Stabilizing Polymer-Based Solid-State Electrolytes with 4 V Class Cathodes: From Interface Modification to Electrode Designs
Bibliographic record
Abstract
Developing safe solid-state lithium batteries with high-energy-density is important for the application in electric vehicles (EVs). Nowadays, solid-state batteries (SSBs) with polymer-based solid-state electrolyte (SSE) is one of the most promising candidates for the application in EVs. However, SSEs based on polyethylene oxide (PEO) and lithium salt complex delivers poor electrochemical performance in 4 V class high-energy-density cathodes such as lithium cobalt oxide (LiCoO2) and lithium nickel manganese cobalt oxide (NMC). This is because PEO has low electrochemical oxidation window which is lower than 3.8 V vs.Li/Li+.[1] This abstract mainly focus on developing several strategies for stabilizing polymer-based SSEs with 4 V class cathodes including interface modification to electrode designs. Firstly, inspired by previous reports that coating can enhance the cycling performance of SSBs with polymer-based SSE,[2] we applied atomic layer deposition (ALD), a versatile chemical deposition technique which is capable to deposit uniform thin film with controllable thickness at low temperature,[3] for coating cathode to stabilize the polymer-based SSE/4 V class cathode interface under high voltage charge/discharge processes. The coating effect on conductive carbon, active materials, and electrode surface was studied and compared. It is found that carbon is detrimental for the decomposition of polymer-based SSE and the protection of SSE/carbon interface can effectively enhance the performance of 4 V class solid polymer batteries.[4] Secondly, the electrode design is critical for high performance high-energy-density solid polymer batteries. Using freeze-drying method, we had prepared a high-areal-loading cathode for solid polymer batteries with excellent electrochemical performance. This is because the vertically aligned porous electrode can facilitate the Li+ ion transport within the thick electrode.[5] Also, the binder effect in 4 V class solid polymer batteries is carefully studied. Various binders including PEO, polyvinylidene fluoride (PVDF) etc. were applied in 4 V class solid polymer batteries. And the mechanism had been studied by synchrotron X-ray absorption and X-ray photoelectron spectroscopy.[6] References [1] Y. Xia, T. Sakai et al., J. Power Sources, 2001, 92, 234-243 [2] H. Miyashiro, M. Wakihara et al., Chem. Mater. 2005, 17, 23, 5603-5605; S. Seki, T Iwahori et al., Chem. Mater. 2005, 17, 8, 2041-2045; Q. Yang, L. Chen et al., J. Power Source, 2018, 388, 65-70. [3] Y. Zhao, K. Zheng, X. Sun, Joule 2018, 2, 1-22; Y. Zhao, X. Sun, ACS Energy Lett. 2018, 3, 899−914; J. Liu and X. Sun, Nanotechnology 2015, 26, 024001; [4] J. Liang, X. Sun et al., J. Mater. Chem. A, 2019, submitted; J. Liang, X. Sun et al., 2019, to be submitted; [5] X. Yang, J. Liang, X. Sun et al., Nano Energy, 2019, 61, 567-575568; [6] J. Liang, X. Sun et al., 2019, to be submitted
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".