Reinforcing the Adsorption and Conversion of Polysulfides in Li−S Battery by Incorporating Molybdenum into MnS/MnO Nanorods
Bibliographic record
Abstract
Abstract The Sabatier principle suggests that an excessive adsorption of lithium polysulfides (LiPSs) by metal compounds may hinder their conversion in the absence of a conversion module. Therefore, it is imperative to establish a synergetic effect mechanism between “strong adsorption” and “rapid conversion” for LiPSs. To achieve this coexistence, a molybdenum‐doped MnS/MnO@C porous structure is designed as a multifunctional coating on the polypropylene (PP) separator. The incorporation of MnS/MnO@C enhances the adsorption capacity towards LiPSs, while molybdenum facilitates subsequent conversion. Benefiting from the synergistic effect of each component and its large specific surface area, the cell with Mo‐doped MnS/MnO@C coating achieves smooth adsorption‐diffusion‐conversion processes and exhibits an appreciable rate performance with outstanding cycling stability. Even when sulfur loading increases to 9.68 mg cm −2 , the modified battery delivers an excellent initial areal capacity of 11.69 mAh cm −2 and maintains 6.97 mAh cm −2 after 50 cycles at 0.1 C. This study presents a promising approach to simultaneously accomplish “strong adsorption” and “rapid conversion” of polysulfides, offering novel perspectives for devising dual‐functional modified separators.
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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".