Electrochemical Nitrate Reduction to Ammonia on Polycrystalline Copper Electrodes in Alkaline Solutions
Bibliographic record
Abstract
Ammonia has widely utilized as the raw material of fertilizer and feeds in fuel cells and hydrogen storage materials (Orens. S, Proc. Natl. Acad. Sci. U.S.A. 2021, 118 (49)). In general, the Haber-Bosch process has supplied the mass production of ammonia (N 2 + H 2 → 2NH 3 ). However, this chemical process requires high-temperature and high-pressure conditions to cleave the triple N 2 and produces undesired CO 2 gas, as CH 4 is added for H 2 generation. It is attractive to yield ammonia in milder conditions and more cost-effective methods. In addition, the usage of waste and eco-poisoned species, which is inevitably produced, is valuable as the reactant. For this purpose, nitrate (NO 3 - ) conversion using electrochemical methods has drawn attention. Nitrate has a high solubility in aqueous media and better reactivity than N 2 gas at ambient temperature and atmospheric conditions. Nonetheless, the nitrate reduction undergoes multiple electron-transfer processes (NO 3 - + 8e - + 9H + → NH 3 + 3H 2 O), causing low ammonia selectivity competing against byproducts such as hydrogen, nitrogen oxides, and hydroxylamine. Here, I show critical factors determining the conversion selectivity of nitrate using Cu catalysts. I prepared three Cu foils treated by different surface cleaning processes. Surface morphology and roughness of Cu relying on the surface treatments significantly altered the conversion efficiency. In particular, nitric oxide (NO) was a pivotal intermediate to determine the final products, which was sensitive to the Cu surface condition. I will discuss details of the electrochemical nitrate reduction process observed by in-situ and ex-situ gas and spectroscopic analyses and correlate the conversion efficiency with the surface conditions of Cu foils. Figure 1
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".