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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nano Filters for Selective Metal Capture (CRADA Final Report)

As part of the Cyclotron Road program, Sunchem investigated novel nanoporous materials and their metal extraction performance in both batch and dynamic continuous flow operations. The proposed project developed novel nanoporous materials for selective metal extraction from complex industrial mixtures. The technical objectives were to synthesize and characterize the nanoporous materials, structure the nanoporous materials with binders along with structuring techniques and evaluate its performance in both a batch and dynamic continuous flow operation. The nanoporous material was structured through a disc granulation method to obtain particles in the size range of 250 to 500 micron diameter. This structured material was packed into a continuous flow column which achieved up to 48 wt% of gold capture. This work aimed to address the key technical risk of the materials’ capability to be employed in an industrial operation with competitive performance compared to other adsorbents.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Experimental characterization and analysis of phase change material-based thermal energy storage system for refrigerated display case

Refrigerated display cases that are used to store and exhibit food products in supermarkets and retail spaces consume a significant portion of the buildings’ total electricity. Importantly, the refrigeration-related energy cost and demand charges are greatly affected by the time-of-use electricity pricing and demand rates, which are at their maximum during peak hours, typically when refrigeration energy consumption is also high. Using energy storage to shift the refrigeration load from peak to off-peak hours can greatly reduce the operational costs in the supermarket. This study demonstrates a phase change material-based thermal energy storage (TES) system, specifically designed in stackable units, that can be integrated with an open vertical refrigerated display case. We perform numerical and experimental characterization that includes finite-difference modeling for the TES, prototype fabrication, and laboratory evaluation, followed by a preliminary system-level analysis to predict the impact of TES on the refrigerated case performance, energy use, and energy cost. The results show that the dedicated latent TES for refrigerated cases can provide a specific energy of 50.4 Wh/kg and a specific power of 15.5 W/kg. The TES can be charged during 12 h of the off-peak period and discharged at various rates during 4 to 6 h of the peak period, thereby shifting the refrigeration load from the peak to the off-peak period. Consequently, annual cost savings up to 19% can be achieved, depending on the thermal load, the summer/winter peak electricity pricing, and the transition temperature of the phase change material used.

25 ENERGY STORAGE↗
DOE OSTI2023

Characterization and response of three TNT equivalent materials in subsurface explosions

Energetic compositions utilized for inducing seismic responses are often referred to in terms of their TNT equivalent, a metric which is often misused because observations of interest are not often understood with respect to explosive characteristics. Here, in this investigation, we characterize three energetic compositions used to elicit seismic responses in a highly instrumented testbed to further our understanding of subsurface TNT equivalence. Three different energetic sources are prepared and studied for their fidelity to thermochemically predicted detonation behavior. We present effects of subsurface detonation on subsurface rock and seismic emission as a function of energetic source material within the testbed. Field experiments were conducted using a single thermochemical yield of 5 kg using diverse sources consisting of nitromethane, ammonal, and PBXN‐5 (an HMX based plastic bonded explosive) mixtures at the Energetic Materials Research and Testing Center in Socorro, New Mexico. Relative performance of energy density‐related detonation parameters, such as detonation velocity and pressure, affect the observed response in the subsurface. Most notable, equal thermochemical yields described in terms of TNT equivalence do not correlate to equal thermochemical effects in the subsurface.

Blue Canyon Dome↗
DOE OSTI2022

Comprehensive Characterization of Multi-Phase Sulfurized Polyacrylonitrile Cathodes for Lithium-Sulfur Batteries

