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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Chelation-Driven Chemistry Controls Dissolution Pathways for Facile Critical Mineral Recovery from Ultramafic Resources

The efficient recovery of critical minerals, such as nickel (Ni) and manganese (Mn) from the subsurface is of vital importance, given their role in modern technologies ranging from batteries to advanced alloys. This study explores an approach that employs chelation to enhance critical mineral recovery from ultramafic rocks using chelating ligands under ambient pressures and systematically evaluates the extraction process for the first time. Experimental results demonstrate the superior performance of EDTA and PrDTA in achieving high extraction efficiencies for both Ni and Mn. To further highlight the ability for optimization, the fluid exchange and variation of fluid-to-rock ratio experiments were conducted, revealing tunable controls for engineering recovery. We show that under optimized conditions, a Ni extraction efficiency of ?82% and a Mn extraction efficiency of ~60% is achieved at ambient pressures, which is equivalent to over 23 times the current global Ni production, and 2 million metric tonnes (MMT) greater than the current global Mn production. From both in-situ and ex-situ mining perspectives, the results highlight the potential of chelation to advance sustainable mineral processing. Overall, this work highlights the environmental benefits of innovative ligand-assisted recovery methods, addressing the critical need for the efficient utilization of olivine resources.

Krishnan, Keerthana↗
DOE OSTI2021

Conservation laws in coupled cluster dynamics at finite temperature

We extend the finite-temperature Keldysh non-equilibrium coupled cluster theory (Keldysh-CC) [A. F. White and G. K.-L. Chan, J. Chem. Theory Comput. 15, 6137–6253 (2019)] to include a time-dependent orbital basis. When chosen to minimize the action, such a basis restores local and global conservation laws (Ehrenfest’s theorem) for all one-particle properties while remaining energy conserving for time independent Hamiltonians. We present the time-dependent Keldysh orbital-optimized coupled cluster doubles method in analogy with the formalism for zero-temperature dynamics, extended to finite temperatures through the time-dependent action on the Keldysh contour. To demonstrate the conservation property and understand the numerical performance of the method, we apply it to several problems of nonequilibrium finite-temperature dynamics: a 1D Hubbard model with a time-dependent Peierls phase, laser driving of molecular H2, driven dynamics in warm-dense silicon, and transport in the single impurity Anderson model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Examining the Economic Optimality of Automatic Generation Control

The automatic generation control (AGC) system is temporally situated between economic dispatch and synchronous generator dynamics, and its primary role is to regulate frequency within and tie-line flows between control areas. Given appropriate choice of participation factors (feed-forward controller gains that govern the disaggregation of the area-level power requirement to individual generators), the AGC can be engineered to nudge system dynamics toward a steady-state operating point corresponding to economic optimality. This paper establishes necessary and sufficient conditions under which a widely accepted choice of participation factors guarantees the alignment of steady-state synchronous generator outputs with a global minimum of a prototypical economic dispatch problem. In so doing, it resolves several ambiguities and formalizes technical assumptions governing the role of the standard AGC architecture in the context of economic dispatch and steady-state operation. Numerical case studies tailored to a modified version of the New England 39-bus 10-machine test system validate the theoretical results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Microbial necromass in cropland soils: A global meta-analysis of management effects

