20,695 h –1 at −0.6 V (vs RHE) and a maximum Faradaic efficiency of 97.7% for CO production. Furthermore, the dynamic structure evolution monitored by operando X-ray absorption fine-structure spectroscopy unveils the interaction between the Ni center and CO 2 molecules and the synergistic effect of the Ni–Cu atomic pair on CO 2 RR activity.
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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 | 0.001 | 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".