Electrochemical Impedance Spectroscopy Data Research Articles

The Effect of Cation Contamination on the Water Transport for PEM Water Electrolysis

Large-scale hydrogen production by PEM water electrolysis (PEMWE) in the energy sector requires a deep understanding of the system behaviour in failure cases and possible recovery procedures. This includes the purification of the anolyte feed water because the contamination with ions can cause the PEMWE stack to breakdown.[1] For instance, the presence of iron trace cations or other metal cations in the feed water leads to a performance loss due to higher ohmic resistance of the cell and higher iR-free voltages.[2] [3] Moreover, cation contamination might influence the water transport in a PEMWE cell. In principle, the transfer of the water molecules to the cathode is proportional to the proton flux from the cathode to the anode. The proportionality factor is defined as water transport coefficient and is also temperature dependent.[4] In case of the mono- and multivalent cations as trace elements in the feed water, the water loading of the membrane, which is expressed by water molecules per sulfonic acid group of the membrane, λ, decreases over time and leads to successive increase in the ohmic resistance. [5] However, the influence of cation contamination on the water transport in PEMWE is poorly understood to date.In this work, we investigated the effect of cation contamination in the anode feed water on the water crossover for PEMWE. Cation contamination experiments were carried out at a constant current density by adding different concentrations such as K2SO4, Na2SO4, etc. to the anode feed water. All electrochemical measurements were carried out in an in-house test bench equipped with a single cell of 5 cm2 geometric electrode area and potentiostat with booster. A commercially available catalyst coated perfluorosulfonic acid membranes (loading of 0.3 mg cm-2 geo Pt/C and 1 mgcm-2 geo IrOx) was used. The anode and cathode compartments were kept at atmospheric pressure and cell temperature of 80°C. To determine the water crossover from anode to cathode at different current densities, the cathode exhaust was cooled in a heat exchanger and measured by a gravimetric method. [4] Firstly, a conditioning procedure was performed with purified feed water until steady cell performance was achieved. The cell performance was evaluated using polarization curves, electrochemical impedance spectroscopy (EIS) and water crossover measurements. The last two are of particular interest, because they provide information about membrane humidification. The Tafel slope and mass transport overvoltage were established from the polarization curves and high frequency resistance (HFR). We observed a significant rise of the cell voltage by adding cations to the anode water feed within few hours. The analysis of Tafel slope and EIS data also reveal an increase of ohmic and mass transport resistances. In these experiments, we were able to correlate the HFR results with the water crossover as a function of the nature of the cation and its concentrations. Our data shows that the specific water crossover per current density decreases as a consequence of cation contamination. The switch back to purified feed water allows us to monitor the dynamics of the performance recovery process obtained from EIS and water crossover measurements. Additional measurements of the polarization curves and HFR were used to distinguish between reversible and irreversible degradation processes due to the cation contamination.Altogether, we present the effect of cation contamination on the water transport processes and provide deeper insights into the mass transport and performance losses for PEMWE.

Distribution of Relaxation Times Analysis of PEMFC through Loewner Framework: A Direct and Regularization Free Approach

The analysis and interpretation of electrochemical impedance spectroscopy (EIS) data are often complex and challenging, despite its popularity within the field of electrochemistry. Hence, recently, we have introduced novel data-driven approach for extracting the distribution of relaxation times (DRT) from impedance data in order to improve further analysis [1, 2]. This approach is based on the Loewner framework, which is a versatile and straightforward data-driven modeling method that computes reduced-order models in a state-space representation directly from frequency response data. Thus, it allows estimation of DRT without need of regularization procedures or iterative optimization algorithms.The effectiveness of this approach is successfully demonstrated by analysing the experimental EIS data of a polymer electrolyte membrane fuel cell (PEMFC). The results show that this method allows clear identification of the individual polarization processes based on their characteristic time constants (see Figure). Furthermore, the peaks observed in the DRT plot are correctly associated with different processes by examining spectra under various operating conditions. This is further validated by employing a physics-based model [3]. In this way, unambiguous fault identification and monitoring of the degradation state of the cell is possible and it can be the foundation for an on-board diagnostic tool.

Estimating Parameters of Physics-Based Battery Models – What Machine Learning Can and Cannot Do

