3D Microstructures Research Articles

Solid Oxide Fuel Cells with 3D Inkjet Printing Modified LSM-YSZ Interface

Abstract In this study, pillar shaped yttria-stabilized zirconia (YSZ) 3D microstructures with ~60 to 90 m diameter and 12 to 20 m height are fabricated by 3D inkjet printing to improve the topology of the electrolyte/cathode interface. The microstructures increase the surface area of the cell by ~ 2.4% to 4.0% and enhance the connection between the dense YSZ electrolyte and mixed YSZ-lanthanum strontium manganite (LSM) cathode. The morphology and microstructure of the YSZ interface are characterized with scanning electron microscopy. Polarization curves confirm that the power density improves by 47% to 107% at 0.55V, depending on the dimensions of the microstructures, in comparison to a flat interface. The non-linear improvement in power density with the size of microstructures is confirmed by calculating the uncertainty with repeated tests. Based on electrochemical impedance spectroscopy and distribution of relaxation times analysis, the performance improvement is attributed to changes in the oxygen surface exchange kinetics and O2- diffusivity in the cathode.

Text-to-Microstructure Generation Using Generative Deep Learning.

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although recent advances in deep generative networks have enabled the inverse design of material microstructures, most studies involve property-conditional generation and focus on a specific type of structure, resulting in limited generation diversity and poor human-computer interaction. In this study, a pioneering text-to-microstructure deep generative network (Txt2Microstruct-Net) is proposed that enables the generation of 3D material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct-Net model is trained on a large microstructure-caption paired dataset that is extensible using the algorithms provided. Moreover, the model is sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance is also demonstrated in the inverse design of material microstructures and metamaterials. It has promising potential for interactive microstructure design when associated with large language models and could be a user-friendly tool for material design anddiscovery.

Organic Mixed Ionic Electronic Conductor Nanochannels for Vertical Electrochemical and Ionic Transistors

AbstractThin films of organic mixed ionic electronic conductors (OMIECs) constitute the functional layer in organic electrochemical transistors (OECTs), organic bioelectronic transducers and other ionic‐electronic devices. The thin‐film configuration constrains devices to be fabricated on impermeable substrates in the form of 2D microstructures with lateral electrodes to drive an electronic current through the thin film. In order to alleviate such constraints, novel OMIEC deposition methods are needed that produce alternatives to thin‐film devices and that are compatible with permeable substrates and electronic transport in the vertical direction. Here OMIECs filled nanoporous membranes are introduced as functional layer in devices with mixed ionic electronic transport. Electropolymerization of ethylenedioxythiophene (EDOT) monomers is used to fabricate OMIEC filled nanochannels. Electronic and ionic transport through such nanochannels are investigated and modulation of electronic as well as ionic carrier density by action of a third gate electrode is demonstrated. The novel OMIEC nanochannels enable the fabrication of vertical OECTs with high transconductance and organic ionic transistors using only additive fabrication methods.

Freeze-Casting of Alumina and Permeability Analysis Based on a 3D Microstructure Reconstructed Using Generative Adversarial Networks.

In this study, alumina ceramics with hierarchical pores were successfully fabricated using freeze casting. Experimental studies show that both the solid loading of the slurry and the thermal insulation layer at the interface of the slurry and cooling plate can influence the pore characteristics of cast samples. In order to examine the pore characteristics and evaluate the permeability of the freeze-cast samples fabricated under different conditions, a generative adversarial network (GAN) method was employed to reconstruct the three-dimensional (3D) microstructure from two-dimensional (2D) scanning electron microscopy (SEM) images of the samples. Furthermore, GAN 3D reconstruction was validated against X-ray tomography 3D reconstruction results. Based on the GAN reconstructed microstructures, the permeability and pore distribution of the various samples were analyzed. The sample cast with 35 wt.% solid loading shows an optimal permeability.

