Structure of the Solid-State Electrolyte Li3+2xP1-xAlxS4: Lithium-Ion Transport Properties in Crystalline vs Glassy Phases

Tuning Ion Mobility in Lithium Argyrodite Solid Electrolytes via Entropy Engineering

The development of improved solid electrolytes (SEs) plays a crucial role in the advancement of bulk-type solid-state battery (SSB) technologies. In recent years, multicomponent or high-entropy SEs are gaining increased attention for their advantageous charge-transport and (electro)chemical properties. However, a comprehensive understanding of how configurational entropy affects ionic conductivity is largely lacking. Herein we investigate a series of multication-substituted lithium argyrodites with the general formula Li6+x[M1aM2bM3cM4d]S5I, with M being P, Si, Ge, and Sb. Structure-property relationships related to ion mobility are probed using a combination of diffraction techniques, solid-state nuclear magnetic resonance spectroscopy, and charge-transport measurements. We present, to the best of our knowledge, the first experimental evidence of a direct correlation between occupational disorder in the cationic host lattice and lithium transport. By controlling the configurational entropy through compositional design, high bulk ionic conductivities up to 18 mS cm-1 at room temperature are achieved for optimized lithium argyrodites. Our results indicate the possibility of improving ionic conductivity in ceramic ion conductors via entropy engineering, overcoming compositional limitations for the design of advanced electrolytes and opening up new avenues in the field.

Deciphering Alloy Composition in Superconducting Single-Layer FeSe1-xSx on SrTiO3(001) Substrates by Machine Learning of STM/S Data

Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, a plain visual analysis of STM images can be challenging for such determination in multicomponent alloys, particularly beyond the diluted limit due to chemical disorder and electronic inhomogeneity. One viable solution is to use machine learning to analyze STM data and identify hidden patterns and correlations. Here, we apply this approach to determine the Se/S concentration in superconducting single-layer FeSe_1-xS_x alloys epitaxially grown on SrTiO₃(001) substrates via molecular beam epitaxy. First, the K-means clustering method is applied to identify defect-related dI/dV tunneling spectra taken by current imaging tunneling spectroscopy. Then, the Se/S ratio is calculated by analyzing the remaining spectra based on the singular value decomposition method. Such analysis provides an efficient and reliable determination of alloy composition and further reveals the correlations of nanoscale chemical inhomogeneity to superconductivity in single-layer iron chalcogenide films.