With the widespread use of Lithium-ion (Li-ion) batteries in multiple industries, the design space for the electrochemical cells has increased drastically. Li-ion batteries’ design parameters and material properties, such as porosity, electrode thickness, active material loading, and solid phase diffusivities, typically vary substantially due to different design goals and variation in the manufacturing process. Due to the difficulty in obtaining these and other battery cell parameters, many battery management systems used to control and monitor Li-ion batteries opt for the simplified resistor-capacitor circuit-based battery cell representation over using physics-based battery models. However, if these parameters become obtainable, scientists could implement advanced battery management systems using physics-based battery models to capture precisely how the battery performs and degrades in a wide variety of applications. Multiple methods, such as genetic algorithms, statistics, and machine learning, have been attempted for the parametrization and characterization of battery cells to enable accurate battery simulations using physics-based models [1, 2]. However, there are inherent limitations to the type and number of parameters that can be estimated using these methods if only the standard battery cell cycling data is utilized for this purpose. The present work elucidates and quantifies these limitations specifically in the case of machine learning based approach and provides deeper insights into these limitations that may help develop more robust cell characterization protocols. In the present work, model parameters are varied in a continuum-level physics-based model and simulations are performed for: (1) constant current-constant voltage (CC-CV) charging and constant current discharge at different C-rates, and (2) electrochemical impedance spectroscopy (EIS) in the relevant frequency range at multiple states of charge (SOCs). The Single Particle Model (SPM) is used for performing these simulations due to its relative simplicity and small number of parameters. At first, the charge and discharge data are input as training data in a long short-term memory neural network (LSTM NN), and EIS data is input as training data in a convolutional neural network (CNN). Once trained, the machine learning (ML) model uses charge, discharge, and EIS data withheld from the original training data set to predict SPM parameters. The estimated parameters are compared with the true parameters used for SPM charge, discharge, and EIS simulations, and the error in parameter estimation is quantified. A rigorous grid convergence analysis is performed to highlight the importance of ensuring that the spatial discretization error of the model does not influence the parameter estimation accuracy. For the first error quantification, all parameters are varied concurrently to obtain the parameter estimation error that arises from a large, undetermined parameter set. Parameters are then analyzed individually and in clusters related to diffusion, kinetics, and stoichiometric limitations. Further analysis is performed on parameters that could not be estimated with sufficient accuracy, including theoretical explanations for the incurred error and the effect of this error on the internal state predictions.

Development of an Onboard Urea Electrolyser to Reduce NOx Emissions in Vehicles By Enhancing the Selective Catalytic Reduction with the Produced H2 and NH3

In the persistent pursuit of reducing nitrogen oxide emissions from diesel exhaust, challenges arise in achieving optimal performance at lower temperatures, especially in urban driving conditions. This research focuses on a pragmatic approach – development of an onboard urea electrolyser using diesel exhaust fluid (DEF) as the hydrogen-rich carrier. The primary goal is to enhance the NOx reduction efficiency at temperatures below 200oC by providing the electrolyser products – hydrogen and ammonia [1]. The reason behind this innovation lies in recognising DEF as a viable electrolyte, offering both safety and a promise of performance increase due to splitting of urea instead of water. Beyond the contribution to NOx reduction, this approach aligns with the broader societal and political imperatives of integrating cleaner energy solutions within the automotive sector. However, the development of a functional urea electrolyser involves certain complexities. The key considerations are the material stability under operating conditions, anode catalyst dissolution in DEF, mass transport limitations, and the reduction of relatively high cathode overpotentials. A notable aspect of our research involves addressing these challenges in scenarios both with and without potassium hydroxide alongside DEF, requiring a nuanced understanding of the electrochemical processes involved. Additionally, weight, packaging, and system integration play an important role in the development of an onboard urea electrolyser.This study systematically explores these challenges to devise a comprehensive understanding of the underlying electrochemical processes involved as well as the integration and operation strategy. The research methodology adopts single cell tests for which activation and measurement protocols have been established to achieve representative and comparable performance curve recordings and electrochemical impedance spectroscopy (EIS) data. Additionally, to various operating conditions of the electrolyte, the cell components screening adds to a considerable effort of this study. We explore the influence on the materials and design of the bipolar plates (BP) and porous transport layers (PTL); electrode performance and durability as well as the membrane selection. Within this study we consider anion exchange membranes (AEM) and bipolar membranes (BPM) for the performance comparison. Adopting both catalyst-coated membrane (CCM) and catalyst-coated substrate (CCS) approaches enables for examination of electrode preparation techniques and their influence on performance of the electrolysis cell. The initial results are promising, especially when employing 1 M KOH in the DEF electrolyte at 70oC, reaching 0.85 A cm-1 at 2 V. The respective performance curve is presented in Figure 1., together with a diagram of the electrolyser system integration into an existing Diesel exhaust line.This research represents a practical stride towards realising the denitrification of transportation sector especially in city cycle driving when the exhaust gases temperature is too low for the state-of-the-art SCR systems. By addressing identified challenges and optimising key parameters, our study lays a robust foundation for the scalable implementation of a functional urea electrolyser for transport applications and a basic understanding of the electrochemical processes behind the performance and stability of such a system.Figure 1.Depicts a recorded polarisation curve of a single cell using an anion exchange membrane (AEM) and nickel catalyst anode fed DEF and 1 M KOH as an electrolyte at 70oC. Below, the suggested integration of the electrolyser into latest generation Diesel exhaust aftertreatment system. It includes diesel oxidation catalyst (DOC), selective Diesel particulate filter (sDPF), hydrogen supported denitrification catalyst ‘H2-deNOx’ and finally a selective catalytic reduction (SCR) step.Reference: Esser, E., Kureti, S., Heckemüller, L., Todt, A. et al., "Low-Temperature NOx Reduction by H2 in Diesel Engine Exhaust," SAE Int. J. Adv. & Curr. Prac. in Mobility 4(5):1828-1845, 2022, https://doi.org/10.4271/2022-01-0538. Figure 1

A Case of Inverted Crosstalk in High-Voltage Lithium-Ion Batteries with Carbonate-Based Electrolytes and LiNi0.5Mn1.5O4 Cathodes

