Extreme Temperature Sample Environment for Materials Research using Neutron Scattering
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47 OTHER INSTRUMENTATION↗
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Beamline Electrostatic Levitator (BESL) for in-situ High Energy K-Ray Diffraction Studies of Levitated Solids and Liquids at High Temperature
Determinations of the phase formation sequence, the crystal structures and the thermodynamic properties of materials at high temperatures are difficult because of contamination from the sample container and environment. Containerless processing techniques, such as electrostatic (ESL), electromagnetic (EML), aerodynamic, and acoustic levitation, are most suitable these studies. An adaptation of ESL for in-situ structural studies of a wide range of materials, including metals, semiconductors, insulators using high energy (125 keV) synchrotron x-rays is described here. This beamline ESL (BESL) allows the in-situ determination of the atomic structures of equilibrium solid and liquid phases, including undercooled liquids, as well as real-time studies of solid-solid and liquid-solid phase transformations. The use of image plate (MAR345) or GE-Angio detectors enables fast (30 ms - 1s) acquisition of complete diffraction patterns over a wide q-range (4 - 140/mm). The wide temperature range (300 - 2500 K), containerless processing under high vacuum (10(exp -7) - 10(exp -8) torr), and fast data acquisition, make BESL particularly suitable for phase diagram studies of high temperature materials. An additional, critically important, feature of BESL is the ability to also make simultaneous measurement of a host of thermo-physical properties, including the specific heat, enthalpy of transformation, solidus and liquidus temperatures, density, viscosity, and surface tension; all on the same sample and simultaneous with the structural measurements.
Gangopadhyay, A. K.↗
Rheo-Structural Spectroscopy: Fingerprinting the In Situ Response of Fluids to Arbitrary Flow Fields
The objectives of this project were to develop new sample environments, measurement methodologies and associated modeling tools for characterizing the structural response to arbitrarily complex processing flows using small angle scattering, and to apply these new tools for understanding the fundamental physics governing the structuring of anisotropic particulate and polymeric materials under flow histories and conditions relevant to industrial processing flows. The research resulted in the development and implementation of a new sample environment, the fluidic four roll mill (FFoRM), for in situ small angle neutron and X-ray scattering (SANS/SAXS) measurements. These measurements are capable of generating large data sets that “fingerprint” how a complex fluid responds to a wide range of flow histories involving time variations in deformation type and rate. New modeling tools were developed to extract detailed microstructural information from such data sets, including orientation distribution functions and interparticle correlation functions, as well as reduced-order parametric descriptors of these high-dimensional functions that can be used to readily map, visualize and interpret a fluid’s structural response to its flow history. These new tools were applied to a range of model materials involving elongated particle suspensions in order to provide new insights into the physics of how flow couples with orientational and structural order in complex flows, particularly under non-dilute conditions for which no accurate theories currently exist. Using these investigations, we elucidated a number of new insights into the fundamental phenomena driving such process-structure-property relationships. These findings provide guidance for the further development of rheological models, and ultimately can inform the rational and model-based design of flow processes to achieve optimized orientational ordering that is key to the properties and function of a wide range of energy-relevant materials.
36 MATERIALS SCIENCE↗
Combining Spatial and Temporal Resolution in Cryo-TEM of Device Materials: Preprint
Cryogenic transmission electron microscopy (cryo-TEM) has been instrumental to reducing the effects of electron-beam sample interactions and enabling higher spatial resolution studies of beam sensitive materials. However, the process of thinning and freezing samples for cryo-TEM characterization is often complex and frequently involves removing materials from their environment or stimuli of interest for extended times. This can allow for structural relaxation, diffusion, and other dynamic processes to occur prior to characterization; for energy materials like battery electrodes, this makes it difficult to precisely correlate the imaged structure with native structures that evolve in-situ during device processing, operation, or aging. Here, we present methods to prepare cryo-TEM samples from energy materials during these critical processes in the relevant device lifetimes. This offers insights into improving the temporal resolution of cryo-TEM, while still exploiting its ability to achieve high spatial resolutions in characterizing beam sensitive materials.
