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@ARTICLE{Mu:902984,
      author       = {Mu, Rui and Zheng, Yujie and Lambertz, Andreas and Wilks,
                      Regan G. and Bär, Marcus and Zhang, Yufeng},
      title        = {{A} spectrum deconvolution method based on grey relational
                      analysis},
      journal      = {Results in Physics},
      volume       = {23},
      issn         = {2211-3797},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-04730},
      pages        = {104031},
      year         = {2021},
      abstract     = {The extensive usage of X-ray spectroscopies in studying
                      complex material systems is not only intended toreveal
                      underlying mechanisms that govern physical phenomena, but
                      also used in applied studies focused onan insight-driven
                      performance improvement of a wide range of devices. However,
                      the traditional analysismethods for X-ray spectroscopic data
                      are rather time-consuming and sensitive to errors in data
                      preprocessing(e.g., normalization or background
                      subtraction). In this study, a method based on grey
                      relationalanalysis, a multi-variable statistical method, is
                      proposed to analyze and extract information from
                      X-rayspectroscopic data. As a showcase, the valence bands of
                      microcrystalline silicon suboxides probed by hardX-ray
                      photoelectron spectroscopy (HAXPES) were investigated. The
                      results obtained by the proposedmethod agree well with
                      conventionally derived composition information (e.g., curve
                      fit of Si 2p core levelof the silicon suboxides).
                      Furthermore, the uncertainty of chemical compositions
                      derived by the proposedmethod is smaller than that of
                      traditional analysis methods (e.g., the least square fit),
                      when artificial linearfunctions are introduced to simulate
                      the errors in data pre-processing. This suggests that the
                      proposedmethod is capable of providing more reliable and
                      accurate results, especially for data containing
                      significantnoise contributions or that is subject to
                      inconsistent data pre-processing. Since the proposed method
                      is lessexperience-driven and error-prone, it offers a novel
                      approach for automate data analysis, which is of
                      greatinterest for various applications, such as studying
                      combinatorial material “libraries”.},
      cin          = {IEK-5},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IEK-5-20101013},
      pnm          = {1213 - Cell Design and Development (POF4-121)},
      pid          = {G:(DE-HGF)POF4-1213},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000639878400004},
      doi          = {10.1016/j.rinp.2021.104031},
      url          = {https://juser.fz-juelich.de/record/902984},
}