Microstructure Reconstruction Research Articles

Viscoelastic Analysis of Asphalt Concrete with a Digitally Reconstructed Microstructure.

In the finite element analysis of asphalt concrete (AC), it is nowadays common to incorporate the information from the underlying scales to study the overall response of this material. Heterogeneity observed at the asphalt mixture scale is analyzed in this paper. Reliable finite element analysis (FEA) of asphalt concrete comprises a set of complex issues. The two main aspects of the asphalt concrete FEA discussed in this study are: (1) digital reconstruction of the asphalt pavement microstructure using processing of the high-quality images; and (2) FEA of the asphalt concrete idealized samples accounting for the viscoelastic material model. Reconstruction of the asphalt concrete microstructure is performed using a sequence of image processing operations (binarization, removing holes, filtering, segmentation and boundaries detection). Geometry of the inclusions (aggregate) are additionally simplified in a controlled mode to reduce the numerical cost of the analysis. As is demonstrated in the study, the introduced geometry simplifications are justified. Computational cost reduction exceeds of several orders of magnitude additional modeling error occurring due to the applied simplification technique. Viscoelastic finite element analysis of the AC identified microstructure is performed using the Burgers material model. The analysis algorithm is briefly described with a particular focus on the computational efficiency aspects. In order to illustrate the proposed approach, a set of 2D problems is solved. Numerical results confirm both the effectiveness of the self-developed code and the applicability of the Burgers model to the analyzed class of AC analysis problems. Further research directions are also described to highlight the potential benefits of the developed approach to numerical modeling of asphalt concrete.

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework called ‘Micro3Diff’ for 2D-to-3D reconstruction of microstructures using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a concept referred to as ‘multi-plane denoising diffusion’, which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic or experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs.

Utilizing Synchrotron-Based X-ray Micro-Computed Tomography to Visualize the Microscopic Structure of Starch Hydrogels In Situ.

This study aimed to visualize the microstructures of starch hydrogels using synchrotron-based X-ray micro-computed tomography (μCT). Waxy maize starch (WMS, 3.3% amylose, db), pea starch (PS, 40.3% amylose), and high-amylose maize starch (HMS, 63.6% amylose) were cooked at 95 and 140 °C to prepare starch hydrogels. WMS and HMS failed to form a gel after 95 °C cooking and storage, while PS developed a firm gel. At 140 °C cooking, HMS of a high amylose nature was fully gelatinized and generated a rigid gel with the highest strength. Both scanning electron microscopy (SEM) and μCT revealed the unique structural features of various starch hydrogels/pastes prepared at different temperatures, which were greatly affected by the degree of swelling and dispersity of the starches. As a nondestructive method, μCT showed certain advantages over SEM, including minimal shrinkage of the hydrogels, relatively simple sample preparation, and allowing for three-dimensional reconstruction of the hydrogel microstructure. This study indicated that synchrotron-based μCT could be a useful technique in visualizing biopolymer-based hydrogels.

Stochastic reconstruction of heterogeneous microstructure combining sliced Wasserstein distance and gradient optimization

Computational reconstruction methods play an important role in integrated computational materials engineering, providing an efficient and inexpensive way for multi-modal and multi-length datasets construction, material design, and property prediction research. However, existing methods are either not computationally inefficient (traditional methods), or data-dependent and difficult to handle various reconstruction tasks (learning-based methods). Hybrid methods show advantages in addressing these problems, but they are still necessary to advance in low dataset dependence, generalization, and controllability. Herein, we propose a novel descriptor-based gradient optimization method, which utilizes multipoint statistical information and sliced Wasserstein metric for accurate microstructure characterization and reconstruction. Our method aims to respect and reproduce all the local patterns from training images, realizing similar higher-order statistics while better reproducing local features. To this end, sliced Wasserstein metric is introduced to measure the distance between higher-order distributions, and gradient optimization is adopted for fast and efficient reconstructions. Hierarchical and multitemplate strategies are developed to further strengthen characterization and reconstruction ability of heterogeneous structures. Additionally, our method can complete 2D-to-2D, 2D-to-3D, and 3D-to-3D reconstruction tasks even in the case of a single sample. Multiple sets of experiments are conducted under different reconstruction tasks for verification, and comparisons of visualization results, statistical parameters, and physical properties show the superiority of our method.

