Sulfide and Oxide Inorganic Solid Electrolytes for All-Solid-State Li Batteries: A Review
Bibliographic record
Abstract
Energy storage materials are finding increasing applications in our daily lives, for devices such as mobile phones and electric vehicles. Current commercial batteries use flammable liquid electrolytes, which are unsafe, toxic, and environmentally unfriendly with low chemical stability. Recently, solid electrolytes have been extensively studied as alternative electrolytes to address these shortcomings. Herein, we report the early history, synthesis and characterization, mechanical properties, and Li+ ion transport mechanisms of inorganic sulfide and oxide electrolytes. Furthermore, we highlight the importance of the fabrication technology and experimental conditions, such as the effects of pressure and operating parameters, on the electrochemical performance of all-solid-state Li batteries. In particular, we emphasize promising electrolyte systems based on sulfides and argyrodites, such as LiPS5Cl and β-Li3PS4, oxide electrolytes, bare and doped Li7La3Zr2O12 garnet, NASICON-type structures, and perovskite electrolyte materials. Moreover, we discuss the present and future challenges that all-solid-state batteries face for large-scale industrial applications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".