Johannes Landesfeind

Electrolyte motion–induced salt inhomogeneity (EMSI) is a critical degradation mechanism that can limit lifetime of large-format lithium-ion cells. Here, ex-situ scanning micro–X-ray fluorescence (µ-XRF) spectroscopy is employed to map electrolyte salt concentration across entire electrodes harvested from 18 650 cells. Lithium hexafluoroarsenate (LiAsF ₆ ) is used as a spectroscopically favorable salt, enabling arsenic fluorescence as an indicator of local salt concentration. Transport properties of LiAsF ₆ are shown, via Advanced Electrolyte Model calculations, to closely match those of LiPF ₆ in relevant carbonate solvents, validating its use as a surrogate salt for this technique. We confirm that the measured As distributions reflect true electrolyte salt concentration by tracking their growth and relaxation over cycling and rest, and by showing consistent patterns across the separator–anode–cathode stack, indicating a cell-level redistribution of salt. The maps reveal that the axial salt gradient along the entire length of the anode is strongly non-uniform. We observe pronounced tab-correlated banding and localized salt-depleted regions beneath the tab, which we interpret as signatures of pressure-dependent electrolyte pumping and tab-driven modulation of EMSI. This work establishes µ-XRF mapping of LiAsF ₆ as a powerful new technique to quantify electrolyte salt distributions with high spatial resolution.

Accurate determination of porosity in battery electrodes is a key factor in the cell design of lithium-ion batteries, as it influences the ionic and electronic transport as well as the mechanical properties of electrodes. The porosity is important as it affects the energy and power density, thus the overall electrochemical performance of lithium-ion batteries.¹ However, porosity determination of thin commercial electrodes (2 This is valid for electrodes at the beginning of life, but even more relevant over the entire lifetime of a battery, when the porosity of the electrodes may be affected from degradation products.

Despite many commercial and academic approaches to simulate and predict battery operation, the complexity of software tools often limits their broad application. This work presents a novel physical domain library, called BattScape, that has been developed in Simscape from MathWorks® aiming at bridging the gap between experimentalists and simulation experts by providing a framework for user-friendly battery simulations. Our validation with an isothermal P2D Newman model with concentration dependent parameters in COMSOL Multiphysics® shows very good agreement for charge and discharge simulations of identical setups down to 0.4 mV average difference even at 5 C. Runtime benchmarking with COMSOL Multiphysics® shows increased computational costs of ∼130 s vs ∼10 s at runtimes below 3 min allowing to, e.g., create a drag-and-drop digital twin of experiments. To emphasize the useability and adoptability of the framework, three examples are provided: (A) a parameter adjustment of implemented elements to represent a perfect lithium counter electrode, (B) a novel combination of implemented elements to simulate a blended electrode, and (C) a simplified aging mechanism and how BattScape could be used as a digital twin to optimize battery operation control with changing parameters, for example to avoid lithium plating for an aging battery.

In recent years, the electrolyte motion induced salt inhomogeneity (EMSI) effect has attracted lots of interest due to its high relevance for the aging behavior of large format lithium-ion cells. In this study, we show that the EMSI effect also occurs in the 18650-type SDI 35E cell, the first demonstration of the EMSI effect in a publicly available commercial lithium-ion cell. The EMSI-specific conducting salt concentration gradient between jelly roll edge and axial-mid plane is measured as a function of cycle number for various C-rates ranging from 0.1 C to 0.9 C. The salt gradient develops within the first 50 to 100 cycles and reaches a steady state thereafter. The magnitude of the steady state salt gradient increases with increasing C-rate. The stabilization of the salt gradient after 50 to 100 cycles has important implications for the interplay of the EMSI effect, C-rate and cell aging which is discussed in detail. In addition, we present a simple analytical technique for the determination of the salt concentration which can be carried out in every electrochemical laboratory without need for special analytical equipment.

