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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Opening band gaps of low-dimensional materials at the meta-GGA level of density functional approximations

The quasiparticle band structure can be properly described by Hedin's GW approximation (GW), at a high computational cost. For band gaps, semilocal density functionals up to the generalized gradient approximation (GGA) level cannot compete with the accuracy of hybrid-based approximations or GW. Meta-GGA density functionals with a strong dependence on the kinetic energy density ingredient can potentially give wider band gaps compared with GGAs. The recent TASK meta-GGA density functional from Aschebrock and Kümmel [ Phys. Rev. Research 1 , 033082 (2019) ], is constructed with an enhanced nonlocality in the generalized Kohn-Sham scheme and therefore harbors great opportunities for band gap prediction. Although this approximation was found to yield excellent band gaps of bulk solids, this accuracy cannot be straightforwardly transferred to low-dimensional materials. Additionally, the reduced screening of these materials results in larger band gaps compared with their bulk counterparts, as an additional barrier to overcome. In this paper we demonstrate how the alteration of this functional affects the band gaps of monolayers and nanoribbons and present accurate band gaps competing with the revised Heyd-Scuseria-Ernzerhof (HSE06) approximation. In order to achieve this goal, we have modified the TASK functional (a) by changing the tight upper bound for one- or two-electron systems ( h X 0 ) from 1.174 to 1.29 and (b) by changing the limit of the interpolation function f X ( α → ∞ ) of the TASK functional that interpolates the exchange enhancement factor F X ( s , α ) from α = 0 to 1. The resulting modified TASK (mTASK) was tested for various materials from three dimensions to two dimensions to one dimension (nanoribbons) and was compared with the results of the higher-level hybrid functional HSE06 or with the G 0 W 0 approximation within many-body perturbation theory. We find that mTASK systematically improves the band gaps and band structures of two-dimensional (2D) and 1D systems, without significantly affecting the accuracy of the original TASK for the bulk 3D materials, when compared with the Perdew-Burke-Ernzerhof (PBE) GGA and the strongly constrained and appropriately normed (SCAN) meta-GGA. We further demonstrate the applicability of mTASK by assessing the band structures of transition metal dichalcogenide nanoribbons with respect to various bending curvatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Step-by-step growth of complex oxide microstructures

The synthesis of complex and hybrid oxide microstructures is of fundamental interest and practical applications. However, the design and synthesis of such structures is a challenging task. We developed a solution phase process to synthesize complex silica and silica titania hybrid microstructures by exploiting the emulsion droplet based shape control and step by step growth. The strategy is robust and can be extended to make complex hybrid structures made of two or more materials while each having its own shape.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Electrical, Mechanical and Thermal Properties of Single Molecules

The specific aims of the prior DOE grant are to determine the stability of a single molecule bound to two electrodes, study local heating in single molecule junctions due to electron-phonon and electron-electron interactions, measure electron-phonon interactions in single molecule wires; and explore piezoelectric properties of single molecules. We have completed all the major tasks, and also expanded naturally the scope of the project to address several other critical issues in single molecule properties, developed new experimental capabilities, and observed a number of unexpected phenomena. We summarized here some of the findings that are most relevant to the present renewal proposal. More details can be found in the publications resulted from this grant and annual progress reports.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Post-irradiation Examination Plan for ORNL and University of California Santa Barbara Assessment of UCSB ATR-2 Irradiation Experiment

New and existing databases will be combined to support development of physically based models of transition temperature shifts (TTS) for high fluence-low flux (φ < 10 11 n/cm 2 -s) conditions, beyond the existing surveillance database, to neutron fluences of at least 1×10 20 n/cm 2 (>1 MeV). All references to neutron flux and fluence in this report are for fast neutrons (>1 MeV). The reactor pressure vessel (RPV) task of the Light Water Reactor Sustainability (LWRS) Program is working with various organizations to obtain archival surveillance materials from commercial nuclear power plants to allow for comparisons of the irradiation-induced microstructural features from reactor surveillance materials with those from similar materials irradiated under high flux conditions in test reactors

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

ORNL Interim Progress Report on Static CIRFT Testing Curvature Data Update

Since the CIRFT tests reported in NUREG-7198 were generated, a number of factors that influence the recorded curvature measurement data were identified. In 2016, a data reanalysis task was undertaken to implement the lessons learned. This letter report provides the revised results of previous CIRFT tests, after implementing the following data reanalysis procedures: (A) experimental data smoothing and LVDT reset, (B) LVDT probe contact and sensor spacing correction for curvature data, and (C) LVDT probe dynamic vibration adjustment procedure development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Analysis of the ASME Code Rules for Subsection III-5-HHB (Composite Materials) for Current HTR Design Requirements