The deployment of spinel LiNi0.5Mn1.5O4 (LNMO) cathodes can be key to develop lithium-ion batteries (LIBs) with higher energy density and lower costs. However, the most common carbonate-based electrolytes for LIBs suffer from severe degradation at high potentials, potentially causing cell failure and safety hazards.[1,2] Therefore, investigating the degradation mechanisms in battery cells employing high-voltage battery cathodes such as LNMO is paramount. In this work [3], we studied the influence of linear carbonates on cycling and interfacial stability. Electrolytes consisting of LiPF6 dissolved in a mixture of cyclic (ethylene carbonate, EC), and linear carbonates (diethyl carbonate, DEC, or dimethyl carbonate, DMC), in a 3:7 EC:DEC/DMC weight ratio, were compared. Galvanostatic cycling and electrochemical impedance spectroscopy data of LNMO||Li cells indicate that the linear carbonate plays a fundamental role in cell failure. Specifically, cells with DEC-based electrolyte show early rollover failure, reaching 80% and 20% state of health at cycle 31 and 55, respectively. In contrast, cells with DMC-containing electrolyte have better stability, keeping 80% state of health until cycle 92, as shown in Figure 1. Three-electrode measurements and further characterizations, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS), unveil that cell failure occurs due to instability of the cathode-electrolyte interface; when DEC-based electrolytes are used, a huge impedance build-up is correlated with the formation of a spotty deposit on LNMO particles. In contrast, these deposits are absent in cathodes cycled in the DMC-based electrolyte. These deposits can be explained as originating from electrolyte reduction products forming on the anode side as shown previously for layered oxide||Li cells.[4] Overall, this study provides an insightful discussion on an "inverted crosstalk", i.e., from anode to cathode, degradation mechanism. Importantly, improved cycling stability with DMC compared to DEC is also found for LNMO||graphite cells.[1] V. Nilsson, S. Liu, C. Battaglia, R.-S. Kühnel, Electrochim. Acta 427, 2022, 140867.[2] S. Liu, M. Becker, Y. Huang-Joos, H. Lai, G. Homann, R. Grissa, K. Egorov, F. Fu, C. Battaglia, R.-S. Kühnel, Batteries Supercaps 6, 2023, e202300220.[3] A. Curcio, W. Dachraoui, Ø. Dahl, N. P. Wagner, C. Battaglia, R.-S. Kühnel, submitted.[4] H.-J. Peng, C. Villevieille, S. Trabesinger, H. Wolf, K. Leitner, P. Novák, J. Power Sources 335, 2016, 91-97. Figure 1

Evaluation of Electrochemical Techniques for Characterizing the Kinetics of Porous Insertion-Type Electrodes

The composite-type porous electrode remains the most commonly used electrode for a wide variety of electrochemical energy storage devices, including lithium-ion batteries, electrochemical capacitors, Li-S/Li-air batteries, and even all-solid-state batteries. This is due to its ease of manufacturability and ability to maintain a high electrochemical surface area with adequate ion and electron transport. Understanding porous electrode kinetics is essential to maximize the active material utilization at high rates. A variety of electrochemical techniques exist to characterize the kinetic behavior of porous electrodes. The most popular ones include electrochemical impedance spectroscopy, galvanostatic charge/discharge, galvanostatic intermittent titration technique, and cyclic voltammetry. The purpose of this work was to evaluate which of these four techniques provides the most relevant kinetic information for understanding porous electrode kinetics. To do this, we utilized porous electrodes composed of LiCoO2 as a model insertion-type material, and with varying thicknesses (10 - 40 µm). We find that GITT is an accessible and reliable method for obtaining the effective diffusion coefficient of the porous electrode. It yields consistent results, regardless of electrode thickness and component ratio. On the other hand, the cyclic voltammetry peak current behavior can be easily compromised, even at slow scan rates of 0.01 - 0.1 mV/s, and when using a high conducting domain ratio. When comparing the dQ/dV plot and cyclic voltammetry for observing peaks indicating phase transitions in the insertion material, the dQ/dV plot is less kinetically limited. We utilized the differential relaxation time (DRT) method to analyze electrochemical impedance spectroscopy data. Our findings demonstrate that as thickness increases, charge transfer resistance decreases, while diffusion resistance increases. This study provides a comprehensive evaluation on the differences between electrochemical methods to characterize the kinetics of composite porous electrodes.

Water Management in Anion Exchange Membrane Fuel Cells Via Distribution of Relaxation Times Analysis

In response to growing concerns regarding air pollution and energy scarcity, anion exchange membrane fuel cells (AEMFCs) have emerged as a promising technology for power generation due to their high efficiency, zero emissions, and the abundance of hydrogen resources. Within AEMFCs, water serves as a reactant, being consumed at the cathode, and generated at the anode, which can lead to an imbalance in the water content within the fuel cell. Maintaining an appropriate water distribution is essential for efficient charge transport and electrochemical reaction kinetics. Therefore, the development of a comprehensive water management strategy is crucial to ensure the stable operation and durability of AEMFCs.Characterizing the water condition of the anode and cathode in different operation conditions (normal, drying, or flooding) is a critical step that provides data foundation for devising effective water management control strategies. Electrochemical impedance spectroscopy (EIS) offers a means to investigate different resistances (kinetics, mass transport, and ohmic losses) over varying time scales during operation to assess water transport. The conventional interpretation of EIS data typically relies on equivalent circuit models, necessitating prior assumptions and initial component selections. In this study, we employed the distribution of relaxation times (DRT) methodology, known for its powerful ability to separate components, to conduct a more precise analysis of polarization processes. Initially, electrochemical impedance dynamics related to hydroxide transport, hydrogen oxidation reactions, and oxygen reduction reactions were effectively extracted. Subsequently, DRT was employed to investigate the loss and variation trends of each polarization process under conditions of anode and cathode water flooding and drying conditions, respectively. Furthermore, to gain a deeper understanding of the effects of water content on mass transport and electrochemical reaction kinetics, a three-dimensional multi-physics model for AEMFCs was developed. This model incorporated water phase transition, heat and mass transfer, liquid water and dissolver water transport, and electrochemical reactions. These endeavors represent a comprehensive and systematic approach to guide the utilization of the distribution of relaxation times in fuel cells.

