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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Development of PNNL’s Plutonium Metallography Capability

The goal of this project was to develop plutonium metallography capabilities and establish workflows for characterization at PNNL’s Radiological Processing Facility (RPL). Developing and expanding on this capability opens more opportunities for PNNL to better support science through collaborations with other labs, plants, and sites withing the Department of Energy Complex and through programs within the National Nuclear Security Administration. This project established metallography equipment in an air glovebox, polishing protocols, as well as radiological and facility controls and procedures so that gram quantities of plutonium metal could be analyzed on multiple characterization tools. Data was collected and analyzed on two different delta phase plutonium - Gallium samples and a calciothermically reduced alpha plutonium metal using a combination of optical and electron microscopy, powder X-ray diffraction, and atom probe tomography. This report highlights the metallography capabilities, some preliminary data collections, and plans at PNNL to support plutonium material science.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

An Accelerated Creep Testing Program For Nickel Based Superalloys

In order to improve the energy production within the United States, new materials are developed with the goal of increased functionality. These materials are required to perform at extreme pressures and temperatures (up to and beyond 200MPa and 650â??) as well as be sustainable beyond 105 hours. Before developing these materials into components for power generation, mechanical testing is required to better understand the behavior of these materials at various loads and temperatures. The longest of these tests is the conventional creep test which is a real time test that can beyond 105 hours depending on service conditions. Hundreds of specimens must be tested before being approved for use in power generation, but it is extremely difficult to rapidly qualify when each material is required to exceed 105 hours making testing through conventional means unfeasible. While others have designed accelerated testing methods to help qualify materials, many of these experiments use an empirical approach that requires test data to fit already existing creep data. In this study, an accelerated creep test called the stepped isostress method (SSM) is then applied to wrought Inconel 718 (IN 718) specimens. A test matrix is designed using deformation mechanism maps, time-temperature-transformation diagrams, and time-temperature-precipitation diagrams, in order to ensure that microstructurally, the accelerated creep tests fail similarly to the conventional creep tests. The data from the stepped isostress method is then applied to the McVetty and Sin-hyperbolic (Sinh) constitutive models in order to generate a creep master curve. The results from the model fits are then compared to the conventional creep data done from experiments as well as from the NIMS database for Inconel 718. Optical microscopy and scanning electron microscopy are used to compare the evolution of the microstructure from the conventional creep and accelerated creep tests. It is found that using the Sinh model on the accelerated creep data provides reasonable creep rupture predictions when compared to the scatter band of creep tests. It was also observed that the microstructural evolution between the SSM experiments and creep tests were very similar in terms of the volume fraction of precipitates and phases.

Mach, Robert↗
DOE OSTI2020

Dynamic compressed sensing for real-time tomographic reconstruction

Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO 3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, we report reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Human XPG nuclease structure, assembly, and activities with insights for neurodegeneration and cancer from pathogenic mutations

Xeroderma pigmentosum group G (XPG) protein is both a functional partner in multiple DNA damage responses (DDR) and a pathway coordinator and structure-specific endonuclease in nucleotide excision repair (NER). Different mutations in the XPG gene ERCC5 lead to either of two distinct human diseases: Cancer-prone xeroderma pigmentosum (XP-G) or the fatal neurodevelopmental disorder Cockayne syndrome (XP-G/CS). To address the enigmatic structural mechanism for these differing disease phenotypes and for XPG’s role in multiple DDRs, here we determined the crystal structure of human XPG catalytic domain (XPGcat), revealing XPG-specific features for its activities and regulation. Furthermore, XPG DNA binding elements conserved with FEN1 superfamily members enable insights on DNA interactions. Notably, all but one of the known pathogenic point mutations map to XPGcat, and both XP-G and XP-G/CS mutations destabilize XPG and reduce its cellular protein levels. Mapping the distinct mutation classes provides structure-based predictions for disease phenotypes: Residues mutated in XP-G are positioned to reduce local stability and NER activity, whereas residues mutated in XP-G/CS have implied long-range structural defects that would likely disrupt stability of the whole protein, and thus interfere with its functional interactions. Combined data from crystallography, biochemistry, small angle X-ray scattering, and electron microscopy unveil an XPG homodimer that binds, unstacks, and sculpts duplex DNA at internal unpaired regions (bubbles) into strongly bent structures, and suggest how XPG complexes may bind both NER bubble junctions and replication forks. Collective results support XPG scaffolding and DNA sculpting functions in multiple DDR processes to maintain genome stability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Inversion of Many-Beam Bragg Intensities for Phasing by Iterated Projections: Removal of Multiple Scattering Artifacts from Diffraction Data

