Microstructural engineering of high-power redox flow battery electrodes via non-solvent induced phase separation
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Jacquemond, Remy Richard↗
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High-throughput, super-resolution 3D reconstruction of nano-structured solid oxide fuel cell electrodes and quantification of microstructure-property relationships
Nano-structuring methods are actively applied to solid oxide fuel cell electrodes to reduce the operating temperature while preserving a high electro-catalytic activity. The unique nanoscale microstructure is vital to electrochemical performance, yet not well quantified in three dimensions. Here, with a multi-stage recovering principle of distance correlation functions, the three-dimensional microstructures of La 0.8 Sr 0.2 MnO 3-δ nanoparticles infiltrated porous Y 0.16 Zr 0.84 O 2-δ electrodes are reconstructed with a dimension of 1024 × 1024 × 1024 voxels at a resolution of 7.5 nm from one two-dimensional micrograph. The key geometric characteristics, such as tortuosity factors, active surface/interface areas and three-phase boundary length, are calculated from the reconstructed three-dimensional microstructures at various loadings of La 0.8 Sr 0.2 MnO 3-δ . Combining with the analysis of distribution of relaxation times, the active three-phase boundary length is shown to be the main factor governing the electrode impedance, and is related quantitatively to the electrochemical process at high frequency. The accuracy of capturing nanoscale features is validated by the focused ion beam sectioning dataset of a Ni-Y 0.16 Zr 0.84 O 2-δ electrode at nanoscale resolution. Lastly, this work provides a promising strategy for reconstructing three-dimensional heterogeneous nanostructures from one super-resolution two-dimensional micrograph, and demonstrates a quantitative approach for uncovering processing-structure-property relationships of nanostructured electrodes and beyond.
25 ENERGY STORAGE↗
SOC Microstructural Analyzer
This program was designed to analyze the 3-phase microstructure of the electrodes of a solid oxide fuel cell (SOFC) or electrolysis cell (SOEC), both referred to in combination as a solid oxide cell (SOC). It is agnostic to the exact system, so it could be repurposed to analyze any 3-phase microstructure. This tool directly analyzes segmented voxel-based data that has been segmented into phase IDs (1,2,3). The voxels will be analyzed directly for: - tortuosity factors - triple phase boundaries - 2-phase interfacial areas, using a meshed isosurface - mean diameters of each phase, using an inscribed sphere method - standard deviation of the diameters of each phase, from the same inscribed sphere data - connectivity information Comprehensive information is available in the readme file (within the zipped repository in Markdown language, and also available here as a rendered PDF). Please cite this page / DOI, as well as https://doi.org/10.1111/jace.14775, for usage.
3D microstructure↗
Methodology of Atomic Force Microscopy Visualization of Electrode–Electrolyte Interfaces
Electrochemical atomic force microscopy (EC-AFM) provides unprecedented insights into the microstructure of electrode–electrolyte interfaces during electrochemical reactions. However, performing EC-AFM measurements has many challenges, for example, drift, contamination, and probe degradation. We present solutions to these experimental issues through electrochemical cell design and carefully chosen experimental parameters. The possibility that the probes can react with the interface during scanning, generating false-positive electrochemical dynamics, is discussed as an example of the challenge of high-fidelity EC-AFM measurement. Here, we demonstrate this effect in highly ordered pyrolytic graphite and show that we could use electrochemical control of the AFM probe to enable high-fidelity in situ AFM visualization of solid–liquid interfaces during electrochemical reactions.
