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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs

One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

36 MATERIALS SCIENCE↗
DOE OSTI2022

ADAPTIVE GROUP TESTING WITH MISMATCHED MODELS

Accurate detection of infected individuals is one of the critical steps in stopping any pandemic. When the underlying infection rate of the disease is low, testing people in groups, instead of testing each individual in the population, can be more efficient. In this work, we consider noisy adaptive group testing design with specific test sensitivity and specificity that select the optimal group given previous test results based on pre-selected utility function. As in prior studies on group testing, we model this problem as a sequential Bayesian Optimal Experimental Design (BOED) to adaptively design the groups for each test. We analyze the required number of group tests when using the updated posterior on the infection status and the corresponding Mutual Information (MI) as our utility function for selecting new groups. More importantly, we study how the potential bias on the ground-truth noise of group tests may affect the group testing sample complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Advancements in NbTiN based circuits for Superconducting Digital Logic

Superconducting (SC) electronics have emerged as a promising platform for high-speed, energy-efficient computing and quantum information processing. This work, centered on NbTiN, presents recent advances in material science and fabrication methods leading to significant improvements in performance, scalability and vertical integration. We specifically report on fabrication and characterization of key components, including Josephson junctions (JJs), flux trapping structures and SC interconnects. Together, these efforts represent critical steps towards realizing practical, complex, dense and large-scale SC integrated circuits.

Pokhrel, A. [Imec,Heverlee,Belgium]↗
DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
DOE OSTI2024

The Path to Autonomous Cyberdefense

Defenders are overwhelmed by attacks against their networks, which will only be exacerbated as attackers leverage artificial intelligence to automate workflows. Here, we propose a path to autonomous cyberagents able to augment defenders by automating critical steps in the cyberdefense lifecycle.

Oesch, Sean↗
DOE OSTI2025

Assembly of the MOLLER Toroidal Magnets at Jefferson Lab

The MOLLER experiment at the Thomas Jefferson National Accelerator Facility (JLab) aims to precisely measure the weak interaction between electrons. This experiment leverages the 12 GeV electron beam and will run for three years. A crucial component of MOLLER is a system of five uniquely shaped water-cooled toroidal magnets. These magnets, possessing seven-fold symmetry, are designed to focus particles by separating electrons scattered off hydrogen in a liquid hydrogen target. The five magnets separate electrons scattered off electrons (Møller scattering) and protons (elastic e-p scattering) within target into approximately circular rings at the detector. Here, this paper presents the assembly process for these five toroidal magnets, detailing critical steps including epoxy application, pin insertion, soldering and brazing, coil assembly, and alignment, all performed to meet stringent magnet specifications. Additionally, it discusses challenges encountered during construction and highlights lessons learned, offering insights for future magnet development projects.

Lamont, Joseph [Thomas Jefferson National Accelera↗
DOE OSTI2025

Seeing the forest for all the trees: Mycorrhizal‐associated nutrient economies are modulated by stem density and the synchrony between overstorey and understorey communities

1. Identifying relationships between plant communities and soil characteristics is a critical step in understanding the consequences of species gains and losses in ecosystems. The mycorrhizal associated nutrient economy (MANE) hypothesis predicts that the degree to which tree species and their mycorrhizal associates affect soil properties is driven by the relative dominance of different mycorrhizal types (often measured by relative basal area). While this approach emphasizes the importance of canopy trees, it does not account for how other factors (e.g. the density and composition of understorey trees) may alter tree–soil relationships. 2. We analysed tree–soil data from an eastern deciduous forest in southern Indiana, USA that contains >29,000 georeferenced stems, including 21 species that associate with arbuscular mycorrhizal (AM) fungi and 14 that associate with ectomycorrhizal (ECM) fungi. We sampled soils (upper 5 cm) from across the plot and modelled soil characteristics to tree communities. Given differences in soil characteristics among AM-dominated and ECM-dominated neighbourhoods, we hypothesized that relationships between tree-mycorrhizal dominance and soils would be affected by both the density of stems in the plot (owing to ‘Zinke’ individual plant effects) and composition of the understorey trees (owing to ‘trait divergence effects’) 3. In support of our Zinke hypothesis, we found that the relationships between tree-mycorrhizal dominance and soil variables (soil pH, nitrification rates and carbon to nitrogen ratio) were strengthened as stand density increased. In support of the trait divergence hypothesis, we found that as the mismatch between overstorey and understorey composition increased (e.g. AM-dominated understories beneath ECM-dominated overstories and vice versa), the relationship between tree dominance and soil variables weakened. We were able to use these insights to create mycorrhizal metrics to predict soil variables that were sensitive to the structural composition of neighbourhoods. 4. Synthesis. Our results indicate that relationships between plants and soils in forests are not only shaped by dominant overstorey trees but also the density and composition of understorey trees. Thus, efforts to predict the ecosystem consequences of species gains and losses may benefit from considering these elements of forest structure and not only the basal area of the dominant trees.

