Structure Optimization for Cellulose‐Based Separator through Fiber Size Regulation for High Performance Lithium Metal Batteries
Bibliographic record
Abstract
Abstract Cellulose‐based separator exhibits excellent electrolyte affinity, thermal stability, and mechanical strength, which acts as a promising alternative to commercial polyolefin separators in lithium metal batteries (LMBs). Fiber size in cellulose‐based separators plays a crucial role in determining their physicochemical structure and mechanical strength, as well as the electrochemical performance of corresponding LMBs. Herein, the fiber size in cellulose‐based separators was first time regulated to optimize their mechanical stability and the related battery performance. The influences of fiber size in the separator on chemical structure, mechanical properties, surface morphology, electrochemical behavior were investigated in detail, in which the underlying mechanism between separator structure and the related performance was elucidated. As a result, the separator optimized by fiber size regulation exhibited excellent thermal stability under 180 °C, good tensile strengths of 6.0 MPa and Young's moduli of 315.9 MPa, superior room temperature ionic conductivity of 1.87 mS cm −1 , as well as significantly improved electrochemical performance of corresponding batteries. It can be concluded that structure optimization for cellulose‐based separator through fiber size regulation is an effective and indispensable approach towards high safety and high performance LMBs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".