Sulfide-Based All-Solid-State Batteries: From Electrolyte Synthesis to Interface Design
Bibliographic record
Abstract
All-solid-state Li-metal batteries (ASSLMBs) have attracted increasing attentions because of their high specific energy density and advantage of safety compared with conventional liquid electrolyte-based Li-ion batteries (LIBs). Solid-state electrolytes (SSEs) are the key component for ASSLMBs. Sulfide-based SSEs exhibit a very competitive ionic conductivity compared with oxides, polymer-based, and other kinds of SSEs. However, the poor electrode/sulfide SSEs interface and the moisture-sensitivity hinder the development.1 From the point of sulfide SSEs synthesis, we demonstrate that using fluorine (F) incorporation in an Argyrodite Li6PS5Cl sulfide electrolyte (LPSCl) can achieve an ultra-stable Li metal/sulfide SSEs interface. The performance can be comparable to that in the symmetric cells based on liquid electrolytes. The condensed interfacial structure and high fluorinated chemical composition of the in-situ formed interface lead to the high performance.2 In addition, Sn (IV) is employed to partially replace problematic P (V) in Argyrodite sulfide Li6PS5I (LPSI) SSEs to prepare relatively air-stable sulfide SSEs, which is attributed to the strong Sn-S bonding energy in the Sn-substituted LPSI electrolytes (LPSI-Sn). Benefiting from the aliovalent element substitution and I-based chemistry, LPSI-Sn electrolyte can show 125-times increase in the ionic conductivity and significantly improved Li metal compatibility.3 For the interface design, we focus on the improvement of the Li anode/sulfide SSEs interface stability. Inorganic LixSiSy and Li3PS4 protection layer achieved by in-situ growing on the Li metal, which can serve as an effective interlayer to improve chemical/electrochemical stability of Li/sulfide SSEs interface.4-5 The growth of Li dendrites at the interface is suppressed. The side reaction between Li metal anodes and sulfide SSEs is also well controlled.In addition, the Li-ion conductive interlayer has demonstrated favorable ionic conductivity fundamentally. High-rate ASSLMBs can be achieved by using these inorganic Li-ion conductive protection layers. Re ferences 1. Q. Zhang, et al. Sulfide-Based Solid-State Electrolytes: Synthesis, Stability, and Potential for All-Solid-State Batteries. Advanced Materials 2019, 31, 1901131. 2. F. Zhao, X. Sun, et al. Ultra-Stable Li Anode Interface Achieved by Incorporating Fluorine in Argyrodite Sulfide Electrolytes. Submitted. 3. F. Zhao, J. Liang, X. Sun, et al. A Versatile Sn-Substituted Argyrodite Sulfide Electrolyte for All-Solid-State Li Metal Batteries. Submitted. 4. J. Liang, X. Li, X. Sun, et al. An Air-Stable and Dendrite-Free Li Anode for Highly Stable All-Solid-State Sulfide-Based Li Batteries. Advanced Energy Materials 2019, 9, 1902125. 5. J. Liang, X. Li, X. Sun, et al. In Situ Li3PS4 Solid-State Electrolyte Protection Layers for Superior Long-Life and High-Rate Lithium-Metal Anodes. Advanced Materials 2018, 30, 1804684.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".