46 results found
Kench S, Squires I, Dahari A, et al., 2022, MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN, Scientific Data, Vol: 9, ISSN: 2052-4463
3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. This has proven a useful tool for understanding how complex material systems behave under applied stresses, temperatures and chemical conditions. However, 3D imaging of materials is challenging for a number of reasons, including limited field of view, low resolution and difficult sample preparation. Recently, a machine learning method, SliceGAN, was developed to statistically generate 3D microstructural datasets of arbitrary size using a single 2D input slice as training data. In this paper, we present the results from applying SliceGAN to 87 different microstructures, ranging from biological materials to high-strength steels. To demonstrate the accuracy of the synthetic volumes created by SliceGAN, we compare three microstructural properties between the 2D training data and 3D generations, which show good agreement. This new microstructure library both provides valuable 3D microstructures that can be used in models, and also demonstrates the broad applicability of the SliceGAN algorithm.
Brosa Planella F, Ai W, Boyce AM, et al., 2022, A continuum of physics-based lithium-ion battery models reviewed, Progress in Energy, Vol: 4
Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the single particle model, deriving in the process other common models, such as the Doyle-Fuller-Newman model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.
Siripornpitak P, Engel I, Cooper S, et al., 2022, Spatial up-sampling of HRTF sets using generative adversarial networks: a pilot study, Frontiers in Signal Processing, Vol: 2, Pages: 1-10, ISSN: 2673-8198
Headphone-based spatial audio simulations rely on Head Related Transfer Functions (HRTFs) in order to reconstruct the sound field at the entrance of the listener’s ears. A HRTF is strongly dependent on the listener’s specific anatomical structures, and it has been shown that virtual sounds recreated with someone else’s HRTF result in worse localisation accuracy, as well as altering other subjective measures such as externalisation and realism. Acoustic measurements of the filtering effects generated by ears, head and torso has proven to be one of the most reliable ways to obtain a personalised HRTF. However this requires a dedicated and expensive setup, and is time-intensive. In order to simplify the measurement setup, thereby improving the scalability of the process, we are exploring strategies to reduce the number of acoustic measurements without degrading the spatial resolution of the HRTF. Traditionally, spatial up sampling of HRTF sets is achieved through barycentric interpolation or by employing the spherical harmonics framework. However, such methods often perform poorly when the provided HRTF data is spatially very sparse. This work investigates the use of generative adversarial networks (GANs) to tackle the up-sampling problem, offering an initial insight about the suitability of this technique. Numerical evaluations based on spectral magnitude error and perceptual model outputs are presented on single spatial dimensions, therefore considering sources positioned only in one of the three main planes: horizontal, median, and frontal. Results suggest that traditional HRTF interpolation methods perform better than the proposed GAN-based one when the distance between measurements is smaller than 90°, but for the sparsest conditions (i.e. one measurement every 120° to 180°), the proposed approach outperforms the others.
Cooper SJ, Roberts SA, Liu Z, et al., 2022, Methods—Kintsugi imaging of battery electrodes: distinguishing pores from the carbon binder domain using Pt deposition, Journal of The Electrochemical Society, Vol: 169, ISSN: 0013-4651
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 i
Wang AA, OKane SEJ, Brosa Planella F, et al., 2022, Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models, Progress in Energ, Vol: 4, Pages: 1-40, ISSN: 2516-1083
The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based continuum-level description of the chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range of battery designs and chemistries, the framework provides an effective balance between detail, needed to capture key microscopic mechanisms, and simplicity, needed to solve the governing equations at a relatively modest computational expense. Nevertheless, implementation requires values of numerous model parameters, whose ranges of applicability, estimation, and validation pose challenges. This article provides a critical review of the methods to measure or infer parameters for use within the isothermal DFN framework, discusses their advantages or disadvantages, and clarifies limitations attached to their practical application. Accompanying this discussion we provide a searchable database, available at www.liiondb.com, which aggregates many parameters and state functions for the standard DFN model that have been reported in the literature.
