Artificial generation of representative single Li-ion electrode particle architectures from microscopy data

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 LiNi_(0.5)Mn_(0.3)Co_(0.2)O_(2) 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 fullgrain 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.

Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks

For a deeper understanding of the functional behavior of energy materials,it is necessary to investigate their microstructure,e.g.,via imaging techniques like scanning electron microscopy (SEM).However,active materials are often heterogeneous,necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view.Cracks within Li-ion electrode particles are an example of fine features,representative quantification of which requires large volumes of tens of particles.To overcome the trade-off between the imaged volume of the material and the resolution achieved,we deploy generative adversarial networks (GAN),namely SRGANs,to super-resolve SEM images of cracked cathode materials.A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles.This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view,thus enabling representative quantification of fine features.