Electrochemical Impedance Spectroscopy Data Research Articles

ZnO doped PAMAM for asphalt improvement as anti-corrosive coatings.

Asphalt is widely used as a coating resin due to its excellent adhesion strength and cost-effectiveness; however, its limited corrosion protection necessitates enhancement. In this study, poly(amidoamine) (PAMAM), combined with zinc oxide (ZnO) nanoparticles, was incorporated into the asphalt matrix to improve its anticorrosive properties. Various ratios of PAMAM-ZnO nanocomposite (1, 2, 4, and 6% by weight) were added to the asphalt binder, with the materials characterized using XRD, ¹H-NMR, and SEM techniques. The 2% PAMAM-ZnO/asphalt ratio exhibited the most significant improvement, achieving a corrosion protection efficiency (η%) of 97.93%, as confirmed by Tafel analysis, and a charge transport resistance (RCT) of 75.91 Ω cm² according to electrochemical impedance spectroscopy (EIS) data. A combination of barrier formation and sacrificial protection drives the corrosion inhibition mechanism. The PAMAM-ZnO nanocomposite forms a highly uniform layer on the carbon steel surface, creating an effective physical barrier that prevents the penetration of corrosive agents, thereby minimizing defects like pinholes. This barrier effect is complemented by the sacrificial protection provided by the ZnO nanoparticles, which are more reactive than the underlying steel and preferentially interact with corrosive ions (e.g., chloride ions). This interaction leads to the formation of stable ZnO corrosion products, which enhance the barrier and reduce the likelihood of corrosion on the steel surface. Additionally, PAMAM facilitates the even distribution and strong adhesion of ZnO within the asphalt matrix, ensuring a durable protective layer. The synergic impact between the polymer barrier and sacrificial ZnO protection results in the exceptional corrosion resistance observed in the 2% PAMAM-ZnO/asphalt formulation, offering a promising approach for advanced anticorrosive coatings.

A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy

This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Extracting Quantitative Insights from electrochemical impedance spectra using Statistical Methods

Statistical analysis of electrochemical impedance spec- troscopy data provides a systematic way of detecting changes in electrochemical energy systems. Applying concepts of divergence measures directly on electrochemical impedance spectroscopy data, one can reliably detect and quantify statistically significant changes. The results is a set of high- lighted frequency bands where the measured impedance characteristics differ statistically significantly from a ref- erence curve. The approach is evaluated on a solid-oxide electrolyser cell operated under different conditions and proves to be sensitive to even the smallest changes. The complete numerical implementation and corresponding experimental data are available as supplementary material at https://portal.ijs.si/nextcloud/s/xTa2cmtfxXn2jSz

New method for graphing impedance values as an analytical tool suitable for electrochemical impedance spectroscopy

A new method for graphing electrical impedance values named a “differentiation-based Bode plot” is proposed. This method facilitates the analysis procedure and reduces the arbitrariness of the interpretation of electrochemical impedance spectroscopy (EIS) data. Among the various methods for graphing impedance values, Nyquist plots are commonly adopted (primarily in the field of EIS) because of the apparent correlation between their shape and the corresponding equivalent circuit. However, because Nyquist plots do not contain frequency information, Bode phase and magnitude plots are frequently utilized. Although such plots are suitable for determining the absolute magnitude and phase of the impedance, they are not always useful for selecting the structure of an equivalent circuit, which is a hypothetical model for electrochemical cells. In the proposed method, instead of the real (Z’) and imaginary (Z’’) parts, two functions of the impedance derivatives with respect to frequency, i.e., dZ’/d(log f) and dZ’’/d(log f), are plotted. As substitutions for the Bode phase and magnitude plots, arctan{dZ’’/d(log f) ÷ dZ’/d(log f)} and log[sqrt{(dZ’/d(log f))2 + (dZ’’/d(log f))2}] are plotted against log f. Unlike the classical Bode plot, the differentiation-based Bode plot is independent of the lateral shift in the Nyquist plot, which is advantageous for determining an equivalent circuit. To demonstrate this advantage, the impedance values of typical equivalent circuits used in electrochemistry are calculated and plotted to compare differentiation-based Bode plots with Nyquist and classical Bode plots.

