Distribution Of Relaxation Times Research Articles

Uncovering the Possibilities of Ceramic Ba(1−x)CoxTiO3 Nanocrystals: Heightened Electrical and Dielectric Attributes

The hydrothermal synthesis of Ba1−xCoxTiO3 (BCT) ceramic nanocrystals across varied substitution fractions (x = 0, …, 1) is the subject of this study. Hydrothermal synthesis is well known for producing high-purity and well-crystallized nanocrystals. A thorough examination is conducted to examine the effects on the structural and electrical properties of the resultant BCT nanocrystals by altering the cobalt substitution fraction. X-ray diffraction (XRD) is used to analyze the structure, while complex impedance spectroscopy (CIS) is used to analyze the electrical properties. As the cobalt content rises, XRD examination reveals a smooth transition from the ferroelectric BaTiO3 phase to the ferromagnetic CoTiO3 phase, offering extensive insights into the phase composition and crystallographic alterations. This phase shift is important because it creates new opportunities to adjust the properties of the material for particular uses. The electrical activity of BCT nanocrystals is clarified further by CIS measurements. A distribution of relaxation times, frequently linked to complex microstructures or heterogeneous materials, is suggested by the detected non-Debye relaxation. A thermally activated conduction process, in which higher temperatures promote the passage of charge carriers, is suggested by the temperature-dependent increase in conductivity. This behavior is strongly dependent on the cobalt content, suggesting that cobalt enhances electrical conductivity and crystallinity through a catalytic effect. A frequency-dependent dielectric constant that rises with temperature and cobalt content is shown by investigating the dielectric characteristics of BCT nanocrystals. Improved polarization mechanisms inside the material are suggested by this increase in dielectric constant, which may be the result of cobalt ion presence. With a thorough grasp of the dielectric behavior, the examination of the loss angle further validates the non-Debye relaxation process.

Indirect prediction of the 3D printability of polysaccharide gels using multiple machine learning (ML) models

In this paper, the capabilities of NIR spectroscopy and LF-NMR data were compared for rapidly predicting the rheological properties of polysaccharide gels and assessing their printability. Seven machine learning (ML) models were established for rheological property prediction based on partial least squares regression (PLSR), support vector regression (SVR), back propagation artificial neural network (BPANN), one-dimensional convolutional neural network (1D CNN), recurrent neural network (RNN), long short-term memory (LSTM), and Transformer. The results showed that among the seven models, the SVR, BPANN, and 1D CNN models based on NIR spectroscopy effectively predicted the rheological parameters of polysaccharide gels, with the highest R2 in the prediction set reaching 0.9796 and the highest RPD reaching 7.0708. For most polysaccharide gels, using the LF-NMR relaxation time distribution curves provided better predictions of rheological properties than using transverse relaxation time and peak area. Among the seven models, the PLSR, SVR, 1D CNN, and Transformer models effectively predicted the rheological characteristics based on LF-NMR parameters, with the highest R2 in the prediction set reaching 0.9869 and the highest RPD reaching 8.7220. This study successfully established a prediction system for the rheological behaviors and 3D printing performance of polysaccharide gels using NIR spectroscopy and LF-NMR data combined with ML methods, achieving an intelligent assessment of the 3D printing behavior of polysaccharide gels.

Analysis, identification, and application of distribution of relaxation times in CMP slurry

This study delves into the mechanistic roles of oxidants, complexing agents, and inhibitors in slurries through of Distribution of Relaxation Times (DRT) analysis. The influence of varying concentrations of these chemicals on electrochemical processes is discussed. This article divides the spectrum obtained through DRT analysis into four different frequency regions and differentiate naming through electrode reaction process: the double-layer charging step, the charge transfer step, the mass transfer step, and the surface transformation step by adsorption or chemical alterations at the electrode surface. The results reveal that the peak observed in the low-frequency range at 10−2–10° Hz is associated with mass transfer step, while the relaxation peak at 10°–102 Hz corresponds to the charge transfer process, while the relaxation peak occurring at 102–104 Hz is linked to the surface transformation step, and the first peak emerging in the 104–106 Hz range is attributed to double-layer charging step.