From bulky Si to porous medium entropy alloy: Enabling highly efficient Li storage via the simultaneous reconstruction of microstructure and component

We show here that the Li-storage performance of Si anode can be substantially improved by engineering a multilevel nanoporous SiGeTiZnCuAl medium entropy alloy (NP-MEA). The NP-MEA is directly engineered through a corrosion-driven phase and structure simultaneous reconstruction by partially etching Al and Zn from the multi-element master alloy. The unique multi-elemental interactions can revolutionize the chemical microenvironment, and thus stabilize the structure and impel the transportation of the electrons and ions. The metallic sponge offers multilevel pore channels for electrolyte permeation and ample space for volume change accommodation, abundant accessible active sites for lithiation/delithiation, as well as the rigid skeleton for expediting the electron transportation and stabilizing the structure. Benefiting from the synergy of component and structure, the NP-MEA delivers a high reversible capacity of 646.1 mAh g−1 after 500 cycles at a current density of 2 A g−1.

Statistical analysis of effective crack properties by microstructure reconstruction and phase-field modeling

AbstractUnderstanding the relation between the microstructure and the material’s effective behavior is an important aspect in inverse computational materials engineering. Especially in the context of localized, inelastic phenomena like plasticity and crack growth, the microstructure morphology plays a crucial role. Due to the stochastic nature of heterogeneous media, a statistical analysis over multiple simulations is necessary, since even with the same material, the simulated crack paths and effective crack lengths are highly dependent on the specific locations of microstructural features. A relevant factor that limits this type of investigation is the high cost of real microstructure data. This work presents a digital workflow for exploring the fracture properties of materials. Therein, the required statistical analyses are facilitated by an algorithm that reconstructs multiple realization of a material structure given a single example. The reconstructed structures are discretized with a regular non-conforming mesh with a diffuse interface and crack representation. Crack phase-field simulations are conducted in order to analyze the effective response. An in-depth introduction to the required methods is given together with a statistical evaluation of the conducted numerical experiments. It is concluded that the statistical variation of the effective material behavior overshadows morphological trends in the presented case. This confirms the relevance and utility of complementing simulation-based workflows with microstructure reconstruction and statistical analyses.

Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach

The prediction of material properties from a given microstructure and its reverse engineering displays an essential ingredient for accelerated material design. However, a comprehensive methodology to uncover the processing-structure-property relationship is still lacking. Herein, we develop a methodology capable of understanding this relationship for differently processed porous materials. We utilize a multi-method machine learning approach incorporating tomographic image data acquisition, segmentation, microstructure feature extraction, feature importance analysis and synthetic microstructure reconstruction. Enhanced segmentation with an accuracy of about 95% based on an efficient annotation technique provides the basis for accurate microstructure quantification, prediction and understanding of the correlation of the extracted microstructure features and electrical conductivity. We show that a diffusion probabilistic model superior to a generative adversarial network model, provides synthetic microstructure images including physical information in agreement with real data, an essential step to predicting properties of unseen conditions.

Quantifying the mechanical degradation of solid oxide cells based on 3D reconstructions of the real microstructure using a unified multiphysics coupling numerical framework

In this work, a multiphysics coupling numerical framework is developed to quantitatively investigate the initial performances of solid oxide cells (SOCs) based on the thermodynamically consistent integration of the finite element method (FEM) and the phase field method (PFM) to reveal the interaction between species-defect transport, electrochemical reaction kinetics, stress and mechanical damage in SOC electrodes. The modeling framework is validated by comparing the simulation results based on real 3D microstructure reconstructions of specific SOCs with the experimental measurements in electrolysis and fuel cell modes. The phenomena of internal microstructure fracture and delamination observed in the experiment thus can be numerically modeled to quantify the effects of thermal and chemical stresses on the mechanical degradation of heterogeneous electrodes. The framework is also applied in the cross-scale quantification of the possible mechanical damage in SOCs subjected to different mechanical boundary conditions. The framework proposed in this work is flexible, can be superimposed with other fields, and incorporates input from cross-scale simulations. It provides a great potential platform for the optimization of future energy devices considering actual operating conditions and fills the gap in theoretical multiphysics modeling in the field of SOCs.

