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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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DOE OSTI2024

4th Big Data for Nuclear Power Plants Workshop 2023

The Ohio State University and Idaho National Laboratory organized the 4 th Big Data for Nuclear Power Plants Workshop in November, 2023 in Columbus, Ohio. Workshop topics were chosen to understand the challenges and gaps that need to be addressed to maximize the impact of data on the nuclear industry, as well as the associated applications and risks. Discussions were focused around six specific application areas: Operation and Maintenance; Machine Learning in Nuclear Materials and Advanced Manufacturing; Cybersecurity; High-Performance Computing and Massive Computation; Big Data and Digital Twins; and Nuclear Non-Proliferation. The opportunities, challenges, and risks identified in the six focus areas explored in this workshop are diverse, but some common themes emerge, such as the importance of data integrity, quality, coverage, privacy, and traceability. Big data and AI/ML tools can be leveraged to reduce costs, optimize human tasking, and reduce human error across various application areas. In order for the nuclear industry to benefit from big data and advanced analytic capabilities, it is essential to address challenges and risks, such as data privacy, model reliability, and computational resource availability. Learning from other industries that have successfully implemented big data and AI/ML technologies, like the aerospace industry, can help the nuclear industry successfully integrate these technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Investigating Fast Scanning Calorimetry and Differential Scanning Calorimetry as Screening Tools for Thermoset Polymer Material Compatibility with Laser-Based Powder Bed Fusion

As additive manufacturing (AM) technology has developed and progressed, a constant topic of research in the area is expanding the library of materials to be used with these techniques. Among AM methods that utilize polymers, laser-based powder bed fusion (PBF-LB) has preferentially used thermoplastic polymers as its starting materials, but the deposition and material joining method employed in PBF-LB may also be compatible with powdered thermoset polymer precursors as feedstocks. To assess the compatibility of candidate thermosetting polymers and PBF-LB, characterization techniques and protocols that link fundamental material behavior to material behavior in the processing environment are needed. Therefore, the objectives of this work are to compare the curing behavior measured with two different calorimetry techniques that can operate in different heating rate regimes, differential scanning calorimetry (DSC) and fast scanning calorimetry (FSC), and to assess the capabilities of these techniques to act as materials screening tools for PBF-LB. A commercial polyester powder coating is used as a model material to evaluate the potential of obtaining complimentary information for material screening through a combination of calorimetry methods, and its non-isothermal curing behavior is measured at heating rates between 5 °C/min and 7500 °C/min. Curing exotherms are observed with both calorimetry techniques, and comparing the enthalpy associated with curing shows that incomplete curing occurs at higher heating rates, with relative conversion values of approximately 30%. The curing data are fit with two isoconversional models, Friedman and Starink, which show a reduced activation energy at higher heating rates as well, signifying a lower barrier to curing at the conditions used in the FSC experiments. Overall, the results of this work indicate that using these two calorimetry techniques as tiered screening tools can provide valuable information about how curing may proceed in PBF-LB and inform materials selection and design activities for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Can we achieve atmospheric chemical environments in the laboratory? An integrated model-measurement approach to chamber SOA studies

Secondary organic aerosol (SOA), atmospheric particulate matter formed from low-volatility products of volatile organic compound (VOC) oxidation, affects both air quality and climate. Current 3D models, however, cannot reproduce the observed variability in atmospheric organic aerosol. Because many SOA model descriptions are derived from environmental chamber experiments, our ability to represent atmospheric conditions in chambers directly affects our ability to assess the air quality and climate impacts of SOA. Here, we develop an approach that leverages global modeling and detailed mechanisms to design chamber experiments that mimic the atmospheric chemistry of organic peroxy radicals (RO 2 ), a key intermediate in VOC oxidation. Drawing on decades of laboratory experiments, we develop a framework for quantitatively describing RO 2 chemistry and show that no previous experimental approaches to studying SOA formation have accessed the relevant atmospheric RO 2 fate distribution. We show proof-of-concept experiments that demonstrate how SOA experiments can access a range of atmospheric chemical environments and propose several directions for future studies.

