Back to the future: towards the realization of lithium metal batteries using liquid and solid electrolytes
Bibliographic record
Abstract
As the specific energy of traditional lithium-ion batteries (LIBs) approaches theoretical limits, the quest for alternatives intensifies. Lithium metal batteries (LMBs) stand out as a potential solution, promising substantially higher energy densities (∼35% increase in specific energy and ∼50% increase in energy density at the cell level). Historically, challenges with liquid electrolytes (LEs) in LMBs, such as dendrite growth and unstable solid electrolyte interphase (SEI) formation, led to skepticism about their compatibility and most of the focus was on solid-state electrolytes (SSEs) such as polymer electrolytes and recently inorganic electrolytes (oxides, sulphides halides). However, recent strides in LE engineering have repositioned LEs as viable candidates for LMBs, particularly with the strategic use of additives and the careful formulating of solvents. This review delves into the engineering of LEs for LMBs, highlighting their renewed potential and explores the realm of SSEs and report on the recent advancements in both fields. We aim to provide a comprehensive overview of the evolving landscape of LMB research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".