Highly flexible and free-standing NASICON composite electrolyte with composite ceramic electrode for enhanced performance in quasi solid-state lithium metal battery
Bibliographic record
Abstract
The lithium metal batteries offer very high energy density , but commercialization with liquid electrolytes raises certain safety concerns due to the higher incidence of dendrite propagation and short-circuiting. On the other hand, safer and high conducting ceramic electrolytes like NASICON (LATP) are prone to fragility and interface issues. Polymer-based solid state electrolyte composites are in this case a promising stable electrolyte for lithium metal batteries due to their significant advantage of mechanical, electrochemical, and thermal stability with flexibility. Herein, we report a facile method of preparing free-standing composite ceramic polymer electrolytes (LATP-LiClO 4 -PVDF) via the tape casting method. The prepared free-standing composite ceramic polymer membrane exhibits superior electrochemical properties along with mechanical and thermal stability. The combination of ceramic and Li salt offered high ionic conductivity (1.9 × 10 −4 S cm −1 at 60℃). The full cell performance employing composite LiFePO 4 as the cathode delivers exceptional capacity and good cyclic performance.
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".