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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Mechanistic modeling of lifetime distribution of SiC/SiC composite claddings

Silicon carbide (SiC) fiber-reinforced SiC matrix (SiC/SiC) composites have emerged as a new material candidate for fuel claddings in light water reactors. Recent studies showed that the load capacity of SiC/SiC materials exhibits a considerable statistical variation. Therefore, reliability analysis plays a critical role in design of SiC/SiC composite claddings. Here, this paper presents a probabilistic model for the lifetime distribution of SiC/SiC composites. The model is anchored by a multiaxial stress-based failure criterion and subcritical damage accumulation mechanism. Based on the kinetics of subcritical damage growth, the lifetime distribution of a laboratory test specimen for any given loading history can be calculated. A finite weakest-link model is used to extrapolate the lifetime distribution of test specimens to full-length claddings. It is shown that the damage accumulation mechanism has a strong influence on the lifetime distribution of the cladding. This finding highlights the importance of understanding the static fatigue behavior of SiC/SiC composites. The present analysis also demonstrates an intricate length effect on the failure probability of the cladding, which is expected to play a crucial role in design extrapolation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SCA Tools - SCRM Value Add or Lossy Noise Machines

Software supply chain risk management (SCRM) depends upon accurate information regarding the software components that comprise any given software system. The collection of components included in a software package can be organized within a software bill of materials, or SBOM. SBOMs are ideally generated when the software components are put together, such as at compile time, but for many reasons that has not and is not always possible. For example, legacy or proprietary software packages often do not have SBOMs available to downstream consumers of that software. It’s not just end users that are affected, manufacturers themselves also must deal with this problem. To answer these questions, the market has seen the rise of several commercial software composition analysis (SCA) tools. These tools aim to peer into completed software systems, automatically identifying hidden software dependencies and looking up known vulnerabilities associated with those dependencies to enable end-users to enhance their cyber supply chain risk management processes. These tools are potentially a huge boon to end users of legacy and proprietary software – and a potential bane, depending on how accurate they are. This research asks that question – how accurate are currently available binary SCA tools – and provides answers to several other questions: What does it mean to be “accurate”? What limitations do the tools have in identifying common edge cases that take place in modern software development? Can they help you avoid a devastating supply chain attack, or is it all just noise? After researching SCA tools on the market, we identified three vendors that fit our use case and would provide analysis on compiled binaries. Using these tools, we submitted firmware for critical infrastructure devices for analysis and SBOM generation. The SBOM outputs were then cross referenced with SBOMs generated through manual analysis for comparison. In addition to the firmware samples, we also submitted edge case samples based off a popular open-source library that were specifically crafted to evaluate each tools’ ability to accurately identify components. These samples were customized to be consistent with modifications we have seen in modern software development as well as a couple that are representative of supply chain attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Estimating National-Scale Wind Potential Using Spatially Explicit Turbine Layout Optimization

National renewable energy potential assessments play a broad and critical role in analysis of the clean energy transition by providing foundational estimates of developable clean resources. Common to all past wind potential assessments is an assumption that wraps the complexity of wind plant layout (arrangement of turbines) into a single metric known as capacity density, or rated power capacity per unit of land area. Quite often, a singular capacity density or rotor-diameter driven capacity density is used in wind potential assessments across broad geographies despite the complexities of local drivers. Here, we present a new wind technical potential assessment for the United States, leveraging a spatial optimization approach in lieu of the traditional uniform capacity density. The optimization approach is a spatially explicit method for determining the potential locations of individual wind turbines-taking into account the turbine configuration, plant economics and losses, wind resource, and siting considerations. Our approach accounts for the interactions between wind technology design, wind plant layout, and the vast array of regulatory, land use and infrastructure conflicts with wind development. Our results highlight the potential ability of larger turbines to enable increased wind capacity, up to a point, and increased generation when siting turbines in and around spatial constraints; moreover, they demonstrate and capture the LCOE benefit of relatively lower capacity densities and reduced wake losses when land is relatively abundant. These insights provide foundational knowledge for the wind sector as it develops and pursues future turbine models and as wind energy markets expand in zero-carbon futures. Further, when applied in capacity expansion models, supply curves developed by these methods can provide detailed local insights about where wind turbines might be deployed in and among known siting constraints for those regions where wind energy is determined to be economic, providing critical nuance to local decision-makers and stakeholders.