Exploring the Barrier Property of High-Performance Polyurethane-Silica-Lignin Coatings on Carbon Steel

The use of lignin in anticorrosive coatings has provided efficient corrosion protection to metallic alloys.1,2 We have combined lignin-based polyurethane (PU) with silica to produce eco-friendly anticorrosive coatings with high anticorrosive performance, mechanical strength and bactericidal action.3 The PU was synthesized from methyl diphenyl diisocyanate (MDI), glycerol and lignin; the PU pre-polymer was functionalized with APTES to later form a hybrid with the polysilanol network from TEOS. The reference coating4 was prepared using 100% glycerol (PU-Si-0Lig) as the polyol source, while the other two specimens contain glycerol and 0.2 g (Pu-Si-0.2Lig) or 0.4 g (Pu-Si-0.4Lig) of lignin, keeping the predetermined proportion NCO:OH = 1.16. The incorporation of lignin into the polymer matrix led to an increase in the thermal stability (295 to 304 °C), smoothness of the coating surface (4.3 to 1.7 nm) and improvement of the coating mechanical properties, such as coating hardness (362 to 376 MPa). In addition, the lignin-containing coatings present bactericidal activity against Escherichia coli. The corrosion protection of the PU-Si-Lignin hybrid coatings deposited on carbon steel was analyzed by electrochemical impedance spectroscopy (EIS) in 3.5 wt.% NaCl at room temperature. The tests were conducted in a three-electrode cell, consisting of the coated carbon steel substrate as the working electrode, a platinum mesh as a counter electrode, and an Ag|AgCl|KClsat as the reference electrode. PU-Si-0Lig and PU-Si-0.2Lig present fair corrosion protection with low-frequency impedance |Zlf| values around 106 Ω.cm2 after immersion for up to 14 days. Conversely, PU-Si-0.4Lig presents |Zlf| ~ 1010 Ω.cm2, phase angle near -90 ° over seven frequency decades, and a lifespan of 100 days immersed in seawater. The equivalent electric circuit (EEC) used to fit the PU-Si-0.4Lig revealed that the EIS data of the initial 14 days can be represented by a model containing only one time constant. This means the coating has a highly capacitive behavior with insignificant water uptake in the first two weeks. Altogether, these results demonstrated the potential of PU-Si-Lig coating to be applied as an efficient anticorrosive barrier with enhanced mechanical and antibacterial properties.Acknowledgements: This work was supported by the Brazilian funding agencies CNPq, FAPESP, CAPES and PROPe UNESP.Keywords: Renewable feedstock, green coating, PU-silica, lignin, corrosion protection.(1) Harb, S. V.; Cerrutti, B. M.; Pulcinelli, S. H.; Santilli, C. V.; Hammer, P. Siloxane–PMMA Hybrid Anti-Corrosion Coatings Reinforced by Lignin. Surf Coat Technol 2015, 275, 9–16. https://doi.org/http://dx.doi.org/10.1016/j.surfcoat.2015.05.002.(2) Cao, Y.; Liu, Z.; Zheng, B.; Ou, R.; Fan, Q.; Li, L.; Guo, C.; Liu, T.; Wang, Q. Synthesis of Lignin-Based Polyols via Thiol-Ene Chemistry for High-Performance Polyurethane Anticorrosive Coating. Compos B Eng 2020, 200, 108295. https://doi.org/https://doi.org/10.1016/j.compositesb.2020.108295.(3) Alinejad, M.; Henry, C.; Nikafshar, S.; Gondaliya, A.; Bagheri, S.; Chen, N.; Singh, S. K.; Hodge, D. B.; Nejad, M. Lignin-Based Polyurethanes: Opportunities for Bio-Based Foams, Elastomers, Coatings and Adhesives. Polymers (Basel) 2019, 11 (7). https://doi.org/10.3390/polym11071202.(4) Braz, Á. G.; Pochapski, D. J.; Pulcinelli, S. H.; Santilli, C. V. Effects of Curing Temperature and Silica Content on the Structure and Properties of a Sol–Gel PU-Silica Hybrid Coating for Corrosion Protection. ACS Applied Engineering Materials 2023, 1 (7), 1739–1751. https://doi.org/10.1021/acsaenm.3c00148.

