Prediction of Effective Properties of Porous Carbon Electrodes from a Parametric 3D Random Morphological Model

Abstract

2Torben Prill et al.Abstract Pore structures have a major impact on the transport and electrical properties of electrochemical devices, such as batteries and electric double-layer capacitors (EDLCs). In this work we are concerned with the prediction of the electrical conductivity, ion diffusivity and volumetric capacitance of EDLC electrodes, manufactured from hierarchically porous carbons. To investigate the dependence of the effective properties on the pore structures, we use a structurally resolved parametric model of a random medium. Our approach starts from 3D FIB-SEM imaging, combined with automatic segmentation. Then, a random set model is fitted to the segmented structures and the effective transport properties are predicted using full field simulations by iterations of FFT on 3D pore space images and calculations based on the geometric properties of the structure model. A parameter study of the model is used to investigate the sensitivity of the effective conductivity and diffusivity to changes in the model parameters. Finally, we investigate the volumetric capacitance of the EDLC electrodes with a geometric model, make a comparison with experimental measurements and do a parameter study to suggest improved microstructures.

Large-Scale Statistical Learning for Mass Transport Prediction in Porous Materials Using 90,000 Artificially Generated Microstructures

“…This yields structures with a semicontinuous solid phase in contrast to the systems of hard ellipsoids described in Section 4.6, which consist of discrete particles. Note that similar structures have been used in Prill et al (2017) as a model for porous electrodes, where spheres instead of random ellipsoids are considered.…”

“…These microstructure models can be calibrated with experimental data gained, e.g., by tomographic imaging or simply be inspired by experimentally observed structures. There are numerous examples for artificial generation and virtual testing of functional materials, including applications for lithium ion batteries (Feinauer et al, 2015;Hein et al, 2016;Westhoff et al, 2018a;Prifling et al, 2019;Hein et al, 2020;Allen et al, 2021;Prifling et al, 2021a;Birkholz et al, 2021;Furat et al, 2021), solid oxide fuel cells (Abdallah et al, 2016;Neumann et al, 2016;Moussaoui et al, 2018), amorphous silica (Prifling et al, 2021b), gas diffusion electrodes (Neumann et al, 2019a), open-cell foams (Westhoff et al, 2018b), organic semiconductors (Westhoff et al, 2015), mesoporous alumina (Wang et al, 2015), solar cells (Stenzel et al, 2011), electric double-layer capacitors (Prill et al, 2017), platelet-filled composites (Röding et al, 2018), fiber-based materials (Röding et al, 2016;Townsend et al, 2021), and pharmaceutical coatings for controlled drug release (Barman et al, 2019).…”

Quantifying the influence of microstructure on effective conductivity and permeability: Virtual materials testing

“…Efforts have focused, notably, on the Boolean model [10], see e.g. [11,12], on dilated edge systems of Laguerre tessellations [13,14], and, recently, on excursion sets of Gaussian random fields [15]. Moreover, virtual materials testing has been applied in [16] with respect to effective conductivity, where a large variety of virtual microstructures is generated by means of a specifically developed stochastic microstructure model.…”

A review of nonlinear FFT-based computational homogenization methods

“…Also, FFT-based methods were applied to compute the through-thickness conductivity of heterogeneous plates [204] and for an inverse reconstruction of the local conductivity [205]. Furthermore, extensions to compute Fickian diffusion [206] and ionic conductivity [207] were reported, as were applications to the effective conductivity and diffusivity of porous carbon electrodes [208], the electronic conductivity of lithium ion positive electrodes [209] and the conductivity of solid oxide fuel cell anodes [210].…”