MANE framework↗
DOE OSTI2021

Solving the sample size problem for resource selection functions

Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.

Street, Garrett M.↗
DOE OSTI2022

Predicting initial dissolution rates using structural features from molecular dynamics simulations

Predicting chemical durability of glass materials is important for various applications from daily life such as drink glass and kitchen ware to advanced technologies such as nuclear waste disposal and biomedicine. In this work, we explored prediction of initial dissolution rate through structural features from molecular dynamics (MD) simulations for a wide range of glass compositions (total 28) including borosilicate and aluminosilicate glasses, ZrO 2 -containing and V 2 O 5 -containing boroaluminosilicate glasses. The initial dissolution rates (r 0 ) measured experimentally at 90 °C with varying solution conditions were correlated with structural features (e.g., polyhedral linkages and non-bridging oxygen species) obtained from MD simulations, either from this study or from literature. Since hydrolysis of the glass network through breaking of the network former linkages (e.g., Si-O-Si, Si-O-Al, etc.) is a critical step of network glass dissolution, the statistics of these linkages obtained from MD were correlated to r0 through linear regression, where the coefficient of determination (R 2 ) and root mean square error are found to be 0.949 and 0.681, respectively. This model was compared and discussed with existing models developed by various approaches including machine learning, the kinetic rate equation, topological constraint theory, and other descriptors from MD simulations. The discussion provides insights on future model improvements to predict glass dissolution. In addition, the impact of V 2 O 5 on the glass dissolution was examined in detail, implicating that the impact is not the same across all glass compositions and test conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Viral‐mediated delivery of morphogenic regulators enables leaf transformation in Sorghum bicolor (L.)

Recent advancements in monocot transformation, using leaf tissue as explant material, have expanded the number of grass species capable of transgenesis. However, the complexity of vectors and reliance on inducible excision of essential morphogenic regulators have so far limited widespread application. Plant RNA viruses, such as Foxtail Mosaic Virus (FoMV), present a unique opportunity to express morphogenic regulator genes, such as Babyboom (Bbm), Wuschel2 (Wus2), Wuschel-like homeobox protein 2a (Wox2a) and the GROWTH-REGULATING FACTOR 4 (GRF4) GRF-INTERACTING FACTOR 1 (GIF1) fusion protein transiently in leaf explant tissues. Furthermore, altruistic delivery of conventional and viral vectors could provide opportunities to simplify vectors used for leaf transformation—facilitating vector optimization and reducing reliance on morphogenic regulator gene integration. In this study, both viral and conventional T-DNA vectors were tested for their ability to promote the formation of embryonic calli, a critical step in leaf transformation protocols, using Sorghum bicolor leaf explants. Although conventional leaf transformation vectors yielded viable embryonic calli (43.2 ± 2.9%: GRF4-GIF1, 50.2 ± 3%: Bbm/Wus2), altruistic conventional vectors employing the GRF4-GIF1 morphogenic regulator resulted in improved efficiencies (61.3 ± 4.7%). Altruistic delivery was further enhanced with the use of viral vectors employing both GRF4-GIF1 and Bbm/Wus2 regulators, resulting in 75.1 ± 2.3% and 79.2 ± 2.5% embryonic calli formation, respectively. Embryonic calli generated from both conventional and viral vectors produced shoots expressing fluorescent reporters, which were confirmed using molecular analysis. This work provides an important proof-of-concept for the use of both altruistic vectors and viral-expressed morphogenic regulators for improving plant transformation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Acquisition and Processing Considerations for Infrared Images of Rotating Turbine Blades

Abstract As designers aim to increase efficiency in gas turbines for aircraft propulsion and power generation, spatially resolved experimental measurements are needed to validate computational models and compare improvement gains of new cooling designs. Infrared (IR) thermography is one such method for obtaining spatially resolved temperature measurements. As technological advances in thermal detectors enable faster integration times, surface temperature measurements of rotating turbine blades become possible to capture including the smallest features. This paper outlines opportunities enabled by the latest IR detector technologies for capturing spatially resolved rotating blade temperatures, while also addressing some of the challenges of implementing IR for turbine rigs such as the one in the Steady Thermal Aero Research Turbine (START) Laboratory. This paper documents critical steps in achieving accurate measurements including calibration, integration times, spatial noise, and motion blur. From these results, recommendations are provided for achieving accurate IR measurements collected in a rotating turbine facility to study film cooling.

