Ceramic Li Ion Electrolytes for Next Generation Solid-State Li Ion Batteries
Bibliographic record
Abstract
At present, there is a growing interest in the development of safe and low-cost all-solid-state Li ion batteries for transport applications due to their higher practical volumetric and gravimetric energy densities compared to other known secondary batteries. The commercial Li ion battery utilizes LiPF6 dissolved organic polymer as an electrolyte, graphite anode and LiCoO2 electrode that exhibit several issues including safety, low energy density and inadequate chemical stability and durability. To this end, numerous solid-state Li ion electrolytes such as two-dimensional layered structures Li-beta alumina and Li3N, and three-dimensional structured Li4SiO4, Li2+2xZn1-xGeO4, Li1+xTi2−xAlxP3O12, (Li,Ln)TiO3 and Li5La3M2O12 (M = Nb, Ta) have been studied for all-solid-state Li ion batteries. Among these materials, garnet-like oxides have recently gained much attention because of their high Li ion conductivity, low electronic conductivity at high Li activity, and high electrochemical stability window, up to 6 V/Li. The lithium stuffing in the parent garnet-type Li5La3M2O12 increases Li ion conduction. For example, x = 0.75 members of Li5+2xLa3M2-xYxO12 show the highest Li ion conductivity of 10-4 Scm-1 at room temperature. In this talk, role of crystal chemistry on Li ion conductivity of the garnet-type solid Li ion electrolytes will be presented.
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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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".