Electrolyte Development for Solid-state Lithium Batteries
Bibliographic record
Abstract
In this chapter, we report on the recent progress in the development of Li-ion electrolytes for next- generation Li batteries. With a brief overview of state-of-the-art organic polymer electrolytes for Li-ion batteries, the status of solid-state (ceramic) Li-ion electrolyte research based on various inorganic compounds including Li3N, Li-β-alumina, Li3PO4, Li4SiO4, Li-based sodium super ionic conductors (NASICON) structure, LiM2(PO4)3 (M = Zr, Ti, Ge), lithium super ionic conductor (LISICON) Li14Zn(GeO4), perovskite-type La(2/3)−xLi3xTiO3 (LLTO), anti-perovskite Li3OX (X = Cl, Br) and garnet-type structure Li5La3M2O12 (M = Nb, Ta, Sb), Li6La2AM2O12 (A = Ca, Sr, Ba; M = Nb, Ta), and Li7La3M2O12 (M = Zr, Hf) are reviewed. Among these solid Li-ion electrolytes, some of the Zr and Ta-based Li-stuffed garnet-type oxides such as Li5La3Ta2O12, Li7La3Zr2O12 and Li7−xLa3Zr2−xTaxO12, and Li4−xSi1−xPxO4 membranes were found to be stable against chemical reaction with elemental Li and electrochemically stable at high voltages, which may enable high energy density Li-ion batteries. Application of selected solid-state Li-ion electrolytes in all-solid-state Li-ion batteries is presented in this chapter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".