Insight into MoS<sub>2</sub>–MoN Heterostructure to Accelerate Polysulfide Conversion toward High‐Energy‐Density Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur batteries are deemed as optimal energy devices for the next generation of high‐energy‐density energy storage. However, several problems such as low energy density and short cycle life hinder their application in industry. Here, MoS 2 –MoN heterostructure nanosheets grown on carbon nanotube arrays as free‐standing cathodes are reported. In this heterostructure, MoN works as a promoter to provide coupled electrons to accelerate the redox reaction of polysulfides while the MoS 2, with a two‐dimensional layered structure, provides smooth Li + diffusion pathways. Through their respective advantages, both MoN and MoS 2 could mutually boost the process of “adsorption‐diffusion‐conversion” of polysulfides, which have a synergy enhancement effect to restrain the lithium polysulfides from shuttling. The designed cathodes show excellent long‐term cycling performances of 1000 cycles at 1C with a low decay rate of 0.039% per cycle and a high rate capability up to 6C. A high initial areal capacity of 13.3 mAh cm −2 is also achieved under a low electrolyte volume/sulfur loading (E/S) ratio of 6.3 mL g −1 . This strategy of promoting polysulfide conversion by heterostructure MoS 2 –MoN as presented in this work can provide a more structured design strategy for future advanced Li–S energy storage systems.
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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".