To accelerate the development of innovative batteries, we can enhance or partially substitute time-intensive experimental processes with simulations of physical batteries. So far, there are many models that have been developed for specific needs: from simple single particle models (SPM) to focus on solid diffusion in the active material particles, up to full battery cells including thermal aspects. ^1–4 We have recently developed BattScape, a flexible pseudo 2-dimensional (P2D) MATLAB Simscape based simulation environment for intercalation batteries. ⁵ This framework was validated with COMSOL Multiphysics ^® , obtaining an average voltage difference below 0.5 mV at a 5C discharge of an isothermal NMC/graphite cell with concentration dependent parameters, e.g. for the electrolyte transport properties. ^5,6 All these models, including BattScape, require a broad parameter set, covering physical, electrochemical and sometimes thermal properties, often as a function of concentration and temperature. ^6,7 The determination of these parameters is often built on experimental assumptions and mathematical simplifications. In this work we use BattScape to check some of these assumptions and their implications on the validity of obtained parameters from experimental works. An example for such a parameter is the solid diffusion coefficient, for which multiple electrochemical determination procedures are utilized in the literature. ^8–10 One assumption in all these models is that the active material particles, in which the lithium diffuses, are of identical size everywhere in the studied electrode. During a GITT experiment the voltage polarization and relaxation during and after a current pulse is analyzed and related to the solid diffusion coefficient. Figure 1 shows the BattScape simulated voltage response for such a GITT experiment for an NMC half cell with an ideal lithium counter and reference electrode (negligible small overpotentials at the lithium electrode), where the NMC open circuit voltage is linear. A fixed C/20 pulse is applied for half an hour. Changing the particle size distribution from a uniform radius to broader ones, while maintaining the same active surface area, shows a pronounced effect on the voltage evolution during and after the pulse. Application of the analysis scheme, with the average particle sizes, yields diffusion coefficients of 4.74x10 ^-15 m ² /s, 4.50x10 ^-15 m ² /s and 4.07x10 ^-15 m ² /s for the particle size distributions, while the implemented, fixed value in the model was 5.00x10 ^-15 m ² /s. In our presentation we will demonstrate how parametrization experiments (such as GITT for diffusion coefficients) can be skewed by only partially met mathematical conditions and experimental restrictions and how such apparent parameters could be adopted to better reflect their actual values. Extending the careful analysis of mathematical assumptions in parameterization of lithium-ion batteries to other properties, e.g., kinetic properties or electrolyte transport parameters will allow us to improve the fundamental understanding of thermodynamic and transport mechanisms inside intercalation batteries and help to improve simplified models and operation controls. Figure 1: Comparison of GITT pulse simulations obtained with BattScape for a uniform particle size (9.8 μm) and three different particle size distributions, using different blends presented in a form of table. Simulation performed on an NMC111 half cell (non-polarizable lithium and linear NMC OCV in analyzed region) with C/20 charge pulses for 0.5 h and 49.5 h of rest. Cell areal capacity used: 0.0171 mAh/cm ² . Introduced solid diffusion coefficient for the NMC: 5x10 ^-15 m ² /s. References W. Lai and F. Ciucci, Electrochimica Acta , 56 , 4369–4377 (2011). M. Ecker, S. Käbitz, I. Laresgoiti, and D. U. Sauer, J. Electrochem. Soc. , 162 , A1849–A1857 (2015). A. Latz and J. Zausch, Beilstein J. Nanotechnol. , 6 , 987–1007 (2015). K.-J. Lee, K. Smith, A. Pesaran, and G.-H. Kim, Journal of Power Sources , 241 , 20–32 (2013). R. Pakuła, W. A. Appiah, M. Parmananda, B. Suthar, and J. Landesfeind, manuscript in preparation (2025). J. Schmalstieg, C. Rahe, M. Ecker, and D. U. Sauer, J. Electrochem. Soc. , 165 , A3799–A3810 (2018). J. Landesfeind and H. A. Gasteiger, J. Electrochem. Soc. , 166 , A3079 (2019). J. Kim, S. Park, S. Hwang, and W.-S. Yoon, J. Electrochem. Sci. Technol , 13 , 19–31 (2022). M. Ball, M. Cormier, E. Zsoldos, N. Phattharasupakun, M. Johnson, M. Metzger, C. Yang, J. Dahn, I. Hamam, and N. Zhang, ECS Meeting Abstracts , MA2023-02 , 189–189 (2023). E. Zsoldos, M. Cormier, N. Phattharasupakun, A. Liu, and J. Dahn, Journal of The Electrochemical Society , 170 (2023). Acknowledgments We gratefully acknowledge the funding from the Regionale Forskningsfond Agder for its financial support under the auspices of the Agder Battery Design project (grant number ES711295-RFFAGDER). Figure 1

