Unraveling the Enhanced N<sub>2</sub> Activity on CuNi Alloy Catalysts for Ammonia Production: Experiments, DFT, and Statistical Analysis
Bibliographic record
Abstract
One of the main challenges in designing catalysts for ammonia synthesis is to create active sites on the surface of the catalyst that prefers to reduce the strong N 2 molecule despite its highly stable structure. Binary alloys have been demonstrated as potential ammonia synthesis catalysts in the literature. However, for binary alloys to be commercially viable, one needs to improve their catalytic activity for N 2 reduction by strategically manipulating the several unique active sites present on their surface. Herein, by using computational tools, we created five different compositions of Cu x Ni 1– x (0.5 ≤ x ≤ 0.9) alloys via special quasi-random structure (SQS) and genetic algorithm (GA). The alloy with about 50% of Cu and 50% of Ni is predicted to have the highest catalytic activity based on the shift of the d-band center toward the Fermi level. We then synthesized MgO-supported Cu 0.5 Ni 0.5 nanoparticles and compared their activity for ammonia synthesis with that of Ni/MgO and Cu/MgO. It was found that the MgO-supported Cu 0.5 Ni 0.5 alloy possesses 21 times higher activity than Cu/MgO and 3 times higher than Ni/MgO for ammonia synthesis, confirming the computational results. To demonstrate the impact of alloying on the catalytic activity, we further investigated all the possible unique sites on the surface of the Cu 0.5 Ni 0.5 alloy for nitrogen reduction reaction (NRR) via density functional theory (DFT). The investigation of the 96 unique active sites on the Cu 0.5 Ni 0.5 surface demonstrated that the position and concentration of Ni atoms near each investigated adsorption site have a linear correlation with the adsorption energy of the N 2 . Along with the structural and electronic properties of the active sites modified by Ni, orientation of the N 2 molecule also plays an important role in determining the activity of the CuNi catalyst. These findings not only explained the notable increase in the activity of CuNi catalysts compared to the pure metals for NH 3 synthesis but also offered critical insights required to tailor the specific surface environment of CuNi catalysts for NRR. This knowledge can serve as a foundation for further developments in designing binary alloy catalysts for sustainable ammonia synthesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".