Integrated Cascade Catalysts for Electrochemical Nitrate Reduction to Ammonia
Bibliographic record
Abstract
Electrocatalytic nitrate reduction reaction (NO 3 – RR) powered by renewable energy sources offers a promising approach to achieve ammonia (NH 3 ) synthesis with zero-carbon emission. However, sluggish proton-coupled electron transfer and byproduct formation challenge efficient NH 3 synthesis. Here, we construct an integrated cascade catalytic system to elucidate the governing principles of active hydrogen (*H) generation and utilization during NO 3 – RR. A representative catalyst, composed of atomically dispersed Fe sites anchored on an N-doped carbon matrix and encapsulated Ru nanoparticles, exhibits an NH 3 yield up to 2336.43 μg NH3 h –1 mg cat –1 while maintaining a Faradaic efficiency of 96.03% at a low potential of 0 V vs RHE. In addition, operando SR-FTIR spectroscopy and DFT calculations reveal that electron transfer from Fe atom to Ru particle not only enhances the affinity of Fe sites for NO x – species but also enriches H coverage on Ru sites, thereby accelerating hydrogenation steps and sustaining a steady *H generation-consumption cycle. This work reveals the mechanistic origin of active hydrogen in tandem catalytic structures and provides fundamental insights for advancing highly selective, energy efficient, and durable NH 3 electrosynthesis and wastewater treatment.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".