Random and systematic error as a function of sample area in electrochemical impedance spectroscopy data

Electrochemical impedance spectroscopy (EIS) is one of the most important non-destructive diagnosis tools in lithium-ion battery (LIB) technology. EIS is routinely used for characterization of battery materials, optimization of manufacturing processes, and understanding of degradation mode. However, interpretation of EIS spectra is not a straightforward procedure. Typical EIS data of LIB have heavily overwrapped capacitive semicircles in Nyquist (complex impedance) plot because LIBs’ anodes and cathodes both involve several conduction processes. Widely used method for information separation is least-squares fitting based on discrete equivalent circuit model. However, this method requires a priori assumption on the number of capacitive processes. Destructive EIS measurement methods, such as insertion of reference electrode and symmetry cell construction, are powerful for separation of cathode/anode information. But these involve meticulous work, and battery structure or degradation state often make it difficult to obtain an accurate measurement result.Recently, distribution of relaxation times (DRT) analysis has increasingly used for EIS interpretation. This analysis treats an EIS spectrum as a continuous function of relaxation time, or time constant of infinitesimal RC equivalent circuit element. DRT analysis can distinguish several different capacitive processes without the need of prior model assumption. Application field of DRT analysis currently focuses on solid oxide fuel cell, but several LIB studies have been reported for LiFePO4 cathode system[1] and LiMn2O4-LiNixCoyMnzO2 cathode system[2]. Here we demonstrate that DRT analysis is a powerful tool for degradation analysis of LIBs with LiCoO2(LCO)-based cathode and graphite anode, and provide experimental verification of information obtained by DRT analysis.Commercial-size prototype LIBs with LCO-based cathode and graphite anode are used for this work. Charge-discharge cycling tests of the cells are performed at different temperature conditions. EIS measurements are conducted at fully charged state of every fifty cycles, with frequency range of 1 MHz to 10 mHz. For better separation of different processes, the EIS spectra are obtained at 23 deg C, 10 deg C and 0 deg C in a climate chamber. DRT is calculated by MATLAB with in-house code, which is based on Fourier transfer methods utilizing window function preprocessing[3]. To understand DRT results in physical viewpoints, several electrode analyses of disassembled cells are conducted at initial and the cycled state. Figure 1 shows Nyquist plots and the corresponding DRT plots of (a) low-temperature and (b) high-temperature cycled LIBs obtained at 10 deg C. In Nyquist plot, all spectra exhibit the shape of two depressed capacitive semicircles. At a first glance, both low-temperature cycling and high-temperature cycling appear to increase the size of low-frequency semicircles. But closer look shows that low-temperature cycling cause “merge” or heavier overwrap of two semicircles, whereas high-temperature cycling retains the separated feature. These characteristics are further clarified by DRT analysis. In each cell, we can identify four different peaks in DRT plots. The high-temperature cycled cell shows gradual increase in the lowest-frequency (1) peak, whereas the peak remains almost the same intensity in the low-temperature cycled cell and the middle-frequency (2) peak increases significantly. No change is observed for the (3) peak during the cycle test. Both cycling condition cause the increase in the highest-frequency (4) peak. Further teardown analyses verify that these DRT peaks can be attributed to different electrode degradation phenomena, which are related to cycling temperature. These results demonstrate the ability of DRT analysis as a degradation diagnostics tool for LIBs with various kinds of material systems. [1] J. P. Schmidt, T. Chrobak, M. Ender, J. Illig, D. Klotz, E. Ivers-Tiffée, J. Power Sources, 196 5342 (2011). [2] B. Stiaszny, J. Ziegler, E. Krauß, J. P. Schmidt, E. Ivers-Tiffée, J. Power Sources, 251 439 (2014).[3] H. Schichlein, A. C. Müller, M. Voigts, A. Krügel, E. Ivers-Tiffée, J. Appl. Electrochem., 32 875 (2002). Figure 1

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically.This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance.The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

