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Imaging is critical to the characterisation of materials. However, even with careful sample preparation and microscope calibration, imaging techniques can contain defects and unwanted artefacts. This is particularly problematic for applications where the micrograph is to be used for simulation or feature analysis, as artefacts are likely to lead to inaccurate results. Microstructural inpainting is a method to alleviate this problem by replacing artefacts with synthetic microstructure with matching boundaries. In this paper we introduce two methods that use generative adversarial networks to generate contiguous inpainted regions of arbitrary shape and size by learning the microstructural distribution from the unoccluded data. We find that one benefits from high speed and simplicity, whilst the other gives smoother boundaries at the inpainting border. We also describe an open-access graphical user interface that allows users to utilise these machine learning methods in a ‘no-code’ environment.

Materials characterization is fundamental to our understanding of lithium ion battery electrodes and their performance limitations. Advances in laboratory-based characterization techniques have yielded powerful insights into the structure–function relationship of electrodes, yet there is still far to go. Further improvements rely, in part, on gaining a deeper understanding of complex physical heterogeneities in the materials. However, practical limitations in characterization techniques inhibit our ability to combine data directly. For example, some characterization techniques are destructive, thus preventing additional analyses on the same region. Fortunately, artificial intelligence (AI) has shown great potential for achieving representative, 3D, multi-modal datasets by leveraging data collected from a range of techniques. In this Perspective, we give an overview of recent advances in lab-based characterization techniques for Li-ion electrodes. We then discuss how AI methods can combine and enhance these techniques, leading to substantial acceleration in our understanding of electrodes.

Acquiring precise information about the mode content of a laser is critical for multiplexed optical communications, optical imaging with active wave-front control, and quantum-limited interferometric measurements. Hologram-based mode decomposition devices, such as spatial light modulators, allow a fast, direct measurement of the mode content, but they have limited precision due to cross-coupling between modes. Here we report the first proof-of-principle demonstration of mode decomposition with a metasurface, resulting in significantly enhanced precision. A mode-weight fluctuation of $6\times 10^{-7}$ was be measured with 1 second of averaging at a Fourier frequency of 80 Hz, an improvement of more than three orders of magnitude compared to the state-of-the-art spatial light modulator decomposition. The improvement is attributable to the reduction in cross-coupling enabled by the exceptional small pixel size of the metasurface. We show a systematic study of the limiting sources of noise, and we show that there is a promising path towards complete mode decomposition with similar precision., Peer-reviewed version. Accepted for publication in Physical Review A. 6 pages, 8 figures

The mesostructure of porous electrodes used in lithium-ion batteries strongly influences cell performance. Accurate imaging of the distribution of phases in these electrodes would allow this relationship to be better understood through simulation. However, imaging the nanoscale features in these components is challenging. While scanning electron microscopy is able to achieve the required resolution, it has well established difficulties imaging porous media. This is because the flat imaging planes prepared using focused ion beam milling will intersect with the pores, which makes the images hard to interpret as the inside walls of the pores are observed. It is common to infiltrate porous media with resin prior to imaging to help resolve this issue, but both the nanoscale porosity and the chemical similarity of the resins to the battery materials undermine the utility of this approach for most electrodes. In this study, a technique is demonstrated which uses in situ infiltration of platinum to fill the pores and thus enhance their contrast during imaging. Reminiscent of the Japanese art of repairing cracked ceramics with precious metals, this technique is referred to as the kintsugi method. The images resulting from applying this technique to a conventional porous cathode are presented and then segmented using a multi-channel convolutional method. We show that while some cracks in active material particles were empty, others appear to be filled (perhaps with the carbon binder phase), which will have implications for the rate performance of the cell. Energy dispersive X-ray spectroscopy was used to validate the distribution of phases resulting from image analysis, which also suggested a graded distribution of the binder relative to the carbon additive. The equipment required to use the kintsugi method is commonly available in major research facilities and so we hope that this method will be rapidly adopted to improve the imaging of electrode materials and porous media in general.

