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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Mechanical Characterization of High-Temperature Carbon Fiber-Polyphenylene Sulfide Composites for Large Area Extrusion Deposition Additive Manufacturing

Additive manufacturing (AM) is evolving from rapid prototyping to production of structural components. The widespread application of AM demands a high level of mechanical performance from these components, and it is therefore essential to improve feedstock material in order to meet these mechanical expectations. However, compared to traditional manufacturing techniques, the mechanical properties of AM materials and their resulting components are not well understood. In this study, we investigated the processability, microstructure, and mechanical performance of twin-screw compounded short carbon fiber reinforced polyphenylene sulfide (PPS) pellets as a feedstock material for big area AM (BAAM). The performance of the AM components was compared to that of traditional processing methods, namely injection molding (IM) and extrusion-compression molding (ECM). It was found that the AM composites exhibited 118% lower tensile strength and 55% lower tensile modulus when compared to traditional injection molding composite specimens; however, AM composites exhibited comparable properties to ECM composites. This response was attributed to highly aligned fibers in IM and AM samples. However, the AM composites contained porosity (15.5% volume), which reduced their mechanical properties in comparison to ECM composites. Finally, the IM process showed the maximum amount of fiber attrition with minimum porosity (0.007% volume), while the ECM process exhibited the least fiber attrition with 4.3% volume porosity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Post-process annealing of large-scale 3D printed polyphenylene sulfide composites

Large-scale extrusion-based additive manufacturing of high-performance thermoplastic composites like fiber reinforced polyphenylene sulfide (PPS) is well-suited for tooling applications to lower manufacturing costs and lead times. Autoclave tooling requires good mechanical performance at temperatures even above the glass transition temperature (T g ). In this work, the authors have investigated a post-process isothermal annealing technique to improve the mechanical properties of various grades of carbon fiber reinforced PPS components printed on the Big Area Additive Manufacturing (BAAM) system. Since PPS is a semi-crystalline polymer, crystallinity can change during annealing and affect the mechanical properties of the part. In addition, isothermal annealing can also lead to solid-state structural changes in the form of thermal and oxidative branching and/or crosslinking reactions in some grades of PPS which can alter the crystallization process. This work reports the effect of annealing on dynamic mechanical properties of BAAM printed components, along with studies to determine the effect of annealing on crystallinity and the occurrence of oxidative reactions. Results showed that isothermal annealing at 250 °C for 18 h improved the storage modulus of all selected grades (neat and reinforced) of PPS at temperatures above T g . Annealing led to an overall increase in the degree of crystallinity, with secondary crystallization taking place. Although oxidative structural changes were observed to occur more on the surface of the PPS parts, they primarily influenced the size of crystals formed and did not significantly alter the degree of crystallinity at various regions within the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterizing material transitions in large-scale Additive Manufacturing

Integrating Multiple Materials (MM) into large-scale Additive Manufacturing (AM) is a key for various industrial applications wishing to incorporate site-specific properties into geometrically complex designs that are difficult to manufacture with traditional techniques. Printing with multiple materials is typically accomplished by using layers as natural material boundaries, but having the capability to switch between materials within a single layer without pausing would further expand MM possibilities. This study used Cincinnati Incorporated’s Big Area Additive Manufacturing (BAAM) system to explore material transitions with a novel dual-hopper that enables in-situ material blending of a pelletized feedstock. Constructing MM and functionally graded material (FGM) structures requires depositing a specific material composition at a specific geometric location to achieve a desired performance. Furthermore, accurately implementing this with the BAAM’s blended extrusion system requires a thorough understanding of the transition between distinct material compositions. This study characterizes a step-change transition between neat acrylonitrile butadiene styrene (ABS) and carbon fiber-reinforced ABS. Three distinct techniques were compared for analyzing the fiber content, and the transition zone between materials was characterized as a function of transition direction. The transition process was consistent to within 0.7 wt% carbon fiber variation between different layers and prints. The transition between materials was found to be directionally dependent, with ABS to CF/ABS having a transition length of 3.5 m compared to 3.2 m for CF/ABS to ABS. Furthermore, the transition from Material A to Material B was found to be repeatable with a possible variance in transition length of 0.3 m.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Investigating the effect of different shielding gas mixtures on microstructure and mechanical properties of 410 stainless steel fabricated via large scale additive manufacturing

