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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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Forged ingot Nb is a novel processing method to obtain Nb discs used for the fabrication of superconducting radio-frequency cavities. This contribution reports on the fabrication, surface treatment and cryogenic RF testing of two 1.5 GHz single-cell cavities made of this material, with a residual resistivity ratio of ~100, at Jefferson Lab.
This project explores new classes of shape memory alloys (SMAs), through integration of novel processing, manufacturing, and composition to modulate the SMA thermomechanical response.
Hydrogen is essential in the transition to sustainable energy, and developing low-carbon production methods is a key research focus. Traditional steam methane reforming (SMR) dominates the hydrogen industry but contributes substantially to CO2 emissions. In response, Gas Switching Reforming (GSR) has emerged as a novel process that integrates carbon capture and utilizes process heat more efficiently. Unlike other reforming methods, GSR consolidates oxidation and reduction reactions within a single reactor, which minimizes external energy inputs and simplifies scaling. Like conventional steam methane reforming (SMR), GSR can be integrated with water-gas shift and pressure swing adsorption units for pure hydrogen production. This work presents a comprehensive business case analysis of GSR technology based on experimental results in Technology Readiness Level 3, Life Cycle Assessment (LCA) and Techno-Economic (TEA) evaluation incorporating ASPEN Plus process modeling considering different configurations and energy scenarios. The TEA incorporates data from kinetic experiments from various catalysts to evaluate the GSR process under various conditions. The goal of this work is to evaluate GSR’s potential to serve as a low-carbon alternative to SMR, focusing on global warming potential and additional impact categories to evaluate a wide spectrum of environmental impacts. Comparative assessments were conducted with SMR, chemical loop reforming (CLR), and proton exchange membrane (PEM) electrolysis to explore trade-offs across environmental metrics. The environmental impact assessment of this work encompasses the entire hydrogen production lifecycle from raw material extraction to plant decommissioning, using a cradle-to-gate boundary. Preliminary findings highlight that GSR, when integrated with low-carbon energy sources, could significantly reduce environmental impacts, making it a promising candidate for low-carbon hydrogen infrastructure. The insights from this business case evaluation aim to guide industry in scale-up and commercialization of this promising clean energy technology.
Rhodococcus bacterial strains are characterized by wide metabolic versatility and extraordinary resistance to environmental stresses. The high versatility and adaptability of Rhodococcus strains is partly related with large and complex genomes (up to 10.1 Mbp) including high genetic redundancy and the presence of several circular and linear (mega)plasmids, which harbour peculiar catabolic and biosynthetic genes. Within Rhodococcus genus, R. opacus strain PD630 is considered a model oleaginous strain for its ability to produce and accumulate lipids (mostly triacyglycerols, TAGs) using different carbon sources, including low-cost and renewable resources such as lignocellulose. Notably, under specific growth conditions, this strain is capable of accumulating up to 80% of its cellular dry weight in TAGs; that is a rare feature in the prokaryotic and eukaryotic kingdoms. Multi-omic approaches have been applied to obtain system-level information about metabolic and regulatory pathways involved in these biosynthetic processes. Novel molecular tools for genome editing (CRISPR/Cas9 and recombineering) have been recently developed highlighting the possible utilization of R. opacus PD630 as synthetic biology platform for lipids production.
We report g – r and r – i new colors for 21 Saturn Irregular Satellites; among them, four previously unreported. This is the highest number of Saturn Irregular satellites reported in a single survey. These satellites were measured by "stacking" their observations to increase their signal without trailing. This work describes a novel processing algorithm that enables the detection of faint sources under significant background noise and in front of a severely crowded field. Our survey shows these new color measurements of Saturn Irregular Satellites are consistent with other Irregular Satellites populations as found in previous works and reinforcing the observation that the lack of ultrared objects among the irregular satellites is a real feature that separates them from the trans-Neptunian objects (their posited source population).
