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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 OSTI2024

Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Abstract Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain ( https://centilebrain.org/#/brainAGE2 ), an open‐science, web‐based platform for individualized neuroimaging metrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Hidden Features: How Subsurface and Landscape Heterogeneity Govern Hydrologic Connectivity and Stream Chemistry in a Montane Watershed

ABSTRACT Hydrologic connectivity is defined as the connection among stores of water within a watershed and controls the flux of water and solutes from the subsurface to the stream. Hydrologic connectivity is difficult to quantify because it is goverened by heterogeniety in subsurface storage and permeability and responds to seasonal changes in precipitation inputs and subsurface moisture conditions. How interannual climate variability impacts hydrologic connectivity, and thus stream flow generation and chemistry, remains unclear. Using a rare, four‐year synoptic stream chemistry dataset, we evaluated shifts in stream chemistry and stream flow source of Coal Creek, a montane, headwater tributary of the Upper Colorado River. We leveraged compositional principal component analysis and end‐member mixing to evaluate how seasonal and interannual variation in subsurface moisture conditions impacts stream chemistry. Overall, three main findings emerged from this work. First, three geochemically distinct end members were identified that constrained stream flow chemistry: reach inflows, and quick and slow flow groundwater contributions. Reach inflows were impacted by historic base and precious metal mine inputs. Bedrock fractures facilitated much of the transport of quick flow groundwater and higher‐storage subsurface features (e.g., alluvial fans) facilitated the transport of slow flow groundwater. Second, the contributions of different end members to the stream changed over the summer. In early summer, stream flow was composed of all three end members, while in late summer, it was composed predominantly of reach inflows and slow flow groundwater. Finally, we observed minimal differences in proportional composition in stream chemistry across all four years, indicating seasonal variability in subsurface moisture and spatial heterogeneity in landscape and geologic features had a greater influence than interannual climate fluctuation on hydrologic connectivity and stream water chemistry. These findings indicate that mechanisms controlling solute transport (e.g., hydrologic connectivity and flow path activation) may be resilient (i.e., able to rebound after perturbations) to predicted increases in climate variability. By establishing a framework for assessing compositional stream chemistry across variable hydrologic and subsurface moisture conditions, our study offers a method to evaluate watershed biogeochemical resilience to variations in hydrometeorological conditions.

Johnson, Keira [College of Earth, Ocean, and Atmos↗
DOE OSTI2023

Exploring young adult texts within the Historically Responsive Literacy Framework with preservice teachers

Abstract While culturally responsive texts have become more common in teacher education, too often, preservice teachers (PTs) are not asked to examine how to use these books pedagogically. To address this issue, in a young adult (YA) literature course, the Framework for Culturally and Historically Responsive Literacy (HRL) (Muhammad, 2000) Cultivating genius: An equity framework for culturally and historically responsive literac y, 2020; Scholastic) was used in conjunction with representative and inclusive YA trade books. Data analysis was conducted on PT's course work, including HRL unit plans, to determine whether PTs were able to implement Muhammad's (2020) framework using YA texts. The findings of this study show that PTs' understanding of the HRL tenets was at varying levels. While there were unit plans with learning goals that matched Muhammad's purpose and definition for each tenet and YA connections for every tenet, PTs showed a better understanding of the tenets of identity and intellect than skills and Criticality. Implications of these findings including exposing PTs to learning frameworks “written by people of color and designed for children of color” (Muhammad, 2020, p. 11) to create instruction that meets their future students' needs and does not just skim the surface of culturally responsive pedagogies.