Sulfurized polyacrylonitrile (SPAN) is considered one of the most promising cathode materials to overcome the operational challenges that plague lithium-sulfur (Li-S) batteries. However, material properties and electrochemical performance implications of SPAN prepared under different synthesis conditions are not yet fully investigated. In this study, we show the impacts of different synthesis conditions on the formation of sulfur to PAN bonds and redox reaction mechanisms of multi-phase SPAN via comprehensive material and electrochemical characterizations. In-situ Raman analysis was first applied to study the multi-phase SPAN-based Li-S cells. We found that both elemental sulfur and chemically bonded sulfur are present under the synthesis condition of 300 degree C/3 h along with unreacted PAN. The incompletely sulfurized, multi-phase SPAN exhibited an unusually rapid capacity degradation in the resultant Li-S cells, which is attributed to polysulfide formation and continuously growing interfacial impedance in the Li-S cells. On the other hand, SPAN samples prepared under the synthesis condition of 350 degree C/3 h are found completely sulfurized with chemically bonded sulfur to the PAN backbone without the presence of free elemental sulfur. Complete sulfurization of SPAN led to exceptionally stable cycle performance due to excellent reversible redox processes of chemically bonded sulfur with Li+ in the Li-S cells.

DIRECT ENERGY CONVERSION,ENERGY STORAGE↗
DOE OSTI2023

Detection and Mitigation of Radionuclides in the Environment: Toward a Clean Ecosystem

This research describes a straightforward approach to producing surface-engineered nanomaterials for the detection and mitigation of radionuclides generated in nuclear facilities. Here, a micelle forming surfactant ligand, namely cetyltrimethylammonium bromide (CTAB), was engineered on the surface of iron oxide nanoparticles and explored for the removal of radioactive materials, such as pertechnetate (TcO 4 – ), from aqueous environments. A series of analytical tools were employed to characterize the nanocomposite materials, such as SEM, EDS, UV-Vis spectroscopy, DLS, and PALS, and evaluated for their ability to capture a model analyte, perrhenate (ReO 4 – ) ions. The iron oxide magnetic nanoparticles retain their magnetic properties after surface functionalization and can be easily manipulated and collected with a magnet. Therefore, these nanocomposite materials can be used to remotely remediate environments by scavenging and collecting radionuclide species at the desired location.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Project 7.3 Characterization of Kevlar®-reinforced Composites for Predictive Simulation

The IACMI 7.3 project between DuPont and Purdue University required the mechanical testing of a 2x2 twill Kevlar® weave with Hexion EPIKOTE resin. The tension, shear, flexure, and compressive behavior of this material system was characterized, and the results can be seen in Table 1. These material properties can now be further evaluated for use in performance simulations of automotive components to represent the properties of composites based on woven materials containing Kevlar. This report will provide details regarding the effort required to arrive at these material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Research tool development for catalyst, catalyst/ionomer, and ionomer characterization above 100°C (CRADA Final Report)

There is a need to develop experimental tools that address the catalyst and ionomer interface under high-temperature proton-exchange-membrane (HT-PEM) operating conditions (dry, >100°C). These conditions mean traditional water-based measurements for membrane and ionomer properties are insufficient; new ex-situ techniques are required to address and characterize expected novel materials. In this project we develop a microelectrode cell for high temperature kinetics and gas transport properties and ex-situ ionomer thin-film characterization under these conditions. We use baseline Nafion and phosphoric-acid imbibed membranes as prototype materials for measuring the membrane/acid/ionomer systems with Pt microelectrodes and demonstrate feasibility of the tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Revealing local order via high energy EELS

Short range order (SRO) is critical in determining the performance of many important engineering materials. However, accurate characterization of SRO with high spatial resolution – which is needed for the study of individual nanoparticles and at material defects and interfaces – is often experimentally inaccessible. Here, we locally quantify SRO via scanning transmission electron microscopy with extended energy loss fine structure analysis. Specifically, we use novel instrumentation to perform electron energy loss spectroscopy out to 12 keV, accessing energies which are conventionally only possible using a synchrotron. Our data is of sufficient energy resolution and signal-to-noise ratio to perform quantitative extended fine structure analysis, which allows determination of local coordination environments. To showcase this technique, we investigate a multicomponent metallic glass nanolaminate and locally quantify the SRO with <10 nm spatial resolution; this measurement would have been impossible with conventional synchrotron or electron microscopy methods. Finally, we discuss the nature of SRO within the metallic glass phase, as well as the wider applicability of our approach for determining processing–SRO–property relationships in complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel

Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries and electrocatalysts. Experimental characterizations of such materials by operando microscopy produce rich image datasets, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Learning heterogeneous reaction kinetics from X-ray movies pixel-by-pixel

Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries 1 and electrocatalysts 2. Experimental characterizations of such materials by operando microscopy produce rich image datasets 3–6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation 7. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.

