001049792 001__ 1049792
001049792 005__ 20251217202233.0
001049792 037__ $$aFZJ-2025-05576
001049792 1001_ $$0P:(DE-Juel1)208523$$aAkue-Goeh, Adole Imelda$$b0
001049792 245__ $$aDEVELOPMENT OF ACCESSIBLE AUTOMATED QUANTIFICATION METHODS ON FIB/SEM TOMOGRAPHY FOR INVESTIGATING SOLID OXIDE ELECTROLYZER CELL  (SOEC) AND PROTON EXCHANGE MEMBRANE WATER ELECTROLYZER (PEMWE) DEGRADATION$$f - 2025-10-20
001049792 260__ $$c2025
001049792 300__ $$a61p
001049792 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
001049792 3367_ $$02$$2EndNote$$aThesis
001049792 3367_ $$2BibTeX$$aMASTERSTHESIS
001049792 3367_ $$2DRIVER$$amasterThesis
001049792 3367_ $$0PUB:(DE-HGF)19$$2PUB:(DE-HGF)$$aMaster Thesis$$bmaster$$mmaster$$s1765991919_10803
001049792 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
001049792 502__ $$aMasterarbeit, Félix-Houphouët-Boigny of Abidjan (CôTE D'IVOIRE), 2025$$bMasterarbeit$$cFélix-Houphouët-Boigny of Abidjan (CôTE D'IVOIRE)$$d2025
001049792 520__ $$aThe expanding hydrogen economy relies on efficient and durable electrochemical devices, such as Solid Oxide Electrolysis Cells (SOECs) and Proton Exchange Membrane Water Electrolyzers (PEMWEs). The performance and lifetime of these devices are closely linked to their microstructural properties. Despite the development of advanced microstructural characterization techniques, like Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) for quantitative analysis, data processing remains challenging due to the complexity of the data, the large volume generated and the limited access to advanced computational tools. This study proposes an automated, modular pipeline that combines traditional image processing and Random Forest-based supervised learning to segment the electrode-catalyst part composed of four layers in SOECs (Layer 1 to Layer 4 ) and to quantitatively evaluate key microstructural parameters such as porosity and thickness. The pipeline is intentionally designed to require minimal computational resources and remain accessible even to non-expert users.The training on a representative annotated dataset of FIB-SEM images (10 training images out of a total of 200) achieved a layer segmentation accuracy of  68% on the test dataset. Even though this indicates the need for additional improvement, it was enough to identify meaningful structural variations. The utilization of the pipeline across multiple experimental FIB-SEM datasets enables the extraction of statistically consistent trends in porosity and thickness under different operational conditions: pristine, 100-hour and 200-hour run cells. layer 4 exhibits a distinct rise in porosity, whereas layer 2 displayed a noticeable change in thickness. These findings show that a lightweight machine learning approach combined with traditional image processing can provide meaningful insights into microstructural parameters. it also emphasizes the potential for developing a user-friendly and automated pipeline to assess the complex FIB-SEM datasets of these electrochemical devices quantitatively in a record time. Potential enhancement could look closer to the annotation protocol, feature engineering and combined machine learning approaches. Keywords: FIB-SEM, microstructural analysis, Random Forest, image processing, electrolysis.
001049792 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
001049792 909CO $$ooai:juser.fz-juelich.de:1049792$$pVDB
001049792 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)208523$$aForschungszentrum Jülich$$b0$$kFZJ
001049792 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$$x0
001049792 9141_ $$y2025
001049792 920__ $$lyes
001049792 9201_ $$0I:(DE-Juel1)IET-1-20110218$$kIET-1$$lGrundlagen der Elektrochemie$$x0
001049792 980__ $$amaster
001049792 980__ $$aVDB
001049792 980__ $$aI:(DE-Juel1)IET-1-20110218
001049792 980__ $$aUNRESTRICTED