Review—Multifunctional Separators: A Promising Approach for Improving the Durability and Performance of Li-Ion Batteries
Bibliographic record
Abstract
Electrified vehicles require Li-ion batteries (LIBs) with 10-year useful life. We review herein our progress since 2016 in the understanding of dissolved Mn species and LIB performance degradation by multifunctional materials. Multifunctional separators (MFSs), can trap Mn cations, scavenge acid species, and/or dispense alkali metal ions, with significant battery performance benefits: increased capacity retention during electrochemical cycling and improved rate performance. Cells with LiMn 2 O 4 (LMO), LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NCM622), or LiNi 0.5 Mn 1.5 O 4 (LNMO) positive electrodes, graphite negative electrodes, and LiPF 6 /mixed organic carbonate solutions were investigated. XRD, ICP-OES, XANES, HR-SEM, FIB-SEM, and MAS-NMR on harvested cell components complemented and aided the interpretation of electrochemical test results. While in LIBs with positive electrodes affected by Mn dissolution cell performance improvements can be enabled by delaying and then impeding the deposition of transition metal (TM) ions deposition at negative electrodes through their trapping by MFS, acid scavenging separators provide a more general approach that can enable performance benefits in cells with non-spinel positive electrode materials such as NCM622, as well as with 5 V class positive electrode materials such as LNMO. We illustrate our discussion with both previously published and new data. We conclude with a discussion of remaining challenges, new opportunities, and recommendations for future work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".