Distribution of relaxation times-based analysis of aging mechanisms and prediction of heating domain for alternating current pulse self-heating lithium-ion batteries

Lithium-ion batteries (LIBs)-based electric vehicles (EVs) often encounter challenges such as the reduction in endurance mileage and the increase in charging time at low temperatures. To address these issues, the alternating current (AC) pulse self-heating in LIBs emerges as a pivotal method to overcome performance limitations of EVs at low temperatures. However, employing AC pulsing with high amplitude and low frequency can potentially lead to battery degradation. In this study, the state transitions and aging causes of LIBs during the self-heating are meticulously examined using the distribution of relaxation times (DRT) method. The results reveal that at an AC frequency of 50 Hz or higher, even with amplitudes up to 12 C, there is no degradation for the battery used in this study. For aged LIBs, the significant rise in impedance of solid electrolyte interphase (SEI) due to lithium plating, is the main factor driving battery aging during AC self-heating. Subsequently, an electrochemical-thermal coupling (ETC) model grounded in the degradation mechanism has been developed and experimentally validated to accurately predict the optimal heating domain for self-heating. This approach holds significant implications for the rapid selection of self-heating parameters for facilitating efficient battery performance enhancement.

Bimetallic FeCu-MOF derivatives as heterogeneous catalysts with enhanced stability for electro-Fenton degradation of lisinopril

A bimetallic FeCu/NC core-shell catalyst, consisting in nanoparticles where zero-valent Fe and Cu atoms, slightly oxidized on their surface, are encapsulated by carbon has been successfully prepared by modifying the synthesis route of MIL(Fe)-88B. FeCu/NC possessed well-balanced textural and electrochemical properties. According to voltammetric responses, in-situ Fe(III) reduction to Fe(II) by low-valent Cu was feasible, whereas the high double-layer capacitance confirmed the presence of a great number of electroactive sites that was essential for continuous H2O2 activation to •OH via Fenton's reaction. Electrochemical impedance and distribution of relaxation times (DRT) analysis informed about the strong leaching resistance of FeCu/NC. To validate the promising features of this catalyst, the advanced oxidation of the antihypertensive lisinopril (LSN) was investigated for the first time. The heterogeneous electro-Fenton (HEF) treatment of 16.1 mg L−1 LSN solutions was carried out in a DSA/air-diffusion cell. At pH 3, complete degradation was achieved within 6 min using only 0.05 g L−1 FeCu/NC; at near-neutral pH, 100 % removal was also feasible even in actual urban wastewater, requiring 60–75 min. The FeCu/NC catalyst demonstrated high stability, still maintaining 86.5 % of degradation efficiency after 5 cycles and undergoing low iron leaching. It outperformed the monometallic (Fe/NC and Cu/NC) catalysts, which is explained by the Cu(0)/Cu(I)-catalyzed Fe(II) regeneration mechanism that maintains the Fenton's cycle. LC-MS/MS analysis allowed the identification of two main primary LSN by-products. It can then be concluded that the FeCu/NC-based HEF process merits to be further scaled up for wastewater treatment.

In Situ Analysis of Binder Degradation during Catalyst-Accelerated Stress Test of Polymer Electrolyte Membrane Fuel Cells.

High-oxygen-permeability ionomers (HOPIs) are being actively developed to enhance the performance and durability of high-power polymer electrolyte membrane fuel cells (PEMFCs). While methods for evaluating binder performance are well-established, techniques for assessing binder durability and measuring its degradation in situ during the AST process remain limited. This study examines the distribution of relaxation times (DRT) and Warburg-like response (WLR) methods as in situ analysis techniques during the catalyst-accelerated stress test (AST) process. We conducted catalyst-ASTs (0.6-0.95 V cycling) for 20,000 cycles, monitoring changes using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV). Contrary to expectations, during the catalyst-AST, the ion transport resistance of the binder decreased, indicating no binder degradation. Scanning electron microscopy/energy dispersive spectrometer (SEM/EDS) analysis revealed that the degradation rate of the catalyst and the support was relatively higher than that of the binder, leading to a reduction in catalyst layer thickness and improved binder network formation. By applying the DRT method during the catalyst-AST process, we were able to measure the increase in oxygen reduction reaction (ORR) resistance and the decrease in proton transport resistance in situ. This allowed for the real-time detection of the reduction in catalyst layer thickness and improvements in ionomer networks due to catalyst and support degradation. These findings provide new insights into the complex interplay between catalyst degradation and binder performance, contributing to the development of more durable PEMFC components.

