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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A unifying equation for fermentation sustainability across the titer-rate-yield landscape

Industrial fermentation is central to the sustainable production of fuels and chemicals, yet commercial viability of emerging technologies hinges on improving fermentation titer, rate, and yield (TRY). How these metrics shape system cost remains difficult to generalize due to complex interactions among feedstocks, fermentation, separations, catalytic upgrading, waste management, and facility design. Here, we systematically map theoretical fermentation performance spaces (formed by all potential TRY combinations) for 32 representative biomanufacturing facilities—spanning distinct choices for feedstocks, fermentation regimes and products, separations, and catalytic upgrading—by simulating and evaluating them (via techno-economic analysis, TEA) under uncertainty (600,000 Monte Carlo simulations) and across TRY combinations (7500 TRY combinations for each of 32 configurations). Across this wide design and thermodynamic simulation space, we find the relationship between fermentation TRY and system cost is captured by a simple, generalizable mathematical equation (R 2 of 0.992 − 1.000 across our simulations; 0.954 − 1.000 when validated against prior studies that used different tools). We use this equation to elucidate key drivers that shape cost sensitivity to fermentation performance, generating widely applicable insights. By demonstrating a unifying relationship governs the impact of fermentation on biomanufacturing economics, this work establishes a foundation for agile, holistically predictive, resource-efficient strategies to prioritize fermentation research and development needs and accelerate commercialization of emerging biomanufacturing technologies.

applied mathematics↗
DOE OSTI2025

Physical patterning of high-Q superconducting niobium resonators via ion beam etching

The development of superconducting quantum circuits increasingly involves the exploration of chemically distinct materials and complex multilayered structures. Accelerating this trend may benefit from low-damage, materials-agnostic patterning techniques that are compatible with a broad range of materials. Here, in this work, we investigate the utility of low-energy ion beam etching (IBE), a physical patterning technique, as an alternative to reactive ion etching for fabricating low-loss superconducting resonators. We use niobium (Nb) resonators as a test platform, leveraging their well-characterized performance metrics for benchmarking. To address IBE-induced surface redeposition, we introduce an in situ aluminum capping layer combined with targeted post-fabrication chemical treatment. This strategy yields resonators with internal quality factors as high as 6 × 10 5 in the single-photon regime at 50 mK. These results establish low-energy IBE as a promising patterning technique for superconducting devices, with the potential to accelerate development across chemically diverse and multilayered material platforms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Structure of an ancestral mammalian family 1B1 cytochrome P450 with increased thermostability

Mammalian cytochrome P450 enzymes often metabolize many pharmaceuticals and other xenobiotics, a feature that is valuable in a biotechnology setting. However, extant P450 enzymes are typically relatively unstable, with T 50 values of ~30–40 °C. Reconstructed ancestral cytochrome P450 enzymes tend to have variable substrate selectivity compared with related extant forms, but they also have higher thermostability and therefore may be excellent tools for commercial biosynthesis of important intermediates, final drug molecules, or drug metabolites. In this work, the mammalian ancestor of the cytochrome P450 1B subfamily was characterized structurally and functionally, revealing differences from the extant human CYP1B1 in ligand binding, metabolism, and potential molecular contributors to its thermostability. Whereas extant human CYP1B1 has one molecule of α-naphthoflavone in a closed active site, we observed that subtle amino acid substitutions outside the active site in the ancestor CYP1B enzyme yielded an open active site with four ligand copies. A structure of the ancestor with 17β-estradiol revealed only one molecule in the active site, which still had the same open conformation. Detailed comparisons between the extant and ancestor forms revealed increases in electrostatic and aromatic interactions between distinct secondary structure elements in the ancestral forms that may contribute to their thermostability. To the best of our knowledge, this represents the first structural evaluation of a reconstructed ancestral cytochrome P450, revealing key features that appear to contribute to its thermostability.

16beta-estradiol↗
DOE OSTI2020

Photometric classification of Hyper Suprime-Cam transients using machine learning

Abstract The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network with highway layers. This algorithm is trained by actual observed cadence and filter combinations such that we can directly input the observed data array without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes.