Usseglio-Viretta FLE, Patel P, Bernhardt E, et al., 2022, MATBOX: An Open-source Microstructure Analysis Toolbox for microstructure generation, segmentation, characterization, visualization, correlation, and meshing, SOFTWAREX, Vol: 17, ISSN: 2352-7110
Simon BA, Gayon-Lombardo A, Pino-Muñoz CA, et al., 2022, Combining electrochemical and imaging analyses to understand the effect of electrode microstructure and electrolyte properties on redox flow batteries, Applied Energy, Vol: 306, Pages: 1-22, ISSN: 0306-2619
Reducing the cost of redox flow batteries (RFBs) is critical to achieving broad commercial deployment of largescale energy storage systems. This can be addressed in a variety of ways, such as reducing component costs orimproving electrode design. The aim of this work is to better understand the relationship between electrodemicrostructure and performance. Four different commercially available carbon electrodes were examined – twocloths and two papers (from AvCarb® and Freudenberg Performance Materials) – and a comprehensive study ofthe different pore-scale and mass-transport processes is presented to elucidate their effect on the overall cellperformance. Electrochemical measurements were carried out in a non-aqueous organic flow-through RFB withthese different electrodes, using two supporting solvents (propylene carbonate and acetonitrile) and at a varietyof 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 tocalculate the pressure drops and permeabilities, which were found to be within 1.26 × 10− 11 and 1.65 × 10− 11m2 for the papers and between 8.61 × 10− 11 and 10.6 × 10− 11 m2 for the cloths. A one-dimensional model wasdeveloped and fit to polarisation measurements to obtain mass-transfer coefficients, km, which were found to bebetween 1.01 × 10− 6 and 5.97 × 10− 4 m s− 1 with a subsequent discussion on Reynolds and Sherwood numbercorrelations. 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 masstransfer coefficients, displayed better performances.
Ouyang M, Bertei A, Cooper SJ, et al., 2021, Model-guided design of a high performance and durability Ni nanofiber/ceria matrix solid oxide fuel cell electrode, Journal of Energy Chemistry, Vol: 56, Pages: 98-112, ISSN: 2095-4956
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.
Kench S, Cooper SJ, 2021, Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion, Nature Machine Intelligence, Vol: 3, Pages: 299-305, ISSN: 2522-5839
Generative adversarial networks (GANs) can be trained to generate three-dimensional (3D) image data, which are useful for design optimization. However, this conventionally requires 3D training data, which are challenging to obtain. Two-dimensional (2D) imaging techniques tend to be faster, higher resolution, better at phase identification and more widely available. Here we introduce a GAN architecture, SliceGAN, that is able to synthesize high-fidelity 3D datasets using a single representative 2D image. This is especially relevant for the task of material microstructure generation, as a cross-sectional micrograph can contain sufficient information to statistically reconstruct 3D samples. Our architecture implements the concept of uniform information density, which ensures both that generated volumes are equally high quality at all points in space and that arbitrarily large volumes can be generated. SliceGAN has been successfully trained on a diverse set of materials, demonstrating the widespread applicability of this tool. The quality of generated micrographs is shown through a statistical comparison of synthetic and real datasets of a battery electrode in terms of key microstructural metrics. Finally, we find that the generation time for a 108 voxel volume is on the order of a few seconds, yielding a path for future studies into high-throughput microstructural optimization.
Mistry A, Franco AA, Cooper SJ, et al., 2021, How machine learning will revolutionize electrochemical sciences, ACS Energy Letters, Vol: 6, Pages: 1422-1431, ISSN: 2380-8195
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.
Finegan DP, Zhu J, Feng X, et al., 2021, The application of data-driven methods and physics-based learning for improving battery safety, Joule, Vol: 5, Pages: 316-329, ISSN: 2542-4351
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.