Identification of Degradation Reasons for a CO2 MEA Electrolyzer Using the Distribution of Relaxation Times Analysis.

The application of membrane electrode assembly (MEA) in electrocatalytic CO2 reduction (ECO2R) technology is an essential step toward industrialization. Nevertheless, the issue of ECO2R failure in MEA during extended operation hinders its industrial application. In this work, we employed in situ, nondestructive electrochemical impedance spectroscopy (EIS) techniques combined with distribution of relaxation times (DRT) methodology to diagnose the causes of the failure. By systematically investigating the variations in polarization resistance throughout the degradation process and utilizing a controlled variable approach to identify the origin of polarization, we diagnosed the degeneration of ionomers as the primary cause of the performance degradation of ECO2R in this instance. We further confirmed the reliability of our findings through material characterization and respraying ionomers onto the catalyst surface. This research provides an effective diagnostic method for the failure analysis of ECO2R performance in MEA, which is crucial for advancing industrialization of ECO2R technology.

Direct computations of viscoelastic moduli of biomolecular condensates.

Biomolecular condensates are viscoelastic materials defined by time-dependent, sequence-specific complex shear moduli. Here, we show that viscoelastic moduli can be computed directly using a generalization of the Rouse model that leverages information regarding intra- and inter-chain contacts, which we extract from equilibrium configurations of lattice-based Metropolis Monte Carlo (MMC) simulations of phase separation. The key ingredient of the generalized Rouse model is a graph Laplacian that we compute from equilibrium MMC simulations. We compute two flavors of graph Laplacians, one based on a single-chain graph that accounts only for intra-chain contacts, and the other referred to as a collective graph that accounts for inter-chain interactions. Calculations based on the single-chain graph systematically overestimate the storage and loss moduli, whereas calculations based on the collective graph reproduce the measured moduli with greater fidelity. However, in the long time, low-frequency domain, a mixture of the two graphs proves to be most accurate. In line with the theory of Rouse and contrary to recent assertions, we find that a continuous distribution of relaxation times exists in condensates. The single crossover frequency between dominantly elastic vs dominantly viscous behaviors does not imply a single relaxation time. Instead, it is influenced by the totality of the relaxation modes. Hence, our analysis affirms that viscoelastic fluid-like condensates are best described as generalized Maxwell fluids. Finally, we show that the complex shear moduli can be used to solve an inverse problem to obtain the relaxation time spectra that underlie the dynamics within condensates. This is of practical importance given advancements in passive and active microrheology measurements of condensate viscoelasticity.

Synergistic effect of holey graphene and CoSe2-NiSe2 heterostructure to enhance fast Na-ion transport

Inspired by the “Barrel principle” and based on the working principle of the “rocking chair”. We propose two shortboards that “bulk diffusion rate” and “ion reaction rate”, which affect the fast charging of sodium ion batteries (SIBs). The holey reduced graphene oxide (HrGO) with abundant nanopores provides longitudinal diffusion channels, enabling rapid Na+ diffusion along this direction, which solves the shortboard of bulk diffusion rate. Additionally, heterojunctions are considered a viable approach for enhancing fast Na+ storage performance by providing more storage sites and accelerating the kinetics of Na+ reaction, which solve the shortboard of ion reaction rate. Herein, we construct HrGO combined with CoSe2-NiSe2 heterojunctions has been rationally fabricated to synergistically enhance the ionic and electronic diffusion kinetics. As expected, the CoSe2-NiSe2/HrGO anode in SIBs shows an excellent fast-charging performance (392.4mAh/g at 15 A/g), surpassing most Co/Ni-based selenide anodes. Additionally, the assembled CoSe2-NiSe2/HrGO||NVP full cell delivers a high specific capacity of 116.1 and 95.4mAh/g at 0.1 and 20C, respectively, coupled with 97.8 % capacity retention rate for 100 cycles. Furthermore, we elucidate the mechanism of Na+ storage and fast transport through electrochemical kinetic analysis, in situ and ex situ characterizations, density functional theory (DFT) calculations, and distribution of relaxation times (DRT). Importantly, this study provides theoretical insights into accelerating rapid Na+ transfer.

