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Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.

From both economic and environmental points of view, the reuse of dredged sediments in the direct onsite casting of concrete represents a promising method for replacing sand. The aim of this study was to develop a cementitious material that (i) reuses the thin particles of sediments; (ii) has a low density due to the incorporation of air foam in the material; and (iii) achieves a minimum mechanical strength of 0.5 MPa for embankment applications. This study focused on the characterization of a non-standard "concrete", which is a mixture of a synthetic soil (80% montmorillonite and 20% calibrated sand) and cement. To reduce its density, air foam was incorporated into the material during the manufacturing process (air-foamed lightweight clay concrete—AFLCC). The study results highlight that a density around 1.2 (unit: g/cm3/1 g/cm3) can be obtained. This density reduction can be obtained with a certain degree of workability when the material is in a fresh state. To obtain this workability, a certain amount of water must be added; however, the addition of water has a significant impact on the compressive strength of the AFLCC. As such, a mathematical equation correlating the compressive strength, the density, and the percentage of cement is proposed in this study. The mechanical strength results of the AFLCC at different times, in conjunction with the Vicat results, show that the porosity created by the air foam has the effect of slowing down the hydration mechanism of the cement. The porosities obtained are consistent with the density results. The characteristic radii indicate large pore sizes for formulations with low fluidity in the fresh state when air bubbles are incorporated. [ABSTRACT FROM AUTHOR]

The difficult determination of morphological properties in metal foams stands behind the reasons why metal foams are not widely used in industry, since quality control of the batches produced is limited to destructive methods. To approach this challenge, a new method of analysis of morphological properties based on 2D X‐Ray radiograms and the employment of a new Convolutional Neural Network architecture is proposed. The training of this model is based on a combined approach of simulating simplified foams as pretraining data and the acquisition of real experimental data, extracted from X‐Ray computer tomographies. The network is trained successfully with 41 foams to obtain predictions for cell size distribution between 0.3 and 5 mm, as well as sphericities in ranges from 0.4 to 1. In addition, tests are carried out to get an insight into the robustness of the model when confronted with similar data that are not included in the training process. It is found that the effectiveness of the neural network increases with a larger number of cells in the observed volume where above 500 cells per volume 92.5% of sphericity predictions and 99.4% of cell size predictions passed the Kolmogorov‐Smirnov test. [ABSTRACT FROM AUTHOR]

A review of various mechanisms of suprathermal electron generation in tokamak plasma and an analysis of diagnostic systems for the study of the spatial evolution of electron beams are presented. It is planned to use detectors based on CdTe crystals to detect suprathermal x-ray emission (20–300 keV) at the T-15MD tokamak (R = 1.5 m, a = 0.67 m). Spatial resolution is provided by a system of tube collimators placed in detector chambers mounted in vertical, inclined, and equatorial diagnostic ports. To measure suprathermal x-ray emission spectrum, a set of CdTe spectrometric detectors and LaBr3(Ce) scintillation crystal detectors are used. The tomographic program is used to reconstruct spatial localization of the X-ray emission. The received spatial and temporal distribution of local suprathermal electron beams will be used to study a variety of physical phenomena, such as reconnection of magnetic field lines, nonlinear transport processes, distortion of the electron energy distribution function during powerful additional heating and current drive, and plasma perturbances that are important for the study of kinetic instabilities in plasma with fast ions. [ABSTRACT FROM AUTHOR]

A promising candidate for X‐ray detection is layered hybrid double perovskites (LHDPs) with excellent structural stability, but their sensitivity is generally limited by unsatisfactory interlayer charge transport. Herein, employing one ethylenediamine (EDA) chain as a structural inducer, we successfully obtain unusual Dion‐Jacobson (DJ) phase LHDPs, (EDABr)4AgBiBr8 and (EDABr)4CuBiBr8, featuring a Ruddlesden‐Popper‐like (RP‐like) interlayer. Thanks to the bridging of bromine anions, organic cations are linked via charge‐assisted hydrogen bonds, where two ionic spacers are orderly woven into a supramolecular framework. Consequently, the RP‐like interlayer space is regulated by the dual‐ion‐woven supramolecular framework with embedded charge‐assisted hydrogen bond networks, remarkably enriching interlayer interactions and boosting charge transport. Through theoretical calculations, structural roles of the supramolecular framework are elucidated by extra orbital contribution and large diffusion barrier of Br anions. As proof of concept, the sensitivity of RP‐like devices up to 5250 μC Gyair−1 cm−2 is a record‐high of LHDP‐based X‐ray detectors for now, while a low detection limit (91 nGyair s−1) and outstanding radiation‐resistant capability (50 Gyair) are achieved. Moreover, an oriented membrane device is prepared to demonstrate high‐performance X‐ray tomography. These findings offer a brand‐new interlayer‐modulation strategy for the construction of sensitive and stable scintillation semiconductors. [ABSTRACT FROM AUTHOR]

