Efficient Ammonia Electrosynthesis from Pure Nitrate Reduction via Tuning Bimetallic Sites in Redox‐Active Covalent Organic Frameworks
Bibliographic record
Abstract
Abstract Electrocatalytic nitrate reduction reaction (NITRR) represents a promising approach for ammonia synthesis, but existing application has been constrained by the complex proton‐coupled electron transfer and the sluggish kinetics induced by various intermediates. Herein, we synthesized a series of metalized covalent organic frameworks: NiTP‐MTAPP MCOFs (M = 2H, Co, Cu, and Fe), based on dual redox‐active centers: thiophene‐substituted Ni‐bis(dithiolene) ligand‐Ni[C 2 S 2 (C 4 H 2 SCHO) 2 ] 2 and metallic porphyrin. Through regulating the adsorption and desorption of species at the catalytic sites, we have identified the optimal NITRR electrocatalyst: NiTP‐CoTAPP MCOF, which achieved the highest faradaic efficiency (FE) of approximately 85.6% at −0.8 V (vs. RHE) in pure nitrate solution, with an impressive yield rate of 160.2 mmol h −1 g −1 cat. The generation of active hydrogen at [NiS 4 ] sites achieved dynamic equilibrium with the timely hydrogenation reaction at CoN 4 sites, effectively suppressing the hydrogen evolution reaction. Moreover, the incorporation of thiophene (TP) groups and metal ions facilitates charge transfer. Density functional theory (DFT) calculations demonstrated the reduction in energy barriers at different catalytic sites. The CoN 4 −NiS 4 system exhibited the optimal adsorption‐to‐desorption capability and the lowest energy barrier (0.58 eV) for the rate‐determining step (*NO → *HNO), which is supported by the moderate d‐band center and Bader charge value.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".