Rational Ligand Design of Conjugated Coordination Polymers for Efficient and Selective Nitrate Electroreduction to Ammonia
Bibliographic record
Abstract
Abstract Electrocatalytic nitrate reduction to ammonia (NRA) offers an attractive route for converting nitrate pollutants to ammonia under mild conditions. Among other catalysts, single‐atom catalysts (SACs) with high metal‐atom‐utilization efficiency and low‐coordinated metal sites hold immense potential to be extensively applied, which unfortunately encounter a formidable challenge to obtain simultaneous improvement of NRA activity and selectivity. Here, a novel and general strategy is reported to achieve efficient and selective NRA catalysis on conjugated coordination polymers featuring with high‐density and well‐defined nitrogen (N)‐coordinated single‐atom metal sites via precise regulation of N‑heterocyclic ligands toward accelerating the hydrogenation kinetics necessitated in the NRA pathway. Taking cobalt (Co) as an example, two CoN 4 ‐centered conjugated coordination polymer electrocatalysts (CoN 4 ‐pyrr and CoN 4 ‐pyri) are synthesized with pyrrole and pyridine ligands are investigated as a proof‐of‐concept study. As revealed, the CoN 4 ‐pyrr can markedly outperform the CoN 4 ‐pyri toward NRA electrocatalysis. Experimental and theoretical results suggest that, relative to the N atoms of pyridine ligand in CoN 4 ‐pyri, the N atoms of pyrrole ligand in CoN 4 ‐pyrr can enable a faster transfer of hydrogen radicals to the Co active sites for accelerating the hydrogenation kinetics of * NO intermediate at the rate‐determining step of NRA pathway.
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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".