Framework for the Generation of 3D Fiber Composite Structures from 2D Observations

AbstractSynthetic generation of realistic materials for testing of process–structure–property (PSP) relationships in computational materials science has gained significant traction over the past two decades. Generation tools continue to lag in some aspects of realism, leading to uncertainty and errors in simulating material response. The experimental collection of information to guide generation of 3D synthetic structures remains costly, time consuming, and challenging. These challenges are compounded by limitations of stereology, which permits estimation of 3D microstructural statistics from 2D observations under restrictive assumptions on constituent morphologies and size distributions and can be difficult to apply to microstructure metrics like constituent orientation distribution and clustering. This work seeks to overcome these challenges by introducing a framework for learning probable 3D microstructure statistics by minimizing the loss determined by matching statistics obtained from 2D observations via synthetic microstructure generation software (e.g. Dream.3D) and stereological principles. This framework is applied to short carbon fiber composite structures printed from an additive manufacturing process, Direct Ink Writing, with the hope that the framework could generalize to other particulate structures as well as allow for simulation and design of materials structure.

Multiphoton Lithography of Interpenetrating Polymer Networks for Tailored Microstructure Thermal and Micromechanical Properties.

Multiphoton lithography (MPL), an emerging truly 3D microfabrication technique, exhibits substantial potential in biomedical applications, including drug delivery and tissue engineering. Fabricated micro-objects are often expected to undergo shape morphing or bending of the entire structure or its parts. Furthermore, ensuring precise property tuning is detrimental to the realization of the functionality of MPL microstructures. Herein, novel MPL materials based on interpenetrating polymer networks (IPNs) are presented that effectively combine the advantages of acrylate and epoxy systems. IPNs with varying component ratios are investigated for their microfabrication performance and structural integrity with respect to thermal and micromechanical properties. A variety of high-resolution techniques is applied to comprehensively evaluate IPN properties at the bulk, micron, and segmental levels. This study shows that the MPL laser scanning velocity and power, photoinitiator content, and multi-step exposure can be used to tune the morphology and properties of the IPN. As a result, a library of 3D MPL IPN microstructures with high 3D structural stability and tailored thermal and micromechanical properties is achieved. New IPN microstructures with Young's moduli of 3-4MPa demonstrate high-to-fully elastic responses to deformations, making them promising for applications in morphable microsystems, soft micro-robotics, and cell engineering.

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.

Poisson Effect‐Assisted Replication Lithography for Rapid Fabrication of Three‐Dimensional Microstructures

Demands for micro‐ and nano‐fabrication techniques have been increasing over recent decades due to their foundational importance in fields such as electronics, sensors, displays, biotechnologies, and energy technologies. Still, the rapid and efficient fabrication of complex 3D microstructures has long been a challenge due to the inherent limitations of conventional imprint lithography and the slow fabrication speed of maskless lithography systems using femtosecond lasers. This study introduces a novel lithographic replication method for the rapid replication of intricate 3D microstructures with closed‐loops by leveraging the Poisson effect‐driven lateral deformation of soft molds. Specifically, the suggested technique employs an elastomeric soft mold, engraved with negative cavity parts of the target structure separated by intentional gaps. Lateral deformation of the material allows the separated cavities to assemble for replication of target microstructure and defectless release from the soft mold. In addition to the experimental demonstrations of the proposed method using well‐known materials like polydimethysliloxane (PDMS) for the soft mold and UV‐curable polyurethane acrylate (PUA) for replication, essential considerations such as material selection and master mold design are discussed. The presented method not only broadens the capabilities of imprint lithographic techniques but also holds promise for the large scale, continuous fabrication of complex 3D microstructures.

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure under rapid solidification conditions in metal additive manufacturing. High fidelity simulations of solidification microstructures are typically performed using multicomponent partial differential equations (PDEs) with moving interfaces. The inherent randomness of the PDE initial conditions (grain seeds) necessitates ensemble simulations to predict microstructure statistics, e.g., grain size, aspect ratio, and crystallographic orientation. Currently such ensemble simulations are prohibitively expensive and surrogates are necessary.In GrainGNN, we use a dynamic graph to represent interface motion and topological changes due to grain coarsening. We use a reduced representation of the microstructure using hand-crafted features; we combine pattern finding and altering graph algorithms with two neural networks, a classifier (for topological changes) and a regressor (for interface motion). Both networks have an encoder-decoder architecture; the encoder has a multi-layer transformer long-short-term-memory architecture; the decoder is a single layer perceptron.We evaluate GrainGNN by comparing it to high-fidelity phase field simulations for in-distribution and out-of-distribution grain configurations for solidification under laser power bed fusion conditions. GrainGNN results in 80%–90% pointwise accuracy; and nearly identical distributions of scalar quantities of interest (QoI) between phase field and GrainGNN simulations compared using Kolmogorov-Smirnov test. GrainGNN's inference speedup (PyTorch on single x86 CPU) over a high-fidelity phase field simulation (CUDA on a single NVIDIA A100 GPU) is 150×–2000× for 100-initial grain problem. Further, using GrainGNN, we model the formation of 11,600 grains in 220 seconds on a single CPU core.

