Towards Mixed Ionic and Electronic Conducting Li-Stuffed Garnets
Bibliographic record
Abstract
Li-stuffed garnet-type Li 7 La 2.75 Ca 0.25 Zr 1.75 Nb 0.25 O 12 (LLCZNO) and Li 7+y La 2.75 Ca 0.25 Zr 1.75-y Nb 0.25 M y O 12 (M = Mn, Fe, Co, Ni; y = 0.1, 0.2, referred to as yM-LLCZNO) were synthesized in air by ceramic synthesis at different sintering temperatures. The resulting compositions were characterized for their phase formation and microstructure, and their ionic and electronic conductivities in air and 7% H 2 /N 2 . Powder X-ray diffraction showed that transition metal-doped LLCZNO garnets contain several impurity phases. The transition metal-substituted samples have lower conductivities than that of the parent LLCZNO. The electronic conductivities of selected transition metal-doped LLCZNO samples were found to be about 4 to 5 orders of magnitude lower than their corresponding ionic conductivity, highlighting the challenge to design single-phase mixed electronic and ionic conducting Li-stuffed garnets in both oxidizing and reducing atmospheres. The research area of single-phase mixed conducting Li-garnet is relatively unexplored at the moment, and a theoretical study will help to elucidate the underlying challenge in doping transition metal in Li-based garnets structure and its effect on transport properties.
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 | 0.000 | 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".