Ammonia Synthesis by Nitrate Reduction Catalyzed by Copper Porphyrin Metal–Organic Framework in Tandem with Cuprous Oxide
Bibliographic record
Abstract
Electrocatalytic nitrate reduction (NO 3 RR) methods are promising in addressing nitrate (NO 3 – ) pollution and green ammonia (NH 3 ) synthesis. However, the NO 3 RR process is complex, and overcoming the high energy barrier of the reaction is crucial for improving NH 3 selectivity. In this study, Cu 2 O was combined with two-dimensional copper(II) tetrakis (4-carboxyphenyl) porphyrin (Cu-TCPP) nanosheets. The Cu-TCPP/Cu 2 O/CF tandem catalytic electrode was reported, demonstrating enhanced catalytic performance through synergistic interactions across multiple active sites. After 4 h of electrocatalytic nitrate reduction tests, the Cu-TCPP/Cu 2 O/CF catalysts achieved NH 3 yields up to 0.0937 mmol h –1 cm –2 and NH 3 faraday efficiency (FE NH3 ) up to 90.22% at a potential of −1 V vs RHE. In addition, the source of nitrate reduction activity was analyzed under different initial conditions, in situ Raman characterization and the NO 3 RR pathway on the catalyst surface was investigated. Interestingly, the Zn-nitrate (Zn-NO 3 – ) battery constructed with Cu-TCPP/Cu 2 O/CF as the cathode showed a FE NH3 of 98.89% and an NH 3 yield of 199.25 μmol h –1 cm –2 . The constructed Zn-NO 3 – battery could be discharged continuously for more than 24 h while synthesizing NH 3 efficiently. Cu-TCPP/Cu 2 O/CF has good potential for practical applications and provides a reference for subsequent work on metal–organic framework tandem catalysts.
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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) | 0.000 | 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 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".