Nikolaus L. Cordes

Synchrotron-based X-ray computed tomography during compression loading of cellular materials

Three-dimensional X-ray computed tomography (CT) of in situ dynamic processes provides internal snapshot images as a function of time. Tomograms are mathematically reconstructed from a series of radiographs taken in rapid succession as the specimen is rotated in small angular increments. In addition to spatial resolution, temporal resolution is important. Thus temporal resolution indicates how close together in time two distinct tomograms can be acquired. Tomograms taken in rapid succession allow detailed analyses of internal processes that cannot be obtained by other means. This article describes the state-of-the-art for such measurements acquired using synchrotron radiation as the X-ray source.

Laboratory-based characterization of plutonium in soil particles using micro-XRF and 3D confocal XRF

The measurement of plutonium (Pu) in a soil matrix is of interest in safeguards, nuclear forensics, and environmental remediation activities. The elemental composition of two Pu contaminated soil particles was characterized nondestructively using micro X-ray fluorescence spectrometry (micro-XRF) techniques including high resolution X-ray (hiRX) and 3D confocal XRF. The three dimensional elemental imaging capability of confocal XRF permitted the identification two distinct Pu particles within the samples: one external to the Fe-rich soil matrix and another co-located with Cu within the soil matrix. The size and morphology of the particles was assessed with X-ray transmission microscopy (XTM) and micro X-ray computed tomography (micro-CT) providing complementary information. Limits of detection for a 30 μm Pu particle are <15 ng for each of the XRF techniques. This study highlights the capability for lab-based, nondestructive, spatially resolved characterization of heterogeneous matrices on the micrometer scale with nanogram sensitivity.

Analysis of thermal history effects on mechanical anisotropy of 3D-printed polymer matrix composites via in situ X-ray tomography

The tensile behavior of an additively manufactured (AM) polymer matrix composite (PMC) is studied with in situ X-ray computed microtomography (CT) and digital volume correlation (DVC). In this experiment, the effects of recycled material content and print direction on the selective laser-sintered (SLS) material’s mechanical response are explored. The PMC samples are printed in a tensile specimen geometry with gage lengths parallel to all three orthogonal, primary sintering directions. In situ tensile-CT experiments are conducted at Argonne National Laboratory’s Advanced Photon Source 2-BM beamline. Analysis of the AM PMC’s tensile response, failure, and strain evolution is analyzed both from a conventional standpoint, using the load–displacement data recorded by the loading fixture, and from a microstructural standpoint by applying DVC analysis to the reconstructed volumes. Significant variations on both strength and ductility are observed from both vantages with respect to print direction and the recycled material content in the printed parts. It is found that the addition of recycled source material with a thermal history reduces the tensile strength of the SLS composite for all directions, but the effect is drastic on the strength in the layering direction.

A Route to Integrating Dynamic 4D X-ray Computed Tomography and Machine Learning to Model Material Performance

Machine learning has recently been implemented in materials science where it has opened new pathways to modeling and predicting material performance [1]. A material property that is difficult to model and predict (as well as difficult to tailor during material fabrication) is the compressive performance of polymeric foams. These materials are often used in applications where high strength, light weight, low density and/or low cost components are desired and their compressive properties are often essential to the intended application. However, a population of polymer foams samples with analogous polymeric material properties may exhibit extremely different compressive stress-strain curves which can be attributed to the various micrometer-scale void morphologies present in the population. By capturing these void microstructures at various stress-strain states via dynamic 4D (i.e., 3D + time) X-ray computed tomography (X-ray CT), quantitative void microstructure descriptors can used as inputs into machine learning algorithms for the purposes of developing a polymeric foam compressive performance model. This will allow a deeper understanding of exactly how the void microstructure affects a polymer foam’s compressive response in a mathematical framework as well as establish a methodology for the study of other systems undergoing a physical dynamic external stimulus. The work presented here is a proof-of-concept study combining 4D X-ray CT data with a traditional multivariate regression technique, partial least squares regression (PLS), and a machine learning technique, Artificial Neural Network (ANN), for the purposes of modeling the stress-strain response using void microstructural information. Results of these analyses will be shown as well as a path forward for providing a robust study of this material property.