Microbial necromass is a large and persistent component of soil organic carbon (SOC), especially under croplands. The effects of cropland management on microbial necromass accumulation and its contribution to SOC have been measured in individual studies but have not yet been summarized on the global scale. For this study, we conducted a meta-analysis of 481-paired measurements from cropland soils to examine the management effects on microbial necromass and identify the optimal conditions for its accumulation. Nitrogen fertilization increased total microbial necromass C by 12%, cover crops by 14%, no or reduced tillage (NT/RT) by 20%, manure by 21%, and straw amendment by 21%. Microbial necromass accumulation was independent of biochar addition. NT/RT and straw amendment increased fungal necromass and its contribution to SOC more than bacterial necromass. Manure increased bacterial necromass higher than fungal, leading to decreased ratio of fungal-to-bacterial necromass. Greater microbial necromass increases after straw amendments were common under semi-arid and in cool climates in soils with pH <8, and were proportional to the amount of straw input. In contrast, NT/RT increased microbial necromass mainly under warm and humid climates. Manure application increased microbial necromass irrespective of soil properties and climate. Management effects were especially strong when applied during medium (3–10 years) to long (10+ years) periods to soils with larger initial SOC contents, but were absent in sandy soils. Close positive links between microbial biomass, necromass and SOC indicate the important role of stabilized microbial products for C accrual. Microbial necromass contribution to SOC increment (accumulation efficiency) under NT/RT, cover crops, manure and straw amendment ranged from 45% to 52%, which was 9%–16% larger than under N fertilization. In summary, long-term cropland management increases SOC by enhancing microbial necromass accumulation, and optimizing microbial necromass accumulation and its contribution to SOC sequestration requires site-specific management.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data for Sustainable Co-Production of Plant Lipids and Cellulosic Sugars from Transgenic Energycane at an Industrially Relevant Scale: A Proof of Concept for Alternative Feedstocks

Development of sustainable and scalable technologies to convert lignocellulosic biomass to biofuels is critical to achieving carbon neutrality. The potential of transgenic bioenergy crops as a renewable source of sugars and lipids has been demonstrated at bench-scale. However, scaling up these processes is important for holistic analysis. Here proof-of-concept for chemical-free hydrothermal pretreatment of transgenic energycane-oilcane line L13 at an industrially relevant scale to recover vegetative lipids along with cellulosic sugars is presented. Pilot-scale processing of 97 kg of transgenic energycane-oilcane L13 stems and high solids pretreatment of bagasse enhanced the recovery of cellulosic glucose and xylose by 5-fold as compared to untreated bagasse and helped in the enrichment of vegetative lipids in the biomass residues which allowed its recovery at the end of the bioprocess. Palmitic and oleic acids were the predominant fatty acids (FAs) extracted from stems and leaves. The processing did not affect lipid composition. The efficiency of lipid recovery from untreated biomass was 75.9% which improved to 88.7% upon pretreatment. The vegetative tissues of transgenic energycane-oilcane L13 contained 0.42 metric tons/hectare of lipids. Processing vegetative tissues yielded 0.38 metric tons/hectare of lipids. This approaches an oil yield similar to soybean (global average 0.44 metric tons/hectare) and is almost twice as high as the oil yield from sugarcane engineered to hyperaccumulate lipids (0.20 metric tons/hectare). The study suggests that further optimization by state-of-the-art metabolic engineering and biomass processing can establish transgenic bioenergy crops for commercial drop-in fuel production.

Biomass Analytics↗
DOE OSTI2022

Energy-dependent optimization of the prompt fission neutron spectrum with CGMF

Throughout the course of FY21, significant effort was put into investigating models within the LANL developed Hauser-Feshbach fission fragment decay code, CGMF, to understand and potentially solve the long-standing challenge of a too-soft prompt fission neutron spectrum, PFNS. Several inputs and models to CGMF were investigated, including the discrete nuclear levels, the optical model potential, level densities, and the fission fragment initial conditions. Some of the global models within CGMF led to a slight hardening of the neutron spectrum—particularly the likely incomplete discrete levels in through which γ-rays decay—but none of the changes where large enough for the tail of the PFNS to reproduce experimental data. A significant hardening of the spectrum tail was observed when the fission fragment initial conditions were optimized based on their sensitivities to the PFNS data for thermal incident neutrons. In this way, the parameters for the CGMF mass and total kinetic energy distributions, along with the spin cutoff factor were adjusted to better reproduce the experimental PFNS measurements. This optimization hardened the tail of the PFNS slightly but led to unphysical mass distributions for the fission fragments before neutron emission. It was clear from the above that we do not expect to be able to produce an evaluation-quality PFNS with CGMF in the near future. Challenges at thermal will persist–and possibly worsen–with increasing incident energy, where more models are needed to completely describe the fission. Basic-science research funding exceeding the amount available and scope of our NCSP funds would be needed to tackle this decade-long challenge impacting many fission-fragment event generator. And, in fact, Amy Lovell won LDRD ECR funding to do so over the next few years. Therefore, we focused in FY22 on extending evaluation capabilities beyond thermal incident neutrons, to take into account the incident energy dependence of the PFNS and fission fragment initial condition distributions in CGMF. We chose to set up the evaluation methodology to perform PFNS evaluations with CGMF across incident-neutron energies, in order to have it readily available for future NCSP evaluations when the PFNS from CGMF has improved. In this report, we outline the evaluation methodology, along with the results of the optimization, including full model calculations with CGMF using the evaluated parameters.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Optimization of simulated high-field side lower hybrid current drive coupling using machine learning predictions of scrape-off layer density

