Lattice Oxygen-Driven Co-Adsorption of Carbon Dioxide and Nitrate on Copper: A Pathway to Efficient Urea Electrosynthesis
Bibliographic record
Abstract
The electrochemical coupling of CO 2 and NO 3 – on copper-based catalysts presents a sustainable strategy for urea production while simultaneously addressing wastewater denitrification. However, the inefficient random adsorption of CO 2 and NO 3 – on the copper surface limits the interaction of the key carbon and nitrogen intermediates, thereby impeding efficient C–N coupling. In this study, we demonstrate that the residual lattice oxygen in oxide-derived copper nanosheets (O L -Cu) can effectively tune the electron distribution, thus activating neighboring copper atoms and generating electron-deficient copper (Cu δ+ ) sites. These Cu δ+ sites enhance CO 2 adsorption and stabilize *CO intermediates, which enables the directional NO 3 – adsorption at adjacent Cu δ+ sites. This mechanism shortens the C–N coupling pathway and achieves a urea yield of up to 298.67 mmol h –1 g –1 at −0.7 V versus RHE, with an average Faradaic efficiency of 31.71% at a high current density of ∼95 mA cm –2 . In situ spectroscopic measurements confirmed the formation of Cu δ+ sites and tracked the evolution of the key intermediates (i.e., *CO, *NO, *OCNO, and *NOCONO) during urea synthesis. Density functional theory calculations revealed that Cu δ+ sites promote adjacent coadsorption of *CO and *NO 3, as well as *OCNO and *NO 3, significantly improving C–N coupling kinetics. This study underscores the critical role of lattice oxygen in facilitating adjacent coadsorption and improving C–N coupling selectivity.
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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".