Reviving Low-Temperature Performance of Lithium Batteries by Emerging Electrolyte Systems
Bibliographic record
Abstract
Open AccessRenewablesREVIEWS14 Jan 2023Reviving Low-Temperature Performance of Lithium Batteries by Emerging Electrolyte Systems Tingzhou Yang, Yun Zheng, Yizhou Liu, Dan Luo, Aiping Yu and Zhongwei Chen Tingzhou Yang Google Scholar More articles by this author , Yun Zheng Google Scholar More articles by this author , Yizhou Liu Google Scholar More articles by this author , Dan Luo Google Scholar More articles by this author , Aiping Yu Google Scholar More articles by this author and Zhongwei Chen Google Scholar More articles by this author https://doi.org/10.31635/renewables.022.202200007 SectionsAboutPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked InEmail Although lithium batteries have been successfully commercialized in the past two decades, they are particularly sensitive to ultra-low temperatures. Most of battery’s capacity and power will be lost in sub-zero temperatures, mainly due to the increased electrolyte viscosity, insufficient ionic conduction, slow charge-transfer kinetics, and reduced ion diffusing constant. In this review, we sorted out the critical factors leading to the poor low-temperature performance of electrolytes, and the comprehensive research progress of emerging electrolyte systems for the ultra-low temperature lithium battery is classified and highlighted. We further provide a systematic summary of the advanced characterization and computational simulation for low-temperature electrolyte systems to guide researchers in screening the low-temperature electrolytes, monitoring solvation/de-solvation behavior, and investigating reaction mechanisms. Besides their fundamental significance, our review may also forge a new opportunity and prospects in the effective design of electrolytes for the ultra-low temperature application of energy storage devices. Download figure Download PowerPoint Previous article FiguresReferencesRelatedDetails Issue AssignmentNot Yet Assigned Copyright & Permissions© 2023 Chinese Chemical Society Downloaded 1 times PDF downloadLoading ...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".