Bidirectional Nitrogen Neutralization via Coupled Electrocatalytic Processes of Nitrate Reduction and Hydrazine Oxidation
Bibliographic record
Abstract
Abstract The electrocatalytic coupling of the nitrate reduction reaction (NO3−RR) and the hydrazine oxidation reaction (HzOR), denoted as NO3−RR||HzOR, not only holds promise for the synthesis of high‐value‐added products (such as NH3) but also facilitates bidirectional nitrogen neutralization. Here, the synthesis of sponge‐like porous nitrogen‐doped carbon encapsulated Cu nanoparticles electrocatalysts is presented for the electrochemical NO3−RR||HzOR. Substituting the traditional oxygen evolution reaction (OER) with the HzOR as the anode reaction notably accelerates the kinetic process of NH3 synthesis via NO3−RR. Moreover, the spatial confinement of Cu nanoparticles within a sponge‐like porous nitrogen‐doped carbon (NDC) structure not only addresses the aggregation and detachment issues of Cu NPs from the catalyst support surface but also effectively modulates the electronic structure of Cu NPs through electronic interactions between NDC and Cu NPs. This, in turn, enhances the adsorption and activation of nitrate ions. Consequently, the combined advantages of optimized surface electronic structure and spatial confinement of Cu NPs significantly improve the activity and stability for the electrocatalytic NO3−RR to NH3. This work offers significant reference value for sustainable nitrogen neutrality development by leveraging a mild, energy‐efficient, and environmentally friendly electrocatalytic process that concurrently eliminates nitrogen pollutants in both high and low oxidation states.
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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 | 0.001 | 0.001 |
| 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".