Energy-dense, fast-charging lithium-ion batteries are in high demand due to their essential role in various applications, from consumer electronics to electric vehicles.¹ However, one significant challenge with fast charging is that it can accelerate lithium plating which is a key degradation mechanism that reduces battery life and performance.² Gaining a deeper understanding of this degradation process in commercial batteries, along with its root causes, is essential for advancing battery technology. ³ Recent research has focused on this critical issue, revealing new insights into the aging mechanisms at play during fast charging.⁴ Solchenbach et al.,have shown that fast charging can induce motion of the electrolyte, leading to salt concentration imbalances, or "salt inhomogeneity".⁴ This salt imbalance leads to localized lithium plating on the anode over cycling and ultimately decrease the cell lifespan. This finding suggests that addressing electrolyte motion induced salt inhomogeneity, “EMSI”, is important not only for mitigating lithium plating and extending the lifespan of fast-charging Li-ion batteries, but also for improving cell design and testing methodologies, particularly for large-format commercial cells under high compression.⁴ The salt concentration across the electrode in their work was measured using ion chromatography (IC) and liquid chromatography-mass spectrometry, LC-MS, both of which are costly and not widely accessible in most research facilities. Additionally, the process of punching coin-shaped samples, diluting them with precision in solvent, and performing subsequent analysis on the extracted solvent is cumbersome. Furthermore, even small punched samples provide only averaged results over the entire area of the coin. For instance, punching 15 mm diameter coins across the electrode limits the spatial resolution to only maximum four individual sampling points along the electrode height for commercially available 18650 and 21700 cells. At each sampling point, the salt gradient is averaged over an area of approximately 1.7 cm² (the coin area), which cannot resolve variations in the salt gradient over smaller scales. This lack of spatial resolution and complex analysis technique makes it challenging to effectively quantify the salt inhomogeneity in the electrode. Given the importance of EMSI effects for large-format battery aging studies, we have developed a novel technique that can effectively quantify the salt concentration gradient across the electrode with higher spatial resolution. Here, we present a custom-designed measurement rig featuring a probe head with multiple wire electrodes, enabling the mapping of salt concentration from ionic conductivity derived through impedance measurements. A sketch of the measurement rig developed is shown in Figure 1 as well as the preliminary results on the cathode electrode of an uncycled 18650 LG cell and a fast charged 18650 Samsung cell. References P. Grey and D. S. Hall, Nat Commun, 11, 6279 (2020). Waldmann, B.-I. Hogg, and M. Wohlfahrt-Mehrens, Journal of Power Sources, 384, 107–124 (2018). Münster, M. Diehl, J. E. Frerichs, M. Börner, M. R. Hansen, M. Winter, and P. Niehoff, Journal of Power Sources, 484, 229306 (2021). Solchenbach, C. Tacconis, A. Gomez Martin, V. Peters, L. Wallisch, A. Stanke, J. Hofer, D. Renz, B. Lewerich, G. Bauer, M. Wichmann, D. Goldbach, A. Adam, M. Spielbauer, P. Lamp, and J. Wandt, Energy Environ. Sci., 17, 7294–7317 (2024). Figure 1. a) A simplified illustration of a measurement setup featuring a probe head equipped with an array of wire electrode probes positioned on top of a separator and a battery electrode. b) Preliminary results showing the LiPF₆ concentration profile across the cathode electrode of an uncycled 18650 LG MJ1 cell (green) and a fast-charged, cycled 18650 Samsung 35E cell (red). The profiles reveal differences in salt concentration for the fast-charged cell, with the minimum concentration observed at the bottom region (near the negative terminal). c) Li-plating pattern on the anode electrode of the fast-charged, cycled 18650 Samsung cell, showing that the extent of Li-plating follows the measured salt concentration trend. The most severe Li-plating is observed at the bottom of the electrode, where the salt concentration is the lowest. Figure 1

As the demand for Li-ion batteries continues to grow, accelerating their development is crucial. An essential aspect of enhancing battery performance involves obtaining a deeper understanding of fundamental electrochemical processes. This understanding can be achieved by applying advanced measurement techniques such as electrochemical impedance spectroscopy (EIS), a promising method for investigating batteries, providing insights into reactions, material properties, and interphases.¹ However, when EIS is conducted on a full cell, the resulting spectra combine information from both electrodes, limiting the characterization of each electrode.² To deconvolute the influence of the electrodes, a three-electrode setup can be used. This setup introduces a reference electrode (RE) which allows deconvolution of the full cell impedance into the impedance of the separate electrodes. The choice and placement of the RE significantly impact impedance spectra.³ Ideally, the RE should maintain a stable potential, allow ion flow, and not obstruct any part of the other electrodes.