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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.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation

Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses m ≳ 10 8.5 M ⊙ . Training in this way allows the network to learn the gravitational lensing of light, and, remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from 10 9 –10 10 M ⊙ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Interpreting Neutron Reflectivity Profiles of Diblock Copolymer Nanocomposite Thin Films Using Hybrid Particle-Field Simulations

Mixtures of block copolymers and nanoparticles (block copolymer nanocomposites) are known to microphase separate into a plethora of microstructures, depending on the composition, length scale and nature of interactions among its different constituents. Theoretical and experimental works on this class of nanocomposites have already high-lighted intricate relations among chemical details of the polymers, nanoparticles, and various microstructures. Confining these nanocomposites in thin films yields an even larger array of structures, which are not normally observed in the bulk. In contrast to the bulk, exploring various microstructures in thin films by the experimental route remains a challenging task. Here in this work, we construct a model for the thin films of lamellar forming diblock copolymers containing spherical nanoparticles based on a hybrid particle-field approach. The model is benchmarked by comparison with the depth profiles obtained from the neutron reflectivity experiments for symmetric poly(deuterated styrene-b-n butyl methacrylate) copolymers blended with spherical magnetite nanoparticles covered with hydrogenated poly(styrene) corona. We show that the model based on a hybrid particle-field approach provides details of the underlying microphase separation in the presence of the nanoparticles through a direct comparison to the neutron reflectivity data. This work benchmarks the application of the hybrid particle-field model to extract the interaction parameters for exploring different microstructures in thin films containing block copolymers and nanocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A study on the sintering of ultrafine grained tungsten with Ti-based additives

Tungsten (W) is a brittle material at room temperature making it very difficult to fabricate. Although the lack of ductility remains a difficult challenge, nano-sized and ultrafine grain (UFG) microstructures offer potential for overcoming tungsten's room temperature brittleness. One way to manufacture UFG W is to compact and sinter nanosized W powder, however, it is a non-trivial task to control grain growth during sintering. In an effort to inhibit grain growth, the effect of Ti-based additives on the densification and grain growth of nano-W powders was investigated here in this study. The addition of 1 wt.% Ti into tungsten led to more than a 63% decrease in average grain size of sintered samples at comparable density levels. It was also found that sintering in Ar yielded a finer grain size than sintering in H 2 at similar densities. Compared to conventional high temperature sintering, a lower temperature sintering cycle for a longer hold time resulted in both near-full density and fine grain size. The roles of the Ti additive include not only the inhibition of grain growth, but also the potential absorption of oxygen from W particles.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels

Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanism is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training of a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction, and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.

36 MATERIALS SCIENCE↗
DOE OSTI2026

pathSQE : an automated workflow for single-crystal inelastic neutron scattering data processing and analysis

Inelastic neutron scattering (INS) experiments utilizing modern time-of-flight spectrometers enable the comprehensive mapping of the energy (E)- and momentum (Q)-resolved dynamical structure factor of single crystals, probing both the lattice and magnetic excitations. Yet, the large size and complexity of four-dimensional INS data are challenging current analysis workflows, often resulting in an underutilization of the measured information. To help address this issue, this paper introduces new software interfaced with the Mantid framework, pathSQE, designed to streamline the processing, analysis and interpretation of 4D single-crystal INS data. By automating key tasks such as 1D/2D slicing, symmetrization, Brillouin zone folding, data visualization, prioritization and filtering, and comparisons with simulations, pathSQE facilitates and accelerates INS data analysis workflows. Here, this paper outlines the features and implementation and provides several illustrations of the use of pathSQE on data collected on single crystals using direct-geometry time-of-flight spectrometers at the Spallation Neutron Source, including Ge, FeSi, MnO and SnS single-crystal measurements on the ARCS, HYSPEC and CNCS neutron spectrometers. Beyond streamlining post-experiment data processing, pathSQE establishes an automated and modular processing pipeline that could support future real-time experiment steering.

