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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 OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗
DOE OSTI2023

[18F]Tosyl fluoride as a versatile [18F]fluoride source for the preparation of 18F-labeled radiopharmaceuticals

Abstract Positron emission tomography (PET) is an in vivo imaging technology that utilizes positron-emitting radioisotope-labeled compounds as PET radiotracers that are commonly used in clinic and in various research areas, including oncology, cardiology, and neurology. Fluorine-18 is the most widely used PET-radionuclide and commonly produced by proton bombardment of 18 O-enriched water in a cyclotron. The [ 18 F]fluoride thus obtained generally requires processing by azeotropic drying in order to completely remove H 2 O before it can be used for nucleophilic radiofluorination. In general, the drying step is important in facilitating the radiofluorination reactions and the preparation of 18 F-labeled PET radiotracers. In this communication, we have demonstrated the feasibility of using [ 18 F]tosyl fluoride ([ 18 F]TsF) as a versatile [ 18 F]fluoride source for radiofluorination to bypass the azeotropic drying step, and we have developed a continuous flow solid-phase radiosynthesis strategy to generate [ 18 F]TsF in a form that is excellent for radiofluorination. [ 18 F]TsF shows high reactivity in radiofluorination and provides the features suitable for preparing PET radiotracers on a small scale and exploring novel radiolabeling technologies. Thus, using [ 18 F]TsF as a [ 18 F]fluoride source is a promising strategy that facilitates radiofluorination and provides a convenient and efficient solution for the preparation of 18 F-labeled radiopharmaceuticals that is well matched to the emerging trends in PET imaging technologies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

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

Structural and thermal properties of ultralow thermal conductivity Ba 3 Cu 2 Sn 3 Se 10

The thermal properties of Ba 3 Cu 2 Sn 3 Se 10 were investigated by measurement of the thermal conductivity and heat capacity. The chemical bonding in this diamagnetic material was investigated using structural data from Rietveld refinement and calculated electron localization. This quaternary chalcogenide is monoclinic (P2 1 /c), has a large unit cell with 72 atoms in the primitive cell, and a high local coordination environment. Additionally, the Debye temperature (162 K) and average speed of sound (1666 m s -1 ) are relatively low with a very small electronic contribution to the heat capacity. Ultralow thermal conductivity (0.46 W m -1 K -1 at room temperature) is attributed to the relatively weak chemical bonding and intrinsic anharmonicity, in addition to a large unit cell. This work is part of the continuing effort to explore quaternary chalcogenides with intrinsically low thermal conductivity and identify the features that result in a low thermal conductivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Metal-insulator transition and magnetism of SU(3) fermions in the square lattice

We study the SU(3) symmetric Fermi-Hubbard model (FHM) in the square lattice at 1/3-filling using numerically exact determinant quantum Monte Carlo and numerical linked-cluster expansion techniques. We present the different regimes of the model in the T–U plane, which are characterized by local and short-range correlations, and capture signatures of the metal-insulator transition and magnetic crossovers. These signatures are detected as the temperature scales characterizing the rise of the compressibility, and an interaction-dependent change in the sign of the diagonal spin-spin correlation function. The analysis of the compressibility estimates the location of the metal-insulator quantum critical point at U c /t ~ 6, and provides a temperature scale for observing Mott physics at finite T. Furthermore, from the analysis of the spin-spin correlation function we observe that for U/t ≳ 6 and T ~ J = 4⁢t 2 /U there is a development of a short-range two-sublattice (2SL) antiferromagnetic structure, as well as an emerging three-sublattice (3SL) antiferromagnetic structure as the temperature is lowered below T/J ≲ 0.57. This crossover from 2SL to 3SL magnetic ordering agrees with Heisenberg limit predictions, and has observable effects on the density of on-site pairs. Finally, we describe how the features of the regimes in the T–U plane can be explored with alkaline-earth-like atoms in optical lattices with currently achieved experimental techniques and temperatures. Furthermore, the results discussed in this paper provide a starting point for the exploration of the SU(3) FHM upon doping.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Absence of moment fragmentation in the mixed B -site pyrochlore Nd 2 GaSbO 7

Nd-based pyrochlore oxides of the form Nd 2 GaSbO 7 have garnered a significant amount of interest owing to the moment fragmentation physics observed in Nd 2 GaSbO 7 and speculated in Nd 2 GaSbO 7 . Notably this phenomenon is not ubiquitous in this family, as it is absent in Nd 2 GaSbO 7 , which features a smaller ionic radius on the B site. Here, we explore the necessary conditions for moment fragmentation in the Nd pyrochlore family through a detailed study of the mixed B-site pyrochlore Nd 2 GaSbO 7 . Furthermore, the B site of this system is characterized by significant disorder and an extremely small average ionic radius. Similarly to Nd 2 GaSbO 7 , we find no evidence for moment fragmentation through our bulk characterization and neutron scattering experiments, indicating that chemical pressure (and not necessarily the B-site disorder) plays a key role in the presence or absence of this phenomenon in this material family. Surprisingly, the presence of significant B-site disorder in Nd 2 GaSbO 7 does not generate a spin-glass ground state and instead the same all-in-all-out magnetic order identified in other Nd pyrochlores is found here.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterizing Sub-Cohorts via Data Normalization and Representation Learning

