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001038800 037__ $$aFZJ-2025-01640
001038800 1001_ $$0P:(DE-Juel1)188204$$aColliard, Andre$$b0$$eCorresponding author$$ufzj
001038800 1112_ $$aHelmholtz Imaging Conference 2024$$cHeidelberg$$d2024-05-14 - 2024-05-15$$wGermany
001038800 245__ $$aUTILE: A Deep Learning-Driven Imaging Journey Across Dimensions
001038800 260__ $$c2024
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001038800 520__ $$aBridging 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.
001038800 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
001038800 7001_ $$0P:(DE-Juel1)181057$$aMalek, Kourosh$$b1$$ufzj
001038800 7001_ $$0P:(DE-Juel1)178034$$aEikerling, Michael$$b2$$ufzj
001038800 7001_ $$0P:(DE-Juel1)181059$$aEslamibidgoli, Mohammad Javad$$b3$$ufzj
001038800 909CO $$ooai:juser.fz-juelich.de:1038800$$pVDB
001038800 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188204$$aForschungszentrum Jülich$$b0$$kFZJ
001038800 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181057$$aForschungszentrum Jülich$$b1$$kFZJ
001038800 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178034$$aForschungszentrum Jülich$$b2$$kFZJ
001038800 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181059$$aForschungszentrum Jülich$$b3$$kFZJ
001038800 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$$x0
001038800 9141_ $$y2024
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001038800 9201_ $$0I:(DE-Juel1)IET-3-20190226$$kIET-3$$lIET-3$$x0
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