An iterated projection algorithm (N-Phaser) is developed that reconstructs a scattering potential from N-beam multiple Bragg scattered intensities. The method may be used to eliminate multiple scattering artifacts from electron diffraction data, solving the phase problem and increasing the thicknesses of samples used in materials science, solid-state chemistry, and small molecule crystallography. For high-energy transmission electron diffraction, we show that the algorithm recovers accurate complex structure factors from a wide range of thicknesses, orientations, and relativistic beam energies, and does not require known thickness or atomic-resolution data if sufficient multiple scattering occurs. Furthermore, extensions to Cryo-electron microscopy and Micro-electron diffraction are suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Decoding Cancer Variants of Unknown Significance for Helicase–Nuclease–RPA Complexes Orchestrating DNA Repair During Transcription and Replication

All tumors have DNA mutations, and a predictive understanding of those mutations could inform clinical treatments. However, 40% of the mutations are variants of unknown significance (VUS), with the challenge being to objectively predict whether a VUS is pathogenic and supports the tumor or whether it is benign. To objectively decode VUS, we mapped cancer sequence data and evolutionary trace (ET) scores onto crystallography and cryo-electron microscopy structures with variant impacts quantitated by evolutionary action (EA) measures. As tumors depend on helicases and nucleases to deal with transcription/replication stress, we targeted helicase–nuclease–RPA complexes: (1) XPB-XPD (within TFIIH), XPF-ERCC1, XPG, and RPA for transcription and nucleotide excision repair pathways and (2) BLM, EXO5, and RPA plus DNA2 for stalled replication fork restart. As validation, EA scoring predicts severe effects for most disease mutations, but disease mutants with low ET scores not only are likely destabilizing but also disrupt sophisticated allosteric mechanisms. For sites of disease mutations and VUS predicted to be severe, we found strong co-localization to ordered regions. Rare discrepancies highlighted the different survival requirements between disease and tumor mutations, as well as the value of examining proteins within complexes. In a genome-wide analysis of 33 cancer types, we found correlation between the number of mutations in each tumor and which pathways or functional processes in which the mutations occur, revealing different mutagenic routes to tumorigenesis. We also found upregulation of ancient genes including BLM, which supports a non-random and concerted cancer process: reversion to a unicellular, proliferation-uncontrolled, status by breaking multicellular constraints on cell division. Together, these genes and global analyses challenge the binary “driver” and “passenger” mutation paradigm, support a gradient impact as revealed by EA scoring from moderate to severe at a single gene level, and indicate reduced regulation as well as activity. The objective quantitative assessment of VUS scoring and gene overexpression in the context of functional interactions and pathways provides insights for biology, oncology, and precision medicine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Statistically elucidated responses from low-signal contrast mechanisms in ultrafast electron microscopy

The emergence of ultrafast electron microscopy (UEM) has enabled the discovery of strongly correlated dynamic mechanisms, including electron–phonon coupling, structural phase transitions, thermal transport, and electromagnetic deflection. Most UEM systems operate stroboscopically, meaning that the technique is susceptible to artifacts, mistakes, and misinterpretation of the data due to extensive experimental effort. In contrast to the ultrafast designation, data acquisition is extraordinarily slow because the electron beam has significantly reduced signal compared to traditional transmission electron microscopy due to pulsing the electron beam. Consequently, the sample may drift, tilt, or undergo irreversible structural changes that are independent of the time-resolved dynamics throughout the experimental time frame. Furthermore, these datasets require significant user interpretation that can be problematic when proper controls are not implemented thoroughly. Here, we demonstrate a new algorithm designed to separate ultrafast structural dynamics from long-term artifacts using a LiNbO 3 sample experiencing electrically driven surface acoustic wave propagation. Additionally, we provide examples of the impact of user bias when analyzing the data and provide a methodology, which enables the extraction of time-resolved responses when the image signal is extraordinarily low. Overall, the goal of this publication is to provide methods that validate the experimental results and reduce researcher biases during UEM data interpretation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Transmission electron microscopy investigation of phase transformation and fuel constituent redistribution in neutron irradiated U-10wt.%Zr fuel

