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001038802 041__ $$aEnglish
001038802 1001_ $$0P:(DE-Juel1)188204$$aColliard, Andre$$b0$$eCorresponding author$$ufzj
001038802 1112_ $$aHelmholtz AI Conference 2024$$cDüsseldorf$$d2024-06-12 - 2024-06-14$$gHAICON 2024$$wGermany
001038802 245__ $$aEnhanced 3D Bubble Characterization in Vanadium Redox Flow Batteries Using Synchrotron X-ray Imaging and Deep Learning
001038802 260__ $$c2024
001038802 3367_ $$033$$2EndNote$$aConference Paper
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001038802 520__ $$aThe 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.
001038802 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
001038802 7001_ $$0P:(DE-Juel1)181057$$aMalek, Kourosh$$b1$$ufzj
001038802 7001_ $$0P:(DE-Juel1)178034$$aEikerling, Michael$$b2$$ufzj
001038802 7001_ $$0P:(DE-Juel1)181059$$aEslamibidgoli, Mohammad Javad$$b3$$ufzj
001038802 909CO $$ooai:juser.fz-juelich.de:1038802$$pVDB
001038802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188204$$aForschungszentrum Jülich$$b0$$kFZJ
001038802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181057$$aForschungszentrum Jülich$$b1$$kFZJ
001038802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178034$$aForschungszentrum Jülich$$b2$$kFZJ
001038802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181059$$aForschungszentrum Jülich$$b3$$kFZJ
001038802 9131_ $$0G:(DE-HGF)POF4-123$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1231$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vChemische Energieträger$$x0
001038802 9141_ $$y2024
001038802 9201_ $$0I:(DE-Juel1)IET-3-20190226$$kIET-3$$lIET-3$$x0
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