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@INPROCEEDINGS{Colliard:1038801,
      author       = {Colliard, Andre and Malek, Kourosh and Eikerling, Michael
                      and Eslamibidgoli, Mohammad Javad},
      title        = {{D}eep {L}earning-driven {A}utonomous {I}maging
                      {C}haracterization of {P}olymer {E}lectrolyte {M}embrane
                      {F}uel {C}ell {T}echnologies},
      reportid     = {FZJ-2025-01641},
      year         = {2024},
      abstract     = {Seamless integration of deep learning and computer
                      vision-driven tools has become essential to accelerate
                      research addressing global energy challenges. This is
                      particularly true in the field of energy technology
                      characterization, where the need for high throughput and
                      advanced methods creates a bottleneck in the analysis and
                      interpretation of large data volumes. In this study, we
                      introduce three different use-cases related to autonomous
                      image analysis of polymer electrolyte membrane technologies
                      across nano-, micro-, and macro-scales, aimed at
                      accelerating and enhancing insights in this domain. For each
                      case, a comprehensive tool was developed, integrating image
                      preprocessing, deep learning model predictions, feature
                      extraction, and results visualization, thereby facilitating
                      usage by experimentalists.At the nanoscale, we have
                      developed a tool for autonomous 2D Transmission Electron
                      Microscopy (TEM) image analysis to assess the size and shape
                      of Platinum nanoparticles supported on Carbon. aimed at
                      investigating the catalyst layer of polymer electrolyte
                      membrane fuel cells (PEMFC).[1] Progressing to the
                      microscale, we introduce a deep learning-based approach for
                      analyzing micro-CT 3D volumes, focusing on pore analysis.
                      This software is designed to perform multi-class
                      segmentation of pores and liquid channels within gas
                      diffusion layers (GDL), providing detailed insights into
                      pore structures and water transport properties.[2] Lastly,
                      at the macroscale, we showcase a tool developed for
                      detecting oxygen bubbles in optical videos from polymer
                      electrolyte water electrolyzers (PEMWE). This application
                      enables the time-resolved analysis of bubble dynamics within
                      the water electrolyzer’s flow field, offering a
                      macroscopic view of gas evolution and its impact on device
                      performance.[3] Through these tools, our work demonstrates
                      the potential of deep learning and computer vision to bridge
                      the gap between scales in energy materials research,
                      offering novel insights and expediting the analysis
                      process.[1] André Colliard-Granero et al., “Deep
                      learning for the automation of particle analysis in catalyst
                      layers for polymer electrolyte fuel cells,” vol. 14, no.
                      1, pp. 10–18. [2] André Colliard-Granero et al., “Deep
                      Learning for Automation of 3D Pore Analysis in Micro-CT
                      Tomographs,” under preparation. [3] André
                      Colliard-Granero et al., “Deep Learning-Enhanced
                      Characterization of Bubble Dynamics in Proton Exchange
                      Membrane Water Electrolyzers,” Physical Chemistry Chemical
                      Physics, 2024, Accepted Manuscript. DOI:
                      https://doi.org/10.1039/D3CP05869G},
      month         = {Oct},
      date          = {2024-10-06},
      organization  = {Pacific Rim international meeting
                       2024, Honolulu (USA), 6 Oct 2024 - 11
                       Oct 2024},
      subtyp        = {After Call},
      cin          = {IET-3},
      cid          = {I:(DE-Juel1)IET-3-20190226},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123)},
      pid          = {G:(DE-HGF)POF4-1231},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1038801},
}