Engineering↗
DOE OSTI2026

TRIZ-Based Design Improvement for Facilitating Transmission Control Unit Remanufacturing

Design for Remanufacturing (DfRem) centers on enhancing product design and operations to facilitate remanufacturing while increasing both economic and environmental sustainability. DfRem requires a comprehensive understanding of product characteristics, production conditions, and operational constraints. Thus, DfRem requires a systematic methodology to identify and implement alternative designs. Among many critical steps in remanufacturing, disassembly is essential because it directly supports remanufacturing by separation of product components for cleaning, inspection, and other subsequent process steps. This study proposes a TRIZ-based framework to improve product design for disassembly in support of remanufacturing. The framework is applied to a transmission control unit (TCU) case study. The current TCU design prevents remanufacturing due to the sealant-based component joinery, which complicates disassembly and risks damaging the printed circuit board (PCB). After defining technical contradictions for the TCU product design and reviewing the suggested TRIZ principles to solve the conflicts, a cantilever snap-fit design alternative is developed. The economic feasibility of the snap-fit design is assessed by comparing the current and snap-fit design costs for three life cycles. The cost analysis demonstrates that the cost of the snap-fit design remains the same as the current design. Additionally, the snap-fit design offers substantial cost savings for three life cycles compared to the current design. We demonstrate how snap-fit design supports both environmental and economic sustainability.

42 ENGINEERING↗
DOE OSTI2020

Highly selective Si 3 N 4 /SiO 2 etching using an NF 3 /N 2 /O 2 /H 2 remote plasma. I. Plasma source and critical fluxes

Highly selective plasma etching of silicon nitride (Si 3 N 4 ), while not etching silicon dioxide (SiO 2 ), is a critical step in the fabrication of microelectronics devices. In many applications, this etching must be damage-free and isotropic, which then motivates the use of remote plasmas where the reactants interacting with the substrate are dominantly neutral species. In this paper and Paper II, mechanisms for highly selective Si 3 N 4 etching in remote plasmas are discussed based on results from experiments and simulations. It has been shown experimentally that high Si 3 N 4 /SiO 2 etch selectivity (≈380) can be achieved in the downstream effluent of an NF 3 /N 2 /O 2 /H 2 plasma. The authors found that H2 plays a principal role in the reaction mechanism as Si 3 N 4 /SiO 2 selectivity shows a sharp maximum as a function of the H2 flow rate. Based on this observation, and measured densities of F-atoms and H2 in the process chamber, a mechanism of selective Si 3 N 4 /SiO 2 etching is proposed in which HF molecules in vibrationally excited states accelerate etching reactions. A reaction mechanism for NF 3 /N 2 /O 2 /H 2 plasmas and its afterglow was developed to computationally determine the species densities and fluxes on the wafer level, validated by comparing with experimentally measured F-atom and H 2 densities. The calculated species densities and fluxes were used as input to an analytical model of Si 3 N 4 and SiO 2 etching based on the results of quantum chemistry simulations. This paper presents experimental results (etching data and species densities), the reaction mechanism for NF 3 /N 2 /O 2 /H 2 plasmas, and the results of simulations of gas phase chemistry. Quantum chemistry simulations of elementary etching reactions, description of the analytical model of Si 3 N 4 and SiO 2 etching, calculations of the etch rates, and Si 3 N 4 and SiO 2 selectivity with this model are presented in Paper II.