The process of identifying a cohort of interest is a very challenging task. It requires manually inspecting many patient records of complex structure that might include medical coding errors and missing data. This paper presents a computational pipeline for refining the process of cohort selection based on medical concepts recorded in the electronic health records (EHRs). The pipeline extracts EHR data for a given cohort and normalizes this data using standard vocabularies. Then a stacked denoising autoencoder is used to embed the normalized patient vectors in a low dimensional space, where the patients are subsequently clustered into sub-cohorts. The goal is to represent the cohort in a standard format and abstract variants of sub-populations. As a use-case, we applied the pipeline to 1.8 million Veterans diagnosed with major depressive disorder (MDD), and identified four meaningful sub-cohorts using the features learned by the autoencoder. Then, each sub-cohort was explored using a set of keywords for interpretation.

Rush III, Everett↗
DOE OSTI2022

Deconstructing the root system of grasses through an exploration of development, anatomy and function

Abstract Well‐adapted root systems allow plants to grow under resource‐limiting environmental conditions and are important determinants of yield in agricultural systems. Important staple crops such as rice and maize belong to the family of grasses, which develop a complex root system that consists of an embryonic root system that emerges from the seed, and a postembryonic nodal root system that emerges from basal regions of the shoot after germination. While early seedling establishment is dependent on the embryonic root system, the nodal root system, and its associated branches, gains in importance as the plant matures and will ultimately constitute the bulk of below‐ground growth. In this review, we aim to give an overview of the different root types that develop in cereal grass root systems, explore the different physiological roles they play by defining their anatomical features, and outline the genetic networks that control their development. Through this deconstructed view of grass root system function, we provide a parts‐list of elements that function together in an integrated root system to promote survival and crop productivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Revisiting Temporal Blocking Stencil Optimizations

Iterative stencils are used widely across the spectrum of High Performance Computing (HPC) applications. Many efforts have been put into optimizing stencil GPU kernels, given the prevalence of GPU-accelerated supercomputers. To improve the data locality, temporal blocking is an optimization that combines a batch of time steps to process them together. Under the observation that GPUs are evolving to resemble CPUs in some aspects, we revisit temporal blocking optimizations for GPUs. We explore how temporal blocking schemes can be adapted to the new features in the recent Nvidia GPUs, including large scratchpad memory, hardware prefetching, and device-wide synchronization. We propose a novel temporal blocking method, EBISU, which champions low device occupancy to drive aggressive deep temporal blocking on large tiles that are executed tile-by-tile. We compare EBISU with state-of-the-art temporal blocking libraries: STENCILGEN and AN5D. We also compare with state-of-the-art stencil auto-tuning tools that are equipped with temporal blocking optimizations: ARTEMIS and DRSTENCIL. Over a wide range of stencil benchmarks, EBISU achieves speedups up to 2.53x and a geometric mean speedup of 1.49x over the best state-of-the-art performance in each stencil benchmark.

Zhang, Lingqi↗
DOE OSTI2025

Modeling the Nucleation and Growth of Lead Sulfate Particles on Lead Electrodes

Lead-acid batteries (LABs) play a pivotal role in the energy storage sector with applications spanning from starting-lighting-ignition batteries to grid energy storage. Passivation of lead negative electrodes by PbSO 4 particles is a fundamental mechanism limiting the performance of LAB. In this regard, an electrochemical model is developed that simulates the nucleation and growth (N&G) dynamics of PbSO 4 particles on a flat lead electrode, responsible for its passivation. The model considers the electrochemical reactions between lead electrode and sulfuric acid, N&G of PbSO 4 particles, passivation of the lead surface, and the ternary transport of PbSO 4 (aq), bisulfate, and protons in H 2 SO 4 electrolyte. The model is validated with a dataset of cyclic voltammetry (CV) responses collected at several scan rates and H 2 SO 4 concentrations. The model shows remarkable qualitative and quantitative agreement with the experimental data including CV peak features, discharge capacity, and particle size. The model was employed to explore key N&G quantities, such as supersaturation, nucleation rate, particle count, growth rate, particle size, and surface coverage, and to examine how their interactions influence electrode utilization. Parametric studies were also conducted to evaluate how scan rates and acid concentrations influence the previously mentioned N&G quantities and, subsequently, the utilization of the electrode.

25 ENERGY STORAGE↗
DOE OSTI2025

On-chip terahertz emission from Floquet-Bloch states [Invited]

Floquet engineering uses time-periodic electromagnetic fields to modify the electronic properties of quantum materials via the creation of Floquet-Bloch states. These photon-dressed states inherit features from both the material and the driving field, enabling the exploration and control of quantum phenomena in light-matter hybrid systems. In non-centrosymmetric materials, shift currents can arise from the quantum geometric properties of electronic wavefunctions. However, shift currents from Floquet-Bloch states remain experimentally unexplored. Here, we employ an on-chip optoelectronic circuit to detect intrinsic terahertz emission from Floquet-Bloch states in T d -WTe 2 under intense optical driving. We observe strong edge-localized terahertz emission that scales linearly with the driving field, consistent with the theoretical prediction for shift currents generated by Floquet-Bloch states. The results advance our understanding of strongly driven quantum materials and provide insights for developing efficient, bias-free terahertz sources for future optoelectronic technologies.