This document includes the critical analysis review of the American Society of Mechanical Engineers (ASME) Section III Division 5 Subsection HH Subpart B (HHB), including Mandatory Appendices, that was published in 2023. In the context of this document, reference to “the code” is specific to this subsection unless otherwise specified. A specific composites task group within the ASME Nonmetallic Design and Materials Working Group, with the support of external experts, was established to perform a gap analysis review. The significant findings are summarized here. The committee response with suggested action items are detailed in the body of the report.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Preparation of Aluminum Oxide Films Under Water Exposure – Preliminary Report on 6061 Series Alloys

Aluminum fuel cladding will undergo general corrosion with resulting formation of adherent oxide films during in-reactor and post-discharge exposures under various water conditions and temperatures. The presence of oxides with chemically-bound water can pose challenges for extended dry storage, and an action plan to identify needed technical and engineering activities and analyses to address the identified knowledge gaps and technical data needs was previously prepared. This report is part of the Task 6 activities of that plan. This report describes laboratory experiments performed to produce hydroxide (oxide) films on aluminum 6061 series alloy substrates immersed in water at controlled temperatures. The experimental set-up and characterization results to determine the oxide morphology, thickness, and chemical composition are described.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Feedstock powder processing research needs for additive manufacturing development

Additive manufacturing (AM) promises to redesign traditional manufacturing by enabling the ultimate in agility for rapid component design changes in commercial products and for fabricating complex integrated parts. Here, by significantly increasing quality and yield of metallic alloy powders, the pace for design, development, and deployment of the most promising AM approaches can be greatly accelerated, resulting in rapid commercialization of these advanced manufacturing methods. By successful completion of a critical suite of processing research tasks that are intended to greatly enhance gas atomized powder quality and the precision and efficiency of powder production, researchers can help promote continued rapid growth of AM. Finally, other powder-based or spray-based advanced manufacturing methods could also benefit from these research outcomes, promoting the next wave of sustainable manufacturing technologies for conventional and advanced materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficient Reversible Operation and Stability of Novel Solid Oxide Cells

This project aims to develop and test novel Reversible Solid Oxide Cells (ReSOCs) specifically designed to yield low area specific resistance, as required to achieve high round trip efficiency in reversible operation at high current density. A key objective is to improve long-term reversible SOC durability; mechanistic degradation models will be used to predict long-term durability using input data from accelerated testing that combines electrochemical life testing with quantitative microstructural and chemical evaluation. The modeling tasks seek to design low-loss, low-cost ReSOC systems using input data from the experimental cell studies. A key challenge is to develop a thermal management strategy that maintains thermally self-sustaining operation while operating the stack at a potential lower than the thermo-neutral steam electrolysis voltage of ~1.3 V, to maintain high efficiency.

08 HYDROGEN↗
DOE OSTI2021

Demo 5: RapidClave Technology Demonstrations – Round II (Task 2)

The purpose of this project task was to make automotive composite part manufacturing more cost competitive for low volume production and thereby drive composite application innovation. The strategy was to incorporate low-cost preforming, snap cure resins, and RapidClave® processing to create an alternative to conventional automotive composite manufacturing based on SMC. The program used RapidClave® technology from Globe Machine Manufacturing, fast-curing (“snap cure”) epoxy resins from Hexion, and conformable/stretchable glass fiber reinforcement mats produced by Owens Corning for rapid preforming. O’Gara Armoring retrofits vehicles to meet special security needs, such as larger doors to facilitate easier entry into the vehicle. O’Gara has a variety of custom vehicles in need of custom doors that require affordable tooling to produce approximately 100 ship sets/year. The project focused on a composite door panel application provided by O’Gara that is currently made by manual chopped fiber spray-up processing. UDRI reverse-engineered the current composite door panel and created tooling for use in the RapidClave®. UDRI designed an improved composite door using MultiMat fiberglass reinforcement from Owens Corning. UDRI made snap cure resin films from Hexion resin. Finally, UDRI conducted molding trials to compare autoclave processing with RapidClave® processing. The technical goals of the project were to reduce tooling cost by 50% as compared to SMC compression molding and to reduce cycle time by 50% compared to current manual spray-up process. The approach to reduce tool costs is based on use of single sided tooling for use at 100 psi, as compared to matched metal SMC tooling. An added benefit realized from the program is that the Owens Corning mat provided more uniform thickness and improved performance. Tensile and flexural strengths and moduli were increased by at least 50%. This task also demonstrated a 75% decrease in cycle time. O’Gara is evaluating the technology demonstrated in this project for some of their current production. Additionally, there are new products O’Gara is pursuing which require higher production rates than their current products. O’Gara has identified these new products as good candidates for the RapidClave® technology. The cost advantages demonstrated by this project would then lead to significant economic development. In conclusion, this task successfully combined the RapidClave® technology from Globe with a snap cure epoxy resin film system from Hexion and preform material from Owens Corning. Further research is needed to better map the technical limits such as cycle time of these technologies. Additionally, the technology should be extended in terms of size and shape to include large parts outside of automotive applications, such as a small aircraft fuselage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Analytical Results for MOX Colemanite Samples Received on July 22, 2013

