MST e-News July 2017
This is the July 2017 edition of the Materials Science and Technology Division newsletter.
36 MATERIALS SCIENCE↗
SEARCH · Search NASA
Results for “materials Science”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Determination of the Critical Parameters for Remote Microscope Control
As part of a program to determine the capabilities of Telescience as applied to Microgravity Materials Science the need for a remotely controlled microscope was recognized. For this purpose we equipped a microscope with an X-Y-Z positioning device and motors on the zoom and focus controls. Computer control of these devices allowed remote operation. A standard TV camera was mounted to the computer controlled video board which could compress the image in resolution and grey scale. The operator control console was programmed to display three still video pictures as well as provide command access. A standard data transfer network was used to transmit the video data files and the command interaction was via a high speed phone modem. This system, with the microscope in the Microgravity Materials Science Laboratory (MMSL) at LeRC and the control at RPI, was used to determine the accuracy of setting, time required to achieve setting and the operator ease factor. It was found that the focus setting could be established well within the resolution limit of the TV system and that each motion took about 50 seconds and approximately 12 minutes was required to reach ?best? focus. These times could be reduced significantly with operator experience. The operators were provided with ancillary equipment which provided assistance in making the necessary decisions and they reported satisfaction with the control.
Hahn, R. C.↗
High-throughput simulations for insight into grain boundary structure-property relationships and other complex microstructural phenomena
High-throughput simulations can be a powerful tool in the discovery of new materials and behaviors. As part of a special issue on Rising Stars in Computational Materials Science, this paper uses the work of the author to show how high-throughput simulations have had an impact in grain boundary structure-property relationships and other complex microstructural phenomena. The work demonstrates how new tools designed to analyze large datasets produced by high-throughput simulations are enabling comprehensive grain boundary structure-property relationships to be obtained. High-throughput simulations are also used to demonstrate the impact descriptive and inferential statistics has had in extracting key aspects of deformation in metallic glasses. Finally, several different examples are used to show a balanced approach between simulations designed to survey and simulations designed for detailed analysis. Together, the two approaches provide a comprehensive picture of the variety of behaviors that exist, while ensuring that the physics underlying the behaviors are thoroughly understood.
36 MATERIALS SCIENCE↗
MST e-News March 2014
This is the March 2014 edition of the newsletter for the Materials Science and Technology Division at LANL.
36 MATERIALS SCIENCE↗
Preface: High-pressure studies with x-rays
The study of matter at extreme conditions represents a forefront area of research activity across the sciences, including physics, chemistry, materials science, and even biology. Advancing the frontier of extreme conditions science requires powerful micro-sampling probes to access minute samples through the vessel walls and to separate weak sample signals from the background signals arising from the much more massive surrounding vessel materials. Here, the development of synchrotron radiation sources has provided the essential breakthrough in extreme conditions field.
47 OTHER INSTRUMENTATION↗
Attention-based explainability for structure–property relationships
Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.
Slautin, Boris [Independent Researcher]↗
A two-set order parameters phase-field modeling of crack deflection/penetration in a heterogeneous microstructure
Modeling of crack deflection/penetration or manifested as intergranular/transgranular fracture in polycrystals has long been a challenge in both fracture mechanics and materials science. A phase-field model formulated with two-set order parameters describing the crack field and the microstructure field respectively, is herein established to investigate the competition between crack penetration and deflection at an interface. The advantages of the proposed model are three-fold: (1) the fracture toughness of grain boundaries and/or interfaces, besides single crystal properties (e.g., Young’s modulus, Poisson ratio, and fracture toughness), can be automatically incorporated, (2) cohesive interfaces are treated in one phase-field framework in comparison with the integrated phase-field and cohesive zone model in which cohesive elements have to be manually placed at the interface, and (3) two-set order parameters enable to describe the concurrent crack propagation and microstructure evolution. The proposed model is validated by comparing the simulated critical deflection angles with the results from theoretical analysis of the linear elastic fracture mechanics and the experiments. The model is then applied to a heterogeneous Silicon Nitride polycrystal to determine the effect of interface fracture toughness on the intergranular/transgranular fracture transition and the effective toughness of the materials.
