Sulfuration of an Fe–N–C Catalyst Containing Fe<i><sub>x</sub></i>C/Fe Species to Enhance the Catalysis of Oxygen Reduction in Acidic Media and for Use in Flexible Zn–Air Batteries
Bibliographic record
Abstract
Abstract During the preparation of atomically dispersed Fe–N–C catalysts, it is difficult to avoid the formation of iron‐carbide‐containing iron clusters (“FexC/Fe”), along with the desired carbon matrix containing dispersed FeNx sites. As a result, an uncertain amount of the oxygen reduction reaction (ORR) occurs, making it difficult to maximize the catalytic efficiency. Herein, sulfuration is used to boost the activity of FexC/Fe, forming an improved system, “FeNC–S–FexC/Fe”, for catalysis involving oxygen. Various spectroscopic techniques are used to define the composition of the active sites, which include Fe–S bonds at the interface of the now‐S‐doped carbon matrix and the FexC/Fe clusters. In addition to outstanding activity in basic media, FeNC–S–FexC/Fe exhibits improved ORR activity and durability in acidic media; its half‐wave potential of 0.821 V outperforms the commercial Pt/C catalyst (20%), and its activity does not decay even after 10 000 cycles. Interestingly, the catalytic activity for the oxygen evolution reaction (OER) simultaneously improves. Thus, FeNC–S–FexC/Fe can be used as a high‐performance bifunctional catalyst in Zn–air batteries. Theoretical calculations and control experiments show that the original FeNx active centers are enhanced by the FexC/Fe clusters and the Fe–S and C–S–C bonds.
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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".