A Copper–Zinc Cyanamide Solid-Solution Catalyst with Tailored Surface Electrostatic Potentials Promotes Asymmetric N-Intermediate Adsorption in Nitrite Electroreduction
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The electrocatalytic nitrite reduction (NO 2 RR) converts nitrogen-containing pollutants to high-value ammonia (NH 3 ) under ambient conditions. However, its multiple intermediates and multielectron coupled proton transfer process lead to low activity and NH 3 selectivity for the existing electrocatalysts. Herein, we synthesize a solid-solution copper–zinc cyanamide (Cu 0.8 Zn 0.2 NCN) with localized structure distortion and tailored surface electrostatic potential, allowing for the asymmetric binding of NO 2 – . It exhibits outstanding NO 2 RR performance with a Faradaic efficiency of ∼100% and an NH 3 yield of 22 mg h –1 cm –2, among the best for such a process. Theoretical calculations and in situ spectroscopic measurements demonstrate that Cu–Zn sites coordinated with linear polarized [NCN] 2– could transform symmetric [Cu–O–N–O–Cu] in CuNCN-NO 2 – to a [Cu–N–O–Zn] asymmetric configuration in Cu 0.8 Zn 0.2 NCN-NO 2 –, thus enhancing adsorption and bond cleavage. A paired electro-refinery with the Cu 0.8 Zn 0.2 NCN cathode reaches 2000 mA cm –2 at 2.36 V and remains fully operational at industrial-level 400 mA cm –2 for >140 h with a NH 3 production rate of ∼30 mg NH3 h –1 cm –2 . Our work opens a new avenue of tailoring surface electrostatic potentials using a solid-solution strategy for advanced electrocatalysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".