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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reactions of atomic thorium and uranium cations with CF 4 studied by guided ion beam tandem mass spectrometry

The fundamental chemistry of the actinides continues to be an area of interest because of their use in nuclear fuel cycles. Here, the kinetic energy dependences of the reactions of thorium and uranium cations with CF 4 were studied using guided ion beam tandem mass spectrometry. The products observed include AnF x + (An = Th or U, x = 1-3) and CF y + (y = 1-3). Although most reactions are exothermic according to literature thermochemistry, all cross sections display strong endothermic features indicating a restriction in the reactivity at low energies. For both actinides, only the AnF + + CF 3 product channel is observed at low energies; however, its formation proceeds very inefficiently. Furthermore, these results indicate that the reactions of Th + and U + with CF 4 are not thermodynamically driven and suggest that the low-energy reactivity results from precise alignment of the reactants.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Persistence of Late Substantial Patient-Reported Symptoms (LAPERS) After Radiochemotherapy Including Image Guided Adaptive Brachytherapy for Locally Advanced Cervical Cancer: A Report From the EMBRACE Study

This report describes the persistence of late substantial treatment-related patient-reported symptoms (LAPERS) in the multi-institutional EMBRACE study on magnetic resonance image guided adaptive brachytherapy in locally advanced cervical cancer (LACC).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Initial Results of a Phase 2 Trial of 18F-DOPA PET-Guided Dose-Escalated Radiation Therapy for Glioblastoma

Our previous work demonstrated that 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine ({sup 18}F-DOPA) positron emission tomography (PET) is sensitive and specific for identifying regions of high density and biologically aggressive glioblastoma. The purpose of this prospective phase 2 study was to determine the safety and efficacy of biologic-guided, dose-escalated radiation therapy (DERT) using {sup 18}F-DOPA PET in patients with glioblastoma.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Machine learning guided search for energetically favorable metal borocarbide ternary compounds

In this work, we employ machine-learning (ML) combined with first principles calculations to efficiently search for the energetically favorable metal borocarbide (M-B-C) ternary compounds with M being the group 1–3 metal elements. Using a crystal graph convolutional neural network (CGCNN) ML approach followed by first-principles calculations, we predicted 47 energetically favorable stable and metastable ternary Na-B-C, Ca-B-C, and La-B-C ternary compounds with their decomposition energy (E d ) below or within 100 meV/atom from the currently known convex hulls. Phonon spectra and electronic structures of the 14 energetically favorable stable structures are also investigated by first-principles calculations. By substituting the metal atoms in the 29 energetically favorable non-equivalent template structures of Na (Ca, La)-B-C with other group 1–3 elements in the periodic table, we further obtain 22 stable structures and 52 metastable structures (E d ≤100 meV/atom with respect to the known convex hulls) for Li-B-C, K-B-C, Rb-B-C, Mg-B-C, Sr-B-C, Ba-B-C, Sc-B-C and Y-B-C ternary compounds. New convex hulls including our newly predicted stable ternary structures and the known stable structures are constructed for the M-B-C systems. The results obtained by our ML guided first-principles calculations enrich our knowledge in the structure and energy landscape of metal borocarbide ternary compounds and provide useful guidance for further experimental synthesis and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Guided skyrmion motion along pinning array interfaces

We examine ac driven skyrmions interacting with the interface between two different obstacle array structures. We consider drive amplitudes at which skyrmions in a bulk obstacle lattice undergo only localized motion and show that when an obstacle lattice interface is introduced, directed skyrmion transport can occur along the interface. The skyrmions can be guided by a straight interface and can also turn corners to follow the interface. For a square obstacle lattice embedded in a square pinning array with a different obstacle radius, we find that skyrmions can undergo transport in all four primary symmetry directions under the same fixed ac drive. We map where localized or translating motion occurs as a function of the ac driving parameters. Our results suggest a new method for controlling skyrmion motion based on transport along obstacle lattice interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

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

A practical guide to characterizing irradiated nuclear fuels using FIB tomography