Stochastic reconstruction and performance prediction of cathode microstructures based on deep learning

The effective properties of lithium-ion battery (LIB) cathode are determined by both the volume fractions of constituents and the morphological features of microstructure. However, it is difficult to establish an accurate quantitative relationship between the macroscopic effective properties and microstructural features. Deep learning techniques, due to their exceptional nonlinear fitting capabilities, have been widely applied in various complex fields. Our study presents a generation scheme of numerous three-dimensional (3D) digital microstructures of cathode, using a deep convolutional neural network (CNN)-based stochastic reconstruction algorithm combining with the scanning electron microscope (SEM) images. The reconstructed samples are substituted with the corresponding finite element (FE) models, and the effective mechanical and electrochemical properties are assessed through the FE-based homogenization theory. Finally, the generated cathode samples and their effective properties are used to train the 3D CNN for performance prediction. This study demonstrates that the deep learning approaches can accurately and rapidly reconstruct the microstructure of cathode and predict their effective properties. Furthermore, the established framework can be extended to other heterogeneous materials.

Process-Structure-Property Relationship Development in Large-Format Additive Manufacturing: Fiber Alignment and Ultimate Tensile Strength.

Parts made through additive manufacturing (AM) often exhibit mechanical anisotropy due to the time-based deposition of material and processing parameters. In polymer material extrusion (MEX), printed parts have weak points at layer interfaces, perpendicular to the direction of deposition. Poly(lactic acid) with chopped carbon fiber was printed on a large-format pellet printer at various extrusion rates with the same tool pathing to measure the fiber alignment with deposition via two methods and relate it to the ultimate tensile strength (UTS). Within a singular printed bead, an X-ray microscopy (XRM) scan was conducted to produce a reconstruction of the internal microstructure and 3D object data on the length and orientation of fibers. From the scan, discrete images were used in an image analysis technique to determine the fiber alignment to deposition without 3D object data on each fiber's size. Both the object method and the discrete image method showed a negative relationship between the extrusion rate and fiber alignment, with -34.64% and -53.43% alignment per extrusion multiplier, respectively, as the slopes of the linear regression. Tensile testing was conducted to determine the correlation between the fiber alignment and UTS. For all extrusion rates tested, as the extrusion multiplier increased, the percent difference in the UTS decreased, to a minimum of 8.12 ± 14.40%. The use of image analysis for the determination of the fiber alignment provides a possible method for relating the microstructure to the meso-property of AM parts, and the relationship between the microstructure and the properties establishes process-structure-property relationships for large-format AM.

Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models

Microstructure reconstruction serves as a crucial foundation for establishing process–structure–property (PSP) relationship in material design. Confronting the limitations of variational autoencoder and generative adversarial network within generative models, this study adopted the denoising diffusion probabilistic model (DDPM) to learn the probability distribution of high-dimensional raw data and successfully reconstructed the microstructures of various composite materials, such as inclusion materials, spinodal decomposition materials, chessboard materials, fractal noise materials, and so on. The quality of generated microstructure was evaluated using quantitative measures like spatial correlation functions and Fourier descriptor. On this basis, this study also achieved the regulation of microstructure randomness and the generation of gradient materials through continuous interpolation in latent space using denoising diffusion implicit model (DDIM). Furthermore, the two-dimensional microstructure reconstruction was extended to three-dimensional framework and integrated permeability as a feature encoding embedding. This enables the conditional generation of three-dimensional microstructures for random porous materials within a defined permeability range. The permeabilities of these generated microstructures were further validated through the application of the lattice Boltzmann method. The above methods provide new ideas and references for material reverse design.

