Solid‐State Lithium/Selenium–Sulfur Chemistry Enabled via a Robust Solid‐Electrolyte Interphase
Bibliographic record
Abstract
Abstract Lithium/selenium‐sulfur batteries have recently received considerable attention due to their relatively high specific capacities and high electronic conductivity. Different from the traditional encapsulation strategy for suppressing the shuttle effect, an alternative approach to directly bypass polysulfide/polyselenide formation via rational solid‐electrolyte interphase (SEI) design is demonstrated. It is found that the robust SEI layer that in situ forms during charge/discharge via interplay between rational cathode design and optimal electrolytes could enable solid‐state (de)lithiation chemistry for selenium‐sulfur cathodes. Hence, Se‐doped S 22.2 Se/Ketjenblack cathodes can attain a high reversible capacity with minimal shuttle effects during long‐term and high rate cycling. Moreover, the underlying solid‐state (de)lithiation mechanism, as evidenced by in situ 7 Li NMR and in operando synchrotron X‐ray probes, further extends the optimal sulfur confinement pore size to large mesopores and even macropores that have been long considered as inferior sulfur or selenium host materials, which play a crucial role in developing high volumetric energy density batteries. It is expected that the findings in this study will ignite more efforts to tailor the compositional/structure characteristics of the SEI layers and the related ionic transport across the interface by electrode structure, electrolyte solvent, and electrolyte additive screening.
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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) | 0.002 | 0.001 |
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".