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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Recent Advances and Prospects in Design of Hydrogen Permeation Barrier Materials for Energy Applications—A Review

The hydrogen infrastructure involves hydrogen production, storage and delivery for utilization with clean energy applications. Hydrogen ingress into structural materials can be detrimental due to corrosion and embrittlement. To enable safe operation in applications that need protection from hydrogen isotopes, this review article summarizes most recent advances in materials design and performance characterization of barrier coatings to prevent hydrogen isotopes’ absorption ingress and permeation. Barriers are crucial to prevent hydride formation and unwanted hydrogen effects to increase safety, materials’ lifetime and reduce cost for applications within nuclear and renewable energy. The coating may be applied on a material that requires protection from hydrogen pick-up, transport and hydride formation in hydrogen storage containers, in pipelines, spent nuclear fuel storage or in nuclear reactors. While existing, commercial coatings that have been much in use may be satisfactory for various applications, it is desirable to evaluate whether alternative coating concepts can provide a greater resistance to hydrogen isotope permeation along with other improved properties, such as mechanical strength and thermal resistance. The information presented here is focusing on recent findings within the past 5–7 years of promising hydrogen barriers including oxides, nitrides, carbon, carbide, MAX-phases and metals and their mechanical strength, hydrogen pick-up, radiation resistance and coating manufacturing techniques. A brief introduction to hydrogen permeation is provided. Knowledge gaps were identified to provide guidance for material’s research prospects.

08 HYDROGEN↗
DOE OSTI2020

Sectioning of NBG-17 Graphite Billet

The Advanced Reactor Technologies (ART) Baseline Graphite Characterization program at the Idaho National Laboratory (INL) is performing a comprehensive evaluation of inter-billet variations in as-produced nuclear-grade graphite. Before graphite test specimens are machined, the full-size billets must be pre-sectioned into sub-blocks that can be more easily handled and shipped. As manufactured, NBG-17 billets are rectangular in shape, nearly 6 feet in length, and may exceed 1,500 pounds in weight. Sectioning of these billets must be accomplished via a supplier that is capable of handling and cutting the billets into individual slabs and quartering these slabs into sub-blocks. Billets will be pre-labeled by the Purchaser prior to leaving controlled storage such that traceability of any individual sub-block is never lost during sectioning operations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Bioconversion of Thermochemical Intermediates

Thermochemical (TC) biofuels production via both pyrolysis and hydrothermal liquefaction produces aqueous waste streams, typically with organic compounds at concentrations of -50-100 g/L. These streams represent a wastewater treatment cost and carbon loss for the TC biorefinery, but the concentration range for these compounds is ideal for bioconversion. To that end, the Bioconversion of Thermochemical Intermediates (BTI) project is developing advanced analytics and engineered microbes to convert these waste streams to co-products, with the overall aim of improving the economics and carbon conversion efficiency of TC biorefining. To date, we have primarily focused on development of advanced analytical chemistry approaches to fully characterize TC aqueous streams and engineering of Pseudomonas putida for conversion of non-conventional substrates, including methylated phenolics, cyclic ketones, furans, and C1-C3 light oxygenates, into atom-efficient products. Two primary challenges are the rapid deployment of aqueous-compatible analytics to changing upstream conditions and dealing with the toxicity of the feed streams to engineered microbes. The project efforts have resulted in engineered strains of P. putida able to consume 90% of the organic compounds in aqueous waste streams from catalytic fast pyrolysis, more than 300-fold toxicity tolerance improvements in P. putida, and carbon closures exceeding 90% for TC wastewater streams across multiple processing technologies.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2020

Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part II - Autonomous Navigation

Nuclear sites across the Unites States and other nations in the world, are prone to extreme radiation effects. To understand the radiation hazards, plan the cleanup efforts; and to meet the quality standards and guidelines, it is important to accurately characterize any nuclear facility. One of the current methods in characterizing facilities is the use of advanced autonomous systems. These are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. Conventional methods of taking radiation measurements by hand within or around the containment areas, and analyzing the collected data to obtain the result are: Ineffective, Puts scientists at risk of Radiation, Not cost-effective. The platform uses the Robotic Operating Systems (ROS) to integrate multiple sensors seamlessly. It can operate near real time by using a distributed processing system to handle large amounts of data: Surrounding cameras, A multi-channel 3D Lidar, Manipulator arm, High-performance gamma-ray spectrometer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

Siriwardane, Hema↗
DOE OSTI2025

Temperature effects on Thermal Neutron Scattering in Beryllium Metal

Many advanced reactor concepts being considered are characterized by a thermal neutron energy spectrum, which traditional light water reactor systems utilized. Beryllium metal, a material with a long-standing operational history in reactor environments, continues to be of significant interest due to its large scattering cross section, low absorption cross section, and well-characterized behavior under irradiation conditions.