An integrated microstructure reconstruction and meshing framework for finite element modeling of woven fiber-composites

Critical to finite element (FE) analysis of fiber-reinforced composites is accurately reproducing microstructural features via high-quality meshes such that the material heterogeneity and anisotropy are properly captured. Here we present an integrated computational framework for generating realistic FE models of woven composites with high fiber volume fractions (>50%). This framework relies on a virtual microstructure reconstruction algorithm that first generates a geometric model of loosely-woven yarns (i.e., bundles of fibers), followed by performing an FE compaction simulation to create the final textile composite microstructure. A non-iterative meshing algorithm, i.e., conforming to interface structured adaptive mesh refinement (CISAMR), has been adapted to automatically transform synthesized microstructures into conforming FE meshes. CISAMR can handle challenging geometrical features such as thin resin interstices and yarn interpenetrations (an artifact of the FE compaction) without the need to reprocess the digital geometry before mesh generation. We show that the homogenized elastic moduli obtained from FE simulations of an 8-harness satin woven composite laminate agree well with experimental measurements (within 3%), thereby validating the accuracy of the framework. We also present several other mechanical analysis examples, including a nonlinear damage simulation, that further demonstrate the ability of this framework to construct high-fidelity FE models of intricate woven composites with varying 2D and 3D woven architectures.

Gaussian random field-based characterization and reconstruction of cancellous bone microstructure considering the constraint of correlation structure

The macro scale physical properties of cancellous bone materials are governed by the microstructural features, which is of great significance for the multi-scale research of cancellous bone and the inverse design of bone-mimicking materials. Therefore, it is essential to characterize the natural cancellous bone samples, and reconstruct the microstructures with the biomimetic osteointegration and mechanical properties. In this research, a novel approach for the characterization and reconstruction of cancellous bone was proposed, based on the medical image analysis and anisotropic three-dimensional Gaussian random field (GRF). The geometric similarity, i.e. the interface curvature distribution (ISD), was meticulously studied, which is important to the osteointegration ability. And the mechanical properties were validated by the stress-strain curves under the large compressive strain simulated by the smoothed particle hydrodynamic (SPH) method. In addition, the effects of the generation parameters of GRF-based biomimetic microstructures on the apparent properties were analyzed. The ISD results demonstrated that both GRF and micro-CT groups had the similar columnar morphological properties, while the latter had more hyperbolic features. And it was found that the GRF-based biomimetic microstructures and the natural bone samples based on micro-CT (MCT) had the similar failure mode. The concordance correlation coefficient between MCT and GRF pairs was 0.8685, with a Pearson ρ value of 0.8804, and significance level p<0.0001. The Bland-Altman LoA was 0.1647 MPa with 95 % (1.96SD) lower and upper bound value between −0.2892 and 0.6185 MPa. The two groups had almost the same elastic modulus with the mean absolute percentage error (MAPE) of 7.84 %. While the yield stress and total conversion energy of the GRF-based samples were lower than those of the natural bone samples, and the MAPE were 16.99 % and 16.27 %, respectively. Although it meant the lower structural efficiency, the huge design space of this approach and advanced 3D printing technology can provide great potential for the design of orthopedic implants.

Unlocking the potential: analyzing 3D microstructure of small-scale cement samples from space using deep learning

Due to the prohibitive cost of transporting raw materials into Space, in-situ materials along with cement-like binders are poised to be employed for extraterrestrial construction. A unique methodology for obtaining microstructural topology of cement samples hydrated in microgravity environment at the International Space Station (ISS) is presented here. Distinctive Scanning Electron Microscopy (SEM) micrographs of hardened tri-calcium silicate (C3S) samples were used as exemplars in a deep learning-based microstructure reconstruction framework. The proposed method aids in generation of an ensemble of microstructures that is inherently statistical in nature, by utilizing sparse experimental data such as the C3S samples hydrated in microgravity. The hydrated space-returned samples had exhibited higher porosity content (~70 %) with the portlandite phase assuming an elongated plate-like morphology. Qualitative assessment of the volumetric slices from the reconstructed volumes showcased similar visual characteristics to that of the target 2D exemplar. Detailed assessment of the reconstructed volumes was carried out using statistical descriptors, and was further compared against micro-CT virtual data. The reconstructed volumes captured the unique microstructural morphology of the hardened C3S samples of both space-returned and ground-based samples, and can be directly employed as Representative Volume Element (RVE) to characterize mechanical/transport properties.