Coking is a common catalyst deactivation route in industrial processes involving carbonaceous species. While coking is easy to diagnose, this is often performed by bulk analysis. Understanding specific symptoms such as pore blockage and obstruction of active sites is especially challenging for technical catalysts and requires a spatially‐resolved approach. Here a combination of ptychographic X‐ray computed tomography (PXCT) and X‐ray fluorescence nanotomography (XRF‐CT) could identify and allocate regions of coke deposition within a technical zeolite‐based propane dehydrogenation catalyst. PXCT is sensitive to the quantitative electron density of the sample, therefore indirectly visualising coke deposition in meso‐ and macropores with 56–61 nm 3D spatial resolution. For more direct visualisation the catalysts were treated with Cu solution as fluorescent marker, whereby complementary XRF‐CT analysis could distinguish accessible and blocked pores based on the presence or absence of adsorbed Cu. This strategy was used to assess coking as a function of time on stream, to evaluate coke removal by oxidative regeneration, and to distinguish the presence of coke deposits separately within the zeolite and binder components. This strategy is applicable to virtually any porous solid catalyst and can deliver previously unknown insights into the common phenomenon of coke deposition particularly in technical catalysts. [ABSTRACT FROM AUTHOR]

The onset of brittle failure in rocks includes dilatancy and strain localization. To better understand this nucleation process, we analyze the evolution of the local three‐dimensional strain tensor using X‐ray tomograms acquired during triaxial compression experiments on granite and sandstone. The onset of the localization of the compaction, dilation, and shear strain occurs when ∼65% of the rock volume experiences dilation. Tracking the locations of the high strains throughout loading suggests that the deformation that occurs early in loading influences the location of the system‐sized fracture network that produces macroscopic failure. This influence is larger in the sandstone experiments than the granite experiments, likely due to the microstructure of the sandstone. These results have important implications for detecting precursors to catastrophic failure. Plain Language Summary: We investigate the fundamental processes that lead to brittle failure in rocks. We deform two common types of crustal rocks, granite and sandstone, under upper crustal stress conditions. As the stress applied to the rock increases, the rock tends to expand (dilate) more than compact, particularly as it approaches catastrophic, macroscopic failure. A larger portion of the rock undergoes dilation when the strain field starts to localize, indicating that accelerating dilation is a precursor to macroscopic failure. We observe different localization patterns in the rocks: in sandstone, strain localization progresses monotonically with increasing stress, whereas phases of delocalization can occur in the granite. Two competing models describe the development of the system‐sized fracture network that produces macroscopic failure: the network develops from (a) the coalescence of fractures that form early in loading, or from (b) the propagation of a process zone of interacting fractures through relatively intact rock. We find that the high strain events persist at the same location throughout the experiments more than expected by chance, particularly in experiments on sandstone. The results provide perhaps the most robust experimental confirmation yet that the fracture network that causes macroscopic failure evolves from the deformation that occurs earlier in loading. Key Points: X‐ray tomography quantifies the evolving spatial evolution of high strain events during brittle failureAt the onset of strain localization, on average 65% of the volume of the rock cores undergo dilationLocalized zones of high strain events persist in space from the onset of loading [ABSTRACT FROM AUTHOR]

Hydrogel foams are widely used in many applications such as biomaterials, cosmetics, foods, or agriculture. However, controlling precisely foam morphology (bubble size or shape, connectivity, wall and strut thicknesses, homogeneity) is required to optimize their properties. Therefore, a method is proposed here for generating, controlling, and characterizing the morphology of hydrogel foams from liquid foam templates: Using the example of Alginate‐CaHPO4‐based hydrogel foams, a highly controllable foaming process is provided by bubbling nitrogen through nozzles into the solution, which produces hydrogel foams with millimeter‐sized bubbles. A rheological characterization protocol of the foam's constituent material is first implemented and highlights the impact of the initial liquid foam properties and of the competition between the solidification kinetics and the foam aging mechanisms on the resulting morphology. X‐ray tomographic characterization performed on solidifying and solidified samples then demonstrates that by controlling the temporal evolution of the foam via its formulation, it is possible to tune the final morphology of the alginate foams. This method can be adapted to other hydrogel or polymer formulations, foam characteristics and length scales, as soon as solidification processes happen on timescales shorter than foam destabilization mechanisms. [ABSTRACT FROM AUTHOR]

Compositional and anatomical studies of silicified wood have been carried out extensively all around the world. The classification of silicified wood as such deals with all the forms and phases of silica that come under its umbrella. One such class of silicified wood is fossil wood with a high content of quartz, and there are very limited mentions of this category of fossilized wood. The examined wood belongs to gymnosperm and comes from the Upper Triassic deposits of Madagascar. A fresh approach to such samples is adopted by studying the crystallographic texture of the fossil wood to understand the orientation of the crystals replacing the organic matter within the sample. This work focuses on crystallographic texture analysis based on pole figures measured by X-ray diffraction. The intensity of the pole density maxima on the pole figures measured on the heartwood surface part of the analyzed samples is higher than that on the sapwood. This affirms that the crystallographic texture is sharper at the heartwood part compared to the sapwood. The X-ray tomography study, conducted to understand the difference in mineral distribution within the sample, reveals a greater X-ray absorbing phase on the sapwood of both samples. This is due to the concentration of iron compounds, which both replace the remaining conductive structures of the wood and fill the cavities inside them. We believe that this research on silicified wood is the first research work that encompasses crystallographic texture analysis with pole figures, an approach not previously undertaken in similar studies. We hope that our research can be useful in understanding the processes of replacement of organic matter by minerals. [ABSTRACT FROM AUTHOR]

