Battery performance is strongly correlated with electrode microstructural properties. Of the relevant properties, the tortuosity factor of the electrolyte transport paths through microstructure pores is important as it limits battery maximum charge/discharge rate, particularly for energy-dense thick electrodes. Tortuosity factor however, is difficult to precisely measure, and thus its estimation has been debated frequently in the literature. Herein, three independent approaches have been applied to quantify the tortuosity factor of lithium-ion battery electrodes. The first approach is a microstructure model based on three-dimensional geometries from X-ray computed tomography (CT) and stochastic reconstructions enhanced with computationally generated carbon/binder domain (CBD), as CT is often unable to resolve the CBD. The second approach uses a macro-homogeneous model to fit electrochemical data at several rates, providing a separate estimation of the tortuosity factor. The third approach experimentally measures tortuosity factor via symmetric cells employing a blocking electrolyte. Comparisons have been made across the three approaches for 14 graphite and nickel-manganese-cobalt oxide electrodes. Analysis suggests that if the tortuosity factor were characterized based on the active material skeleton only, the actual tortuosities would be 1.35-1.81 times higher for calendered electrodes. Correlations are provided for varying porosity, CBD phase interfacial arrangement and solid particle morphology.

Ionic mass transport including electrolyte diffusivity and conductivity depends on the geometric tortuosity of the electrode. This paper compares two experimental methods that determine tortuosity based on diffusivity or conductivity. The polarization-interrupt method previously developed by our group determines tortuosity in terms of effective diffusivity. The blocking-electrolyte method proposed by Gasteiger and coworkers determines tortuosity in terms of effective ionic conductivity and is analyzed using a generalized transmission-line model to account for multiple sources of impedance. Tortuosity of several commercial-quality electrodes was measured using both methods, producing reasonable agreement between the two methods in most cases. The advantages and disadvantages of each method and variables that can affect the accuracy of the measurement, such as electrode wetting and model fitting, are discussed. For particular electrodes, one method may be advantageous or more conveniently applied than the other.

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Lateral microstructure heterogeneity in anodes is known to induce nonuniform current density, state of charge, and lithium plating. This means that such electrode heterogeneity can limit fast charging of lithium-ion batteries. In this work, a combination of experiments and simulation is employed to understand the effect of mm scale lateral heterogeneity on cell aging. A previously developed model was extended to efficiently simulate SEI formation and Li plating for independent regions of an electrode. The model consists of three parallel regions each described under a P2D framework and with a distinct ionic resistance and possibly active material loading. The results suggest that during fast charge when the active material is uniformly distributed across the three regions, the region with the highest resistance reaches the end of life sooner than the other regions. There is also positive feedback from Li metal filling the pores near the separator interface that further accelerates lithium plating. Finally, when there is a non-uniform active material distribution associated with the ionic resistance heterogeneity, tight competition between regions can occur, leading to less overall lithium plating and plating that is more uniform between regions.

Tortuosity is a geometric parameter of porous electrodes that quantifies the tortuous path ions take to meet electrons in order for the chemical reaction to take place on the surface of the active material. Ionic resistance in the electrodes, which is directly related to the tortuosity, is a key factor that influences battery performance, and must be accurately represented in battery models. This work is intended to help battery researchers understand and be able to reliably determine the tortuosity of different electrodes. The polarization–interrupt method previously developed in our research group is an effective way to directly measure electrodes tortuosity [1]. This method determines tortuosity based on effective diffusivity in the sample by solving diffusional equations along with the polarization-interrupt experiment. It requires that an electrode film be delaminated from its current collector. The blocking electrolyte method is another technique used for measuring tortuosity that was recently introduced by Gasteiger and coworkers [2]. This method is based on an AC-impedance measurement of the electrode sample using a non-intercalating or blocking electrolyte. In this method the tortuosity is determined based on an effective conductivity fit with a transmission-line model. In this work the two methods were used to determine the tortuosity of the several Li-ion cathodes and anodes. The results from each method are compared and the advantages, disadvantages, and validity of each method are discussed. Furthermore, for the blocking electrolyte method, we investigated the effects of different salts and solvents on the obtained tortuosity and discuss the implications on choosing an electrolyte for this method. We also added extra terms to the transmission-line model to account for contact impedance in the active material film and current collector interface in both the electronic and ionic path to help the model to better capture the experimental data. This work was supported by the U.S. Department of Energy through the BMR program. -------------------------------------------------------------------------------------------------------- [1] Thorat et. al., Journal of Power Sources, vol. 188, p. 592–600, 2009. [2] Landesfeind et. al., Journal of The Electrochemical Society, vol. 163, p. A1373-A1387, 2016. Figure 1: Example results for the two techniques: a) cell potential during a polarization-interrupt experiment and b) Nyquist impedance plot of blocking-electrolyte experiment. Figure 1

Li-ion battery performance is greatly affected by how well ions travel through the electrodes, which in turn depends on the microstructure of the porous pathways through the electrodes. During cell formation and cycling, changes in the microstructure therefore lead to changes in ionic transport. Currently what causes these changes and their magnitude is poorly understood. Changes in effective ionic conductivity and diffusivity can measured in terms of tortuosity, a dimensionless geometric factor. This report focuses on the effect of cell assembly and repeated cycling on tortuosity. Using a method previously developed by our group, namely the polarization-interrupt method [1], as well as AC impedance techniques, we were able to determine electrode tortuosity for various electrode films. There is clear evidence of changes in tortuosity during cell formation and cycling steps. These experiments also suggest that electrolyte wetting is a time-dependent process that changes the measured tortuosity. Three different stages of electrodes were compared: (1) pristine electrodes which had never been in contact with electrolyte, (2) electrodes harvested from newly assembled batteries, and (3) electrodes harvested from cycled batteries. This was done for both anodes and cathodes. In the case of the harvested electrodes, a careful washing procedure is needed to remove Li salt. To validate our results we used two different experimental methods, namely the polarization-interrupt method and a blocking-electrolyte method recently discussed by Gasteiger and coworkers [2]. This research was funded by the BMR program of the US Department of Energy. [1] Zacharias et al. , J. Electrochem. Soc. 160, A306 (2013). [2] Landesfeind et al., J. Electrochem. Soc. 163, A1373, (2016). Fig. 1 SEM/FIB cross section of Li-ion anode showing tortuous paths created by the pores in between active material. Figure 1