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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".