A Ceramic Rich Quaternary Composite Solid-State Electrolyte for Solid-State Lithium Metal Batteries
Bibliographic record
Abstract
Solid-state lithium metal batteries are one of the most promising candidates to take over the traditional liquid-based lithium ion batteries as they not only allow us to circumvent safety issues but also boost energy density far over the current limits imposed by the present chemistries. We have recently demonstrated that the combination of highly conductive inorganic solid electrolyte (ISE), Li0.33La0.55TiO3 (LLTO), with the mechanically durable solid polymer electrolyte (SPE), polyethylene oxide: Lithium bis(trifluoromethanesulfonyl)imide (PEO:LiTFSI), alongside a solid plasticizer, Succinonitrile, has proved to be successful in making highly performing polymer-rich (70% polymer) quaternary composite solid electrolytes (CSEs) that evade both the brittleness of ceramics and the poor conductivity of polymers. Herein, we extend the work to ceramic rich quaternary CSEs (70% ceramic). Ceramic-rich films were fabricated using tape casting technique and have reasonable ionic conductivity of 1.5 × 10−4 S cm−1 at 55 °C, decent mechanical properties and displays impressive endurance in Li ∣∣ Li symmetrical cells (> 800 h). Solid-state coin-type cells assembled with composite cathode show satisfactory cycling performance at 0.05 C and 55 °C reaching specific discharge capacity of 160.6 mAh g−1, maintaining high Coulombic efficiency (> 95%) and high capacity retention of 90.3% after 30 cycles.
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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".