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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Synthesis and Photonic Sintering of Proton Conducting Lanthanide Nickelates

Lanthanide nickelate perovskites are known proton conductors at intermediate temperatures (350-600°C). They are attractive targets for proton conducting fuel cells and electrolyzers due to their operational temperature range and their stability in the presence of CO 2 . These materials have the formula LNiO 3 , with the lanthanide L = La, Ce, Pr, Nd, or Sm. Sintering is challenging due to phase transformations that occur at lower temperatures than those required for sintering. In this project, a series of lanthanide nickelate perovskites was synthesized via both a precursor solid solution method and the glycine nitrate process. Powders derived from the precursor solid solution method for LaNiO 3 and NdNiO 3 were sintered at Utility Global Inc. at various temperatures utilizing fast photonic sintering. LaNiO 3 was successfully sintered while maintaining the perovskite phase. NdNiO 3 underwent a phase transformation at all the conditions tested. The stability of the perovskite phase decreases moving right across the lanthanides in the periodic table, while the expected proton conductivity increases. These results will inform further investigations into lanthanide nickelate perovskites toward the goal of producing a free-standing, sintered, proton-conducting membrane.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Production of Readily Compressible Dies for the Enhanced Sintering of Solids (PRESS) Preliminary Investigations

Non-oxide ceramics, which consist of predominantly carbides, borides, and nitrides, are of great interest to modern engineering because of their resistance to extreme conditions. Properties of such materials include high temperature resistance, high hardness, chemical resistance, and high fracture toughness. However, the main method for manufacturing parts of these materials is by machining cylindrical billets to the desired geometry. Not only does this produce waste, but the cost of machining is high since the part materials are of such high hardness. This method is used because these non-oxide ceramics must be sintered to high density using simultaneous heat and pressure within a hot-pressing unit. These units are inherently restricted to cylindrical geometries. A proposed way to expand the capabilities of a hot-press is by using intermediate compressible dies. This method consists of surrounding a ceramic part green body with a material that will shrink at the same rate as the part and survive the hot-pressing conditions. At the end, the compressible die material would be removed leaving a nearly net shaped hot-pressed part. In this study, graphite powder was investigated as the compressible die material with its shrinkage rate being controlled by particle size mixing. Both ceramic materials and graphite were then cast into parts and compressible dies respectively to show the feasibility of the compressible die hot-pressing method.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Iodine Waste Form Corrosion: PNNL Summary Testing Report

A test method to correlate the laboratory corrosion behavior of iodine-containing waste forms (IWFs) to a conceptual long-term corrosion models is required. Several candidate methods have been evaluated previously, and each method presents unique challenges and limitations. Dynamic leach tests have emerged as the preferred candidate test method to evaluate IWFs and parameterize an eventual long-term corrosion model. This report was prepared in support of this effort to define the test suite to evaluate the corrosion of IWFs, with the goal of defining and parameterizing a conceptual long-term corrosion model. A review was presented highlighting the high degree of variability between the IWF corrosion data that is available in the literature and the need for a standardized test suite. The targeted test suite is composed of a single interval screening test, a dynamic leach test, and post-test solid characterization of the monolithic IWF samples. The data presented serves two purposes: to expand the range of IWFs evaluated with the test suite to assess method variability and to further understand the corrosion of IWFs to best inform conceptual long-term corrosion model development. A series of IWF types were included in the testing, including hot isostatically pressed Ag-mordenite, silver functionalized silica-based aerogels, iodosodalites (hot isostatic pressing, sintered, glass bonded ceramic), iodoapatite, tellurite glass and glass composite materials.