GASKET↗
DOE OSTI2025

Measurement of Charged-current Muon Neutrino–argon Interactions Without Final-state Pions Using the MicroBooNE Detector

This thesis presents a new high-statistics measurement of flux-integrated single- and double-differential cross sections for charged-current muon neutrino interactions on argon nuclei without final-state pions. The analysis utilizes the full 1.3 $\times$ $10^{21}$ protons-on-target dataset collected by the MicroBooNE liquid argon time projection chamber between 2015 and 2020 at Fermilab's Booster Neutrino Beam. The results of this study are reported with respect to final-state muon kinematic variables and compared with predictions from commonly used neutrino event generators. In one-dimensional distributions, all generators perform reasonably well. However, in two dimensions, only a few demonstrate good agreement with the data. These findings provide valuable insight into characterizing neutrino-nucleon interactions. Such advancements are vital for future long-baseline neutrino experiments, which aim to accurately measure neutrino oscillation and investigate beyond-the-Standard-Model physics with high sensitivity.

Englezos, Panagiotis [Rutgers U., Piscataway (main↗
DOE OSTI2023

Characterizing PuO 2 Powder at Oak Ridge National Laboratory for the Multi-Lab Plutonium Process Signatures Campaign FY 2022

This report highlights the development of advanced analytical capabilities at Oak Ridge National Laboratory to characterize PuO 2 powder to support the Multi-Lab Plutonium Process Signatures Campaign in FY 2022. Two samples from a batch of 76 statistical precipitation runs were packaged and shipped from Pacific Northwest National Laboratory to Oak Ridge National Laboratory at the beginning of FY 2022. Techniques were used to characterize the sample, including powder X-ray diffraction, Raman spectroscopy, and diffuse reflectance spectroscopy. A brief description of each technique, the results and discussion, and future work are described in this report. Opportunities to expand current capabilities and include additional analytical capabilities for the next campaign will also be discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Measurements and Characterization

The Measurements and Characterization scientists at the National Renewable Energy Laboratory provide characterization support, conduct collaborative research, and develop new measurement capabilities and diagnostics to advance the photovoltaic (PV) generation of energy. We use state-of- the-art characterization tools to solve problems in all phases of material and device development. Results lead to an increased understanding that drives advances in the performance, reliability, cost, and manufacturability of PV materials, devices, and systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Achieving clean production with nanostructured coated milling tools dry machining low carbon steel

The advancement of clean production using nanostructured materials in subtractive manufacturing processes has focused on replacing the use of liquid lubrication with solid lubricants coated to conventional cutting tools machining low carbon steel (~ 0.2 wt. % carbon). However, little is known about the wear mechanisms that dominate such tools when coated with functional graded nanostructured coatings. The present study follows an international standard for characterizing such wear (ISO 8688) but uses advanced measurement techniques such as x-ray fluorescence technology to measure the diffusion of chemical species from tool to workpiece and vice versa, that contributes to tool wear with the view to enhance the principles of cleaner production. The results show that when commercially-available tools coated with the appropriate functionally graded industrial coating, diffusion does not take place and creates an effective chemical barrier during machining. The experimental methods used in the study include machining workpieces using a CNC milling machine, tachometer for measuring spindle speed, dynamometer for measuring cutting forces, infra-red camera for measuring cutting temperatures, chemical species diffusion wear using x-ray florescence detector, and an optical microscope for measuring tool wear. Coupled with the physical measurement of wear, it is concluded that mechanical wear dominates the milling of a low carbon steel and that thermal properties of the coatings do not correlate with such wear. It is also concluded that flank wear as a function of volume of workpiece removed and machining duration (machining index) is a sound method for assessing the transition of the stages of wear during clean machining operations. The present study not only has implication for the design of better cutting tools, but also advances dry machining processes that eliminate the use of liquid lubricants making subtractive production processes cleaner. The main conclusions drawn from this study show that coated tools have a longer life under dry conditions compared to uncoated tools and that there is no correlation between coating properties and tool wear. In conclusion, diffusion of chemical species into the cut chips did not occur because of the lack of thermally induced chemical wear of the cutting tool and that wear is caused by the gradual erosion of the flank face due to the chip abrading the cutting tool.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Forecasting Tropical Annual Maximum Wet-Bulb Temperatures Months in Advance From the Current State of ENSO

Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet-bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TW max ). We develop a multiple linear regression model that explains 80% of variance in tropical mean TW max and significant regional TW max variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong-to-very-strong El Niño at the end of 2023, with an Oceanic Niño Index of ~2.0, suggests a 2024 tropical land mean TW max of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TW max in specific areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes

Focal Area: Advanced methods to glean insights from complex data (Focal Area 3). Science Challenge: Fusing and interpreting the vast amount of data from disjoint sources for the purpose of elevating our understanding of aerosol-cloud interaction presents an enormous challenge and opportunity and is necessary to improve uncertainty quantification and predictions, especially for extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Interpreting strain tensor data to characterize and monitor reservoirs for CO2 storage and other applications

Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and this has opened the door to new opportunities for characterization and monitoring. We have deployed strainmeters and then conducted injection well tests in an underlying reservoir at 530m depth. The resulting data indicated that the horizontal strain at shallow strainmeters (30 to 40m depth) was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 n/d over a few days. The signal at two strainmeters at shallow depth were consistent, although the magnitude of the horizontal strains were different reflecting the different radial directions from the well. The signal at a deep strainmeter deployed at reservoir depth was much different, with tensile vertical strains and compressive horizontal strains. These data can be interpreted by inverting poroelastic forward models developed using numerical and analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. This model is fast and can be inverted to estimate reservoir stiffness and geometry. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. The proxy model is periodically updated and refined using the finite element model to ensure accuracy. This approach significantly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. We have shown that the strain tensor in the caprock is sensitive to pressure in the reservoir, boundaries in the reservoir, and pressure in the caprock caused by leaks. These results indicate that coupling strain tensor data with inversion has the potential to help evaluate reservoirs during initial characterization, and to monitor them during the CO2 injection and storage process.

Murdoch, Larry↗
DOE OSTI2020

Nanotechnology for catalysis and solar energy conversion

This roadmap on Nanotechnology for Catalysis and Solar Energy Conversion focuses on the application of nanotechnology in addressing the current challenges of energy conversion: 'high efficiency, stability, safety, and the potential for low-cost/scalable manufacturing' to quote from the contributed article by Nathan Lewis. This roadmap focuses on solar-to-fuel conversion, solar water splitting, solar photovoltaics and bio-catalysis. It includes dye-sensitized solar cells (DSSCs), perovskite solar cells, and organic photovoltaics. Smart engineering of colloidal quantum materials and nanostructured electrodes will improve solar-to-fuel conversion efficiency, as described in the articles by Waiskopf and Banin and Meyer. Semiconductor nanoparticles will also improve solar energy conversion efficiency, as discussed by Boschloo et al in their article on DSSCs. Perovskite solar cells have advanced rapidly in recent years, including new ideas on 2D and 3D hybrid halide perovskites, as described by Spanopoulos et al 'Next generation' solar cells using multiple exciton generation (MEG) from hot carriers, described in the article by Nozik and Beard, could lead to remarkable improvement in photovoltaic efficiency by using quantization effects in semiconductor nanostructures (quantum dots, wires or wells). These challenges will not be met without simultaneous improvement in nanoscale characterization methods. Terahertz spectroscopy, discussed in the article by Milot et al is one example of a method that is overcoming the difficulties associated with nanoscale materials characterization by avoiding electrical contacts to nanoparticles, allowing characterization during device operation, and enabling characterization of a single nanoparticle. Besides experimental advances, computational science is also meeting the challenges of nanomaterials synthesis. The article by Kohlstedt and Schatz discusses the computational frameworks being used to predict structure–property relationships in materials and devices, including machine learning methods, with an emphasis on organic photovoltaics. The contribution by Megarity and Armstrong presents the 'electrochemical leaf' for improvements in electrochemistry and beyond. In addition, biohybrid approaches can take advantage of efficient and specific enzyme catalysts. These articles present the nanoscience and technology at the forefront of renewable energy development that will have significant benefits to society.

