Synchrotron Imaging of Li Metal Anodes in Solid State Batteries Aided by Machine Learning

Abstract

Reversible lithium metal anodes that can achieve high rate capabilities are necessary for next generation energy storage systems. Solid electrolyte can act as a barrier for unwanted physical and chemical decomposition that lead to unstable electrodeposition (e.g. dendrite and filament growth). The formation and growth of filaments is tied to unique chemo-mechanical properties that exists at buried solid|solid interfaces. Herein,<i> in situ</i> tomography of Li|LLZO|Li cells is carried out to track morphological transformations in Li metal electrodes and buried solid|solid interfaces during stripping and plating processes. Optimized experimental parameters provide high resolution, high contrast reconstructions that enable lithium metal visualization. Machine learning and image processing tools are combined to quantify changes in lithium metal during stripping and plating. The analysis enables quantifying local current densities and pore size distribution in lithium metal during cycling experiments. Hotspots in lithium metal are correlated with microstructural anisotropy in the solid electrolyte. Modeling studies show large heterogeneity in transport and mechanical properties of electrolyte at the electrode|electrolyte interfaces. Regions with lower effective properties (transport and mechanical) are nuclei for failure. Failure is attributed to microstructural heterogeneities in the solid electrolyte that lead to high local stress and flux distributions.

Effect of a layer-by-layer assembled ultra-thin film on the solid electrolyte and Li interface

“…The limited operation of ASSLB could be explained by these fractures that occurred because of chemomechanical stresses upon cycling. This kind of outcome was found in many reports on the solid electrolytes research [85][86][87][88][89][90][91][92][93][94][95][96][97][98]. Regardless of the chemomechanical fractures, the lbl assembled ultra-thin polymer significantly improved the interfaces in a solid-state cell and conserved its morphology and uniformity over 20 galvanostatic charge discharge cycles.…”

A review of the recent progress in battery informatics

“…ML is becoming a new weapon in the arsenal to provide much desired high-level analysis of the data from these advanced analysis techniques. Leveraging the capability of image analysis beyond manual annotation and object recognition, ML, especially CNN-based method, is well-suited for the in-depth visualization, 3D reconstructing and comprehensive understanding of electrode microstructures [214][215][216][217] . Jiang et al trained a Mask R-CNN to perform the segmentation of images taken from the quantitative X-ray phase-contrast nano-tomography of the Ni-rich LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC) composite cathode (Fig.…”

“…For example, in the work of Furat et al 215 , each stack of EBSD data included 91 individual images for the analysis of convolution neural network. Dixit collected the tomography data with the size greater than 30 GB from each scan 217 . Their neural network model was trained on 800 images from one electrode in a single electrochemical cycle and tested on another 200 images from the same electrode.…”