42 ENGINEERING↗
DOE OSTI2021

Synthesis and behavior of bulk iron nitride soft magnets via high-pressure spark plasma sintering

Abstract In this study, dense bulk iron nitrides (Fe x N) were synthesized for the first time ever using spark plasma sintering (SPS) of Fe x N powders. The Fe 4 N phase of iron nitride in particular has significant potential to serve as a new soft magnetic material in both transformer and inductor cores and electrical machines. The density of SPSed Fe x N increased with SPS temperature and pressure. The microstructure of the consolidated bulk Fe x N was characterized with X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and superconducting quantum interference device (SQUID) magnetometry. XRD revealed a primary phase of Fe 4 N with secondary phases of Fe 3 N and metallic iron. Finite element analysis (FEA) was also applied to investigate and explain localized heating and temperature distribution during SPS. The effects of processing on interface bonding formation and phase evolution were investigated and discussed in detail to provide insight into fundamental phenomena and microstructural evolution in SPSed Fe x N. Graphic abstract

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine-learning-based, online estimation of ceramic’s microstructure upon the laser spot brightness during laser sintering

The ceramic microstructure strongly influences its properties. During manufacturing, the online monitoring of microstructure is critical to ensure the desired material properties. So far, the microstructure on the relevant scale is usually characterized offline using scanning electron microscopy (SEM), which is time and cost-consuming. In this work, we demonstrate a cost-effective, machine learning (ML)-based approach to simulate the SEM micrographs in real-time from the laser spot brightness. We experimentally observed a strong correlation between the laser spot brightness and the corresponding microstructure at the exact locations. The brightness values obtained from thermal emission images and the corresponding SEM micrographs were used in the training datasets. The ML algorithm was a style-based conditional generative adversarial network (CGAN). After training, the ML model could generate high-fidelity microstructure images within 0.1 seconds based on in-situ captured brightness at the laser sintering spot. We used the average grain sizes as the metric to evaluate the accuracy of the ML-predicted micrographs. Here, the ML-predicted microstructures were in good agreement, with less than 5% in difference from the real SEM images. In conclusion, we demonstrate the cost-effective, online microstructure estimation during laser sintering with a simple setup (a camera, a regular computer, and the ML model).

08 HYDROGEN↗
DOE OSTI2021

Coupling of Spark Plasma Sintering with Advanced Modeling to Enable Process Scale-Up: LDRD Final Project Poster

Electric-field assisted sintering (EFAS) manufacturing techniques reduce energy usage requirements, fabricate near-net-shape parts, and produce bulk nanostructured materials through rapid heating and cooling rates achievable through Joule heating. Process parameters of pulsed DC voltage and constant pressure are applied through stainless steel rams and graphite tooling to the ceramic yttrium oxide powder to be sintered. Our high-fidelity modeling code application advances manufacturing for extreme environments by enabling process-informed design and improving microstructure consistency through multiphysics multiscale process-structure-property-performance correlation simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Influence of metal organic framework glasses on thermoelectric properties of AgSb 0.96 Zn 0.04 Te 2 alloy

In recent years, a significant number of chalcogenides-based thermoelectric (TE) materials have been investigated theoretically and experimentally. However, the efficiency of TE materials is often limited by physical and chemical stability issues. Therefore, it is crucial to identify additive materials that can reduce thermal conductivity and improve the efficiency of crystalline TE materials. One well-known approach to lower thermal conductivity is introducing porosity into the material structure, which helps scatter phonon energy. Metal-organic framework (MOF) crystalline materials, known for their porosity and physical and chemical stability, offer unique advantages in TE applications. In this study, we investigate the influence of 20 weight% amorphous Zeolitic Imidazolate Framework (ZIF)-62 to p-type AgSb 0.96 Zn 0.04 Te 2 (ASTZ), a well-known TE material. We synthesized composites, ASTZ, ASTZ-Zn, and ASTZ-Co, by sintering ASTZ with amorphous ZIF-62(Zn) and ZIF-62(Co) respectively. The addition of amorphous ZIF-62(Zn) leads to a significant enhancement in the Seebeck coefficient of ASTZ increasing from 151 µV/K to 229 µV/K at 586K. Moreover, the thermal conductivity of the ASTZ TE materials drops drastically from 0.491 Wm -1 K -1 to around 0.22 Wm -1 K -1 at 623K with the addition of either amorphous ZIF-62(Zn) or ZIF-62(Co). Remarkably, ASTZ containing amorphous ZIF-62(Zn) achieves a maximum thermoelectric figure of merit (zT max ) of approximately 0.078 at 573K, surpassing any MOF-based thermoelectric material reported to date. In conclusion, these findings highlight the potential of amorphous ZIF-62 as an effective additive for enhancing the properties of thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experimental and computational investigations on the SO 2 poisoning of (La 0.8 Sr 0.2 ) 0.95 MnO 3 cathode materials