Comparison between 2D and 3D microstructures and implications for metamorphic constraints using a chloritoid–garnet-bearing mica schist

Abstract. Despite the fact that rock textures depend on the 3D spatial distribution of minerals, our tectono-metamorphic reconstructions are mostly based on a 2D visualisation (i.e. thin sections). This work compares 2D and 3D investigations of petrography and microstructures, modal abundances, and local bulk rock composition and their implication for P–T estimates, showing the pros and cons and reliability of 2D analysis. For this purpose, a chloritoid–garnet-bearing mica schist from the Dora-Maira Massif in the Western Alps has been chosen. In particular, for 2D a thin section scan has been combined with chemical X-ray maps, whereas for 3D the X-ray computerised axial microtomography (µCT) has been applied. Two-dimensional investigations are readily accessible and straightforward but do not consider the entire rock volume features. Conversely, the rise of 3D techniques offers a more comprehensive and realistic representation of metamorphic features in the 3D space. However, they are computationally intensive, requiring specialised tools and expertise. The choice between these approaches should be based on the research aims, available resources, and the level of detail needed to address specific scientific questions. Nevertheless, despite differences in the modal distribution, the estimated bulk rock compositions and relative thermodynamic modelled phase fields show similarities when comparing the 2D and 3D results. Also, since different thin section cut orientations may influence the results and consequent interpretations, three different cuts from the 3D model have been extrapolated and discussed (i.e. XZ, YZ, and XY planes of the finite-strain ellipsoid). This study quantitatively corroborates the reliability of the thin section approach for tectono-metamorphic reconstructions, still emphasising that 3D visualisation can help understand rock textures.

The evolution of intergranular networks during grain growth and its effect on percolation behavior

Triple junctions (TJs) are line defects in three-dimensional (3D) polycrystalline materials where three grains meet. Transport along the TJs depends on the connectivity between them. Here, we investigate the connectivity of more than 6000 TJs in a 3D microstructure of pure iron (Fe) through the lens of bond percolation. Our efforts are made possible by synchrotron-based x-ray diffraction tomography, which allows us to resolve the TJ network both temporally and spatially, during grain growth. In the framework of standard percolation theory, we determine a percolation threshold of the TJs, above which there exists a continuous pathway of TJs that travels infinitely far. Our experimental results indicate a surprisingly different percolation threshold (by 16%) compared to models and theories. The reason for the discrepancy is that the real TJ network has a topological disorder that is not present in the idealized microstructures considered in prevailing models and theories. Leveraging the wealth of time-resolved data we trace the origin of this disorder by following the topological transitions that accompany grain growth. Overall, the insights obtained in this study can help guide the design of polycrystalline materials via defect engineering.

Deformation and fracture of SiCp/Al composites under complex loading conditions: A simulation study

An accurate depiction of the deformation and fracture mechanisms of metal matrix composites is the key to their rational structure and property design. In the present work, the Advanced Guerson Tvergaard Needleman (AGTN) constitutive model is firstly combined with an algorithm to reconstruct realistic 3D microstructures within the finite-element framework. We employ this model to probe the elastoplastic response and fracture behaviors of aluminum matrix composites with different volume fractions of reinforcing particles (7, 14, and 21 %) and under various loading conditions (tension, compression and shear). Real microstructures are referred to reconstruct the three-dimensional model of the composites. The AGTN constitutive model is integrated with the micromechanical damage model, which is capable of independently describing the constitutive behavior of various components, including the elastoplastic damage of the metal matrix, particle-enhanced elastic-brittle failure, and the interfacial traction separation. By using the conventional SiCp/Al composite system as a model material, we examine the stress state of the matrix and fracture mechanisms under different loading conditions by using triaxiality, Lode angle and statistical analysis. In general, the damage of SiCp/Al composites can be attributed to interfacial debonding and particle fracture, whereas the latter dominates when the volume fraction of SiCp is high. Furthermore, the critical volume fraction for the transition of damage mechanisms is found to be largely dependent on the loading conditions. Our work provides a comprehensive understanding of particle-reinforced aluminum matrix composite under in-service conditions.

