Artificial Generation of Representative Single Li-Ion Electrode Particle Architectures from Microscopy Data: Article No. 105

Orkun Furat, Lukas Petrich, Donal Finegan, David Diercks, Francois Usseglio-Viretta, Kandler Smith, Volker Schmidt

Research output: Contribution to journalArticlepeer-review

20 Scopus Citations

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.

Original languageAmerican English
Article number105
Number of pages16
Journalnpj Computational Materials
Volume7
Issue number1
DOIs
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

NREL Publication Number

  • NREL/JA-5700-78934

Keywords

  • 3D grain architecture
  • electron backscatter diffraction
  • lithium-ion battery
  • nano-computed tomography
  • NMC particle
  • random field
  • random tessellation
  • spatial stochastic modeling

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