To study the formation of detrimental phases under the sulfur gas impurity to the long-term degradation in the LSM cathode material, the classic cathode material, (La 0.8 Sr 0.2 ) 0.95 MnO 3 (LSM), was prepared, sintered, and annealed at 800, 900, and 1000°C in the sulfur-containing atmospheres, respectively. Through X-Ray Diffraction (XRD), Scanning Electron Microscope (SEM), and Transmission electron microscopy (TEM) techniques, as well as the CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) methodology, the secondary phases, especially the detrimental ones, under different conditions were predicted and experimentally verified correspondingly. Furthermore, sulfur poisoning results indicate that the accelerated tests might have degradation mechanisms different from actual operation conditions. More importantly, comprehensive comparisons among various impurity-containing conditions were also made to recommend better operation parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Embedded Fiber Optic Sensors in Structural Materials for Sensing in Extreme Environments

This slide deck presents the research results in embedding fiber optic sensors in structural materials. Fiber optic sensors were embedded in stainless steel and nickel via electric field assisted sintering. The embedded sensors were evaluated in terms of fiber integrity, fiber-matrix bonding, fiber functionality, mechanical properties, and machinability.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Uniformity Is Key in Defining Structure–Function Relationships for Atomically Dispersed Metal Catalysts: The Case of Pt/CeO 2

Catalysts consisting of atomically dispersed Pt (Pt iso ) species on CeO 2 supports have received recent interest due to their potential for efficient metal utilization in catalytic convertors. However, discrepancies exist between the behavior (reducibility, interaction strength with adsorbates) of high surface area Pt iso /CeO 2 systems and of well-defined surface science and computational model systems, suggesting differences in Pt local coordination in the two classes of materials. Here, we reconcile these differences by demonstrating that high surface area Pt iso /CeO 2 synthesized at low Pt loadings (<0.1% weight) exhibit resistance to reduction and sintering up to 500 °C in 0.05 bar H 2 and minimal interactions with CO—properties previously seen only for model system studies. Alternatively, Pt loadings >0.1 weight % produce a distribution of sub-nanometer Pt structures, which are difficult to distinguish using common characterization techniques, and exhibit strong interactions with CO and weak resistance to sintering, even in 0.05 bar H 2 at 50 °C—properties previously seen for high surface area materials. This work demonstrates that low metal loadings can be used to selectively populate the most thermodynamically stable adsorption sites on high surface area supports with atomically dispersed metals. Further, the site uniformity afforded by this synthetic approach is critical for the development of relationships between atomic scale local coordination and functional properties. Comparisons to recent studies of Pt iso /TiO 2 suggest a general compromise between the stability of atomically dispersed metal catalysts and their ability to interact with and activate molecular species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

Zhang, Xin↗
DOE OSTI2023

The Riddle of Dark LLZO: Cobalt Diffusion in Garnet Separators of Solid‐State Lithium Batteries

