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Exploring the Underlying Correlation between the Structure and Ionic Conductivity in Halide Spinel Solid-State Electrolytes with Neutron Diffraction.

Jiangyang PanLei GaoXinyu ZhangDubin HuangJinlong ZhuLiping WangYadong WeiWen YinYuanguang XiaRuqiang ZouYusheng ZhaoSongbai Han
Published in: Inorganic chemistry (2024)
The development of cutting-edge solid-state electrolytes (SSEs) entails a deep understanding of the underlying correlation between the structure and ionic conductivity. Generally, the structure of SSEs encompasses several interconnected crystal parameters, and their collective influence on Li + transport can be challenging to discern. Here, we systematically investigate the structure-function relationship of halide spinel Li x MgCl 2+ x (2 ≥ x ≥ 1) SSEs. A nonmonotonic trend in the ionic conductivity of Li x MgCl 2+ x SSEs has been observed, with the maximum value of 8.69 × 10 -6 S cm -1 achieved at x = 1.4. The Rietveld refinement analysis, based on neutron diffraction data, has revealed that the crystal parameters including cell parameters, Li + vacancies, Debye-Waller factor, and Li-Cl bond length assume diverse roles in influencing ionic conductivity of Li x MgCl 2+ x at different stages within the range of x values. Besides, mechanistic analysis demonstrates Li + transport along three-dimensional pathways, which primarily governs the contribution to ionic conductivity of Li x MgCl 2+ x SSEs. This study has shed light on the collective influence of crystal parameters on Li + transport behaviors, providing valuable insights into the intricate relationship between the structure and ionic conductivity of SSEs.
Keyphrases
  • solid state
  • ion batteries
  • single cell
  • stem cells
  • machine learning
  • cell therapy
  • big data
  • deep learning
  • crystal structure
  • data analysis