Uranium-10wt.%zirconium (U-10wt.%Zr) is a primary candidate for fast reactor nuclear fuels. However, there is a lack of data characterizing neutron irradiated crystallographic phases and chemistry. In the current study, the microstructural evolution of a U-10wt.%Zr fuel neutron irradiated to a burnup of 5.7 at.% was investigated by scanning transmission electron microscopy, energy dispersive spectroscopy, and selected area electron diffraction to determine the major phases, alterations in microstructure, and variations in local chemical composition at different localities of a fuel cross-section. The current study revealed that the irradiated U-10wt.%Zr fuel was comprised of various major phases, including a-U, ß-U, and d-UZr2, as well as amorphous and crystalline solid fission product (FP) precipitates within different regions of the fuel cross-section. Regions A and A/B in the center of the fuel were comprised of U-rich, U-intermediate, and U-lean localities with a-U and d-UZr 2 composing the major phases. Regions B and C in the intermediate and peripheral fuel localities, respectively, were comprised of a-U grains, U-rich (ß-U) grains with Zr-rich precipitates, U-intermediate grains (d-UZr2), and solid FP precipitates. The major phases identified were associated with the nanoscopic chemical concentrations, the phase diagram, and the as-characterized specimen temperature, with the exception of the non-equilibrium ß-U phase identified in region B. The U-lean localities in regions A, A/B, B, and C were Zr-enriched pathways along subgrain/grain boundaries. This phenomenon suggests that Zr is susceptible to radiation-induced segregation in U-Zr fuels and indicates a new mechanism for constituent redistribution.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. Finally, this study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Designing Particle Morphologies for Materials with Solid Transport Limitations: A Case Study of Lithium and Manganese Rich Cathode Oxides

A lithium and manganese rich nickel-manganese-cobalt oxide (LMR-NMC) cathode is a promising candidate for next-generation batteries due to its high specific capacity, low cost, and low cobalt content. However, the material suffers from poor rate capability due to the diffusion limitations of lithium in the cathode particles. Understanding the material performance requires careful control of the morphology of the cathode particles, taking into account the primary and agglomerated diffusion pathways and the presence of pores, some of which could be closed from electrolyte infiltration. Here, in this study, we use a microstructure-based mathematical model combined with experimental data to understand the role of the complex cathode particle morphology in the rate performance of the material. Scanning electron microscopy images of cathodes made under different synthesis conditions, which results in different agglomerate morphologies, serve as the input into the mathematical model. The model is then compared to rate data to understand the controlling parameters. The presence of intra-agglomerate closed pores results in a large agglomerate diffusion length in comparison to the ideal condition, where the primary particles are agglomerated in an open and dispersed manner such that the entire interfacial area is available for electrochemical reaction. Smaller primary and agglomerate diffusion lengths result in better electrochemical performance. This points us toward designing the morphology of the cathode particles to compensate for the diffusion limitation of LMR-NMC while maximizing the density.

Tewari, Deepti↗
DOE OSTI2023

Design of Diverse Asymmetric Pockets in De Novo Homo-oligomeric Proteins

A challenge for design of protein–small-molecule recognition is that incorporation of cavities with size, shape, and composition suitable for specific recognition can considerably destabilize protein monomers. This challenge can be overcome through binding pockets formed at homo-oligomeric interfaces between folded monomers. Interfaces surrounding the central homo-oligomer symmetry axes necessarily have the same symmetry and so may not be well suited to binding asymmetric molecules. To enable general recognition of arbitrary asymmetric substrates and small molecules, we developed an approach to designing asymmetric interfaces at off-axis sites on homo-oligomers, analogous to those found in native homo-oligomeric proteins such as glutamine synthetase. We symmetrically dock curved helical repeat proteins such that they form pockets at the asymmetric interface of the oligomer with sizes ranging from several angstroms, appropriate for binding a single ion, to up to more than 20 Å across. Of the 133 proteins tested, 84 had soluble expression in E. coli, 47 had correct oligomeric states in solution, 35 had small-angle X-ray scattering (SAXS) data largely consistent with design models, and 8 had negative-stain electron microscopy (nsEM) 2D class averages showing the structures coming together as designed. Both an X-ray crystal structure and a cryogenic electron microscopy (cryoEM) structure are close to the computational design models. The nature of these proteins as homo-oligomers allows them to be readily built into higher-order structures such as nanocages, and the asymmetric pockets of these structures open rich possibilities for small-molecule binder design free from the constraints associated with monomer destabilization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Nucleation and Initial Stages of Growth during the Atomic Layer Deposition of Titanium Oxide on Mesoporous Silica

