Engineering Oversaturated Fe‐N<sub>5</sub> Multifunctional Catalytic Sites for Durable Lithium‐Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium‐sulfur (Li‐S) batteries are regarded as a promising next‐generation system for advanced energy storage owing to a high theoretical energy density of 2600 Wh kg−1. However, the practical implementation of Li‐S batteries has been thwarted by the detrimental shuttling behavior of polysulfides, and the sluggish kinetics in electrochemical processes. Herein, a novel single atom (SA) catalyst with oversaturated Fe‐N5 coordination structure (Fe‐N5‐C) is precisely synthesized by an absorption–pyrolysis strategy and introduced as an effective sulfur host material. The experimental characterizations and theoretical calculations reveal synergism between atomically dispersed Fe‐N5 active sites and the unique carbon support. The results exhibit that the sulfur composite cathode built on the Fe‐N5‐C can not only adsorb polysulfides via chemical interaction, but also boost the redox reaction kinetics, thus mitigating the shuttle effect. Meanwhile, the robust three‐dimensional nitrogen doped carbon nanofiber with large surface area, and high porosity enables strong physical confinement and fast electron/ion transfer process. Attributed to such unique features, Li‐S batteries with S/Fe‐N5‐C composite cathode realize outstanding cyclability and rate capability, as well as high areal capacities under raised sulfur loading, which demonstrates great potential in developing advanced Li‐S batteries.
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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".