Simon BA, Gayon Lombardo A, Pino C, et al., 2020, Combining Electrochemical, Fluid Dynamic, and Imaging Analyses to Understand the Effect of Electrode Microstructure and Electrolyte on Redox Flow Batteries, ECS Meeting Abstracts, Vol: MA2020-02, Pages: 3032-3032
<jats:p> 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.</jats:p> <jats:p>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).  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 me
Gayon Lombardo A, Mosser L, Brandon NP, et al., 2020, Pores for Thought: Reconstructing 3D, Multi-Phase Electrode Microstructures with Periodic Boundaries Using Generative Adversarial Networks for Battery Design, ECS Meeting Abstracts, Vol: MA2020-02, Pages: 332-332
<jats:p> 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 . Recent improvements in 3D imaging techniques have allowed the characterisation of porous electrodes at a nanoscale . 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.</jats:p> <jats:p>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  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.</jats:p> <jats:p>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
Nguyen T-T, Demortière A, Fleutot B, et al., 2020, The electrode tortuosity factor: why the conventional tortuosity factor is not well suited for quantifying transport in porous Li-ion battery electrodes and what to use instead, npj Computational Materials, Vol: 6, ISSN: 2057-3960
The tortuosity factor of porous battery electrodes is an important parameter used to correlate electrode microstructure with performance through numerical modeling. Therefore, having an appropriate method for the accurate determination of tortuosity factors is critical. This paper presents a numerical approach, based on simulations performed on microstructural image data, which enables a comparison between two common experimental methods. Several key issues with the conventional “flow through” type tortuosity factor are highlighted, when used to characterise electrodes. As a result, a new concept called the “electrode tortuosity factor” is introduced, which captures the transport processes relevant to porous electrodes better than the “flow through” type tortuosity factor. The simulation results from this work demonstrate the importance of non-percolating (“dead-end”) pores in the performance of real electrodes. This is an important result for optimizing electrode design that should be considered by electrochemical modelers. This simulation tool is provided as an open-source MATLAB application and is freely available online as part of the TauFactor platform.
Gayon-Lombardo A, Lukas M, Brandon N, et al., 2020, Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries, npj Computational Materials, Vol: 6, ISSN: 2057-3960
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. The same network architecture is successfully applied to two very different three-phase microstructures: A lithium-ion battery cathode and a solid oxide fuel cell anode. A comparison between the real and synthetic data is performed in terms of the morphological properties (volume fraction, specific surface area, triple-phase boundary) and transport properties (relative diffusivity), as well as the two-point correlation function. The results show excellent agreement between datasets and they are also visually indistinguishable. By modifying the input to the generator, we show that it is possible to generate microstructure with periodic boundaries in all three directions. This has 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.
Nguyen T-T, Demortiere A, Fleutot B, et al., 2020, Resolving the Discrepancy in Tortuosity Determination for Battery Porous Electrodes Via a Numerical Approach, ECS Meeting Abstracts, Vol: MA2020-01, Pages: 2724-2724
<jats:p> 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.</jats:p> <jats:p>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, <jats:italic>i.e.,</jats:italic> between two parallel constant-value boundaries. Although this approach has been widely used for porous materials, including both electronic insulators (<jats:italic>e.g.,</jats:italic> a battery separator), and electronic conductors (<jats:italic>e.g.</jats:italic>, battery porous electrodes), it may be the case that the definition needs to be adjusted depending on the scenario being observed.</jats:p> <jats:p>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   was implemented in the TauFactor software package  to compare with the already-implemented diffusion-based method . 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
Finegan DP, Cooper SJ, 2019, Battery safety: data-driven prediction of failure, Joule, Vol: 3, Pages: 2599-2601, ISSN: 2542-4351
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.
Smith CTG, Mills CA, Pani S, et al., 2019, X-ray micro-computed tomography as a non-destructive tool for imaging the uptake of metal nanoparticles by graphene-based 3D carbon structures., Nanoscale, Vol: 11, Pages: 14734-14741
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.