Chen, Hu↗
International symposium on clusters and nanomaterials (energy and life-sciences applications)
The International Symposium on Clusters and Nanomaterials was held in Richmond, Virginia during October 26-29, 2015. The symposium focused on the roles clusters and nanostructures play in solving outstanding problems in clean and sustainable energy and life sciences applications; two of the most important issues facing science and society. Many of the materials issues in renewable energies, environmental impacts of energy technologies as well as beneficial and toxicity issues of nanoparticles in health are intertwined. Realizing that both fundamental and applied materials issues require a multidisciplinary approach the symposium provided a forum by bringing researchers from physics, chemistry, materials science, and engineering fields to share their ideas and results, identify outstanding problems, and develop new collaborations. Clean and sustainable energy sessions addressed challenges in production, storage, conversion, and efficiency of renewable energies such as solar, wind, bio, thermo-electric, and hydrogen. Environmental issues dealt with air- and water-pollution and conservation, environmental remediation and hydrocarbon processing. Topics in life sciences included therapeutic and diagnostic methods as well as health hazards attributed to nanoparticles. Cross-cutting topics such as reactions, catalysis, electronic, optical, and magnetic properties were also covered. The symposium attracted 132 participants from 24 countries in the world. It featured 39 invited speakers in 14 plenary sessions, in addition to one key-note session. Eighty-five contributed papers were presented in two poster sessions and 14 papers from this list were selected to be presented orally at the end of each session to highlight hot topics. Papers presented at the symposium were reviewed and published in SPIE so that these can reach a wide audience. The symposium was highly interactive with ample time allotted for discussions and making new collaborations. The participants’ response was that this was a high quality conference and covered topics at the cutting edge of science and technology. The symposium was endorsed by the American Physical Society, The Materials Research Society, SPIE, The Metallurgical Society, and the American Vacuum Society. The symposium was supported by external grants from the National Science Foundation and the Department of Energy as well as by internal grants from Virginia Commonwealth University (Offices of the President, Provost and Vice President of Academic Affairs, Vice President of Research, Vice Provost for Life Sciences, Dean of the College of Humanities and Sciences, and the Dean of the School of Engineering). The funding from DOE was used to support partial expenses of invited speakers, students and postdoctoral fellows.
36 MATERIALS SCIENCE↗
Fast inversion, preconditioned quantum linear system solvers, fast Green's-function computation, and fast evaluation of matrix functions
Preconditioning is the most widely used and effective way for treating ill-conditioned linear systems in the context of classical iterative linear system solvers. We introduce a quantum primitive called fast inversion, which can be used as a preconditioner for solving quantum linear systems. The key idea of fast inversion is to directly block encode a matrix inverse through a quantum circuit implementing the inversion of eigenvalues via classical arithmetics. We demonstrate the application of preconditioned linear system solvers for computing single-particle Green's functions of quantum many-body systems, which are widely used in quantum physics, chemistry, and materials science. We analyze the complexities in three scenarios: the Hubbard model, the quantum many-body Hamiltonian in the plane-wave-dual basis, and the Schwinger model. We also provide a method for performing Green's function calculation in second quantization within a fixed-particle manifold and note that this approach may be valuable for simulation more broadly. Aside from solving linear systems, fast inversion also allows us to develop fast algorithms for computing matrix functions, such as the efficient preparation of Gibbs states. Furthermore, we introduce two efficient approaches for such a task, based on the contour-integral formulation and the inverse transform, respectively.