The call to reduce global CO2 emissions is at the forefront of research and development in the transportation and petrochemical industries. A potential solution which intersects both sectors is the adoption and commercialization of more sustainable technologies for methanol production. Currently, methanol is gaining popularity as a possible fuel additive, and as a polymer precursor via established methanol-to-olefins routes which are increasing in capacity and utilization, putting methanol in the spotlight and global methanol production on the rise. At the same time, experimental and computational studies have surged, illuminating a wide array of novel process designs to introduce sustainable methanol into the market. However, with numerous competing pathways identified across a variety of fossil and non-fossil feedstocks, there is a need to harmonize the analyses of these pathways using a consistent techno-economic approach and compare them to current commercial pathways to better understand the benefits or limitations inherent to each technology. In this work we conduct a consistent comparative analysis of three sustainable methanol synthesis pathways versus a commercial baseline, with biomass, CO2, and natural gas as the carbon sources. The three sustainable pathways of interest are biomass gasification to syngas with subsequent catalytic conversion of syngas to methanol, indirect conversion of CO2 to syngas via electrolysis with subsequent catalytic conversion of syngas to methanol, and direct conversion of CO2 to methanol via electrolysis. To elucidate the challenges and opportunities in the path to commercialization of each sustainable pathway, we investigate five key process metrics: production cost, carbon efficiency, carbon intensity, energy efficiency, and technology readiness level (TRL).
The equations of motion (EOM) for the heavy symmetrical top with one point fixed are highly nonlinear. The literature describes the numerical methods that are used to resolve this classical system, including modern tools, such as the Runge–Kutta fourth–order method. Finding the derivate of closed-form solutions for the EOM is more difficult and, as mentioned in the literature, discovering the solution is not always possible for all the EOM. Fortunately, a few examples are available that serve as a guide to move further in this topic. The purpose of this paper is to find a methodology that will produce the solutions for a given subset of EOMs that fulfill certain requisites. This paper summarizes the literature available on this topic and then follows with the derivation of the EOM using the Euler–Lagrange method. The Routhian method will be used to reduce the size of the expression, and it continues with the formulation of the classical cubic function, ƒ(u), through a novel process. The roots of ƒ(u) are of the utmost importance in finding the EOM closed-form solution, and once the final roots are selected, the general method that will produce the closed-form solutions is presented. Two sets of examples are included to show the validity of the process, and comparisons of the results from the closed-form solutions vs. the numerical results for these examples are shown.
A novel process for making PBI films starting from gel PBI membranes polymerized and casted in the PPA process wherein acid-imbibed gel PBIs are neutralized in a series of water baths and undergo controlled drying in association with a substrate material, yielding a PBI film without the use of organic solvents.
The National Renewable Energy Laboratory (NREL) has pioneered novel processing technology for silicon nanoparticles (Si NPs) synthesized from plasma-enhanced chemical vapor deposition (PECVD) that will disrupt current lithium-ion battery technology. This revolutionary Si NP-based anode leverages a molecular coating applied to PECVD Si NPs that transforms them into homogeneous colloids suitable for conventional slurry formulation and electrode deposition resulting in delivered capacities of >2000 mAh/g (total electrode mass) at >90 wt% Si content anodes.
Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards end-to-end deep learning system for accelerating advanced manufacturing research and development.
A novel process for making PBI films starting from gel PBI membranes polymerized and casted in the PPA process wherein acid-imbibed gel PBIs are neutralized in a series of water baths and undergo controlled drying in association with a substrate material, yielding a PBI film without the use of organic solvents.
The objectives of this project are to develop a cost-effective design and fabrication process for a novel transformational membrane and its membrane modules that capture CO 2 from flue gas. Optimization of the novel transformational membrane, scale-up of the membrane to a prototype size of about 20" wide in continuous roll-to-roll fabrication, and construction and testing of a skid for the integrated membrane process will be performed. For the design of this membrane, we use a cost-effective polymer support and coat a thin top layer of the membrane. The simplicity of this membrane design offers a low cost for the membrane element in commercial spiral-wound (SW) configuration (<$2.00/ft 2 or $21.5/m 2 ). The prototype membrane will be used to fabricate 6 pilot-size membrane modules (each about 20" length, 3 modules each at the commercial-size diameter of 8 inches with 35 m 2 membrane area for Stage 1 and 3 modules each at 5-inch diameter with 12 m 2 membrane area for Stage 2) for testing with simulated flue gas at OSU and with actual flue gas at the National Carbon Capture Center, Wilsonville, AL using the skid to capture the CO 2 (at 60 – 90%) with at least 95% CO 2 purity. The prototype membrane modules will be in commercial SW configuration with a minimal pressure drop (<0.103 bar/meter (1.5 psi/meter)). These objectives have been achieved successfully.
