期刊
ACS APPLIED MATERIALS & INTERFACES
卷 12, 期 34, 页码 37957-37966出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsami.9b19091
关键词
solid ion conductor; solid electrolyte; Li-ion battery; density functional theory; ion transport; electrochemical stability; kinetics
资金
- TomKat Center for Sustainable Energy at Stanford University
- Toyota Research Institute Accelerated Materials Design and Discovery program
We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We report exceptional density functional theory (DFT)-based room-temperature single-crystal ionic conductivity values for two phases within the crystalline lithium-boron-sulfur (Li-B-S) system: 62 (+9, -2) mS cm(-1) in Li5B7S13 and 80 (-56, -41) mS cm(-1) in Li9B19S33. We report significant ionic conductivity values for two additional phases: between 0.0056 and 0.16 mS/cm(-1) in Li2B2S5 and between 0.0031 and 9.7 mS cm(-1) in Li3BS3 depending on the room-temperature extrapolation scheme used. To our knowledge, our prediction gives Li9B19S33 and Li5B7S13 the second and third highest reported DFT-computed single-crystal ionic conductivities of any crystalline material. We compute the thermodynamic electrochemical stability window widths of these materials to be 0.50 V for Li5B7S13, 0.16 V for Li2B2S5, 0.45 V for Li3BS3, and 0.60 V for Li9B19S33. Individually, these materials exhibit similar or better ionic conductivity and electrochemical stability than the best-known sulfide-based solid-state Li-ion electrolyte materials, including Li10GeP2S12 (LGPS). However, we predict that electrolyte materials synthesized from a range of compositions in the Li-B-S system may exhibit even wider thermodynamic electrochemical stability windows of 0.63 V and possibly as high as 3 V or greater. The Li-B-S system also has a low elemental cost of approximately 0.05 USD/m(2) per 10 mu m thickness, which is significantly lower than that of germanium-containing LGPS, and a comparable mass density below 2 g/cm(3). These fast-conducting phases were initially brought to our attention by a machine learning-based approach to screen over 12,000 solid electrolyte candidates, and the evidence provided here represents an inspiring success for this model.
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