This review considers the use of fractal concepts to improve the development, fabrication, and characterisation of catalytic materials and supports. First, the theory of fractals is discussed, as well as how it can be used to better describe often highly complex catalytic materials and enhance structural characterisation via a variety of different methods, including gas sorption, mercury porosimetry, NMR, and several imaging modalities. The review then surveys various synthesis and fabrication methods that can be used to create catalytic materials that are fractals or possess fractal character. It then goes on to consider how the fractal properties of catalysts affect their performance, especially their overall activity, selectivity for desired products, and resistance to deactivation. Finally, this review describes how the optimum fractal catalyst material for a given reaction system can be designed on a computer. [ABSTRACT FROM AUTHOR]

The X-ray tomography method has several advantages, including its non-destructiveness and the ability to visualize the rock skeleton and pore space in three dimensions. However, one of the main challenges of this method is the limited resolution when studying core samples that are 30 millimeters in diameter, which is typical for petrophysical analysis. In these samples, a significant portion of pores have dimensions smaller than the resolution capabilities of most X-ray tomographic systems, making it impossible to accurately determine the boundary between the pore and skeleton structures in tomograms, nor visualize the entire pore volume.To verify this hypothesis, tomograms from real oil and gas samples were analyzed. The resulting histograms of X-ray densities revealed that it is not possible to directly measure the threshold value of X-ray density that defines the “skeleton-pore” boundary. In order to solve the problem of estimating boundary values, a technique is proposed in this work that suggests using artificial digital models – phantoms. This approach has been previously used mainly in computer modeling, but it has not been used much in petroleum geology. The main advantage of using phantoms is complete control over the set pore space parameters and X-ray density of the skeleton, which cannot be achieved on real samples.A computational experiment was conducted in the work, where 124 core phantoms with specific porosity characteristics were generated using numerical modeling. These phantoms were then converted into tomograms, allowing us to determine statistical characteristics of the values for X-ray densities of the samples at the reconstruction stage.Based on the statistical analysis of the X-ray density distribution in the sample, we determined the boundary values that are most suitable for reliable void space detection. Using regression and correlation methods, we developed a model to estimate the optimal boundary value for X-ray density in void space allocation.We proposed an algorithm for determining and applying this value in the analysis of core X-ray CT data.This model was tested on real samples that were not used in the development of the forecast model. The use of the proposed model for predicting boundary values on obtained tomograms demonstrated a high degree of consistency with actual data.

Abstract Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1–2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.

X-ray-based computed tomography is a well established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in situ and in operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projection often results in poor quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time. Here, tomoCAM, is introduced, a new GPU-accelerated implementation of model-based iterative reconstruction that leverages non-uniform fast Fourier transforms to efficiently compute Radon and back-projection operators and asynchronous memory transfers to maximize the throughput to the GPU memory. The resulting code is significantly faster than traditional MBIR codes and delivers the reconstructive improvement offered by MBIR with affordable computing time and resources. tomoCAM has a Python front-end, allowing access from Jupyter-based frameworks, providing straightforward integration into existing workflows at synchrotron facilities.

Highlights The extensive use of multidirectional composite laminates requires to understand the delamination behavior at interfaces between plies with different fiber orientations. In this study, double cantilever beam testing was performed to investigate the mode I interlaminar fracture of carbon fiber reinforced polymer laminates with different central interfaces (0//0, 0//+45 and +45//−45). X‐ray microtomography revealed the curved crack front shape that was incorporated to calculate fracture toughness. The fracture toughness varies with the crack length in a typical delamination resistance curve, which can be quantified by an empirical equation. Incorporation of variable fracture toughness into a cohesive zone model can better predict the delamination fracture of the laminate, compared to constant toughness. It was found that the delamination mechanism is independent of central interfaces. However, compared to unidirectional laminates, multidirectional laminates are less resistant to crack initiation, but more resistant to propagation with higher toughness and shorter fiber bridging zone. Mode I interlaminar fracture of CFRP laminates with different central interfaces was investigated experimentally and numerically. Curved crack front shape was incorporated to calculate fracture toughness. Incorporation of variable fracture toughness into a cohesive zone model can better predict the delamination fracture of the laminate. Multidirectional laminates are less resistant to crack initiation but more resistant to propagation with higher toughness and shorter fiber bridging zone. [ABSTRACT FROM AUTHOR]

Molten chloride salts represent a very corrosive medium due to the amount of impurities they contain and that essentially comes from moisture. In this work, an industrial nickel-based alumina-forming alloy was preoxidized and corroded for 500 h in the NaCl–MgCl2 eutectic. Electrochemistry and SEM analyses were used to prepare and analyse the corrosion test. Both the nickel-rich matrix and the alumina scale formed during preoxidation seemed to remain stable during the corrosion test contrary to some of the chromium carbides initially present in the columnar microstructure of the alloy. The use of X-ray tomography coupled with SEM observation revealed a preferential dissolution of the chromium carbides connected to the alloy/salt interface. X-ray tomography reveals a chromium carbides network enabling a deep molten salt infiltration within the alloy due to their preferential dissolution. Molten salt infiltration in the dissolved carbides network then leads to the oxidation of aluminium present in the alloy into a mixed MgAl2O4 spinel. An oxoacido-basic reaction between the alumina scale formed at the alloy surface during preoxidation and MgO dissolved in the salt is also discussed. This work shows that nickel-based alumina-forming alloy present a realistic interest and that the microstructure of the alloy should be optimized in further work to enhance corrosion resistance. [ABSTRACT FROM AUTHOR]