36 MATERIALS SCIENCE↗
DOE OSTI2018

X-ray characterization of atomistic defects causing irradiation creep of SIC

Silicon carbide (SiC) fiber–reinforced SiC matrix composites have been widely studied for use in developing an accident-tolerant fuel (ATF) cladding for light water reactors (LWRs) in recent years because of their superior oxidation and irradiation resistance and low neutron absorption cross section. An important task is the establishment of the thermo-mechanical analysis capability to predict the in-pile performance and failure probability of the cladding. Irradiation creep is known to be a key property in modeling the stress state under irradiation. To better understand the radiation creep mechanism from an atomic point of view, this study conducted synchrotron-based X-ray diffraction (XRD) experiments on SiC neutron-irradiated with and without applied stress at the National Synchrotron Light Source-II (NSLS-II). This report presents progress of the XRD study and future plans. The important finding obtained was that the macroscopic creep strain and strain due to the lattice expansion were similar in case of neutron irradiation at 300°C to 0.1 displacement per atom (dpa), which indicates that the lattice strain is a major source of the transient creep strain.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A publicly available PyTorch-$\mathrm{ABAQUS}$ $\mathrm{UMAT}$ deep-learning framework for level-set plasticity

Here this paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a family of plasticity models where the yield surface is implicitly represented by a scalar-valued function. In particular, our focus is to introduce a practical framework that can be deployed for engineering analysis that employs a user-defined material subroutine (UMAT/VUMAT) for ABAQUS, which is written in FORTRAN. To accomplish this task while leveraging the back-propagation learning algorithm to speed up the neural-network training, we introduce an interface code where the weights and biases of the trained neural networks obtained via the PyTorch library can be automatically converted into a generic FORTRAN code that can be a part of the UMAT/VUMAT algorithm. To enable third-party validation, we purposely make all the data sets, source code used to train the neural-network-based constitutive models, and the trained models available in a public repository. Furthermore, the practicality of the workflow is then further tested on a dataset for anisotropic yield function to showcase the extensibility of the proposed framework. A number of representative numerical experiments are used to examine the accuracy, robustness and reproducibility of the results generated by the neural network models.

36 MATERIALS SCIENCE↗
DOE OSTI2014

An automated pipette puller for fabrication of glass micropipettes

Glass micropipettes are versatile probing tools for performing micro- and nano-manipulation tasks. This paper presents the design and development of an automated pipette puller system for fabrication of glass micropipettes. The pipette puller employs a new strategy for fabrication of micropipettes that enables achieving independent control of their taper, tip diameter, and bend-angle, and also facilitates theoretical derivation of simple, approximate relationships between the pipette shape and the pulling parameters. Subsequently, the design and fabrication of the pipette puller is described, which include that of the pipette heating system, the mechanical motion stages, and the control electronics of the pipette puller. The fabricated pipette puller is experimentally evaluated to demonstrate control of the taper, tip diameter, and the bend-angle of the micropipette. Further, the dependence of the taper and tip diameter on the pulling parameters is evaluated and is shown to be in alignment with the proposed theoretical relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sensing and arresting metal corrosion in molten chloride salts at 800 degrees Celsius

Originally this was planned as a 5 task, 3 year project, starting in May 2019. Task 1 Detecting oxidants and metal corrosion in molten chloride salt at 800°C. Task 2 Effectiveness of Zr getter using head gas sensors of inlet and outlet gases. Task 3 Cathodic protection of metal in molten salt at reducing potential. Task 4 Find reduction potential of metals in molten chloride salts & quantum modeling. Task 5 Develop corrosion test sections integrated into molten salt flow loop. After Q4, the project was scaled back and the reorganized SOPO was reduced to 3 tasks to be completed at the end of Q7 on 01/05/2021. The 3 SOPO tasks are: Task 1 Sense oxidants and the metal corrosion in molten Na-K-Mg-Cl salt at 800°C. Task 3 Cathodically protect H230 alloy in molten Na-K-Mg-Cl salt at 800C by making H230 alloy the cathode of a power supply with a chromium-rich-metal as the anode. Task 4 Find the reduction potential of Cr metal in molten Na-K-Mg-Cl salt at 800°C. The total timeline was reduced from 12 to 7 quarters with the end date being, January 5, 2021. This amounted to task 1,3 and 4 being done in Q5,6 and 7 of an extended year 1, ending on January 5, 2021. Task 1, 3 and 4 were completed. For task1) the increase of oxidants in molten salt is detected by two things: i) a positive shift of the electrode potential for a Haynes 230 alloy (H230) metal coupon versus a silver/silver-chloride reference electrode (SSE) and ii) an increase in the corrosion rate of a H230 electrode at 800C. For task 3) applying a cathodic potential (negative overpotential) to a H230 electrode versus SSE arrested the corrosion of the cathodically protected H230 compared to an unprotected “spectator H230 electrode” in the same molten chloride salt. Lastly, for task 4) the reduction potential of chrome and chrome rich, and nickel metal electrodes were measured. The most valuable outcome of this work is that a power supply can arrest metal corrosion for metal in molten chloride salt when the power supply applies a cathodic potential to bare metal, like H230, in molten chloride salts, like ternary Mg-K-Na-chloride salt. H230 is a high strength metal for containing molten chloride salt and ternary Mg-K-Na-chloride salt is used as heat transfer fluids in concentrating solar power plants. When to use cathodic protection for protecting metal in molten chloride salts? Two scenarios are envisioned: 1) for protecting high-valued components, like solar collectors and heat exchangers and 2) for protecting the bare surface in holidays (point defects) of corrosion inhibitors that are to intended to cover the entire surface of metal.

