Quantitative FIB/SEM tomogram analysis of closed and open porosity of spheroidized graphite anode materials for LiBs applications

March 18, 2023

Stefan Sailer (1), Manuel Mundszinger (1), Jan Martin (2), Marilena Mancini (2), Margret Wohlfahrt-Mehrens (2), Ute Kaiser (1)
Micron. Volume 166 (March 2023). DOI: https://doi.org/10.1016/j.micron.2022.103398


FIB-SEM tomography, slice and View, porous carbon, lithium-ion battery, machine learning, quantitative 3D-reconstruction


The electrochemical behaviour of rounded graphite particles as anode material in a lithium-ion battery strongly depends on the particle properties. The spheroidization process directly affects these properties, including the open porosity that determines the extent of direct contact between liquid electrolyte and carbon surface. Therefore, the quantification of the proportion between open and closed pores is of great interest. Here, we quantify the open and closed porosity of spheroidized porous graphite particles from FIB-SEM tomograms. Quantification is achieved based on two developments: (1) a new sample preparation strategy and (2) a newly developed image evaluation scheme based on neural networks. The sample preparation strategy involves embedding of many graphite powder particles in indium enabling the investigation of several graphite particles in one FIB/SEM tomogram with high stability and with high contrast between the conductive embedding material and porous graphite. A quantitative evalutation of closed and open porosity is achieved by machine learning in form of convolutional neural networks. The convolutional neural network is used to detect the bulk graphite and by further morphological operations, closed and open pores are identified. An error is determined by comparing automatically created quantifications with manual reference values. Our porosity data for two differently spheroidized graphite samples agree qualitatively well with corresponding results from nitrogen physisorption measurements. This approach may allow quantitative data evaluation from porous powders and support understanding of the correlation to the electrochemical behaviour in the lithium-ion battery.

How Our Software Was Used

Manually segmented images, created with Dragonfly were used as training and validation data.

Author Affiliation

(1) Central Facility for Electron Microscopy, Electron Microscopy Group of Materials Science, Albert-Einstein-Allee 11, Universität Ulm, 89081 Ulm, Germany
(2) Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW), Helmholtzstraße 8, 89081 Ulm, Germany