The ability to obtain 3D microstructural information from techniques such as x-ray computed tomography (XCT) has become an imperative means for understanding the relationship between processing, structure, and performance of materials. Due to the large datasets that are a product of XCT, the development of automated characterization techniques are required to efficiently and accurately quantify microstructural features. Segmentation techniques such as thresholding are common for simplistic datasets, while machine learning tools such as deep learning used more complex datasets. For example, composites, specifically ceramic matrix composites, are often difficult to analyze due to the low contrast between constituents. Deep learning has proven to be a promising tool for segmenting multi-phase microstructures. For composite materials, fiber classification and fiber tracking are also growing areas of interest to understand how fiber variation affects the local microstructure. Faster R-CNNs have been developed for feature identification and have been proven to be able to identify features, such as fibers in extremely noisy XCT datasets. Fiber tracking is performed using association algorithms and Kalman filtering to provide linkages between composite structure and performance. [ABSTRACT FROM AUTHOR]

The simple addition of a Zn(II) precursor to a preoptimized poly(carboxylic acid) binder solution enhances the electrochemical performance and cycle life of silicon‐based electrodes. The binder/cation couple forms a cross‐linked coordinated binder that plays a key role in enhancing the mechanical and chemical stability of the electrode microstructure. The impact of the addition of the Zn precursor on the microstructural evolution of the electrode during cycling is investigated at different scales (from cell/electrode to silicon particle scale) using complementary operando, in situ, and ex situ X‐ray tomography techniques. Comparative analyses conducted on the reference and with Zn electrode formulations using operando and in situ X‐ray micro‐tomography allow for monitoring of the electrode morphological deformations along with the crack pattern formation and evolution during cycling. The benefits of the precursor addition include enhancing the mechanical stability of the electrode through a strengthened microstructure more apt to maintaining its integrity, as well as a better anchoring to the current collector leading to decreased electrical disconnections and capacity fade. Moreover, complementary ex situ X‐ray nano‐tomography measurements highlight the benefits of the precursor addition in terms of chemical stability with mitigated solid electrolyte interface (SEI) formation over the electrode cycling. [ABSTRACT FROM AUTHOR]

Magnesium‐based alloys are excellent materials for temporary medical implants, but understanding and controlling their corrosion performance is crucial. Most nanoscale corrosion studies focus on the surface, providing only 2D information. In contrast, macro‐ and microscale X‐ray tomography offers representative volume information, which is, however, comparatively low in resolution and rather qualitative. Here a new mesoscale approach overcomes these drawbacks and bridges the scale gap by combining 3D measurements using ptychographic X‐ray computed tomography (PXCT) with electron microscopy. This combination allows to observe the corrosion progress non‐destructively in 3D and provides high‐resolution chemical information on the corrosion products. A medical Mg–Zn–Ca alloy is used and compared the same sample in the pristine and corroded states. With PXCT an isotropic resolution of 85 and 123 nm is achieved for the pristine and corroded states respectively, which enables to distinguish nanoscale Mg2Ca precipitates from the matrix. The corroded state in best approximation to the in situ conditions is imaged and reveals the complexity of corrosion products. The results illustrate that the corrosion‐layer is dense and defect‐free, and the corrosion of the material is grain‐orientation sensitive. The developed workflow can advance research on bioactive materials and corrosion‐sensitive functional materials. [ABSTRACT FROM AUTHOR]

Pre-Roman glass craftsmanship reached its summit with the development of complex polychrome glass beads, being the Phoenician glass pendants the most exquisite and elaborate example. The uniqueness and complexity of such findings could reveal key information for the understanding of the production and trade of glass pieces at that age. However, these findings have practically never been studied from a physic-chemical perspective. In this work, a remarkable polychrome glass pendant (2nd -1st c. BC) found at the archaeological site of Pintia (Padilla de Duero, Valladolid, Spain) is studied by a multi-analytical non-destructive approach, employing X-ray tomography to understand its fabrication procedure, as well as X-ray fluorescence (XRF) and Raman spectroscopy, both employed in microscopic mode, to determine the composition of each glass employed in its fabrication. The outstanding preservation state and well-defined archaeological context of this glass pendant offered a unique opportunity to expand the understanding of pre-Roman glass pieces, while the combination of the experimental techniques employed provided the first complete and detailed study of a Phoenician glass pendant. The fabrication procedure of the pendant has been identified step-by-step, showing evidence of the use of pre-made pieces for the eyes, as well as hints of its fabrication in a secondary workshop. Moreover, the microchemical analysis of the vividly colored glasses by XRF and Raman spectroscopy revealed a composition compatible with the use of natron as fluxing agent, typical of Phoenician glass, the presence of surface alterations corresponding to carbonatation processes, as well as the nature of the employed chromophores or pigments: Mn, Cu, and Co for the blue, Fe-S for the black, CaSb2O7 and CaSb2O7 + TiO2 for two diverse white glasses, and a pyrochloric triple oxide (Pb2Sb2 − xSnxO7−x/2) and lead oxides for the yellow. Remarkably, the use of pyrochloric triple oxides as yellow pigments has scarcely been previously reported at that age. Finally, the identification by Raman spectroscopy of CaSb2O7 and the β-phase of CaSiO3, as well as the Raman spectra features of the glass matrix corresponding to the blue glass, indicated maximum firing temperatures below 1100 °C. [ABSTRACT FROM AUTHOR]

