Progress and Prospects of Inorganic Solid‐State Electrolyte‐Based All‐Solid‐State Pouch Cells
Bibliographic record
Abstract
All-solid-state batteries have piqued global research interest because of their unprecedented safety and high energy density. Significant advances have been made in achieving high room-temperature ionic conductivity and good air stability of solid-state electrolytes (SSEs), mitigating the challenges at the electrode-electrolyte interface, and developing feasible manufacturing processes. Along with the advances in fundamental study, all-solid-state pouch cells using inorganic SSEs have been widely demonstrated, revealing their immense potential for industrialization. This review provides an overview of inorganic all-solid-state pouch cells, focusing on ultrathin SSE membranes, sheet-type thick solid-state electrodes, and bipolar stacking. Moreover, several critical parameters directly influencing the energy density of all-solid-state Li-ion and lithium-sulfur pouch cells are outlined. Finally, perspectives on all-solid-state pouch cells are provided and specific metrics to meet certain energy density targets are specified. This review looks to facilitate the development of inorganic all-solid-state pouch cells with high energy density and excellent safety.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".