A new approach to three-dimensional microstructure reconstruction of a polycrystalline solar cell using high-efficiency Cu(In,Ga)Se2

A new method for efficiently converting electron backscatter diffraction data obtained using serial sectioning by focused ion beam of a polycrystalline thin film into a computational, three-dimensional (3D) structure is presented. The reported data processing method results in a more accurate representation of the grain surfaces, reduced computer memory usage, and improved processing speed compared to traditional voxel methods. The grain structure of a polycrystalline absorption layer from a high-efficiency Cu(In,Ga)Se2 solar cell (19.5%) is reconstructed in 3D and the grain size and surface distribution is investigated. The grain size distribution is found to be best fitted by a log-normal distribution. We further find that the grain size is determined by the [Ga]/([Ga] + [In]) ratio in vertical direction, which was measured by glow discharge optical emission spectroscopy. Finally, the 3D model derived from the structural information is applied in optoelectronic simulations, revealing insights into the effects of grain boundary recombination on the open-circuit voltage of the solar cell. An accurate 3D structure like the one obtained with our method is a prerequisite for a detailed understanding of mechanical properties and for advanced optical and electronic simulations of polycrystalline thin films.

Stochastic Reconstruction of 3D Heterogeneous Microstructure Using a Column-Oriented Multiple-Point Statistics Program

Abstract Three-dimensional (3D) microstructure reconstruction is a key approach to exploring the relationship between pore characteristics and physical properties. Viewing the training image as a prior model, multiple-point statistics (MPS) focus on reproducing spatial patterns in the simulation grid. However, it is challenging to efficiently generate 3D nonstationary models with varying microstructures. In this work, we propose column-oriented simulation (ColSIM) to achieve the stochastic reconstruction of 3D porous media. A heterogeneous system is understood as a spatially evolving process that consists of frequent transitions of small magnitude and abrupt changes of large magnitude. First, a training image selection step is suggested to find representative microstructures. Our program applies modified Hausdorff distance, t-distributed stochastic neighboring embedding, and spectral clustering to organize two-dimensional (2D) candidate images. The medoid of each group is applied to guide the following programs. Second, we introduce column-oriented searching into MPS. To save simulation time, a subset of conditioning points is checked to find desired instances. Our program suggests an early stopping strategy to address complex microstructures. Third, a contrastive loss term is designed to create 3D models from 2D slice. To automatically calibrate the volume fraction and simplify parameter specification, the computer consistently monitors the difference between the present model and the target. The performance of ColSIM is examined by 3D multiphase material modeling and 3D heterogeneous shale simulation. To achieve quantitative evaluation, we compute various statistical functions and physical descriptors on simulated realizations. The proposed ColSIM exhibits competitive performance in terms of calculation efficiency, microstructure reproduction, and spatial uncertainty.

Recursive aggregate segmentation by erosion and reconstitution (RASER) to characterize concrete microstructure using complementarity of X-ray and neutron computed tomography

The reconstruction of concrete microstructure with computed tomography (CT) images is a challenging problem because concrete is associated with poly-mineral aggregates, various aggregate sizes, high volume fraction of aggregates, rough aggregate surface, small and large connected aggregates, and others. To tackle some of the issues in concrete microstructure reconstruction, CT images are obtained using the complementarity of X-ray and neutron while a recursive aggregate segmentation by erosion and reconstitution (RASER) process is proposed to separate connected aggregates for both 3D and 2D. The RASER algorithm is based on the recursive and iterative processes of erosion and reconstitution of aggregate surface while increasing the eroding layer thickness until a constant number of aggregates is reached. Based on the RASER technique, the 3D segmented concrete microstructure illustrates that aggregates are not skewed based on their sizes and locations while neither under- nor over-segmented in this study. Furthermore, we found that most 2D microstructure analysis can lead to over-segmentation because of artificial section segmentation and rough aggregate surface.