Takahashi, Ichiro↗
DOE OSTI2020

Improved description of light nuclei through chiral effective field theory at leading order

Here, we propose an arrangement of the most commonly invoked version of the two-nucleon chiral potential such that the low-lying amplitude zero of the 1 S 0 partial wave is captured at leading order of the effective expansion. Adopting other partial waves from the LENPIC interaction, we show how this modification yields an improved description of ground-state energies and point-proton radii of the 3 H, 4 He, and 6 He nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

On the length scale and rate-dependent mechanical behavior of monolithic (oxy)sulfidic glassy solid-state electrolytes

In the controlled atmosphere of a dedicated glove box, nanoindentation performed with a diamond Berkovich indenter tip has been used to examine the mechanical behavior of three (oxy)sulfide solid-state electrolytes (SSEs), 70Li 2 S·(30–x)P 2 S 5 ·xP 2 O 5 (x = 0, 2, and 5). At a drive frequency of 120 Hz, the elastic modulus is found to be predominantly depth independent over the range of 100 nm to 1 μm and generally insensitive to the varying mol fraction of oxygen (0, 2, and 5%) as well as the imposed strain rates of 0.025, 0.05, and 0.1 1/s. All three SSEs exhibit significant room-temperature creep. Strain burst activity observed during loading (potentially representative of pore collapse or cracking) is attenuated with the addition of oxygen. The hardness is found to be insensitive to the imposed strain rates but varying with depth and oxygen content. Furthermore, the highest oxygen concentration yields the lowest hardness and strongest depth dependence.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electron Cloud Simulations in the Fermilab Booster

As part of Fermilab's Proton Improvement Plan-II (PIP-II), the Fermilab Booster synchrotron will operate at a higher intensity, increasing from 4.5×1012 to 6.7×1012 protons per pulse [ppp]. A potential challenge for achieving high-intensity performance arises from rapid transverse instabilities induced by electron cloud (EC). This research presents EC simulations using PyECLOUD, which is an advanced computational tool that incorporates measurements of the secondary electron yield (SEY) from the Booster's combined function magnet material. By systematically varying beam parameters in PyECLOUD, such as bunch structure, bunch length, and intensity, the EC effects on beam stability and overall performance of Booster can be predicted.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Irradiation of Mo-100 Targets and Testing Fe(ll)CI 2 Precipitation for Tc-99 Removal

The production of molybdenum-99 (Mo-99) is a critical step in the generation of technetium-99m (Tc-99m), a radioisotope widely used in medical imaging. NorthStar Medical Radioisotopes, LLC is planning to produce the important medical radioisotope Mo-99 through a photonuclear reaction on molybdenum-100 (Mo-100). Accelerator production of Mo-99 using enriched Mo-100 targets yields undesirable isotopes, such as niobium-95 (Nb-95) and zirconium-95 (Zr-95). Previous studies reported that Nb and Zr isotopes can be effectively removed from Mo-99 products through coprecipitation with Fe(III). To enable the potential utilization of the first milking of Tc-99m from a Mo-99/Tc-99m generator, it is desirable to remove long-lived Tc-99. This can be accomplished by substituting Fe(III) with Fe(II) during the precipitation step, without affecting the removal of Zr and Nb isotopes. Previous experiments demonstrated up to 99+% removal of Tc using this method. It was also observed that the presence of hydrogen peroxide significantly impacts Tc removal. This was evident in the processing of enriched Mo-100 targets, where only 25% of Tc was removed. To further investigate this, another irradiation of enriched Mo-100 disks was conducted, followed by Tc-99 removal using Fe(II). Prior to irradiation, experimental conditions for Tc removal were optimized through several test runs using Mo-100 disks spiked with Tc-99. The results of these tests are discussed in this report.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Liquid organic hydrogen carriers for long-duration energy storage

Liquid organic hydrogen carrier (LOHC) systems are an excellent alternative to pressurized gas and liquid hydrogen storage technologies due to their high volumetric storage capacities and straightforward adaptation to existing infrastructure. Here, we investigate various molecular and heterogenized Ru, Mn, and Fe catalysts for the reversible (de)hydrogenation of polyols. Mn catalysts, Mn-MACHO(Ph) (1) and Mn-MACHO(iPr)BH 4 (2-BH 4 ) were found to maintain catalytic activity for hydrogen production comparable to the Ru analogs, with greater than 98% conversion of 1,4 butanediol and quantitative hydrogen production. Furthermore, to assess the viability of utilizing heterogenized molecular catalysts for 1,4 butanediol (de)hydrogenation, molecular catalysts, Mn-MACHO(Ph), Ru-MACHO(Ph), and Ru-9, were polymerized to form Mn-MACHO-Poly, Ru-MACHO-Poly, and Ru-9-Poly respectively. These catalysts were then used to assess (de)hydrogenation of polyols, ethylene glycol and 1,4 butanediol. These studies reveal that Ru-MACHO-Poly is an efficient dehydrogenation catalyst with 99% conversion and a hydrogen percent yield of 96%. In addition, Ru-MACHO-Poly is a competent hydrogenation catalyst with approximately 98% conversion back to 1,4 butanediol based on quantitative NMR measurements. Overall, the data suggest these molecular and heterogenized catalysts have potential for practical use in polyalcohol-based LOHC systems.

