Inter‐Metal Interaction with a Threshold Effect in NiCu Dual‐Atom Catalysts for CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Abstract Dual‐atom catalysts (DACs) have become an emerging platform to provide more flexible active sites for electrocatalytic reactions with multi‐electron/proton transfer, such as the CO 2 reduction reaction (CRR). However, the introduction of asymmetric dual‐atom sites causes complexity in structure, leaving an incomprehensive understanding of the inter‐metal interaction and catalytic mechanism. Taking NiCu DACs as an example, herein, a more rational structural model is proposed, and the distance‐dependent inter‐metal interaction is investigated by combining theoretical simulations and experiments, including density functional theory computation, aberration‐corrected transmission electron microscopy, synchrotron‐based X‐ray absorption fine structure, and Monte Carlo experiments. A distance threshold around 5.3 Å between adjacent NiN 4 and CuN 4 moieties is revealed to trigger effective electronic regulation and boost CRR performance on both selectivity and activity. A universal macro‐descriptor rigorously correlating the inter‐metal distance and intrinsic material features (e.g., metal loading and thickness) is established to guide the rational design and synthesis of advanced DACs. This study highlights the significance of identifying the inter‐metal interaction in DACs, and helps bridge the gap between theoretical study and experimental synthesis of atomically dispersed catalysts with highly correlated active sites.
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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 | 0.001 |
| 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 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".