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.

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.

Effect of In and Sc Co-doping on electrochemical performance on bulk and grain boundaries of La1.9In0.05Sc0.05Ce2O7 proton conductors

In this paper, fluorite-type proton conductors La1.9In0.1Ce2O7, La1.9In0.05Sc0.05Ce2O7 and La1.9Sc0.1Ce2O7 (referred to as LCO-I, LCO-IS and LCO-S), were synthesized by the low-cost solid-state reaction method. The formation process and structure of single-doped LCO-I and LCO-S and co-doped LCO-IS were studied using X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). The electrochemical performance was studied using alternating current electrochemical impedance spectroscopy (EIS). XRD results showed that the synthesized material had a fluorite structure, without any impurity phase. SEM micrographs showed that LCO-I, LCO-S and LCO-IS had a fairly dense grain. EDS analysis confirmed that the elemental composition of LCO-I, LCO-S and LCO-IS was evenly distributed and contained no impurities. LCO-IS exhibited superior conductivity among related materials, and the total conductivity of LCO-IS in oxygen was 9.49 × 10−3 S cm−1 at 800 °C. The effects of co-doping on bulk and grain boundary properties were analyzed using the distribution of relaxation time (DRT). The bulk and grain boundary conductivity of co-doped LCO-IS was higher than that of single-doped LCO-I and LCO-S. LCO-IS showed good proton conductivity. In the temperature range of 300–800 °C, the proton conductivity was 1.35 × 10−6–4.43 × 10−3 S cm−1. For LCO-I and LCO-IS electrolyte supported symmetric cells (Ag ǀ LCO-I (LCO-IS) ǀ Ag), their open circuit voltages were 0.99 V and 0.97 V, respectively, at 700 °C, and the power densities were 8.8 mW cm−2 and 9.34 mW cm−2. This demonstrates the potential of LCO-IS to be used in various devices as a proton-conducting electrolyte.

Surface to bulk design empowering Ni-rich layered oxide cathode in sulfide-based All-Solid-State batteries

All-solid-state batteries (ASSBs) employing sulfide solid electrolytes (SSE) and high-nickel layered oxide cathodes have attracted considerable attention, attributed to their superior safety and great potential for high energy densities. However, the interface compatibility between SSE and nickel-rich oxide cathode remains a challenge. Here, a dual-functional strategy of Li6.25La3Zr2Al0.25O12 surface coating and bulk Zr doping in LiNi0.8Co0.1Mn0.1O2 was proposed (NCM811@CD-LLZAO), aiming to enhance the interfacial compatibility toward sulfide-based ASSBs. The fabricated ASSBs exhibit an impressive capacity retention rate of 91.1% after 100cycles at 0.1C and can sustain an ultra-long cycling performance over 1000cycles at 1.0C. Remarkably, even at a high rate of 11.0C, the battery still maintains a high capacity, highlighting its excellent rate performance. The distribution of relaxation time (DRT) analysis reveals that the LLZAO buffering layer can enhance the kinetics at the cathode/electrolyte interface. Theoretical calculation further confirms that the strong Zr-O bond formed by Zr doping can stabilize the lattice oxygen and effectively prevent the sulfide solid electrolyte from further electrochemical oxidation, thereby enhancing the interfacial dynamic and stability. These encouraging results provide a new strategy for the practical application of high-energy–density ASSBs, enabling fast charge transfer, extended cycle longevity and enhanced safety.

Carbon Nanotube Supported Fluorine Substituted Iron Phthalocyanine Enabling Boosted Polysulfide Redox Conversion Kinetics and Cyclic Stability.

The sluggish transition and shuttle of polysulfides (LiPS) significantly hinder the application and commercialization of Li-S batteries. Herein, carbon nanotubes (CNTs) supported 10 nm sized iron Hexadecafluorophthalocyanine (FePcF16/CNTs) are prepared using a solid synthesis approach. The well-exposed FePcF16 molecular improve the LiPS capture efficiency and redox kinetics by its central Fe-N4 units and F functional groups. The strong electron withdraw F groups significantly promote the conjugate effect and decrease the steric hindrance during mass migration procedure. Distribution of relaxation time (DRT) analysis shows that the Fe-N4 units exhibit strong affinity towards LiPS and the F groups further improve the Li+ diffusion rate in Li2S nucleation and oxidation procedure, accomplishing a porous surface on cathode. As a result, the FePcF16/CNTs separator exhibits a high initial capacity of 1136.2 mAh g-1 at 0.2 C, outstanding rate capacity of 624.9 mAh g-1 at 5 C and superior long-term stability at 2 C surviving 300 cycles with a low capacity decay of 0.43 ‰ per cycle.

