Ying Huang (1) (2), David Perlmutter (3), Andrea Fei-Huei Su (2) (4), Jerome Quenum (3) (5), Pavel Shevchenko (6), Dilworth Y. Parkinson (7), Iryna V. Zenyuk (1) (2) (5), Daniela Ushizima (3) (8)
Computational Materials. (1 June 2023). DOI: https://doi.org/10.1038/s41524-023-01039-y

Keywords

batteries, imaging techniques

Abstract

Operando X-ray micro-computed tomography (µCT) provides an opportunity to observe the evolution of Li structures inside pouch cells. Segmentation is an essential step to quantitatively analyzing µCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the datasets. Herein, we report a computational approach, batteryNET, to train an Iterative Residual U-Net-based network to detect Li structures. The resulting semantic segmentation shows singular Li-related component changes, addressing diverse morphologies in the dataset. In addition, visualizations of the dead Li are provided, including calculations about the volume and effective thickness of electrodes, deposited Li, and redeposited Li. We also report discoveries about the spatial relationships between these components. The approach focuses on a method for analyzing battery performance, which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

How Our Software Was Used

Dragonfly was used to create labels, perform image processing, and generate rendered images.

Author Affiliation

(1) Department of Materials Science & Engineering, University of California Irvine, Irvine, California, USA
(2) National Fuel Cell Research Center, University of California Irvine, Irvine, California, USA
(3) Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
(4) Department of Chemical & Biomolecular Engineering, University of California Irvine, Irvine, California, USA
(5) Department of Electrical Engineering and Computer Sciences, College of Engineering, University of California Berkeley, Berkeley, California, USA
(6) Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois, USA
(7) Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California, USA
(8) Berkeley Institute for Data Science, University of California Berkeley, Berkeley, California, USA