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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 OSTI2025

Insights into catalyst degradation during alkaline water electrolysis under variable operation

Energy conversion technologies that are key to decarbonization efforts face significant durability challenges due to variable operation. Understanding the impact of variable operation on catalytic stability and identifying the key variables that dictate degradation is crucial for developing robust technologies. Here, we present a comprehensive investigation of the effects of variable operation on liquid alkaline water electrolysis. Our findings reveal that variable operation induces severe degradation of Ni, Fe, and Co catalytic films that is not observed during steady-state operation. By systematically interrogating the electrode discharge during simulated shutdown tests using Raman spectroscopy and mass spectrometry techniques, we uncover significant alterations caused by reverse currents in real time. These include changes in crystal structure, composition, film thickness, electronic conductivity, and dissolution rates. Lab-scale electrolyzer experiments further highlight the impact of variable operation on catalyst materials under relevant conditions. Finally, we provide guidelines for leveraging these insights to advance electrocatalysis research. This work underscores the importance of integrating realistic stressors into stability testing and offers practical guidelines for catalyst design, performance evaluation, and industrial implementation. Collectively, insights from this study will drive the development of more resilient energy conversion technologies.

Marquez, Raul A. [Univ. of Texas, Austin, TX (Unit↗
DOE OSTI2024

Learning thermodynamically constrained equations of state with uncertainty

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material’s thermodynamic state variables—specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived, and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty is reduced by considering thermodynamic constraints.

Sharma, Himanshu (ORCID:000900050235718X)↗
DOE OSTI2023

Tomographic optical emission spectroscopy of an atmospheric pressure plasma jet and surface ionization waves on planar and structured surfaces

Here in this paper, an approach for 3D plasma structure diagnostics using tomographic optical emission spectroscopy (Tomo-OES) of a nanosecond pulsed atmospheric pressure plasma jet (APPJ) is presented. In contrast to the well-known Abel inversion, Tomo-OES does not require cylindrical symmetry to recover 3D distributions of plasma light emission. Instead, many 2D angular projections are measured with intensified cameras and the multiplicative algebraic reconstruction technique is used to recover the 3D distribution of light emission. This approach solves the line-of-sight integration problem inherent to optical diagnostics, allowing recovery of localized OES information within the plasma that can be used to better infer plasma parameters within complex plasma structures. Here, Tomo-OES was applied to investigate an APPJ operated with helium in ambient air and impinging on planar and structured dielectric surfaces. Surface charging caused the guided streamer from the APPJ to transition to a surface ionization wave (SIW) that propagated along the surface. The SIW experienced variable geometrical and electrical material properties as it propagated, leading to 3D configurations that were non-symmetric and spatially complex. Light emission from He, N$_2^+$, and N2 were imaged at ten angular projections and the respective time-resolved 3D emission distributions in the plasma were then reconstructed. The spatial resolution of each tomographic reconstruction was 7.4 µm and the temporal resolution was 5 ns, sufficient to observe the guided streamer and the effects of the structured surface on the SIW. Emission from He showed the core of the jet and emission from N$_2^+$ and N2 indicated effects of entrainment of ambient air. Penning ionization of N2 created a ring or outer layer of N$_2^+$ that spatially converged to form the 'plasma bullet' or spatially diverged across a surface as part of a SIW. The SIW entered trenches of size 150 µm, leading to decreases in plasma light emission in regions above the trenches. The plasma light emission was higher in some regions with trenches, possibly due to effects of field enhancement.

plasma interactions with complex surfaces↗
DOE OSTI2023

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring

A combined machine learning–physics–based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system’s free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.

42 ENGINEERING↗
DOE OSTI2024

Synthesizing, Compounding, and Characterizing a Heat Labile Polyurethane Foam

ABSTRACT A need exists for a packaging foam material that can be converted from solid to gaseous degradation products at reasonably low energy levels or temperatures, such as 100O C. This paper will primarily discuss the approaches currently being used to synthesize and characterize such a material. These approaches include the incorporation of novel polyols such as azo containing diols, polycarbonate diols, and polypropylene carbonate polyols into polyurethane foams. Characterization methods include NMR, FTIR, TGA, finite element analysis, impact strength, and others. This project will be funded for a duration of three years. Year 1 focused on developing the proposed test methods and producing an initial rigid polyurethane foam. Year 2 focuses on refining the materials and test methods. If appropriate, design of experiment (doe) techniques will be used to optimize components, component levels, density and other variables to attain required final material properties (TGA weight loss, impact strength, etc.). Year 3 focuses on scaling up to larger engineering quantities. The application for this material is in load securement for transportation of low level radioactive waste materials within the US Department of Energy (DOE) complex. Foam in place process equipment and operators will be shielded using this novel method over current practice. Current practice can involve time consuming methods of load securement in low level radiation environments. This new technique would eliminate exposure time securing the load and greatly improve the As Low As Reasonably Attainable (ALARA) conditions. The objective is to progress to higher Technical Readiness Levels (TRL) and larger pilot scale quantities. This paper discusses methodologies and presents current results to date.