Electrochemical cells↗
Crossover as Determinant for Safety and Performance Tradeoffs in Proton Exchange Membrane Water Electrolyzers
Hydrogen (H2) crossover is a pressing challenge constraining safe and efficient operation of proton exchange membrane water electrolyzers (PEMWEs) especially amongst strides to employ thinner membranes, which enables improved energy efficiency, and elevated cathode pressures, that reduces the energy burden on downstream compressors. Here, we develop a microstructure-aware multicomponent reactive-transport framework that resolves dissolved and gaseous H2 transport pathways and mechanistically links electrode architecture to crossover related safety and performance. We show that operability is co-governed by the cathode catalyst layer (CCL) and the anode porous transport layer (APTL) which sets the H2 crossover flux and the egress capacity respectively. Elevated Pt/C ratio in the CCL suppresses crossover flux by up to 23% while a higher APTL porosity lowers H2 in O2 fraction by 0.6% in the anode effluent. We condense the findings into (cathode pressure-current density) maps overlaid with safety limits and performance targets and ultimately define two safety-performance unified metrics to gauge the size and quality of the operating window. Given the push towards higher pressure and deeper turndown for renewable integration, this study provides mechanistic design guidance to prevent crossover-induced safety risks while preserving the desired performance.
Electrolysis↗
Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes
Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.
25 ENERGY STORAGE↗
Evaluating microstructure evolution in an SOFC electrode using digital volume correlation
Degradation mechanisms within solid oxide fuel cells (SOFC) during thermal cycling limit operational start-up times and cell lifetime, and must therefore be better understood and mitigated. This work explores such mechanisms using digital volume correlation (DVC) techniques applied to lab-based X-ray tomograms where the microstructural evolution is evaluated during the operational cycling of a Ni–YSZ/YSZ cell. To emulate reduced start-up times, five tomograms were collected over four operational thermal cycles to 750 °C at various ramp-rates: 3, 10, 20 and 30 °C min –1 . Two key features are observed in the 3D strain profiles. Firstly, during low ramp-rates the material produces microscopic channels towards cluster-points. This is thought to be caused by the ceramic skeleton inhibiting the effects of sintering. Secondly, previously unseen macroscopic ‘waves’ developed after high-rate cycling, consisting of linear regions of compression and tension throughout the sample. These wave features decay away from a heterogeneous defect which is thought to be responsible for the non-uniform strain profile. This work demonstrates the first use of sub-micron DVC computations applied to an SOFC exposed to operationally relevant temperatures. Lastly, these findings will assist in the development of new electrode materials from their fabrication to operation, ultimately supporting commercial viability of SOFCs.
30 DIRECT ENERGY CONVERSION↗
Enabling high rate capability, low internal resistance, and excellent cyclability for vanadium redox flow batteries utilizing ultrafast laser-structured graphite felt
The electrochemical performance of vanadium redox flow batteries (VRFBs) was enhanced via laser-patterned graphite felt (GF) electrodes. The laser-structured GF electrodes engineered via preparing a series of well-ordered microscopic channel structures with an average width of 200 μm, creating a three-dimensional carbon framework. The ultrafast laser patterning increased the porosity by 10%, as compared to pristine electrode. The analysis of the overpotential distribution using electrochemical impedance spectroscopy revealed that the electrode polarization involving charge transfer and diffusion resistance is strongly alleviated with the aid of carbon micro-perforation. The advanced design of laser-structured GF electrode also exhibits high rate capability, low internal resistance, and excellent cyclability. The improved performance was attributed to the synergistic effect involving (i) high electrochemically active surface area for rapid electrochemical reactions (i.e., V(II)/V(III) and V(IV)/V(V) redox couples) and (ii) excellent transport properties for facile electron/ion/species transport. The micro-scale channels created with the laser-ablation technique act as capillary structures and enabled homogeneous and rapid wetting of GF electrodes as formulated with the classical Washburn equation. The VRFB equipped with the laser-structured GF electrodes delivered a high discharge capacity with exceptional capacity retention upon extended cycling (>84.9%). In conclusion, accordingly, the design of laser-structured electrode paves the pathway towards finely tuning the electrode internal microstructure for improved cyclability, durability, and capacity retention in various electrochemical energy storage/conversion devices (e.g. all-vanadium redox flow batteries).