International audience; During the operation of Solid Oxide Cell (SOC) fuel electrodes, the mobility of nickel can lead to significant changes in electrode morphology, with accompanying degradation in electrochemical performance. In this work, the dewetting of nickel films supported on yttria-stabilized zirconia (YSZ), hereafter called 2D cells, is studied by coupling in-situ environmental scanning electron microscopy (E-SEM), image analysis, cellular automata simulation and electrochemical impedance spectroscopy (EIS). Analysis of experimental E-SEM images shows that Ni dewetting causes an increase in active triple phase boundary (aTPB) length up to a maximum, after which a sharp decrease in aTPB occurs due to Ni de-percolation. This microstructural evolution is consistent with the EIS response, which shows a minimum in polarization resistance followed by a rapid electrochemical degradation. These results reveal that neither evaporation-condensation nor surface diffusion of Ni are the main mechanisms of dewetting at 560–800 °C. Rather, the energy barrier for pore nucleation within the dense Ni film appears to be the most important factor. This sheds light on the relevant mechanisms and interfaces that must be controlled to reduce the electrochemical degradation of SOC electrodes induced by Ni dewetting.

International audience; Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.

Summary Enabling accurate prediction of battery failure will lead to safer battery systems, as well as accelerating cell design and manufacturing processes for increased consistency and reliability. Data-driven prediction methods have shown promise for accurately predicting cell behaviors with low computational cost, but they are expensive to train. Furthermore, given that the risk of battery failure is already very low, gathering enough relevant data to facilitate data-driven predictions is extremely challenging. Here, a perspective for designing experiments to facilitate a relatively low number of tests, handling the data, applying data-driven methods, and improving our understanding of behavior-dictating physics is outlined. This perspective starts with effective strategies for experimentally replicating rare failure scenarios and thus reducing the number of experiments, and proceeds to describe means to acquire high-quality datasets, apply data-driven prediction techniques, and to extract physical insights into the events that lead to failure by incorporating physics into data-driven approaches.

Mixed ionic electronic conductors (MIECs) have attracted increasing attention as anode materials for solid oxide fuel cells (SOFCs) and they hold great promise for lowering the operation temperature of SOFCs. However, there has been a lack of understanding of the performance-limiting factors and guidelines for rational design of composite metal-MIEC electrodes. Using a newly-developed approach based on 3D-tomography and electrochemical impedance spectroscopy, here for the first time we quantify the contribution of the dual-phase boundary (DPB) relative to the three-phase boundary (TPB) reaction pathway on real MIEC electrodes. A new design strategy is developed for Ni/gadolinium doped ceria (CGO) electrodes (a typical MIEC electrode) based on the quantitative analyses and a novel Ni/CGO fiber–matrix structure is proposed and fabricated by combining electrospinning and tape-casting methods using commercial powders. With only 11.5 vol% nickel, the designer Ni/CGO fiber–matrix electrode shows 32% and 67% lower polarization resistance than a nano-Ni impregnated CGO scaffold electrode and conventional cermet electrode respectively. The results in this paper demonstrate quantitatively using real electrode structures that enhancing DPB and hydrogen kinetics are more efficient strategies to enhance electrode performance than simply increasing TPB.

The tortuosity factor of porous electrode microstructure is a crucial input parameter for numerical models of batteries as it strongly influences the electrode performance. As such, it is very important to have a method to determine this parameter accurately, based on a definition that reflects the design of the cell. Various experimental methods have been developed for either directly measuring or indirectly inferring the tortuosity factor; however, numerical approaches, based on 3D image data, are now gaining interest in the battery community, due to the advances in nanoscale tomographic imaging methods. The standard definition of the tortuosity factor solves the Fick diffusion equation at steady-state, i.e., between two parallel constant-value boundaries. Although this approach has been widely used for porous materials, including both electronic insulators (e.g., a battery separator), and electronic conductors (e.g., battery porous electrodes), it may be the case that the definition needs to be adjusted depending on the scenario being observed. In this study, we intend to give an insight into the appropriate way to determine the tortuosity factor of battery porous electrodes and the impact of various tortuosity determination methods is investigated. An additional module that relies on the symmetric cell method [1] [2] was implemented in the TauFactor software package [3] to compare with the already-implemented diffusion-based method [4]. This symmetric cell method refers to the measurement of the ionic current distribution inside the pores using AC impedance based on a symmetric cell setup. Figure 1 shows the workflow for tortuosity determination applied in this study. The integration of this module in TauFactor might be interesting for tortuosity determination at the microscale since it is the same method as at macroscopic scale observed in various experimental approaches. Figure 1 . Illustration of the workflow for tortuosity determination applied in this work. The module recently implemented in TauFactor allows calculation based directly on tomographic data in symmetric cell configuration, and generates a simulated impedance spectrum. A macroscopic model such as TLM or Newman’s model is used to extract the tortuosity value of the electrode. References: [1] Landesfeind, J. et al.; J. Electrochem. Soc. 2016, 163 (7), A1373–A1387 [2] Malifarge, S. et al.; J. Electrochem. Soc. 2017, 164 (11), E3329–E3334 [3] Cooper, S. J. et al.; SoftwareX 2016, 5, 203–210 [4] Cooper, S. J. et al.; Electrochimica Acta 251 (2017) 681–689 [5] Pouraghajan, F. et al.; J. Electrochem. Soc. 2018, 165 Figure 1

