Enabling High‐Performance NASICON‐Based Solid‐State Lithium Metal Batteries Towards Practical Conditions
Bibliographic record
Abstract
Abstract Solid‐state lithium metal batteries (SSLMBs) are promising next‐generation high‐energy rechargeable batteries. However, the practical energy densities of the reported SSLMBs have been significantly overstated due to the use of thick solid‐state electrolytes, thick lithium (Li) anodes, and thin cathodes. Here, a high‐performance NASICON‐based SSLMB using a thin (60 µm) Li1.5Al0.5Ge1.5(PO4)3 (LAGP) electrolyte, ultrathin (36 µm) Li metal, and high‐loading (8 mg cm−2) LiFePO4 (LFP) cathode is reported. The thin and dense LAGP electrolyte prepared by hot‐pressing exhibits a high Li ionic conductivity of 1 × 10−3 S cm−1 at 80 °C. The assembled SSLMB can thus deliver an increased areal capacity of ≈1 mAh cm−2 at C/5 with a high capacity retention of ≈96% after 50 cycles under 80 °C. Furthermore, it is revealed by synchrotron X‐ray absorption spectroscopy and in situ high‐energy X‐ray diffraction that the side reactions between LAGP electrolyte and LFP cathode are significantly suppressed, while rational surface protection is required for Ni‐rich layered cathodes. This study provides valuable insights and guidelines for the development of high‐energy SSLMBs towards practical conditions.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".