Synergistic Engineering of Defects and Architecture in Binary Metal Chalcogenide toward Fast and Reliable Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries have great promise to support the next‐generation energy storage if their sluggish redox kinetics and polysulfide shuttling can be addressed. The rational design of sulfur electrodes plays key roles in tacking these problems and achieving high‐efficiency sulfur electrochemistry. Herein, a synergetic defect and architecture engineering strategy to design highly disordered spinel Ni–Co oxide double‐shelled microspheres (NCO‐HS), which consist of defective spinel NiCo 2 O 4– x ( x = 0.9 if all nickel is Ni 2+ and cobalt is Co 2.13+ ), as the multifunctional sulfur host material is reported. The in situ constructed cation and anion defects endow the NCO‐HS with significantly enhanced electronic conductivity and superior polysulfide adsorbability. Meanwhile, the delicate nanoconstruction offers abundant active interfaces and reduced ion diffusion pathways for efficient Li–S chemistry. Attributed to these synergistic features, the sulfur composite electrode achieves excellent rate performance up to 5 C, remarkable cycling stability over 800 cycles and good areal capacity of 6.3 mAh cm −2 under high sulfur loading. This proposed strategy based on synergy engineering could also inform material engineering in related energy storage and conversion fields.
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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) | 0.000 | 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".