25 ENERGY STORAGE↗
X-ray Micro-Computed Tomography for Structural Analysis of All-Solid-State Battery at Pouch Cell Level
Characterizing the microstructure of all-solid-state batteries (ASSBs) during fabrication and operation is vital for their advancement, particularly as scaling to pouch cell levels introduces challenges in probing large-scale microstructural evolution. This work highlights the potential of synchrotron X-ray micro-computed tomography (sXCT) as a nondestructive, rapid (<30 min), and high-resolution technique for visualizing and quantifying key microstructural features, including overhang, porosity, contact loss, active surface area, and tortuosity, in all-solid-state pouch cells. The large field of view (up to millimeters) of sXCT enables detailed analysis at an industry-relevant scale, bridging the gap between laboratory research and commercial applications. Furthermore, integrating realistic sXCT-derived 3D models into multiphysics simulations could provide insights into chemo-mechanical degradation, particularly at the edges of the pouch cells, offering a pathway for designing robust, high-performance ASSBs. This perspective establishes sXCT as an indispensable tool for advancing both the understanding and the engineering of next-generation energy storage systems.
25 ENERGY STORAGE↗
Voltage cycling as a dynamic operation mode for high temperature electrolysis solid oxide cells
Solid Oxide Electrolysis Cells (SOECs) have emerged as a promising technology for the efficient production of H2 via high-temperature electrolysis. However, power input from dynamic energy sources remains a significant challenge for their long-term stability. It is important to analyze the tolerance of cells under dynamic operation conditions. This study focuses on evaluating the impact of voltage cycling on the performance and durability of electrode-supported SOECs. We explore the operational limits and degradation mechanisms of SOECs subjected to various voltage conditions and find that the cells have high tolerance for dynamic voltage. Voltage cycling between 1.3 V and 1.5 V for 9000 cycles does not damage the cell. Conversely, cycling to higher voltages (≥1.7 V) results in accelerated degradation. Advanced characterization is used to screen for various degradation modes post operation. Within the oxygen electrode, XRD and STEM EDS find compositional and phase evolution in all voltage cycled samples including increased decomposition of the air electrode resulting in cation migration. Microstructural analysis of the fuel electrode from nano-CT data shows minimal change throughout the sample set and no evidence of Ni migration, indicating the fuel electrode is stable and not impacted by cycling to higher voltages within the timeframe studied.
Zhu, Zhikuan↗
Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. Finally, the ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.
36 MATERIALS SCIENCE↗
Understanding the role of water-soluble guar gum binder in reducing capacity fading and voltage decay of Li-rich cathode for Li-ion batteries
The practical application of high-capacity Li-rich cathode materials is hindered by capacity fading and voltage decay. The capacity fading and voltage decay could be effectively overcome by using water-soluble guar gum (GG) binder instead of traditional polyvinylidene fluoride (PVDF). However, the specific role of the GG binder is not clear yet, though the GG binder can significantly improve the electrochemical performance of Li-rich cathode. To understand the effect of GG binder on the morphology, microstructure of electrode and electrode/electrolyte interfaces, ex-situ scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray adsorption near edge spectroscopy (XANES), in-situ electrochemical impedance spectroscopy (EIS) were applied to comparatively study the charge-discharge processes of Li-rich Li 1.2 Ni 0.2 Mn 0.6 O 2 cathode when using GG and PVDF as binders. The results indicate that the GG binder can prevent electrode crack and active material loss, ascribing to the strong mechanical adhesion of GG binder with active material particles and current collector. It has found that the GG binder can also induce the formation of a uniform layer on Li 1.2 Ni 0.2 Mn 0.6 O 2 particles’ surface. As a consequence, both the electrolyte decomposition and the electrode corrosion were significantly inhibited. The strong chelation between Mn 2+ and polar OH group restrain Mn ion dissolution, which contributes to surface structural transformation mitigation. Here, our study reveals the role of water-soluble GG binder in reducing capacity fading and voltage decay of Li-rich material and is of great importance in design functional binders for high-performance Li-rich electrodes.
25 ENERGY STORAGE↗
Roll-to-roll solvent-free manufactured electrodes for fast-charging batteries
In response to the growing demand for lithium-ion batteries (LIBs), we demonstrate a solvent-free manufacturing technology that can avoid toxic organic solvents and form unique electrode structures to overcome the bottlenecks in low costs and fast charging. The lower tortuosity achieved by the open pores in the dry-printed (DP) electrode allows for a shorter Li+ diffusion pathway, which leads to better rate performance. The DP pouch cells exhibit higher capacity retention of 78% and 69% at 3C and 4C, respectively, compared with 67% and 52% for the slurry cast (SL) cells at the same rates. Moreover, the coating layer on the surface of active materials prevents the excess side reaction between active materials and electrolytes, which prolongs the cycle life of the DP cells. This manufacturing process is a roll-to-roll system with immense potential to be scaled up, providing a more efficient and economical way for battery manufacturing.