This study addresses the imbibition kinetics of finite water amounts in kaolinite plasters. Using an X-ray tomograph with in-situ liquid addition, 2D radiograph images precisely measure the water imbibition and distribution. Two distinct regimes are observed in the presence of a limited water reservoir. Following a classical penetration scaling with the square root of time, the progression continues slowly. The addition of sand minimally impacts these kinetics, only modifying the porosity volume. Imbibition front kinetics is described by analyzing unsaturated flow under limited water conditions, particularly in scenarios where water availability is restricted. The study underscores the importance of investigating imbibition under finite water amounts, a common degradation scenario. The physical understanding aims to contribute to the enhanced design of earthen plaster. [ABSTRACT FROM AUTHOR]

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Geopolymers in radioactive waste management have in recent times gained dominance in disposal module acceptance. Here is a comparative study on formulations prepared from industrial wastes to be utilized as radionuclide disposal barriers in near surface disposal facilities (NSDFs). Different tools such as isocalorimetry, compressive strength, chemical durability were utilized for screening the formulations. Water leaching of the samples shows that the release of the ions to the leachant is minimal. Durability studies in acids (H2SO4, HCl and HNO3) show that the samples are mostly acid resistant. X-ray tomography suggests low pore volume change over a period of 2 years. The study concludes that the NSDF requirement of low permeability of water and better chemical durability criterion is met by the optimized geopolymerization formulation. [ABSTRACT FROM AUTHOR]

Most additive manufacturing (AM) technologies use heat to fuse materials together to create the manufactured part. The heat used in the AM process distorts the parts. Powder bed–based 3D printers can print multiple parts in their build chamber. The distortion is not uniform across the different locations of the build volume. Parts printed in different locations will have different thermal histories and therefore different distortions. In some cases, the achievable accuracy of the parts is insufficient due to the distortion. Subtractive processes such as milling, turning, and grinding make it difficult or impossible to improve part accuracy. For AM to produce more accurate parts, a distortion reduction method must be implemented. To take advantage of the ability to print multiple parts in a powder-based polymer 3D printing process in one build unit, a distortion mitigation technique must be applied to all the parts being printed simultaneously in the build chamber. The performance of the distortion mitigation method can be evaluated by measuring the dimensional accuracy of the uncompensated and compensated parts. Uncompensated 3D printing uses the nominal 3D model, which is the normal use of the 3D printers. Compensated 3D printing uses a distorted 3D model that is used for the printing. The 3D model is compensated with the reversed distortion data obtained from uncompensated manufacturing. X-ray computed tomography (XCT) is the chosen measurement method to extract the point cloud for the dimensional measurements. Unlike optical 3D scanners and coordinate measuring machines (CMM), the XCT is able to measure undercut and internal surfaces. The nominal difference % is improved by 18% by using compensation for the 3D models in the case of distances between two parallel planes. The standard deviation of the measured values was also improved. The distortion reduction method studied can significantly reduce the calibration errors of the 3D printer build chamber. When the tolerances of the parts are close to the limit of the 3D printer, this method can reduce the number of rejected parts. The XCT measurement of the parts is costly, so this method can be cost effective for high value parts or large production volumes. [ABSTRACT FROM AUTHOR]

Pore structural characterisation is important for rocks and their constituent minerals in order to understand physico-chemical processes occurring therein. Rather than a broad general survey of potential pore characterisation techniques, this review focuses on an in-depth discussion of some key current issues in this topic. A so-called 'brute-force' characterisation approach involving a single imaging modality is seldom possible for rocks due to their high degree of heterogeneity. This work surveys alternate strategies suitable for rocks. Further, this work addresses some misapprehensions and misunderstandings that have arisen concerning some experimental techniques offering alternate strategies to the brute-force approach, such as gas overcondensation and mercury porosimetry. It also considers some pore structural characterisation techniques, such as cryoporometry, that are seldom used in the context of natural materials and surveys their capabilities. [ABSTRACT FROM AUTHOR]

The topology and surface characteristics of lyophilisates significantly impact the stability and reconstitutability of freeze-dried pharmaceuticals. Consequently, visual quality control of the product is imperative. However, this procedure is not only time-consuming and labor-intensive but also expensive and prone to errors. In this paper, we present an approach for fully automated, non-destructive inspection of freeze-dried pharmaceuticals, leveraging robotics, computed tomography, and machine learning. [ABSTRACT FROM AUTHOR]

There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE's capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. [ABSTRACT FROM AUTHOR]

Abstract Hydrogel foams are widely used in many applications such as biomaterials, cosmetics, foods, or agriculture. However, controlling precisely foam morphology (bubble size or shape, connectivity, wall and strut thicknesses, homogeneity) is required to optimize their properties. Therefore, a method is proposed here for generating, controlling, and characterizing the morphology of hydrogel foams from liquid foam templates: Using the example of Alginate‐CaHPO4‐based hydrogel foams, a highly controllable foaming process is provided by bubbling nitrogen through nozzles into the solution, which produces hydrogel foams with millimeter‐sized bubbles. A rheological characterization protocol of the foam's constituent material is first implemented and highlights the impact of the initial liquid foam properties and of the competition between the solidification kinetics and the foam aging mechanisms on the resulting morphology. X‐ray tomographic characterization performed on solidifying and solidified samples then demonstrates that by controlling the temporal evolution of the foam via its formulation, it is possible to tune the final morphology of the alginate foams. This method can be adapted to other hydrogel or polymer formulations, foam characteristics and length scales, as soon as solidification processes happen on timescales shorter than foam destabilization mechanisms.