Electrochemical impedance spectroscopy (EIS) is a powerful diagnostic tool used in a wide range of applications, including solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs). While EIS measurements on single cell level are commonplace, implementation of EIS on stack level remains limited, primarily due to the extremely low impedance of large-footprint cells, which imposes stringent demands on the impedance spectrometer and experimental setup (1). Furthermore, compared to single-cell EIS data, stack-level data reported in the literature are rarely subjected to the same degree of equivalent circuit modeling, making a detailed analysis of underlying electrochemical processes difficult.Here, we report a framework for analyzing stack-level impedance spectroscopy data. The quality of the acquired data is first verified using Kramers-Kronig transformations (2), followed by the application of distribution of relaxation times (DRT) and analysis of the difference of impedance spectra (ADIS) methods to experimental data collected under a set of pre-determined operating conditions (electrochemical test procedure) (3-4). DRT and ADIS results were incorporated into the complex non-linear least squares (CNLS) fitting procedure and the validity of the chosen equivalent circuit model was confirmed by reconstruction of Nyquist, Bode, the individual electrochemical contributions, and DRT plots of the fitted data.The developed framework was successfully implemented on a commercial 75-cell SOEC stack (TSP-1) developed by Haldor Topsoe (5). An electrochemical impedance spectrum is recorded simultaneously on all 13 cell groups in the stack using Zahner instrumentation. In this work, the electrochemical impedance data obtained from a state-of-the-art TSP-1 stack, containing three different types of Ni/YSZ based cells, is reported. The stack was initially investigated by EIS, using an electrochemical test procedure including CO2 electrolysis under constant conversion, H2O electrolysis under constant conversion, variations in the pO2 on the oxygen electrode, and finally temperature variations in CO2 electrolysis operation. This electrochemical test procedure was performed to develop an equivalent circuit model for the stack. Afterward, the stack was subjected to a degradation in steam electrolysis under 580 mA/cm2 (70% conversion), with an impedance spectrum being recorded every 24 hours. The operational point for this degradation test was chosen to match the real-life operation point of these stacks and hence to give a realistic image of any potential degradation. The developed framework and the equivalent circuit model developed for the stack was finally used to model the EIS data obtained during the degradation test. The modeling of these EIS data utilizes the full potential of electrochemical impedance spectroscopy and help identify any degradation mechanisms taking place in the stack, an analysis not previously reported on stack level.(1) R. R. Mosbæk, J. Hjelm, R. Barfod, and P. V. Hendriksen. ECS transactions, 35, 1 (2011).(2) B. Boukamp, J. Electrochem. Soc., 142, 1885 – 1894 (1995).(3) S. H. Jensen, A. Hauch, P.V. Hendriksen, M. Mogensen, N. Bonanos, and T. Jacobsen. Journal of the Electrochemical Society, 154(12), B1325-B1330, (2007)(4) H. Schichlein, and E. Ivers-Tiffee. Journal of Applied Electrochemistry, 32, 875-882, (2002).(5) J. Rass-Hansen, P. Blennow, T. Heiredal-Clausen, R. Küngas, T. Holt Nørby, and S. Primdahl, Proceedings of the 12th European SOFC & SOE Forum, 318-325 (2016). Figure 1

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

An increase in photocurrent has been observed at silicon electrodes coated with nanostructured porous silica films as compared to bare, unmodified silicon. Ultimately, to utilize this effect in devices such as sensors or microchip power supplies, the physical phenomena behind this observation need to be well characterized. To this end, Electrochemical Impedance Spectroscopy (EIS) was used to characterize the effect of surfactant-templated mesoporous silica films deposited onto silicon electrodes on the electrical properties of the electrode space-charge region in an aqueous electrolyte solution, as the electrical properties of this space-charge region are responsible for the photobehavior of semiconductor devices. A significant shift in apparent flat-band potential was observed for electrodes modified with the silica film when compared to bare electrodes; the reliability of this data is suspect, however, due to contributions from surface states to the overall capacitance of the system. To assist in the interpretation of this EIS data, a series of measurements at Pt electrodes was performed with the hope of decoupling electrode and film contributions from the EIS spectra. Surprisingly, the frequency-dependent impedance data for Pt electrodes coated with a surfactant-templated film was nearly identical to that observed for bare Pt electrodes, indicating that the mesoporous film had little effect on the transport of small electrolyte ions to the electrode surface. Pore-blocking agents (tetraalkylammonium salts) were not observed to inhibit this transport process. However, untemplated (non-porous) silica films dramatically increased film resistance, indicating that our EIS data for the Pt electrodes is reliable. Overall, our preliminary conclusion is that a shift in electrical properties in the space-charge region induced by the presence of a porous silica film is responsible for the increase in observed photocurrent.

