Regulating Local Reaction Environments for Efficient Nitric Oxide Reduction to Ammonia via Strengthening Interactions Between Heteroatom‐Doped Carbon and Metallic Alloys
Bibliographic record
Abstract
Abstract Electrocatalytic nitric oxide reduction reaction (NORR) is a feasible strategy for ammonia (NH 3 ) synthesis and restoring the nitrogen cycle. Electronic structure modulation of metal sites through strengthening metal‐support interactions represents a plausible approach to enhance NORR yield and Faradaic efficiency (FE), primarily by facilitating NO hydrogenation and inhibiting the hydrogen evolution reaction (HER). In this work, a boron and nitrogen co‐doped carbon‐supported CuNi alloy (CuNi@BCN) catalyst is designed and fabricated, which achieved a high NH 3 yield rate of 573.70 µmol cm −2 h −1 in a flow cell and a FE of 95.13% in an H‐cell. These newly achieved performances are outperforming the most recently developed NORR electrocatalysts. Theoretical calculations and in situ tests clarify that heteroatom‐doped carbon can lead to an electron‐rich alloy and thus facilitate NO hydrogenation with efficient participation of proton (*H) and inhibition of HER. The precise modulation of the alloy's electronic structure originates from heteroatom doping, which regulates the local reaction environments and successfully strengthens the alloy‐support interaction. This work demonstrates a route for optimizing the catalyst's electrocatalytic performance by regulating the local reaction environments of the metal active center.
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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 | 0.000 |
| 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) | 0.000 | 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".