Microstructure Quantification Research Articles

Machine Learning-Assisted Microstructural Quantification of Multiphase Cathode Composites in All-Solid-State Batteries: Correlation with Battery Performance.

Microstructure optimization and high-performance material development are crucial for improving the electrochemical performance of all-solid-state batteries (ASSBs). Researchers frequently record numerous micro-scale or nano-scale electron micrographs for unbiased post-mortem analysis, performance evaluation, and improvement of ASSBs. However, these micrographs are often underutilized and typically analyzed qualitatively without ensuring an accurate representation of the experimental objectives. This study explores machine learning (ML)-based quantitative analysis techniques using electron microscopy images, combined with a stereology-driven linear-intercept concept method and semantic segmentation, to extract quantitative microstructural parameters for optimizing ASSB performance. The applicability of ML-assisted image analytics is demonstrated by employing composite cathodes in ASSBs to achieve unbiased automation and deep semantic segmentation during microstructural characterization. Furthermore, the ramifications of this ML-assisted method are discussed, along with its advantages and disadvantages in battery research.

Quantification of 3D microstructures in Achilles tendons during in situ loading reveals anisotropic fiber response.

While the number of studies investigating Achilles tendon pathologies has grown exponentially, more research is needed to gain a better understanding of the complex relation between its hierarchical structure, mechanical response, and failure. At the microscale, collagen fibers are, with some degree of dispersion, primarily aligned along the principal loading direction. However, during tension, rearrangements and reorientations of these fibers are believed to occur. As 3D micro-movements are hard to capture, the precise nature of this fiber reorganization remains unknown. This study aimed to visualize and quantify the intricate fiber changes occurring within rat Achilles tendons under tension. Rat tendons were in situ loaded with concurrent synchrotron phase contrast microCT imaging. The results are heterogenous and show that collagen fibers' response to loading is nonuniform and depends on anatomical orientation. Furthermore, damage propagation could be visualized, revealing that in the presence of heterotopic ossification damage proceeds within the ossified deposits rather than at the interface between hard and soft tissues. Our approach could effectively capture the microstructural changes occurring during loading and shows promise in understanding the relation between microstructure and mechanical response for ex-vivo Achilles tendons and other biological tissues. STATEMENT OF SIGNIFICANCE: Achilles tendons endure high mechanical loads during daily motion and physical activities. Understanding the structural and mechanical responses of Achilles tendons to such loads is vital for elucidating their function in health and pathology. We have combined the use of synchrotron phase contrast microCT with in situ mechanical loading contributing a better understanding of the relation between microstructural response and organ scale mechanical properties. The proposed methodology will be valuable for future research into the interplay between structure, mechanics, and pathology of tendons, and for the development of more effective strategies to preserve tendon function and possibly mitigating musculoskeletal disorders.

Multiscale Three-Dimensional Evaluation and Analysis of Murine Lung Vasculature From Macro- to Micro-Structural Level.

The pulmonary vasculature plays a pivotal role in the development and progress of chronic lung diseases. Due to limitations of conventional two-dimensional histological methods, the complexity and the detailed anatomy of the lung blood circulation might be overlooked. In this study, we demonstrate the practical use of optical serial block face imaging (SBFI), ex vivo microcomputed tomography (micro-CT), and nondestructive optical tomography for visualization and quantification of the pulmonary circulation's 3D architecture from macro- to micro-structural levels in murine lung samples. We demonstrate that SBFI can provide rapid, cost-effective, and label-free visualization of mouse lung macrostructures and large pulmonary vessels. Micro-CT offers high-resolution imaging and captures microvascular and (pre)capillary structures, with microstructural quantification. Optical microscopy techniques such as optical projection tomography (OPT) and light sheet fluorescence microscopy, allows noninvasive, mesoscopic imaging of optically cleared mouse lungs, still enabling 3D microscopic reconstruction down to the precapillary level. By integrating SBFI, micro-CT, and nondestructive optical microscopy, we provide a framework for detailed and 3D understanding of the pulmonary circulation, with emphasis on vascular pruning and rarefaction. Our study showcases the applicability and complementarity of these techniques for organ-level vascular imaging, offering researchers flexibility in selecting the optimal approach based on their specific requirements. In conclusion, we propose 3D-directed approaches for a detailed whole-organ view on the pulmonary vasculature in health and disease, to advance our current knowledge of diseases affecting the pulmonary vasculature.