Applying Pattern Recognition to the Analysis of X-ray Computed Tomography Data of Polymer Foams

X-ray computed tomography (CT) of materials provides large, three dimensional (3D) image data sets (i.e., tomograms), resolving both surface and subsurface features. Tomograms of open-cell polymer foams typically reveal a two-phase material consisting of the supporting polymer ligament material and the void structure (Fig. 1, left). Segmenting the tomograms for the void structure, rather than the polymer ligaments, allows for measuring the void structures in 3D (Fig. 1, right). The equivalent diameter of a void, which is the measure of the void diameter assuming the void is a perfect sphere, is a common singular metric used to describe and differentiate polymer foams. However, for stochastic and irregular void structures, this singular metric can be insufficient when comparing two or more polymer foam samples (Fig. 2). Therefore, multiple 3D void descriptors are typically required, though more than three measurements can lead to difficulty in interpretation. Thus, a statistically-based pattern recognition technique, Principal Components Analysis (PCA), has been implemented to aid in the interpretation of multivariate tomogram data sets of polymer foam systems. PCA transforms N-dimensional data into a reduced number of dimensions which capture most of the data’s variance and is commonly used for pattern recognition in experimental sciences, thus enabling easy visualization of sample groupings based on several descriptors.

In situ Synchrotron X-ray Tomographic Imaging of 3D Printed Materials During Uniaxial Loading

The 3D printing of polymeric materials offer a wealth of possibilities, in that unique structures may be printed with geometries that are not possible with traditional molding, extrusion, casting, or machining techniques. Polymer structures may be optimized for weight, strength, or form, to improve their overall function. Mechanical testing indicates that print orientation as well as the use of recycled print material can affect the ultimate mechanical performance. Due to these problems, there are very few demonstrated high performance applications of 3D printed materials, especially polymers. In order to understand the adhesion between the printed layers as well as adhesion between the polymer and fillers and crack initiation, propagation, and ultimately failure, in situ analysis techniques are needed. To further complicate the analysis, these materials are typically hyper-elastic in nature. As such, experiments cannot be ‘paused’ during data collection as the material will continue to deform and respond to the applied stress.

X-ray microscopy for in situ characterization of 3D nanostructural evolution in the laboratory

X-ray microscopy (XRM) has emerged as a powerful technique that reveals 3D images and quantitative information of interior structures. XRM executed both in the laboratory and at the synchrotron have demonstrated critical analysis and materials characterization on meso-, micro-, and nanoscales, with spatial resolution down to 50 nm in laboratory systems. The non-destructive nature of X-rays has made the technique widely appealing, with potential for “4D” characterization, delivering 3D micro- and nanostructural information on the same sample as a function of sequential processing or experimental conditions. Understanding volumetric and nanostructural changes, such as solid deformation, pore evolution, and crack propagation are fundamental to understanding how materials form, deform, and perform. We will present recent instrumentation developments in laboratory based XRM including a novel in situ nanomechanical testing stage. These developments bridge the gap between existing in situ stages for micro scale XRM, and SEM/TEM techniques that offer nanometer resolution but are limited to analysis of surfaces or extremely thin samples whose behavior is strongly influenced by surface effects. Several applications will be presented including 3D-characterization and in situ mechanical testing of polymers, metal alloys, composites and biomaterials. They span multiple length scales from the micro- to the nanoscale and different mechanical testing modes such as compression, indentation and tension.

Characterization of Metal Doped Polymer Capsules using Confocal Micro X-ray Fluorescence Spectroscopy and X-ray Computed Tomography

Defect Induced Mix Experiment (DIME) spherical capsules, utilized as National Ignition Facility (NIF) targets, are composed of a 42 μm-thick polymer shell which has been doped with a 2 μmthick inner layer of 1.5 at.% germanium and a 2 μm-thick outer layer of 1.5 at.% gallium. The metal-doped layers are separated by a 3 μm-thick polymer layer. The LANL DIME campaign requires that the characterization of these capsules must provide better accuracy than the fabrication tolerances.

Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization

Since its development in the 1970s (Hounsfield, Br J Radiol 46(552):1016–1022, 1973) [1], X-ray tomography has been used to study the three dimensional (3D) structure of nearly every type of material of interest to science, both in the laboratory (Elliott and Dover, J Microsc 126(2):211–213, 1982) [2] and at synchrotron facilities (Thompson et al., Nucl Instrum Methods Phys Res 222(1):319–323, 1984) [3]. The ability to nondestructively image internal structures is useful in the medical community for patient diagnosis. For this same reason, it is critical for understanding material structural morphology. X-ray tomography of static materials can generate a true 3D structure to map out content and distribution within materials including voids, cracks, inclusions, microstructure, and interfacial quality. This technology is even more useful when applying a time component and studying the changes in materials as they are subjected to non-equilibrium stimulations. For example, testing mechanical properties (e.g., compressive or tensile loading), thermal properties (e.g., melting or solidification), corrosion, or electrostatic responses, while simultaneously imaging the material in situ, can replicate real world conditions leading to an increase in the fundamental understanding of how materials react to these stimuli. Mechanical buckling in foams, migration of cracks in composite materials, progression of a solidification front during metal solidification, and the formation of sub-surface corrosion pits are just a few of the many applications of this technology. This chapter will outline the challenges of taking a series of radiographs while simultaneously stressing a material, and processing it to answer questions about material properties. The path is complex, highly user interactive, and the resulting quality of the processing at each step can greatly affect the accuracy and usefulness of the derived information. Understanding the current state-of-the-art is critical to informing the audience of what capabilities are available for materials studies, what the challenges are in processing these large data sets, and which developments can guide future experiments. For example, one particular challenge in this type of measurement is the need for a carefully designed experiment so that the requirements of 3D imaging are also met. Additionally, the rapid collection of many terabytes of data in just a few days leads to the required development of automated reconstruction, filtering, segmentation, visualization, and animation techniques. Finally, taking these qualitative images and acquiring quantitative metrics (e.g., morphological statistics), converting the high quality 3D images to meshes suitable for modeling, and coordinating the images to secondary measures (e.g., temperature, force response) has proven to be a significant challenge when a materials scientist ‘simply’ needs an understanding of how material processing affects its response to stimuli. This chapter will outline the types of in situ experiments and the large data challenges in extracting materials properties information.

Quantitative Analysis of Ultralow-Density Materials Using Laboratory-Based Quasi-Monochromatic Radiography

Abstract The measurement of the density of materials, especially ultralow-density foams, is difficult in that the measurement must be precise and localizable. The density of the material is often governed by its cellular (i.e., porous) structure, and many techniques exist to create that structure. Often, the cellular structure can vary from one location within the material to another, and when at low densities (i.e., densities lower than ~500 mg/cm3), it can vary due to shrinkage during syneresis, collapse under the weight of gravity, or gas/water vapor uptake. Quantifying this variation is important for a variety of applications, especially when used in plasma physics targets. Knowing the density and its variation across the sample is critical for experimental results to be accurately predicted by physics calculations and for modeling the results of the physics targets. The use of quasi-monochromatic radiography provides a means to image the two-dimensional (2-D) distribution of density variation within silica aerogel materials and to quantitatively measure that variation from sample to sample and lot to lot. For this study, two batches of silica aerogels with targeted densities of ~20 mg/cm3 were created, one batch at Lawrence Livermore National Laboratory, and the other batch at Los Alamos National Laboratory. Outlined here is a quasi-monochromatic radiography system using various X-ray sources coupled to a doubly curved crystal optic and X-ray charge-coupled device camera to image and characterize these materials. It was found that measuring the density both gravimetrically and using quasi-monochromatic radiography were statistically identical, although the two batches were found to be slightly higher than their targeted density due to shrinkage. The radiography system also provided 2-D information as to the aerogel quality, i.e., presence of voids, chipped material, or inclusions.