14 SOLAR ENERGY↗
DOE OSTI2024

New Directions for Thermoelectrics: A Roadmap from High‐Throughput Materials Discovery to Advanced Device Manufacturing

Thermoelectric materials, which can convert waste heat into electricity or act as solid‐state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high‐throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high‐throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed‐loop process to generate and analyze broad datasets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to realize scalable, low‐cost, and energy‐efficient fabrication of thermoelectric devices. This review provides an overview of recent advances in discovering thermoelectric materials using high‐throughput methods, including processing, characterization, and screening. Advanced manufacturing methods of thermoelectric devices are also introduced to realize the broad impacts of thermoelectric materials in power generation and solid‐state cooling. In the end, this article also discusses the future research prospects and directions.

Song, Kaidong↗
DOE OSTI2022

MIDAS: Modeling Individual Differences using Advanced Statistics

This research explores novel methods for extracting relevant information from EEG data to characterize individual differences in cognitive processing. Our approach combines expertise in machine learning, statistics, and cognitive science, advancing the state-of-the art in all three domains. Specifically, by using cognitive science expertise to interpret results and inform algorithm development, we have developed a generalizable and interpretable machine learning method that can accurately predict individual differences in cognition. The output of the machine learning method revealed surprising features of the EEG data that, when interpreted by the cognitive science experts, provided novel insights to the underlying cognitive task. Additionally, the outputs of the statistical methods show promise as a principled approach to quickly find regions within the EEG data where individual differences lie, thereby supporting cognitive science analysis and informing machine learning models. This work lays methodological ground work for applying the large body of cognitive science literature on individual differences to high consequence mission applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Applications of analytical electron microscopy to guide the design of boron carbide

Compositional analysis of boron carbide on nanometer length scales to examine or interpret atomic mechanisms, for example, solid-state amorphization or grain-boundary segregation, is challenging. This work reviews advancements in high-resolution microanalysis to characterize multiple generations of boron carbide. First, ζ-factor microanalysis will be introduced as a powerful (scanning) transmission electron microscopy ((S)TEM) analytical framework to accurately characterize boron carbide. Three case studies involving the application of ζ-factor microanalysis will then be presented: (1) accurate stoichiometry determination of B-doped boron carbide using ζ-factor microanalysis and electron energy loss spectroscopy, (2) normalized quantification of silicon grain-boundary segregation in Si-doped boron carbide, and (3) calibration of a scanning electron microscope X-ray energy-dispersive spectroscopy (XEDS) system to measure compositional homogeneity differences of B/Si-doped arc-melted boron carbides in the as-melted and annealed conditions. Overall, the improvement and application of advanced analytical tools have helped better understand processing–microstructure–property relationships and successfully manufacture high-performance ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparative S/TEM study of superconducting Ta quantum resonators by wet and dry etching types

Superconducting resonators play a pivotal role in various quantum technology applications, such as quantum computing and high-frequency communication systems. The performance of these resonators is closely tied to the properties of the superconducting films used in their fabrication. Here, in this study, we investigated the impact of wet and dry etching on tantalum (Ta) films leveraging advanced scanning transmission electron microscopy-based characterization methods and examined the morphological, chemical, and strain changes caused by the etching processes. Consequently, we report the significant differences between the two etching methods, with dry etching resulting in straight slanted sidewalls and a thinner oxidized layer, while wet etching produced curved sidewalls and undercuts. Both methods led to the formation of a residual Ta wedge at the lower part of the sidewall, causing lattice deformation, which could adversely influence the homogeneous operations of superconducting devices. These insights enhance our understanding of how etching influences superconducting films, offering valuable guidance for optimizing resonators and related devices. Our findings mark a significant stride in advancing quantum technologies and high-frequency communication by enhancing our practical understanding of superconducting material fabrication.

36 MATERIALS SCIENCE↗