Improving the frequency resolution of distribution of relaxation times by integrating elastic net regularization and quantum particle swarm optimization

The distribution of relaxation times (DRT) method is an efficient approach for analyzing electrochemical impedance spectroscopy (EIS) of fuel cells. However, facing electrochemical processes with close characteristic times/frequencies, low frequency resolution of DRT puts a damper on the analysis of results. To address this problem, a new DRT method integrating elastic net regularization and quantum particle swarm optimization (QPSO) algorithm is proposed in this study. Elastic net regularization combines the advantages of Lasso's feature selection and Tikhonov's coefficient shrinkage, thereby enhancing the frequency resolution of DRT. The use of the QPSO algorithm avoids the manual selection of parameters. A comprehensive evaluation of the developed method is conducted using both synthetic EIS and experimental EIS. The results show that the developed DRT method shows good performance and the reconstructed DRT functions exhibit high frequency resolution and accuracy. Overall, the proposed DRT method holds promise for advancing the characterization of fuel cells.

Remarkable difference in structural relaxation dynamics of conventionally prepared bulk glass and vapor-deposited thin films.

The structural relaxation dynamics of conventionally prepared bulk glass of N,N'-bis(3-methylphenyl)-N,N'-diphenyl-benzidine (TPD) was measured by differential scanning calorimetry. The calorimetric data were quantitatively described in terms of the Tool-Narayanaswamy-Moynihan (TNM) model. The TNM parameters were evaluated using a combination of linearization and non-linear optimization methodologies: h*/R = 109.5 kK, ln(A/s) = -321, β = 0.37, x = 0.64. In addition, the TNM phenomenology was used to describe recently reported normalized thickness data of stable and aged thin TPD films measured by ellipsometry. The structural relaxation was found to proceed at a markedly higher rate in these thin films prepared by the physical vapor deposition compared to that of conventional bulk glass. This feature appears to be associated with the significantly narrower distribution of relaxation times (β ≅ 0.8) observed for stable thin film in "as-deposited" form with uniquely dense molecular packing. Interestingly, very similar attributes of the relaxation kinetics were also found in the aged thin film with a previously erased thermal history associated with the deposition.

Novel approach to detect encasement failures in the anchorage system of guyed transmission towers by distribution of relaxation times

Anchor rods of guyed transmission towers encapsulated in Portland cement pastes and mortar are usually affected by corrosion processes when buried in soils with chemical aggressiveness. Encasement failures, such as cracks and leaks, cause preferential points of degradation of the inner metal bar, affecting its mechanical integrity over the years and causing, in many cases, the collapse of the transmission tower. However, the nondestructive testing (NDT) used for diagnosis has not shown conclusive results. In this study, a methodology to detect failures in the cement paste encasement was developed using electrochemical impedance spectroscopy (EIS) by evaluating the distribution of relaxation time constants (DRT) to identify different time constants of water in the pore structure of cement paste. These parameters were typically determined by the nuclear magnetic resonance (NMR) relaxometry technique, which was not correctly applied to the field tests. From the results, it was possible to detect the presence of cement paste in soils with electrical resistivities varying from 4.0 to 200.0 Ω.m by identifying their time constant at 0.10 ms, which was attributed to the water present in the hydrated calcium silicate gel (C-S-H) interlayers.

Characterization of Microscopic Structures in Living Tumor by In Vivo Measurement of Magnetic Relaxation Time Distribution of Intratumor Magnetic Nanoparticles.