Reducing the cost of redox flow batteries (RFBs) is critical to achieving broad commercial deployment of large-scale energy storage systems. This can be addressed in a variety of ways, such as reducing component costs or improving electrode design. The aim of this work is to better understand the relationship between electrode microstructure and performance. Four different commercially available carbon electrodes were examined – two cloths and two papers (from AvCarb® and Freudenberg Performance Materials) – and a comprehensive study of the different pore-scale and mass-transport processes is presented to elucidate their effect on the overall cell performance. Electrochemical measurements were carried out in a non-aqueous organic flow-through RFB with these different electrodes, using two supporting solvents (propylene carbonate and acetonitrile) and at a variety of flow rates. Electrode samples were scanned using X-ray computed tomography, and a customised segmentation technique was employed to extract several microstructural parameters. A pore network model was used to calculate the pressure drops and permeabilities, which were found to be within 1.26 × 10−11 and 1.65 × 10−11 m2 for the papers and between 8.61 × 10−11 and 10.6 × 10−11 m2 for the cloths. A one-dimensional model was developed and fit to polarisation measurements to obtain mass-transfer coefficients, km, which were found to be between 1.01 × 10−6 and 5.97 × 10−4 m s−1 with a subsequent discussion on Reynolds and Sherwood number correlations. This work suggests that, for these fibrous materials, permeability correlates best with electrochemical cell performance. Consequently, the carbon cloths with the highest permeability and highest mass-transfer coefficients, displayed better performances.

We present an all-fiber flexible supercapacitor with composite nanofiber electrodes made via electrospinning and an electrospun separator. With the addition of manganese acetylacetonate (MnACAC) to polyacrylonitrile (PAN) as a precursor for the electrospinning process and subsequent heat treatment, the performance of pure PAN supercapacitors was improved from 90 F g−1 to 200 F g−1 (2.5 mV s−1) with possible mass loadings of MnACAC demonstrated as high as 40 wt%. X-ray diffraction measurements showed that after thermal treatment, the MnACAC was converted to MnO, meanwile, the thermal decomposition of MnACAC increased the graphitic degree of the carbonised PAN. Scanning electron microscopy and image processing showed that static electrospinning of pure PAN and PAN-Mn resulted in fiber diameters of 460 nm and 480 nm respectively after carbonisation. Further analysis showed that the fiber orientation exhibited a slight bias which was amplified with the addition of MnACAC. Use of focused ion beam scanning electron microscopy tomography also showed that MnO particles were evenly distributed through the fiber at low MnACAC concentrations, while at a 40 wt% loading the MnO particles were also visible on the surface. Comparison of the electrospun separators showed improved performance relative to a commercial Celgard separator (200 F g−1 vs 141 F g−1).

Accurate prediction of battery failure, both online and offline, facilitates design of safer battery systems through informed-engineering and on-line adaption to unfavorable scenarios. With the wide range of batteries available and frequently evolving pack designs, accurate prediction of cell behavior under different conditions is very challenging and extremely time consuming. In this issue of Joule, Li et al. 1 used data from a previously reported finite-element model to train machine learning algorithms to predict whether a cell will undergo an internal short circuit when exposed to a selection of mechanical abuse conditions. The presented approach aims to alleviate, and yet is still limited by, a common challenge facing data-driven prediction methods: access to robust, plentiful, high-quality, and relevant experimental data.