Kranjc, Mark D.↗
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗
DOE OSTI2025

Strength and ductility of additively manufactured 316L stainless steel: Impact of neutron irradiation and data variability

Here, this article presents the mechanical properties of additively manufactured (AM) 316L stainless steel processed via the laser powder bed fusion (LPBF) method, focusing on the effects of neutron irradiation on mechanical properties and the variability in strength and ductility data. The rapid melting-solidification process and multiple heating-cooling cycles inherent in LPBF typically result in a fine, metastable microstructure with significant local variability. AM 316L builds of varying thicknesses were fabricated, and SS-J3 miniature tensile specimens were machined from six different locations. These specimens were irradiated with fast neutrons to doses of 2 and 10 dpa at target temperatures of 300 °C and 600 °C. Post-irradiation tensile tests were conducted at room temperature, 300 °C, and 600 °C. Compared to conventional 316L stainless steel, AM 316L exhibited higher initial strength but lower ductility. Irradiation at 300 °C caused significant hardening and prompt necking at yield, with limited uniform ductility, although embrittlement was not observed up to 10 dpa. While neutron irradiation, particularly at 600 °C, increased the variability in strength and ductility data, no clear dependence of mechanical properties on build thickness or sampling location was found—contrary to the conventional perception that AM materials may exhibit high property variability. Furthermore, we observed that the variability in property data for LPBF-processed 316L was relatively low compared to that of wrought 316L stainless steel. This reduced variability in AM 316L steel may be attributed to its highly metastable, stress-containing microstructure, which is discussed in the context of general tensile property variations.

Additively manufactured 316L stainless steel↗
DOE OSTI2023

Thermodynamic controls on the synthesis, structure and reactivity of materials for energy (Final Report)

The major objectives have been: a) to advance and use unique calorimetric capabilities to determine the energetics of oxide and other materials having possible energy applications, with an emphasis on new materials for batteries, fuel cells and solar energy conversion, b) to understand, in terms of both macroscopic energetics and microscopic structure and bonding, the interplay of defect chemistry, oxidation-reduction, and size effects at the bulk and nanoscale in determining materials properties. This project emphasizes mixed ionic-electronic conduction materials containing transition metals of variable valence, especially perovskites and lithium containing phases, with M = Mn, Fe, Co, Ni. The thermochemistry of new families of complex halide, chalcogenide, nitride and hybrid (organic-inorganic) perovskite materials were explored. The major and unique technique employed is high temperature oxide melt solution calorimetry, with careful attention given to materials synthesis and characterization. The thermochemical studies will be coupled with structural studies, especially to characterize order-disorder phenomena by in situ high temperature diffraction techniques.

14 SOLAR ENERGY↗
DOE OSTI2020

Tensile properties of 3D-printed wood-filled PLA materials using poplar trees

Fabrication of renewable materials through additive manufacturing using wood-filled polylactic acid (PLA) is an emerging field of study. The variability in the tensile properties of 3D-printed materials due to the variability in woody biomass properties has not been studied. Biomass size reduction, filament extrusion, and 3D-printing methods were investigated to determine the conditions that resulted in consistent printing. Using 20% ball-milled poplar reinforcement in PLA, 210 °C filament extrusion temperature and 230 °C printing temperature were found to be the best conditions. Subsequently, seventy poplar samples from a common garden were used to test the tensile properties of the printed materials. The median tensile strength at yield was 50 MPa, with 5–95 percentiles ranged in 37–54 MPa. Strain% at break had a median value of 2.1%, and 5–95 percentiles were 1.7–2.7%. The median Young's modulus was 3.65 GPa, and 5–95 percentiles ranged in 2.9–4.1 GPa. Biomass density was correlated to composite density. Median particle size of ball-milled poplar was negatively correlated with tensile strength. Composite density affected tensile strength of the composite. β-glycosidic bond of polysaccharides in biomass affected Young's modulus of the composite. Overall, these data show that genotypic variation among Populus trichocarpa have substantial effect on tensile properties of 3D printed PLA-poplar materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A pseudo-two-dimensional (P2D) model for FeS 2 conversion cathode batteries