Friction stir welding (FSW) is a promising technology for joining dissimilar metal alloys, such as aluminum alloy (Al 2024) and titanium alloy (Ti-6Al-4V). However, optimizing FSW parameters to enhance joint strength and reliability remains a challenge. To address this, this manuscript presents a novel concept of using tool rotational speed as a key parameter to investigate joint formation mechanisms and associated mechanisms in FSW. The study found that tool rotational speed significantly affects the deformation and mechanical mixing of the two metals in the weld nugget. Optimal tool rotational speed produces a defect-free weld with superior mechanical properties. The fragmentation of joint interfaces and the formation of new particles of different sizes in titanium lead to deformation and fracture mechanisms. X-ray tomography results demonstrate that fine particles are evenly dispersed in the Al matrix compared to coarse particles. Moreover, the study provides valuable insights into the microstructural development in Al, attributed to dynamic recovery (DRV), continuous dynamic recrystallization (CDRX), and particle-stimulated nucleation (PSN). The type of intermetallic compounds (IMCs) formation is not affected by the tool rotational speed, and a proposed mechanism of IMCs formation is presented from a thermodynamic perspective. Overall, this study improvises the current understanding of joint formation mechanisms in FSW and suggests using tool rotational speed as a parameter for optimizing FSW parameters for enhanced joint strength and reliability. [ABSTRACT FROM AUTHOR]

Recent research has shown that some intermetallic compound particles with high interfacial hydrogen trap energies (e.g., Mg2Si) are prone to damage at high hydrogen concentrations. In this study, the acceleration of particle damage in an A6061 alloy was observed in-situ via X-ray CT. The damage behavior of the particles that are located in the crack tip stress field, where high stress triaxiality causes a local increase in the hydrogen concentration, was analyzed. The influence of hydrogen on the damage behavior of the dispersed Mg2Si particles was investigated by preparing a material charged with hydrogen to achieve extremely high hydrogen concentration, and further hydrogen enrichment in a crack tip region was also utilized. Interfacial debonding of Mg2Si particles was frequently observed in the vicinity of a crack tip immediately prior to tensile fracture. Even though the fracture is typical of ductile fracture, hydrogen accelerates particle damage and reduces the macroscopic ductility of the aluminum alloy. This can be considered as a form of hydrogen embrittlement of aluminum alloys. Even in materials with relatively low hydrogen concentrations (0.85 mass ppm), interfacial debonding occurred in the hydrogen-enriched crack tip regions. A higher hydrogen concentration promoted interfacial debonding over a wider range of particle sizes and particle shapes. It can be inferred that localized hydrogen enrichment, which is expected to occur due to external hydrogen exposure, stress corrosion cracking, corrosion or crack tips, can directly contribute to debonding at the Mg2Si particle/aluminum matrix interface. According to the analysis, reduction of the diameter and simplification of the shape of Mg2Si particles are effective method for suppressing such hydrogen-induced debonding. [ABSTRACT FROM AUTHOR]

The plating of lithium metal on the graphite electrode is a major degradation mechanism in lithium‐ion batteries (LIBs). It brings a significant risk of internal shortcircuit by penetration of dendritic lithium through the separator, leading to short cycle life and safety issues. Understanding how and when plating occurs is crucial for the development of mitigation strategies, e. g. tuning the electrolyte composition. Here we present an operando X‐ray tomographic microscopy (XTM) study to directly monitor the plating of lithium metal in a lithium/graphite cell. XTM enables a non‐destructive and quantitative characterization at operando conditions of lithium deposition on a graphite electrode at relevant conditions. In this work it allows us to probe the role of the electrolyte additives vinylene carbonate (VC) and lithium bis(fluorosulfonyl)imide (LiFSI) in the standard LIB electrolyte LP57 (base electrolyte without additives). The additives show overall better performances in terms of delayed onset of lithium plating which is important for the utilisation of the full capacity of graphite intercallaiton. We show that there is a transition during lithiation of the dominating mechanism, once lithium plating is initiated this rapidly becomes dominating and hinders further intercalation. For the base electrolyte a homogeneous and dense morphology of plated lithium is found, whereas a more dendritic morphology is observed in the presence of additives. During delithiation, there is a rapid stripping of some of the plated lithium followed by deintercalation. In addition, our work provides a general methodology to track the morphology of plated lithium, which is crucial for fundamental research about battery safety. [ABSTRACT FROM AUTHOR]

The mechanical response of rubbers has been ubiquitously assumed to be only a function of the imposed strain. Using innovative X-ray measurements capturing the three-dimensional spatial volumetric strain fields, we demonstrate that rubbers and indeed many common engineering polymers undergo significant local volume changes. But remarkably, the overall specimen volume remains constant regardless of the imposed loading. This strange behavior which also leads to apparent negative local bulk moduli is due to the presence of a mobile phase within these materials. Combining X-ray tomographic observations with high-speed radiography to track the motion of the mobile phase, we have revised classical thermodynamic frameworks of rubber elasticity. The work opens broad avenues to understand not only the mechanical behavior of rubbers but a large class of widely used engineering polymers. [ABSTRACT FROM AUTHOR]