Graphene-based carbon sponges can be used in different applications in a large number of fields including microelectronics, energy harvesting and storage, antimicrobial activity and environmental remediation. The functionality and scope of their applications can be broadened considerably by the introduction of metallic nanoparticles into the carbon matrix during preparation or post-synthesis. Here, we report on the use of X-ray micro-computed tomography (CT) as a method of imaging graphene sponges after the uptake of metal (silver and iron) nanoparticles. The technique can be used to visualize the inner structure of the graphene sponge in 3D in a non-destructive fashion by providing information on the nanoparticles deposited on the sponge surfaces, both internal and external. Other deposited materials can be imaged in a similar manner providing they return a high enough contrast to the carbon microstructure, which is facilitated by the low atomic mass of carbon.

Ionogels are semi-solid, ion conductive and mechanically compliant materials that hold promise for flexible, shape-conformable and all-solid-state energy storage devices. However, identifying facile routes for manufacturing ionogels into devices with highly resilient electrode/electrolyte interfaces remains a challenge. Here we present a novel all-in-gel supercapacitor consisting of an ionogel composite electrolyte and bucky gel electrodes processed using a one-step method. Compared with the mechanical properties and ionic conductivities of pure ionogels, our composite ionogels offer enhanced self-recovery (retaining 78% of mechanical robustness after 300 cycles at 60% strain) and a high ionic conductivity of 8.7 mS cm−1, which is attributed to the robust amorphous polymer phase that enables facile permeation of ionic liquids, facilitating effective diffusion of charge carriers. We show that development of a supercapacitor with these gel electrodes and electrolytes significantly improves the interfacial contact between electrodes and electrolyte, yielding an area specific capacitance of 43 mF cm−2 at a current density of 1.0 mA cm−2. Additionally, through this all-in-gel design a supercapacitor can achieve a capacitance between 22–81 mF cm−2 over a wide operating temperature range of −40 °C to 100 °C at a current density of 0.2 mA cm−2.

Separators are an integral component for optimising performance and safety of lithium-ion batteries; therefore, a clear understanding of how their microstructure affects cell performance and safety is crucial. Phase contrast X-ray microscopy is used here to capture the microstructures of commercial monolayer, tri-layer, and ceramic-coated lithium-ion battery polymer separators. Spatial variations in key structural parameters, including porosity, tortuosity factor and pore size distribution, are determined through the application of 3D quantification techniques and stereology. The architectures of individual layers in multi-layer membranes are characterised, revealing anisotropy in porosity, tortuosity factor and mean pore size of the three types of separator. Detailed structural properties of the individual layers of multi-layered membranes are then related with their expected effect on safety and rate capability of cells.

© 2016 American Chemical Society.The degradation of intermediate temperature solid oxide fuel cell (ITSOFC) cathodes has been identified as a major issue limiting the development of ITSOFCs as high efficiency energy conversion devices. In this work, the effect of Cr poisoning on (La0.6Sr0.4)0.95(Co0.2Fe0.8)O3-δ (LSCF6428), a particularly promising ITSOFC cathode material, was investigated on symmetrical cells using electrochemical impedance spectroscopy and multiscale structural/chemical analysis by advanced electron and ion microscopy. The systematic combination of bulk and high-resolution analysis on the same cells allows, for the first time, direct correlation of Cr induced performance degradation with subtle and localized structural/chemical changes of the cathode down to the atomic scale. Up to 2 orders of magnitude reduction in conductivity, oxygen surface exchange rate, and diffusivity were observed in Cr poisoned LSCF6428 samples. These effects are associated with the formation of nanometer size SrCrO4; grain boundary segregation of Cr; enhanced B-site element exsolution (both Fe and Co); and reduction in the Fe valence, the latter two being related to Cr substitution in LSCF. The finding that significant degradation of the cathode happens before obvious microscale change points to new critical SOFC degradation mechanisms effective at the nanometer scale and below.

A new approach to generate high-fidelity 3D microstructure reconstructions by leveraging resolution and sample volume characteristics from 2D and 3D microscopy methods is presented. This approach is employed to model the microstructure of a highly orthotropic polypropylene separator used in lithium-ion batteries, which have challenging multi-scale features of fibrils ( 100 nm) to resolve in 3D. Phase contrast nano X-ray computed tomographic data are used to reconstruct the lamellae phase, while 2D scanning electron microscopy data are used to characterize sub-100 nm microstructure features such as the thin fibrils that are beyond the effective resolution of X-ray computed tomography. Fibril geometries are reconstructed stochastically based on the 2D higher resolution data, and integrated with the lamellae geometries in 3D space. Transport property analyses are performed to investigate the bias of microstructure models without considering the fibrils. A sensitivity study is also conducted to facilitate understanding of the relationship between microstructure characteristics and transport properties.

