Elucidating the Impact of the Metal Center Identity and Macrocycle Structure of Molecular Catalysts for Electrochemical Reduction of Nitrate to Ammonia
Bibliographic record
Abstract
Ammonia (NH 3 ) is a vital fertilizer and industrial chemical predominantly produced via the energy-intensive Haber–Bosch process. The electrochemical reduction of nitrate (NO 3 – ) to NH 3 offers an alternative that can source nitrogen from NO 3 – in wastewater or industrial processes. In this work, we evaluate the impact of the local atomic environment of four molecular catalysts supported on carbon nanotubes (CNTs): copper phthalocyanine (CuPc/CNT), copper tetraphenylporphyrin (CuTPP/CNT), iron phthalocyanine (FePc/CNT), and iron tetraphenylporphyrin (FeTPP/CNT). FePc/CNT coated electrodes achieved the highest performance, exhibiting a partial current density of 61.2 mA cm –2 at −0.9 V RHE and a Faradaic efficiency of 98.9% toward NH 3 at −0.6 V RHE . Notably, the phthalocyanine catalysts outperformed their porphyrin analogues, underscoring the impact of the second shell coordination environment on the activity and stability of the catalysts. Density functional theory (DFT) calculations revealed that Fe-based catalysts facilitate stronger π-back bonding to *NO, reducing the thermodynamic barrier for NO reduction, which is typically a rate limiting step in the NO 3 – reduction mechanism. In situ X-ray absorption spectroscopy (XAS) coupled with post-mortem ex situ transmission electron microscopy (TEM) and X-ray diffraction (XRD) showed that FePc/CNT retained Fe–N coordination at potentials as negative as −0.8 V RHE, whereas the metal centers of the other catalysts were reduced into metallic clusters at potentials more negative than −0.6 V RHE . We attribute the enhanced stability and selectivity of FePc/CNT to its local coordination environment. By integrating experimental and theoretical insights, this work elucidates the impact of metal identity and the local atomic environment that synergistically governs the electrocatalytic performance and stability.
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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 | 0.000 |
| 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".