Lower hybrid current drive (LHCD) is a potential source of non-inductive off-axis current drive (CD) for tokamaks. Although LHCD has been successfully deployed on a number of tokamaks, it is highly sensitive to the scrape-off layer (SOL) conditions local to the LHCD launcher. Large gaps between the launcher and plasma core, SOL turbulence, or edge density perturbations due to edge-localized modes can hamper CD or cause large reflected power. These coupling issues in part motivated the installation of an LHCD launcher on the high-field side (HFS) of DIII-D. On the HFS, the SOL is less turbulent and more controllable compared to the low-field side. This quiescence may result in more predictable edge conditions and thus a more predictable CD. Here, in this work, HFS SOL reflectometry measurements are predicted from global plasma parameters using machine learning models. The SOL predictions coupled with the full-wave simulation of the LHCD launcher allow for the prediction of reflected power, directivity, and arcing risk before the discharge. Launcher performance is then optimized using multi-objective Bayesian optimization, finding the shot parameters that result in an optimal SOL density that maximizes CD while minimizing the risk of arcing. The predictions and optimizations of LHCD performance are then accelerated using a surrogate model of the full-wave LHCD simulation.

Bayesian optimization↗
DOE OSTI2023

Stochastic optimization of a uranium oxide reaction mechanism using plasma flow reactor measurements

Abstract In this work, a coupled Monte Carlo Genetic Algorithm (MCGA) approach is used to optimize a gas phase uranium oxide reaction mechanism based on plasma flow reactor (PFR) measurements. The PFR produces a steady Ar plasma containing U, O, H, and N species with high temperature regions (3000–5000 K) relevant to observing UO formation via optical emission spectroscopy. A global kinetic treatment is used to model the chemical evolution in the PFR and to produce synthetic emission signals for direct comparison with experiments. The parameter space of a uranium oxide reaction mechanism is then explored via Monte Carlo sampling using objective functions to quantify the model-experiment agreement. The Monte Carlo results are subsequently refined using a genetic algorithm to obtain an experimentally corroborated set of reaction pathways and rate coefficients. Out of 12 reaction channels targeted for optimization, four channels are found to be well constrained across all optimization runs while another three channels are constrained in select cases. The optimized channels highlight the importance of the OH radical in oxidizing uranium in the PFR. This study comprises a first step toward producing a comprehensive experimentally validated reaction mechanism for gas phase uranium molecular species formation.

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

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Valence Disproportionation of GeS in the PbS Matrix Forms Pb 5 Ge 5 S 12 Inclusions with Conduction Band Alignment Leading to High n-Type Thermoelectric Performance

