Electrolyte Design for Anode-Free Lithium Metal Batteries
Bibliographic record
Abstract
Current lithium-ion batteries (LIBs) are approaching their energy density limits and thus may not keep up with the ever-increasing demand for higher specific energy density in today’s energy storage and power applications. Anode-free lithium metal batteries (AFLMBs) utilize the full theoretical capacity of Li metal anode (3860 mAh g-1, ten times higher than lithiated graphite) and offer lower cost and better safety than cells with Li excess. However, due to the low efficiency of Li deposition and stripping, AFLMBs suffer from rapid capacity loss. In this presentation, we will discuss a unique coin cell configuration design with high compression for AFLMBs. The high pressure leads to more stable cycling performance, providing a more accurate assessment of AFLMBs.1 A carbonate-glyme hybrid electrolyte for AFLMB is demonstrated with a capacity retention of 73% for 50 cycles. The hybrid electrolyte possesses a unique solvation structure, where diglyme solvates both Li-ions and film-forming additive, while carbonates dilute the mixture, enabling facile ion migrations.2 C. Zhou, A. J. Samson, M. A. Garakani, and V. Thangadurai, J. Electrochem. Soc., 168, 060532 (2021). C. Zhou et al., Energy Storage Mater., 42, 295–306 (2021) https://doi.org/10.1016/j.ensm.2021.07.043.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".