Enhanced ammonia electrosynthesis over phosphorus-doped nickel across broad nitrate concentrations via regulating trade-offs in pathways
Bibliographic record
Abstract
Electrocatalytic nitrate reduction reaction (NO 3 RR) holds promise for carbon-neutral ammonia production but requires efficient electrodes to minimize the formation of nitrite and hydrogen by-products, from either concentrated or dilute feedstocks. Here, we report a trade-off effect upon phosphorus incorporation into Ni, wherein Ni- and P-rich surfaces tend to inhibit nitrite and hydrogen formation, respectively, while promoting the generation of the respective opposite by- products. Under 0.1 M nitrate concentration, the selectivity switch between the by-products presented a correlation with the intrinsic HER activity of surfaces, highlighting the critical role of *H supply. Through ex-situ and in-situ spectroscopies, the reconstructed P-doped Ni (R-NiP) was found to exhibit an oxidation state and local atomic environment intermediate between those of Ni and P-doped Ni (NiP), effectively integrating the suppressing traits of both Ni and P. Consequently, an ammonia Faradaic efficiency of 89 % was achieved at a reduced potential on R-NiP, along with a 2- and 8-fold enhancement in intrinsic activity compared to NiP and Ni, respectively. By leveraging this trade-off, we also showcase the adaptability of NiP platform for effective operation across both lower and higher nitrate concentrations, emphasizing that nitrate bulk concentration should be factored in when regulating *H supply for efficient ammonia electrosynthesis. • Nitrate reduction to ammonia is enabled on a modified nickel catalyst. • An ammonia production rate of 2.462 mmol h −1 cm −2 was achieved on R-NiP sample. • Efficient ammonia production requires a balanced availability of nitrate and *H. • Promoting the supply of *H does not inherently yield positive outcomes. • Managing *H supply must be done with consideration of nitrate bulk concentration.
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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".