We report focused ion beam (FIB) tomography with combined electron backscatter diffraction (EBSD) and energy dispersive x-ray spectroscopy (EDS) is a technique capable of statistically characterizing the microstructure and spatial compositional variation of nuclear fuel in three-dimensions (3D). The 3D visualization from FIB tomography provides a comprehensive picture of the interconnected microstructural and compositional features that can impact fuel performance. While these features are often characterized with surface examination, the complexity and relationship of fission products and grain boundary networks may not fully be captured by these 2D methods. This work presents a practical guide to FIB tomography that is tailored to nuclear fuel characterization. The steps used to collect and process the data are provided along with the scripts used to process the data. Additionally, suggestions for future characterization efforts utilizing this approach are given.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Responsibility for emissions and mitigation capability should guide use of carbon removal offsets

M Gidden conducted this work prior to joining PNNL and no DOE OFFICE OF SCIENCE, OTHER DOE, OTHER FEDERAL, OR PNNL OVERHEAD FUNDING is being used. Stabilizing and then drawing down global temperature requires achieving net-negative CO2 emissions globally. Carbon removal is needed to offset “hard-to-abate” sectors that remain sources of emissions at net-zero and beyond. We argue the use of scarce carbon removal resources should be guided by the principles enshrined in the Paris Agreement.

Gidden, Matthew↗
DOE OSTI2022

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, in this work, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Endothelial surface translocation of mitochondrial PDCE2 involves the non-canonical secretory autophagy pathway: Putative molecular target for radiation-guided drug delivery

Highlights: • Radiation induces ectopic endothelial surface exposure of mitochondrial PDCE2. • PDCE2 translocation is associated with late-stage inhibition of autophagic flux. • Radiation damage overloads the degradative lysosomal pathway of autophagy. • PDCE2 trafficking occurs via autophagosomes and non-canonical secretory autophagy. • Surface PDCE2 provides a novel target for radiation-guided vascular drug delivery. Radiation has been proposed as a priming agent to induce discriminatory luminal biomarkers for vascular targeting and drug delivery in disorders such as brain arteriovenous malformations and cancers. We previously observed ectopic expression of intracellular proteins such as mitochondrial PDCE2 on irradiated endothelium in animal models. In this study we examined the mechanism of PDCE2 trafficking in human endothelial cells to better understand its suitability as a vascular target. Ionizing radiation induced PDCE2 surface localization in association with accumulation of autophagosome markers (L3CB and p62) indicative of late-stage inhibition of autophagic flux. This effect was abolished in the presence of Rapamycin, an autophagy-inducer, but replicated in the presence of Bafilomycin A, an autophagy blocker. PDCE2 co-localized with lysosomal markers of the canonical degradative autophagy pathway in response to radiation but also with recycling endosomes and SNARE proteins responsible for autophagosome-plasma membrane fusion. These findings demonstrate that radiation-induced blockade of autophagic flux stimulates redirection of intracellular molecules such as PDCE2 to the cell surface via a non-canonical secretory autophagy pathway. Intracellular membrane proteins trafficked in this way could provide a unique pool of radiation biomarkers for therapeutic drug delivery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Improved accuracy in degenerate variational integrators for guiding centre and magnetic field line flow

First-order-accurate degenerate variational integration (DVI) was introduced in Ellison et al. ( Phys. Plasmas , vol. 25, 2018, 052502) for systems with a degenerate Lagrangian, i.e. one in which the velocity-space Hessian is singular. In this paper we introduce second-order-accurate DVI schemes, both with and without non-uniform time stepping. We show that it is not in general possible to construct a second-order scheme with a preserved two-form by composing a first-order scheme with its adjoint, and discuss the conditions under which such a composition is possible. We build two classes of second-order-accurate DVI schemes. We test these second-order schemes numerically on two systems having non-canonical variables, namely the magnetic field line and guiding centre systems. Variational integration for Hamiltonian systems with non-uniform time steps, in terms of an extended phase space Hamiltonian, is generalized to non-canonical variables. It is shown that preservation of proper degeneracy leads to single-step (one-step) methods without parasitic modes, i.e. to non-uniform time step DVIs. This extension applies to second-order-accurate as well as first-order schemes, and can be applied to adapt the time stepping to an error estimate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Abstract Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

47 OTHER INSTRUMENTATION↗