Science & Technology - Other Topics↗
DOE OSTI2025

Identifying a next-generation antimalarial trioxolane in a landscape of artemisinin partial resistance

For over two decades, artemisinin-based combination therapy (ACT) has been the standard of care for the treatment of uncomplicated falciparum malaria. However, artemisinin partial resistance (ART-R) is now prevalent in Southeast Asia and has emerged in eastern Africa, threatening ACT efficacy. Artefenomel, a synthetic 1,2,4-trioxolane, exhibits an extended pharmacokinetic exposure profile that predicts for efficacy against ART-R parasites. Unfortunately, the development of artefenomel was halted recently after almost a decade in the clinic. Here, we describe studies of an artefenomel-adjacent chemotype that combines potent in vitro activity against clinical ART-R parasites, an extended pharmacokinetic profile with single-exposure efficacy in a murine malaria model, and enhanced stability in human microsomes and hepatocytes. Overall, our studies reveal a heretofore underexplored trioxolane chemotype with the potential to address ART-R in a next-generation trioxolane development candidate.

Science & Technology - Other Topics↗
DOE OSTI2025

Plutonium Oxidation State Distribution under WIPP Relevant Conditions (Rev. 2)

The oxidation state of plutonium in the Waste Isolation Pilot Plant (WIPP) environment has been a topic of interest since the initial compliance certification application. Plutonium (Pu) was initially expected to be present primarily as Pu(IV), but since the presence of Pu(III) could not be ruled out, it was also included in performance assessment (PA) calculations. The redox of the other variant actinides, uranium (U) and neptunium (Np), are also coupled to the plutonium redox. This led to a position in performance assessment calculations that is commonly referred to as “50/50”. In which half of the PA realizations assume solubility is dominated by the low oxidation state [U(IV), Np(IV), Pu(III)] and the other half assume solubility is dominated by the higher oxidation state [U(VI), Np(V), Pu(IV)]. Recently, a joint group of WIPP project participants from Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), and the Department of Energy’s Carlsbad Field Office (DOE-CBFO) agreed on a new path forward for the oxidation state model in PA which will decouple the redox active actinides and will instead consider their redox as a function of E h . This will take the current binary approach and turn it into a system with four possible redox states.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Intricacies of frustrated magnetism in the Kondo metal YbAgGe

The combination of localized magnetic moments, their frustration and interaction with itinerant electrons is a key challenge of condensed matter physics. Frustrated magnetic interactions promote degenerate ground states with enhanced fluctuations, a topic that is predominantly studied in magnetic insulators. The coupling between itinerant and localized electrons in metals add complexity to the problem, and is presently formulated only for extreme cases in which the itinerant electrons mediate exchange between localized spins (RKKY interaction) or suppress the formation of magnetic moments (Kondo screening). Here, we report an in-depth experimental study of the distorted Kagome metal YbAgGe, unravelling the open questions of how frustration, localized magnetism and itinerant electrons are intertwined in frustrated Kondo metals. We find that coupled itinerant and localized electrons give rise to dynamic magnetic correlations below T* ≈ 20 K. At lower temperature, frustrated magnetic interactions establish anisotropic magnetic short-range correlations that culminate into antiferromagnetic long-range order below T N = 0.68 K with a significantly reduced modulated magnetic moment. We show that local moment Hamiltonians can yield limited understanding of the microscopic behaviour in frustrated metals, and prompt the extension of more sophisticated model Hamiltonians incorporating itinerant effects.

Mazzone, Daniel G. [PSI Center for Neutron and Muo↗
DOE OSTI2024

Trapped-ion quantum simulation of electron transfer models with tunable dissipation

Electron transfer is at the heart of many fundamental physical, chemical, and biochemical processes essential for life. The exact simulation of these reactions is often hindered by the large number of degrees of freedom and by the essential role of quantum effects. Here, we experimentally simulate a paradigmatic model of molecular electron transfer using a multispecies trapped-ion crystal, where the donor-acceptor gap, the electronic and vibronic couplings, and the bath relaxation dynamics can all be controlled independently. By manipulating both the ground-state and optical qubits, we observe the real-time dynamics of the spin excitation, measuring the transfer rate in several regimes of adiabaticity and relaxation dynamics. Our results provide a testing ground for increasingly rich models of molecular excitation transfer processes that are relevant for molecular electronics and light-harvesting systems.