97 MATHEMATICS AND COMPUTING↗
Modeling and Qualification of a Modified Emission Unit for Radioactive Air Emissions Stack Sampling Compliance
A planned laboratory space and exhaust system modification to the Pacific Northwest National Laboratory Material Science and Technology Building indicated a new evaluation of the mixing at the air sampling system location would be required for compliance to ANSI/HPS N13.1-2011. The modified exhaust system would add a third fan thereby increasing the overall exhaust rate out the stack thus voiding the previous mixing study. Prior to modifying the radioactive air emissions exhaust system, a three-dimensional computational fluid dynamics computer model was used to evaluate the mixing at the sampling system location. Modeling of the new original three-fan system indicated that not all mixing criteria could be met. A second modeling effort was conducted with the addition of an air blender downstream of the confluence of the three fans which then showed satisfactory mixing results. The final installation included an air blender, and the exhaust system underwent full-scale tests to verify velocity, cyclonic flow, gas, and particulate uniformity. The modeling results and those of the full-scale tests show agreement between each of the evaluated criteria. The use of a computational fluid dynamics code was an effective aid in the design process and allowed the sampling system to remain in its original location while still meeting the requirements for sampling at a well-mixed location.
atmospheric emissions, air monitoring, environment↗
Installation and commissioning of the ion source systems for the new spallation neutron source 2.5 MeV injector
The U.S. Spallation Neutron Source (SNS) is a state-of-the-art neutron scattering facility delivering the world’s most intense pulsed neutron beams to a wide array of instruments, which are used to conduct investigations in many fields of engineering, physics, chemistry, material science, and biology. Neutrons are produced by spallation of liquid Hg by the bombardment of short (∼1 µs), intense (∼35 A) pulses of protons delivered at 60 Hz by an accumulator ring which is fed by a high-intensity, 1 GeV, H− LINAC (linear accelerator). This facility has operated nearly continuously since 2006 but has recently undergone a 4-month maintenance period, which featured a complete replacement of the 2.5 MeV injector feeding the LINAC. The new injector was developed at ORNL in an off-line beam test facility and consists of an ion source, low energy beam transport, and a Radio Frequency Quadrupole (RFQ). This report first describes the installed configuration of the new injector detailing the ion source system. The first beam current, RFQ transmission, emittance, and energy measurements from the injector installed on the SNS are reported. These data not only show a significant performance improvement for our existing facility but will also make accessible the higher beam current requirements for future SNS upgrade projects: the proton power upgrade and second target station.
Welton, R. F.↗
Cryo-STEM Tomography with Inpainting
Traditionally, microscopists have worked with the Nyquist-Shannon theory of sampling, which states that to be able to reconstruct the image fully it needs to be sampled at a rate of at least twice the highest spatial frequency in the image. This sampling rate assumes that the image is sampled at regular intervals and that every pixel contains information that is crucial for the image (it even assumes that noise is important). Images in general, and especially low dose S/TEM images, contain significantly less information than can be encoded by a grid of pixels (which is why image compression works). Mathematically speaking, the image data has a low dimensional or sparse representation. Through the application of compressive sensing methods [1,2,3] this representation can be found using pre-designed measurements that are usually random for implementation simplicity. These measurements and the compressive sensing reconstruction algorithms have the added benefit of reducing noise. This reconstruction approach can be extended into higher dimensions, whereby the random sampling in each 2-D image can be extended into: a sequence of tomographic projections (i.e. tilt images); a sequence of video frames (i.e. incorporating temporal resolution and dynamics); spectral resolution (i.e. energy filtering an image to see the distribution of elements); and ptychography (i.e. sampling a full diffraction image at each location in a 2-D grid across the sample). This approach has been employed experimentally for materials science samples requiring low-dose imaging [2], and can be readily applied to biological samples. Figure 1 shows the resolution possible in a complex biological system, mouse pancreatic islet beta cells [4], when tomogram slices are reconstructed using subsampling. Reducing the number of pixels (1/6 pix and 1/3*1/3) shows minimal degradation compared to the reconstructions using all pixels (all data and 1/3 tilt). Although subsampling 1/6 of the tilts (1/6 of overall dose) degrades the reconstruction to