Chueh, William↗
DOE OSTI2022

Processing of Ultrasonic Measurements for an Additive Manufacturing Application

Resonant ultrasonic spectroscopy is an effective methodology capable of measuring a change in modal resonant frequencies of material structures. It encompasses techniques based on ultrasonic frequencies employed to estimate elastic coefficients in solids. It is these frequencies that are uniquely characterized by the material shape, coefficients, symmetry and density that are used to improve and detect changes especially during the additive manufacturing (AM) process. In this report a variety of techniques to extract and validate material resonances are developed starting first with the background theory required to comprehend the approaches. Once the theory is established, various signal processing methodologies are applied to estimate the underlying resonances leading to the extraction of the critical elastic coefficients characterizing the specimen under test. A detailed case study based on wire arc additive manufacturing is discussed demonstrating the applicability of the various approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Update on Activities Related to the Library of Graphite Microstructures

This report provides an overview and update of the ongoing efforts to create a comprehensive library of microstructures for nuclear graphite and carbon-based materials under consideration for nuclear applications. The library includes data on microstructural characterization of unirradiated graphite materials, a guide to the techniques used to analyze graphite (which complements the ASME guidelines and ASTM standards), a summary of characterization data for neutron-irradiated or oxidized material, and a compendium of microstructural information for carbon-based materials. These efforts are being conducted at various length scales for the filler and binder phases in graphite to better understand graphite’s local structure and property relationships. This report is a follow-up to the previous milestone report titled Report on initial development of a database of nuclear graphite characteristics based on microstructural characterization, ORNL/TM/-2023/2992, published in July 2023.The effort to develop the library of microstructures supports the US Department of Energy Office of Advanced Reactor Technologies program objectives of aiding the material selection, licensing, management, and core assessments of a graphite core by documenting the unirradiated microstructure of relevant grades or characterizing the microstructure’s evolution under the reactor environment. Additionally, this project aims to provide (1) information and guidelines for the characterizing of graphite and (2) a protocol to assess a nuclear graphite grade.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An Overview of Nano Multilayers as Model Systems for Developing Nanoscale Microstructures

The microstructural transformations of binary nanometallic multilayers (NMMs) to equiaxed nanostructured materials were explored by characterizing a variety of nanoscale multilayer films. Four material systems of multilayer films, Hf-Ti, Ta-Hf, W-Cr, and Mo-Au, were synthesized by magnetron sputtering, heat treated at 1000 °C, and subsequently characterized by transmission electron microscopy. Binary systems were selected based on thermodynamic models predicting stable nanograin formation with similar global compositions around 20–30 at.%. All NMMs maintained nanocrystalline grain sizes after evolution into an equiaxed structure, where the systems with highly mobile incoherent interfaces or higher energy interfaces showed a more significant increase in grain size. Furthermore, varying segregation behaviors were observed, including grain boundary (GB) segregation, precipitation, and intermetallic formation depending on the material system selected. The pathway to tailored microstructures was found to be governed by key mechanisms and factors as determined by a film’s initial characteristics, including global and local composition, interface energy, layer structure, and material selection. This work presents a global evaluation of NMM systems and demonstrates their utility as foundation materials to promote tailored nanomaterials.