Aging Behavior Analysis of Al-Ce-Based Alloys Using Hardness and Microstructural Quantification

Aging Behavior Analysis of Al-Ce-Based Alloys Using Hardness and Microstructural Quantification

Uncertainty Quantification of Microstructures: A Perspective on Forward and Inverse Problems for Mechanical Properties of Aerospace Materials

In this review, state‐of‐the‐art studies on the uncertainty quantification (UQ) of microstructures in aerospace materials is examined, addressing both forward and inverse problems. Initially, it introduces the types of uncertainties and UQ algorithms. In the review, the forward problem of uncertainty propagation in process–structure and structure–property relationships is then explored. Subsequently, the inverse UQ problem, also known as the design under uncertainty problem, is discussed focusing on structure–process and property–structure linkages. Herein, the review concludes by identifying gaps in the current literature and suggesting key areas for future research, including multiscale topology optimization under uncertainty, implementing physics‐informed neural networks to UQ problems, investigating the effects of uncertainty on extreme mechanical behavior, reliability‐based design, and UQ in additive manufacturing.

Automated cell tracking using 3D nnUnet and Light Sheet Microscopy to quantify regional deformation in zebrafish.

Light Sheet Microscopy (LSM) in conjunction with embryonic zebrafish, is rapidly advancing three-dimensional, in vivo characterization of myocardial contractility. Preclinical cardiac deformation imaging is predominantly restricted to a low-order dimensionality image space (2D) or suffers from poor reproducibility. In this regard, LSM has enabled high throughput, non-invasive 4D (3d+time) characterization of dynamic organogenesis within the transparent zebrafish model. More importantly, LSM offers cellular resolution across large imaging Field-of-Views at millisecond camera frame rates, enabling single cell localization for global cardiac deformation analysis. However, manual labeling of cells within multilayered tissue is a time-consuming task and requires substantial expertise. In this study, we applied the 3D nnU-Net with Linear Assignment Problem (LAP) framework for automated segmentation and tracking of myocardial cells. Using binarized labels from the neural network, we quantified myocardial deformation of the zebrafish ventricle across 4-6 days post fertilization (dpf). Our study offers tremendous promise for developing highly scalable and disease-specific biomechanical quantification of myocardial microstructures.

Microstructure quantification and phased array ultrasonic inspection of GH3535 alloy weld

Microstructure quantification and phased array ultrasonic inspection of GH3535 alloy weld

Investigation on the compressive strength, chloride migration of MK-FA-LS blended cementitious materials with electrochemical techniques

Blended cementitious materials has become an efficient method to reduce the CO2 emission while improving the long-time performance in construction filed, especially for the application of combined supplementary cementitious materials. However, the microstructure and performance evolution of the new materials need further deep investigation. In this study, metakaolin (MK), fly ash (FA) and limestone (LS) was introduced in cementitious materials to form a complicated quaternary system. The compressive strength and chloride migration performance are evaluated, with different MK/FA proportions. Then through the calorimetry test, X-ray Diffraction (XRD)/Rietveld analysis, free water content measurements, and electrochemical impedance spectroscopy (EIS) test, the phase assemblage and microstructure quantification of the blended systems are compared. Finally, to better understand the performance change, the correlation between the compressive strength/chloride migration coefficient and the electrochemical parameters are established. Results showed that the combination of MK, FA and LS is beneficial for the improvement of compressive strength and the chloride resistance in a proper mix design. EIS is a proper nondestructive method to understand the microstructure evolution of blended systems and even predict the chloride resistance of similar systems. This study shields a light for the application of the quaternary system, with a better durability, higher CO2 emission reduction and lower price.

