Selectively Reducing Nitrate into NH<sub>3</sub> in Neutral Media by PdCu Single-Atom Alloy Electrocatalysis
Bibliographic record
Abstract
Electrocatalytic nitrate reduction reaction (NO 3 – RR) technology provides a promising solution to recover the nitrate nutrition from wastewater through catalyzing nitrate reduction into value-added NH 3 . However, the selectivity and efficiency of electrocatalysts are frustrated due to the imbalance of *H adsorption (for NO 3 hydrogenation) and unavoidable adjacent *H self-coupling on active sites, resulting in competitive hydrogen evolution reaction (HER). Here, we report a PdCu single-atom alloy (SAA) catalyst that allows isolated Pd sites to produce *H for the hydrogenation process of *NO 3 on neighboring Cu sites, which can restrain the *H self-coupling through extending the distance between two *H and thus effectively suppress competitive HER. Consequently, the PdCu SAA catalyst exhibits an ultrahigh NH 3 Faraday efficiency (FE) of 97.1% with a yield of 15.4 μmol cm –2 h –1 from the electrocatalytic NO 3 – RR in the neutral electrolyte, outperforming most of the reported catalysts. Single-crystal experiments and theoretical calculations further prove that the introduction of atomic Pd on the Cu (100) surface could serve as the main active site and greatly decrease the energy barrier of the rate-determining step (RDS) on Cu from Δ G = 0.39 eV (*NOO → *NOOH) to Δ G = 0.10 eV of *NOH → *NHOH on PdCu SAA.
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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 | 0.000 | 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 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".