14 SOLAR ENERGY↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Moving Toward Quantifying Reliability - The Next Step in a Rapidly Maturing PV Industry: Preprint

Some may say that PV modules are moving toward being a simple commodity, but most major PV customers ask: 'How can I minimize chances of a module recall?' Or, 'How can I quantify the added value of a 'premium' module?' Or, 'How can I assess the value of an old PV system that I'm thinking of purchasing?' These are all questions that PVQAT (the International PV Quality Assurance Task Force) and partner organizations are working to answer. Defining standard methods for ensuring minimal acceptable quality of PV modules, differentiating modules that provide added value in the toughest of environments, and creating a process (e.g. through IECRE [1]) that can follow a PV system from design through installation and operation are tough tasks, but having standard approaches for these will increase confidence, reduce costs, and be a critical foundation of a mature PV industry. This paper summarizes current needs for new tests, some challenges for defining those tests, and some of the key efforts toward development of international standards, emphasizing that meaningful quantification of reliability (as in defining a service life prediction) must be done in the context of a specific product with design parameters defined through a quality management system.

14 SOLAR ENERGY↗
DOE OSTI2018

Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures

Highlights: • Microstructure quantification based on directional chord length distribution and principal component analysis is presented • A boundary-based algorithm of calculations of chord length distributions is introduced • Utility of the quantification framework is shown in three case studies on synthetic, experimental, and simulation datasets • Angularly resolved chord length distribution captures essential details of grain size and shape in diverse microstructures • Low-dimensional representations of directional chord length distributions are obtained using principal component analysis - Abstract: Quantification of mesoscale microstructures of polycrystalline materials is important for a range of practical tasks of materials design and development. The current protocols of quantifying grain size and morphology often rely on microstructure metrics (e.g., mean grain diameter) that overlook important details of the mesostructure. In this work, we present a quantification framework based on directionally resolved chord length distribution and principal component analysis as a means of extracting additional information from 2-D microstructural maps. Towards this end, we first present in detail a method for calculating chord length distribution based on boundary segments available in modern digital datasets (e.g., from microscopy post-processing) and their low-rank representations by principal component analysis. The utility of the proposed framework for capturing grain size, morphology, and their anisotropy for efficient visualization, representation, and specification of polycrystalline microstructures is then demonstrated in case studies on datasets from synthetic generation, experiments (on Ni-base superalloys), and simulations (on steel during recrystallization).

36 MATERIALS SCIENCE↗
DOE OSTI2017

Ultrahigh Elastic Strain Energy Storage in Metal-Oxide-Infiltrated Patterned Hybrid Polymer Nanocomposites

Modulus of resilience, the measure of a material’s capacity to store and release elastic strain energy, is critical for realizing advanced mechanical actuation technologies in micro/nanoelectromechanical systems. In general, engineering the modulus of resilience is difficult because it requires asymmetrically increasing yield strength and Young’s modulus against their mutual scaling behavior. This task becomes further challenging if it needs to be carried out at the nanometer scale. Here, we demonstrate organic–inorganic hybrid composite nanopillars with one of the highest modulus of resilience per density by utilizing vapor-phase aluminum oxide infiltration in lithographically patterned negative photoresist SU-8. In situ nanomechanical measurements reveal a metal-like high yield strength (~500 MPa) with an unusually low, foam-like Young’s modulus (~7 GPa), a unique pairing that yields ultrahigh modulus of resilience, reaching up to ~24 MJ/m 3 as well as exceptional modulus of resilience per density of ~13.4 kJ/kg, surpassing those of most engineering materials. The hybrid polymer nanocomposite features lightweight, ultrahigh tunable modulus of resilience and versatile nanoscale lithographic patternability with potential for application as nanomechanical components which require ultrahigh mechanical resilience and strength.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Radiation dermatitis caused by a bolus effect from an abdominal compression device

