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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Why Do Oceanic Nonlinearities Contribute Only Weakly to Extreme El Niño Events?

Abstract Extreme El Niño events have outsized global impacts and control the El Niño Southern Oscillation (ENSO) warm/cold phases asymmetries. Yet, a consensus regarding the relative contributions of atmospheric and oceanic nonlinearities to their genesis remains elusive. Here, we isolate the contribution of oceanic nonlinearities by conducting paired experiments forced with opposite wind stress anomalies in an oceanic general circulation model, which realistically simulates extreme El Niño events and oceanic nonlinearities thought to contribute to ENSO skewness (Tropical Instability Waves (TIWs), Nonlinear Dynamical Heating (NDH)). Our findings indicate a weak contribution of oceanic nonlinearities to extreme El Niño events in the eastern Pacific, owing to compensatory effects between lateral (NDH and TIWs) and vertical processes. These results hold across different vertical mixing schemes and modifications of the upper‐ocean heat budget mixed layer criterion. Our study reinforces previous research underscoring the pivotal role of atmospheric nonlinearities in shaping extreme El Niño events.

Geology↗
DOE OSTI2024

Machine learning surrogates for surface complexation model of uranium sorption to oxides

Abstract The safety assessments of the geological storage of spent nuclear fuel require understanding the underground radionuclide mobility in case of a leakage from multi-barrier canisters. Uranium, the most common radionuclide in non-reprocessed spent nuclear fuels, is immobile in reduced form (U(IV) and highly mobile in an oxidized state (U(VI)). The latter form is considered one of the most dangerous environmental threats in the safety assessments of spent nuclear fuel repositories. The sorption of uranium to mineral surfaces surrounding the repository limits their mobility. We quantify uranium sorption using surface complexation models (SCMs). Unfortunately, numerical SCM solvers often encounter convergence problems due to the complex nature of convoluted equations and correlations between model parameters. This study explored two machine learning surrogates for the 2-pK Triple Layer Model of uranium retention by oxide surfaces if released as U(IV) in the oxidizing conditions: random forest regressor and deep neural networks. Our surrogate models, particularly DNN, accurately reproduce SCM model predictions at a fraction of the computational cost without any convergence issues. The safety assessment of spent fuel repositories, specifically the migration of leaked radioactive waste, will benefit from having ultrafast AI/ML surrogates for the computationally expensive sorption models that can be easily incorporated into larger-scale contaminant migration models. One such model is presented here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

Abstract Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s −1 ) and damaging (≥25.7 m s −1 ) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Probing the Mechanical Properties of 2D Materials via Atomic‐Force‐Microscopy‐Based Modulated Nanoindentation

Abstract As the field of low‐dimensional materials (1D or 2D) grows and more complex and intriguing structures are continuing to be found, there is an emerging need for techniques to characterize the nanoscale mechanical properties of all kinds of 1D/2D materials, in particular in their most practical state: sitting on an underlying substrate. While traditional nanoindentation techniques cannot accurately determine the transverse Young's modulus at the necessary scale without large indentations depths and effects to and from the substrate, herein an atomic‐force‐microscopy‐based modulated nanomechanical measurement technique with Angstrom‐level resolution (MoNI/ÅI) is presented. This technique enables non‐destructive measurements of the out‐of‐plane elasticity of ultra‐thin materials with resolution sufficient to eliminate any contributions from the substrate. This method is used to elucidate the multi‐layer stiffness dependence of graphene deposited via chemical vapor deposition and discover a peak transverse modulus in two‐layer graphene. While MoNI/ÅI has been used toward great findings in the recent past, here all aspects of the implementation of the technique as well as the unique challenges in performing measurements at such small resolutions are encompassed.

