Influence of AlPO<sub>4</sub> Impurity on the Electrochemical Properties of NASICON‐Type Li<sub>1.5</sub>Al<sub>0.5</sub>Ti<sub>1.5</sub>(PO<sub>4</sub>)<sub>3</sub> Solid Electrolyte
Bibliographic record
Abstract
Abstract Densification of ceramic electrolytes is a key enabler in producing electrolyte pellets for solid‐state batteries. This requires understanding the correlation between the starting grain size of electrolytes, chemical phase evolution and degree of compaction which determine ion conductivity and chemical stability of solid electrolytes. In our work we were able to optimize the densification process of LATP at 700 °C with a high total ionic conductivity of 3.45×10−4 S cm−1 after hot pressing, balancing pristine LATP crystallite size and AlPO4 impurity formation. By combining several techniques such as in situ heating X‐ray diffraction (XRD), scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR), we explored the formation mechanism of AlPO4 during the synthesis process of NASICON‐type Li1.5Al0.5Ti1.5(PO4)3 (LATP) electrolyte and we showed the effects of the annealing temperature on the crystal size of the material. Density functional theory (DFT) calculations on the chemical stability of the electrolyte imply a metastable behaviour of LATP furtherly enhanced by particle nano‐structuring at high temperature. Our results point to facile manufacturing of ceramic electrolytes towards energy dense and safe solid‐state battery technology.
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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 | 0.000 | 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".