Regulating Fe-spin state by atomically dispersed Mn-N in Fe-N-C catalysts with high oxygen reduction activity
Bibliographic record
Abstract
Abstract As low-cost electrocatalysts for oxygen reduction reaction applied to fuel cells and metal-air batteries, atomic-dispersed transition metal-nitrogen-carbon materials are emerging, but the genuine mechanism thereof is still arguable. Herein, by rational design and synthesis of dual-metal atomically dispersed Fe,Mn/N-C catalyst as model object, we unravel that the O2 reduction preferentially takes place on FeIII in the FeN4 /C system with intermediate spin state which possesses one eg electron (t2g4eg1) readily penetrating the antibonding π-orbital of oxygen. Both magnetic measurements and theoretical calculation reveal that the adjacent atomically dispersed Mn-N moieties can effectively activate the FeIII sites by both spin-state transition and electronic modulation, rendering the excellent ORR performances of Fe,Mn/N-C in both alkaline and acidic media (halfwave positionals are 0.928 V in 0.1 M KOH, and 0.804 V in 0.1 M HClO4), and good durability, which outperforms and has almost the same activity of commercial Pt/C, respectively. In addition, it presents a superior power density of 160.8 mW cm−2 and long-term durability in reversible zinc–air batteries. The work brings new insight into the oxygen reduction reaction process on the metal-nitrogen-carbon active sites, undoubtedly leading the exploration towards high effective low-cost non-precious catalysts.
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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) | 0.000 | 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 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".