Key Roles of Initial Calcination Temperature in Accelerating the Performance in Proton Ceramic Fuel Cells via Regulating 3D Microstructure and Electronic Structure

Key Roles of Initial Calcination Temperature in Accelerating the Performance in Proton Ceramic Fuel Cells via Regulating 3D Microstructure and Electronic Structure

Ultrathin and Deformable Graphene Etch Mask for Fabrication of 3D Microstructures.

Three-dimensional (3D) microfabrication techniques play a crucial role across various research fields. These techniques enable the creation of functional 3D structures on the microscale, unlocking possibilities for diverse applications. However, conventional fabrication methods have limits in producing complex 3D structures, which require numerous fabrication steps that increase the costs. Graphene is an atomically thin material known for its deformability and impermeability to small gases and molecules, including reactive gases like XeF2. These features make graphene a potential candidate as an etch mask for 3D microfabrication. Here, we report the fabrication of various 3D microstructures using graphene etch masks directly grown and patterned on a Si substrate. The patterned graphene deforms and wraps the etched structures, allowing for the fabrication of complicated 3D microstructures, such as mushroom-like and step-like microstructures. As a practical demonstration of the graphene etch mask, we fabricate an omniphobic surface of reentrant 3D structures on a Si substrate. Our work provides a method for fabricating complex 3D microstructures using a graphene etch mask, contributing to advancements in etching and fabrication processes.

Synchrotron micro-computed tomography analysis of neutron-irradiated U-Mo fuel

The three-dimensional (3D) microstructure of neutron-irradiated uranium-10 wt.% molybdenum (U-10Mo) fuel with a burn-up of 9.8 × 1021 fissions/cm3 was characterized using a novel, multi-modal synchrotron micro-computed tomography approach combining propagation-based phase-contrast enhanced and absorption contrast techniques. The porosity development, porosity interconnectedness, swelling, composition, local thickness of the zirconium (Zr) diffusion barrier, and the influence of the fuel–cladding interaction on the local composition and pore morphology, were uniquely determined in 3D. Two cuboids were produced using a focused ion beam-scanning electron microscope at the Zr diffusion barrier–fuel interface and in the bulk fuel. The bulk fuel sample swelled by 53.3 [+9.7/−3.1]%, while the fuel near the Zr–fuel interface swelled by 63.3 [+14.7/−7.2]%. The average local thickness of the Zr diffusion barrier decreased by 53 %, compared to the expected pre-irradiated thickness. Four pore morphology regions were identified initiating parallel to the fuel–Zr interaction region: (1) an interaction layer of suppressed porosity, (2) a layer of elongated and interconnected porosity, (3) a transition zone of low porosity, and (4) a layer of unoriented porosity representative of the bulk fuel behavior. The increase in porosity near the diffusion barrier corresponded to a higher U concentration compared to that in the bulk fuel. The interconnected porosity in the fuel near the diffusion barrier was extensive and oriented parallel to the diffusion barrier, while the bulk fuel had more compact and isolated pore networks. The interaction layer, despite having suppressed porosity, was nearly 100 wt.% U. Porosity suppression at the diffusion barrier corresponds to the expected reduction in radiation-driven diffusion of Xe at the interface despite the anticipated increase in fission product nucleation originating from a higher U concentration. The novel 3D insights of the porosity, swelling, and compositional variations characterized herein can improve the fidelity of fuel performance codes for proliferation-resistant fuels for research and test reactors.

R‐Vine Copulas for Data‐Driven Quantification of Descriptor Relationships in Porous Materials

AbstractLocal variations in the 3D microstructure can control the macroscopic behavior of heterogeneous porous materials. For example, the permittivity through porous sheets or membranes is governed by local high‐volume pathways or bottlenecks. Due to local variations, unfeasibly large amounts of microstructure data may be needed to reliably predict such material properties directly from image data. Here it is demonstrated that a vine copula approach provides parametric models for local microstructure descriptors that compactly capture the 3D microstructure including its local variations and efficiently probe it with respect to selected, measurable properties. In contrast to common methods of complexity reduction, the proposed approach creates parametric models for the multivariate probability distribution of high‐dimensional descriptor vectors that inherently contain the complex, nonlinear dependencies between these descriptors. Therein, material properties are offered in physically motivated distributions of microstructure descriptors rather than as normally distributed data. Applied to porous fiber networks (paper) before and after unidirectional compression, it is shown that the copula‐based models reveal material‐characteristic relationships between two or more microstructure descriptors. In this way, the presented modeling approach can provide deeper insight into the microscopic origin of effective macroscopic properties of heterogeneous porous materials.

