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work. A model is to be developed to estimate the material mass breakdown for various cell designs and cell formats. The model will be validated from teardown analysis of commercial lithium-ion battery
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and evaluation of composite tanks and steel piping under cyclic thermal and hydrogen exposure. Predictive modeling of material degradation, fatigue, and embrittlement. Assessment of thermal management
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at cell membranes; Apply machine-learning models trained on simulation data to study how lipid composition and genetic variation influence the conformational and phase properties of membrane-associated
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imaging and spectroscopy) to evaluate performance of electrodes and to map electrolyte chemical composition in micrometer resolution, allowing validation of the model predictions. Validation and evaluation
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at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as
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, it is crucial to evaluate whether increasing broadleaf stands effectively enhances biodiversity and how these effects vary with landscape context. Additionally, the interaction of forest composition