The Mixed Oxide Fuel Fabrication Facility (MFFF) will use colemanite bearing concrete neutron absorber panels credited with attenuating neutron flux in the criticality design analyses and shielding operators from radiation. The Savannah River National Laboratory (SRNL) is tasked with measuring the boron oxide content of the colemanite raw aggregate material prior to it being mixed into the concrete. SRNL received ten samples of colemanite for analysis on July 22, 2013. The elemental boron content of each sample was measured according to ASTM C 1301. The boron oxide content was calculated using the oxide conversion factor for boron.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Microscopic Imprints of Learned Solutions in Tunable Networks

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Thermodynamic Assessment of the Ternary Alloy System Li-Sn-Zn

The goal of this project is to predict thermodynamics and structure-properties relationships of a series of Li-based alloys for the development of advanced tritium breeding materials that exhibit the beneficial properties of pure lithium while mitigating its chemical reactivity issues for fusion applications. This project makes use of phenomenological thermodynamics based on the CALPHAD (CALculation of PHAse Diagrams), and the use of the Thermo-Calc software (see Appendix A), and experimental calorimetric data and structural characterization for the purpose of validating the data. The initial selection of Sn and Zn as solutes is considered in this first report. Originally Li-Sn alloys have shown promise in meeting tritium breeding ratio (TBR) and energy multiplication requirements. However with the addition of Zn, we expect an improvement in the nuclear, thermal, and chemical requirements of the working fluid. Other substitutes will be considered once neutronic calculations are carried out. Hence the objective of this first task is to carry out a thermodynamic assessment of the Li-Sn-Zn alloy system, as part of the development of a self-consistent and validated thermodynamic database that will include other solute elements for optimizing the properties of blanket materials in fusion-based reactors. This obviously involves the thermodynamic assessment of the three binary subsystems, namely Li-Sn, Li-Zn, and Sn-Zn.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Interband Cascade Photovoltaic Cells

In this project, we are performing basic and applied research to systematically investigate our newly proposed interband cascade (IC) photovoltaic (PV) cells [1]. These cells follow from the great success of infrared IC lasers [2-3] that pioneered the use of quantum-engineered IC structures. This quantum-engineered approach will enable PV cells to efficiently convert infrared radiation from the sun or other heat source, to electricity. Such cells will have important applications for more efficient use of solar energy, waste-heat recovery, and power beaming in combination with mid-infrared lasers. The objectives of our investigations are to: achieve extensive understanding of the fundamental aspects of the proposed PV structures, develop the necessary knowledge for making such IC PV cells, and demonstrate prototype working PV cells. This research will focus on IC PV structures and their segments for utilizing infrared radiation with wavelengths from 2 to 5 μm, a range well suited for emission by heat sources (1,000-2,000 K) that are widely available from combustion systems. The long-term goal of this project is to push PV technology to longer wavelengths, allowing for relatively low-temperature thermal sources. Our investigations address material quality, electrical and optical properties, and their interplay for the different regions of an IC PV structure. The tasks involve: design, modeling and optimization of IC PV structures, molecular beam epitaxial growth of PV structures and relevant segments, material characterization, prototype device fabrication and testing. At the end of this program, we expect to generate new cutting-edge knowledge in the design and understanding of quantum-engineered semiconductor structures, and demonstrate the concepts for IC PV devices with high conversion efficiencies.

36 MATERIALS SCIENCE↗