Song B, Bertei A, Wang X, et al., 2019, Unveiling the mechanisms of solid-state dewetting in Solid Oxide Cells with novel 2D electrodes, Journal of Power Sources, Vol: 420, Pages: 124-133, ISSN: 0378-7753
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 yttriastabilized zirconia (YSZ), hereafter called 2D cells, is studied by coupling in-situ environmentalscanning 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. Thismicrostructural 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.
Yin C, Liu X, Wei J, et al., 2019, “All-in-Gel” design for supercapacitors towards solid-state energy devices with thermal and mechanical compliance, Journal of Materials Chemistry A, Vol: 7, Pages: 8826-8831, ISSN: 2050-7488
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.
Ouyang M, Bertei A, Cooper S, et al., 2019, Design of Fibre Ni/CGO Anode and Model Interpretation, 16th International Symposium on Solid Oxide Fuel Cells (SOFC-XVI)
Liu X, Taiwo O, Yin C, et al., 2019, Aligned ionogel electrolytes for high‐temperature supercapacitors, Advanced Science, Vol: 6, Pages: 1-7, ISSN: 2198-3844
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.
Ouyang M, Bertei A, Cooper SJ, et al., 2019, Design of fibre Ni/CGO anode and model interpretation, ECS Transactions, Vol: 91, Pages: 1721-1739, ISSN: 1938-6737
A new structure of Ni/gadolinium-doped ceria (CGO) is prepared by a highly tuneable and facile combination of electrospinning and tape-casting method. The structure consists of a network made by continuous Ni fibres and filled in with CGO matrices. When used as the anode of solid oxide fuel cell (SOFC), though it has a lower triple phase boundary (TPB) density, it exhibits better performance compared with impregnated and cermet Ni/CGO with higher nickel loading. An algorithm is developed to determine the ceria-pore double phase boundary (DPB) density with different distance from nickel phase. Using the results, the relative electrochemical reaction rate on DPB and TPB of three different electrodes are calculated and proves that fibre-matrices structure has the morphology advantage of efficiently making use of all ceria-pore DPB. The relative contribution of DPB and TPB in anode reaction of SOFC is quantified in the first time and the importance of DPB is further stressed. This work provides new inspirations in material design of SOFC/SOEC and develops a novel strategy to evaluate the performance of electrodes quantitatively.
Usseglio-Viretta FLE, Colclasure A, Mistry AN, et al., 2018, Resolving the discrepancy in tortuosity factor estimation for Li-Ion battery electrodes through micro-macro modeling and experiment, Journal of The Electrochemical Society, Vol: 165, Pages: A3403-A3426, ISSN: 1945-7111
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.
Daemi SR, Tan C, Volkenandt T, et al., 2018, Visualizing the carbon binder phase of battery electrodes in three dimensions, ACS Applied Energy Materials, Vol: 1, Pages: 3702-3710, ISSN: 2574-0962
This study presents a technique to directly characterize the carbon and binder domain (CBD) in lithium-ion (Li-ion) battery electrodes in three dimensions and use it to determine the effective transport properties of a LiNi0.33Mn0.33Co0.33O2 (NMC) electrode. X-ray nanocomputed tomography (nano-CT) is used to image an electrode composed solely of carbon and binder, whereas focused ion beam–scanning electron microscopy is used to analyze cross-sections of a NMC electrode to gain morphological information regarding the electrode and CBD porosity. Combining the information gathered from these techniques reduces the uncertainty inherent in segmenting the nano-CT CBD data set and enables effective diffusivity of its porous network to be determined. X-ray microcomputed tomography (micro-CT) is then used to collect a NMC data set that is subsequently segmented into three phases, comprised of active material, pore, and CBD. The effective diffusivity calculated for the nano-CT data set is incorporated for the CBD present in the micro-CT data set to estimate the ensemble tortuosity factor for the NMC electrode. The tortuosity factor greatly increases when compared to the same data set segmented without considering the CBD. The porous network of the NMC electrode is studied with a continuous pore size distribution approach that highlights median radii of 180 nm and 1 μm for the CBD and NMC pores, respectively, and with a pore throat size distribution calculation that highlights median equivalent radii of 350 and 700 nm.