Appleget, Chelsea D.↗
DOE OSTI2022

Daylight simulation workflows incorporating measured bidirectional scattering distribution functions

Daylight predictions of architectural spaces depend on good estimates of light transfer through skylights, windows and other fenestration systems. For clear glazing and painted surfaces, parametric transmission and reflection models have proven adequate, but there are many cases where light-scattering, semi-specular shading and daylighting materials defy simple characterization. Something as commonplace as fabric roller shades and venetian blinds may turn daylight prediction into guesswork, and numerous advanced systems on the market tuned specifically to enhance daylight are not sufficiently characterized to distinguish their performance. In this paper, we describe new tools available to handle novel and specialized fabrics, materials, and devices using data-driven modelling of bi-directional scattering distribution functions (BSDFs). These representations are usually tabulated at constant or adjustable angular resolution for efficient point-in-time and annual daylight simulations. We describe a variety of BSDF simulation workflows, including some of the tools and methods that make advanced analysis possible, and highlight some of the current challenges. We conclude with a discussion of future work and how such data might be created and shared worldwide.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Enhancement of second-order optical nonlinearities and nanoscale periodic domain patterning in ferroelectric boron-substituted aluminum nitride thin films

The discovery and development of CMOS-compatible, nonlinear optical materials is essential to produce integrated photonic devices with advanced functionalities. AlN is a strong candidate for on-chip device demonstration due to its intrinsic second-order optical nonlinearities, large bandgap, and well-established fabrication techniques. However, AlN is not easily phase matched for the largest coefficient d 33 ; the coefficients that could potentially be dispersion phase-matched, d 31 and d 15 , have weak nonlinearities. This work investigates ferroelectric Al 1-x B x N (x = 0 to 0.11) for viability as a large bandgap nonlinear optical material with unique suitability towards ultraviolet light generation using second harmonic generation. The linear and nonlinear optical properties are characterized accounting for material anisotropy. With increasing B concentration, a large enhancement from near negligible values to d 31 = 0.9 ± 0.1 pm/V and d 15 = 1.2 ± 0.1 pm/V is observed. This compares favorably to other large bandgap materials like β-Ba(BO 2 ) 2 , where the largest nonlinear coefficient is d 22 ~ 2.3 pm/V at 800 nm. This is accompanied by a change in the bandgap from 6.1 eV to 5.8 eV as B substitution goes from 0 to 11%. A periodically poled, quasi-phase-matched ferroelectric domain pattern with 400 nm domain size and a wall roughness of <16 nm is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Radiation hard Ga 2 O 3 detectors for high energy physics

In this project, we explore the use of ultra-wide bandgap Ga 2 O 3 materials for fabricating next-generation radiation hard solid-state detectors for high energy physics (HEP) applications. As an emerging semiconductor, Ga 2 O 3 has ultra-wide bandgap (4.5-4.9 eV), high breakdown electric field (8 MV/cm) and much lower production cost compared with radiation hard diamond detectors, all of which make Ga 2 O 3 a great candidate material working in harsh radiation environment of future HEP experiments. The recent breakthrough of growth technologies of Ga 2 O 3 significantly improves the availability of large area single crystalline Ga 2 O 3 . We focus on an early proof-of-principle demonstration of Ga 2 O 3 detectors and conduct comprehensive material and detector characterization to evaluate the potential of the emerging Ga 2 O 3 as a new radiation-hard detector material. Our endeavors directly support the instrumentation development and update need of HEP experiments and fits very well into the DOE HEP “Detector R&D” research subprogram.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Automated Threat Recognition For Aviation Security Applications

We have developed a framework for automated threat recognition (ATR) of explosive threat materials for both single-energy and dual-energy X-ray CT systems. Under this framework, two different types of ATR have been developed. The first type of ATR employs supervised machine learning with statistical characterization of target materials for threat identification training. The reliance only on statistical characterization information for threat training uniquely enables this style of ATR to adapt quickly to evolving threats. The second type of ATR employs deep learning through convolutional neural networks. Convolutional neural networks are attractive due to their human-like capacity for learning and strong ability to identify trends and patterns. Although this method is more powerful than the first type, it requires large amounts of training data and therefore is less agile. Each ATR performs threat characterization at the voxel neighborhood level. This approach avoids the use of threat shape as a detection criterion, which is prohibited by DHS and TSA guidelines.

97 MATHEMATICS AND COMPUTING↗