Tumor microscopic structure is crucial for determining properties such as cancer type, disease state (key for early diagnosis), and novel therapeutic strategies. Magnetic particle imaging is an early cancer diagnostic tool using magnetic nanoparticles as a tracer, which actualizes cancer theranostics in combination with hyperthermia treatment using the abilities of magnetic nanoparticles as a heat source. This study focuses on the microscopic structures associated with cancer cell distribution, the stromal compartment, and vascularization in different kinds of living tumors by analyzing the intratumor magnetic relaxation response of magnetic nanoparticles injected into the tumors. Furthermore, this study describes a sequential system for the measurement of magnetic relaxation time and analysis of the intratumor structure using nonbiological samples such as viscous fluids and solidified magnetic nanoparticles. Particularly, the fine discriminability achieved by reconstructing a distribution map representing the relationship between magnetic relaxation time and viscosity of medium is demonstrated, based on experimental data with a limited condition number. Observing tumor microscopic structure through the dynamic magnetization response of intratumor magnetic nanoparticles is a low-invasive tool for analyzing tumor tissue without dissection. It holds promise for the advancement of biomedical applications, such as early cancer theranostics, using magnetic nanoparticles.

Scalable, Flexible, Magnetic-Field-Guided rULGO Sponge-BN-Cobalt Oxide-Based Supercapacitors: Mechanistic Insights into Multiple Charge Transfer Pathways by the Distribution of Relaxation Times.

Scalable and flexible supercapacitors are in high demand from an application point of view. Through our exploration, we have attained promising performance of electrochemical energy storage under the influence of an external magnetic field for future energy-based applications. In this work, a commercial sponge is used as a template for ultra-large graphene oxide (rULGO) functionalization, followed by the incorporation of Co3O4:BN without the inclusion of binders or conductive additives. The fabricated electrodes, namely, SPG-rULGO and SPG-rULGO-Co3O4:BN, demonstrate superior performance with a potential window of 2.2 V at a magnetic field strength of 13.5 and 28 mT, respectively. A specific capacitance of 218 ± 5% F·g-1 and 312 ± 5% F·g-1, respectively, with retention rates of 80 and 88% over 5000 charge-discharge cycles are achieved. In contrast to the conventional fabrication of the asymmetric device, both electrodes are made using flexible substrates with SPG-rULGO-Co3O4:BN as the positive electrode and SPG-rULGO as the negative electrode eliminating the need to use activated carbon. This configuration yields a specific capacitance of 153 ± 5% F·g-1 at 1 Ag-1, leading to a high energy density of 103 ± 5% W·h·kg-1 at a power density of 1.10 ± 5% kW kg-1 with an 85% retention rate. The charge-discharge mechanism of bare and modified electrodes is probed by the distribution of relaxation time analysis of the coupled electrochemical impedance spectra. The integration of magnetic field with advanced electrode materials opens up other possibilities for optimizing energy storage systems and advancing the field of flexible and mechanically robust supercapacitors.

Distinguishing Electrochemical Processes in PEM Electrolysis By Impedance Modeling – Distribution of Relaxation Times

Proton exchange membrane (PEM) water electrolysis is a key technology in hydrogen production to provide capacity and flexibility for renewable energy power sources. Various research topics for electrolyzer development focus on cost reduction, performance optimization and lifetime extension with minimum platinum group metal (PGM) loading required [1]. Understanding degradation mechanisms in PEM electrolysis cells becomes necessary for developing mitigation strategies in dynamic durability tests, which facilitates the importance of advanced electrochemical characterization methods.Electrochemical impedance spectroscopy (EIS) is a commonly used diagnostic technique in electrochemical systems. For impedance interpretation, equivalent circuit model (ECM) can be an option with prior knowledge about the system and appropriate circuit elements to prevent overfitting. Alternatively, distribution of relaxation times (DRT) provides a model-free pathway to address electrochemical mechanisms with different time constants and it has been applied to fuel cells, batteries and capacitors in the past years [2, 3]. Impedance deconvolution by DRT, however, has not been well adapted for PEMECs. In this study, electrochemical processes are successfully identified from the DRT profile by varying membrane electrode assembly (MEA) compositions and different operational conditions. In a single cell testing, four main peaks in DRT results associated with (1) mass transport losses (2) reaction kinetic (3) ionic conductivity within the catalyst layer and (4) ohmic properties at the interface of membrane and catalyst layers can be separated and quantified. Examination of electron microscopy provides further explanation of different MEA characteristics in performance analysis. This study verifies DRT is a promising tool to better understand PEM electrolysis processes and will be applied to degradation mitigation in the future.