Solid Li- and Na-Ion Electrolytes for Next Generation Rechargeable Batteries
Bibliographic record
Abstract
We dedicate this paper to Prof. John B. Goodenough’s 100th birthday, who has made several seminal contributions to the modern electrochemical energy storage and conversion technologies that made significant social and economic impacts on humankind. This review paper reports two battery systems that he contributed in the early stage of solid-state ionics (SSIs). The development of advanced Li and or Na batteries based on solid-state (ceramic) electrolytes (SSEs) is being focused on because of their safety, high energy density, and design flexibility for high power and energy density applications. Several SSEs exhibit a higher electrochemical stability window, enabling various high voltage cathodes to improve the power density compared to organic liquid electrolytes-based batteries (except for sulfide-based electrolytes). However, most SSEs have lower (at least an order of magnitude) ionic conductivity and poor interface compatibility compared to liquid electrolytes. Attempts have been made to improve the ionic conductivity and interface of SSEs and electrodes and develop hybrid solid electrolytes with improved ionic conductivity and stability with an elemental anode and high voltage cathodes. Here, we discuss the materials aspects of SSEs and hybrid SSEs for next-generation Li and Na batteries. Various solid-state electrolytes, including hydride-type, silicates, LISICONs, NASICON-type oxides, glassy-type oxides, covalent organic frameworks, perovskite-type oxides, antiperovskites, Li-stuffed garnet-related structure oxides, and metal halides have been developed. The chemical composition–structure–ionic conductivity relationship of several key SSEs and the ion transport mechanism have been discussed in this study. Moreover, interfacial engineering methods for some typical SSEs and battery applications have also been discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".