42 ENGINEERING↗
DOE OSTI2021

Self-assembled vertically aligned nanocomposite systems integrated on silicon substrate: Progress and future perspectives

Silicon (Si) integration is a critical step for implementing functional oxides into Si-based electronic devices, considering the advantages of low-cost and scalability of Si substrates. In the past decade, self-assembled vertically aligned nanocomposites (VANs) have attracted enormous research interest owing to their unique multifunctionalities and highly tunable physical properties as well as their one-step self-assembly process. Most of the VAN thin films have been reported to grow epitaxially on single crystalline oxide substrates, however, with limited systems reported on Si substrates due to the very large lattice mismatch between oxides and Si lattices. In this review, the current progress for self-assembled VAN systems integrated on a Si substrate is summarized. Buffer layered enabled VAN growth has been proven to be an effective approach for improving the epitaxial quality of oxide-oxide and oxide-metal VAN systems, while direct growth is preferred in nitride-metal VAN systems. Furthermore, the material versatility enables the Si-integrated VAN thin films to exhibit distinct physical properties such as ferromagnetism, ferroelectricity, magnetoresistance, as well as unique optical properties. The review also summarizes the various parameters for tuning the growth morphologies and corresponding properties for the VAN systems, including phase molar ratio, deposition frequency, buffer layers, background pressure, etc. Finally, future perspectives are discussed including new VAN system exploration, physical properties tuning, as well as design and fabrication of Si-based nanoelectronics and nanophotonic devices applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Size-dependent influence of NO x on the growth rates of organic aerosol particles

Atmospheric new-particle formation (NPF) affects climate by contributing to a large fraction of the cloud condensation nuclei (CCN). Highly oxygenated organic molecules (HOMs) drive the early particle growth and therefore substantially influence the survival of newly formed particles to CCN. Nitrogen oxide (NO x ) is known to suppress the NPF driven by HOMs, but the underlying mechanism remains largely unclear. Here, we examine the response of particle growth to the changes of HOM formation caused by NO x . We show that NO x suppresses particle growth in general, but the suppression is rather nonuniform and size dependent, which can be quantitatively explained by the shifted HOM volatility after adding NO x . By illustrating how NO x affects the early growth of new particles, a critical step of CCN formation, our results help provide a refined assessment of the potential climatic effects caused by the diverse changes of NO x level in forest regions around the globe.

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

Emergence of SARS-CoV-2 through recombination and strong purifying selection

COVID-19 has become a global pandemic caused by the novel coronavirus SARS-CoV-2. Understanding the origins of SARS-CoV-2 is critical for deterring future zoonosis, discovering new drugs, and developing a vaccine. We show evidence of strong purifying selection around the receptor binding motif (RBM) in the spike and other genes among bat, pangolin, and human coronaviruses, suggesting similar evolutionary constraints in different host species. We also demonstrate that SARS-CoV-2’s entire RBM was introduced through recombination with coronaviruses from pangolins, possibly a critical step in the evolution of SARS-CoV-2’s ability to infect humans. Similar purifying selection in different host species, together with frequent recombination among coronaviruses, suggests a common evolutionary mechanism that could lead to new emerging human coronaviruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Verwey transition as evolution from electronic nematicity to trimerons via electron-phonon coupling

Understanding the driving mechanisms behind metal-insulator transitions (MITs) is a critical step toward controlling material’s properties. Since the proposal of charge order–induced MIT in magnetite Fe 3 O 4 in 1939 by Verwey, the nature of the charge order and its role in the transition have remained elusive. Recently, a trimeron order was found in the low-temperature structure of Fe 3 O 4 ; however, the expected transition entropy change in forming trimeron is greater than the observed value, which arises a reexamination of the ground state in the high-temperature phase. Here, we use electron diffraction to unveil that a nematic charge order on particular Fe sites emerges in the high-temperature structure of bulk Fe 3 O 4 and that, upon cooling, a competitive intertwining of charge and lattice orders arouses the Verwey transition. Our findings discover an unconventional type of electronic nematicity in correlated materials and offer innovative insights into the transition mechanism in Fe 3 O 4 via the electron-phonon coupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

La- and Mn-doped cobalt spinel oxygen evolution catalyst for proton exchange membrane electrolysis

Discovery of earth-abundant electrocatalysts to replace iridium for the oxygen evolution reaction (OER) in a proton exchange membrane water electrolyzer (PEMWE) represents a critical step in reducing the cost for green hydrogen production. Here we report a nanofibrous cobalt spinel catalyst codoped with lanthanum (La) and manganese (Mn) prepared from a zeolitic imidazolate framework embedded in electrospun polymer fiber. The catalyst demonstrated a low overpotential of 353 millivolts at 10 milliamperes per square centimeter and a low degradation for OER over 360 hours in acidic electrolyte. A PEMWE containing this catalyst at the anode demonstrated a current density of 2000 milliamperes per square centimeter at 2.47 volts (Nafion 115 membrane) or 4000 milliamperes per square centimeter at 3.00 volts (Nafion 212 membrane) and low degradation in an accelerated stress test.

36 MATERIALS SCIENCE↗