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| Home > Publications database > Deep Learning-driven Autonomous Imaging Characterization of Polymer Electrolyte Membrane Fuel Cell Technologies > print |
| 001 | 1038801 | ||
| 005 | 20250220092010.0 | ||
| 037 | _ | _ | |a FZJ-2025-01641 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Colliard, Andre |0 P:(DE-Juel1)188204 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Pacific Rim international meeting 2024 |g PRiME 2024 |c Honolulu |d 2024-10-06 - 2024-10-11 |w USA |
| 245 | _ | _ | |a Deep Learning-driven Autonomous Imaging Characterization of Polymer Electrolyte Membrane Fuel Cell Technologies |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1738582663_11962 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Seamless 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 |
| 536 | _ | _ | |a 1231 - Electrochemistry for Hydrogen (POF4-123) |0 G:(DE-HGF)POF4-1231 |c POF4-123 |f POF IV |x 0 |
| 700 | 1 | _ | |a Malek, Kourosh |0 P:(DE-Juel1)181057 |b 1 |u fzj |
| 700 | 1 | _ | |a Eikerling, Michael |0 P:(DE-Juel1)178034 |b 2 |u fzj |
| 700 | 1 | _ | |a Eslamibidgoli, Mohammad Javad |0 P:(DE-Juel1)181059 |b 3 |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:1038801 |p VDB |
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| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Energie |l Materialien und Technologien für die Energiewende (MTET) |1 G:(DE-HGF)POF4-120 |0 G:(DE-HGF)POF4-123 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-100 |4 G:(DE-HGF)POF |v Chemische Energieträger |9 G:(DE-HGF)POF4-1231 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 920 | _ | _ | |l no |
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