Development of High-Performance Cathodes for Inorganic Halide All-Solid-State Li-Ion Batteries
Bibliographic record
Abstract
Electric vehicles (EVs), representing the future of transportation, are predicted to be one of the ultimate solutions to eliminate greenhouse gas emissions. Li-ion batteries (LIBs), as the most promising sustainable energy devices, are critical for the development of EVs because of their higher operating voltages compared to other energy storage technologies. However, the development of conventional LIBs touched the ceiling because of three main challenges. (1) The use of flammable and toxic liquid electrolytes brings high safety risks. (2) The limited energy density of LIBs cannot satisfy the requirement for long-range EVs. (3) The increasing market demand for LIBs will cause resource shortage and rise of cost.[1] Accordingly, all-solid-state Li-ion batteries (ASSLIBs) have recently emerged as promising alternative batteries for next-generation EVs because of their ability to overcome the drawbacks of conventional LIBs. Among the developed solid-state electrolytes (SSEs), inorganic SSEs (e.g., sulfide SSEs, halide SSEs) have high Li+ conductivity at 10-3 S cm-1 and improved interfacial stability, demonstrating stable cycling performance in ASSLIBs.[2] However, inorganic ASSLIBs still face some key challenges hindering their application in EVs. For example, the rate capability of inorganic ASSLIBs still falls far below the requirements of fast-charging EVs (full-charged in 15 min). In addition to the effect of Li+ conductivity in SSEs, Li+/e- transfer kinetics at the cathode interface and in cathode materials are also critically important for the high-rate capability of inorganic ASSLIBs. In this talk, I will first review our previous studies on cathode interface in inorganic ASSLIBs.[3-7] After that, I will introduce our most recent research on unveiling the kinetic limitation of cathode interfaces in inorganic ASSLIBs by synchrotron-based spectromicroscopy. Our research suggests that poor Li+/e- transfer kinetics at the cathode/SSE interface and in the cathode bulk structure are the key limitation for the low capacity of inorganic ASSLIBs under high-rate charge-discharge cycles. References [1] H. Li, Joule, 3 (2019) 911-914. [2] S. Randau, D.A. Weber, O. Kötz, R. Koerver, P. Braun, A. Weber, E. Ivers-Tiffée, T. Adermann, J. Kulisch, W.G. Zeier, Nature Energy, 5 (2020) 259-270. [3] S. Deng, X. Li, Z. Ren, W. Li, J. Luo, J. Liang, J. Liang, M.N. Banis, M. Li, Y. Zhao, Energy Storage Materials, 27 (2020) 117-123. [4] S. Deng, Y. Sun, X. Li, Z. Ren, J. Liang, K. Doyle-Davis, J. Liang, W. Li, M. Norouzi Banis, Q. Sun, ACS Energy Letters, 5 (2020) 1243-1251. [5] S. Deng, Q. Sun, M. Li, K. Adair, C. Yu, J. Li, W. Li, J. Fu, X. Li, R. Li, Energy Storage Materials, 35 (2021) 661-668. [6] S. Deng, M. Jiang, N. Chen, W. Li, M. Zheng, W. Chen, R. Li, H. Huang, J. Wang, C.V. Singh, Advanced Functional Materials, 32 (2022) 2205594. [7] S. Deng, M. Jiang, A. Rao, X. Lin, K. Doyle‐Davis, J. Liang, C. Yu, R. Li, S. Zhao, L. Zhang, Advanced Functional Materials, 32 (2022) 2200767.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".