Algorithmic structural segmentation of defective particle systems: A lithium-ion battery study

We describe a segmentation algorithm that is able to identify defects (cracks, holes and breakages) in particle systems. This information is used to segment image data into individual particles, where each particle and its defects are identified accordingly. We apply the method to particle systems that appear in Li-ion battery electrodes. First, the algorithm is validated using simulated data from a stochastic 3D microstructure model, where we have full information about defects. This allows us to quantify the accuracy of the segmentation result. Then we show that the algorithm can successfully be applied to tomographic image data from real battery anodes and cathodes, which are composed of particle systems with very different morpohological properties. Finally, we show how the results of the segmentation algorithm can be used for structural analysis

Degradation of Li S battery electrodes on 3D current collectors studied using x ray phase contrast tomography

Lithium sulphur batteries are promising candidates for future energy storage systems, mainly due to their high potential capacity. However low sulphur utilization and capacity fading hinder practical realizations. In order to improve understanding of the system, we investigate Li S electrode morphology changes for different ageing steps, using X ray phase contrast tomography. Thereby we find a strong decrease of sulphur loading after the first cycle, and a constant loading of about 15 of the initial loading afterwards. While cycling, the mean sulphur particle diameters decrease in a qualitatively similar fashion as the discharge capacity fades. The particles spread, migrate into the current collector and accumulate in the upper part again. Simultaneously sulphur particles lose contact area with the conducting network but regain it after ten cycles because their decreasing size results in higher surface areas. Since the capacity still decreases, this regain could be associated with effects such as surface area passivation and increasing charge transfer resistanc

Fabrication of composites via spouted bed granulation process and simulation of their micromechanical properties

In this contribution numerical simulation of Young’s modulus of copper-polymer composites is presented. For the simulation of the composites the Bonded-Particle-Model was applied. The model allows representing of the structure of composite materials realistically. The polymer matrix, which surrounds the particles, was represented as network of solid bonds connecting copper particles. Simulation results were validated based on mechanical determination of modulus of elasticity. The modulus of elasticity was approximated in experiments as well as in simulation by four-point-bending tests. It was observed, that obtained simulation results are in good agreement with experimental results

Structural 3D characterization of silica monoliths extraction of rod networks

Based on experimental 3D image data, we analyze a highly porous silica monolith consisting of a network of rod like structures. Because the rods are often hard to recognize even by visual inspection of the image data, a simple binarization with e.g. thresholding techniques is problematic. Therefore we extract a voxel based skeleton directly from the filtered grayscale image, which is then transformed into vector data, i.e., a system of line segments describing the rod network. In a final step we complete the extraction by estimating a radius for every line segment, using the concept of the Hough transform applied to the gradient image. These steps yield a structural segmentation with advantages over global or local thresholding techniques and allow the statistical analysis and characterization of the given sampl

Simulation-based investigation of core-shell agglomerates: Influence of spatial heterogeneity in particle sizes on breakage characteristics

The stability and breakage behavior of agglomerates is of interest in many applications. It is well known that the internal microstructure is of great influence thereupon. However, the precise relationship of structural properties and mechanical behavior is not yet known for many scenarios. In this paper, we consider a flexible stochastic model to analyze the strength of spherical agglomerates consisting of spherical primary particles, arranged as core and shell. Structural properties can be varied in core and shell independently. Applying the bonded-particle model (BPM), we investigate the influence of the primary particle size distributions in core and shell on the breakage behavior under uniaxial compressive load. To get more meaningful results, we perform numerical studies of the same agglomerate with different directions of force and investigate the variation in breakage behavior

Stochastic 3D modeling of the microstructure of lithium ion battery anodes via Gaussian random fields on the sphere

The performance and durability of lithium ion batteries are highly dependent on the microstructures of their components. Recently, methods have been developed that make possible the simulation of electrochemical processes on 3D representations of lithium ion batteries. However, it is difficult to obtain realistic microstructures on which these simulations can be carried out. In this paper, we develop a stochastic model that is able to produce realistic microstructures of lithium ion battery anodes, which can serve as input for the simulations. We introduce the use of Gaussian random fields on the sphere as models for the particles that form the anodes. Using this new approach, we are able to model realistic particle geometries. The stochastic model also uses a number of techniques from stochastic geometry and spatial statistics. We carry out validation of our model, in order to demonstrate that it realistically describes the key features of the anode s microstructur

Quantitative comparison of segmentation algorithms for FIB-SEM images of porous media

International audienceFocused ion beam tomography has proven to be capable of imaging porous structures on a nano-scale. However, due to shine-through artefacts, common segmentation algorithms often lead to severe dislocation of individual structures in z-direction. Recently, a number of approaches have been developed, which take into account the specific nature of focused ion beam-scanning electron microscope images for porous media. In the present study, we analyse three of these approaches by comparing their performance based on simulated focused ion beam-scanning electron microscope images. Performance is measured by determining the amount of misclassified voxels as well as the fidelity of structural characteristics. Based on this analysis we conclude that each algorithm has certain strengths and weaknesses and we determine the scenarios for which each approach might be the best choice

Inverting Laguerre tessellations

ALaguerre tessellation is a generalization of aVoronoi tessellation where the proximity between points is measured via a power distance rather than the Euclidean distance. Laguerre tessellations have found significant applications in materials science, providing improved modeling of (poly)crystalline microstructures and grain growth. There exist efficient algorithms to construct Laguerre tessellations from given sets of weighted generator points, similar to methods used for Voronoi tessellations. The purpose of this paper is to provide theory and methodology for the inverse construction; that is, to recover the weighted generator points from a given Laguerre tessellation. We show that, unlike the Voronoi case, the inverse problem is in general non-unique: different weighted generator points can create the same tessellation. To recover pertinent generator points, we formulate the inversion problem as a multimodal optimization problem and apply the cross-entropy method to solve it

Bonded-particle extraction and stochastic modeling of internal agglomerate structures

The discrete element method (DEM) is an effective computational technique that is used to investigate the mechanical behavior of various particle systems like, for example, agglomerates. However, for systems of perfectly spherical and non-overlapping particles, the structural input is almost always based only qualitatively on experimentally observed structures. In this paper, we consider the case of agglomerates where particles are nearly spherical and connected by bonds. A novel bonded-particle extraction (BPE) method is proposed for the automated approximation of such agglomerate structures from tomographic data sets. This method can be effectively used in conjunction with various commercial or open-source DEM simulation systems. By BPE, sphere-like primary particles are represented each by exactly one (perfect) sphere, and the set of spheres is non-overlapping. Furthermore, the solid bridge bonds between primary particles are retained. Having derived such a simple description of complex tomographic data sets, one can perform DEM simulations with well-established models like the bonded-particle model. Moreover, it is shown that a larger data base of statistically equivalent microstructures can be generated by a stochastic modeling approach. This approach reduces the need for (time-consuming) experimental agglomerate production and characterization