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 (N2 + H2 → 2NH3). However, this chemical process requires high-temperature and high-pressure conditions to cleave the triple N2 and produces undesired CO2 gas, as CH4 is added for H2 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 (NO3 -) conversion using electrochemical methods has drawn attention. Nitrate has a high solubility in aqueous media and better reactivity than N2 gas at ambient temperature and atmospheric conditions. Nonetheless, the nitrate reduction undergoes multiple electron-transfer processes (NO3 - + 8e- + 9H+ → NH3 + 3H2O), 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 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".