Li, Xinyu [Max Planck Institute for the Structure ↗
DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Machine Learning for Neutron Resonance Evaluations [Slides]

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit a resonance structure whose shape is determined in part by the angular momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. In this presentation, we describe the application of machine learning to automate the quantum number assignments. Scikit-learn classifiers were trained on simulated resonance data whose statistical properties were chosen to mimic real data. We explored the use of several physics (and random matrix theory)-motivated features for training the classifiers, including the nearest neighbor spacing distribution, cumulative level distribution, and channel width distributions. Initial results demonstrated that we can determine resonance spin groups somewhat reliably. We are now investigating the application of our approach to 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Planning for Material Control and Accountancy at Liquid Fueled Molten Salt Reactors

The purpose of this report is to provide molten salt reactor (MSR) developers and future US Nuclear Regulatory Commission (NRC) license applicants with recommendations for developing an effective and practical material control and accounting (MC&A) plan, focused primarily on MSR designs that use circulating liquid fuel. Because of the breadth of MSR designs, there is no single, generic, detailed MC&A plan that will work for every design. The wide variation of fresh fuel salts, the method and frequency of loading fresh fuel, the reactor system design components (e.g., tanks, filtration systems, chemical processing streams), and waste streams will determine the specific measurement locations and instrumentation that can best meet MC&A objectives throughout an MSR facility. Additionally, MSR designs are rapidly evolving, and new design features and deployment scenarios that will affect MC&A are being explored and pursued. This report defines a generic MC&A approach that was developed for terrestrial (as opposed to maritime) deployments to meet the intent of NRC domestic safeguards and MC&A. MSR license applicants should consider nuclear safeguards (both domestic and international) and security throughout the design, as early as the preconceptual design phase. MC&A of special nuclear material (SNM) is an aspect of the NRC’s domestic safeguards program, alongside physical protection. Because liquid-fueled MSRs are reactors with SNM in nondiscrete (or item) form, it is likely that the NRC may require liquid-fueled MSR license applicants to submit a formal MC&A plan as a part of their license application. Currently, the NRC licensing protocol presents a challenge because the NRC MC&A regulations have not been updated to accommodate advanced reactors, including types of MSRs. Because no liquid-fueled MSR has been licensed for operation at the time of this report, no template or precedence for a successfully licensed MSR MC&A plan exists. However, the MSR license applicant can take advantage of the NRC’s published commitments to performance-based regulations. The authors recommend that the license applicant, or MSR designers, develop an MC&A plan throughout the design lifecycle and plan to submit a detailed MC&A program description, or MC&A plan, to the NRC as a part of a license application. No MC&A plan template or guidance exists that is specific to liquid-fueled MSRs. The authors recommend that license applicants discuss the topic of MC&A during preapplication engagement. Because of the uniqueness of MC&A for liquid fueled MSRs, the authors recommend that liquid fueled MSR developers engage with the NRC on the topic of MC&A in the early phases of its design development and follow up any time there are significant modifications in design plans that would affect MC&A. For example, topics like modifications in fuel handling processes, changes in uranium enrichment, or additional chemical processing streams added to the design could be discussed with the NRC specifically on the topic of MC&A.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1976

Coincident observations of ionospheric troughs and the equatorial plasmapause

Electron-density observations made in the topside ionosphere by the Ariel 4 and Isis 2 satellites are examined in conjunction with results obtained by Explorer 45 when it traversed the near-equatorial plasmapause with one hour (both UT and MLT) of the Ariel and Isis traversals of the same L coordinate. Both dusk and night observations are analyzed, and an attempt is made to show that depressions in ionospheric electron density occur in the vicinity of the plasmapause field line. It is concluded that the electron distributions observed in the electron-density troughs at 550 km near dusk by Ariel and at 1400 km near midnight by Isis do not always parallel variations in the light-ion distribution inferred from the Explorer plasmapause traversals and that there appears to be no specific feature of the main ionospheric trough which can be used to identify the plasmapause field line except in a statistical sense.

Grebowsky, J. M.↗
NASA NTRS1985

Narrow Absorption Components in Be Star Winds

The stars omega Ori (B2 IIIe), 66 Oph (B2 IVe), and 59 Cyg (B1.5 IVe) are discussed. The extent to which the narrow absorption components in these Be stars differs from narrow components in the O stars and gamma Cas is explored. Any models or mechanisms for the formation of narrow absorption features in the UV resonance lines of Be star spectra must account for the presence of multiple narrow absorption features which are variable in number, radial velocity, and strength. Models predicting a high and low density structure to the stellar wind caused by instabilities in a flow driven by radiation pressure or by variable mass loss may be more successful in describing the behavior of winds in early Be stars. These models appear to be capable of producing single absorption components in the velocity range observed for O stars and very early Be stars.

Grady, C. A.↗