Physics-Based Impedance Model for Understanding Proton Transport in PEM Fuel Cells

The electrochemical impedance spectroscopy (EIS) diagnostics using H2/N2 operation condition developed for fuel cell is useful tools to estimate the catalyst layer (CL) proton resistance. Currently, the resistance is estimated by fitting the measured EIS data to an equivalent circuit model. However, it is challenging to correctly determine the transmission line circuit and accurately estimate the proton resistance [1]. Moreover, the capacitive and resistance elements in the transmission line model are assumed similar throughout the CL even if there is nonhomogeneous distribution of ionomer. To understand the EIS response in CL, we present a physics-based model. Utilizing the model, we are able to investigate the H2/N2 cell impedance response under various operating conditions. The continuum scale model will be combined with agglomerate scale model to provide a detail study of the electrode parameters like ionomer loading and ionomer coverage The hybrid theoretical model allows us to study the effect of nonhomogeneous CL and its corresponding EIS response. Acknowledgement This research is supported by U.S. Department of Energy (DOE) Hydrogen and Fuel Cell Technologies Office, through the Million Mile Fuel Cell Truck Consortium (M2FCT).

On-Board Diagnostics for Li-Ion Batteries Using Electrochemical Impedance Spectroscopy (EIS)-Based Health Estimation Models

There exists a growing need for standardized On-Board Diagnostics (OBD) for electric vehicles [1] to provide accurate health metrics and guarantees to both consumers and manufacturers.Previous work has shown the powerful LIB capacity-based State-of-Health (SoH) estimation capability of Electrochemical Impedance Spectroscopy (EIS) measurements and data-driven models [2] [3]. Since EIS measurements are dependent not only on SoH but also State-of-Charge (SoC) and temperature [4], it is important that the measurements are conducted after the cell reaches an equilibrium, and that these other variables are also tracked. Although EIS measurements are somewhat quicker than some traditional capacity-determination experimental methods, the time taken for such measurements is not insignificant. Therefore, building a pipeline to determin the EIS frequency measurements most important for SoH estimation is an important task in developing a suitable EIS-based OBD. By exploring a frequency range between 0.1 and 200 Hz, we study EIS measurements related to diffusion and charge transfer processes in LIB operation [5], with each process being partially distinguishable due to their varying timescales.In this work, using EIS data collected from 5Ah LIBs with an NMC-111 cathode and a graphite anode at various SoCs (0, 25, 50, 75 and 100%) and cell lifetime (0, 10, 20, 40 and 90 days) as input features, we develop sequential, data-driven health estimation models for LIBs. The 22 cells used for this analysis have been aged over a period of 90 days in two different ways: either through active cycling at different C-rates (0.2 and 1C) and temperatures (0, 25 and 40⁰C), or passive “calendar aging” where the cells are left without use at a specific temperature and SOC. Using feature attribution techniques (Shapley values, feature occlusion, etc.), we find the most influential of the frequency ranges in the EIS measurements that relate strongly with cell performance degradation. To develop a streamlined and efficient SoH estimation framework, we formulate an optimization problem to find the EIS experimental design in terms of frequency ranges that delivers maximum accuracy in SoH estimation at different points in the lifetime of the cell. These streamlined and optimized EIS experimental designs and SoH estimation models can be used directly in the development of rapid and efficient on-board diagnostic tools.

Integrating Electrochemical Analysis and Machine Learning for Enhanced Discovery of Conductive MOFs: A Database-Driven Approach

Metal-organic frameworks (MOFs) are highly versatile structures composed of metal ions linked by organic molecules. They offer a broad range of applications in electronics and light-based technologies due to their flexibility. However, the connection between metal ions and organic components often hinders efficient charge movement, which is essential for electrical conduction. To address this challenge, researchers have been innovatively designing MOFs to enhance their conductivity, which is typically low. This process is complex and time-consuming. To overcome these barriers, and to save both time and costs, we integrated machine learning (ML) with electrochemical testing. Our aim was to pinpoint ion and electron conductive MOFs from the Computation-Ready, Experimental Metal–Organic Framework (CoREMOF) Database, which contains over 14,000 MOFs. The ML approach predicted 84 intrinsically conductive MOFs, which were then subjected to rigorous experimental validation through synthesis and conductivity performance testing. Further experimental data confirmed that only two conductive MOFs, containing copper and pyrazole structures with carbonyl groups, exhibited both proton and electron conductivity. The differences between them lie solely in the preparation techniques and solvents used. Sample 1 was prepared using hydrothermal methods and alcohol as the solvent, and NH4@1 was prepared with an ammonium solution at room temperature. To understand deeper their intrinsic conductivity and semiconductor properties, we employed temperature-dependent conductivity and electrochemical measurements, including Cyclic Voltammetry (CV), Electrochemical Impedance Spectroscopy (EIS), Linear Sweep Voltammetry (I-V), and Mott-Schottky analysis. The EIS data showed a smaller semicircular arc for synthesized NH4@1, indicating its lower charge transfer resistance due to the presence of two different positive charge carriers on its surface. We investigated the effect of increasing positive charge carriers by raising the humidity up to 98% RH. EIS data showed that increasing positive charge has a significantly reduced charge transfer resistance of sample 1 compared to NH4@1. Because NH4@1 consists of ammonium and H3O+ simultaneously, causing saturation of positive charges and increasing charge transfer resistance. Mott-Schottky analysis further elucidated the electrical properties of compounds 1 and NH4@1, revealing their p-type behavior with negative slopes and flat band potentials of 0.61 V and 0.9 V, respectively. The CV analysis of pyrazole carboxylic acid in a 0.1 M KOH solution, using three reference electrodes, revealed reduction and oxidation peaks vs. Ag/AgCl. These peaks highlight the redox behavior of the linker, generating a stable carboxylate anion (-COO−) that facilitates charge hopping. This reversible redox activity is crucial for the charge transport mechanism within the MOFs. CV analyses confirmed the presence of different copper oxidation states in both compounds, with cathodic peaks indicating the reduction from Cu(II) to Cu(I), suggesting active charge transfer. Extensive electrochemical studies, encompassing both Mott-Schottky and EIS analyses, provided deeper insights into charge carriers and transfer resistance, areas not extensively explored in previous research on intrinsically conductive MOFs. Our results underscore the critical role of experimental validation of ML predictions in accelerating the discovery and characterization of conductive MOFs for potential applications in batteries.