Abstract Solid‐state batteries (SSBs) with a Li 7 La 3 Zr 2 O 12 (LLZO) garnet electrolyte are attracting much attention as robust and safe alternative to conventional lithium‐ion batteries. Technical challenges in the practical implementation of garnet SSBs are related to the need for high‐temperature sintering, which often leads to undesirable chemical reactions with the cathode material. While these reactions are well understood for composite cathodes, very little is known about similar processes between cathode and separator during battery fabrication. This work focuses on understanding the processes between the composite LiCoO 2 ‐LLZO cathode and the LLZO separator and how they affect the battery performance. The extensive diffusion of Co‐ions within LLZO, which leads to the often‐observed LLZO darkening, is shown to have a significant impact on ionic conductivity, electronic conductivity, and dendrite stability of the separator. Experimental data coupled with large‐scale molecular dynamics simulations uncover the diffusion mechanism for Co‐ions and identify secondary phases that form during these interactions. In addition to extensive Co‐ion diffusion within the grains, a non‐uniform segregation of Co‐ions at grain boundaries is found leading to the formation of three distinct Co‐containing phases. This work offers a general approach to studying the fundamental ion diffusion processes that occur during the fabrication of oxide SSBs.

electrochemical properties↗
DOE OSTI2024

Glass-bonded ceramic waste forms for immobilization of radioiodine from caustic scrubber wastes

Glass-bonded sodalite composite waste forms have been developed for the immobilization of liquid radioactive wastes resulting from off-gas treatment during aqueous reprocessing of used nuclear fuel, with a particular focus on 129I. The proposed composite waste form is comprised of aluminosilicate ceramic phases containing volatile radionuclides bonded with a glassy matrix. In this work, a suite of ten candidate low-temperature glass binders (ZnO-Bi2O3-based glasses and a Na2O-B2O3-SiO2 glass) were examined. Six glasses were mixed with caustic scrubber waste simulant previously converted into a sodalite-rich material (to provide glass fractions of 10 and 20 wt.%), uniaxially pressed into pellets, and sintered at 350 °C or 550 °C for 8 h in air. Iodine retention after heat treatment was assessed by neutron activation analysis, showing retention of 67-100 % of expected iodine. The aqueous durabilities of the resulting materials were then determined, following the ASTM C1308 standard test, showing iodine releases of 1 to 23 g m-2 after 4 d. The cumulative iodine release for the best performing system (a zinc-bismuth-borate glass binder) was <1 g m-2, and its iodine retention from processing was 67 %. The iodine releases compared favorably with other waste forms. In parallel, this best-performing composition was also consolidated via hot isostatic pressing (HIP) in a stainless-steel canister at 550 °C for 2 h under 100 MPa pressure. The HIPed sample was produced at the ~20 g scale and showed improved densification and minimal reaction with the canister.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Grand potential sintering simulations of doped UO 2 accident-tolerant fuel concepts

Chromium-doped UO is a widely-studied near-term deployable accident-tolerant fuel concept because it results in a dense, large-grain structure that increases the fuel resistance to densification, swelling, and fission gas release. A new charged-interstitial mechanism was recently proposed to describe the behavior of dopants like chromium in sintered UO 2 . Based on that mechanism, manganese was suggested as an even stronger dopant than chromium. In the current work we use mesoscale sintering simulations in an effort to validate the new mechanism. We compare the relative behavior of Cr-doped and undoped UO 2 against experimental data in the literature. We also make predictions of the relative behavior of Mn-doped UO 2 . Simulations are done using the phase field-based grand potential sintering model. Dopants have two effects on sintered UO 2 . They marginally increase the densification rate and greatly increase the average grain size. Both of these effects are individually tested in small-scale simulations. Then large 3D sintering simulations are used to test the combined behavior of both effects. The results for the densification rate simulations are consistent with experiments for Cr-doped fuels. However, we note the grain growth rates are lower than what is found in the literature for Cr-doped fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

High-throughput, Ultra-fast Laser Sintering of Ceramics and AI Based Prediction on Processing-Microstructure-Property Relationships

We report high-throughput, ultra-fast laser sintering of alumina sample array and characterization of sample units’ microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. These experimental data were used to train machine-learning (ML) models. Accurate ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power and (2) prediction of the expected microstructure from the desired hardness. An independent neural network was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in terms of projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.

36 MATERIALS SCIENCE↗