Metal Big Area Additive Manufacturing (mBAAM) offers the potential to fabricate large scale tools at high deposition rates (15 lb/h+). 410 martensitic steel is a potential tooling material, owing to its low cost, good machinability and reasonable printability. During the mBAAM process, the shielding gas can have a significant impact on the material properties as well as the process cost. Therefore, the current study aims to understand the effect of different shielding gas mixtures on large-scale additive manufacturing of 410 martensitic stainless steel. We show that an argon mixture with 3% nitrogen gas produced the best performance in terms of maximum hardness and tensile strength, with much less scatter in tensile strength. He-Ar-CO 2 or tri-mix shielded samples showed a low tensile strength with wide scatter, due to stabilized delta ferrite in microstructure during printing. Both tri-mix and Ar-CO 2 shielded samples showed slightly higher porosity. Thus, we recommend the use of argon-3% nitrogen as a shielding gas mixture for processing 410 steel for tool applications, based on the relatively low cost of this gas mixture and the resulting higher hardness, higher dimensional stability, and lower porosity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Material properties and mechanical behaviour of functionally graded steel produced by wire-arc additive manufacturing

Metal Big Area Additive Manufacturing is an additive manufacturing technique based on Gas Metal Arc Welding (GMAW) with the option to use many shielding gases, and materials. The system is equipped with a dual torch design allowing for printing different materials; in our study, AISI 410 stainless steel and AWS ER70S-6 mild steel are both printed in the same component. Different print strategies were designed to highlight changes in material and mechanical properties. Deformation behaviour of a materials’ interface was analysed by two-dimensional digital image correlation of uniaxial tensile specimens in displacement-controlled tests. Instances of non-homogeneous local strains adjacent to the interface are observed, as well as variability in mechanical behaviour and microstructure based on location within the print. Optical and electron microscopy are used to evaluate three microstructural zones in a 5 mm range of the interface between mild steel and stainless steel. Areas far from the interface produced polygonal ferrite and pearlite, while areas close to the interface produced acicular ferrite and bainite. Chromium redistribution profiles are dependent on the print strategy used, as shown by scanning electron microscopy with Energy dispersive spectroscopy. Evidence produced via electron backscatter diffraction is shown to support the argument that transformation induced plasticity is not the cause for the non-homogeneous deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Massless charged particles tunneling radiation from a RN-dS horizon and the linear and quadratic GUP

Highlights: • Generalized Uncertainty Principle (GUP). • Reissner–Nordstrom de Sitter (RNdS) spacetime. • Massless charged particles. • Tunneling through cosmological horizon. • Tunneling of spin fields. In this paper, we investigate the massless Reissner–Nordstrom de Sitter metric in the context of minimal length scenarios. We prove not only the confinement of the energy density of massless charged particles, both fermions and bosons, but also their ability to tunnel through the cosmological horizon. These massless particles might be interacting with Dirac sea and in this case they will appear outside the cosmological horizon in the context of dS/CFT holography. This result may formulate a fundamental reason for the expansion of the Dirac sea. Therefore, a spacetime Big Crunch may occur.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Co-existence of atomically dispersed Ru and Ce 3+ sites is responsible for excellent low temperature N 2 O reduction activity of Ru/CeO 2

Nitrous oxide N 2 O reduction is a big challenge due to high global warming potential of N 2 O. (~300 times higher compared with CO 2 ). The best known catalysts, such as Rh/ceria, require relatively high temperatures for N 2 O decomposition. Herein, we report that Ru/ceria catalysts with low Ru loading of ~0.25 wt% efficiently catalyze low temperature N 2 O reduction by CO starting at 100 °C (full N 2 O conversion below 200 °C) under industrially relevant flow rates and gas concentrations. Further, this remarkable performance stems from maintaining isolated Ru cations even on reduced ceria surface and, simultaneously, the propensity of Ru to affect ceria surface to form labile surface oxygen thereby creating large number of oxygen vacancies (Ce +3 cations) in the presence of CO. In contrast, for Rh/CeO 2 catalysts with equivalent metal loading, the activity is much lower because atomically dispersed Rh sinters into metallic clusters at the onset reaction temperature (~200 °C): these clusters are much less effective than isolated single Ru ions, with lower Ce +3 concentration maintained on reduced Rh/CeO 2 catalyst. Our study highlights the benefits of gaining molecular-level insight into the dynamic nature of catalytically active sites under reaction conditions for preparing catalysts containing low loading of precious metals with unsurpassed low temperature activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A highly oxygen reduction reaction active and CO 2 durable high-entropy cathode for solid oxide fuel cells

