Epoxy‐rich Fe Single Atom Sites Boost Oxygen Reduction Electrocatalysis
Bibliographic record
Abstract
Electrocatalysts for highly efficient oxygen reduction reaction (ORR) are crucial for energy conversion and storage devices. Single-atom catalysts with maximized metal utilization and altered electronic structure are the most promising alternatives to replace current benchmark precious metals. However, the atomic level understanding of the functional role for each species at the anchoring sites is still unclear and poorly elucidated. Herein, we report Fe single atom catalysts with the sulfur and oxygen functional groups near the atomically dispersed metal centers (Fe1/NSOC) for highly efficient ORR. The Fe1/NSOC delivers a half-wave potential of 0.92 V vs. RHE, which is much better than those of commercial Pt/C (0.88 V), Fe single atoms on N-doped carbon (Fe1/NC, 0.89 V) and most reported nonprecious metal catalysts. The spectroscopic measurements reveal that the presence of sulfur group induces the formation of epoxy groups near the FeN4S2 centers, which not only modulate the electronic structure of Fe single atoms but also participate the catalytic process to improve the kinetics. The density functional theory calculations demonstrate the existence of sulfur and epoxy group engineer the charges of Fe reactive center and facilitate the reductive release of OH* (rate-limiting step), thus boosting the overall oxygen reduction efficiency.
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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) | 0.002 | 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".