Advancements in Low-Temperature Lithium Metal Batteries: A Comprehensive Review
Bibliographic record
Abstract
Lithium metal batteries (LMB) represent a major advance in energy storage technology, offering a huge high energy density, and have the potential to be used in important fields such as electric vehicles and aerospace. However, their practical application at low temperatures still faces major challenges. This paper reviews recent advances in overcoming these obstacles, including the development of ether-based and fluorated electrolytes to reduce dendrite formation, improve SEI stability, and the use of 3D collectors and protective coatings to create more stable interfaces. By combining these methods, we conclude from a large number of studies that the problem of LMB at low temperatures is not the result of a single aspect, but the result of multiple factors. SEI instability leads to uneven lithium deposition and electrolyte decomposition in LMB. At the same time, the ionic conductivity of the electrolyte decreases at low temperature, slowing down the transmission rate of lithium ions, resulting in an increase in overpotential and promoting the uneven deposition of lithium. Therefore, this paper proposed a feasible path for the future development of LMB, that is, strengthening the three-dimensional lipophilic skeleton while improving the electrolyte solution, and constructing the three-dimensional lipophilic skeleton of SEI to solve the problems of lithium dendrite growth of LMB at low temperature and SEI instability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".