One big obstacle for the oxygen reduction reaction (ORR) electrode in solid oxide fuel cells (SOFCs) is the poor reaction activity and fast degradations caused by CO 2 poisoning. Here, in this study, we report our design of an active A-site Ca-rich high-entropy Pr 0.1875 Ba 0.1875 Sr 0.1875 La 0.1875 Ca 0.25 CoO 3-δ (PBSLC 25 C) electrode, guided by the O p-band theory. Here, when applied as a cathode in solid oxide fuel cells (SOFCs), it demonstrates high ORR activity and excellent CO 2 tolerance under realistic operating conditions. Ni-YSZ-based anode-supported cells with PBSLC 25 C cathodes demonstrate excellent peak power densities of 1.14 W cm -2 , 1.04 W cm -2 , and 0.77 W cm -2 in the air with 1%, 5%, and 10% CO 2 , respectively, at 750 °C. The engineered high-entropy PBSLC 25 C effectively diminishes the CO 2 poisoning effect and maintains active surfaces for fast oxygen exchange, as confirmed by the cell durability test in air containing CO 2 (5 and 10 vol%), Raman spectroscopy, and density functional theory calculations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

In this paper, we present mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Selenium-doped copper oxide nanoarrays: Robust electrocatalyst for the oxygen evolution reaction with ultralow overpotential

The oxygen evolution reaction (OER) is the key anodic reaction in electrochemical water splitting. OER is a four-electron process with sluggish reaction kinetics. RuO 2 and IrO 2 are the benchmark catalysts for OER. Thus, finding a low-cost, earth-abundant, and stable electrocatalyst for OER is a big challenge. Here in this work, we report OER over Se-CuO/CF nanoarrays with an ultralow overpotential of 440 mV (at 50 mA/cm 2 current density). Se-CuO/CF nanoarray has a TOF of 0.05 s -1 at 300 mV. The catalyst showed consistent OER performance upto1000 cycles run over a period of 10 h. The OER performance of Se-CuO/CF nanoarrays was compared with RuO 2 /CF catalyst and the overpotential for RuO 2 /CF nanoarray was only 10 mV lower than Se-CuO/CF 50 mA/cm 2 current density. Se-CuO/CF nanoarray showed 50 mV lower overpotential than the state of the art RuO 2 /CF catalyst at a current density of 100 mA/cm 2 . The Tafel slope of Se-CuO/CF is 21 mV/decade lower compared to RuO 2 /CF suggesting faster reaction kinetics of Se-CuO/CF. The electrochemical surface area and the conductivity were increased upon doping Se into CuO/CF nanoarrays which can be attributed to enhanced OER performance of the Se-CuO/CF nanoarrays.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Snowmass2021 - Letter of interest cosmology intertwined I: Perspectives for the next decade

The standard Λ Cold Dark Matter cosmological model provides an amazing description of a wide range of astrophysical and astronomical data. However, there are a few big open questions, that make the standard model look like a first-order approximation to a more realistic scenario that still needs to be fully understood. In this Letter of Interest we will list a few important goals that need to be addressed in the next decade, also taking into account the current discordances present between the different cosmological probes, as the Hubble constant H0 value, the σ8S8 tension, and the anomalies present in the Planck results. Finally, we will give an overview of upgraded experiments and next-generation space-missions and facilities on Earth that will be of crucial importance to address all these questions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

We report that computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evapotranspiration of advanced perennial bioenergy grasses produced on marginal land in the U.S. Midwest

The production of dedicated energy crops for biofuel and co-products can help develop a sustainable and viable bioeconomy under a changing climate. The co-production of perennial bioenergy and commodity crops in an agricultural landscape has the potential to reduce greenhouse gas emissions and provide numerous ecosystem services. However, as production of advanced, higher-yielding cultivars occurs, their impact on water resources needs to be considered. Estimates of evapotranspiration (ET) of perennial grasses compared to commodity crops are not always consistent in the literature. In this study, ET was estimated using an energy-balance model with Landsat satellite imagery and ground-based weather data to compare the large-scale production of advanced switchgrass cultivars (‘Independence’, ‘Liberty’, and ‘Carthage’), predecessor cultivars (‘Shawnee’ and ‘Sunburst’), and other perennial grasses (big bluestem and a low-diversity mixture) to corn grown continuously on marginal soils in the U.S. Midwest. Results showed significant differences in ET between corn and perennial grass treatments at four of the five field sites. However, differences between crop treatments were not consistent across sites, nor were the differences between advanced and predecessor switchgrass varieties. Production year played a large role in daily and cumulative ET across all sites. Differences in crop biomass yield between establishment and post-establishment years may contribute to these interannual differences. Site and interannual variations in precipitation and temperature may also be major contributing factors. Overall, the results of this study indicate that differences in ET between perennial bioenergy grasses and corn are likely dependent upon location of production and weather conditions.