Converting waste heat into useful electricity using solid-state thermoelectrics has a potential for enormous global energy savings. Lead chalcogenides are among the most prominent thermoelectric materials, whose performance decreases with an increase in chalcogen amounts (e.g., PbTe>PbSe>PbS). Herein, we demonstrate the simultaneous optimization of the electrical and thermal transport properties of PbS-based compounds by alloying with GeS. The addition of GeS triggers a complex cascade of beneficial events as follows: Ge 2+ substitution in Pb 2+ and discordant off-center behavior; formation of Pb 5 Ge 5 S 12 as stable second phase inclusions through valence disproportionation of Ge 2+ to Ge0 and Ge 4+ . PbS and Pb 5 Ge 5 S 12 exhibit good conduction band energy alignment that preserves the high electron mobility; the formation of Pb 5 Ge 5 S 12 increases the electron carrier concentration by introducing S vacancies. Sb doping as the electron donor produces a large power factor and low lattice thermal conductivity (κ lat ) of ~0.61 Wm -1 K -1 . The highest performance was obtained for the 14% GeS-alloyed samples, which exhibited an increased room temperature electron mobility of ~121 cm 2 V -1 s -1 for 3 × 10 19 cm -3 carrier density, and a ZT, of 1.32 at 923 K. This is ~ 55% greater that the corresponding Sb-doped PbS sample and is one of the highest reported for the n-type PbS system. Moreover, the average ZT (ZT avg ) of ~0.76 from 400 to 923 K is the highest for PbS-based systems.

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

Constraining the nuclear gluon PDF with inclusive hadron production data

The nuclear parton distribution functions (nPDFs) of gluons are known to be difficult to determine with fits of deep inelastic scattering (DIS) and Drell-Yan (DY) data alone. Therefore, the nCTEQ15 analysis of nuclear PDFs added inclusive neutral pion production data from RHIC to help in constraining the gluon. In this analysis, we present a new global analysis of nuclear PDFs based on a much larger set of single inclusive light hadron data from RHIC and the LHC. Using our new nCTEQ code (nCTEQ++) with an optimized version of INCNLO we study systematically the limitations of the theory and the impact of the fragmentation function uncertainty.

Duwentäster, Pit↗
DOE OSTI2022

Co-Optimization of Fuels & Engines Findings and Impact (FY2015-FY2021 Report)

Breakthroughs from the U.S. Department of Energy Co-Optimization of Fuels & Engines (Co-Optima) initiative could make it possible to more rapidly cut emissions, reduce dependence on international petroleum, and contribute to ambitious national goals to slow global warming on the land, in the air, and across the water. Findings from the 6-year collaborative undertaking are now available in this report.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Global study of separable pairing interaction in covariant density functional theory

A systematic global investigation of pairing properties based on all available experimental data on pairing indicators has been performed for the first time in the framework of covariant density functional theory. It is based on the separable pairing interaction. The optimization of the scaling factors of this interaction to experimental data clearly reveals its isospin dependence in the neutron subsystem. However, the situation is less certain in the proton subsystem since similar accuracy of the description of pairing indicators can be achieved both with isospin-dependent and mass-dependent scaling factors. The differences in the functional dependencies of scaling factors lead to uncertainties in the prediction of proton and neutron pairing properties which are especially pronounced at high isospin and could have a significant impact on some physical observables. For a given part of the nuclear chart the scaling factors for spherical nuclei are smaller than those for deformed ones; this feature exists also in nonrelativistic density functional theories. Furthermore, its origin is traced back to particle-vibration coupling in odd-A nuclei which is missing in all existing global studies of pairing. Although the present investigation is based on the NL5(E) covariant energy density functional (CEDF), its general conclusions are expected to be valid also for other CEDFs built at the Hartree level.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Controlling circuitry underlies the growth optimization of Saccharomyces cerevisiae

Microbial growth emerges from coordinated synthesis of various cellular components from limited resources. In Saccharomyces cerevisiae, cyclic AMP (cAMP)-mediated signaling is shown to orchestrate cellular metabolism; however, it remains unclear quantitatively how the controlling circuit drives resource partition and subsequently shapes biomass growth. Here we combined experiment with mathematical modeling to dissect the signaling-mediated growth optimization of S. cerevisiae. We showed that, through cAMP-mediated control, the organism achieves maximal or nearly maximal steady-state growth during the utilization of multiple tested substrates as well as under perturbations impairing glucose uptake. However, the optimal cAMP concentration varies across cases, suggesting that different modes of resource allocation are adopted for varied conditions. Under settings with nutrient alterations, S. cerevisiae tunes its cAMP level to dynamically reprogram itself to realize rapid adaptation. Moreover, to achieve growth maximization, cells employ additional regulatory systems such as the GCN2-mediated amino acid control. This study establishes a systematic understanding of global resource allocation in S. cerevisiae, providing insights into quantitative yeast physiology as well as metabolic strain engineering for biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multi-plane moment-of-fluid interface reconstruction in 3D

