Boosting Electrochemical Nitrogen Reduction via Axial Coordination Engineering on Single‐Iron‐Atom Catalysts
Bibliographic record
Abstract
Abstract Electrocatalytic nitrogen (N2) reduction reaction (NRR) presents a sustainable alternative to the Haber–Bosch process for ammonia (NH3) synthesis. Iron phthalocyanine (FePc) is demonstrated as a promising catalyst for the electrocatalytic NRR. However, FePc with planar symmetric Fe‐N4 sites exhibits poor N2 adsorption and activation capabilities, resulting in an unsatisfactory NRR performance. Herein, an axial oxygen coordination strategy is developed to optimize the local electron distribution on FePc for improving N2 adsorption and activation. The as‐obtained FePc‐O‐CP shows a superior NH3 yield rate (59.72 µg h−1 mg−1cat.) and a considerable Faradaic efficiency (13.76%) in 0.1 m HCl. Density functional theory (DFT) calculations verify that the axial oxygen ligand on FePc inhibits the adsorption of H+ and enhances the N2 adsorption and activation, thereby greatly promoting NH3 generation. This work reveals the significance of regulating the local coordination environment of single‐atom catalysts for improving electrocatalytic NRR performance and provides a feasible strategy for the rational design of atomic‐scale active sites.
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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".