Regulating the Electronic Synergy of Asymmetric Atomic Fe Sites with Adjacent Defects for Boosting Activity and Durability toward Oxygen Reduction
Bibliographic record
Abstract
Abstract The oxygen reduction reaction (ORR) plays a fundamental role in sustainable energy technologies. However, the creation of non‐precious metal electrocatalysts with high ORR activity and durability under all pH conditions is of great significance but remains challenging. Herein, the aim is to overcome this challenge by creating a Fe single atom catalyst on a 2D defect‐containing nitrogen‐doped carbon support (Fe 1 /DNC) via a microenvironment engineering strategy. Microkinetic modeling reveals that FeN 4 (OH) moieties are the real active sites under reaction conditions. Due to the synergistic promotion effect of denser accessible FeN 4 (OH) moieties and defect‐induced electronic properties, Fe 1 /DNC catalyst achieves extraordinary ORR activity under alkaline, acidic, and neutral conditions, with half‐wave potentials of 0.95, 0.82, and 0.70 V, respectively. Moreover, a negligible performance decay is observed with this Fe catalyst in stability and methanol tolerance tests. Zn‐air battery employing Fe 1 /DNC delivers remarkable peak power density and long‐term operational durability. Theoretical analysis provides compelling evidence that the defects adjacent to FeN 4 (OH) moieties can endow an inductive effect to reshape electronic properties to balance the OOH* formation and OH* reduction. This work offers insight into the regulation of asymmetric coordination structure and electronic properties of metal sites for boosting electrocatalytic activity and stability.
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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".