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001038801 041__ $$aEnglish
001038801 1001_ $$0P:(DE-Juel1)188204$$aColliard, Andre$$b0$$eCorresponding author$$ufzj
001038801 1112_ $$aPacific Rim international meeting 2024$$cHonolulu$$d2024-10-06 - 2024-10-11$$gPRiME 2024$$wUSA
001038801 245__ $$aDeep Learning-driven Autonomous Imaging Characterization of Polymer Electrolyte Membrane Fuel Cell Technologies
001038801 260__ $$c2024
001038801 3367_ $$033$$2EndNote$$aConference Paper
001038801 3367_ $$2DataCite$$aOther
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001038801 3367_ $$2ORCID$$aLECTURE_SPEECH
001038801 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1738582663_11962$$xAfter Call
001038801 520__ $$aSeamless 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
001038801 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
001038801 7001_ $$0P:(DE-Juel1)181057$$aMalek, Kourosh$$b1$$ufzj
001038801 7001_ $$0P:(DE-Juel1)178034$$aEikerling, Michael$$b2$$ufzj
001038801 7001_ $$0P:(DE-Juel1)181059$$aEslamibidgoli, Mohammad Javad$$b3$$ufzj
001038801 909CO $$ooai:juser.fz-juelich.de:1038801$$pVDB
001038801 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188204$$aForschungszentrum Jülich$$b0$$kFZJ
001038801 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181057$$aForschungszentrum Jülich$$b1$$kFZJ
001038801 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178034$$aForschungszentrum Jülich$$b2$$kFZJ
001038801 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181059$$aForschungszentrum Jülich$$b3$$kFZJ
001038801 9131_ $$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
001038801 9141_ $$y2024
001038801 920__ $$lno
001038801 9201_ $$0I:(DE-Juel1)IET-3-20190226$$kIET-3$$lIET-3$$x0
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