⁴ Micro-REs are often favored for EIS due to these considerations. Additionally, the RE placement can distort impedance spectra if positioned in a geometrically or electrically asymmetric region of the cell.³ Several cell designs have been proposed for three-electrode configurations.⁴ An innovative approach incorporates a gold micro-RE into a Swagelok T-cell setup.⁵ This work involves a 50 µm thick insulated gold wire sandwiched between two separators which could be lithiated to form a gold-Li alloy at its tip. This lithiated tip shows a stable potential for extended periods, resulting in reliable impedance spectra of the electrodes. While the proposed setup has been instrumental in facilitating valuable research, the cell format has room for improvement. Even though effective, Swagelok T-cells come with high costs and require intricate assembly. Furthermore, disassembly, cleaning, and drying of cell components for re-use substantially prolong the preparation time. Other studies have explored the coin cell format for the three-electrode setup⁴, offering several advantages, including simplified and rapid assembly procedures. Moreover, the disposable nature of this cell format significantly reduces unit costs and shortens preparation times. Consequently, some researchers have sought to leverage these benefits by incorporating the RE through a coaxial configuration within a hole in the electrodes.⁴ However, these adaptations often necessitate substantial modifications to cell components and electrodes and are not well-suited for reliable impedance measurements. In some other approaches that involve placing the RE between separators and electrodes⁴, achieving consistent cell assembly appears to be a persistent challenge, and cell sealing issues may arise in these setups as well. In this work, we have devised a reliable, cost-efficient approach for three-electrode coin cell construction that employs common coin cell parts. Our approach eliminates the need for specialized tools, relying instead on conventional laboratory-scale equipment and accessible commercial materials. In this configuration, as depicted in the inset of Fig.1, the assembly process is carefully adapted so that the micro-RE wire remains straight and lies as flat as possible throughout the cell, positioned between the electrodes and separators. The sealing in our setup is ensured with a special resin compatible with the micro-RE and the components inside the cell, effectively preventing the penetration of moisture and/or air into the cell. We have validated the effectiveness of this sealing method by electrochemical tests that are sensitive to the water and oxygen content within the cell (see Fig. 1). Here, we present comprehensive details about the assembly modifications and custom-designed cell holder. This study also includes examples of measurement results, especially impedance spectra. The proposed setup holds significant promise for various applications in research laboratories and industrial settings, enabling fast, reliable, and cost-efficient setup for impedance characterization tests. References [1] L. Andrzej, Electrochemical Impedance Spectroscopy and its Applications, Springer, New York, NY, (2014). [2] M. Dollé, F. Orsini, A. S. Gozdz, and J.-M. Tarascon, J. Electrochem. Soc., 148, A851–A857 (2001). [3] M. Ender, A. Weber, and I.-T. Ellen, J. Electrochem. Soc., 159, A128 (2011). [4] R. Raccichini, M. Amores, and G. Hinds, Batteries, 5, 12 (2019). [5] S. Solchenbach, D. Pritzl, E. Jia Yi Kong, J. Landesfeind, and H. Gasteiger, J. Electrochem. Soc., 163, A2265–A2272 (2016). Fig. 1. Electrochemical sealing test results from a lithium graphite half-cell coin cell in a three-electrode setup. The graphite electrode lithiated to 500 mV vs. Li/Li⁺ maintains its stable potential in the absence of current over extended periods, demonstrating reliable sealing and no water and or oxygen penetration in the cell. Inset: a simplified sketch of the three-electrode coin cell design and components with micro-RE placed between two separators, allowing to detangle impedance contributions from the anode and cathode side individually. Figure 1

Accurate determination of electrolyte transport properties in lithium-ion batteries (LIBs) is pivotal for understanding battery performance and optimizing their design. Electrolyte transport properties, encompassing ionic conductivity, diffusion coefficients, and transference numbers, are critical parameters that influence charge and discharge rates, ion mobility, and energy efficiency.^[1,2] However, experimental measurements are inevitably accompanied by uncertainties stemming from experimental conditions, instrument calibration, and data analysis methods. Error propagation in electrolyte transport properties has substantial ramifications for battery research and development. Inaccurate measurements can lead to flawed conclusions about battery behavior and hinder the design of optimal electrolyte formulations.