Enhanced Nitrate-to-Ammonia Activity on Copper–Nickel Alloys via Tuning of Intermediate Adsorption
Bibliographic record
Abstract
Electrochemical conversion of nitrate (NO 3 – ) into ammonia (NH 3 ) recycles nitrogen and offers a route to the production of NH 3, which is more valuable than dinitrogen gas. However, today’s development of NO 3 – electroreduction remains hindered by the lack of a mechanistic picture of how catalyst structure may be tuned to enhance catalytic activity. Here we demonstrate enhanced NO 3 – reduction reaction (NO 3 – RR) performance on Cu 50 Ni 50 alloy catalysts, including a 0.12 V upshift in the half-wave potential and a 6-fold increase in activity compared to those obtained with pure Cu at 0 V vs reversible hydrogen electrode (RHE). Ni alloying enables tuning of the Cu d- band center and modulates the adsorption energies of intermediates such as *NO 3 –, *NO 2, and *NH 2 . Using density functional theory calculations, we identify a NO 3 – RR-to-NH 3 pathway and offer an adsorption energy–activity relationship for the CuNi alloy system. This correlation between catalyst electronic structure and NO 3 – RR activity offers a design platform for further development of NO 3 – RR catalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".