2D materials↗
DOE OSTI2022

libwfa: Wavefunction analysis tools for excited and open‐shell electronic states

Abstract An open‐source software library for wavefunction analysis, libwfa, provides a comprehensive and flexible toolbox for post‐processing excited‐state calculations, featuring a hierarchy of interconnected visual and quantitative analysis methods. These tools afford compact graphical representations of various excited‐state processes, provide detailed insight into electronic structure, and are suitable for automated processing of large data sets. The analysis is based on reduced quantities, such as state and transition density matrices (DMs), and allows one to distill simple molecular orbital pictures of physical phenomena from intricate correlated wavefunctions. The implemented descriptors provide a rigorous link between many‐body wavefunctions and intuitive physical and chemical models, for example, exciton binding, double excitations, orbital relaxation, and polyradical character. A broad range of quantum‐chemical methods is interfaced with libwfa via a uniform interface layer in the form of DMs. This contribution reviews the structure of libwfa and highlights its capabilities by several representative use cases. This article is categorized under: Software > Quantum Chemistry Theoretical and Physical Chemistry > Spectroscopy

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

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *

Abstract We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum with a fixed body centered tetragonal lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Unraveling the Stable Cathode Electrolyte Interface in all Solid‐State Thin‐Film Battery Operating at 5 V

Abstract Spinel‐type LiNi 0.5 Mn 1.5 O 4 (LNMO) is one of the most promising 5 V‐class cathode materials for Li‐ion batteries that can achieve high energy density and low production costs. However, in liquid electrolyte cells, the high voltage causes continuous cell degradation through the oxidative decomposition of carbonate‐based liquid electrolytes. In contrast, some solid‐state electrolytes have a wide electrochemical stability range and can withstand the required oxidative potential. In this work, a thin‐film battery consisting of an LNMO cathode with a solid lithium phosphorus oxynitride (LiPON) electrolyte is tested and their interface before and after cycling is characterized. With Li metal as the anode, this system can deliver stable performance for 600 cycles with an average Coulombic efficiency >99%. Neutron depth profiling indicates a slight overlithiated layer at the interface prior to cycling, a result that is consistent with the excess charge capacity measured during the first cycle. Cryogenic electron microscopy further reveals intimate contact between LNMO and LiPON without noticeable structure and chemical composition evolution after extended cycling, demonstrating the superior stability of LiPON against a high voltage cathode. Consequently, design guidelines are proposed for interface engineering that can accelerate the commercialization of a high voltage cell with solid or liquid electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2025

X-ray near-field multi-slice ptychography for in-situ imaging

Abstract In-situ imaging of chemical reactions can provide valuable insight into nanoparticle growth and structural evolution. Hard X-ray imaging is an excellent tool for this purpose, as it combines high spatial resolution with high penetration depth, allowing for realistic reaction environments. While far-field ptychography is a well-established method at synchrotron radiation sources, its near-field analog has received less attention. In this work we show that near-field multi-slice ptychography is a competitive method for high-resolution in-situ imaging by studying the formation of gold nanocages via galvanic replacement. Our work extends near-field X-ray ptychography to sub-50 nm spatial resolution by using multilayer Laue lenses. These high numerical aperture optics enable to distinguish sample layers that are separated by less than 100 µm by multi-slicing techniques.

Science & Technology - Other Topics↗
DOE OSTI2021

Graph convolutional networks applied to unstructured flow field data

Abstract Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for an unstructured dataset. Therefore, a graph based data-driven model to perform inference on fields defined on an unstructured mesh, using a graph convolutional neural network (GCNN) is presented. The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements. The network can infer from field samples at different resolutions, and is invariant to the order in which the measurements within each sample are presented. The GCNN method, using inductive convolutional layers and adaptive pooling, is able to predict this quantity with a validation R 2 above 0.98, and a Normalized Mean Squared Error below 0.01, without relying on spatial structure.

Ogoke, Francis (ORCID:0000000224327783)↗
DOE OSTI2020

From hydrogen to helium: the spectral evolution of white dwarfs as evidence for convective mixing

ABSTRACT We present a study of the hypothesis that white dwarfs undergo a spectral change from hydrogen- to helium-dominated atmospheres using a volume-limited photometric sample drawn from the Gaia-DR2 catalogue, the Sloan Digital Sky Survey (SDSS), and the Galaxy Evolution Explorer (GALEX). We exploit the strength of the Balmer jump in hydrogen-atmosphere DA white dwarfs to separate them from helium-dominated objects in SDSS colour space. Across the effective temperature range from 20 000 to 9000 K, we find that 22 per cent of white dwarfs will undergo a spectral change, with no spectral evolution being ruled out at 5σ. The most likely explanation is that the increase in He-rich objects is caused by the convective mixing of DA stars with thin hydrogen layers, in which helium is dredged up from deeper layers by a surface hydrogen convection zone. The rate of change in the fraction of He-rich objects as a function of temperature, coupled with a recent grid of 3D radiation-hydrodynamic simulations of convective DA white dwarfs – which include the full overshoot region – lead to a discussion on the distribution of total hydrogen mass in white dwarfs. We find that 60 per cent of white dwarfs must have a hydrogen mass larger than MH/MWD = 10−10, another 25 per cent have masses in the range MH/MWD = 10−14–10−10, and 15 per cent have less hydrogen than MH/MWD = 10−14. These results have implications for white dwarf asteroseismology, stellar evolution through the asymptotic giant branch and accretion of planetesimals on to white dwarfs.

