A Combined Ordered Macro‐Mesoporous Architecture Design and Surface Engineering Strategy for High‐Performance Sulfur Immobilizer in Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract The practical application of lithium–sulfur (Li–S) batteries is hindered by the “shuttle” of lithium polysulfides (LiPS) and sluggish Li–S kinetics issues. Herein, a synergistic strategy combining mesoporous architecture design and defect engineering is proposed to synthesize multifunctional defective 3D ordered mesoporous cobalt sulfide (3DOM N‐Co9S8−x) to address the shuttling and sluggish reaction kinetics of polysulfide in Li–S batteries. The unique 3DOM design provides abundant voids for sulfur storage and enlarged active interfaces that reduce electron/ion diffusion pathways. Meanwhile, X‐ray absorption spectroscopy shows that the surface defect engineering tunes the CoS4 tetrahedra to CoS6 octahedra on Co9S8, endowing abundance of S vacancies on the Co9S8 octahedral sites. The ever‐increasing S vacancies over the course of electrochemical process further promotes the chemical trapping of LiPS and its conversion kinetics, rendering fast and durable Li–S chemistry. Benefiting from these features, the as‐developed 3DOM N‐Co9S8−x/S cathode delivers high areal capacity, superb rate capability, and excellent cyclic stability with ultralow capacity fading rate under raised sulfur loading and low electrolyte content. This design strategy promotes the development of practically viable Li–S batteries and sheds lights on the material engineering in related energy storage application.
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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".