Perovskite Solid-State Electrolytes for Lithium Metal Batteries
Bibliographic record
Abstract
Solid-state lithium metal batteries (LMBs) have become increasingly important in recent years due to their potential to offer higher energy density and enhanced safety compared to conventional liquid electrolyte-based lithium-ion batteries (LIBs). However, they require highly functional solid-state electrolytes (SSEs) and, therefore, many inorganic materials such as oxides of perovskite La2/3−xLi3xTiO3 (LLTO) and garnets La3Li7Zr2O12 (LLZO), sulfides Li10GeP2S12 (LGPS), and phosphates Li1+xAlxTi2−x(PO4)3x (LATP) are under investigation. Among these oxide materials, LLTO exhibits superior safety, wider electrochemical window (8 V vs. Li/Li+), and higher bulk conductivity values reaching in excess of 10−3 S cm−1 at ambient temperature, which is close to organic liquid-state electrolytes presently used in LIBs. However, recent studies focus primarily on composite or hybrid electrolytes that mix LLTO with organic polymeric materials. There are scarce studies of pure (100%) LLTO electrolytes in solid-state LMBs and there is a need to shed more light on this type of electrolyte and its potential for LMBs. Therefore, in our review, we first elaborated on the structure/property relationship between compositions of perovskites and their ionic conductivities. We then summarized current issues and some successful attempts for the fabrication of pure LLTO electrolytes. Their electrochemical and battery performances were also presented. We focused on tape casting as an effective method to prepare pure LLTO thin films that are compatible and can be easily integrated into existing roll-to-roll battery manufacturing processes. This review intends to shed some light on the design and manufacturing of LLTO for all-ceramic electrolytes towards safer and higher power density solid-state LMBs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".