Understanding the interplay between electrode microstructure and cell performance of electrochemical devices is important both for modelling and experimental design. Redox Flow Batteries (RFBs) are an electrochemical energy storage technology with potential for grid-scale energy storage applications, although costs need to be further reduced to be competitive. One pathway to lowering the costs involves increasing the power density of each cell, such that fewer cells are required. This can be achieved in a variety of ways, including by improving the design of the electrode microstructures. The aim of this work is to better understand the relationship between electrode microstructure and RFB performance and, ultimately, to make inferences about electrode utility. The performances of a variety of commercially available carbon electrodes are examined via a series of commonly used microstructural and electrochemical analyses (Figure a-c). [1] We present a comprehensive study of pore-scale mass-transport processes occurring in each of the electrodes and rationalize their effect on the overall cell performance. A matrix of electrochemical tests were carried out in a flow-through RFB cell using incremental flow rates (Figure b-c) and two non-aqueous TEMPO electrolytes with distinct viscosity and diffusivity properties. Scanning electron microscopy (SEM) was used to image the electrodes and large 3 mm samples of each were scanned using X-ray computed tomography (Figure a). A customized segmentation technique was subsequently developed that resamples the image data to ensure the fiber dimensions agree with SEM images, improving the validity of the various extracted metrics. From these images, calculations and electrochemical tests, several microstructural parameters were extracted and a pore network model was used to calculate the permeabilities of the electrodes. A 1D model was developed across half the symmetric membrane electrode assembly and, using the parameters extracted from XCT, fit to galvanostatic polarization measurements to obtain the mass transfer coefficients at each unique operating condition – these were found to be in the range of and m s-1. Transport processes and distributions through the cell are analysed and correlations between dimensionless Reynolds and Sherwood numbers are subsequently discussed, as demonstrated in previous work. [2, 3] Given a ‘good performance’ is associated with a system being able to reach a high current with a low overpotential, the following findings were made. Firstly, while volume-specific surface area, porosity and tortuosity are useful descriptors for evaluating porous media, they are not good indicators of performance in these systems. Instead the permeability and, by extension, the ease with which convective transport can occur is found to be directly correlated with performance in this system. It is asserted that systems using solvents with higher viscosity have a reduced performance due, in part, due to regions in the electrode being starved of new electrolyte. Consistently, across all electrodes and in each electrolyte a better performance is observed when a higher flow rate is used. This effect is, in part, attributed to the reduction in size of the boundary layer at the electrode surface. Finally, in agreement with previous works, [1, 4] the carbon cloths tested in this work are shown to have higher mass-transfer coefficients and permeabilities than the carbon papers and we conclude that the best performing electrode is the thicker carbon cloth, which has the lowest resistance to convective flow, highest permeability, and highest mass-transfer coefficients in both solvents. [1]: A. Forner-Cuenca, E. E. Penn, A. M. Oliveira and F. R. Brushett, Exploring the Role of Electrode Microstructure on the Performance of Non-Aqueous Redox Flow Batteries. J. Electrochem. Soc., 166, 2230-2241 (2019). [2]: Y. A. Gandomi, D. S. Aaron, T. A. Zawodzinski and M. M. Mench, In Situ Potential Distribution Measurement and Validated Model for All-Vanadium Redox Flow Battery. J. Electrochem. Soc., 163, A5188-A5201 (2016). [3]: K. M. Tenny, A. Forner-Cuenca, Y.-M. Chiang and F. R. Brushett, Comparing Physical and Electrochemical Properties of Different Weave Patterns for Carbon Cloth Electrodes in Redox Flow Batteries. J. Electrochem. En. Conv. Stor., 17, 041108 (2020). [4]: M. A. Sadeghi, M. Aganou, M. D. R. Kok, M. Aghighi, G. Merle, J. Barralet and J. Gostick, Exploring the Impact of Electrode Microstructure on Redox Flow Battery Performance Using a Multiphysics Pore Network Model. J. Electrochem. Soc., 166, A2121-A2130 (2019). Figure 1

