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Updated: Jun 7, 2026

Solid-state Graft Copolymer Electrolytes for Lithium Battery Applications
Published on: August 12, 2013
Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes
Haoyue Guo1, Matthew R Carbone2, Chuntian Cao3
1Department of Chemical Engineering, Columbia University, New York, New York, 10027, USA. haoyue1619@gmail.com.
Researchers created a large database of sulfur X-ray absorption spectra (XAS) for lithium thiophosphates. This resource aids in understanding solid electrolytes by linking spectral features to atomic structures.
Area of Science:
- Materials Science
- Computational Chemistry
- Spectroscopy
Background:
- X-ray absorption spectroscopy (XAS) is crucial for characterizing material properties.
- Understanding the local chemical environment of atoms is key in materials development.
- Lithium thiophosphates are important in energy storage applications.
Purpose of the Study:
- To develop a comprehensive database of sulfur K-edge XAS spectra for lithium thiophosphate materials.
- To establish correlations between spectral features and local atomic structures.
- To provide a valuable resource for the analysis of sulfide-based solid electrolytes.
Main Methods:
- Atomic structures of crystalline and amorphous lithium thiophosphates were used as input.
- Simulations were performed using the excited electron and core-hole pseudopotential approach.
- The Vienna Ab initio Simulation Package (VASP) was employed for calculations.
Main Results:
- A database of 2681 S K-edge XAS spectra for 66 distinct crystalline and glassy models was generated.
- This represents the most extensive collection of first-principles computational XAS spectra for these materials.
- The database facilitates the identification of different sulfur species based on their coordination and short-range order.
Conclusions:
- The developed XAS database is a significant resource for researchers studying sulfide-based solid electrolytes.
- It enables spectral fingerprinting and matching with experimental data.
- The data supports the development of machine learning models for materials characterization.
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