Reversible transition of an amorphous Cu-Al oxyfluoride into a highly active electrocatalyst for nitrate reduction to ammonia
Bibliographic record
Abstract
The electrochemical conversion of nitrate (NO3-) to ammonia (NH3) is an emerging alternative to valorize this aqueous pollutant to an essential chemical feedstock and potential fuel. Underpinning the maturation of the field and eventual viability of this technology is the discovery of efficient electrocatalysts, coupled with fundamental insights into the reaction mechanism. Until now, fluorinated materials have not yet been explored in this direction. In this work, we present the first fluorinated catalyst used for electrochemical NO3- reduction to NH3. A new copper-based oxyfluoride, Cu3Al2OF10, prepared through a facile coprecipitation and annealing of the corresponding hydrated fluoride r-Cu3Al2F12(H2O)12, was found to be exceptionally active, attaining a NH3 Faradaic Efficiency (FE) of up to 57% for the 8-electron NO3- to NH3 pathway (-0.4 VRHE) with a mass activity of up to 1220 A.g-1 at -0.6 VRHE, the highest yet recorded. Additionally, Cu3Al2OF10 continually produced NH3 for 2.5 days while maintaining a reasonable FE (55%). Finally, electroanalytical and operando spectroscopic investigations revealed a reversible transition to a phase entailing Cu nanoparticles embedded within the amorphous oxyfluoride matrix that was predominantly responsible for the catalyst’s activity. Overall, this work stands to open avenues for transition metal fluoride materials within the field nitrogen-based electrocatalysis.
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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 | 0.000 |
| 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) | 0.001 | 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".