the point that the cellular structures cannot be identified. Using 1/3 of both the pixels and the tilts provides a high quality image at 1/9 the overall dose even for this most basic and rapid demonstration of the CS methods. Figure 2 demonstrates the theoretical tomogram reconstruction quality (vertical axis) as undersampling (horizontal axis) is increased; we examined subsampling pixels and tilt-angles individually and a combined approach in which both pixels and tilts are sub-sampled. Note that subsampling pixels maintains high quality reconstructions (solid lines). Using the inpainting algorithm to obtain tomograms can automatically reduce the dose applied to the system by an order of magnitude. Perhaps the best way to understand the impact is to consider that by using inpainting (and with minimal hardware changes), a sample that can normally withstand a dose of ~10 e/Å2 can potentially be imaged with an “equivalent quality” to a dose level of 103 e/Å2. To put this in perspective, this is approaching the dose level used for the most advanced images, in terms of spatial resolution, for inorganic systems. While there are issues for biological specimens beyond dose (structural complexity being the most important one), this sampling approach allows the methods that are traditionally used for materials science to be applied to biological systems [5]. References: [1] A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [2] L Kovarik, A Stevens, A Liyu et al. Appl. Phys. Lett. 109, 164102 (2016) [3] A Stevens, L Kovarik, P Abellan et al. Adv. Structural and Chemical Imaging 1(10), (2015), pp. 1. [4] MD Guay, W Czaja, MA Aronova et al. Scientific Reports 6, 27614 (2016) [5] Supported by the Chemical Imaging, Signature Discovery, and Analytics in Motion Initiatives at PNNL. PNNL is operated by Battelle Memorial Inst. for the US DOE; contract DE-AC05-76RL01830.
Stevens, Andrew↗
Properties of perhalogenated {closo-B10} and {closo-B11} multiply charged anions and a critical comparison with {closo-B12} in the gas and the condensed phase
Anionic closo-borate anions [closo-BnXn]2- are part of the most famous textbook examples of polyhedral compounds. Substantial differences in their reactivity and interactions with other compounds depending on the substituent X and clustersize n have been recognized, which favors specific closo-borates for different applications in cancer treatment, chemical synthesis, and materials science. Surprisingly, a fundamental understanding of the molecular properties underlying these differences is lacking. Here, we report our study of electronic structure and reactivity of closo-borate anions [closo-BnXn]2- (X = Cl, Br, I, n = 10, 11, 12 in all combinations). We investigated the free dianions and the ion pairs [nBu4N]+[closo-BnXn]2- by gas phase anion photoelectron spectroscopy accompanied by theoretical investigations. Strong similarities in electronic structure for n = 10 and 11 were observed, while n = 12 clusters are different. A systematic picture of the development in electronic stability along the dimension X is derived. Collision induced dissociation shows that fragmentation of the free dianions is mainly dependent on the substituent X and gives access to a large variety of boron-rich molecular ions. Fragmentation of the ion pair depends strongly on n. The results reflect the high chemical stability of cluster with n = 10 and 12, while those with n = 11 are much easier prone to dissociation. We bridge our study to the condensed phase by performing comparative electrochemistry and reactivity studies on closo-borates in solution. The trends found at the molecular level are also reflected in the condensed-phase properties. We discuss how the gas phase values allow to evaluate the influence of the condensed phase on the electronic stability of closo-borates. A novel synthetic method via an oxidation/chlorination reaction yielding [closo-B10Cl10]2– from highly chlorinated {closo-B11} clusters is introduced, which underlines the intrinsically high reactivity of the {closo-B11} cage.
Warneke, Jonas↗
Dynamic and Programmable Cellular-Scale Granules Enable Tissue-like Materials
Living tissues are an integrated, multiscale architecture consisting of dense cellular ensembles and extracellular matrices (ECMs). The cells and ECMs cooperate to enable specialized mechanical properties and dynamic responsiveness. However, the mechanical properties of living tissues are difficult to replicate. A particular challenge is identification of a cell-like synthetic component, which is tightly integrated with its matrix and also responsive to external stimuli. Here, we demonstrate that cellular-scale hydrated starch granules, an underexplored component in materials science, can turn conventional hydrogels into tissue-like materials when composites are formed. By using several synchrotron-based X-ray techniques, we reveal the mechanically induced organization and training dynamics of the starch granules in the hydrogel matrix. These dynamic behaviors enable multiple tissue-like properties such as programmability, anisotropy, strain-stiffening, mechanochemistry, and self-healability.