Chapter 10 Interlaboratory and intralaboratory surveys Reference methods and reference materials

To better understand degradation in electrochemical converters and helping to correlate certain phenomena with specific operating conditions, machine learning (ML) methods are increasingly being applied. Success has already been achieved in the field of degradation analysis and prediction of capacity of lithium ion batteries1, for instance. In terms of Solid Oxide Cell (SOC) stacks ML methods have been applied mainly with the aim of identification of faulty operation modes and degradation related fault diagnosis2. ML approaches usually require a considerable amount of real training data, when used for forecasting models. A data consolidation and curation strategy was developed with the aim of processing the historic long-term test bench data of SOCs collected by Forschungszentrum Jülich over the past years. In comparison to other datasets developed in this field3, the one presented in this work contains SOC stack tests in fuel cell operation with significantly longer operating times under load. A compilation of the sample experiments and the consolidation into a hierarchical data format are presented. Further, an essential part of the strategy is the automatic curation and analysis of electrochemical impedance spectroscopy (EIS) measurements, using a specifically developed procedure in Python. The varying quality of measurements from past years, as well as recurring artefacts such as parasitic inductances, can be addressed in this way. Additional distribution of relaxation times (DRT) deconvolutions and equivalent circuit modelling (ECM) are performed, as part of the procedure to automatically retrieve feature values from measurements (cf. Fig. 1). The novel dataset, which to the authors’ knowledge includes some of the longest SOC stack tests available, serves as the basis for several evaluations. In addition to classification and clustering work to derive degradation patterns, in particular based on the EIS data, another focus is on the development of forecasting models. The current work is primarily concerned with long short-term memory (LSTM), as well as regression models that make use of both the time series data and the characterisation measurements, such as EIS. Acknowledgement The authors would like to thank their colleagues at Forschungszentrum Jülich GmbH for their great support and the Helmholtz Society as well as the German Federal Ministry of Education and Research for financing these activities as part of the WirLebenSOFC project (03SF0622B). References 1: Jones, P.K., Stimming, U. & Lee, A.A. Impedance-based forecasting of lithium-ion battery performance amid uneven usage. Nature Communications 13, 4806 (2022).2: B. Yang et al. Solid oxide fuel cell systems fault diagnosis: Critical summarization, classification, and perspectives. Journal of Energy Storage 34, 102153 (2021).3: A.K. Padinjarethil, S. Pollok & A. Hagen. Degradation studies using machine learning on novel solid oxide cell database. Fuel Cells 21, 566–576 (2021). Figure caption: Fig.1: Flow diagram of EIS data curation pipeline and curation results for example EIS measurement. Figure 1

EIS and ENM measurements for three different organic coatings on aluminum

The effect of corrosion protection performance of epoxy coatings containing zirconium oxide (ZrO2) nanoparticles on carbon steel was analyzed using scanning electrochemical microscopy (SECM) and electrochemical impedance spectroscopy (EIS). Localized measurements such as oxygen consumption and iron dissolution were observed using SECM in 3.5% NaCl in the epoxy coated sample. Line profile and topographic image analysis were measured by applying −0.70 and +0.60 V as the tip potential for the cathodic and anodic reactions, respectively. The tip current at −0.70 V for the epoxy coated sample with ZrO2 nanoparticles decreased rapidly, which is due to cathodic reduction of dissolved oxygen. The EIS measurements were conducted in 3.5% NaCl after wet and dry cyclic corrosion test. The film resistance (Rf) and charge transfer resistance (Rct) values were increased by the addition of ZrO2 nanoparticles in the epoxy coating. SEM/EDX analysis showed that oxides of Zr were enriched in corrosion products at a scratched area of the coated steel after corrosion testing. Focused ion beam-transmission electron microscope analysis confirmed the presence of the nanoscale oxide layers of Zr in the rust of the steel, which had a beneficial effect on the corrosion resistance of coated steel by forming protective corrosion products in the wet/dry cyclic test.