BASIC BIOLOGICAL SCIENCES,MATERIALS SCIENCE↗
Materials science research in microgravity
There are several important attributes of an extended duration microgravity environment that offer a new dimension in the control of the microstructure, processing, and properties of materials. First, when gravitational effects are minimized, buoyancy driven convection flows are also minimized. The flows due to density differences, brought about either by composition or temperature gradients will then be reduced or eliminated to permit a more precise control of the temperature and the composition of a melt which is critical in achieving high quality crystal growth of electronic materials or alloy structures. Secondly, body force effects such as sedimentation, hydrostatic pressure, and deformation are similarly reduced. These effects may interfere with attempts to produce uniformly dispersed or aligned second phases during melt solidification. Thirdly, operating in a microgravity environment will facilitate the containerless processing of melts to eliminate the limitations of containment for reactive melts. The noncontacting forces such as those developed from electromagnet, electrostatic, or acoustic fields can be used to position samples. With this mode of operation, contamination can be minimized to enable the study of reactive melts and to eliminate extraneous crystal nucleation so that novel crystalline structures and new glass compositions may be produced. In order to take advantage of the microgravity environment for materials research, it has become clear that reliable processing models based on a sound ground based experimental experience and an established thermophysical property data base are essential.
Perepezko, John H.↗
Erbium-implanted materials for quantum communication applications
Erbium-doped materials can serve as spin-photon interfaces with optical transitions in the telecom C band, making them an exciting class of materials for long-distance quantum communication. However, the spin and optical coherence times of Er 3+ ions are limited by currently available host materials, motivating the development of new Er 3+ -containing materials. Here we demonstrate the use of ion implantation to efficiently screen prospective host candidates, and show that disorder introduced by ion implantation can be mitigated through post-implantation thermal processing to achieve inhomogeneous linewidths comparable to bulk linewidths in as-grown samples. We present optical spectroscopy data for each host material, which allows us to determine the level structure of each site, allowing us to compare the environments of Er 3+ introduced via implantation and via doping during growth. Here, we demonstrate that implantation can generate a range of local environments for Er 3+ , including those observed in bulk-doped materials, and that the populations of these sites can be controlled with thermal processing.
74 ATOMIC AND MOLECULAR PHYSICS↗
Lunar vertical-shaft mining system
This report proposes a method that will allow lunar vertical-shaft mining. Lunar mining allows the exploitation of mineral resources imbedded within the surface. The proposed lunar vertical-shaft mining system is comprised of five subsystems: structure, materials handling, drilling, mining, and planning. The structure provides support for the exploration and mining equipment in the lunar environment. The materials handling subsystem moves mined material outside the structure and mining and drilling equipment inside the structure. The drilling process bores into the surface for the purpose of collecting soil samples, inserting transducer probes, or locating ore deposits. Once the ore deposits are discovered and pinpointed, mining operations bring the ore to the surface. The final subsystem is planning, which involves the construction of the mining structure.
Introne, Steven D.↗
In vitro investigation of protein assembly by combined microscopy and infrared spectroscopy at the nanometer scale
The nanoscale structure and dynamics of proteins on surfaces has been extensively studied using various imaging techniques, such as transmission electron microscopy and atomic force microscopy (AFM) in liquid environments. These powerful imaging techniques, however, can potentially damage or perturb delicate biological material and do not provide chemical information, which prevents a fundamental understanding of the dynamic processes underlying their evolution under physiological conditions. Here, we use a platform developed in our laboratory that enables acquisition of infrared (IR) spectroscopy and AFM images of biological material in physiological liquids with nanometer resolution in a cell closed by atomically thin graphene membranes transparent to IR photons. In this work, we studied the self-assembly process of S-layer proteins at the graphene-aqueous solution interface. The graphene acts also as the membrane separating the solution containing the proteins and Ca 2+ ions from the AFM tip, thus eliminating sample damage and contamination effects. The formation of S-layer protein lattices and their structural evolution was monitored by AFM and by recording the amide I and II IR absorption bands, which reveal the noncovalent interaction between proteins and their response to the environment, including ionic strength and solvation. Our measurement platform opens unique opportunities to study biological material and soft materials in general.