American Association of Physicists in Medicine (AAPM) Task Group 176 evaluated the dosimetric effects caused by couch tops and immobilization devices. The report analyzed the extensive physics-based literature on couch tops, stereotactic body radiation therapy (SBRT) frames, and body immobilization bags, while noting the scarcity of clinical reports of skin toxicity because of external devices. Here, we present a clinical case report of grade 1 abdominal skin toxicity owing to an abdominal compression device. We discuss the dosimetric implications of the utilized treatment plan as well as post hoc alternative plans and quantify differences in attenuation and skin dose/build-up between the device, a lower-density alternative device, and an open field. The description of the case includes a 66-year-old male with HER2 amplified poorly differentiated distal esophageal adenocarcinoma treated with neoadjuvant chemo-radiation and the use of an abdominal compression device. Radiation was delivered using volumetric modulated arc therapy (VMAT) with 2 arcs using abdominal compression and image guidance. The total dose was 50.4 Gy delivered over 40 elapsed days. With 2 fractions remaining, the patient developed dermatitis in the area of the compression device. The original treatment plan did not include a contour of the device. Alternative post hoc treatment plans were generated, one to contour the device and a second with anterior avoidance. In conclusion, replanning with the device contoured revealed the bolus effect. The skin dose increased from 27 to 36 Gy. planned target volume (PTV) coverage at 45 Gy was reduced to 76.5% from 95.8%. The second VMAT treatment plan with an anterior avoidance sector and more oblique beam angles maintained PTV coverage and spared the anterior wall, however at the expense of substantially increased dose to lung. This case report provides an important reminder of the bolus effect from external devices such as abdominal compression. Special consideration must be given to contour and/or avoiding beam entrance to the device, and to the use of such devices in patients who may have heightened radiosensitivity, such as those who are human immunodeficiency virus (HIV)–positive.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Abstract Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2017

The ultimate mineral processing challenge: Recovery of rare earths, phosphorus and uranium from Florida phosphatic clay

We report phosphate beneficiation in Florida generates more than one tonne of phosphatic clay, or slime, per tonne of phosphate rock produced. Since the start of the practice of large-scale washing and desliming for phosphate beneficiation, more than 2 Gt of slime has accumulated, containing approximately 600 Mt of phosphate rock, 600 kt of rare earth elements (REEs) and 80 million kilograms of uranium. The recovery of these valuable elements from the phosphatic clay is one of the most challenging endeavors in mineral processing, because the clay is extremely dilute, with an average solids concentration of 3 percent, and fine in size, with more than 50 percent having particle size smaller than 2 μm, and it contains nearly 50 percent clay minerals as well as large amounts of magnesium, iron and aluminum. With industry support and under funding from the Critical Materials Institute, the Florida Industrial and Phosphate Research Institute in conjunction with the Oak Ridge National Laboratory undertook the task to recover phosphorus, rare earths and uranium from Florida phosphatic clay. This paper presents the results from the preliminary testing of two approaches. The first approach involves three-stage cycloning using cyclones with diameters of 12.4 cm (5 in.), 5.08 cm (2 in.) and 2.54 cm (1 in.), respectively, to remove clay minerals followed by flotation and leaching. The second approach is a two-step leaching process. In the first step, selective leaching was conducted to remove magnesium, thus allowing the production of phosphoric acid suitable for the manufacture of diammonium phosphate (DAP) in the second leaching step. The results showed that multistage cycloning with small cyclones is necessary to remove clay minerals. Finally, selective leaching at about pH 3.2 using sulfuric acid was found to be effective for removing more than 80 percent of magnesium from the feed with minimal loss of phosphorus.

36 MATERIALS SCIENCE↗