09 BIOMASS FUELS↗
DOE OSTI2025

Process design and techno-economic analysis for bio-based graphite and liquid hydrocarbons production from lignocellulosic biomass

The worldwide demand for graphite, as the main anode material for Li-ion batteries, is expected to double by 2028 since it supports the use of electricity, including transient renewable sources, for energy storage, sustainable mobility, and automation. However, the dependence on non-renewable and external resources jeopardizes the world supply chain. This study explores the technical and economic performance of transforming lignocellulosic biomass into biographite and fuel-grade hydrocarbons through pyrolysis bio-oil upgrading. According to simulation results, the total power demand for the biorefinery reached 10,784 kWh per tonne of biographite, of which 36 % can be supplied by the heat integration network and power plant. Sensitivity and risk analyses were conducted to evaluate the economics, with process yields identified as the most relevant indicators to the minimum selling price (MSP). The analysis revealed a promising cost-competitive range for biographite MSP against fossil-based graphite (medium quality synthetic graphite Chinese market price ~$\$$4.2/kg). Case D, which includes biofuels as a byproduct, presents the best metrics, reaching a MSP of $\$$3.3/kg of anode-grade biographite with a profit margin of 27 %. While including biofuels in the product slate provides the best economic performance, the uncertainty associated with the big capital investment makes its risk 13 % higher to attain an IRR >20 % than the case in which biographite is produced as a standalone product. Overall, this study demonstrates that integrated biorefineries can produce a cost-competitive bio-based anode material for Li-ion batteries.

09 BIOMASS FUELS↗
DOE OSTI2025

Machine learning-assisted design of metal–organic frameworks for hydrogen storage: A high-throughput screening and experimental approach

Various theoretical approaches, including big data and high-throughput screening techniques, have been explored in developing new materials due to their significant potential time-saving advantages. However, it remains a significant challenge to experimentally realize new materials that are predicted. In this study, we propose a novel materials design strategy that utilizes machine-learning (ML) techniques to predict new porous materials that show promise for hydrogen storage and are likely to be feasible to synthesize. By leveraging ML techniques and metal–organic framework (MOF) databases, we are able to predict the synthesizability of MOF structures. This is evidenced by the successful synthesis of a new vanadium-based MOF that exhibits excellent performance for cryogenic H 2 storage. Notably, the total gravimetric and volumetric H 2 uptakes are as high as 9.0 wt% and 50.0 g/L at 77 K and 150 bar. This ML-assisted materials design offers an efficient and promising approach for developing hydrogen storage materials.

08 HYDROGEN↗
DOE OSTI2025

Properties and performance of lignin-based polyurethane foams from lignin and castor oil as synergistic bio-polyols

Lignin and castor oil with intrinsic hydroxyl groups are attractive green resources for polyurethane (PU) applications. However, lignin's heterogeneous and highly crosslinked structure, poor processability, as well as the feedstock variability, lead to inconsistent and poor performance of the final foam products. Castor oil-based polyurethane foam (PUF) has a relatively high price and density, which are big hurdles to its practical applications. Here, in this study, we hypothesized that synergistic bio-polyol mixtures composed of lignin and castor oil could balance the drawbacks from individual component. Castor oil could improve the dispersity of lignin, while lignin simultaneously addressed the issues caused by PUF prepared with castor oil, such as its high density and low thermal stability. For a comprehensive understanding of the effects of structural properties of lignin on its PUF processing and applications, various lignin fractions were isolated by co-solvent enhanced lignocellulosic fractionation (CELF) from different species, including hardwood, softwood, and herbaceous plants, and the processed lignins with different molecular weights were applied with castor oil. The lignin fractions with lower molecular weight showed good dispersity in castor oil with a high lignin content (up to 50 wt%) and completely replaced petroleum-based polyols. The produced foam with 50 wt% low-molecular-weight lignin fractions from woody biomass showed comparable/higher compressive strength (up to 20 psi) and thermal insulation performance (up to 5.69 R-value in −1 for 50 % L-Pine foam). In addition, this study revealed the relationship between lignin's structural properties and foam performance, providing insights for practical applications of lignin-based PUF.

Jeong, Soyeon [State Univ. of New York (SUNY), Syr↗
DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