Pule, Heather↗
DOE OSTI2022

Particulate and dissolved metabolite distributions along a latitudinal transect of the western Atlantic Ocean

Abstract Metabolites, or the small organic molecules that are synthesized by cells during metabolism, comprise a complex and dynamic pool of carbon in the ocean. They are an essential currency in interactions at the population and community levels of biological organization. Characterizing metabolite distributions inside microbial cells and dissolved in seawater is essential to understanding the controls on their production and fate, as well as their roles in shaping marine microbial food webs. Here, we apply a targeted metabolomics method to quantify particulate and dissolved distributions of a suite of biologically relevant metabolites including vitamins, amino acids, nucleic acids, osmolytes, and intermediates in biosynthetic pathways along a latitudinal transect in the western Atlantic Ocean. We find that, in the upper 200 m of the water column, most particulate or intracellular metabolites positively covary with the most abundant microbial taxa. In contrast, dissolved metabolites exhibited greater variability with differences in distribution between ocean regions. Although fewer particulate metabolites were detected below 200 m, the particulate metabolites identified in the deep ocean may be linked to adaptive physiological strategies of deep‐sea microbes. Based on the identified metabolite distributions, we propose relationships between certain metabolites and microbial populations, and find that dissolved metabolite distributions are not directly related to their particulate abundances.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗
DOE OSTI2021

Multiscale design of nonlinear materials using a Eulerian shape optimization scheme

Motivated by recent advances in manufacturing, the design of materials is the focal point of interest in the material research community. One of the critical challenges in this field is finding optimal material microstructure for a desired macroscopic response. This work presents a computational method for the mesoscale-level design of particulate composites for an optimal macroscale-level response. The method relies on a custom shape optimization scheme to find the extrema of a nonlinear cost function subject to a set of constraints. Three key “modules” constitute the method: multiscale modeling, sensitivity analysis, and optimization. Multiscale modeling relies on a classical homogenization method and a nonlinear NURBS-based generalized finite element scheme to efficiently and accurately compute the structural response of particulate composites using a nonconformal discretization. A three-parameter isotropic damage law is used to model microstructure-level failure. An analytical sensitivity method is developed to compute the derivatives of the cost/constraint functions with respect to the design variables that control the microstructure's geometry. The derivation uncovers subtle but essential new terms contributing to the sensitivity of finite element shape functions and their spatial derivatives. Several structural problems are solved to demonstrate the applicability, performance, and accuracy of the method for the design of particulate composites with a desired macroscopic nonlinear stress-strain response.

42 ENGINEERING↗
DOE OSTI2022

Photovoltaic module antireflection coating degradation survey using color microscopy and spectral reflectance

Abstract Commercial antireflective coatings (ARCs) on photovoltaic (PV) module glass can improve module power by 2.5%–3.0%, but their long‐term field performance requires additional study. In this paper, we investigate ARC performance on fielded modules using two nondestructive techniques: reflectance spectroscopy and RGB microscopy, finding a large variation in coating durability and performance. For new coatings, the fleet average nominal power enhancement is 2.8%. This power enhancement is observed to degrade at approximately −0.05%/year absolute over the first 8 years of fielding in arrays that are not regularly cleaned. Interferometry and imaging results imply that coating loss for these modules is due to slow chemical thinning of the initial 125 nm thick coating by 1.4 to 5.0 nm/year. Due to the physics of interference coatings, the performance loss is projected to accelerate as the coating is further thinned, resulting in a coating lifetime (time to 80% of initial performance) of 7.5 to 25 years. At the higher 5.0 nm/year coating loss rates, we estimate that −0.14%/year power degradation of the module can be attributed solely to ARC degradation over the first 20 years. Extreme coating loss is observed on some modules where after 8 years fielding, the coating can be completely removed with a single wet wipe with a lens tissue. A case study is also presented comparing ARC and noncoated modules installed at a single site in 2013. We find that the ARC modules have a 2.6% higher soiling power loss than the noncoated modules; this exceeds the ARC power enhancement of 2.4% and leads to the surprising conclusion that the ARC lowers production for modules in this location due to increased soiling. These results demonstrate that RGB microscopy is a powerful, field‐capable, and quantitative characterization technique for assessing degradation of PV module ARCs.