Improved photocatalytic activity triggered by UV light, as well as electrochemical sensing characteristics of MgO nanoparticles

ABSTRACT The current study’s objective was to investigate the potential applications of synthesised magnesium oxide nanoparticles (MgO NPs) as green fuel through solution combustion with leaves of tulsi (holy basil). The end product was further calcined for 3 h at 800°C to produce a well-crystalline nanomaterial. Transmission electron microscopy (TEM) examinations indicated a typical crystallite size of approximately 52 nm, determined by applying Scherer’s formula. The results from the scanning electron microscope (SEM) revealed a morphology that is clumped, puffy, and porous. The energy band gap (Eg) values for MgO NPs were determined to be 4.31 eV using DRS data, verifying the semiconductor behaviour of the NPs. Degradation experiments were conducted on two dyes, Fast Blue (FB) and Fast Orange (FO), exposed to UV light for 90 minutes. Under UV light, the photocatalytic degradation of FO (80.35%) and FB (80.5%) reached their maximum. A graphite paste electrode was used for cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and sensor investigations. The cyclic CVs of the MgO NPs-modified graphite paste electrode in 0.1 M NaOH were scanned at 10 to 50 mV/s to determine the specific capacitance values of 250.3 Fg−1. Based on the acquired EIS data, the MgO NPs used as the electrode displayed pseudocapacitive capacitor characteristics. Further, the proposed sensor was employed to determine the Pb2+ and Li+ ions in water and showed sensitivity in the order of 0.005 and 0.006 A. Therefore, MgO NPs synthesised via green solution combustion may have excellent applications as photocatalysts and electrochemical sensors.

Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor

The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack.

Vertical Microfluidic Trapping System for Capturing and Simultaneous Electrochemical Detection of Cells.

Electrochemical impedance spectroscopy (EIS) is a non-invasive and label-free method widely used for characterizing cell cultures and monitoring their structure, behavior, proliferation and viability. Microfluidic systems are often used in combination with EIS methods utilizing small dimensions, controllable physicochemical microenvironments and offering rapid real-time measurements. In this work, an electrode array capable of conducting EIS measurements was integrated into a multichannel microfluidic chip which is able to trap individual cells or cell populations in specially designed channels comparable to the size of cells. An application-specific printed circuit board (PCB) was designed for the implementation of the impedance measurement in order to facilitate connection with the device used for taking EIS spectra and for selecting the channels to be measured. The PCB was designed in consideration of the optical screening of trapped cells in parallel with the EIS measurements which allows the comparison of EIS data with optical signals. With continuous EIS measurement, the filling of channels with cell suspension can be followed. Yeast cells were trapped in the microfluidic system and EIS spectra were recorded considering each individual channel, which allows differentiating between the number of trapped cells.

Na+ conductivity of (100−x)Na3PS4−xCa3N2 glass and glass−ceramics

AbstractThe ionic conductivity of Na3PS4 can be improved via the aliovalent substitution of P and S as well as multivalence cation substitution of Na. Although the enhanced ionic conductivities of tetragonal and cubic Na3PS4 have been theoretically and experimentally studied, the dynamics of sodium ions in these systems have been rarely reported. In this study, (100−x)Na3PS4−xCa3N2 (0 ≤ x ≤ 10) glass samples were synthesizing using high‐energy planetary ball‐milling method. These samples were subjected to heat treatment at 280°C for 1 h in an argon atmosphere to obtain glass–ceramic samples. The structures of these samples were characterized via Raman spectroscopy and X‐ray diffraction analysis. The lattice parameter and cell volume of the glass–ceramic samples strongly depended on the x value in (100−x)Na3PS4−xCa3N2 (0 ≤ x ≤ 10). Their ionic conductivities were determined via alternating current electrochemical impedance spectroscopy (EIS). 92Na3PS4−8Ca3N2 glass–ceramics exhibited an ionic conductivity of 8.14 × 10−4 Scm−1 at 25°C. Charge carrier concentration and hopping rate were extracted from the EIS data to determine the ionic conductivities of the samples. The activation energies of DC conductivity and mobile ion formation and migration were also derived from the EIS data to analyze the effect of Ca3N2 doping on the ionic conductivity of the synthesized samples.

