CoFe<sub>2</sub>O<sub>4</sub>@rGO as a Separator Coating for Advanced Lithium–Sulfur Batteries
Bibliographic record
Abstract
Lithium–sulfur (Li–S) batteries are hindered by the undesired shuttle effect and sluggish electrochemical conversion kinetics. Herein, a well‐designed CoFe2O4@reduced graphene oxide (CFO@rGO) composite is used to modify the separator to develop a multifunctional polysulfide barrier. Density functional theory (DFT) calculations confirm that highly electronegative oxygen ions in CFO tend to bond with transition metal (TM) ions at octahedral (Oh) sites, which induces the formation of FeS and CoS bonds between CFO and polysulfides. This indicates that CFO can effectively anchor polysulfides. Furthermore, the low Li2S decomposition energy barrier and Li+ diffusion energy barrier reveal that CFO can accelerate the redox reaction kinetics of sulfur species. Electronic structure calculations speculate that the low‐energy barrier can be attributed to the electron‐hopping phenomenon between TM ions of different valence states at Oh sites. Benefiting from these advantages, a CFO@rGO/PP separator demonstrates satisfactory cycling performance (0.087% capacity decay rate at 2C with 500 cycles) and superb rate performance (686 mAh g−1 at 5C). This work provides a valuable reference for future research on spinel‐type materials as electrocatalysts for 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".