Karin, Todd↗
DOE OSTI2026

Persistence and potential of soil organic carbon in nature‐based climate solutions: A review of managed disturbances

Societal Impact Statement Implementing nature-based climate solutions is important for mitigating climate change, which is a global issue, but requires local adjustments in management practices. Using the association between soil carbon and minerals as a proxy for carbon persistence, we evaluated the effect of different management regimes on soil carbon sequestration and loss. We identified areas where management practices that increase carbon inputs should be prioritized and areas where management should focus on avoiding severe disturbances. Using this storage-potential-and-persistence framework to identify how to increase or maintain soil organic carbon storage locally will increase the effectiveness of nature-based climate solutions globally. Summary Increasing soil organic carbon storage could reduce the pace of climate change, but the longevity of this nature-based climate solution depends on the persistence of carbon in soils, not just the input rates into soils. We apply a framework for considering how soil carbon persistence—namely, via the association with minerals—sheds light on soil carbon sequestration. We review how management of disturbances, such as prescribed burning, forestry, and grazing, can change soil carbon storage, persistence, and potential. Past work demonstrated that management of disturbances can sequester soil carbon, but it remains unclear how the potential stabilization of that accrual and vulnerability to loss varies across disturbance types and geographies. We found that there is substantial geographical heterogeneity in the overlap among estimates of carbon accrual, disturbance occurrence, and potential stabilization: Fire-prone grasslands and intensively grazed rangelands occur in areas estimated to have high potential to store mineral-associated organic carbon, and studies also find that adjusted fire and grazing can promote mineral-associated organic carbon. Plantation forestry and burned area span large regions where particulate organic matter is the dominant form, and studies find that particulate organic carbon is disproportionately lost following intense wildfires and forest harvests. Thus, areas with high mineral-associated organic carbon deficits should be prioritized for practices that increase carbon inputs; whereas areas with high proportions of particulate organic carbon should be prioritized for practices that help to avoid severe disturbances. Taken together, the distribution of and changes in persistence mechanisms shed light on the durability of nature-based climate solutions.

fire↗
DOE OSTI2025

An evolutionarily conserved tryptophan cage promotes folding of the extended RNA recognition motif in the hnRNPR ‐like protein family

Abstract The heterogeneous nuclear ribonucleoprotein (hnRNP) R‐like family is a class of RNA binding proteins in the hnRNP superfamily with diverse functions in RNA processing. Here, we present the 1.90 Å X‐ray crystal structure and solution NMR studies of the first RNA recognition motif (RRM) of human hnRNPR. We find that this domain adopts an extended RRM (eRRM1) featuring a canonical RRM with a structured N‐terminal extension (N ext ) motif that docks against the RRM and extends the β‐sheet surface. The adjoining loop is structured and forms a tryptophan cage motif to position the N ext motif for docking to the RRM. Combining mutagenesis, solution NMR spectroscopy, and thermal denaturation studies, we evaluate the importance of residues in the N ext –RRM interface and adjoining loop on eRRM folding and conformational dynamics. We find that these sites are essential for protein solubility, conformational ordering, and thermal stability. Consistent with their importance, mutations in the N ext –RRM interface and loop are associated with several cancers in a survey of somatic mutations in cancer studies. Sequence and structure comparison of the human hnRNPR eRRM1 to experimentally verified and predicted hnRNPR‐like proteins reveals conserved features in the eRRM.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Machine Learning Calabi–Yau Metrics

We apply machine learning to the problem of finding numerical Calabi–Yau metrics. Building on Donaldson's algorithm for calculating balanced metrics on Kähler manifolds, we combine conventional curve fitting and machine-learning techniques to numerically approximate Ricci-flat metrics. We show that machine learning is able to predict the Calabi–Yau metric and quantities associated with it, such as its determinant, having seen only a small sample of training data. Using this in conjunction with a straightforward curve fitting routine, we demonstrate that it is possible to find highly accurate numerical metrics much more quickly than by using Donaldson's algorithm alone, with our new machine-learning algorithm decreasing the time required by between one and two orders of magnitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

On the Search for Low W 0

The magnitude of the vacuum expectation value of the Gukov-Vafa-Witten superpotential |W o | plays a central role in the phenomenology of type IIB flux compactifications. Recent analytical constructions have shown that perturbatively flat vacua can be used to obtain very low values of |W o |. We present systematic algorithms to carry out exhaustive numerical searches for such vacua. Here we also analyse them in the statistical context, as part of the entire ensemble of type IIB flux vacua at low |W o |. Our preliminary analysis indicates that these perturbatively flat vacua are statistically sparse in the whole set of vacua at low |W o | as calculated by Denef and Douglas. Two-moduli examples are used to illustrate these more general findings in specific settings. We find that these simple (two moduli) cases are good examples for existence proofs but they do not feature a large statistical tuning freedom for phenomenological applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Constrained Extrapolation Problem and Order-Dependent Mappings

