Eliminating the Detrimental Effects of Conductive Agents in Sulfide-Based Solid-State Batteries
Bibliographic record
Abstract
Sulfide-based solid-state electrolytes (SSEs) are considered a key part in the realization of high-performance all solid-state lithium-ion batteries (ASSLIBs). However, the incompatibility between conductive additives and sulfide-based SSEs in the cathode composite challenges the stable delivery of high-rate capability. Herein, a poly(3,4-ethylenedioxythiophene) (PEDOT) modification is designed as a semiconductive additive for cathode composites (cathode/SSE/carbon) to realize the high performance. The modified ASSLIB demonstrates a competitive rate capacity of over 100 mAh g –1 at 1 C, which is 10 times greater than that of the bare cathode. Detailed surface chemical and structural evolutions at the cathodic interface indicate the PEDOT modification not only significantly suppresses the side reactions but also realizes effective electron transfer at the cathode/SSE/carbon three-phase interface. Introducing a controllable semiconductive additive for the cathode composites in this study offers a promising design to realize the high-rate performance and overcome long-term challenges in the application of conductive additives in sulfide-based ASSLIBs.
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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".