Boosting Electrocatalytic Nitrogen Reduction on Cobalt‐Based Perovskite via Regulating Reaction Pathway Through Donation‐Back‐Donation Modulation
Bibliographic record
Abstract
Abstract The electrocatalytic approach of combining N 2 and H 2 O to produce ammonia, known as the electrocatalytic N 2 reduction reaction (eNRR), has garnered significant attention due to its environmental benefits and potential for supporting a decentralized agricultural economy. However, the underlying chemistry governing the reaction pathways remains poorly understood, hindering the design of low‐cost and efficient eNRR catalysts. Here we report the enhancement of the electrocatalytic eNRR activity of perovskite oxides by tuning the reaction pathway through a “donation‐back‐donation” mechanism. This is achieved by controlling the spin state via adjusting the distribution of d orbital electrons in low‐cost transition metals, such as cobalt. Specifically, the cobalt in perovskite SrCoO 3 (SC) with a low‐spin state demonstrates an 18 times higher ammonia yield rate compared to that in Co 3 O 4 and 1.5 times higher than cobalt in perovskite LaCoO 3 (LC). The low spin states of cobalt in SC enable better control of the eNRR reaction pathway over the transformation of *N 2 H to *NHNH or *NNH 2 , resulting in alternating hydrogenation in SC rather than distal hydrogenation in LC with a high spin state. The unprecedented improvement in eNRR by regulating the spin state of Co demonstrates the bright of low‐cost Co‐based electrocatalysts for ammonia production.
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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".