2D and 3D Microstructural Reconstruction of Nodular Cast Iron

2D and 3D Microstructural Reconstruction of Nodular Cast Iron

Discontinuous dynamic recrystallization behavior and texture evolution of large-size pure titanium flat ingot via electron beam cold hearth melting during short process direct hot rolling

Large-size pure titanium ingots melted by single electron beam cold hearth melting (EBCHM)) could be rolled into large-width and heavy-weight titanium strip coils using short process without forging. It is crucial to study the large-span refinement mechanism from highly coarse grains, whose sizes are in the order of ∼cm, to micronmeter. In this study, the recrystallization mechanism and texture evolution of pure titanium with a size of 1570 × 200 × > 8000 mm was investigated by multi-pass simulated rolling and experimental rolling. The results indicated the hot deformation can result in marked reduction in grain size of the as-cast grain, from 3 to 5 cm to 17–23 μm. The discontinuous dynamic recrystallization (DDRX) mechanism of a pure titanium ingot during direct multi-pass hot deformation causes the reconstruction of as-cast microstructure and refinement of hot rolling microstructure. Furthermore, DDRX occurred, during hot deformation, and was accompanied with weakening of deformation texture. Raising the initial hot deformation temperature results in a modification of the texture type of the {0001} crystal plane. The strength of the texture experiences a decrease initially and then eventually an increase. This alteration in strength is inversely related to the degree of dynamic recrystallization. Based on the simulated rolling experiment, the results of experimental rolling also found the coarse as-cast grains was broken and reconstructed. It has strength (average yield strength: 334 MPa, ultimate tensile strength: 371 MPa), ductility (total elongation: 30 %). Such a combination of mechanical properties makes the hot rolling plate a potential choice for sheet practical application.

Bubble and Dissolved Gas Distribution in Flow through Membraneless Water Electrolysis

Membraneless water electrolysis devices offer an alternative method for hydrogen production without the use of a membrane separator used in traditional alkaline (porous diaphragm) or PEM water electrolysis (polymer electrolyte membrane). This could offer lower capital cost devices (only requiring two electrodes and a cell body) with simple and scalable manufacturing. It could also result in lower operating costs if highly conductive liquid electrolytes are used. Membraneless devices come in a variety of forms including parallel plate, diverging flow through and buoyancy driven devices to name a few. However, the key concept between them is using fluid forces in the bulk electrolyte (controlled by device or porous electrode structure) to remove bubbles from the electrode surfaces and attempt to reduce cross-over of dissolved hydrogen and oxygen, as well as their two-phase flow [1]. Since there is no separator or membrane to restrict transport between the anode and cathode the fluid convection with laminar flows must be used to guide the products to the separate outlets with minimal cross-over and to minimise bubble accumulation on the electrode surfaces. Bubbles on the electrodes increase the activation overpotential by reducing the active electrochemical surface area and decrease the effective conductivity of the flowing electrolyte by inducing tortuous pathways in the electrode gap. In this study, we look at the single-phase and two-phase flow response of flow through membraneless electrolyser by coupling secondary current distribution (which includes the effects of ionic and activation overpotentials) with the volume of fluid method in OpenFOAM. The effects of increasing the length of the device and the electrolyte flow rate on the product cross-over and the dynamic potential of the devices are evaluated to give guidelines for process scale up from 1 to 10 cm. Furthermore, the effect of device porous electrode microstructure on the current density distribution and bubble accumulation is evaluated using custom microstructure reconstruction algorithms built into PMG 1.6 [2]. It was found that there is a balance in flow rate required for uniform flow over the electrodes which depends on the device length and the porous electrode properties. Ordered, graded or unstructured electrodes have different electrochemical performance, and this study highlights some electrode designs that could improve if future manufacturing efforts are increased. Depending on the current density distribution, dissolved gas accumulation and the wettability conditions of the porous electrodes, bubbles may emerge in the electrode gap.Figure 1 - Effect of the hydrogen and oxygen bubble accumulation (yellow) on the ionic current density distribution in a flow through membraneless water electrolyser