^[3] Based on the literature, measurements of concentration cells for the determination of electrolyte properties were conducted at different temperatures and concentrations to study its impact on transport properties, and the Gaussian error propagation method was used to analyze the data. The concentration cell experiment is essential to determine the transport factor a. ^[4] The equation to determine the transport factor is shown in Figure 1a and the exemplary results are represented in Figure 1b. The results show that the error bars are significantly higher at certain concentration pairs. The main aim of this research is to identify the sources that contribute to the prominent difference in the error bars across different concentrations and temperatures. It also discusses the significance of systematic errors, which arise due to calibration issues or instrumental biases, and random errors stemming from variations in experimental conditions.^[5] Recognizing the nature of these errors is critical for accurate interpretation of experimental data. To determine the electrolyte transport properties and the corresponding error or uncertainty, experimental datasets from the literatureare studied and repeated in our laboratories. References [1] Boz. B, et al. J. Electrochem. Soc. 168, 90501 (2021) [2] Valøen. L.O, Reimers. J.N. J. Electrochem. Soc. 152, A882 (2005) [3] Taylor. J, et al. Journal of Energy Storage. 24, 100761 (2019) [4] Landesfeind. J, Ebner. M, Eldiven. A, Wood. V and Gasteiger .H.A. J. Electrochem. Soc. 165, A469 (2018). [5] Singh. A, Chaturvedi. P. J. Resonance. 26, 853-861 (2021) [6] Landesfeind. J, Gasteiger. H.A. J. Electrochem. Soc. 166(14), A3079-A3097 (2019) [Figure captions] Figure 1 (a) Equation to determine transport factor aWhere U_cc represents the concentration cell potential, c₀ + Δc and c₀ -Δc represent the different cell concentrations whereas F, R, and T represent Faraday constant, ideal gas constant, and temperature respectively. (b) Concentration potentials U_cc (averaged over 3 min) measured in pouch concentration cells with LiPF₆ EC: DMC (1:1, w:w) at varying concentrations for temperatures ranging from −10°C to +50°C. Reprinted from Ref 6^[6] Figure 1

To speed up the development of novel batteries, time-consuming experimental work can be supported or partially replaced with simulations of physical batteries. Since the demand for laboratory research is growing rapidly, the same can be applied to mathematical models. So far, there is a huge variety of tools used to mimic the processes occurring in different scales: from a single active material particle up to a whole battery cell.^1-4 Existing battery modeling software suites such as COMSOL, Siemens Simcenter, Python, or Matlab, require the user to hold a high degree of competence in informatics, computer science, and modeling.^5-7 This may limit the use of simple battery models for laboratory workers in development and research. With all the above in mind, in our work, we want to present the concept of an easy-in-use battery twin model. In our project, new domains (mass/electrode and electrolyte) have been developed in Simscape – a library provided by Simulink which is part of the Matlab suite (Mathworks TM). With an intuitive, drag-and-drop interface and clear error and warning communication, Simscape is a promising tool that can be applied to simulate battery performance and other electrochemical systems. Our first implementation follows the nomenclature from Xia et al.⁸ The very first digital twin proposed here is represented by Newman’s P2D model where the battery as well as the particles in each electrode are discretized (i.e., divided into layers or shells, respectively). A set of non-linear partial derivative equations⁸ is solved with the application of the Butler-Volmer equation used to define the electrochemical reaction rate occurring on the surface of the particle in each layer. A schematic representation of such a battery setup with 5 layers of the separator, cathode, anode electrodes, and 10 shells describing the active material particles is depicted in Figure 1. The ionic concentration gradients in the electrolyte are shown for an exemplary charge simulation with a C-rate of 1/h. The current model will be extended by modeling the thermal properties occurring inside the battery during (dis)charging after in-depth numerical validation has confirmed its validity. In future work, parameters will be determined in the laboratory, and experiments will be conducted to compare with the results obtained in the digital twin. References [1] W. Lai, F. Ciucci, Electrochimica Acta, 56, 4369-4377, 2011. [2] M. Ecker et al. Electrochem. Soc., 162, A1849-A1857, 2015. [3] A. Latz, J. Zausch, J. Beilstein, Nanotechnol., 6, 987-1007, 2015. [4] K.-J. Lee et al., Journal of Power Sources, 241, 20-32, 2013. [5] V. Goel, K. Thornton, MethodsX, 8, 101425, 2021. [6] P. X. Mai et al., Sustainable Development, 2022. [7] M. D. Berliner et al., J. Electrochem. Soc., 168, 090504, 2021. [8] L. Xia et al., IFAC-PapersOnLine, 50, 2169–2174, 2017. Acknowledgments We would like to gratefully acknowledge the funding by the Regional Innovation Project in Agder for their financial support and collaboration with Morrow Batteries ASA on the project number ES711295 -RFFAGDER. Figure 1: Custom elements created in Simscape for the simulation of the batteries (top); a graphical representation of a battery cell with distinct single particles in each discrete element (middle); evolution of Li⁺ concentration in the electrolyte generated with custom battery domains created in Simscape (bottom). Figure 1