The microstructure of porous electrodes significantly impacts the performance of electrochemical energy storage (EES) devices [1,2]. Thus, their morphological optimisation is vital for developing the next generation of EES technologies [3]. Recent improvements in 3D imaging techniques have allowed the characterisation of porous electrodes at a nanoscale [4]. However, a variety of challenges remain, including how to extract the key metrics or “essence” of the microstructure with which synthetic volumes with equivalent properties can be generated. This work implements a machine learning technique called deep-convolutional generative adversarial networks (GANs) to generate 3D synthetic realisations of n-phase electrode microstructures (Figure 1 (a)). The same network architecture is successfully applied to generate two very different three-phase microstructures: a Li-ion cathode and a SOFC anode. GANs implicitly capture the probability distribution function that fully defines the microstructure [5] into a low dimensional space, which is crucial for the follow-up process of optimisation. A statistical comparison between the real and synthetic data is performed in terms of the morphological properties (volume fraction, surface area, triple phase boundary) and transport properties (relative diffusivity), as well as the two-point correlation function. The results show excellent agreement between both datasets and they are also visually indistinguishable. The impact of this work lies in the ability to obtain a fully differentiable function that constitutes a “virtual representation” of the electrode microstructure. We show that with these virtual representations, it is possible to implement physical simulations based on which we can design an optimised porous microstructure. Furthermore, we introduce the possibility of generating spatially periodic microstructures with the same microstructural properties as the real tomographic data (Figure 1 (b)). These periodic microstructures have the potential to significantly reduce the simulated volume required to be considered “representative" and therefore massively reduce the computational cost of the electrochemical simulations necessary to predict the performance of a particular microstructure during optimisation. Keywords : Electrode microstructure, machine learning, generative adversarial networks, optimisation [1] G. Wei, C. Jia, J. Liu and C. Yan, Journal of Power Sources 220, 185 (2012). [2] H. Moussaoui, J. Laurencin, Y. Gavet, G. Delette, M. Hubert, P. Cloetens, T. Le Bihan and J. Debayle, Computational Materials Science. 143, 262 (2018). [3] N. Ni, S.J. Cooper, R. Williams, N. Kemen, D.W. McComb, and S.J. Skinner, ACS Applied Materials and Interfaces. 8(27), 17360 (2016). [4] F.L.E. Usseglio-Viretta, A. Colclasure, A.N. Mistry, K.P.Y. Claver, F. Pouraghajan, D.P. Finegan, T.M.M. Heenan, D. Abraham, P.P. Mukherjee, D. Wheeler, P. Shearing, S.J. Cooper and K. Smith, Journal of The Electrochemical Society. 165, A3403 (2018). [5] A. Gayo-Lombardo, L. Mosser, N.P. Brandon, S.J. Cooper, npj Computational Materials (2020) (Pre-print arXiv https://arxiv.org/abs/2003.11632) Figure 1

Ionogels are a new class of promising materials for use in all‐solid‐state energy storage devices in which they can function as an integrated separator and electrolyte. However, their performance is limited by the presence of a crosslinking polymer, which is needed to improve the mechanical properties, but compromises their ionic conductivity. Here, directional freezing is used followed by a solvent replacement method to prepare aligned nanocomposite ionogels which exhibit enhanced ionic conductivity, good mechanical strength, and thermal stability simultaneously. The aligned ionogel based supercapacitor achieves a 29% higher specific capacitance (176 F g−1 at 25 °C and 1 A g−1) than an equivalent nonaligned form. Notably, this thermally stable aligned ionogel has a high ionic conductivity of 22.1 mS cm−1 and achieves a high specific capacitance of 167 F g−1 at 10 A g−1 and 200 °C. Furthermore, the diffusion simulations conducted on 3D reconstructed tomography images are employed to explain the improved conductivity in the relevant direction of the aligned structure compared to the nonaligned. This work demonstrates the synthesis, analysis, and use of aligned ionogels as supercapacitor separators and electrolytes, representing a promising direction for the development of wearable electronics coupled with image based process and simulations.

