GenAI-enhanced 4D nano-tomography for advanced battery microstructure analysis

Abstract

Monitoring the evolution of electrode microstructures at the sub-micron scale during electrochemical charging is crucial for elucidating battery aging mechanisms. Four-dimensional (4D) synchrotron X-ray nano-tomography (SXCT), defined as time-resolved three-dimensional (3D) imaging, allows the perception of such dynamic processes. Yet, 4D SXCT faces a dilemma between resolution and field-of-view including the troublesome invasive beam exposure. In this work, we demonstrate the potential of generative AI (genAI) models to significantly enhance 4D SXCT image datasets. Specifically, we apply a trained Inversion by Direct Iteration (InDI) algorithm suitable to improve the spatial resolution of measured unseen 4D SXCT image data, obtained during a lithiation cycle, by a factor of about two, while achieving an eight times larger field-of-view (FOV) as typically provided by such a resolution. Compared to other image enhancement algorithms like CNNs or GANs, InDI exhibits improved contrast and training stability. Besides, when compared to classical diffusion models, the number of iterations is reduced from several hundreds to roughly one dozen. Our results demonstrate the tremendous potential of the InDI model, facilitating enhanced possibilities to quantify the microstructure evolution during cycling with sufficient FOV and spatial resolution. Furthermore, the InDI framework has the potential to generalize as well as illustrate a broad applicability across many areas of materials science and imaging-driven fields, enabling the observation of dynamic processes on the nano-scale.