The Butler-Volmer equation is a fundamental equation extensively used to describe electrochemical kinetics and relates the reaction current at an electrode interface to the voltage. Newman and coworkers suggested an equation (Fig. 1a) for the exchange current density, which includes the state of charge (SoC) dependence of Li-ion batteries as well as theory-based anodic and cathodic transfer coefficients. This description of interface kinetics, part of the commonly used Newman model, is widely used for battery modeling.^1,2 Because of the lack of experimental data, several assumptions were made in the derivations,³ such as assuming the intercalation reaction to be a first order reaction, and assuming the transfer coefficients to be 0.5. As new battery materials emerge, it is important to validate whether these assumptions are applicable, or whether the model needs to be extended. In this work, we compare experimentally obtained interface resistances from different cathode materials with the theoretical behavior of the interface kinetics predicted from the Newman model as well as experimental characterizations published in the research literature. The experimental data for the interface resistance is determined using electrochemical impedance spectroscopy,⁴ where the electrodes are built into a three-electrode cell setup, using a gold wire reference electrode, which allows studying the cathode impedance separately.⁵ As anode, we use graphite that is prelithiated to ~20% to make sure there is enough lithium in the cell to be able to relithiate the cathode completely. The loading of the electrodes is low to ensure negligible pore resistance contributions, so that the semicircle in the Nyquist-plot originates from kinetics only. The obtained charge transfer resistance is analyzed as a function of Li-content to show how the kinetics of the cathode depend on the degree of lithiation. From the Newman model, it is expected that the interface resistance shows a U-shape over the degree of lithiation with a minimum at 50% SoC with a drastic increase in charge transfer resistance for the first and last 10%. This is because the kinetics are slower when the cathode is completely empty and completely full, which can be seen from the equation in Fig. 1a. When c=c _max or c=0, i ₀ becomes 0. Fig. 1b. shows the theoretical behavior based on the Newman model, the experimental results found in this study, and experimental results found in the literature for NMC111⁶. Figure 1: a) Exchange current density suggested by Newman and coworkers. i ₀ is the exchange current density, F is the Faraday constant, k _a and k _c are reaction rate constants for the anodic and cathodic reactions, α _a and α _c are the anodic and cathodic transfer coefficients, c _max is the maximum lithium concentration in the material, c is the lithium concentration in the material, and c _l is the salt concentration in the electrolyte. b) Charge transfer resistance vs. Li-content for ~0.5 mAh/cm² NMC111 (Gelon Lib) with prelithiated graphite (Sigma Aldrich) anode, gold wire reference electrode with a core diameter of 50 µm coated with a 7 µm polyimide insulation (Goodfellow), 80 µl 1 M LiPF6 in EC:EMC (3:7) (Gotion), two glass fiber separators (260 µm, VWR) at 25 °C determined from semi-circle width of cathode impedance (100 kHz to 10 mHz, 10 mV amplitude) using a potentiostat (VMP3, BioLogic) recorded in three electrode Swagelok cell (average of 3 measurements) is represented by dark grey points. The red points show theory according to Eq. 1 with, and the light brown points show NMC111 from Ref. 6. The lines between the points are drawn to enhance the features of the figure. Comparison of experimental data and the theoretical framework shows pronounced discrepancies, which cannot be resolved if the transfer coefficients are kept at 0.5. Similar measurements are conducted for a wide range of active materials and changed environmental conditions to further elaborate validity and/or necessity to modify the commonly used description of the interface kinetics relation. This will help to better understand interface resistance in battery cells and thereby allow optimization of material properties by, e.g., surface modification. References M. Ecker et al., J. Electrochem. Soc., 162, A1836 (2015). T. F. Fuller, M. Doyle, and J. Newman, J. Electrochem. Soc., 141, 1–10 (1994). M. Doyle, T. F. Fuller, and J. Newman, J. Electrochem. Soc., 140, 1526–1533 (1993). A. Lasia, in Electrochemical Impedance Spectroscopy and its Applications, A. Lasia, Editor, p. 203–250, Springer, New York, NY (2014). S. Solchenbach, D. Pritzl, E. J. Y. Kong, J. Landesfeind, and H. A. Gasteiger, J. Electrochem. Soc., 163, A2265–A2272 (2016). R. Morasch, H. A. Gasteiger, and B. Suthar, J. Electrochem. Soc., 170, 080522 (2023). Figure 1