Science & Technology - Other Topics↗
DOE OSTI2024

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Abstract Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Overview of ST40 results and future: expanding the physics basis of high-field spherical tokamaks

The goal of the ST40 programme is to explore the physics of high-field spherical tokamaks (STs), to validate empirical and theoretical models and, hence, to build confidence in predictions required to support the design of future generations of STs. ST40 is a compact high-field ST that has achieved the following parameters: R 0 = 0.4–0.55 m, I p = 0.20–0.85 MA, B t (R= 0.4 m) = 0.7–2.1 T, κ ⩽ 1.9, and A = 1.6–1.9. Highlights of recent experimental results include (i) H-mode and confinement studies at B t ⩽ 2.1 T, (ii) observation of bifurcation of the scrape-off-layer power fall-off width, λ q , into a ‘wide’ branch that follows existing H-mode scalings and a ‘narrow’ branch that exhibits λ q values that are up to 10 times lower than the predictions of established scalings, (iii) development of high-performance scenarios with plasma current, I p , up to 0.85 MA, (iv) development of highly non-inductive scenarios with high β p , and (v) the first ST40 experiments utilising the newly commissioned impurity powder dropper. The work on all these topics has been supported by a number of advancements in ST40 hardware and software, from plasma control to data analysis and interpretation. At the end of 2025, ST40 embarked on a major upgrade to further expand its capabilities by introducing, among other improvements, all-metal plasma-facing components, 1 MW of electron cyclotron heating, a pellet injector, and a pair of lithium evaporators for wall conditioning.

confinement↗
DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗
DOE OSTI2025

Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning

Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems and the absence of time-invariance. The nonequilibrium properties of many-body systems can be described by the dynamics of the correlator, or the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff–Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time numerical simulation. In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. We utilize RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ where N t is the number of steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through various numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Engineering a protease-stable, oral single-domain antibody to inhibit IL-23 signaling

Interleukin (IL)-23 is a validated therapeutic target in inflammatory bowel disease. While antibodies targeting IL23 demonstrate clinical efficacy, they face challenges such as high costs, safety risks, and the necessity of parenteral administration. Here, we present a workflow to simultaneously enhance the affinity and protease stability of an inhibitory anti-IL23R VHH for oral use. Cocrystal structure analysis reveals that the anti-IL23R VHH employs both CDR and framework residues to achieve picomolar affinity for IL23R. The engineered VHH remains stable for over 8 h in intestinal fluid and 24 h in fecal samples. Oral administration of this VHH achieves deep pathway inhibition in a murine colitis model. Furthermore, a single pill provides sustained IL23R inhibition in nonhuman primate blood for over 24 h. With high potency, gut stability, high production yield, and favorable drug-like properties, oral VHHs offer a promising approach for inflammatory bowel diseases.

Science & Technology - Other Topics↗
DOE OSTI2025

Disentangling climate and policy uncertainties for the Colorado River post-2026 operations

Abstract Lakes Mead and Powell in the Colorado River Basin underpin water and hydropower supply for the western United States. While the policies currently regulating the basin will expire by 2026, planning remains challenging due to intertwined climate variability and policy uncertainties. Based on streamflow projections from 10 dynamically downscaled CMIP6 global climate models and unique methods that add and remove internal variability, we evaluate future conditions at Powell and Mead under existing and alternative policies. Due to projected streamflow declines, under existing policy, both reservoirs will face substantial risks (>80% likelihood) of reaching dead pool before 2060. Adopting recently proposed alternative policies reduces but doesn’t eliminate such risks. All policies also exhibit tipping points where reservoir levels can change rapidly with a slight change in streamflow. A sustainable policy may require larger reductions to further reduce the reservoirs’ dead pool risks and provide better buffers from sudden changes.

Science & Technology - Other Topics↗
DOE OSTI2024

Phonon screening and dissociation of excitons at finite temperatures from first principles

The properties of excitons, or correlated electron–hole pairs, are of paramount importance to optoelectronic applications of materials. A central component of exciton physics is the electron–hole interaction, which is commonly treated as screened solely by electrons within a material. However, nuclear motion can screen this Coulomb interaction as well, with several recent studies developing model approaches for approximating the phonon screening of excitonic properties. While these model approaches tend to improve agreement with experiment, they rely on several approximations that restrict their applicability to a wide range of materials, and thus far they have neglected the effect of finite temperatures. Here, we develop a fully first-principles, parameter-free approach to compute the temperature-dependent effects of phonon screening within the ab initio GW -Bethe–Salpeter equation framework. We recover previously proposed models of phonon screening as well-defined limits of our general framework, and discuss their validity by comparing them against our first-principles results. We develop an efficient computational workflow and apply it to a diverse set of semiconductors, specifically AlN, CdS, GaN, MgO, and SrTiO 3 . We demonstrate under different physical scenarios how excitons may be screened by multiple polar optical or acoustic phonons, how their binding energies can exhibit strong temperature dependence, and the ultrafast timescales on which they dissociate into free electron–hole pairs.