Abstract Legacy radioactive waste from nuclear weapons material processing is the biggest environmental challenge in the state of South Carolina, and one of the biggest in the United States. Although substantial progress has been made in processing and vitrifying high‐level radioactive waste at the Savannah River site, approximately 35 million gallons remains to be treated and dispositioned. In this article, we show the development of a new defoamer for use in the processing of high‐level radioactive waste. The application of the new defoamer is not only more effective at controlling foam but will shorten the batch processing time leading to safer and more efficient processing. This has the potential to help shorten the overall site mission lifetime, saving the federal government hundreds of millions of dollars. The novel aspect of the new defoamer is its effectiveness and stability in the harsh conditions needed for processing high‐level radioactive waste: high temperatures, pH 4–13, strong oxidizing and reducing agents, and numerous metal catalysts. The defoamer will replace an existing antifoam agent that decomposes to form multiple flammable gases. Because the existing antifoam agent decomposes quickly, processing changes increased processing time. Statement of novelty We demonstrate that a new defoamer has been developed for use in processing high‐level radioactive waste. This novel defoamer is effective and stable in the harsh processing conditions with no detectable flammable by‐products. Because the existing antifoam agent decomposes quickly, processing changes, including lowering acid addition rates and evaporation rates, increased processing time. The new defoamer is not only more effective at controlling foam but will shorten the batch processing time and lead to safer and more efficient processing by eliminating the production of flammable gases. Statement of industrial relevance Foaming is of great concern in many industrial processes involving three‐phase gas/liquid/fine‐solids systems, like in water evaporation, mineral floatation, air sparging in situ remediation techniques, and in those found in the paper industry.
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In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.
A key trailblazer in the development of thin-film solid-state electrolytes has been lithium phosphorous oxynitride (LiPON), the success of which has led to recent progress in thin-film ion conductors. Here we compare the structural, electrochemical, and processing parameters between previously published LiPON and NaPON ALD processes with a novel ALD process for the K analogue potassium phosphorous oxynitride (KPON). In each ALD process, alkali tert-butoxides and diethylphosphoramidate are used as precursors. To understand the ALD surface reactions, this work proposes a reaction mechanism determined by in-operando mass spectrometry for the LiPON process as key to understanding the characteristics of the APON (A = Li, Na, K) family. As expected, NaPON and LiPON share similar reaction mechanisms as their structures are strikingly similar. KPON, however, exhibits similar ALD process parameters but the resulting film composition is quite different, showing little nitrogen incorporation and more closely resembling a phosphate glass. Due to the profound difference in structure, KPON likely undergoes an entirely different reaction mechanism. Furthermore, this paper presents a comprehensive summary of ALD ion conducting APON films as well as a perspective that highlights the versatility of ALD chemistries as a tool for the development of novel thin film ion-conductors.
Coal is a versatile energy resource and was a driver of the industrial revolution that transformed the economies of Europe and North America and the trajectory of civilization. In this work, a technoeconomic analysis was performed for a coal-to-carbon-fiber manufacture process developed at the University of Kentucky’s Center for Applied Energy Research. According to this process, coal, with decant oil as the solvent, was converted to mesophase pitch via solvent extraction, and the mesophase pitch was subsequently converted to carbon fiber. The total cost to produce carbon fibers from coal and decant oil via the solvent extraction process was estimated to be $\$$11.50/kg for 50,000-tow pitch carbon fiber with a production volume of 3750 MT/year. The estimated carbon fiber cost was significantly lower than the current commercially available PAN-based carbon fiber price ($\$$20–$\$$30/kg). With decant oil recycling rates of 50% and 70% in the solvent extraction process, the manufacturing cost of carbon fiber was estimated to be $\$$9.90/kg and $\$$9.50/kg of carbon fiber, respectively. A cradle-to-gate energy assessment revealed that carbon fiber derived from coal exhibited an embodied energy of 510 MJ/kg, significantly lower than that of conventionally produced carbon fiber from PAN. This notable difference is primarily attributed to the substantially higher conversion rate of coal-based mesophase pitch fibers into carbon fiber, surpassing PAN fibers by 1.6 times. These findings indicate that using coal for carbon fiber production through solvent extraction methods could offer a more energy-efficient and cost-competitive alternative to the traditional PAN based approach.