Understanding Key Limiting Factors of Electrode and Cell Designs in Solid-State Lithium Batteries
Bibliographic record
Abstract
Solid-state batteries (SSBs) use solid electrolytes (SE) to replace flammable liquid electrolytes that result in safer batteries with increased energy density enabled by lithium metal as an anode. Even though there have been recent major breakthroughs at the material level and some at the cell level, there are still many more challenges to overcome that are mostly related to the solid-solid interfaces besides the grand challenge of manufacturing these type of batteries with or without existing manufacturing processes with the promised energy density ( > 400Wh/Kg) at targeted low costs (< 100$/KWh). For this to happen, it is important to look into all the changes that need to be made at the material, electrode and cell levels compared to what is currently used in Li-ion cells and critically evaluate their impact on the cell energy density. In this work, we have first used an equation that we have previously modified and used to calculate the impact of Si content in Si-graphite composite on the full-cell energy density (2) to evaluate the impact of the following parameters (type of anode-or no anode- and cathode materials. N/P ratio, thickness of SE and most importantly the composite cathode formulation on the full-cell energy density. In the latter, we have evaluated the thickness of two electrodes, amount and volume of cathode active material, and amount of catholyte). We will present the impact of each of the above-mentioned factors on the cell energy density supported by data from half and full cells using composite cathodes coupled with and without lithium metal as an anode. References: (1) Mauro Pasta et al 2020 J. Phys. Energy 2 032008. (2) Chae-Ho Yim, Svetlana Niketic, Nuha Salem, Olga Naboka and Yaser Abu-Lebdeh, 2017, J. Electrochem. Soc. 164 A6294.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".