In this work, we consider the problem of extrapolating the perturbation series for the dilute Fermi gas in three dimensions to the unitary limit of infinite scattering length and into the BEC region, using the available strong-coupling information to constrain the extrapolation problem. In this constrained extrapolation problem (CEP) the goal is to find classes of approximants that give well converged results already for low perturbative truncation orders. First, we show that standard Padé and Borel methods are too restrictive to give satisfactory results for this CEP. A generalization of Borel extrapolation is given by the so-called Maximum Entropy extrapolation method (MaxEnt). However, we show that MaxEnt requires extensive elaborations to be applicable to the dilute Fermi gas and is thus not practical for the CEP in this case. Instead, we propose order-dependent-mapping extrapolation (ODME) as a simple, practical, and general method for the CEP. Here, we find that the ODME approximants for the ground-state energy of the dilute Fermi gas are robust with respect to changes of the mapping choice and agree with results from quantum Monte Carlo simulations within uncertainties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Investigation of Interlayer Dielectric in BaTiO 3 /III–Nitride Transistors

In this paper, the impact of varying the thickness of the Al 2 O 3 interlayer dielectric on the electrical characteristics of BaTiO 3 /III-nitride transistors is investigated. In the findings, it is revealed that a minimum thickness of 8 nm for the Al 2 O 3 layer is crucial to maintain high device performance and protect against sputtering-induced damage during BaTiO 3 deposition. The fabricated BaTiO 3 /Al 2 O 3 /AlGaN/GaN high electron mobility transistors exhibit exceptional electrical properties, including a maximum current density of 700 mA mm –1 , an on-resistance of 5 Ω mm, an I ON /I OFF ratio of 10 7 , a subthreshold slope of 119 mV dec –1 , and significantly reduced gate leakage current. The devices with the optimal 8 nm Al 2 O 3 thickness demonstrates excellent agreement between theoretical and experimental values for effective mobility, achieving a value of 1188 cm 2 V –1 ·s at a 2D electron gas density of 10 13 cm –2 . Furthermore, in the study, it is confirmed that increasing the Al 2 O 3 thickness also improves the quality of interface charge density, as evidenced by the results obtained from capacitance–voltage measurements. In these findings, the critical role of controlling the Al 2 O 3 thickness in optimizing the electrical characteristics and overall performance of BaTiO 3 /III-nitride transistors are highlighted.

42 ENGINEERING↗
DOE OSTI2022

Probing eddy size and its effective mixing length in stably stratified roughness sublayer flows

Stably stratified roughness sublayer flows are ubiquitous yet remain difficult to represent in models and to interpret using field experiments. Here, continuous high-frequency potential temperature profiles from the forest floor up to 6.5 times the canopy height observed with distributed temperature sensing (DTS) are used to link eddy topology to roughness sublayer stability correction functions and coupling between air layers within and above the canopy. The experiments are conducted at two forest stands classified as hydrodynamically sparse and dense. Near-continuous profiles of eddy sizes (length scales) and effective mixing lengths for heat are derived from the observed profiles using a novel conditional sampling approach. The approach utilizes potential temperature isoline fluctuations from a statically stable background state. The transport of potential temperature by an observed eddy is assumed to be conserved (adiabatic movement) and we assume that irreversible heat exchange between the eddy and the surrounding background occurs along the (vertical) periphery of the eddy. This assumption is analogous to Prandtl's mixing-length concept, where momentum is transported rapidly vertically and then equilibrated with the local mean velocity gradient. A distinct dependence of the derived length scales on background stratification, height above ground, and canopy characteristics emerges from the observed profiles. Implications of these findings for (1) the failure of Monin–Obukhov similarity in the roughness sublayer and (2) above-canopy flow coupling to the forest floor are examined. The findings have practical applications in terms of analysing similar DTS data sets with the proposed approach, modelling roughness sublayer flows, and interpreting nocturnal eddy covariance measurements above tall forested canopies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges

