Improving the High Temperature Performance of Li-Ion Batteries with Transition Metal Ion Trapping Separators - a Brief Review
Bibliographic record
Abstract
One of the main performance degradation mechanisms in Li-ion batteries is initiated by the dissolution of transition metal (particularly manganese) cations from positive electrode active materials. These ions will electro-migrate to and deposit on the negative electrode surface. The deposited manganese species induce the catalytic decomposition of solvent molecules and the concurrent depletion of electrochemically active lithium, leading surface film growth, gassing, exfoliation of graphite particles, and an overall degradation of battery performance (increased cell resistance, reduced power capability, and a shortened battery life). Several measures for mitigating manganese dissolution or its consequences have been reported over the years in the literature,1,2 including elemental substitutions (doping) in the bulk of the positive electrode active material,3 surface coatings4 and the application an inorganic barrier coatings onto electrodes by atomic layer deposition,5 passivating additives in the electrolyte solution,6and the reduction of the state-of-charge swing during battery operation. Unfortunately, no single mitigation measure has proven completely successfully so far, i.e., without negatively affecting other properties of the LIB such as energy density and internal resistance. A different - and complementary - approach, that of using a separator containing manganese ion chelating agents, may avoid the previously described drawbacks.7-9 Such a separator captures the manganese ions in the inter-electrode space by means of a polymeric chelating agents with cyclic or open structures, thus preventing the migration of manganese ions to, and subsequent contamination of, the negative electrode. We will review the present status of the chelating agents approach for mitigating the consequences of Mn dissolution for battery performance and will discuss some remaining challenges. References 1. G. Amatucci, A. Du Pasquier, A. Blyr, T. Zheng, and J.-M. Tarascon, Electrochim. Acta 45(1999) 255-271. 2. Y. Xia and M. Yoshio, Ch. 12 in Lithium Batteries: Science and Technology, G. A. Nazri and G. Pistoia (editors), Springer Verlag, 2003, ISBN 978-1-4020-7628-2. 3. M. Choi and A. Manthiram, J. Electrochem. Soc. 153(2006) A1760-A1764. 4. C. Li, H. P. Zhang, L. J. Fu, H. Liu, Y. P. Wu, E. Rahm, R. Holze, and H. Q. Wu, Electrochim. Acta 51(2006) 3872-2883. 5. Y. S. Jung, A. S. Cavanagh, A. C. Dillon, M. D. Groner, S. M. George, and S.-H. Lee, J. Electrochem. Soc. 157(2010) A75-A81. 6. Y. S. Jung, A. S. Cavanagh, R. A. Leah, S. H. Kang, A. C. Dillon, M. D. Groner, S. M. George, and Y.-H. Lee, Adv. Mater. 22(2010) 2172-2176. 7. B. Ziv, N. Levy, V. Borgel, Z. Li, M. Levi, D. Aurbach, A. D. Pauric, G. R. Goward, T. J. Fuller, M. P. Balogh, and I. C. Halalay, J. Electrochem. Soc. 161 (2014) A1213-A1217. 8. Z. Li, A. D. Pauric, G. R. Goward, T. J. Fuller, J.M. Ziegelbauer, M. P. Balogh, and I. C. Halalay, J. Power Sources 272(2014) 1134-1141. 9. A. Banerjee, B. Ziv, Y. Shilina, S. Luski, D. Aurbach, and I.C. Halalay, J. Electrochem. Soc. 163 (2016) A1083-A1094.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".