Sequential electrocatalytic reactions along a membrane electrode assembly drive efficient nitrate-to-ammonia conversion
Bibliographic record
Abstract
Electrochemical ammonia (NH3) synthesis from nitrate (NO3-) (NO3RR) offers a promising green alternative to the fossil fuel-based Haber-Bosch approach to support the increasing global demand for nitrogen fertilizers, while removing environmental waste. Previous efforts have centered on designing catalysts to promote the direct conversion from NO3- àNH3 while suppressing the two-step reaction pathway: NO3- àNO2-àNH3. We took the view that efficient NO3RR is possible on simple catalysts by instead promoting the two-step reaction process, and using chemical reactor principles in a membrane electrode assembly (MEA) to drive high ammonia current densities and Faradaic efficiency, despite nitrite intermediates. Here we use an unmodified copper catalyst in an MEA and control reactivity through current density, flow rate, and electrolyte recycling. We show that balancing the electrolyte flow rates with current density results in ideal residence times of NO2- by-products, allowing for 91% FENH3 in a 5 cm2 electrolyzer with a NO3- to NH3 partial current 1.8 A. Full NO3- conversion was also achieved via recycling with FENH3 maintained > 60% with H2 as a by-product. Importantly this work shows how traditional engineering principles can substantially boost ammonia production, even for simple catalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".