Science & Technology - Other Topics↗
DOE OSTI2024

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions

Abstract Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic ground states from a combinatorially large set of possibilities. Machine learning offers a path forward by enabling efficient surrogate models that can more readily enumerate these states, but there is a dearth of training data available, and what is available tends to be imbalanced with too much non-magnetic data. In this work we show that the discrete and previously tackled data imbalance that exists at the level of the magnetic ordering leads to an imbalanced continuous distribution with many zeros when the data is unraveled at the atomic magnetic moment level, which subsequently leads to models with low accuracy for magnetic properties. We mitigate this by using a two-part model framework. Our scheme is able to classify atoms into magnetic and non-magnetic with an F1 score and Matthew’s correlation coefficient (MCC) of ~91% and then to provide an implicit embedding representation that maps directly onto the magnitude of the magnetic moment with a mean absolute error of 0.1 μ B . Beyond screening for new magnetic materials, we demonstrate an additional practical use case of our scheme: the provision of good initial guesses for magnetic moments in first-principles electronic relaxations. Such initialization is shown to lead to faster convergence to configurations that lie closer to the ground state.

Computer Science↗
DOE OSTI2025

The C2 domain augments Ras GTPase-activating protein catalytic activity

Regulation of Ras GTPases by GTPase-activating proteins (GAPs) is essential for their normal signaling. Nine of the ten GAPs for Ras contain a C2 domain immediately proximal to their canonical GAP domain, and in RasGAP (p120GAP, p120RasGAP;RASA1) mutation of this domain is associated with vascular malformations in humans. Here, we show that the C2 domain of RasGAP is required for full catalytic activity toward Ras. Analyses of the RasGAP C2-GAP crystal structure, AlphaFold models, and sequence conservation reveal direct C2 domain interaction with the Ras allosteric lobe. This is achieved by an evolutionarily conserved surface centered around RasGAP residue R707, point mutation of which impairs the catalytic advantage conferred by the C2 domain in vitro. In mice,R707Cmutation phenocopies the vascular and signaling defects resulting from constitutive disruption of theRASA1gene. In SynGAP, mutation of the equivalent conserved C2 domain surface impairs catalytic activity. Our results indicate that the C2 domain is required to achieve full catalytic activity of GAPs for Ras.

Science & Technology - Other Topics↗
DOE OSTI2025

Deciphering metabolic differentiation during Bacillus subtilis sporulation

Abstract The bacteriumBacillus subtilisundergoes asymmetric cell division during sporulation, producing a mother cell and a smaller forespore connected by the SpoIIQ-SpoIIIA (or Q-A) channel. The two cells differentiate metabolically, and the forespore becomes dependent on the mother cell for essential building blocks. Here, we investigate the metabolic interactions between mother cell and forespore using genome-scale metabolic and expression models as well as experiments. Our results indicate that nucleotides are synthesized in the mother cell and transported in the form of nucleoside di- or tri-phosphates to the forespore via the Q-A channel. However, if the Q-A channel is inactivated later in sporulation, then glycolytic enzymes can form an ATP and NADH shuttle, providing the forespore with energy and reducing power. Our integrated in silico and in vivo approach sheds light into the intricate metabolic interactions underlying cell differentiation inB. subtilis, and provides a foundation for future studies of metabolic differentiation.

Science & Technology - Other Topics↗
DOE OSTI2024

Debris-induced consequences on turbulence and vorticity in solar photovoltaic module-generated array wakes

Particle-laden flows in solar photovoltaic (PV) systems are inevitable, where wind-swept debris in open environments are carried by high winds and turbulence, coating panel surfaces or damaging structures. Particle deposition, or soiling, is a well-known issue for large-scale plants which rely on uninhibited solar rays for optimal production. But understanding the mechanisms leading to soiling requires a physical and fluid dynamics-centered focus, since turbulence dominates PV panel wakes and is also known to alter particle concentration and trajectories. This study presents an experimental campaign toward consequences of particle-laden flow between two model PV panels using time-resolved particle image velocimetry. The model array was subjected to varied particle volume fractions, including a tracer particle case and a water droplet case. Characterization of mean velocity, turbulence statistics, and mean kinetic energy within the single phase and, separately, particle phase flows showed modified features due to particle inertia. Images captured at a frequency of 1 kHz in the near wake of the upstream panel allow for a first experimental look at vorticity and convective velocity of vortex structures for single-phase and particle-phase flows which are crucial to debris transport and soiling in PV environments.

Energy & Fuels↗