Engineering Energy Level of FeN<sub>4</sub> Sites via Dual‐Atom Site Construction Toward Efficient Oxygen Reduction
Bibliographic record
Abstract
Abstract Single‐atom catalysts based on metal–N4 moieties and embedded in a graphite matrix (defined as MNC) are promising for oxygen reduction reaction (ORR). However, the performance of MNC catalysts is still far from satisfactory due to their imperfect adsorption energy to oxygen species. Herein, single‐atom FeNC is leveraged as a model system and report an adjacent Ru‐N4 moiety modulation effect to optimize the catalyst's electronic configuration and ORR performance. Theoretical simulations and physical characterizations reveal that the incorporation of Ru‐N4 sites as the modulator can alter the d‐band electronic energy of Fe center to weaken the FeO binding affinity, thus resulting in the lower adsorption energy of ORR intermediates at Fe sites. Thanks to the synergetic effects of neighboring Fe and Ru single‐atom pairs, the FeN4/RuN4 catalyst exhibits a half‐wave potential of 0.958 V and negligible activity degradation after 10 000 cycles in 0.1 m KOH. Metal–air batteries using this catalyst in the cathode side exhibit a high power density of 219.5 mW cm−2 and excellent cycling stability for over 2370 h, outperforming the state‐of‐the‐art catalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".