Garnet-type solid-state fast Li ion conductors for Li batteries: critical review
Bibliographic record
Abstract
Batteries are electrochemical devices that store electrical energy in the form of chemical energy. Among known batteries, Li ion batteries (LiBs) provide the highest gravimetric and volumetric energy densities, making them ideal candidates for use in portable electronics and plug-in hybrid and electric vehicles. Conventional LiBs use an organic polymer electrolyte, which exhibits several safety issues including leakage, poor chemical stability and flammability. The use of a solid-state (ceramic) electrolyte to produce all-solid-state LiBs can overcome all of the above issues. Also, solid-state Li batteries can operate at high voltage, thus, producing high power density. Various types of solid Li-ion electrolytes have been reported; this review is focused on the most promising solid Li-ion electrolytes based on garnet-type metal oxides. The first studied Li-stuffed garnet-type compounds are Li5La3M2O12 (M = Nb, Ta), which show a Li-ion conductivity of ∼10(-6) at 25 °C. La and M sites can be substituted by various metal ions leading to Li-rich garnet-type electrolytes, such as Li6ALa2M2O12, (A = Mg, Ca, Sr, Ba, Sr0.5Ba0.5) and Li7La3C2O12 (C = Zr, Sn). Among the known Li-stuffed garnets, Li6.4La3Zr1.4Ta0.6O12 exhibits the highest bulk Li-ion conductivity of 10(-3) S cm(-1) at 25 °C with an activation energy of 0.35 eV, which is an order of magnitude lower than that of the currently used polymer, but is chemically stable at higher temperatures and voltages compared to polymer electrolytes. Here, we discuss the chemical composition-structure-ionic conductivity relationship of the Li-stuffed garnet-type oxides, as well as the Li ion conduction mechanism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".