Glassy/Ceramic Li<sub>2</sub>TiO<sub>3</sub>/Li<sub>x</sub>B<sub>y</sub>O<sub>z</sub> Analogous “Solid Electrolyte Interphase” to Boost 4.5 V LiCoO<sub>2</sub> in Sulfide‐Based All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract Sulfide‐based all‐solid‐state lithium‐ion batteries (ASSLIBs) are the widely recognized approach toward high safety owing to excellent ionic conductivity and nonflammable nature of solid‐state electrolytes (SSEs). However, narrow potential window of SSEs brings about serious interfacial parasitic reactions, resulting in fast degradation of the battery. Herein, a glassy/ceramic analogous solid electrolyte interface (SEI) is constructed on LiCoO2 (LCO) to enhance interfacial stability between LCO and the Li10GeP2S12 (LGPS) SSEs. In which, ceramic Li2TiO3 guarantees good mechanical toughness of analogous SEI, while glassy LixByOz reinforces the coverage to avoid parasitic reactions. Analogous SEI endows ASSLIBs with excellent cycling and rate performance under an upper charge voltage of 4.3 V with 82.3% capacity retention after 300 cycles at 0.2 C. When pushing charge voltage to 4.5 V, analogous SEI also enables desirable performance with an initial capacity of 172.7 mAh g−1 and long lifespan of 200 cycles at 0.2 C. Both experiments and theoretical computation reveal excellent stability between analogous SEI and LGPS, which endows ASSLIBs with small polarization and improved performance. This work provides an insight on glassy/ceramic analogous SEI strategy to boost the interfacial stability of 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".