Chemical clusters are relevant to many applications in catalysis, separations, materials, and energy sciences. Experimentally, the structure of clusters is difficult to determine, but it is very important in understanding their chemistry and properties. Computational methods can be used to examine cluster structure, however finding the most stable structure is not simple, particularly as the cluster size increases. Global optimization techniques have long been used to tackle the problem of the most stable structure, but such approaches would have to look for a global minimum, while sampling local minima over the whole potential energy surface as well. In this review, the state-of-the-art theory of global optimization theory is summarized. First, the definition, significance, relation to experiments, and a brief history of global optimization is presented. We then discuss, in more detail, three versatile global optimization methods: the basin hopping, the artificial bee colony algorithm, and the genetic algorithm. We close with some representative application examples of global optimization of clusters since 2016 and the challenges, open questions and opportunities in this field.

Global optimization, Chemical clusters, Artificial↗
DOE OSTI2020

What can we learn from N 2 O isotope data? – Analytics, processes and modelling

The isotopic composition of nitrous oxide (N 2 O) provides useful information for evaluating N 2 O sources and budgets. Due to the co–occurrence of multiple N 2 O transformation pathways, it is, however, challenging to use isotopic information to quantify the contribution of distinct processes across variable spatiotemporal scales. Here, we present an overview of recent progress in N 2 O isotopic studies and provide suggestions for future research, mainly focusing on: analytical techniques; production and consumption processes; and interpretation and modelling approaches. Comparing isotope–ratio mass spectrometry (IRMS) with laser absorption spectroscopy (LAS), we conclude that IRMS is a precise technique for laboratory analysis of N 2 O isotopes, while LAS is more suitable for in situ/inline studies and offers advantages for site–specific analyses. When reviewing the link between the N 2 O isotopic composition and underlying mechanisms/processes, we find that, at the molecular scale, the specific enzymes and mechanisms involved determine isotopic fractionation effects. In contrast, at plot–to–global scales, mixing of N 2 O derived from different processes and their isotopic variability must be considered. We also find that dual isotope plots are effective for semi–quantitative attribution of co–occurring N 2 O production and reduction processes. More recently, process–based N 2 O isotopic models have been developed for natural abundance and 15 N–tracing studies, and have been shown to be effective, particularly for data with adequate temporal resolution. Here, despite the significant progress made over the last decade, there is still great need and potential for future work, including development of analytical techniques, reference materials and inter–laboratory comparisons, further exploration of N 2 O formation and destruction mechanisms, more observations across scales, and design and validation of interpretation and modelling approaches. Synthesizing all these efforts, we are confident that the N 2 O isotope community will continue to advance our understanding of N 2 O transformation processes in all spheres of the Earth, and in turn to gain improved constraints on regional and global budgets.

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

Strongly Chiral Liquid Crystals in Nanoemulsions

We report Liquid crystal (LC) emulsions represent a class of confined soft matter that exhibit exotic internal organizations and size-dependent properties, including responses to chemical and physical stimuli. Past studies have explored micrometer-scale LC emulsion droplets but little is known about LC ordering within submicrometer-sized droplets. This paper reports experiments and simulations that unmask the consequences of confinement in nanoemulsions on strongly chiral LCs that form bulk cholesteric and blue phases (BPs). A method based on light scattering is developed to characterize phase transitions of LCs within the nanodroplets. For droplets with a radius to the pitch ratio (R v /p 0 ) as small as 2/3, the BP-to-cholesteric transition is substantially suppressed, leading to a threefold increase of the BP temperature interval relative to bulk behavior. Complementary simulations align with experimental findings and reveal the dominant role of chiral elastic energy. For R v /p 0 ≈ to 1/3, a single LC phase forms below the clearing point, with simulations revealing the new configuration to contain a τ -1/2 disclination that extends across the nanodroplet. These findings are discussed in the context of mechanisms by which polymer networks stabilize BPs and, more broadly, for the design of nanoconfined soft matter.

42 ENGINEERING↗