% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Colliard:1038802,
      author       = {Colliard, Andre and Malek, Kourosh and Eikerling, Michael
                      and Eslamibidgoli, Mohammad Javad},
      title        = {{E}nhanced 3{D} {B}ubble {C}haracterization in {V}anadium
                      {R}edox {F}low {B}atteries {U}sing {S}ynchrotron {X}-ray
                      {I}maging and {D}eep {L}earning},
      reportid     = {FZJ-2025-01642},
      year         = {2024},
      abstract     = {The analysis of gas bubbles in vanadium redox flow
                      batteries (VRFBs) plays a crucial role in understanding and
                      improving their performance [1,2]. Traditional methods have
                      struggled with the direct visualization and quantitative
                      analysis of these bubbles, a challenge overcome by recent
                      advances in Synchrotron X-ray tomography. However, the large
                      volume of data and the complexity of interpreting the images
                      necessitate an innovative approach. We present a novel deep
                      learning framework designed for the automated analysis of 3D
                      bubble dynamics in VRFBs, leveraging high-resolution
                      Synchrotron X-ray imaging. This framework employs an
                      optimized U-Net architecture for semantic segmentation,
                      trained on a dataset of 2294 annotated images across diverse
                      battery configurations. Our methodology not only automates
                      the identification and quantification of bubbles but also
                      introduces advanced descriptors for improved understanding
                      of bubble behaviour within the battery's electrolyte and
                      electrode matrices. The optimized model demonstrates
                      exceptional performance on test dataset, with a precision of
                      $98\%,$ recall of $97\%,$ and an F1-score of $97\%$ on a
                      diverse validation set. Beyond segmentation, the framework
                      offers tools for the detailed examination of electrode
                      interfaces, volumetric and morphological analysis of
                      bubbles, and the generation of comprehensive bubble
                      distribution maps. It also provides metrics for evaluating
                      the impact of bubble presence on membrane efficiency and
                      potential blockages. By providing a robust, scalable
                      solution for the high-throughput analysis of complex 3D
                      datasets, our framework paves the way for deeper insights
                      into the operational challenges of VRFBs. The interactive 3D
                      visualization capabilities further enhance the utility of
                      this analysis, offering researchers an intuitive means to
                      explore and interpret the multifaceted data.[1] K. Köble et
                      al., “Insights into the hydrogen evolution reaction in
                      vanadium redox flow batteries: A synchrotron radiation based
                      X-ray imaging study,” Journal of Energy Chemistry, vol.
                      91, pp. 132–144.[2] L. Eifert et al., “Synchrotron
                      X‐ray Radiography and Tomography of Vanadium Redox Flow
                      Batteries—Cell Design, Electrolyte Flow Geometry, and Gas
                      Bubble Formation,” ChemSusChem, vol. 13, no. 12, pp.
                      3154–3165.},
      month         = {Jun},
      date          = {2024-06-12},
      organization  = {Helmholtz AI Conference 2024,
                       Düsseldorf (Germany), 12 Jun 2024 - 14
                       Jun 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)24},
      url          = {https://juser.fz-juelich.de/record/1038802},
}