59 BASIC BIOLOGICAL SCIENCES↗
Multiscale Modeling of Reconstructed Tricalcium Silicate using NASA Multiscale Analysis Tool
To study microstructure characteristics of cementitious materials hydrated in space; previously, cement binder formations were processed under microgravity conditions and was further compared against ground-based experiments. For accurate estimation of process-structure-property linkage, particularly on samples hydrated in the microgravity environment, it is desired to have a high-fidelity volumetric representation of the microstructure. However, owing to small sample size and high porosity of the space-returned samples, conventional experimental characterization techniques are not viable. Hence, a deep learning-based reconstruction algorithm was employed to obtain high fidelity 3D volumes from sparse high resolution 2D Scanning Electron Microscopy (SEM) images, as inputs to micromechanics-based modeling. This machine learning-based reconstruction methodology validated against low-order statistical descriptors, captured the microstructural topology of both sample types (ground, 1g and microgravity, μg). Due to the lack of gravity, hydration products of the samples processed in space differed from those processed-on ground. Such AI-generated virtual samples were analyzed in a multiscale recursive micromechanics approach using the NASA Multiscale Analysis Tool (NASMAT). Here, we present a methodology to rapidly integrate and evaluate these AI-generated volumes in NASMAT. The synthesized microstructural volumes are directly employed as Representative Volume Elements (RVEs) to preserve the fidelity (1 pixel = 0.54 m). Invariably, analysis of such largescale problems (5123 voxels) requires huge amount of computational resources. By taking advantage of the NASMAT architecture, we also focused on systematic multiscale integration of these AI-reconstructed virtual volumes to reduce the computational demands. In this work, this methodology is demonstrated on the ground-based, 1g samples. The estimated stiffness value of 15.90 GPa is comparable to experimentally obtained modulus of hydrated tricalcium silicate sample. The workflow presented here paves the way for utilizing the NASMAT tool to perform multiscale analyses of other multi-phase material systems using either 3D virtual datasets synthesized using AI or obtained via micro-CT.
Machine Learning↗
Effects of transient thermal shock on the microstructures and corrosion properties of a reduced activation high-entropy alloy
The devices would subject transient thermal shocks (TTS) during operation under extremely harsh conditions of nuclear fusion reactors, which inevitably exert significant impact on the microstructure and performance of structural materials. Here, a reduced activation VCrFeTa 0.2 W 0.2 high-entropy alloy (HEA) was developed by vacuum arc melting. The effects of electron beam induced TTS on its microstructure, microhardness, and corrosion properties were investigated. The results indicate that the weight fraction of each phase changes after TTS, showing a significant decrease in the content of BCC1 phase and an increase in the content of BCC2 and Laves phases. The content of BCC1 phase continues to decrease slightly with increasing the power of TTS. Besides, the microhardness of the alloy increases from ~673 HV to ~714 HV after TTS treatments. In the reduced activation HEA, TTS results in a relatively low corrosion current density of ~0.472 μA/cm 2 in 3.5 wt% NaCl solution, around one-third of the current density observed in the as-cast sample. Furthermore, the VCrFeTa 0.2 W 0.2 HEA after TTS exhibits a pitting potential of ~1.165 V SCE , which is much higher than that of the as-cast sample. The refined composite multiscale entropy method is employed to analyze the influence of TTS on current fluctuation behavior during the corrosion process. The reduced activation VCrFeTa 0.2 W 0.2 HEA exhibits excellent properties in harsh environments after TTS, thereby showing advantageous property in the field of nuclear structural materials. Furthermore, TTS is an efficient and controllable strategy for the improvement of the HEAs’ microstructures and performances.
