Nonprecious Triple-Atom Catalysts with Ultrahigh Activity for Electrochemical Reduction of Nitrate to Ammonia: A DFT Screening
Bibliographic record
Abstract
Electrochemical nitrate reduction to ammonia (NO 3 RR) is promising to not only tackle environmental issues caused by nitrate but also produce ammonia at room temperatures. However, two critical challenges are the lack of effective electrocatalysts and the understanding of related reaction mechanisms. To overcome these challenges, we employed first-principles calculations to thoroughly study the performance and mechanisms of triple-atom catalysts (TACs) composed of transition metals (including 27 homonuclear TACs and 4 non-noble bimetallic TACs) anchored on N-doped carbon (NC). We found five promising candidates possessing not only thermodynamic and electrochemical stability, but also high activity and selectivity for ammonia production. Among them, non-noble homonuclear Ni 3 @NC TAC show high activity with low theoretical limiting potential of −0.31 V RHE . Surprisingly, bimetallic Co 2 Ni@NC, Co 2 Cu@NC, and Fe 2 Ni@NC TACs show ultrahigh activity with theoretical limiting potentials of 0.00 V RHE, without a potential determining step in the whole reaction pathways, representing the best theoretical activity been reported up to date. These promising candidates are facilitated by circumventing the limit of scaling relationships, a well-known obstacle for single-atom catalysts. This study indicates that designing suitable TACs can be a promising strategy for efficiently electro-catalyzing NO 3 RR and breaking the limit of the scaling relationship.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".