A chemical approach to the deposition of thin films on solid surfaces is highly desirable but prone to affect the final properties of the film. To better understand the origin of these complications, the initial stages of the atomic layer deposition of titania films on silica mesoporous materials were characterized. Adsorption-desorption measurements indicated that the films grow in a layer-by-layer fashion, as desired, but initially exhibit surprisingly low densities, about one-quarter of that of bulk titanium oxide. Electron microscopy, X-ray diffraction, UV/visible, and X-ray absorption spectroscopy data pointed to the amorphous nature of the first monolayers, and EXAFS and 29 Si CP/MAS NMR results to an initial growth via the formation of individual tetrahedral Ti-oxide units on isolated Si-OH surface groups with unusually long Ti-O bonds. Here, density functional theory calculations were used to propose a mechanism where the film growth starts at the nucleation centers to form an open 2D structure.

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 OSTI2021

Mitigating CO 2 Corrosion of Natural Gas Steel Pipelines by Thermal Spray Aluminum Coatings

We report internal pipeline corrosion due to carbon dioxide (CO 2 ) is a major challenge facing the oil and gas industry. The objective of this study was to investigate the corrosion behavior of aluminum (Al)-based alloys as sacrificial coatings to protect pipelines in a CO 2 -saturated aqueous electrolyte (3.5 wt% NaCl) at 4 bar CO 2 partial pressure (3 bar) and 40°C. The corrosion resistance of Al-based alloys and thermal spray coatings was evaluated in an electrochemical reaction autoclave using electrochemical methods (potentiodynamic polarization, linear polarization resistance, and electrochemical impedance spectroscopy). Post-corrosion surface characterization was performed by scanning electron microscopy equipped with energy-dispersive x-ray spectroscopy. The obtained data show Al-based alloys demonstrated promising protection against CO 2 corrosion with no breakaway degradation issues.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Graded Buffer Bragg Reflectors with High Reflectivity and Transparency for Metamorphic Optoelectronics

A graded buffer Bragg reflector (GBBR) is a bifunctional device component that provides the reflection of a distributed Bragg reflector and the adjustable lattice constant of a compositionally graded buffer. Prior work showed that these properties can be combined with low threading dislocation density and high reflectivity. Here, we design and demonstrate complex GBBRs for specific metamorphic solar cell applications. We design buffers that provide (1) high reflectivity over a narrow bandwidth, for quantum well solar cells, (2) reflectivity over a wide bandwidth, for optically thin solar cells, and (3) low sidelobe reflection, for multijunction devices that demand low out-of-band reflection. Apodized and triple GBBRs are demonstrated, and transparency is always considered, requiring designs with carefully engineered material combinations. A GBBR with a reflection of 99% is demonstrated, as well as a triple GBBR that has over 80% reflection for 100?nm of the spectrum around 800?nm. We also analyze potential deviations in a baseline GBBR from that of a perfect DBR using transmission electron microscopy to analyze imperfections in the material and modeling to analyze the impact of imperfect refractive index data for lattice-mismatched AlGaInAs. Minimal crosshatch roughness and unintentional thickness variation occur throughout the buffer, which likely influences reflection slightly. Small deviations between the calculated AlGaInAs and utilized AlGaAs refractive index exist, giving methods for future GBBR improvement. While the GBBR designs are intended for metamorphic solar cells, the broad and high reflection may also be useful for other optoelectronic devices such as light-emitting diodes or lasers.

41 EE - Solar Energy Technologies Office (EE-4S)↗