17 WIND ENERGY↗
DOE OSTI2024

Insight of Synthesis of Single Crystal Ni-Rich LiNi 1-x-y Co x Mn y O 2 Cathodes

Single-crystal Ni-rich LiNi 1-x-y Co x Mn y O 2 (NCM) cathodes have garnered widespread attention in the lithium-ion battery community due to their unique advantages in mechanical performance and their ability to minimize interfacial electrochemical side reactions. The synthesis of single-crystal materials with monodisperse and appropriate size, minimal lattice defects, and highly ordered structures is the key for high-performance batteries. However, achieving this goal poses challenges due to the lack of in-depth understanding regarding specific experimental parameters and the solid reaction mechanism during the synthesis process. Further, in this review, the aim is to provide an in-depth analysis of the critical process parameters involved in the synthesis and their impact on crystal morphology, structure, and electrochemical performance. Consequently, the first section focuses on the effect of the precursor morphology, lithium salt, atmosphere, and sintering procedure. In the second section, the study delves into an in-depth discussion of the solid reaction and crystal growth mechanism. Lastly, it is concluded by highlighting the prospects and challenges associated with the synthesis and application of single-crystal Ni-rich NCM cathodes.

25 ENERGY STORAGE↗
DOE OSTI2025

Importance of Sample Handling and Preparation for Surface Analysis: A Summary of ISO Standard 20579 Part 1, Documenting and Reporting the Handling of Specimens Prior to Analysis

Reliable and reproducible analysis of surfaces is critically dependent on how samples have been collected, handled, and prepared before analysis. Although there are important, well-established general guidelines to minimize unintended alteration of the surfaces to be analyzed, specific handling steps depend on the nature of the samples and information desired. Assessments of the validity and reliability of the results require adequate information about sampling, handling, and processing. ISO standard 20579 part 1 describes the information to be recorded and reported for samples that are prepared or submitted for surface analysis. This paper summarizes elements of the standard, which includes annexes that justify and inform the sample handling processes. Also included is a checklist of information that needs to be recorded and reported and an appendix containing an example of a form that might be used to report the information.

AES↗
DOE OSTI2021

Charged particle tracking with quantum annealing optimization

Abstract At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none have investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Flexibility of oxygen sublattice and hydrogen bond length predict proton mobility in ternary metal oxides

Discovery of fast proton conductors is important for advancing clean energy technologies. This requires a better understanding of proton migration mechanisms. While structural and chemical traits of ternary metal oxides have been related to proton migration barriers, lattice dynamical effects have not been resolved quantitatively. Here, in this work, we introduce a phonon-based dynamic descriptor, termed ‘‘thermal O…O fluctuation,’’ quantifying the flexibility of donor-acceptor oxide-ion pairs. This enables direct comparison of O-sublattice flexibility across diverse metal oxides. Using regression models, we ranked physical descriptors as predictors of proton mobility, finding that H-bond length and thermal O…O fluctuation were the strongest descriptors. Further analysis revealed a critical O…O spacing of 2.4 A˚ at the transition state, which is easier to reach by more flexible donor-acceptor pairs, enabling facile proton transfer. Our results demonstrate oxygen sublattice flexibility as a dynamic descriptor and provide guiding principles for enhancing proton mobility in ternary metal oxides.

diffusivity↗
DOE OSTI2024

Unveiling the porosity effect of superbase ionic liquid-modified carbon sorbents in CO 2 capture from air

Direct air capture (DAC) of CO 2 represents one of the most promising technologies to achieve negative carbon emissions. In this work, the superbase ionic liquids (ILs)-modified carbon substrates were developed for DAC of CO 2 by harnessing the strong CO 2 binding capability of IL and the ordered porous channels of the carbon supports. Detailed porosity analysis revealed that the IL with an aromatic cation and an oxygenate anion preferred to fill the micropores, and a thin layer was created on the surface of the mesopores. Strong π-π interaction between the IL layer and the carbon surface was disclosed by wide-angle X-ray scattering (WAXS) analysis, leading to enhanced thermal stability of the IL phase. For the same lL coating amount, the DAC of CO 2 evaluation revealed that a larger mesopore size and pore volume in the carbon/IL composite materials led to higher CO 2 uptake capacity by exposing more active sites to integrate CO 2 from diluted sources. Further, the thermodynamic analysis confirmed the critical role of IL coating in providing strong chemisorption sites and significantly improved selectivity to enrich the diluted CO 2 from the air atmosphere. This work provides guidance on leveraging the scaffolds' surface properties and porosities of the scaffolds to optimize DAC of CO 2 behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2020

