D‐Band Center Modulation of Metallic Co‐Incorporated Co<sub>7</sub>Fe<sub>3</sub> Alloy Heterostructure for Regulating Polysulfides in Highly Efficient Lithium‐Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium‐sulfur (Li‐S) batteries, with their high theoretical energy density and cost‐effectiveness, have become one of the most promising next‐generation energy storage devices. However, they still face challenges such as the “shuttle effect” caused by the dissolution of polysulfide intermediates and the slow sulfur conversion kinetics. In this study, based on the Co7Fe3 alloy catalyst, additional Co metal is introduced to form a Co7Fe3Co catalyst with a heterostructure through a simple heat treatment process. This catalyst is incorporated into Ketjenblack (KB) to form a sulfur‐infused cathode material (designated as S/KB/Co7Fe3Co). Li‐S batteries using S/KB/Co7Fe3Co as the cathode demonstrate outstanding electrochemical performance, maintaining a reversible specific capacity of over 500 mAh g−1 after 1000 cycles at a current density of 1 C, with a capacity decay rate of 0.046% per cycle. DFT theoretical calculations and experimental results both reveal that the introduction of additional Co effectively regulates the d‐band center of the material, enhancing the adsorption of polysulfide intermediates by Co7Fe3Co and promoting bidirectional catalytic sulfur conversion. This work highlights the importance of the simple construction of heterostructured catalytic materials and their role in improving the performance of 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".