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@MASTERSTHESIS{AkueGoeh:1049792,
      author       = {Akue-Goeh, Adole Imelda},
      title        = {{DEVELOPMENT} {OF} {ACCESSIBLE} {AUTOMATED}
                      {QUANTIFICATION} {METHODS} {ON} {FIB}/{SEM} {TOMOGRAPHY}
                      {FOR} {INVESTIGATING} {SOLID} {OXIDE} {ELECTROLYZER} {CELL}
                      ({SOEC}) {AND} {PROTON} {EXCHANGE} {MEMBRANE} {WATER}
                      {ELECTROLYZER} ({PEMWE}) {DEGRADATION}},
      school       = {Félix-Houphouët-Boigny of Abidjan (CôTE D'IVOIRE)},
      type         = {Masterarbeit},
      reportid     = {FZJ-2025-05576},
      pages        = {61p},
      year         = {2025},
      note         = {Masterarbeit, Félix-Houphouët-Boigny of Abidjan (CôTE
                      D'IVOIRE), 2025},
      abstract     = {The expanding hydrogen economy relies on efficient and
                      durable electrochemical devices, such as Solid Oxide
                      Electrolysis Cells (SOECs) and Proton Exchange Membrane
                      Water Electrolyzers (PEMWEs). The performance and lifetime
                      of these devices are closely linked to their microstructural
                      properties. Despite the development of advanced
                      microstructural characterization techniques, like Focused
                      Ion Beam-Scanning Electron Microscopy (FIB-SEM) for
                      quantitative analysis, data processing remains challenging
                      due to the complexity of the data, the large volume
                      generated and the limited access to advanced computational
                      tools. This study proposes an automated, modular pipeline
                      that combines traditional image processing and Random
                      Forest-based supervised learning to segment the
                      electrode-catalyst part composed of four layers in SOECs
                      (Layer 1 to Layer 4 ) and to quantitatively evaluate key
                      microstructural parameters such as porosity and thickness.
                      The pipeline is intentionally designed to require minimal
                      computational resources and remain accessible even to
                      non-expert users.The training on a representative annotated
                      dataset of FIB-SEM images (10 training images out of a total
                      of 200) achieved a layer segmentation accuracy of $68\%$ on
                      the test dataset. Even though this indicates the need for
                      additional improvement, it was enough to identify meaningful
                      structural variations. The utilization of the pipeline
                      across multiple experimental FIB-SEM datasets enables the
                      extraction of statistically consistent trends in porosity
                      and thickness under different operational conditions:
                      pristine, 100-hour and 200-hour run cells. layer 4 exhibits
                      a distinct rise in porosity, whereas layer 2 displayed a
                      noticeable change in thickness. These findings show that a
                      lightweight machine learning approach combined with
                      traditional image processing can provide meaningful insights
                      into microstructural parameters. it also emphasizes the
                      potential for developing a user-friendly and automated
                      pipeline to assess the complex FIB-SEM datasets of these
                      electrochemical devices quantitatively in a record time.
                      Potential enhancement could look closer to the annotation
                      protocol, feature engineering and combined machine learning
                      approaches. Keywords: FIB-SEM, microstructural analysis,
                      Random Forest, image processing, electrolysis.},
      cin          = {IET-1},
      cid          = {I:(DE-Juel1)IET-1-20110218},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123)},
      pid          = {G:(DE-HGF)POF4-1231},
      typ          = {PUB:(DE-HGF)19},
      url          = {https://juser.fz-juelich.de/record/1049792},
}