08 HYDROGEN↗
DOE OSTI2023

Nucleon-Pair Shell Model within a Symmetry Broken Basis

The nucleon-pair shell model (NPSM) within the framework of a broken symmetry basis is studied. The results demonstrate the validity of such a scheme, which in turn leads to a significant reduction in the dimensionality of the multi-pair configuration space. Specifically, in the case of an axially-deformed basis, the results yield a satisfactory description of the low-lying spectrum, even without the need for applying the projection procedure. In the case of a triaxially-deformed basis, the result of variation after angular momentum projection suggest a potential for using the NPSM to find exact solutions in a semi-magic system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Elevating zero dimensional global scaling predictions to self-consistent theory-based simulations

In this work, we have developed an innovative workflow, Stability, Transport, Equilibrium, and Pedestal (STEP)-zero-dimensional (0D), within the OMFIT integrated modeling framework. Through systematic validation against the International Tokamak Physics Activity global H-mode confinement database, we demonstrated that STEP-0D, on average, predicts the energy confinement time with a mean relative error of less than 19%. Moreover, this workflow showed promising potential in predicting plasmas for proposed fusion reactors such as the affordable, robust, compact (ARC) reactor, the European demonstration power plant (EU-DEMO), and the China fusion engineering test reactor (CFETR) indicating moderate H-factors between 0.9 and 1.2. STEP-0D allows theory-based prediction of tokamak scenarios, beginning with 0D quantities. The workflow initiates with the PRO-create module, generating physically consistent plasma profiles and equilibrium using the same 0D quantities as the IPB98(y,2) confinement scaling. This sets the starting point for the STEP module, which further iterates between theory-based physics models of equilibrium, core transport, and pedestal to yield a self-consistent solution. Given these attributes, STEP-0D not only improves the accuracy of predicting plasma performance but also provides a path toward a novel fusion power plant design workflow. When integrated with engineering and costing models within an optimization, this new approach could eliminate the iterative reconciliation between plasma models of varying fidelity. This potential for a more efficient design process underpins STEP-0D's significant contribution to future fusion power plant development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Nitrogen Out-of-Plane (NOOP) Mechanism for Imine-Based Light-Driven Molecular Motors

Light-driven molecular motors have generated considerable interest due to their potential applications in material and biological systems. Recently, Greb and Lehn reported a new class of molecular motors, chiral N-alkyl imines, which undergo unidirectional rotation induced by light and heat. The mechanism of unidirectional motion in molecular motors containing a C═N group has been assumed to consist of photoinduced torsion about the double bond. In this work, we present a computational study of the photoisomerization dynamics of a chiral N-alkyl imine motor. We find that the location and energetics of minimal energy conical intersections (MECIs) alone are insufficient to understand the mechanism of the motor. Furthermore, a key part of the mechanism consists of out-of-plane distortions of the N atom (followed by isomerization about the double bond). Dynamic effects and out-of-plane distortions are critical to understand the observed (rather low) quantum yield for photoisomerization. Finally, our results provide hints as to how the photoisomerization quantum yield might be increased, improving the efficiency of this class of molecular motors.

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

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site‐Level Observations in the Central Midwestern United States

Abstract With projected expansion of biofuel production at a global scale, there is a pressing need to develop adequate representation of bioenergy crops in land surface models to help effectively quantify the biogeophysical and biogeochemical effects of its associated land use changes. This study implements two new perennial bioenergy crops, Miscanthus and switchgrass, into the Community Land Model Version 5 based on site‐level observations from the midwestern United States by modifying parameters associated with photosynthesis, phenology, allocation, decomposition, and carbon cost of nitrogen uptake and integrating concomitantly land management practices. Sensitivity analyses indicate that carbon and energy fluxes of the perennial crops are most sensitive to photosynthesis and phenology parameters. Validation of simulated fluxes against site‐level measurements demonstrates that the model is capable of capturing the overall patterns of energy and carbon fluxes, as well as physiological transitions from leaf emergence to senescence. Compared to annual crops, perennial crops feature longer growing season, greater leaf areas, and higher productivity, leading to increased transpiration, lower annual runoff, and larger carbon uptake. The model simulations suggest that with higher CO 2 assimilation rates and lower demands for nutrients and water, high‐yielding perennial crops are promising alternatives of bioenergy feedstocks compared to traditional annual crops not only for mitigating climate change but also for environmental conservation purposes by reducing fertilizer application and therefore alleviating surface‐ and ground‐water contaminations. Although the local‐scale simulations shed light on potential benefits of using perennial grasses as bioenergy feedstocks, quantifying consequences of their plantations at larger scales warrants additional investigation.