Moment-of-fluid (MOF) methods for interface reconstruction approximate the region occupied by material in each mesh element only through reference to its geometric moments. Here, we present a 3D MOF method that represents the material (POM) in each cell as the convex intersection of the cell and multiple half-spaces, each selected to minimize the least-squares error between computed moments of the approximated material and provided reference moments. This optimization problem is highly non-linear and non-convex, making the numerical result very sensitive to the initial guess. To create an effective initial guess in each cell, we construct an ellipsoid from 0th–2nd order reference moments such that its shape corresponds with that of the POM. Within this ellipsoid we inscribe a polyhedron, and initialize the minimization problem with the half-spaces defined by each of its faces. The inscribed polyhedron has minimally 4 faces, and using up to 3rd order moments permits optimization over up to 20 unknown values. We therefore define MOF methods that utilize 4, 5, or 6 half-spaces, correspondingly initialized with the faces of a single inscribed tetrahedron, triangular prism, or hexahedron. Stability of the non-linear optimization is further improved with a prepossessing step that normalizes the reference moments according to the axes of the reference ellipsoid. Using this approach, the non-linear least-squares solver reliably converges to a near-global minimum from a single initial guess. We demonstrate accuracy and robustness using single-cell and multi-cell examples over a wide spectrum of geometry. In particular, we demonstrate our ability to exactly reproduce several important and complex features defined by up to four half-spaces, such as corners, filaments, filament tips, and embedded material in the cell.

3D interface reconstruction↗
DOE OSTI2025

Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah‐MP Land Surface Model: Model Development and Evaluation

Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C ( V cmax,25 ) and the leaf respiration rate at 25°C ( R 25 )) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R 25 and V cmax,25 vary substantially both temporally and spatially within the same PFT ( C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in V cmax,25 (73% and 54% of the variations in R 25 ). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO 2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.

Ren, Yanghang [Department of Earth System Science ↗
DOE OSTI2020

Methane and nitrous oxide porewater concentrations and surface fluxes of a regulated river

Greenhouse gas (GHG) emissions from rivers are a critical missing component of current global GHG models. Their exclusion is mainly due to a lack of in-situ measurements and a poor understanding of the spatiotemporal dynamics of GHG production and emissions, which prevents optimal model parametrization. We combined simultaneous observations of porewater concentrations along different beach positions and depths, and surface fluxes of methane and nitrous oxide at a plot scale in a large regulated river during three water stages: rising, falling, and low. Our goal was to gain insights into the interactions between hydrological exchanges and GHG emissions and elucidate possible hypotheses that could guide future research on the mechanisms of GHG production, consumption, and transport in the hyporheic zone (HZ). Results indicate that the site functioned as a net source of methane. Surface fluxes of methane during river water stages at three beach positions (shallow intermediate and deep) correlated with porewater concentrations of methane. However, fluxes were significantly higher in the intermediate position during low water stage, suggesting that low residence time increased methane emissions. Vertical profiles of methane peaked at different depths, indicating an influence of the magnitude and direction of the hyporheic mixing during the different river water stages on methane production and consumption. The site acted as either a sink or a source of nitrous oxide depending on the elevation of the water column. Nitrous oxide porewater concentrations peaked at the upper layers of the sediment throughout the different water stages. River hydrological stages significantly influenced porewater concentrations and fluxes of GHG, probably by influencing heterotrophic respiration (production and consumption processes) and transport to and from the HZ. Our results highlight the importance of including dynamic hydrological exchanges when studying and modeling GHG production and consumption in the HZ of large rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Science acceleration and accessibility with self-driving labs

In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs’ potential to enhance human–machine and human–human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects.

Canty, Richard B. [North Carolina State University↗