Niania M, Podor R, Britton TB, et al., 2018, In situ study of strontium segregation in La<inf>0.6</inf>Sr<inf>0.4</inf>Co<inf>0.2</inf>Fe<inf>0.8</inf>O<inf>3- δ</inf>in ambient atmospheres using high-temperature environmental scanning electron microscopy, Journal of Materials Chemistry A, Vol: 6, Pages: 14120-14135, ISSN: 2050-7496
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.
Niania M, Podor R, Britton TB, et al., 2018, Correction: In situ study of strontium segregation in La<inf>0.6</inf>Sr<inf>0.4</inf>Co<inf>0.2</inf>Fe<inf>0.8</inf>O<inf>3- δ</inf>in ambient atmospheres using high-temperature environmental scanning electron microscopy (J. Mater. Chem. A (2018) DOI: 10.1039/c8ta01341a), Journal of Materials Chemistry A, Vol: 6, Pages: 14464-14464, ISSN: 2050-7488
© 2018 The Royal Society of Chemistry. Correction for 'In situ study of strontium segregation in La0.6Sr0.4Co0.2Fe0.8O3-δin ambient atmospheres using high-temperature environmental scanning electron microscopy' by Mathew Niania et al., J. Mater. Chem. A, 2018, DOI: 10.1039/c8ta01341a. The authors regret that the name of the first author of ref. 37 was displayed as "F. Pisigkin". The correct name should be "F. Piskin". The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
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).
Téllez Lozano H, Druce J, Cooper SJ, et al., 2017, Double perovskite cathodes for proton-conducting ceramic fuel cells: are they triple mixed ionic electronic conductors?, Science and Technology of Advanced Materials, Vol: 18, Pages: 977-986, ISSN: 1468-6996
Published by National Institute for Materials Science in partnership with Taylor & Francis. 18 O and 2 H diffusion has been investigated at a temperature of 300 °C in the double perovskite material PrBaCo 2 O 5+δ (PBCO) in flowing air containing 200 mbar of 2 H 2 16 O. Secondary ion mass spectrometry (SIMS) depth profiling of exchanged ceramics has shown PBCO still retains significant oxygen diffusivity (~1.3 × 10 −11 cm 2 s −1 ) at this temperature and that the presence of water ( 2 H 2 16 O), gives rise to an enhancement of the surface exchange rate over that in pure oxygen by a factor of ~3. The 2 H distribution, as inferred from the 2 H 2 16 O − SIMS signal, shows an apparent depth profile which could be interpreted as 2 H diffusion. However, examination of the 3-D distribution of the signal shows it to be nonhomogeneous and probably related to the presence of hydrated layers in the interior walls of pores and is not due to proton diffusion. This suggests that PBCO acts mainly as an oxygen ion mixed conductor when used in PCFC devices, although the presence of a small amount of protonic conductivity cannot be discounted in these materials.
This paper describes the use of a frequency domain, finite-difference scheme to simulate the impedance spectra of diffusion in porous microstructures. Both open and closed systems are investigated for a range of ideal geometries, as well as some randomly generated synthetic volumes and tomographically derived microstructural data. In many cases, the spectra deviate significantly from the conventional Warburg-type elements typically used to represent diffusion in equivalent circuit analysis. A key finding is that certain microstructures show multiple peaks in the complex plane, which may be misinterpreted as separate electrochemical processes in real impedance data. This is relevant to battery electrode design as the techniques for nano-scale fabrication become more widespread. This simulation tool is provided as an open-source MatLab application and is freely available online as part of the TauFactor platform.
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