Spark Plasma Sintering of Lithium Aluminum Germanium Phosphate Solid Electrolyte and its Electrochemical Properties
Bibliographic record
Abstract
Sodium superionic conductor (NASICON)-type lithium aluminum germanium phosphate (LAGP) has attracted increasing attention as a solid electrolyte for all-solid-state lithium-ion batteries (ASSLIBs), due to the good ionic conductivity and highly stable interface with Li metal. However, it still remains challenging to achieve high density and good ionic conductivity in LAGP pellets by using conventional sintering methods, because they required high temperatures (>800 °C) and long sintering time (>6 h), which could cause the loss of lithium, the formation of impurity phases, and thus the reduction of ionic conductivity. Herein, we report the utilization of a spark plasma sintering (SPS) method to synthesize LAGP pellets with a density of 3.477 g cm−3, a relative high density up to 97.6%, and a good ionic conductivity of 3.29 × 10−4 S cm−1. In contrast to the dry-pressing process followed with high-temperature annealing, the optimized SPS process only required a low operating temperature of 650 °C and short sintering time of 10 min. Despite the least energy and short time consumption, the SPS approach could still achieve LAGP pellets with high density, little voids and cracks, intimate grain–grain boundary, and high ionic conductivity. These advantages suggest the great potential of SPS as a fabrication technique for preparing solid electrolytes and composite electrodes used in ASSLIBs.
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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) | 0.000 | 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".