Mechano-Electro-Chemical Impedance Spectroscopy (MeIS): Concept, Theory and Experiment

Electrochemical impedance spectroscopy (EIS) is a widely used non-invasive method for characterizing and diagnosing lithium-ion batteries (LIBs)1. The key to utilizing EIS lies in interpreting the measured impedance spectrum. This involves fitting the experimental data to an impedance model to understand the internal states of the battery. However, due to the multiscale and multiphysical nature of LIBs, impedance models can be complex and have many parameters. As a result, there is a high risk of over-fitting the experimental data, making the interpretation of the EIS data challenging2. In addition to electrical signals, the mechanical responses of a LIB, such as pressure and thickness change during charge/discharge, provides valuable information for characterization and diagnosis3-5. Most existing analyses of mechanical signals focus on the time domain. Recently, von Kessel et al6 introduced Mechanical Impedance Spectroscopy (MIS) as a frequency-analyzing tool for characterizing LIBs. The MIS spectrum (the displacement response subject to a cyclic pressure trigger) turned out to vary with the state of the batteries, demonstrating the great potential of using mechanical responses in the frequency domain to characterize and diagnosis the LIBs. Nevertheless, measuring displacement for LIBs in real-world applications is challenging, as it requires a customized testing machine to measure the micrometer-level displacement while exerting a sinusoidal pressure input. This requirement for customized testing equipment limits the application scope of the MIS method.A free LIB cell cyclic changes its dimensions during charge/discharge. Under confinements where dimension change constricted, cyclic pressure change will be generated, which could be used for frequency analysis. This observation led us to propose a new method called mechano-electro-chemical impedance spectroscopy (MeIS). An MeIS spectrum is defined as the ratio of pressure perturbation to the input current, denoted as . Measurements can be obtained by perturbating the battery with sinusoidal current and recording the resulting pressure or displacement response. The basic transfer function for MeIS is derived from the electro-chemo-mechanical coupling of the porous electrode. The MeIS consists of two parts, the MIS term and an electrochemical term resulting from the insertion/extraction deformation of the electroactive particles. The great advantage of MeIS is that it requires only a pressure or displacement sensor and a charger capable of providing sinusoidal current, making it potentially applicable in in-field scenarios such as management of EV batteries or real-time monitoring of a battery-based energy storage facility. Sensitivity analysis reveals that MeIS is highly sensitive to changes in the structure of porous electrodes, thus providing valuable insights into the internal structural integrity and degradation of LIBs. In addition, the experimental design and demonstrational results are also provided. We believe that MeIS could serve as a convenient and useful complement to EIS, enhancing the non-invasive diagnostic toolbox for LIBs.Reference: K. Mc Carthy, H. Gullapalli, K. M. Ryan, and T. Kennedy, Journal of The Electrochemical Society, 168 (8), (2021).F. Ciucci, Current Opinion in Electrochemistry, 13 132-139 (2019).J. Zhu, T. Wierzbicki, and W. Li, Journal of Power Sources, 378 153-168 (2018).B. Rieger, S. Schlueter, S. V. Erhard, J. Schmalz, G. Reinhart, and A. Jossen, Journal of Energy Storage, 6 213-221 (2016).Z. J. Schiffer, J. Cannarella, and C. B. Arnold, Journal of The Electrochemical Society, 163 (3), A427-A433 (2015).O. von Kessel, T. Deich, S. Hahn, F. Brauchle, D. Vrankovic, T. Soczka-Guth, and K. P. Birke, Journal of Power Sources, 508 (2021). Figure 1

How Microstructures, Oxide Layers, and Charge Transfer Reactions Influence Double Layer Capacitances. Part 2: Equivalent Circuit Models

ABSTRACTIn the first part of this study, double layer (DL) capacitances of plane and porous electrodes were related to electrochemical active surface areas based on electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) measurements. Here, these measured data are described with equivalent circuit models (ECMs), aiming to critically assess the ambiguity, reliability, and pitfalls of the parametrization of physicochemical mechanisms. For microstructures and porous electrodes, the resistive–capacitive contributions of DL in combination with resistively damped currents in pores are discussed to require the complexity of convoluted transmission line ECMs. With these ECMs, the frequency‐dependencies of the capacitances of porous electrodes are elucidated. Detailed EIS or CV data‐based reconstructions of complex microstructures are discussed as impossible due to the blending of individual structural features and the related loss of information. Microstructures in combination with charge transfer reactions and weakly conducting parts require parameter‐rich ECMs for an accurate physicochemical description of all physicochemical mechanisms contributing to the response. Nevertheless, the data of such a complex electrode in the form of an oxidized titanium electrode are fitted by an oversimplistic ECM, showing how easily unphysical parameterizations can be obtained with ECM‐based impedance analysis. In summary, trends in how microstructures, charge transfer resistances and oxide layers can influence EIS and CV data are shown, while awareness for the overinterpretation of ECM‐analysis is raised.