Processing of single‐crystal X‐ray diffraction data from area detectors can be separated into two steps. First, raw intensities are obtained by integration of the diffraction images, and then data correction and reduction are performed to determine structure‐factor amplitudes and their uncertainties. The second step considers the diffraction geometry, sample illumination, decay, absorption and other effects. While absorption is only a minor effect in standard macromolecular crystallography (MX), it can become the largest source of uncertainty for experiments performed at long wavelengths. Current software packages for MX typically employ empirical models to correct for the effects of absorption, with the corrections determined through the procedure of minimizing the differences in intensities between symmetry‐equivalent reflections; these models are well suited to capturing smoothly varying experimental effects. However, for very long wavelengths, empirical methods become an unreliable approach to model strong absorption effects with high fidelity. This problem is particularly acute when data multiplicity is low. This paper presents an analytical absorption correction strategy (implemented in new software AnACor) based on a volumetric model of the sample derived from X‐ray tomography. Individual path lengths through the different sample materials for all reflections are determined by a ray‐tracing method. Several approaches for absorption corrections (spherical harmonics correction, analytical absorption correction and a combination of the two) are compared for two samples, the membrane protein OmpK36 GD, measured at a wavelength of λ = 3.54 Å, and chlorite dismutase, measured at λ = 4.13 Å. Data set statistics, the peak heights in the anomalous difference Fourier maps and the success of experimental phasing are used to compare the results from the different absorption correction approaches. The strategies using the new analytical absorption correction are shown to be superior to the standard spherical harmonics corrections. While the improvements are modest in the 3.54 Å data, the analytical absorption correction outperforms spherical harmonics in the longer‐wavelength data (λ = 4.13 Å), which is also reflected in the reduced amount of data being required for successful experimental phasing. [ABSTRACT FROM AUTHOR]

To analyze the self-sealing process in the Callovo-Oxfordian claystone, self-sealing tests were performed on initially fractured samples under different temperatures with water and gas injection. Cylindrical samples oriented in parallel and perpendicularly to the bedding plane with an artificial initial fracture were used in a triaxial compression cell transparent to X-rays. Water and gas permeability were measured and the evolution of cracks volume was analyzed from X-ray tomography 3D images to characterize the self-sealing process. All tests performed at 20 °C with water injection showed a rapid drop in permeability at the beginning followed by a progressive decrease and a stabilization after one month. The permeability of fractured samples decreases significantly after self-sealing but is still higher (by 2 orders of magnitude) than the permeability of healthy claystone. Otherwise, the less calcite the sample contains (i.e., the more clayey it is), the faster the crack self-seals. The smaller the opening of the initial crack is, the faster the water permeability decreases and the crack closes. No significant influence of the sample orientation on the self-sealing kinetic was identified at this stage. It seems that high temperature has a slight retarding effect on the self-sealing process. For the test with water and gas injection, the injection of gas delays the decrease of water permeability and the self-sealing process, which is probably due to the crack desaturation induced by gas injection. Highlights: Self-sealing tests were performed on initially fractured samples of Callovo-Oxfordian claystone with different temperatures, orientations and fluids (water, gas). Self-sealing process was analyzed thanks to permeability measurements and crack volume estimation from X-ray tomography 3D images. The self-sealing process is fast at the beginning of the test and stabilizes after about one month and the initial permeability of the healthy rock is partially restored. The less calcite the sample contains (the more clayey it is) and the smaller the opening of the initial crack is, the faster the crack self-seals. Temperature and gas seem to have a delay effect on the self-sealing process. There is no significant influence of the sample orientation. [ABSTRACT FROM AUTHOR]

Objectives: The artificial intelligence competition in healthcare at TEKNOFEST-2022 provided a platform to address the complex multi-class classification challenge of abdominal emergencies using computer vision techniques. This manuscript aimed to comprehensively present the methodologies for data preparation, annotation procedures, and rigorous evaluation metrics. Moreover, it was conducted to introduce a meticulously curated abdominal emergencies data set to the researchers. Methods: The data set underwent a comprehensive central screening procedure employing diverse algorithms extracted from the e-Nabız (Pulse) and National Teleradiology System of the Republic of Türkiye, Ministry of Health. Full anonymization of the data set was conducted. Subsequently, the data set was annotated by a group of ten experienced radiologists. The evaluation process was executed by calculating F1 scores, which were derived from the intersection over union values between the predicted bounding boxes and the corresponding ground truth (GT) bounding boxes. The establishment of baseline performance metrics involved computing the average of the highest five F1 scores. Results: Observations indicated a progressive decline in F1 scores as the threshold value increased. Furthermore, it could be deduced that class 6 (abdominal aortic aneurysm/dissection) was relatively straightforward to detect compared to other classes, with class 5 (acute diverticulitis) presenting the most formidable challenge. It is noteworthy, however, that if all achieved outcomes for all classes were considered with a threshold of 0.5, the data set's complexity and associated challenges became pronounced. Conclusion: This data set's significance lies in its pioneering provision of labels and GT-boxes for six classes, fostering opportunities for researchers. Clinical relevance statement: The prompt identification and timely intervention in cases of emergent medical conditions hold paramount significance. The handling of patients' care can be augmented, while the potential for errors is minimized, particularly amidst high caseload scenarios, through the application of AI. Key Points: • The data set used in artificial intelligence competition in healthcare (TEKNOFEST-2022) provides a 6-class data set of abdominal CT images consisting of a great variety of abdominal emergencies. • This data set is compiled from the National Teleradiology System data repository of emergency radiology departments of 459 hospitals. • Radiological data on abdominal emergencies is scarce in literature and this annotated competition data set can be a valuable resource for further studies and new AI models. [ABSTRACT FROM AUTHOR]

