Optimization of Catholyte in Composite Cathodes for Garnet-Structured Llzo Electrolyte in Solid-State Batteries
Bibliographic record
Abstract
The application of Li-ion batteries have been expanding at a rapid rate in recent decades due to the tremendous demands in the market for portable electronics, smart grid, and electric vehicles (EVs). All-solid-state lithium metal battery (ASSLB) is the most promising next-generation energy storage device as they can solve the safety issue resulted from liquid electrolytes. Among different types of solid-state electrolytes (SSE), garnet-type structure materials have been shown to be very promising for the development of ASSLBs owing to their high Li-ion conductivity (∼10–3 S cm–1 at RT), wide electrochemical stability window (∼6 V vs Li+/Li), and good chemical stability against Li metal. The high interfacial resistance generated by inadequate contact and interfacial reactions is a substantial impediment to the adaptation of ASSLBs. To address this challenge, a catholyte (a small amount of ionic conductors which are mostly derived from the solid electrolyte formulation) is introduced into the cathode formulation. This necessities a re-design of the cathode into a composite cathode with possibly new components such as carbon additives with high aspect ratio and conductive binders. Understanding their individual and combined impacts on performance is essential in the pursuit of optimized systems. In this work, we designed a series of composite cathode formulations defined with three cathode active materials (LFP, NMC 622 and NMC 811), three carbonaceous additives (carbon black, carbon nanofibers and carbon nanotubes), and a series of organic and inorganic molecular, and polymeric conductors along with LLZO to create a composite cathode with high capacity and stability at high C-rates. A systematic DOE approach has been utilized to evaluate the impact of each component on the electrode and cell performance and the results will be presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".