Rectification of high-frequency artifacts in EIS data of three-electrode Li-ion cells

The use of a reference electrode and the analysis of Electrochemical Impedance Spectroscopy (EIS) data recorded in three-electrode configuration are beneficial for understanding the underlying electrochemical reaction mechanisms in Li-ion batteries. However, analysis of EIS data recorded both in two-electrode and especially three-electrode configurations is prone to misinterpretation due to presence of unidentified artifacts. Using the commercially available instrument (Biologic VMP3), EIS data is recorded simultaneously for both working and counter electrodes in three-electrode five-wire configuration and are algebraically summed to obtain the data for two-electrode configuration. This data is then compared to that recorded in two-electrode configuration. This arrangement of instrument helps in identifying the otherwise hidden and anomalous features, such as a high-frequency arc and extended solid electrolyte interphase (SEI) region, in the three-electrode configuration and the reason for their absence in the two-electrode configuration. Moreover, these additional features are found State-of-Charge (SoC) i.e., cell-electrochemistry dependent and can be highly influenced by the resistance of current/potential measuring leads. Additionally, a new methodology is established to rectify the EIS data and obtain ‘artifact-free’ electrochemical data.

Advanced characterization of alkaline water electrolysis through electrochemical impedance spectroscopy and polarization curves

Improved electrolyzer components are needed to make alkaline water electrolyzers more flexible and durable. The performance of these new components can be assessed through in situ electrochemical characterization in the form of polarization curves and electrochemical impedance spectroscopy (EIS). Presently, EIS is still mostly used for the IR-correction of the polarization curve, but more valuable information can be extracted. In this work we show how EIS data can be used to determine the dependence of ohmic resistance on current density, to derive anodic and cathodic Tafel slopes and exchange current densities from fitted charge transfer resistances and to derive anodic and cathodic capacitances from fitted constant phase elements. We do this for both a two electrode alkaline electrolysis flow cell setup as well as for a three electrode beaker type setup with 2D nickel electrodes. The presented tools can be used in performance studies of new and existing electrodes and membranes in alkaline water electrolysis.

Spark plasma sintering of Ti2AlC/TiC MAX-phase based composite ceramic materials and study of their electrochemical characteristics

A novel method for obtaining the MAX phase Ti2AlC from TiC, Al4C3, and Ti precursors has been demonstrated, involving activation of the mixture in a high-energy ball mill (HEBM) followed by Spark Plasma Sintering (SPS). The phase composition, mechanical characteristics, surface microstructure, and electrochemical behavior in neutral media (0.1 M Na2SO4) of heterogeneous composite ceramic materials TiC/Ti2AlC were studied as a function of sintering temperature (1200–1400 °C). SPS at 1200 °C yields materials with a relative density of 94.42 % and Ti2AlC mass content up to 57 %. These composite materials exhibit capacitive behavior according to cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) data, with a capacitance of 73 mF/g, suggesting their potential application as lead-free ceramic capacitors. Further increase in sintering temperature (1300–1400 °C) leads to increased electrical resistance and enhanced sample homogeneity.

Bayesian impedance deconvolution using timescale distribution for lithium-ion battery state estimation

The distribution of relaxation times (DRT) and distribution of diffusion times (DDT) are widely recognized as effective model-free methods for deconvolving the internal properties of complex electrochemical systems using electrochemical impedance spectroscopy (EIS) data. This study proposes an integrated framework that employs a Bayesian approach to accurately estimate both DRT and DDT and machine learning techniques to enhance capacity estimation, incorporating Gaussian process regression and transformer networks. These methods utilize the inferred DRT and DDT as inputs to predict the discharge capacity. In addition, we perform a peak analysis on the estimated DRT and DDT to extract additional physically meaningful features, which have also proven to be effective inputs for capacity prediction. The applicability of the proposed framework to the EIS experimental data of lithium-ion cells is demonstrated and compared with existing capacity estimation methods. The proposed framework demonstrates a mean absolute error in capacity prediction below 3.03% when using DRT and DDT directly and 2.71% when using extracted features, outperforming existing methods by approximately one to three percentage points. Our Bayesian approach, combined with peak analysis and the integration of machine learning techniques, enables a more robust diagnosis of the internal state of lithium-ion batteries through EIS measurements.

