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@INPROCEEDINGS{Colliard:1038800,
      author       = {Colliard, Andre and Malek, Kourosh and Eikerling, Michael
                      and Eslamibidgoli, Mohammad Javad},
      title        = {{UTILE}: {A} {D}eep {L}earning-{D}riven {I}maging {J}ourney
                      {A}cross {D}imensions},
      reportid     = {FZJ-2025-01640},
      year         = {2024},
      abstract     = {Bridging the gap between the novel advancements in deep
                      learning and computer vision and the pressing challenges in
                      energy materials research is as crucial as the individual
                      pursuits in both domains. Particularly relevant are those
                      innovative techniques in energy materials characterization,
                      where rapid progress is essential to address the current
                      global energy challenges. In the "UTILE: Autonomous Image
                      Analysis of Energy Materials" project, we focused on
                      automating the analysis of images related to energy
                      materials using deep learning approaches to accelerate and
                      enhance the work of experimentalists in these specialized
                      fields. This work presents three distinct use-cases, each
                      with a tool developed to process images, segment regions of
                      interest, extract features from segmentation maps, and
                      visualize the outcomes.The exploration begins with
                      autonomous 2D Transmission Electron Microscopy (TEM) image
                      analysis for the size and shape assessment of Platinum
                      nanoparticles on Carbon supports, aimed at investigating
                      polymer electrolyte membrane fuel cells (PEMFC). [1,2] Next,
                      we introduce a temporal dimension, developing a tool for
                      detecting oxygen bubbles in optical videos from polymer
                      electrolyte water electrolysers (PEMWE). This tool
                      facilitates time-resolved analysis of bubble dynamics in the
                      water electrolyser's flow field. [3] The final stage of our
                      journey incorporates space as the third dimension, enabling
                      3D analysis of hydrogen bubbles within vanadium redox flow
                      batteries using synchrotron X-ray tomographs. This software
                      provides swift and reliable analysis of bubble size, shape,
                      and distribution, coupled with 3D visualization and advanced
                      characterization features. [4][1] André Colliard-Granero et
                      al., “Deep learning for the automation of particle
                      analysis in catalyst layers for polymer electrolyte fuel
                      cells,” Nanoscale, vol. 14, no. 1, pp. 10–18.[2] André
                      Colliard-Granero et al., “UTILE-Gen: Automated Image
                      Analysis in Nanoscience Using Synthetic Dataset Generator
                      and Deep Learning,” ACS Nanoscience Au, vol. 3, no. 5, pp.
                      398–407[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 [4] André
                      Colliard-Granero et al., “Deep Learning for Autonomous 3D
                      Bubble Analysis of Vanadium Flow Batteries from Synchrotron
                      X-ray Imaging,” under preparation.},
      month         = {May},
      date          = {2024-05-14},
      organization  = {Helmholtz Imaging Conference 2024,
                       Heidelberg (Germany), 14 May 2024 - 15
                       May 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/1038800},
}