36 MATERIALS SCIENCE↗
A two-dimensional analytical unit cell model for redox flow battery evaluation and optimization
Cell performance optimization is important for improving the overall system efficiency of a redox flow battery. To gain better insights into key controlling factors of system efficiency, this work first proposed a theoretical model for a unit flow battery cell by extending a two-dimensional analytic model to a full battery cell. Such a model is then used for cell performance optimization after validating it with experimental and numerical modeling data. With the model results, the activation, equilibrium, and pump energy losses are identified as the dominant sources of battery energy losses. Further, a guideline for reducing these sources is also proposed. Following the guideline, the mass transport coefficient is shown as a key control factor of the equilibrium energy loss and Coulombic efficiency (CE). Approaches are then proposed to improve CE and the overall system efficiency. The mass transport is also revealed as the mechanism of pump rate optimization where an optimal pump rate significantly reduces the equilibrium energy loss. With both low equilibrium and pump energy losses, an optimal electrode porosity or specific area design can further improve a battery's system efficiency based on an optimal porosity predicted by the present model. The model also demonstrates distinct behaviors and overestimation in the system efficiency when reduced to a zero-dimensional model with neglected mass transport resistance. With the new model, the guideline, and new insights, this work provides a reliable and efficient tool for the evaluation and optimization of redox flow battery design in practical applications.
25 ENERGY STORAGE↗
Computational design of microarchitected porous electrodes for redox flow batteries
Porous electrodes are used as the core reactive component across electrochemical technologies. In flowing systems, controlling the fluid distribution, species transport, and reactive environment is critical to attaining high performance. However, conventional electrode materials like felts and papers provide few opportunities for precise engineering of the electrode and its microstructure. To address these limitations, architected electrodes composed of unit cells with spatially varying geometry determined via computational optimization are proposed. Resolved simulation is employed to develop a homogenized description of the constituent unit cells. These effective properties serve as inputs to a continuum model for the electrode when used in the negative half-cell of a vanadium redox flow battery. Porosity distributions minimizing power loss are then determined via computational design optimization to generate architected porosity electrodes. The architected electrodes are compared to bulk, uniform porosity electrodes and found to lead to increased power efficiency across operating flow rates and currents. The design methodology is further used to generate a scaled-up electrode with comparable power efficiency to the bench-scale systems. Finally, the variable porosity architecture and computational design methodology presented here thus offers a novel pathway for automatically generating spatially engineered electrode structures with improved power performance.
25 ENERGY STORAGE↗
Effect of Electrode/Electrolyte Coupling on Birnessite (δ-MnO 2 ) Mechanical Response and Degradation
Understanding the deformation of energy storage electrodes at a local scale and its correlation to electrochemical performance is crucial for designing effective electrode architectures. In this work, the effect of electrolyte cation and electrode morphology on birnessite (δ-MnO 2 ) deformation during charge storage in aqueous electrolytes was investigated using a mechanical cyclic voltammetry approach via operando atomic force microscopy (AFM) and molecular dynamics (MD) simulation. In both K 2 SO 4 and Li 2 SO 4 electrolytes, the δ-MnO 2 host electrode underwent expansion during cation intercalation, but with different potential dependencies. When intercalating Li + , the δ-MnO 2 electrode presents a nonlinear correlation between electrode deformation and electrode height, which is morphologically dependent. Further, these results suggest that the stronger cation–birnessite interaction is the reason for higher local stress heterogeneity when cycling in Li 2 SO 4 electrolyte, which might be the origin of the pronounced electrode degradation in this electrolyte.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Scaling deep learning for material imaging with a pseudo 3D model for domain transfer
The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.
25 ENERGY STORAGE↗