A Growing Freshwater Lens in the Arctic Ocean With Sustained Climate Warming Disrupts Marine Ecosystem Function

One of the most robust changes in the hydrological cycle predicted by Earth System Models (ESMs) during the remainder of 21st century is an increase in the difference between precipitation and evapotranspiration (P-E) in arctic and boreal regions. We explore the long-term consequences of this change for marine ecosystems in the Arctic Ocean using the Community Earth System Model forced with a business as usual scenario of future greenhouse gas concentrations. We find that by the year 2300 increases in freshwater delivery considerably reduce Arctic Ocean surface salinity, creating a freshwater lens that has far-reaching impacts on marine biogeochemistry. The expanding freshwater lens limits vertical nutrient supply into the euphotic zone by enhancing vertical stratification and accelerating surface lateral mixing with surface waters in the North Atlantic, which become increasingly nutrient depleted from weakening of the Atlantic Meridional Overturning Circulation (AMOC). The resulting increase in nutrient stress reduces marine export production in the Arctic Ocean by 53% in 2300 relative to the 1990s and triggers a shift in community composition with small phytoplankton replacing diatoms. At the same time, the seasonal timing of export production undergoes a 2-month forward shift, with the peak advancing from July to May. This suggests that the threat to food webs and higher trophic levels may intensify after the year 2100 as gains in productivity from sea ice loss saturate and freshwater impacts on nutrient stress continue to strengthen. Our analysis highlights the critical importance of changing terrestrial hydrology and land-ocean coupling as drivers of long-term biogeochemical change in the Arctic Ocean and the necessity of multi-century climate change projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Two-dimensional-lattice-confined single-molecule-like aggregates

Intermolecular distance largely determines the optoelectronic properties of organic matter. Conventional organic luminescent molecules are commonly used either as aggregates or as single molecules that are diluted in a foreigner matrix. They have garnered great research interest in recent decades for a variety of applications, including light-emitting diodes, lasers and quantum technologies, among others. However, there is still a knowledge gap on how these molecules behave between the aggregation and dilution states. Here we report an unprecedented phase of molecular aggregate that forms in a two-dimensional hybrid perovskite superlattice with a near-equilibrium distance, which we refer to as a single-molecule-like aggregate (SMA). By implementing two-dimensional superlattices, the organic emitters are held in proximity, but, surprisingly, remain electronically isolated, thereby resulting in a near-unity photoluminescence quantum yield, akin to that of single molecules. Moreover, the emitters within the perovskite superlattices demonstrate strong alignment and dense packing resembling aggregates, allowing for the observation of robust directional emission, substantially enhanced radiative recombination and efficient lasing. Molecular dynamics simulations together with single-crystal structure analysis emphasize the critical role of the internal rotational and vibrational degrees of freedom of the molecules in the two-dimensional lattice for creating the exclusive SMA phase. Furthermore, this two-dimensional superlattice unifies the paradoxical properties of single molecules and aggregates, thus offering exciting possibilities for advanced spectroscopic and photonic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

A spectroscopic, structural, and computational study of Ag–oxo interactions in Ag + /UO 2 2+ complexes

For this work, twelve novel Ag + /UO 2 2+ heterometallic complexes have been prepared and characterized via structural, spectroscopic, and computational methods to probe the effects of Ag–oxo interactions on bonding and photophysical properties of the uranyl cation. Structural characterization reveals Ag–oxo interaction distances ranging from 2.475(3) Å to 4.287(4) Å. These interactions were probed using luminescence and Raman spectroscopy which displayed little effect on the luminescence intensity and the energy of the Raman active U═O symmetric stretch peak as compared to previously reported Pb–oxo interactions. Computational efforts via density functional theory-based natural bond orbital analysis revealed that the highest stabilization energy associated with the Ag–oxo interaction had a value of only 11.03 kcal mol –1 and that all other energy values fell at 7.05 kcal mol –1 or below indicating weaker interactions relative to those previously reported for Pb 2+ /UO 2 2+ heterometallic compounds. In contrast, quantum theory of atoms in molecules analysis of bond critical point electron density values indicated higher electron density in Ag–oxo interactions as compared to Pb–oxo interactions which suggests more covalent character with the Ag + . Overall, this data indicates that Ag + has a less significant effect on UO 2 2+ bonding and photophysical properties as compared to other Pb 2+ , likely due to the high polarizability of the cation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Jet Drift in Heavy-Ion Collision: Acoplanarity and v 2

