Computation‐Guided Dual‐Site Electrocatalysts for Record‐Performance Nitrite‐to‐Ammonia Conversion
Bibliographic record
Abstract
Abstract Designing catalysts that can simultaneously accelerate reactant activation and hydrogenation remains a central challenge in electrochemical ammonia synthesis. Here, a computation‐guided, dual‐site electrocatalyst design strategy that bridges first‐principles theory with device‐level validation is reported. Guided by density functional theory, Cu‐doped ZnO is identified as an optimal dual‐site platform: Cu sites upshift the Zn d‐band center, strengthening * NO 2 adsorption and enabling facile deoxygenation, while ZnO sites promote water dissociation to supply protons at the reaction interface. This cooperative synergy precisely tunes nitrite activation and hydrogenation kinetics, suppressing competing hydrogen evolution. The resulting catalyst achieves a record NH 3 yield of 552.16 mg h −1 cm −2 with 87.9% Faradaic efficiency in a membrane electrode assembly—4× and 18× higher than flow‐ and H‐cell configurations, respectively. Operando spectroscopy confirms the predicted mechanism, demonstrating a theory‐to‐device workflow that replaces trial‐and‐error with predictive catalyst design. This approach establishes a generalizable paradigm for developing advanced electrocatalysts for sustainable chemical transformations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".