Artificial generation of representative single Li-ion electrode particle architectures from microscopy data
Abstract Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling. Information is drawn from multimodal microscopy techniques to artificially generate LiNi0.5...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1bcc7b65cadf4ea9833d5279b963535c |
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| Sumario: | Abstract Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling. Information is drawn from multimodal microscopy techniques to artificially generate LiNi0.5Mn0.3Co0.2O2 particles with full sub-particle grain detail. Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an ‘outer shell’ model, and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a ‘grain’ model. A random field model used to characterize and generate the outer shells, and a random tessellation model used to characterize and generate grain architectures, are combined to form a multi-scale model for the generation of virtual electrode particles with full-grain detail. This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance. |
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