Corrosion is a spontaneous process that causes irreversible damage to nearly all metals, which has world-wide implications for architectural and artistic metalwork, such as bridges, buildings, airplanes and sculptures. Protective coatings such as wax, paint, or polymeric clear coatings are used to prolong the lifetime of metals such as steel and bronze. Unfortunately, these coatings fail over time due to oxidative damage by UV rays and by failure to exclude water that can carry salts and pollutants that cause corrosion of the underlying substrate. The current method of coating assessment is visual inspection but when coating failure is detected at this stage, irreversible damage has already occurred to the metal substrate. Diagnosing coating quality on artistic metalwork is a unique challenge as it requires a method that is non-destructive as to not alter the aesthetic of the piece. A non-destructive technique or device that can detect early signs of coating failure in the field (such as at a sculpture park) does not currently exist. The aim of this thesis is to develop a method that can be readily used in the field by a conservator to quickly diagnose the protective state of a coating on a sculpture in order to provide localized treatment. Electrochemical impedance spectroscopy (EIS) is a method currently used to study protective coatings in the lab. The technique itself is non-destructive but the most common electrochemical cell used with it must be used on a planar substrate and requires that a portion of coating be removed. Not only is the current method destructive, but the data produced by EIS is complicated and time consuming to analyze. These issues provide the foundation for this project. This thesis first proposes multiple methods to quickly analyze the complicated data traditionally collected through EIS. Three quick analysis methods, including the estimation of the derivative at one single frequency in EIS spectra, was successfully used to categorize coating quality of five different coating types. Using this quick analysis can aid conservators in assessing coating condition without the need for extensive training in EIS data interpretation. This thesis also proposes a method to measure early warning signs of coating degradation through a co-planar hydrogel electrochemical cell paired with EIS. The configuration of this co-planar hydrogel cell negates the need for the removal of the coating and can be used on multiple types of surfaces because of its flexibility, therefore overcoming the drawbacks of

Development of future battery generations and quality control of current lithium-ion battery (LIB) systems require reliable techniques for the characterization of the complex dynamic processes in batteries. Electrochemical impedance spectroscopy (EIS) is an established tool for the electrochemical characterization of LIBs and the related ageing processes. Nevertheless, ensuring a reliable interpretation and validation of the impedance data remains challenging. Here we show how EIS complemented by microscopy data can be successfully used to follow battery ageing. We connect data from both methods in an electrochemical finite element model (FEM) to extract changes of intrinsic battery parameters, such as double layer capacitance, film resistance, exchange current density, and particle radii. Ageing mechanisms induced by cycling and temperature are investigated by EIS on commercial cells. Postmortem analysis of the anode and cathode electrodes is carried out to determine their initial micrometric and nanometric structure and to independently investigate morphological and electrical changes induced by ageing. While the obtained initial structural dimensions reduce drastically the number of adjustable parameters in the FEM model, which enables us to follow battery ageing processes noninvasively through the EIS spectra, the microscopy results of the aged cells prove independently the validity of the here presented approach.

Quantitative analysis of electrochemical impedance spectroscopy (EIS) and 2nd-harmonic nonlinear EIS (2nd-NLEIS) data from commercial Li-ion batteries is performed using the porous electrode half-cell models developed in Part I. Because EIS and 2nd-NLEIS signals have opposite parity, the full-cell EIS model relies on the sum of cathode and anode half-cells whereas the full-cell 2nd-NLEIS model requires subtraction of the anode half-cell from the cathode. The full-cell EIS model produces a low error fit to EIS measurements, but importing EIS best-fit parameters into the 2nd-NLEIS model fails to ensure robust model-data convergence. In contrast, simultaneously fitting opposite parity EIS and 2nd-NLEIS models to the corresponding magnitude-normalized experimental data provides a lower total error fit, more internally self-consistent parameters, and better assignment of parameters to individual electrodes than EIS analysis alone. Our results quantify the extent that mild aging of cells (<1% capacity loss) results in substantial increases in cathode charge transfer resistance, and for the first time, a breakdown in cathode charge transfer symmetry at 30% and lower state-of-charge (SoC). New avenues for model-based analysis are discussed for full-cell diagnostic and we identify several open questions.