Dual-Wavelength Volumetric Microlithography for Rapid Production of 4D Microstructures.

4D microstructured actuators are micro-objects made of stimuli-responsive materials capable of induced shape deformations, with applications ranging from microrobotics to smart micropatterned haptic surfaces. The novel technology dual-wavelength volumetric microlithography (DWVML) realizes rapid printing of high-resolution 3D microstructures and so has the potential to pave the way to feasible manufacturing of 4D microdevices. In this work, DWVML is applied for the first time to printing stimuli-responsive materials, namely, liquid crystal networks (LCNs). An LCN photoresist is developed and characterized, and large arrays of up to 5625 LCN micropillars with programmable shape changes are produced by means of DWVML in the time span of seconds, over areas as large as ∼5.4 mm2. The production rate of 0.24 mm3 h-1 is achieved, exceeding speeds previously reported for additive manufacturing of LCNs by 2 orders of magnitude. Finally, a membrane with tunable, micrometer-sized pores is fabricated to illustrate the potential DWVML holds for real-world applications.

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.

Three-dimensional biphase fabric estimation from 2D images by deep learning

A pruned VGG19 model subjected to Axial Coronal Sagittal (ACS) convolutions and a custom VGG16 model are benchmarked to predict 3D fabric descriptors from a set of 2D images. The data used for training and testing are extracted from a set of 600 3D biphase microstructures created numerically. Fabric descriptors calculated from the 3D microstructures constitute the ground truth, while the input data are obtained by slicing the 3D microstructures in each direction of space at regular intervals. The computational cost to train the custom ACS-VGG19 model increases linearly with p (the number of images extracted in each direction of space), and increasing p does not improve the performance of the model - or only does so marginally. The best performing ACS-VGG19 model provides a MAPE of 2 to 5% for the means of aggregate size, aspect ratios and solidity, but cannot be used to estimate orientations. The custom VGG16 yields a MAPE of 2% or less for the means of aggregate size, distance to nearest neighbor, aspect ratios and solidity. The MAPE is less than 3% for the mean roundness, and in the range of 5-7% for the aggregate volume fraction and the mean diagonal components of the orientation matrix. Increasing p improves the performance of the custom VGG16 model, but becomes cost ineffective beyond 3 images per direction. For both models, the aggregate volume fraction is predicted with less accuracy than higher order descriptors, which is attributed to the bias given by the loss function towards highly-correlated descriptors. Both models perform better to predict means than standard deviations, which are noisy quantities. The custom VGG16 model performs better than the pruned version of the ACS-VGG19 model, likely because it contains 3 times (p = 1) to 28 times (p = 10) less parameters than the ACS-VGG19 model, allowing better and faster cnvergence, with less data. The custom VGG16 model predicts the second and third invariants of the orientation matrix with a MAPE of 2.8% and 8.9%, respectively, which suggests that the model can predict orientation descriptors regardless of the orientation of the input images.

Self-Assembly for Creating Vertically-Aligned Graphene Micro Helices with Monolayer Graphene as Chiral Metamaterials.

Graphene's emergence enables creating chiral metamaterials in helical shapes for terahertz applications, overcoming material limitations. However, practical implementation remains theoretical due to fabrication challenges. This paper introduces a dual-component self-assembly technique that enables creating vertically-aligned continuous monolayer graphene helices at microscale with great flexibility and high controllability. This assembly process not only facilitates the creation of three-dimensional (3D) microstructures, but also positions the 3D structures from a horizontal to a vertical orientation, achieving an aspect ratio (height/width) of approximately 2700. As a result, an array of vertically-aligned graphene helices is formed, reaching up to 4mm in height, which is equivalent to 4 million times the height of monolayer graphene. The benefit of these 3D chiral structures made from graphene is their capability to infinitely extend in height, interacting with light in ways that are not possible with traditional 2D layering methods. Such an impressive height elevates a level of interaction with light that far surpasses what is achievable with traditional 2D layering methods, resulting in a notable enhancement of optical chirality properties. This approach is applicable to various 2D materials, promising advancements in innovative research and diverse applications across fields. This article is protected by copyright. All rights reserved.