Composite Cathodes for Solid‐State Lithium Batteries: “Catholytes” the Underrated Giants
Bibliographic record
Abstract
To expedite the large‐scale adoption of electric vehicles (EVs), increasing the gravimetric energy density of batteries to at least 250 Wh kg−1 while sustaining a maximum cost of $120 kWh−1 is of utmost importance. Solid‐state lithium batteries are broadly accepted as promising candidates for application in the next generation of EVs as they promise safer and higher‐energy‐density batteries. Nonetheless, their development is impeded by many challenges, including the resistive electrode–electrolyte interface originating from the removal of the liquid electrolyte that normally permeates through the porous cathode and insures efficient ionic conductivity through the cell. One way to tackle this challenge is by formulating composite cathodes (CCs) that employ solid ionic conductors as “catholytes” in their structure. Herein, it is attempted to shed light on this less studied and poorly understood approach. The different classes of catholytes that have been reported in literature alongside the most common fabrication techniques used to prepare CCs are presented. Next, the interplay between the microstructure and design parameters of CCs with the electrochemical performance of solid‐state batteries (SSBs) and the techniques used to measure their transport properties is well documented. Finally, general guidelines surrounding CC research are outlined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".