The growth of the barrier oxide layer on aluminum during low-current anodic polarization in aqueous solutions occurs in accordance with the high-field Cabrera-Mott growth law,1 as recently confirmed for pure aluminum by use of simultaneous ellipsometry and chronoamperometry to measure the transient film growth following a potential step.2 Electrochemical impedance spectroscopy (EIS) was used to determine the steady-state barrier layer thickness between each potential step as in situ calibration for the measurement of the film growth rate by the other two methods. Complementarity of the three techniques was demonstrated for studying the kinetics of growth and dissolution of thin passivating oxides in aqueous solution. The purpose of the present study is to investigate whether the same approach can be used for studying the modification of the interface by contamination by the common trace element Pb as a result of heat treatment. Pb is known to activate the surface by destabilizing the passivating barrier layer.3 The microstructure and composition of the contaminated interface has been characterized by ex-situ STEM and various surface analytical techniques for samples heat treated at high (600°C) temperatures, resulting in detectable Pb segregation in the form of both nanosize (up to 10 nm) Pb particles and a few nm thick Pb-rich film along the metal-oxide interface. Pb segregation by heat treatment at lower temperatures was not possible by these techniques. Supplementary data were necessary for samples heat treated at lower temperatures, especially for modification of the electrochemical properties of the interface. The samples investigated were model AlPb alloys prepared from pure components in the laboratory and cold rolled into 2.2 mm thick plates, followed by polishing metallographically through 1 µm surface finish. They were subsequently heat treated in an air-circulation furnace in the temperature range 300 - 500°C. The amorphous oxide is reported to thicken moderately in the range 10 - 20 nm as a result of 1 h heat treatment at this temperature range.4 Experiments were conducted in a conventional three electrode electrochemical cell in the manner described above. Ellipsometry could not be used because of slow measurement relative to the rate of barrier-layer growth and unexpected loss of specularity of the sample surface during heat treatment. The EIS data were evaluated by use of the equivalent-circuit model derived rigorously by Armstrong and Edmondson,5 which was shown to be applicable to the present electrochemical interface in the case of pure Al.2 Oxide growth was calculated from the current-time data by use of Faraday's law corrected for current leakage (passive current). The growth data were analyzed by use of the integrated form of the Cabrera-Mott law, according to Ghez,6 giving the well-known inverse logarithmic growth law. The changes in the barrier layer thickness appeared to fit the Ghez equation, but not as successfully as pure Al. The EIS data showed that the film thickened with increasing applied potential at the rate of 1.2 nm/V for all heat treatment temperatures except for 600°C, as expected for pure Al. The passive current density increased and the film resistance decreased at a given applied potential, with increasing annealing temperature, indicating reduced passivity. However, the film capacitance, measured by EIS at a given applied potential, increased with annealing temperature, giving an apparent decrease in the film thickness with increasing annealing temperature, in contrast to the expected trend to the opposite.4 The changes in the barrier film properties, measured by EIS, are attributed to the segregation of metallic Pb at the metal-film interface, in the film, and possibly at the film surface, contributing to the increased capacitance of the electrochemical interface. The model used for correlation of the EIS data has to be modified to incorporate the effect of segregated Pb in the form of particles and continuous film for more reliable measurement of the barrier film thickness in future work. References J. W. Diggle, T. C. Downie, and C. W. Goulding, Chem. Rev., 69 (3), 365 (1969). N. H. Giskeødegård, O. Hunderi and K. Nisancioglu, J. Solid State Electrochem. 19, 3473 (2015). Anawati, B. Graver, H. Nordmark, Z. Zhao, G. S. Frankel, J. C. Walmsley, and K. Nisancioglu, J. Electrochem. Soc., 157, C313 (2010). E. Senel, Dissertation, Norwegian University of Science and Technology, Trondheim (2013). R. D. Armstrong and K. Edmondson, Electrochim. Acta, 18,937 (1973). R. Ghez, J. Phys. Chem., 58, 1838 (1973).

