Composite Separators for Robust High Rate Lithium Ion Batteries
Bibliographic record
Abstract
Abstract Lithium ion batteries (LIBs) are one of the most potential energy storage devices among various rechargeable batteries due to their high energy/power density, long cycle life, and low self‐discharge properties. However, current LIBs fail to meet the ever‐increasing safety and fast charge/discharge demands. As one of the main components in LIBs, separator is of paramount importance for safety and rate performance of LIBs. Among the various separators, composite separators have been widely investigated for improving their thermal stability, mechanical strength, electrolyte uptake, and ionic conductivity. Herein, the challenges and limitations of commercial separators for LIBs are reviewed, and a systematic overview of the state‐of‐the‐art research progress in composite separators is provided for safe and high rate LIBs. Various combination types of composite separators including blending, layer, core–shell, and grafting types are covered. In addition, models and simulations based on the various types of composite separators are discussed to comprehend the composite mechanism for robust performances. At the end, future directions and perspectives for further advances in composite separators are presented to boost safety and rate capacity of LIBs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".