Dissociative electrochemical analysis of materials degradation of an anion exchange membrane electrolyser

For anion exchange membrane (AEM) electrolysers, degradation studies provide valuable insights into how individual components degrade. Often, this presents a formidable challenge as each component significantly contributes to overall degradation. Here, we propose a methodology to individually compare the degradation of key components in AEM electrolysers: hydrogen evolution reaction (HER) catalyst, oxygen evolution reaction (OER) catalyst, and the AEM itself. First, to measure the degradation of catalyst-coated substrates (CCS) commonly used in AEM electrolysers, the AEM was replaced with a diaphragm (Zirfon Agfa UTP 500) permeable to OH− ions and stable in alkaline media. Subsequently, a cell with a stable catalytic layer (Nafion-coated Ni-felt substrates) was assembled to focus on membrane degradation with an AEM Sustainion X37-50 Grade 60. Chronopotentiometry and electrochemical impedance spectroscopy (EIS) revealed that the cell with the AEM experienced a threefold increase in ohmic resistance, attributed to the loss of functional groups. Additionally, an ion exchange capacity (IEC) analysis indicated that Sustainion lost approximately 20% of its ion exchange capacity. In contrast, the cell composed of CCS and Zirfon showed lower degradation. EIS data fitting demonstrated increased kinetic-related resistances while the ohmic resistance remained unchanged. This work presents a practical approach for comparing component degradation in AEM electrolysers.

Effect of graphene on mechanical and anti-corrosion properties of TiO2-SiO2 sol-gel coating

PurposeThis paper aims to explore how graphene can improve the mechanical and anti-corrosion properties of TiO2-SiO2 sol-gel coating. This sol-gel coating has been prepared on aluminum alloy substrate using graphene as both nano-filler and corrosion inhibitor.Design/methodology/approachTo examine the effect of graphene on mechanical properties of sol-gel coating, the abrasion resistance, adhesion strength and scratch resistance of coating have been evaluated. To reveal the effect of graphene on the anti-corrosion property of coating for aluminum alloy, the electrochemical impedance spectroscopy (EIS) has been conducted in 3.5 Wt.% NaCl medium.FindingsScanning electron microscopy images indicate that graphene nanoplatelets (GNPs) have been homogeneously dispersed into the sol-gel coating matrices (at the contents from 0.1 to 0.5 Wt.%). Mechanical tests of coatings indicate that the graphene content of 0.5 Wt.% provides highest values of adhesion strength (1.48 MPa), scratch resistance (850 N) and abrasion strength (812 L./mil.) for the sol-gel coating. The EIS data show that the higher content of GNPs improve both R1 (coating) and R2 (coating/Al interface) resistances. In addition to enhancing the coating barrier performance (graphene acts as nanofiller/nano-reinforcer for coating matrix), other mechanism can be at work to account for the role of the graphene inhibitor in improving the anticorrosive performance at the coating/Al interface.Originality/valueApplication of graphene-based sol-gel coating for protection of aluminum and its alloy is very promising.

Exploiting the Electrochemical Impedance Spectroscopy Frequency Profiles for State-of-Health Predication of Lithium-Ion Battery

Battery Management Systems (BMS) are essential for optimizing battery performance and extending lifespan through continuous monitoring and decision-making via control sensors. The State of Health (SOH) is one of the BMS metrics that provides valuable information on battery health and degradation. However, one of the main challenges in the BMS domain development is finding accurate and effective algorithms for battery SOH prediction, especially for electric vehicles and grid-connected energy storage systems. This study introduces a new SOH prediction method using wavelet-convolutional neural regression networks (CNRN) algorithms. The methodology involves extracting detailed frequency profiles from Electrochemical Impedance Spectroscopy (EIS) data, which are processed through wavelet transformation to capture both time and frequency domain features. These transformed profiles are then input into the CNRN model for SOH prediction. The results demonstrate improved SOH prediction accuracy with EIS frequency profiles, evidenced by a reduction in root mean square error (RMSE) compared to the standard EIS profile. This improvement is due to the fact that the wavelet-CNRN algorithm efficiently captures both the time and frequency features of the battery impedance. Moreover, the performance of the proposed algorithm demonstrated robustness in early end-of-life (EOL) prediction, thereby enhancing the reliability and safety of BMS functions.