Cunningham, Tim↗
DOE OSTI2023

Voltage‐Modulated Untwist Deformations and Multispectral Optical Effects from Ion Intercalation into Chiral Ceramic Nanoparticles

Abstract Reconfiguration of chiral ceramic nanostructures after ion intercalation should favor specific nanoscale twists leading to strong chiroptical effects. In this work, V 2 O 3 nanoparticles are shown to have “built‐in” chiral distortions caused by binding of tartaric acid enantiomers to the nanoparticle surface. As evidenced by spectroscopy/microscopy techniques and calculations of nanoscale chirality measures, the intercalation of Zn 2+ ions into the V 2 O 3 lattice results in particle expansion, untwist deformations, and chirality reduction. Coherent deformations in the particle ensemble manifest as changes in sign and positions of circular polarization bands at ultraviolet, visible, mid‐infrared (IR), near‐IR (NIR), and IR wavelengths. The g‐ factors observed for IR and NIR spectral diapasons are ≈100–400 times higher than those for previously reported dielectric, semiconductor, and plasmonic nanoparticles. Nanocomposite films layer‐by‐layer assembled (LBL) from V 2 O 3 nanoparticles reveal cyclic‐voltage‐driven modulation of optical activity. Device prototypes for IR and NIR range problematic for liquid crystals and other organic materials are demonstrated. High optical activity, synthetic simplicity, sustainable processability, and environmental robustness of the chiral LBL nanocomposites provide a versatile platform for photonic devices. Similar reconfigurations of particle shapes are expected for multiple chiral ceramic nanostructures, leading to unique optical, electrical, and magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Transition Metal Dichalcogenides: Making Atomic‐Level Magnetism Tunable with Light at Room Temperature

Abstract The capacity to manipulate magnetization in 2D dilute magnetic semiconductors (2D‐DMSs) using light, specifically in magnetically doped transition metal dichalcogenide (TMD) monolayers ( M ‐doped TX 2 , where M = V, Fe, and Cr; T = W, Mo; X = S, Se, and Te), may lead to innovative applications in spintronics, spin‐caloritronics, valleytronics, and quantum computation. This Perspective paper explores the mediation of magnetization by light under ambient conditions in 2D‐TMD DMSs and heterostructures. By combining magneto‐LC resonance (MLCR) experiments with density functional theory (DFT) calculations, we show that the magnetization can be enhanced using light in V‐doped TMD monolayers (e.g., V‐WS 2 , V‐WSe 2 ). This phenomenon is attributed to excess holes in the conduction and valence bands, and carriers trapped in magnetic doping states, mediating the magnetization of the semiconducting layer. In 2D‐TMD heterostructures (VSe 2 /WS 2 , VSe 2 /MoS 2 ), the significance of proximity, charge‐transfer, and confinement effects in amplifying light‐mediated magnetism is demonstrated. We attributed this to photon absorption at the TMD layer that generates electron–hole pairs mediating the magnetization of the heterostructure. These findings will encourage further research in the field of 2D magnetism and establish a novel design of 2D‐TMDs and heterostructures with optically tunable magnetic functionalities, paving the way for next‐generation magneto‐optic nanodevices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Drivers for the cracking of multilayer polyamide‐based backsheets in field photovoltaic modules: In‐depth degradation mapping analysis