Influence of contouring on the microstructure and micromechanical properties of Laser Powder Bed Fusion (LPBF) processed Al6061 enhanced with reactive particles

Age-hardenable aluminum alloys are of interest for powder bed fusion (PBF) additive manufacturing to produce lightweight, high-performance custom components. The successful processing of these alloys depends on specific alloying and print techniques to overcome defects that significantly degrade mechanical properties of PBF aluminum. The Reactive Additive Manufacturing (RAM) process modifieds alloy feedstock with reactive precursors which inoculate equiaxed microstructures during solidification, resulting in a reduction of columnar grain growth and shrinkage cracks. Additionally, surface quality is addressed by contouring, which retraces the perimeters of printed layers to produce smooth, fully fused surfaces. However, the combination of RAM and contouring methods has not been previously described in the literature, raising questions about the extent of refinement which is achieved when RAM particles are remelted by contouring laser sweeps. In this research, X-ray microcomputed tomography (XCT) was used to provide a complete characterization of RAM particles and defects in an AA6061-RAM alloy in 3D. The non-destructive XCT technique was used to quantify pore and particle distributions in the contoured alloy volumes and compared directly to hatched regions. The resulting three-dimensional analysis was correlated to traditional 2D microstructure quantification and nanoindentation experiments, providing a multimodal quantitative description of sub-micron grains, reaction products, and micromechanical properties. The study reveals that RAM particles inhibit cracking while simultaneously reducing the variance in mechanical properties between hatched and contoured regions of the build volume.

Recent progress of operando transmission electron microscopy in heterogeneous catalysis

As a highly intricate process encompassing multiple length scales, catalysis research evolves into a comprehensive understanding of reaction kinetics across microscopic to atomic dimensions when electron microscopy, particularly the in situ transmission electron microscopy (TEM), emerges to be increasingly relevant. Meanwhile, the absence of effective methodologies for measuring reaction products during catalysis complicates efforts to elucidate the operational state and catalytic activity of the catalyst. With ongoing advancements of refined gas-cell design within TEM and other in situ accessories, diverse methodologies have emerged to ascertain the occurrence of chemical reactions. In this review, we summarized the recent progress of operando TEM while further extending its conceptual boundaries by including newly emerged reaction-detecting approaches capable of bridging microstructure to the reaction process. These methods involve not only traditional ones of product detection, e.g., in situ mass spectrometry and electron energy loss spectroscopy, but also other reaction-correlative characterizations, such as directly imaging reactant molecule, modified in situ reactor for thermogravimetry and temperature-programmed reaction, and TEM image-based microstructure quantification and activity correlation. Applications, inherent challenges, and our perspectives within these operando TEM techniques are deliberated.

Study on the influence of aggregate on rheological properties of recycled aggregate asphalt mixture using microstructural quantification

Recycled aggregate (RA) is generated through the crushing process of Construction and Demolition Waste (CDW). Although there has been extensive research on the mechanical properties of recycled aggregate asphalt mixture (RAAM), the impact of recycled aggregate (RA) on its rheological characteristics remains underexplored. This paper aims to assess the rheological properties of recycled aggregate asphalt mixture (RAAM) during the processes of mixing and compaction. Firstly, different percentages of recycled aggregate (RA) were utilized in the design of the recycled aggregate asphalt mixture (RAAM), and rheological behaviors of RAAM during the mixing and compaction stage were characterized by mixing torque and compaction energy, respectively. Then, high-precision three-dimensional models of RA were reconstructed utilizing CT images, and four indices were established to quantitatively assess the macro shape and micro-morphology of RA particles, utilizing the spatial model data for precise measurements. Finally, a correlation was found between rheological properties of RAAM and RA morphology through a comprehensive analysis of laboratory test results and spatial model indicators. Results show that aggregate particles’ microstructural morphology significantly influences asphalt mixture’s rheological behavior during mixing and compaction. Additionally, the 3D spherical degree and Needle-flake index performed more significant correlations with mixing and compaction than the 3D angular index and 3D texture index. As opposed to natural aggregate, RA exhibits more intricate morphological characteristics stemming from its porous cement mortar composition. Increasing the RA content causes more energy consumption and increases the thickness of asphalt mortar film during the compaction process. In general, methods established in this study offer an efficient mean of quantifying the morphological characteristics of aggregate particles, thereby enabling the assessment of the impact of RA on the rheological properties of asphalt mixtures.

Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures

The foundation of materials science and engineering is the establishment of process–microstructure–property links, which in turn form the basis for materials and process development and optimization. At the heart of this is the characterization and quantification of the material’s microstructure. To date, microstructure quantification has traditionally involved a human deciding what to measure and included labor-intensive manual evaluation. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer exciting new approaches to microstructural quantification, especially classification and semantic segmentation. This promises many benefits, most notably objective, reproducible, and automated analysis, but also quantification of complex microstructures that has not been possible with prior approaches. This review provides an overview of ML applications for microstructure analysis, using complex steel microstructures as examples. Special emphasis is placed on the quantity, quality, and variance of training data, as well as where the ground truth needed for ML comes from, which is usually not sufficiently discussed in the literature. In this context, correlative microscopy plays a key role, as it enables a comprehensive and scale-bridging characterization of complex microstructures, which is necessary to provide an objective and well-founded ground truth and ultimately to implement ML-based approaches.

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.

An improved method for the quantification of microstructures via optical microscopy

Quantitative optical microscopy is a powerful tool for microstructural characterization of relatively large areas of multiphase materials, but accurate quantification often requires skillful sample preparation, imaging, and analysis techniques. Measuring phase fractions in a microstructure is done by segmenting images between features of interest and everything else and can be accomplished by a variety of techniques. With existing methods, in many cases, optimal contrast between the features of interest and the surrounding microstructural constituents is difficult to obtain and inconsistent across both individual images and a sample surface due to normal variations in materials and equipment, which can introduce measurement errors. A new machine-learning tool, the Labkit ImageJ plugin, provides a user-friendly method for training models for quantitative metallography and can be generally applied to study a wide variety of multiphase materials. Sørensen–Dice Indices were calculated to provide accuracy metric and show the improvement in image segmentation using Labkit to other common contrast-based thresholding methods versus manually-segmented representative images throughout the temperature range of this study. An example using intercritically heat treated HY-80 steel containing a duplex microstructure is presented using three different optical microscopy quantitative methods to show the improvement in reliability from the Labkit plugin. These quantitative measurements are augmented by differential scanning calorimetry and compared to CALculation of PHAse Diagram (CALPHAD) models to highlight the importance of experimental data for the design of heat treatments. A method for calibrating CALPHAD models to better predict industrial heat treatments is presented and shown to improve the accuracy of phase-fraction predictions, which is crucial for execution of intercritical heat treatments of steels.

Quantification of microstructure obtained during isothermal bainite transformation: A novel dilatometry-based model

Microstructural quantification of austenite and ferrite phases during isothermal bainite transformation (IBT) is paramount to tailor properties in low-carbon carbide-free bainitic steel. Such quantification carried out at the end of IBT heat treatment through microstructural analyses is fraught with limitations, such as non-consideration of transformation of the untransformed austenite on cooling post IBT. The current work has, therefore, devised a simple model to quantify volume fractions and the average carbon contents of bainitic ferrite and different austenite regions, namely film and blocky present between ferrite platelets and between bainitic sheaves, respectively, formed during IBT treatments at 375 to 450 °C from dilatometric strain itself. Such quantification, through other means would be very demanding in terms of experimentation and characterization. Microstructural quantification from the devised model is validated with experimental findings and highlights the difference in carbon contents of film and blocky austenite during IBT treatments.

