Hierarchical Defective Fe<sub>3‐</sub><i><sub>x</sub></i>C@C Hollow Microsphere Enables Fast and Long‐Lasting Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries present one of the most promising energy storage systems owing to their high energy density and low cost. However, the commercialization of Li–S batteries is still hindered by several technical issues; the notorious polysulfide shuttling and sluggish sulfur conversion kinetics. In this work, unique hierarchical Fe3‐xC@C hollow microspheres as an advanced sulfur immobilizer and promoter for enabling high‐efficiency Li–S batteries is developed. The porous hollow architecture not only accommodates the volume variation upon the lithiation–delithiation processes, but also exposes vast active interfaces for facilitated sulfur redox reactions. Meanwhile, the mesoporous carbon coating establishes a highly conductive network for fast electron transportation. More importantly, the defective Fe3‐xC nanosized subunits impose strong LiPS adsorption and catalyzation, enabling fast and durable sulfur electrochemistry. Attributed to these structural superiorities, the obtained sulfur electrodes exhibit excellent electrochemical performance, i.e., high areal capacity of 5.6 mAh cm−2, rate capability up to 5 C, and stable cycling over 1000 cycles with a low capacity fading rate of 0.04% per cycle at 1 C, demonstrating great promise in the development of practical 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".