Abstract There is a lack of understanding on the root cause of cracking of photovoltaic (PV) backsheets due to the challenge of multilayer characterization and the complicated failure modes at the submodule level. In this work, the in‐depth degradation mapping of field‐exposed polyamide‐based (PA‐based) PV module backsheets was studied, with the major focus on the identification of underlying drivers for the through cracking (in between the solar cells). PV modules were retrieved from five different locations, comprising a variety of climates, including humid subtropical, hot‐summer Mediterranean, tropical savanna climate, and hot arid. A suite of microscale cross‐sectional characterizations, including chemical changes, fluorescence intensity, and modulus as a function of distance from the air surface of the backsheet, was performed. Results showed more advanced signs of degradation of the inner layer than the outer layer in the cracking region. Increases in the modulus were identified as the major indicator for the cracking. Moreover, a rudimentary test by immersion in acetic acid, which forms during photodegradation of the ethylene‐vinyl acetate copolymer (EVA) encapsulant, showed the first‐time direct evidence that acetic acid can largely accelerate the chemical degradation and facilitate the cracking of PA inner layer. This study suggests that the field cracking of PA‐based backsheet can be attributed to the combined effects of chemical degradation and physical reorganization (chemi‐crystallization) under cyclic thermomechanical stresses. Therefore, it is important to consider effects of local microclimates and interlayer infection to understand the heterogeneous nature of backsheet failures.

Lyu, Yadong↗
DOE OSTI2022

Emergent magnetism and exchange bias effect in iron oxide nanocubes with tunable phase and size

Abstract We report a systematic investigation of the magnetic properties including the exchange bias (EB) effect in an iron oxide nanocube system with tunable phase and average size (10, 15, 24, 34, and 43 nm). X-ray diffraction and Raman spectroscopy reveal the presence of Fe 3 O 4 , FeO, and α -Fe 2 O 3 phases in the nanocubes, in which the volume fraction of each phase varies depending upon particle size. While the Fe 3 O 4 phase is dominant in all and tends to grow with increasing particle size, the FeO phase appears to coexist with the Fe 3 O 4 phase in 10, 15, and 24 nm nanocubes but disappears in 34 and 43 nm nanocubes. The nanocubes exposed to air resulted in an α -Fe 2 O 3 oxidized surface layer whose thickness scaled with particle size resulting in a shell made of α -Fe 2 O 3 phase and a core containing Fe 3 O 4 or a mixture of both Fe 3 O 4 and FeO phases. Magnetometry indicates that the nanocubes undergo Morin (of the α -Fe 2 O 3 phase) and Verwey (of the Fe 3 O 4 phase) transitions at ∼250 K and ∼120 K, respectively. For smaller nanocubes (10, 15, and 24 nm), the EB effect is observed below 200 K, of which the 15 nm nanocubes showed the most prominent EB with optimal antiferromagnetic (AFM) FeO phase. No EB is reported for larger nanocubes (34 and 43 nm). The observed EB effect is ascribed to the strong interfacial coupling between the ferrimagnetic (FiM) Fe 3 O 4 phase and AFM FeO phase, while its absence is related to the disappearance of the FeO phase. The Fe 3 O 4 / α -Fe 2 O 3 (FiM/AFM) interfaces are found to have negligible influence on the EB. Our findings shed light on the complexity of the EB effect in mixed-phase iron oxide nanosystems and pave the way to design exchange-coupled nanomaterials with desirable magnetic properties for biomedical and spintronic applications.

Physics↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2021

Modulating the Surface Ligand Orientation for Stabilized Anionic Redox in Li‐Rich Oxide Cathodes

Abstract Anionic redox chemistry is emerging as a key concept in the development of high‐energy lithium‐ion batteries, as it enables a nearly doubled charge storage capacity, aiding the development of high‐capacity batteries. However, the anionic reactivity is frequently irreversible from charge to discharge, leading to rapid decay of the capacity and voltage of batteries over long‐term cycling. Although the possibility of controlling the anionic redox reactions by tuning the geometric and electronic structures has recently been proposed, the implementation of this strategy is still a critical challenge. Herein, a strategy is proposed to improve the anionic redox reversibility of a model anionic redox active cathode material, Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 , by tuning the surface ligand geometry via the growth of a lattice‐compatible spinel LiCoO 2 coating layer on the particle surface. Detailed local structure and first principles investigations reveal that the shape and orientation of the octahedral layer in the host lattice are modified. Accordingly, a two‐band oxygen redox behavior is triggered in the ligand‐orientation‐regulated Li‐rich cathode, leading to enhanced reversibility, and thus, remarkably improved capacity and voltage retention over cycling. This study highlights the importance of controllable ligand orientation, carving a new path for the development and design of Li‐rich cathodes in the future.

Liu, Yanchen↗