36 MATERIALS SCIENCE↗
Lattice Structured Lightweight Structural Materials
The development of lightweight structural materials is crucial for enhancing the performance and deployment feasibility of fission batteries. This study aims to produce lightweight structural materials whose strength-to-weight ratios exceed those of current widely used structural materials. To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structured models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The modeling results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison to the solid material; the increase in solid shell thickness and blend radius enhances the mechanical performance; and the effect of unit cell size on macro scale stress is minimal. Tensile testing was conducted on the solid and lattice-structured materials fabricated by laser powder bed fusion (LPBF) additive manufacturing. The experimental results agreed well with the model prediction. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for new material development.
lightweight material↗
Report on PTT Imaging of Defects in AM Metallic Materials-Part 2
Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study.
36 MATERIALS SCIENCE↗
Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)
Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.
36 MATERIALS SCIENCE↗
Lattice Design and Advanced Modeling to Guide the Design of High-Performance Lightweight Structural Materials
Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those of the current widely used structural materials such as 316L stainless steels (316L SS). To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structures models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The preliminary results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison with the solid material and that the unit cell size of the lattice had a minimal effect. The novelty here is to apply up-front modeling to determine the best structure for the application before actually producing the sample. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for material development.
36 MATERIALS SCIENCE↗
The electrical properties of low-g processed immiscible alloys
When dispersed or mixed immiscibles are solidified on earth, a large amount of separation of the constituents takes place due to differences in densities. However, when the immiscibles are dispersed and solidified in zero-gravity, density separation does not occur, and unique composite solids can be formed with many new and promising electrical properties. By measuring the electrical resistivity and superconducting critical temperature of zero-g processed Ga-Bi samples, it has been found that the electrical properties of such materials are entirely different from the basic constituents and the ground control samples. The finest dispersions are obtained only in a low-gravity environment. Our results indicate that space-processed immiscible materials may form an entirely new class of electronic materials. The interesting electrical properties of directionally solidified Al-Cu eutectic, a two-component alloy consisting of Al and Al2Cu which was processed on Skylab (M566) are briefly reviewed. The electrical resistivity of directionally solidified eutectics with a lamellar structure are highly anisotropic and have been studied both experimentally and theoretically. The local resistivity of the space-processed samples are compared to a ground control sample and to the expected theoretical results.
Otto, G. H.↗
A method for the probabilistic design assessment of composite structures
A formal procedure for the probabilistic design assessment of a composite structure is described. The uncertainties in all aspects of a composite structure (constituent material properties, fabrication variables, structural geometry, service environments, etc.), which result in the uncertain behavior in the composite structural responses, are included in the assessment. The probabilistic assessment consists of design criteria, modeling of composite structures and uncertainties, simulation methods, and the decision making process. A sample case is presented to illustrate the formal procedure and to demonstrate that composite structural designs can be probabilistically assessed with accuracy and efficiency.
Shiao, Michael C.↗
Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components
Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
The Marshall Space Flight Center Drop Tube Facility
The structural and instrumentation hardware and the improved experimental capabilities of a 105-m Drop Tube Facility are described. The structure is designed to provide a high-vacuum facility in a vertical orientation. The low-gravity environment obtained during free-fall and the containerless materials processing capabilities are discussed. Two furnaces, an electron-beam and an electromagnetic levitator, are used to melt metallic samples in a process environment ranging from 10 to the -6th torr to 1 atm. Specifics of the vacuum control and gas delivery systems are provided. The experimental instrumentation and data acquisition systems used to perform predrop and in-flight measurements of the melting and solidification process are discussed. Typical experimental results are presented as an indicator of the performance of the Drop Tube Facility.
Rathz, Thomas J.↗