Circular Economy of Construction and Demolition Waste for Nanocomposite Cement: XRD, NMR, Vickers, Voltammetric and EIS Characterization.

In this paper, we present the structural, mechanical and electrical properties of composite cement materials that can be widely used as substituent for cement. We start with the characterization of a composite cement sample using an analysis of X-ray diffraction (XRD) and nuclear magnetic resonance (NMR) spectra. The measurements of the Vickers hardness, cyclic and sweep linear voltammetry and electrochemical impedance spectroscopy (EIS) of composite cement materials were also recorded. This study compared the effect of the different nanocomposites added to cement on the mitigation of the alkali-silica reaction, which is responsible for the swelling, cracking and deleterious behavior of the material. The enhancement in Vickers hardness was more pronounced for composite cement materials. In contrast, the values of Vickers hardness decreased for the composite cement containing mortar and the control sample, suggesting that the long-term performance of cement was compromised. In order to obtain information about the bulk resistance of the composite cement material, electrochemical impedance spectroscopy (EIS) data were employed. The results suggest that for composite cement materials, there is an improvement in bulk electrical resistance, which can be attributed to the lower amounts of cracks and swelling due to lower expansion. In the control sample, a reduction in the bulk resistance suggests the formation of microcracks, which cause the aging and degradation of the material. The intersection of arcs in the EIS spectrum of the mixed composite cement sample gradually increased by an alkaline exposure of up to 21 days and finally shifted towards a low value of high frequency with an increase in alkaline exposure of up to 28 days.

Bidens pilosa extract as a corrosion inhibitor on 1008 carbon steel in neutral medium

The aim of this work is to evaluate the performance of Bidens pilosa extract as a corrosion inhibitor for 1008 carbon steel in a neutral medium of 0.1 M NaCl. The research has been accomplished by weight loss measurements, linear polarization resistance (Rp) monitoring and electrochemical impedance spectroscopy (EIS). Phytochemical analysis and Fourier transform infrared spectroscopy were performed to determine bioactive components and detect the main functional groups of Bidens pilosa extract, respectively. The morpholo­gical characterization of the substrate was carried out by optical microscopy (OM). Different isotherms were evaluated to understand more clearly the adsorption mechanism of the inhibitor extract molecules on the surface of the 1008 carbon steel substrate, and the best fit was obtained for the Langmuir isotherm. The results showed that the corrosion rate decreased with an increase of the concentration of the inhibitor up to 1000 ppm, reaching a maximum efficiency value of 82.9 % from gravimetric tests, and 73.1 % from the fitting of EIS data to an equivalent electric circuit. The calculated thermodynamic parameters suggested the formation of a monolayer of inhibitor molecules on the metal surface. The ΔGoads value (-22.8 kJ mol-1) determined from the Langmuir isotherm model indicated that adsorption of the inhibitor molecules on the substrate surface follows a physisorption mechanism. This research revealed that Bidens pilosa extract can be used as a corrosion inhibitor and emerges as an alternative to replace synthetic corrosion inhibitors that are harmful to health and cause damage to the environment.

Manufacturing and corrosion characterization of Ti-10%Zr-2%Sn-8%Mo alloy and Ti-10%Zr-2%Sn-8%Ta alloy after various exposure intervals in chloride solution

Ti-10%Zr-2%Sn-8%Mo alloy and Ti-10%Zr-2%Sn-8%Ta alloy were manufactured using the technique of powder metallurgy. The corrosion of the two alloys was performed after 1 h, 24 h, and 48 h immersion in 3.5% NaCl solution using electrochemical impedance spectroscopy (EIS), cyclic polarization (CPP), and the measurement of current over time. EIS (Nyquist and Bode) data indicated that the Ti-10%Zr-2%Sn-8%Ta alloy has higher polarization resistance compared to the Ti-10%Zr-2%Sn-8%Mo alloy. CPP results revealed that the Ti-10%Zr-2%Sn-8%Ta alloy has higher corrosion resistance and lower corrosion currents. The experiments of measuring the change of current over time at 500 mV showed that both the Ti-10%Zr-2%Sn-8%Mo alloy and the Ti-10%Zr-2%Sn-8%Ta alloy do not show pitting corrosion, which confirms the CPP results. Extending the time to 24 h and 48 h increased the corrosion resistance for both alloys. The surface of the alloys after being corroded in NaCl solution were investigated using scanning electron microscopy and energy dispersive spectroscopy. All investigations confirmed that the Ti-10%Zr-2%Sn-8%Ta alloy is more passivated than the Ti-10%Zr-2%Sn-8%Mo alloy and the increase of exposure time to 48 h enhances the passivation of both alloys, which recommends that these alloys are best choice in the biomedical applications.

