Industrial‐current Ammonia Synthesis by Polarized Cuprous Cyanamide Coupled to Valorization of Glycerol at 4,000 mA cm<sup>−2</sup>
Bibliographic record
Abstract
Abstract The electrocatalytic nitrate reduction (NO3RR) holds significance in both NH3 synthesis and nitrate contamination remediation. However, achieving industrial‐scale current and high stability in membrane electrode assembly (MEA) electrolyzer remains challenging due to inherent high full‐cell voltage for sluggish NO3RR and water oxidation. Here, Cu2NCN with positive surface electrostatic potential VS(r) is applied as highly efficient NO3RR electrocatalysts to achieve industrial‐current and low‐voltage stable NH3 production in MEA electrolyzer with coupled anodic glycerol oxidation. This paired electro‐refinery (PER) system reaches 4000 mA cm−2 at 2.52 V and remains stable at industrial‐level 1000 mA cm−2 for 100 h with the NH3 production rate of 97000 µgNH3 h−1 cm−2 and a Faradaic efficiency of 83%. Theoretical calculations elucidate that the asymmetric and electron‐withdrawing [N−C≡N] units enhance polarization and VS(r), promoting robust and asymmetric adsorption of NO3* on Cu2NCN to facilitate O−N bond dissociation. A comprehensive techno‐economic analysis demonstrates the profitability and commercial viability of this coupled system. Our work opens a new avenue and marks a significant advancement in MEA systems for industrial NH3 synthesis.
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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".