We introduce a sub-eikonal anisotropic contribution to jet-broadening, “jet drift,” that couples to the flow of the nuclear medium, showing that this effect results in a deflection of hard partons, and thus jets, in the direction of the medium flow. We study two-jet observables, i.e. v 2 and acoplanarity for √s = 5.02 TeV PbPb collisions at the LHC. We show that jet drift leads to an enhancement of the elliptic flow (v 2 ) of charged hadrons and modification of the acoplanarity of dihadrons and discuss the implications for jet substructure and medium response effects. We also demonstrate that we can obtain independent information about the event geometry encoded in the drift modification to acoplanarity vs v 2 enhancement, whereas the temperature dependence of these observables is qualitatively different. By disentangling this temperature vs geometry dependence of these observables, we can add discriminatory power to our analysis. Next, we study acoplanarity and v 2 across different system sizes and collisional energies i.e. 200 GeV AuAu collision at RHIC using our in-house simulation package (APE). Our analysis will be critical for understanding and generalizing dynamical jet-QGP interactions for different collisional systems and energies which will aid event engineering applications in the future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Blocking C-terminal processing of KRAS4b via a direct covalent attack on the CaaX-box cysteine

RAS is the most frequently mutated oncogene in cancer. RAS proteins show high sequence similarities in their G-domains but are significantly different in their C-terminal hypervariable regions (HVR). These regions interact with the cell membrane via lipid anchors that result from posttranslational modifications (PTM) of cysteine residues. KRAS4b is unique as it has only one cysteine that undergoes PTM, C185. Small molecule covalent modification of C185 would block any form of prenylation and subsequently inhibit attachment of KRAS4b to the cell membrane, blocking its biological activity. We translated this concept to the discovery and development of disulfide tethering screen hits into irreversible covalent modifiers of C185. These compounds inhibited proliferation of KRAS4b-driven mouse embryonic fibroblasts, but not cells driven by N-myristoylated KRAS4b that harbor a C185S mutation and are not dependent on C185 prenylation. Top–down proteomics was used to confirm target engagement in cells. These compounds bind in a pocket formed when the HVR folds back between helix 3 and 4 in the G-domain (HVR-α3-α4). This interaction can happen in the absence of small molecules as predicted by molecular dynamics simulations and is stabilized in the presence of C185 binders as confirmed by small-angle X-ray scattering and solution NMR. NOESY-HSQC, an NMR approach that measures internuclear distances of 6 Å or less, and structure analysis identified the critical residues and interactions that define the HVR-α3-α4 pocket. Further development of compounds that bind to this pocket could be the basis of a new approach to targeting KRAS cancers.

C185↗
DOE OSTI2021

Computation of linear MHD instabilities with the multi-region relaxed MHD energy principle

Over the last decade, a variational principle based on a generalisation of Taylor's relaxation, referred to as multi-region relaxed magnetohydrodynamics (MRxMHDs) has been developed. The numerical solutions of the MRxMHD equilibria have been constructed using the Stepped Pressure Equilibrium Code (SPEC) (Hudson et al 2012 Phys. Plasmas 19 112502). In principle, SPEC could also be established to describe the MRxMHD stability of an equilibrium. In this work, a theoretical framework is developed to relate the second variation of the energy functional to the so-called Hessian matrix, enabling the prediction of MHD linear instabilities of cylindrical plasmas, and is implemented in SPEC. The negative and positive eigenvalues of the Hessian matrix predict the stability of an equilibrium. Verification studies of SPEC stability results with the M3D-C 1 code and the tearing mode $\Delta^{^{\prime}}$ criterion have been conducted for ideal and resistive MHD instabilities, respectively, in a pressureless cylindrical tokamak, and have shown good agreement. Overall, our stability analysis is a critical step towards understanding the MHD stability of three-dimensional MHDs where nested flux surfaces, magnetic islands and stochastic regions co-exist.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Sheaves as a Framework for Understanding and Interpreting Model Fit

As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex systems where extensive structure is available, but must be drawn from peripheral sources. In this paper we argue that in such situations, sheaves can provide a natural framework to analyze how well a statistical model fits at the local level (that is, on subsets of related datapoints) vs the global level (on all the data). The sheaf-based approach that we propose is suitably general enough to be useful in a range of applications, from analyzing sensor networks to understanding the feature space of a deep learning model.

Kvinge, Henry J.↗