Battery performance is strongly correlated with electrode microstructural properties. Of the relevant properties, the tortuosity factor of the electrolyte transport paths through microstructure pores is important as it limits battery maximum charge/discharge rate, particularly for energy-dense thick electrodes. Tortuosity factor however, is difficult to precisely measure, and thus its estimation has been debated frequently in the literature. Herein, three independent approaches have been applied to quantify the tortuosity factor of lithium-ion battery electrodes. The first approach is a microstructure model based on three-dimensional geometries from X-ray computed tomography (CT) and stochastic reconstructions enhanced with computationally generated carbon/binder domain (CBD), as CT is often unable to resolve the CBD. The second approach uses a macro-homogeneous model to fit electrochemical data at several rates, providing a separate estimation of the tortuosity factor. The third approach experimentally measures tortuosity factor via symmetric cells employing a blocking electrolyte. Comparisons have been made across the three approaches for 14 graphite and nickel-manganese-cobalt oxide electrodes. Analysis suggests that if the tortuosity factor were characterized based on the active material skeleton only, the actual tortuosities would be 1.35–1.81 times higher for calendered electrodes. Correlations are provided for varying porosity, CBD phase interfacial arrangement and solid particle morphology.

Samples of the solid oxide fuel cell cathode material La0.6Sr0.4Co0.2Fe0.8O3-δ(LSCF) were annealed using High-Temperature Environmental Scanning Electron Microscopy (HT-ESEM) from room temperature to 1000 °C in atmospheres of pure oxygen, pure water and ambient lab air. Image series of each heat treatment were taken where microstructural changes were observed and compared between samples. Strontium segregation rate was found to be significantly increased in the presence of pure water as compared to pure O2and ambient air. Electron backscattered diffraction (EBSD) was performed in order to assess the effect of crystal orientation on particle formation and surface sensitive chemical analysis techniques were used to determine the chemical changes at the grain surface as a result of the different heat treatments. It was shown that crystal orientation affected the nature and growth rate of strontium-based particles, however, due to the pseudo-symmetry of La0.6Sr0.4Co0.2Fe0.8O3-δ, precise crystal orientation relationships could not be determined. The chemical composition of the grain surface was found to be approximately equal under each atmosphere.

International audience

A novel two-step Isotopic Exchange (IE) technique has been developed to investigate the influence of oxygen containing components of ambient air (such as H

This chapter begins with a brief history of fuel cell development and introduces solid oxide fuel cells (SOFCs) as high efficiency energy conversion devices. Following this the fundamentals of SOFC performance and cell design are explored, with special focus given to the significance of operating temperature and microstructure. Next the current commercial status of SOFCs is outlined in brief. Finally, SOFC degradation, the major theme of this book, is introduced; the various mechanisms are split into the two broad categories of physical and chemical degradation.

In order to accelerate the commercialization of fuel cells and batteries across a range of applications, an understanding of the mechanisms by which they age and degrade at the microstructural level is required. Here, the most widely commercialized Li-ion batteries based on porous graphite based electrodes which de/intercalate Li+ ions during charge/discharge are studied by two phase contrast enhanced X-ray imaging modes, namely in-line phase contrast and Zernike phase contrast at the micro (synchrotron) and nano (laboratory X-ray microscope) level, respectively. The rate of charge cycling is directly dependent on the nature of the electrode microstructure, which are typically complex multi-scale 3D geometries with significant microstructural heterogeneities. We have been able to characterise the porosity and the tortuosity by micro-CT as well as the morphology of 5 individual graphite particles by nano-tomography finding that while their volume varied significantly their sphericity was surprisingly similar. The volume specific surface areas of the individual grains measured by nano-CT are significantly larger than the total volume specific surface area of the electrode from the micro-CT imaging, which can be attributed to the greater particle surface area visible at higher resolution.

In the article number 1801337, Qigang Wang and co‐workers report a self‐initiated cryopolymerization method to prepare nanocomposite ionogels with hierarchical aligned pores for use in high temperature supercapacitors. Diffusion simulations based on X‐ray tomographic 3D reconstructed images are used to explain the origins of the observed electrochemical enhancements in the aligned ionogels, which highlight the potential for structured materials in energy storage devices.

Electrode tortuosity factor is a key input parameter in many fuel cell simulations. Three-dimensional microstructural data obtained from in-situ synchrotron X-ray nano-computed tomography is used as the basis for comparing five approaches to quantify the tortuosity factor. Three of these techniques are based on diffusivity simulations and showed strong correlation, but had consistently different absolute values. A random walk method showed a good degree of correlation to the diffusive approaches, but had the largest values overall. Lastly, a calculation that used a mean pore centroid approach showed little correlation to any of the other three methods, but compared well with the conventional Bruggeman correlation. Due to the diffusive nature of the ionic transport in electrodes, the authors would recommend calculating tortuosity factors using a diffusive approach based on the voxels rather than a remeshed volume.