Metastability in Li–La–Ti–O Perovskite Materials and Its Impact on Ionic Conductivity
Bibliographic record
Abstract
A great number of candidates exist for solid electrolytes in all-solid Li batteries. This study represents the first in a series using combinatorial synthesis, X-ray diffraction (XRD), and impedance spectroscopy to screen for better solid electrolytes. Herein, over 576 Li–La–Ti–O samples are synthesized and characterized by XRD. Phase compositions are determined using automated Rietveld refinement, and the resulting phase stabilities provide important insights into this class of materials. This system includes the lithium lanthanum titanate (LLTO) perovskite structures. Of highest importance, we find that the perovskite structure is not stabilized as a pure phase at any composition but rather as composites wherein LLTO is stabilized by the presence of secondary phases at high temperature, and at some compositions, these composites are further favored during slow cooling. This new means of stabilizing metastable phases is of interest in itself, but it also proves important in designing solid electrolytes as the ionic conductivities vary dramatically with changes in the secondary phase content. We find ionic conductivities as high as 5 × 10 –5 S cm –1 in total and >10 –3 S cm –1 in the bulk in a sample where the secondary phase is TiO 2 with a composition of 9 molar %. Both conductivity values are highly competitive with the state-of-the-art, even though more cost-effective sintering protocols are used herein (far shorter heating times and lower temperatures). We find that TiO 2 helps lower the grain boundary energy in the composite electrolytes and speculate that it may be acting as a sintering agent. This study therefore helps to decouple the effects of composition and synthesis conditions that have plagued the understanding of this class of material. Thus, this work not only serves as a proof of concept for the use of combinatorial methods in studying solid electrolytes but also gives significant insights into the importance of secondary phases in ionic transport, and this is done for a class of materials that has proven to be particularly challenging. Given the negligible focus on secondary phases in the literature of solid electrolytes, these findings will be of use in further explorations of other classes of solid electrolytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".