The in situ study of fractal microstructure in nanocarbon polymers is an actual task for their application and for the improvement in their functional properties. This article presents a visualization of the bulk structural features of the composites using pulsed acoustic microscopy and synchrotron X-ray microtomography. This article presents details of fractal structure formation using carbon particles of different sizes and shapes—exfoliated graphite, carbon platelets and nanotubes. Individual structural elements of the composite, i.e., conglomerations of the particles in the air capsule as well as their distribution in the composite volume, were observed at the micro- and nanoscale. We have considered the influence of particle architecture on the fractal formation and elastic properties of the composite. Acoustic and X-ray imaging results were compared to validate the carbon agglomeration. [ABSTRACT FROM AUTHOR]

3D imaging via X‐ray microscopy (XRM), a form of tomography, is revolutionising materials characterisation. Nondestructive imaging to classify grains, particles, interfaces and pores at various scales is imperative for our understanding of the composition, structure, and failure of building materials. Various workflows now exist to maximise data collection and to push the boundaries of what has been achieved before, either from singular instruments, software or combinations through multimodal correlative microscopy. An evolving area on interest is the XRM data acquisition and data processing workflow; of particular importance is the improvement of the data acquisition process of samples that are challenging to image, usually because of their size, density (atomic number) and/or the resolution they need to be imaged at. Modern advances include deep/machine learning and AI resolutions for this problem, which address artefact detection during data reconstruction, provide advanced denoising, improved quantification of features, upscaling of data/images, and increased throughput, with the goal to enhance segmentation and visualisation during postprocessing leading to better characterisation of samples. Here, we apply three AI and machine‐learning‐based reconstruction approaches to cements and concretes to assist with image improvement, faster throughput of samples, upscaling of data, and quantitative phase identification in 3D. We show that by applying advanced machine learning reconstruction approaches, it is possible to (i) vastly improve the scan quality and increase throughput of 'thick' cores of cements/concretes through enhanced contrast and denoising using DeepRecon Pro, (ii) upscale data to larger fields of view using DeepScout and (iii) use quantitative automated mineralogy to spatially characterise and quantify the mineralogical/phase components in 3D using Mineralogic 3D. These approaches significantly improve the quality of collected XRM data, resolve features not previously accessible, and streamline scanning and reconstruction processes for greater throughput. LAY DESCRIPTION: 3D imaging via X‐ray microscopy (XRM), a form of tomography, is revolutionising the understanding of various human‐made materials. It provides non‐destructive imaging to classify grains, particles, interfaces and pores at various resolutions. It is particularly important for our understanding of the composition, structure, and failure of building materials such as cements and concretes. An evolving area on interest is the XRM data acquisition and data processing workflow; of particular importance is the improvement of the data acquisition process of samples that are challenging to image, usually because of their size, high density and/or the resolution they need to be imaged at. Modern advances include deep/machine learning and AI, which could help with this problem, which are able to pick out image artefacts during data reconstruction, provide advanced denoising, improved quantification of features, upscaling of data/images, and increased sample throughput. All of these improvements enhance data leading to better characterisation and interpretation of samples, and to spot aspects of failure. Here, we apply three AI and machine‐learning‐based reconstruction approaches to cements and concretes to assist with image improvement, faster throughput of samples, upscaling of data, and quantitative phase identification in 3D. We show that by applying advanced machine learning reconstruction approaches, it is possible to (i) vastly improve the scan quality and increase throughput of 'thick' cores of cements/concretes through enhanced contrast and denoising using DeepRecon Pro, (ii) upscale data to larger fields of view using DeepScout and (iii) use quantitative automated mineralogy to characterise and quantify the mineralogical/phase components in 3D using Mineralogic 3D. These approaches significantly improve the quality of collected XRM data for cements and concretes, resolve features not previously seen, and streamline scanning and reconstruction processes so more samples can be scanned. [ABSTRACT FROM AUTHOR]

This paper presents a method for particle tracing in laboratory X-ray micro-computed tomography (µCT) using an adjusted Random Sample Consensus (RANSAC) algorithm combined with least squares ellipse fitting (LSF). For method testing, a setup for the investigation of deep bed filtration (DBF) has been used as an example of a complex process that can be elucidated with such a method. Particle tracking with tomography systems requires high-temporal resolution which can only be achieved with synchrotron radiation computer tomography. Therefore, in this work, it has been demonstrated that instead of particle tracking, particle tracing in opaque systems such as DBF can be performed in laboratory µCT systems. To achieve particle tracing, dynamic µCT scans with a duration between 30 and 110 s combined with an exposure time of 0.13 s/projection were executed and during the scan time the filtration was performed, causing parabola shaped motion artefacts. The developed method exploits the motion artefacts created by the particle motion during the scan. It could be shown that it is possible to trace particles in complex structures within only one 30 s scan. Furthermore, through trace length and time, it is possible to determine the average velocity. Whereby, the accuracy and limits depend on the particle size, particle velocity/data rate and the X-ray attenuation of particle and medium. Article Highlights: A method for the three-dimensional tracing of particles in porous and opaque media was developed using X-ray microtomography. The method was implemented in a way that it can be used under simple laboratory conditions instead of synchrotron facilities. The method was validated experimentally using opaque geometries of different complexities. [ABSTRACT FROM AUTHOR]