Dynamic reversible evolution of vicinal/bonding heteronuclear diatoms drives relay reductive C–N coupling for enhancive urea electrosynthesis
Bibliographic record
Abstract
Abstract The precise construction of dual active sites has been uncovered for the electroreduction of C‐ and N‐based precursors to synthesize urea. However, these strategies often face adsorption scaling constraints and spatial restrictions that hinder C–N coupling, resulting in suboptimal activity and selectivity. Here, we showcase a dynamically reversible evolution between vicinal Fe/Cu diatoms and alloy‐like Fe–Cu sites, enabling cascade protonation and efficient C–N coupling. This approach markedly enhances urea electrosynthesis from CO 2 and NO 3 − , achieving an ultrahigh urea yield of 2421.2 μg h −1 mg −1 , Faraday efficiency (FE) of 70.4%, and C‐selectivity of 96.7%, surpassing state‐of‐the‐art dual‐site electrocatalysts. Operando spectroscopy and theoretical calculations reveal that neighboring Fe/Cu diatoms facilitate the selective adsorption and hydrogenation of NO 3 − and CO 2 into the key intermediates (*NO and *CO). Furthermore, alloy‐like Fe–Cu sites, formed in situ due to declined metal surface free energy driven by electron transfer, facilitate C–N coupling and subsequent protonation to selectively produce urea, while dynamically reverting to vicinal Fe/Cu diatoms. This work provides new insights into the relay catalytic strategy for urea electrosynthesis by modulating the dynamic atomic‐scale evolution of active sites. image
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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) | 0.002 | 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".