Cheng, Yanyan↗
DOE OSTI2020

System-Level Optimization to Improve Biofuel Potential via Genetic Engineering and Hydrothermal Liquefaction

The economic success of biofuels and bioproducts depends on system-level optimization including biomass production and conversion. Hydrothermal liquefaction (HTL) can convert wet biomass such as microalgae into a biofuel intermediate (BFI) under elevated temperatures and pressure. An understanding of the impacts of biomass composition on BFI yield and quality can inform genetic engineering strategies in the improvement of biochemical composition for biofuel production. In this work, wild type cyanobacterium Synechocystis sp. PCC 6803 biomass was doped with various common cellular storage compounds in lab-scale HTL experiments. Doping with glycogen or polyhydroxybutyrate (PHB) significantly reduced BFI yields, while doping with triglycerides (TAG) or medium chain-length polyhydroxyalkanoate (mcl-PHA) increased BFI yield and quality. In light of these observations, a genetically engineered Synechocystis strain deficient in glycogen biosynthesis was cultivated to produce biomass for HTL, leading to a 17% increase in BFI yield. In addition, we built a multiphase component additivity (MCA) model that can predict BFI yield and quality with PHAs in the biomass. This work demonstrates an effective strategy to integrate strain development with downstream biomass conversion to maximize biofuel yield, with lessons applicable to microalgae as well as other biomass.

09 BIOMASS FUELS↗
DOE OSTI2021

Techno-economic and life-cycle assessment of fuel production from mixotrophic Galdieria sulphuraria microalgae on hydrolysate

Outdoor photoautotrophic algal growth is limited by light attenuation and attendant respiratory CO 2 losses during dark periods, limiting its productivity potential and carbon use efficiency. Developing a system that leverages mixotrophic growth (combining the benefits of both heterotrophic and photoautotrophic growth) has the potential to dramatically improve the total productivity and economics of the system. However, it is unknown if the productivity gains offset the added costs of outdoor mixotrophic cultivation using cellulosic hydrolysate as the feedstock. In this study, corn stover-derived cellulosic sugars were evaluated as the mixotrophic organic carbon source for the cultivation of Galdieria sulphuraria, which can metabolize both glucose and xylose from corn stover hydrolysate. A techno-economic analysis (TEA) and a life-cycle assessment (LCA) were conducted based on a detailed engineering process model for both glass helical photobioreactor and covered pond cultivation platforms, coupled with downstream conversion and upgrading to renewable diesel through hydrothermal liquefaction. Results show the minimum biomass selling price for cultivation in the photobioreactor design assuming a productivity of 1.575 kg m -3 day -1 and a substrate yield of 0.57 g g -1 is $2869 per dry metric ton. The costs are dramatically reduced in the covered pond design which assumes a productivity of 0.8 kg m -3 day -1 and a substrate yield of 0.7 g g -1 , 921 dollars per dry metric ton. Expanding the system boundary to include downstream processing results in a minimum fuel selling price of 8.24 dollars and 3.32 dollars dm 3 GE -1 for the photobioreactor and covered pond systems, respectively. Finally, life-cycle results demonstrate a global warming potential of 339 and 9.1 gCO 2 -eq. MJ -1 on a well-to-wheels basis and a net energy ratio of 2.21 and 0.25 MJ MJ -1 for the photobioreactor and covered pond systems, respectively. Discussion focuses on a required co-product selling price as a function of biomass diversion to meet economic parity with conventional fuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Gel‐Derived Amorphous Bismuth–Nickel Alloy Promotes Electrocatalytic Nitrogen Fixation via Optimizing Nitrogen Adsorption and Activation

Abstract To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH 3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. Herein we developed a three‐dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower free‐energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH 3 production with a yield rate of 17.5 μg h −1 mg cat −1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. This work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.

Fang, Zhiwei↗
DOE OSTI2020

Gel-Derived Amorphous Bismuth–Nickel Alloy Promotes Electrocatalytic Nitrogen Fixation via Optimizing Nitrogen Adsorption and Activation

To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH 3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. Herein we developed a three-dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower fre-energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH 3 production with a yield rate of 17.5 μg h -1 mg cat -1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. Furthermore, this work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