Probabilistic deconvolution of the distribution of relaxation times from multiple electrochemical impedance spectra

Electrochemical impedance spectroscopy (EIS) is widely used to study the properties of electrochemical materials and systems. However, analyzing EIS data remains challenging. Among various analysis methods, the distribution of relaxation times (DRT) has emerged as a novel non-parametric approach capable of providing timescale information. Among the various DRT inversion methods, those based on Gaussian processes (GP) are particularly promising because they provide uncertainty estimates for both EIS and DRT. However, current GP-based DRT implementations can only handle one spectrum at a time. This work extends these models to allow concurrent analysis of multiple impedance spectra as a function of experimental conditions. The new method, called the quasi-Gaussian process distribution of relaxation times, treats the DRT as a GP with respect to the experimental state and as a finite approximation of a positively constrained GP with respect to timescales. This new DRT inversion approach is validated against noise-corrupted artificial EIS data and applied to experimental data, allowing us to expedite EIS data analysis of multiple EIS data from a probabilistic perspective.

High Areal Capacitance and Rate Capability of 3D-Printed Thick Electrodes with Optimized Conductive Networks from the Core-Sheath Structure.

Material extrusion 3D printing has received enormous attention to potentially overcome its limits by tailoring and designing thick electrodes. In this work, we prepared a thick reduced graphene oxide/carbon nanotube-reduced graphene oxide/carbon nanotubes/manganese oxide@carbon nanotubes (rGC-rGCMC) electrode with controlled lattice architectures, core-sheath structure, and hierarchical porosity by material coaxial extrusion 3D printing, freeze-drying, and thermal treatment. The volume ratios of core to sheath, including 100%-0%, 0%-100%, 20%-80%, 30%-70%, 40%-60%, and 50%-50%, were designed to investigate the influences of the core-sheath structure on thick electrodes. The electrodes with a core-sheath volume ratio of 30%-70% electrodes exhibited an enhanced areal specific capacitance of 588.27 mF cm-2 (39.48 F g-1) at a scan rate of 0.5 mA cm-2. All capacitance decays from core-sheath electrodes (20%-80%, 30%-70%, 40%-60%, and 50%-50%) were smaller than those from rGCMC (0%-100%) electrodes, indicating the improved rate capability from the core-sheath structure. On comparison of 30%-70% core-sheath electrodes with electrodes made of a homogeneous 30% rGC and 70% rGCMC mixture (30%+70%), lower capacitance (382.27 mF cm-2 and 25.66 F g-1 at 0.5 mA cm-2) of the 30%+70% mixture electrode without a core-sheath structure suggested less efficiency to harvest electrons from the redox reactions. Electrochemical impedance spectroscopy (EIS) data further supported and explained the resistances of thick electrodes with different volume ratios.

Degradation analysis of plasma sprayed metal-supported solid oxide fuel cell: The evolution of electrode processes under polarization

Metal-supported solid oxide fuel cells (MS-SOFCs) prepared by plasma spraying exhibit promising potential for scale-up applications, where the key lies in breaking through the challenges of limited lifetime and durability. Although electrochemical impedance spectroscopy (EIS) has been widely used to unfold the complex electrode processes in electrochemical reactions, there are still some concerns concerning the in-depth understanding and scientific evaluation of the degradation mechanism of cell long-term performance. In this study, the contribution of each resistance to the overall performance degradation is separately determined by coupled equivalent circuit model (ECM) based on the deconvolution of recorded EIS data at different DC biases. It is found that the deterioration of cathode surface oxygen exchange and O2− conduction causes more than 64 % of the total resistance increase, followed by the anode charge transfer reaction and gas-phase transport (24.91 %), and then the ohmic resistance (9.73 %). However, the correlation between the output voltage drop of MS-SOFC under load and the gas-phase diffusion resistance gradually increases, indicating a shift in the cell degradation mechanism. Combined with the post-test characterization of the aged cell, it can be inferred that the degradation is mainly attributed to the densification of the anodic microstructure and the cathodic elemental segregation. Additionally, attention should be paid to the effects of the thickening of the metal substrate oxide layer and thermal mismatch on the structural stability of MS-SOFC.

Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy

The State of Health (SOH) of lithium-ion batteries is crucial for maintaining their safety and reliability. Electrochemical impedance spectroscopy (EIS) provides extensive information about the battery state, but the data across high-, medium-, and low-frequency ranges are not independent. Practical challenges in obtaining full-frequency EIS data include long measurement times, low accuracy due to noise, and high costs. This paper proposes an SOH estimation method based on offline EIS and domain-adversarial neural network (DaNN) transfer. Initially, we use a feature search strategy on experimental EIS data to identify the optimal impedance feature subset through the root mean square error (RMSE) and comprehensive indicators. These features are then input into the DaNN for SOH estimation using transfer algorithms. Finally, based on feature correlation analysis, weights are allocated using a Gaussian process regression model for a secondary prediction, optimizing the SOH results. Experiments on laboratory calendar aging and publicly available cycling aging datasets demonstrate the method's effectiveness, achieving an RMSE of <1 %. The proposed method significantly improves SOH estimation accuracy and efficiency, addressing the limitations of traditional methods and reducing dependency on extensive measurement data. This advancement enhances the safety, reliability, and cost-effectiveness of battery applications.

Fundamental Physical Parameter Fitting via Electrochemical Impedance Spectroscopy

Electrochemical impedance spectroscopy (EIS) is a non-destructive technique that plays a crucial role in the investigations of batteries. EIS enables to analyze essential information regarding what is happening in a battery. The most important information that can be obtained from an EIS data is the effects of kinetic and diffusion related events. Having an idea about these physical events enables doing battery diagnostics and development. Therefore, several methods of EIS data analysis had been developed in order to clearly understand what is underlying an EIS spectra.One of the developed methods for EIS data analysis is using equivalent circuits. The very first example was done by Randles in 1947[1]. In his paper, he was able to separately obtain kinetic and diffusion-related parameters with the equivalent circuit method. However, he focused on just one reaction in a single interface using a three-electrode setup. Therefore, by lowering the complexity of the system, he was able to separately identify kinetic and diffusion related events. However, the systems that are currently investigated are much more complicated. Therefore, using the equivalent circuit method is plagued by ambiguity and multivalued fits.On the other hand, in 2018, Murbach and Schwartz published on a new way of analysis[2]. They have simulated 38800 impedance spectra with variety of physical constants and tried to match the experimentally provided impedance spectra. However, this method suffers from the above-mentioned ambiguity. That is, several impedance spectra calculated with very different physical constants fitted the experimental data equally well. Therefore, reducing or eliminating this ambiguity of EIS data is essential.Temperature-dependent EIS is one of the ways to reduce this ambiguity. In our group, we have previously shown that even with equivalent circuits, it is possible to distinguish diffusion and kinetic-related events with temperature-dependent EIS[3]. Kinetic events such as lithium-ion transfer has a strong dependence of temperature. Therefore, we were able to separate the kinetic and diffusion events by varying the temperature and looking the Arrhenius plots.In addition to the temperature dependence, non-linear impedance can further reduce the complexity of the EIS data. Again, in our group, we have shown that higher harmonics contain valuable information about redox reactions happening in the system[4]. Also, non-linear impedance response, especially the second harmonic, provides crucial information regarding the diffusion parameters at the low frequency region which is also showed by Murbach and Schwartz [5]. Therefore, we will show that using temperature-dependent linear and non-linear EIS can unlock the secrets of an EIS spectrum.In the current study, we will report temperature-dependent linear and non-linear simulations on an Li-ion battery with the NMC/Graphite chemistry. Here, we will show that temperature-dependent impedance measurements will enhance the discernibility of various kinetic parameters. Also, non-linear impedance response will improve the discernibility of diffusion related events. Combination of temperature dependence and non-linear response is collected in an objective function. The derived objective function is employed for the analysis of variations between experimental and simulated data, considering weights assigned to each parameter during the calculation process.By monitoring the value of this difference as a function of various fundamental parameters, we will show that meticulously adjusted weights can further improve the objective function to converge proper values. Ultimately, we will show that it is possible to correctly separate and identify the physical constants related to kinetic and diffusion events with temperature-dependent linear and non-linear EIS in an unequivocal manner.