36 MATERIALS SCIENCE↗
Automated and Accelerated Continuum Model Development for Electrochemical Systems (Abbreviated Report)
Despite the availability of computational resources and advancements in numerical computing capabilities, the multiscale models core to understanding, predicting the behaviors of, and designing energy and environmental systems involving porous media are still 1.) developed through by-hand derivations and 2.) limited by many methodological assumptions employed during model derivation. As a result, the advancement of effective media models for engineering DOE mission-critical systems (e.g., batteries, flow batteries, electrolyzers, geothermal systems, subsurface chemical storage systems, etc.) is slow (i.e., it takes years for models to traverse from stages of “development” to “practical utilization”), hindering our ability to effectively optimize such systems and stay at the cutting-edge of the energy frontier. In this work, we aimed to address these limitations by 1.) automating and accelerating multiscale model derivation via symbolic computing and 2.) develop a novel multiscale modeling methodology for flow and transport through porous media that avoids the typical assumptions hindering previous models. As a result of our efforts, we 1.) developed a hybrid symbolic-numeric code called Fouriera for fully-automating the implementation of multiphysical and phase-field models via the Fourier spectral method for materials science research, and 2.) advanced a multiscale modeling methodology called The Method of Finite Averages that rigorously predicts the behaviors of flow and transport through heterogeneous porous media under the influence of non-local effects and strong advection. Ultimately, these deliverables provide strong foundations from which further efforts can advance multiscale modeling tools and capabilities that do not intrinsically rely on 1.) the speed and mathematical capabilities of humans, nor 2.) the methodological assumptions limiting current models.
36 MATERIALS SCIENCE↗
Data-scarce surrogate modeling of shock-induced pore collapse process
Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. Here, in this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.
97 MATHEMATICS AND COMPUTING↗
Review of structure representation and reconstruction on mesoscale and microscale
Structure representation and reconstruction on mesoscale and microscale is critical in material design, advanced manufacturing and multiscale modeling. Microstructure reconstruction has been applied in different areas of materials science and technology, structural materials, energy materials, geology, hydrology, etc. This review summarizes the microstructure descriptors and formulations used to represent and algorithms to reconstruct structures at microscale and mesoscale. In the stochastic methods using correlation function, different optimization approaches have been adapted for objective function minimization. A variety of reconstruction approaches are compared in efficiency and accuracy.
Microstructure reconstruction, inverse design, mic↗
Measuring the elastic tensor of a monolithic SiC hollow cylinder with resonant ultrasound spectroscopy
Resonant Ultrasound Spectroscopy (RUS) has become a standard tool in material science to measure nondestructively the elastic properties of materials. The widespread adoption of this tool for applications related to nondestructive testing and structural health monitoring in the industry and other places has been restricted thus far by some requirements imposed by RUS on the sample to be tested, in particular the fact that the sample geometry should be relatively simple (e.g., cuboid, cylinder, or sphere). Here, we use RUS to determine the baseline material properties of a hollow cylinder made of monolithic SiC, a surrogate material and geometry representing nuclear fuel cladding for this study. Typically, elastic properties of the nuclear fuel cladding samples are inspected destructively (e.g., indentation) or with acoustic techniquesrequiring multiple and cumbersome measurements. Here, a relatively new RUS approach specifically developed to measure the elastic tensor of samples with arbitrary geometries is used. The results obtained from this RUS approach are compared to values reported in the literature. As a second verification step, vibrational mode shapes computed numerically with the elastic constants are compared to those measured on the sample using a 3D laser Doppler vibrometer, showing an excellent agreement.
36 MATERIALS SCIENCE↗