Attention-based q-space Deep Learning Generalized for Accelerated Diffusion Magnetic Resonance Imaging.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.

A critical catalogue of SEM-EDS multispectral maps analysis methods and their application to hydrated cementitious materials.

Many methods have been proposed to analyse SEM-EDS hypermaps of hydrated cementitious materials but none can fit all purposes. In this presentation, we review existing methods for phase identification, stoichiometry quantification, and microstructure quantification in cementitious materials and related materials. We first discuss the unique contribution of SEM-EDS with respect to the outstanding scientific challenges towards sustainable construction materials. We then compare the SEM-EDS and image analysis techniques which contribute to answering these challenges. Convergence and divergence in current methods and workflows, and knowledge gaps are highlighted in terms of the specificities of the material (phase assemblage complexity, grain sizes, mixtures, etc.). The discussion is weighted by the required expert knowledge for the sample preparation, microscope operation, and data analysis. We conclude by discussing how microscopy and image analysis integrate into the overall experimental toolkit to investigate and improve cementitious materials.

A Novel Image Artifact Removal Scheme for Phase Percent Quantification of Dual-Phase Steel Microstructures

A Novel Image Artifact Removal Scheme for Phase Percent Quantification of Dual-Phase Steel Microstructures

Microstructure quantification of oblique angle sputtered porous a-Si thin films as a basis for structure-property relations of solid phase microextraction coatings

Understanding processing-structure-property (PSP) linkages of solid-phase microextraction (SPME) coating materials is crucial for the rational design and advancement of these new materials. As SPME is a diffusion-based extraction technique, analyzing the morphology of its coating materials is important for optimizing its performance. In this study, we assess the morphological evolution of micro/mesoporous amorphous silicon (a-Si) thin films sputtered at an oblique angle onto silicon, which serve as models for support materials in SPME devices. The contrast of scanning transmission electron microscopy (STEM) images is enhanced via ZnO infiltration by atomic layer deposition (ALD). Various metrics, including physical descriptors and two-point statistics methods, are employed to follow the films' evolution. Analysis of the two-point correlation function reveals a simple ellipse/spherical local pore geometry in contrast to the long-range irregular arrangement of pores identified by a range of traditional and novel metrics. Additionally, analyzing the internal structure of the pores through homology metrics aligns well with the theoretical understanding of morphological evolution in oblique sputtered films. These analyses show that the “average ratio of principal moment of inertia”, “Betti numbers”, and “two-point statistics” based metrics can capture valuable information during film growth.The morphological analysis approach proposed in this study can be applied to analyze any nanoporous medium as a first step towards developing structure-property relationships that tie back to a given preparation method. Ultimately, a more extensive experimental and/or simulation-based study should confirm the correlations between these metrics and actual diffusion properties as the basis for process-structure-properties relations for improved design and optimization of this film.

3D-porous-GAN: a high-performance 3D GAN for digital core reconstruction from a single 3D image

The 3D digital rock technology is extensively utilized in analyzing rock physical properties, reservoir modeling, and other related fields. This technology enables the visualization, quantification, and analysis of microstructures in rock cores, leading to precise predictions and optimized designs of reservoir properties. Although the accuracy of 3D digital rock reconstruction algorithms based on physical experiments is high, the associated acquisition costs and reconstruction processes are expensive and complex, respectively. On the other hand, the 3D digital rock random reconstruction method based on 2D slices is advantageous in terms of its low cost and easy implementation, but its reconstruction effect still requires significant improvement. This article draws inspiration from the Concurrent single-image generative adversarial network and proposes an innovative algorithm to reconstruct 3D digital rock by improving the generator, discriminator, and noise vector in the network structure. Compared to traditional numerical reconstruction methods and generative adversarial network algorithms, the method proposed in this paper is shown to achieve good agreement with real samples in terms of Dykstra-Parson coefficient, porosity, two-point correlation function, Minkowski functionals, and visual display.