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<p>In order to expand the range of synchrotron radiation structural characterization modes, an automated <i>in-situ</i> X-ray absorption fine structure (XAFS) spectroscopy characterization for electrochemical research has been established. An <i>in-situ</i> control system equipped with an automatic trigger capability facilitates automated acquisition of XAFS and electrochemical data. Furthermore, the quick scanning XAFS (QXAFS) terminal, <i>in-situ</i> server and data storage were all controlled by remote users, enabling remote measurement to be achieved. Using this system, the evolution of the local structure near Fe atoms during the charging and discharging of lithium-sulfur battery (LSB) cathode materials was observed, which provides deep insights into the sulfur reaction pathway in LSBs by leveraging structural information. The system established here paves the way for fully automated and intelligent <i>in-situ</i> XAFS experiments.</p>

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Approach to Automated In-situ X-Ray Absorption Fine Structure Spectroscopy Characterization

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Yinghao Xia,
  • Guikai Zhang,
  • Wenchao Liu,
  • Guolei Cai,
  • Song Jin,
  • Pengfei An,
  • Huan Huang,
  • Lirong Zheng,
  • Hengxing Ji,
  • Shengqi Chu,
  • Jing Zhang

摘要

In order to expand the range of synchrotron radiation structural characterization modes, an automated in-situ X-ray absorption fine structure (XAFS) spectroscopy characterization for electrochemical research has been established. An in-situ control system equipped with an automatic trigger capability facilitates automated acquisition of XAFS and electrochemical data. Furthermore, the quick scanning XAFS (QXAFS) terminal, in-situ server and data storage were all controlled by remote users, enabling remote measurement to be achieved. Using this system, the evolution of the local structure near Fe atoms during the charging and discharging of lithium-sulfur battery (LSB) cathode materials was observed, which provides deep insights into the sulfur reaction pathway in LSBs by leveraging structural information. The system established here paves the way for fully automated and intelligent in-situ XAFS experiments.