Anion exchange membrane water electrolysis (AEM-WE) stands out as a promising method for hydrogen production from renewable energy sources. Unlike proton exchange membrane water electrolysis (PEM-WE), AEM-WE offers the advantage of nonprecious metal catalysts due to mild alkaline conditions, while still enabling a compact cell design and operation under differential pressure. Remarkable performances of AEM-electrolysis cells have been demonstrated in literature, achieving current densities of up to 7.68 A·cm−2 [1], surpassing the record current density of PEM-WE at 6 A·cm−2 [2], both measured at 80 °C and 2 V. However, challenges related to efficiency and stability persist and demand effective solutions to unlock the full potential of AEM-WE. In order to achieve this, cell components, particularly the membrane, catalysts, and electrode structure have to be improved and advanced measurement techniques have to be applied.Different measurement tools serve to characterize, separate, and quantify the dynamic processes inherent in AEM-WE. Electrochemical impedance spectroscopy (EIS) serves as a valuable non-destructive, in-situ diagnostic tool for electrochemical cells, enabling the differentiation of various phenomena based on their respective relaxation times. Moreover, EIS enables the quantification of loss mechanisms thereby facilitating a targeted optimization of electrolysis components and the identification of weaknesses limiting long-time stability.Despite its utility, the broad use of EIS is hindered by its complex interpretation. The widely applied approach of fitting equivalent circuit models (ECMs) aims to extract quantitative data from the semi-circular shapes observed in Nyquist plots. However, the assumption of a physico-chemical meaningful ECM is challenging without prior knowledge of the number, size, and time constants of appearing loss mechanisms. Furthermore, the potential for multiple ECMs to fit one and the same experimental result introduces ambiguity, leading to potential misinterpretations of data.An alternative approach to analyse EIS spectra involves converting the measured data into the time (τ) domain and characterize the different mechanisms based on their characteristic relaxation times. The distribution of relaxation times (DRT) analysis has already been applied across diverse electrochemical systems, spanning from solid oxide fuel cells (SO-FC) and PEM fuel cells (PEM-FC) to PEM water electrolysis and AEM fuel cells (AEM-FC). However, to the best of the authors’ knowledge, DRT analysis of AEM-WE is still absent in literature.In this study we present a thorough investigation of an AEM-WE single-cell, employing a combination of electrochemical impedance spectroscopy (EIS) and the equivalent circuit model (ECM). Notably, we demonstrate the distribution of relaxation times (DRT) analysis at AEM-WE cells for the first time, utilizing a reversible hydrogen electrode (RHE) as a reference. Through half-cell EIS measurements and subsequent DRT spectra analysis, anodic and cathodic half-cell reactions are clearly identified and quantified. The DRT analysis differentiates five loss mechanisms within the AEM-WE system, encompassing the hydrogen evolution reaction, the oxygen evolution reaction, and ionic transport losses within the catalyst layers. By systematically varying operating parameters, we successfully attribute DRT peaks to their respective physicochemical origins. These findings provide valuable insights into the electrochemical processes within the AEM-WE single-cell, significantly advancing our understanding of underlying mechanisms.In fig. 1, we investigate the degradation of AEM-WE single cells through long-term experiments. Periodic EIS measurements enable an in-operando analysis of catalyst degradation. The impedance data is analysed using ECM and DRT. Ohmic contributions resulting from electric and ionic charge transport are distinguished from polarisation resistances arising from electrochemical reactions. A catalyst degradation is observable in fig 1b & c, where the low frequency resistance (LFR) in the Nyquist plot and the DRT peak area increases over time. However, fig. 1b also reveals the reduction of the high frequency resistance (HFR) resulting in an overall performance increase and voltage decrease.[1] N. Chen, S. Y. Paek, J. Y. Lee, J. H. Park, S. Y. Lee, Y. M. Lee, High-performance anion exchange membrane water electrolyzers with a current density of 7.68 a cm −2 and a durability of 1000 hours, Energy & Environmental Science 14 (12) (2021) 6338–6348. doi:10.1039/D1EE02642A.[2] M. Braig, R. Zeis, Distribution of relaxation times analysis of electrochemical hydrogen pump impedance spectra, Journal of Power Sources 576 (2023) 233203. doi:10.1016/j.jpowsour.2023.233203. Figure 1

Electrochemical impedance spectroscopy study of membrane fouling characterization at a conductive sub-stoichiometric TiO2 reactive electrochemical membrane: Transmission line model development