The timescale identification and quantification of complicated kinetic processes in lithium-ion batteries based on micro-reference electrodes

The lack of reliable methods for acquiring accurate and stable electrochemical impedance spectroscopy (EIS) data at both the cathode and anode makes overlapping kinetic processes in commercial lithium-ion batteries (LIBs) with enhanced capacity and size indistinguishable. Micro-reference electrodes offer an elegant solution for obtaining accurate and stable EIS data for both the cathode and anode. The proposed selection model ensures that the mean absolute percentage error between the EIS of the combined cathode and anode and the battery remains below 3.5 % under varying temperatures and states of charge (SOCs). Different SOCs, temperatures, and pressures are utilized on the timescale to identify and quantify the kinetic processes of the cathode and anode. The results indicate that under varying temperature conditions, the process of Li+ traversing the solid electrolyte interphase dominates the total impedance of the battery, contributing over 75 %. At −10 °C, polarization resistance from this process exceeds a 2500 % increase compared to 20 °C. Moreover, the variations in the kinetic processes of LIB cathodes and anodes with temperature are revealed on the timescale, with potential reasons for the overlap of kinetic processes provided. This study offers new insights into LIB electrode kinetics and lays a theoretical groundwork for enhancing battery design and performance.

Phase-controlled synthesis of starch-derived Mo2C–MoC/C heterostructure catalyst for electrocatalytic hydrogen evolution reaction

The electronic structure of molybdenum carbide closely resembles that of Pt, making it a promising candidate material for replacing noble metal catalysts. However, the primary challenge is the controlled and eco-friendly synthesis of molybdenum carbide. Herein, we developed a sustainable and controlled method for synthesizing a heterostructure catalyst for the electrocatalytic hydrogen evolution reaction (HER) using starch as the raw material. Through a hydrothermal redox reaction and subsequent temperature-programmed treatment under an inert atmosphere, the hexagonal Mo2C-cubic MoC heterostructure nanoparticles were dispersed on a carbon matrix. Our investigation into the synthesis process extensively covered the growth of MoO2 nanocrystals and hydrochar during the hydrothermal redox reaction, as well as the phase evolution during the subsequent temperature-programmed treatment. Electrochemical assessments demonstrated the catalyst's remarkable efficiency for the hydrogen evolution reaction across both acidic and alkaline media, achieving a current density of 10 mA/cm2 with an overpotential of 139.4 mV in 1 M KOH solution and 160.7 mV in 0.5 M H2SO4 solution. Combining a series of electrochemical characterizations and applying the distribution of relaxation times (DRT) analysis to operando electrochemical impedance spectroscopy (EIS) data, the superior kinetic performance of the Mo2C–MoC/C was demonstrated, outperforming that of single-phase molybdenum carbide. Furthermore, the catalyst has demonstrated remarkable stability even at high current densities in both acidic and alkaline electrolytes, underscoring its potential as a cathode catalyst in both alkaline water electrolyzers and proton exchange membrane (PEM) electrolyzers.

Halide solid-state electrolyte achieving high ionic conductivity by engineering nanocrystals

Lithium-ion battery (LIB) technologies utilize liquid electrolytes, which can cause safety issues due to electrolyte leakage, uncontrolled side reactions between the liquid electrolyte and electrode, dendrite formation, and flammability of the liquid components with air. These problems can be minimized using solid-state electrolytes (SSEs) containing the functionality of an electrolyte. Our research discovery meets the urgent requirement of developing rapid ionic conductive solid-state electrolytes for lithium metal battery applications, emphasizing safe operation and high energy density. The breakthrough lies in the functionalization and tunability of monoclinic doped Li3InCl6-based solid electrolytes to achieve desirable structural and high ionic conductivity (> 0.15 S cm−1). We report four formulations of solid-state electrolytes obtained using modified sol–gel synthesis and used to assemble symmetrical half cells for electrochemical impedance spectroscopic (EIS) analyses in the frequency ranging from 10–2 to 106 Hz under five different temperatures (15–55 °C). The EIS data of non-doped, F-, Ce-, and Mo-doped electrolytes showed R1 (solid-electrolyte) ranging from 0.05 to 0.10 Ohm and R2 (interfacial) resistance varying from 0.05 to 1.25 Ohm, resulting in superionic conductivity (0.15–0.45 S cm−1), equivalent to the commercially available liquid electrolyte and evidenced two magnitudes increase compared to the published data.Graphical abstract

Influence from Mechanical Stress on State of Health of Large Prismatic Lithium-Ion-Cells under Various Temperatures

The expansion of lithium-ion cells is an aging phenomenon that causes deformation of the cell’s external and internal geometry due to physicochemical reactions during aging and operation. This deformation leads to degradation effects such as capacity loss and increased internal resistance in the cell. In a cell module, expansion of the cells presents a challenge to the mechanical design due to resulting swelling forces. This work presents expansion measurements performed on large prismatic lithium-ion cells cycled at 1 C for up to 1000 cycles at different ambient temperatures and constant compression forces to evaluate the impact of mechanical stress on cell health. Intermediate tests were conducted every 50 cycles to determine cell capacity and perform electrochemical impedance spectroscopy measurements. Thickness measurements showed cell expansion during charging and contraction during discharging due to lithiation and de-lithiation. Additionally, an irreversible change in cell thickness occurred due to aging. Electrochemical impedance spectroscopy data were analyzed using distribution of relaxation time analysis to quantify the increase in internal resistance. The results suggest that compression force has a negligible impact on cells cycled at high temperature. However, at lower temperatures, higher compression force resulted in more rapid aging compared to lower compression force.