Conversion cathode materials are gaining interest for secondary batteries due to their high theoretical energy and power density. However, practical application as a secondary battery material is currently limited by practical issues such as poor cyclability. To better understand these materials, we have, for this study, developed a pseudo-two-dimensional model for conversion cathodes. We apply this model to FeS 2 – a material that undergoes intercalation followed by conversion during discharge. The model is derived from the half-cell Doyle–Fuller–Newman model with additional loss terms added to reflect the converted shell resistance as the reaction progresses. We also account for polydisperse active material particles by incorporating a variable active surface area and effective particle radius. Using the model, we show that the leading loss mechanisms for FeS 2 are associated with solid-state diffusion and electrical transport limitations through the converted shell material. The polydisperse simulations are also compared to a monodisperse system, and we show that polydispersity has very little effect on the intercalation behavior yet leads to capacity loss during the conversion reaction. Finally, we provide the code as an open-source Python Battery Mathematical Modeling (PyBaMM) model that can be used to identify performance limitations for other conversion cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2022

Mechanical evaluation of WC-Co materials with varying microstructures

Tungsten carbide–cobalt materials are useful in a variety of extreme applications due to a desirable blend of properties, yet the technology has not significantly changed since their initial development in the 1920s. The mechanical properties of this class of materials is highly dependent on two variables, the size of the tungsten carbide grains, and the amount of binder phase present in the final body. In this study, the amount of binder phase is isolated across three commercial materials from the same manufacturer with three different grain sizes to investigate the effect on mechanical properties. The mechanical properties investigated are indentation hardness, flexure and tensile strength, as well as fracture toughness. In general, an increase in hardness and tensile strength with decreasing grain size was observed, while the fracture toughness showed the opposite trend with toughness increasing with increasing grain size. The flexure strength results did not show a correlation to grain size. Fractographic analysis identified the dominant strength-limiting flaw for each sample, which largely were in the form of porosity. Other flaws types, such as inclusions from the milling process, clusters of large grains, and machining cracks from the surface finishing process, were also identified. Finally, Weibull analysis was performed and deemed appropriate for analysis of these materials, but strength-size scaling was not conducted due to the variability in the strength-limiting feature between the different specimen geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chapter 2: Evaluating a Concentrating Solar Power Plantas an Extended-Duration Peaking Resource

We explore the ability of a concentrating solar power (CSP) plant with thermal energy storage (TES) to provide peaking capacity. We focus on future power systems, wherein net load patterns may be significantly different than they are today (e.g., due to higher renewable-energy penetrations). We examine 28 locations in the southwestern United States over an 18-year period. The hourly operation of the CSP plants are simulated to determine their potential to provide energy during an eight-hour peak-load window for each day up to 365 days per year. Our result shows that for the large majority of locations and years, CSP plants with certain configurations (i.e., in terms of solar field and TES sizes) can provide nearly 100% peak-load capacity. We examine also the amount of supplemental energy (e.g.,by using natural gas as a supplemental thermal-energy source) that would be required to ensure that a CSP plant could serve the eight highest-load hours of every day of the year. We find that in most cases, a CSP plant supplemented with natural gas would require less than 5% of the fuel that is used by a natural-gas fired power plant providing the same level of reliable capacity. A series of sensitivity analyses show that these results are robust to the number of peak-load hours and days that are considered and the configuration of the CSP plant.

capacity value↗
DOE OSTI2020

Variable voltage electron microscopy: Toward atom-by-atom fabrication in 2D materials

The precisely focused electron beam (e-beam) in scanning transmission electron microscopy has been found to be a versatile tool for patterning matter on the atomic level and enabling atom-by-atom fabrication, however, beam-induced phenomena are known to be extremely sensitive to the e-beam energy and are closely correlated with the proximity of the knock-on threshold. Here we provide a method to control the energy transferred to the sample while facilitating much faster changes in accelerating voltages by maintaining a nearly constant temperature in the electron microscope lenses. We use this method to demonstrate in-situ nano-milling of a graphene film followed rapidly by “gentler” imaging at lower energy. Additionally, the insertion and controlled movement of silicon dopants in graphene is demonstrated by employing a different e-beam energy during each process. We believe the incorporation of variable e-beam energy will broaden the potential for atomic scale e-beam fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed

36 MATERIALS SCIENCE↗
DOE OSTI2021

Load Deflection Data (LDDATA)

The current data set is load deflection data (Stress vs strain for compression stress and stress vs elongation for tension stress) from a material test. The goal of the test was to characterize this rubber-like material by looking at different variables: sample creation process, anisotropy, temperature, etc. The data will be used with the Texas A&M Online Research Experience for Undergraduates (O-REU) program. This program is joint with LANL. The student is not an official LANL student so they will not be able to access data and documents on the yellow. The load deflection data set is unclassified and will be sent to the student for analysis. Analysis will include curve fitting, scalar value analysis (stiffness, toughness, etc.) and analysis of variance to determine what variables statistically affect the response while correcting for covariates (temperature soak time and humidity). Data will be stored as Matlab structs and as HDF5 files. Same data, different formats.

36 MATERIALS SCIENCE↗