High‐Performance Li–SeS<i><sub>x</sub></i> All‐Solid‐State Lithium Batteries
Bibliographic record
Abstract
Abstract All‐solid‐state Li–S batteries are promising candidates for next‐generation energy‐storage systems considering their high energy density and high safety. However, their development is hindered by the sluggish electrochemical kinetics and low S utilization due to high interfacial resistance and the electronic insulating nature of S. Herein, Se is introduced into S cathodes by forming SeS x solid solutions to modify the electronic and ionic conductivities and ultimately enhance cathode utilization in all‐solid‐state lithium batteries (ASSLBs). Theoretical calculations confirm the redistribution of electron densities after introducing Se. The interfacial ionic conductivities of all achieved SeS x –Li 3 PS 4 ( x = 3, 2, 1, and 0.33) composites are 10 −6 S cm −1 . Stable and highly reversible SeS x cathodes for sulfide‐based ASSLBs can be developed. Surprisingly, the SeS 2 /Li 10 GeP 2 S 12 –Li 3 PS 4 /Li solid‐state cells exhibit excellent performance and deliver a high capacity over 1100 mAh g −1 (98.5% of its theoretical capacity) at 50 mA g −1 and remained highly stable for 100 cycles. Moreover, high loading cells can achieve high areal capacities up to 12.6 mAh cm −2 . This research deepens the understanding of Se–S solid solution chemistry in ASSLB systems and offers a new strategy to achieve high‐performance S‐based cathodes for application in ASSLBs.
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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".