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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Comparing Model Representations of Physiological Limits on Transpiration at a Semi-arid Ponderosa Pine Site

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Gravitational wave signatures of a chiral fermion dark matter model

Abstract Theories in which the dark matter (DM) candidate is a fermion transforming chirally under a gauge symmetry are attractive, as the gauge symmetry would protect the DM mass. In such theories, the universe would have undergone a phase transition at early times that generated the DM mass upon spontaneous breaking of the gauge symmetry. In this paper, we explore the gravitational wave signals of a simple such theory based on an SU(2) D dark sector with a dark isospin-3/2 fermion serving as the DM candidate. This is arguably the simplest chiral theory possible. The scalar sector consists of a dark isospin-3 multiple, which breaks the SU(2) D gauge symmetry and also generates the DM mass. We construct the full thermal potential of the model and identify regions of parameter space which lead to detectable gravitational wave signals, arising from a strong first-order SU(2) D phase transition, in various planned space-based interferometers, while also being consistent with dark matter relic abundance. The bulk of the parameter space exhibiting detectable gravitational wave signals in the model also has large WIMP-nucleon scattering cross sections, ℴSI, which could be probed in upcoming direct detection experiments.

Astronomy & Astrophysics↗
DOE OSTI2023

Effective field theory of the two Higgs doublet model

We revisit the effective field theory of the two Higgs doublet model at tree level. The introduction of a novel basis in the UV theory allows us to derive matching coefficients in the effective description that resum important contributions from the Higgs vacuum expectation value. The new basis typically provides a significantly better approximation of the full theory prediction than the traditional approach that utilizes the Higgs basis, particularly for alignment away from the decoupling limit.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Integration of theory and experiment in the modelling of heterogeneous electrocatalysis.

Theoretical modelling is essential to deepen our understanding of heterogeneous electrocatalytic energy conversion processes, such as water splitting. Here, Sharon Hammes-Schiffer and Giulia Galli offer their perspectives on the best strategies for successfully studying such systems. Heterogeneous electrocatalysis is critical to many energy conversion processes. Theoretical and computational approaches are essential to interpret experimental data and provide the mechanistic understanding necessary to design more effective catalysts. However, automated general procedures to build predictive theoretical and computational frameworks are not readily available; specific choices must be made in terms of the atomistic structural model and the level of theory, as well as the experimental data used to inform and validate these choices. Here we outline some best practices for modelling heterogeneous systems and present examples in the context of catalysis at metal electrodes and oxides. The level of theory should be chosen for the specific system and properties of interest, and experimental validation is essential from the beginning to the end of the study. Continuous feedback and ultimate integration between experiment and theory enhances the power of calculations to elucidate mechanisms, identify effective descriptors and clarify design principles.

Hammes-Schiffer, Sharon↗
DOE OSTI2025

String membrane nets from higher-form gauging: An alternate route to 𝑝-string condensation

We present a new perspective on the $p$-string condensation procedure for constructing 3+1D fracton phases by implementing this process via the gauging of higher-form symmetries. Specifically, we show that gauging a 1-form symmetry in 3+1D that is generated by Abelian anyons in isotropic stacks of 2+1D topological orders naturally results in a 3+1D $p$-string condensed phase, providing a controlled non-perturbative construction that realizes fracton orders. This approach clarifies the symmetry principles underlying $p$-string condensation and generalizes the familiar connection between anyon condensation and one-form gauging in two spatial dimensions. We demonstrate this correspondence explicitly in both field theories and lattice models: in field theory, we derive the foliated field theory description of the $\mathbb{Z}_N$ X-Cube model by gauging a higher-form symmetry in stacks of 2+1D $\mathbb{Z}_N$ gauge theories; on the lattice, we show how gauging a diagonal 1-form symmetry in isotropic stacks of $G$-graded string-net models leads to string-membrane-nets hosting restricted mobility excitations. This perspective naturally generalizes to spatial dimensions $d \geq 2$ and provides a step towards building an algebraic theory of $p$-string condensation.

Anyons↗
DOE OSTI2025

Understanding LIB Battery Electrodes Through Classical Electrochemical Interface Theory

Classical models of the electrochemical interface can be applied to complex and dynamic electrodes to understand important mechanisms that are relevant to device-level phenomena like calendar aging or cycle life. Here, we show that negative alloy electrodes with wide electrochemical windows cannot be assumed to have static interfaces throughout all states of charge. Under high states of charge, the interface -including the solid electrolyte interphase - has characteristics that resemble a highly polarized electrode far from the point of zero charge. Under these conditions, the interface can be understood through the Helmholtz model. At lower states of charge, the interface capacitance decreases and the space charge layer length increases. This transformation resembles a metal electrode approaching the point of zero charge which is understood through the Gouy-Chapman-Stern-Grahame model of the electrochemical interface. This transformation happens at both silicon and carbon-coated silicon interfaces. From voltage-limited cycling experiments, in the limit of the diffuse double layer, the silicon electrode impedance rises significantly in the first few cycles. This rise is related to the continuous electrochemical reduction of electrolyte components from an interface that is not electronically screened from the electrolyte. In other words, an electrode interface for batteries should resemble a 'Helmholtz-like' structure.

anode↗
DOE OSTI2024

Liquid state theory of the structure of model polymerized ionic liquids

We employ polymer integral equation theory to study a simplified model of semiflexible polymerized ionic liquids (PolyILs) that interact via hard core repulsions and short range screened Coulomb interactions. The multi-scale structure in real and Fourier space of PolyILs (ions chosen to mimic Li, Na, K, Br, PF 6 , and TFSI) are determined as a function of melt density, Coulomb interaction strength, and ion size. Comparisons with a homopolymer melt, a neutral polymer–solvent-like athermal mixture, and an atomic ionic liquid are carried out to elucidate the distinct manner that ions mediate changes of polymer packing, the role of excluded volume effects, and the influence of chain connectivity, respectively. The effect of Coulomb strength depends in a rich manner on ion size and density, reflecting the interplay of steric packing, ion adsorption, and charge layering. Ion-mediated bridging of monomers is found, which intensifies for larger ions. Intermediate range charge layering correlations are characterized by a many-body screening length that grows with PolyIL density, cooling, and Coulomb strength, in disagreement with Debye–Hückel theory, but in accord with experiments. Qualitative differences in the collective structure, including an ion-size-dependent bifurcation of the polymer structure factor peak and pair correlation function, are predicted. The monomer cage order parameter increases significantly, but its collective ion counterpart decreases, as ions become smaller. Such behaviors allow one to categorize PolyILs into two broad classes of small and large ions. Furthermore, dynamical implications of the predicted structural results are qualitatively discussed.

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

Measurement of Charged Current Inclusive $\bar{V}_\mu$ Interactions and 2p2h Contribution Using the NOvA Near Detector

More precise measurements of the scattering of antineutrinos on nuclei in the few GeV region of incident energy, $E_{\nu}$, can reveal new aspects of $v/\bar{v}$ interactions and of nuclear structure, while providing information crucial to continued progress by neutrino oscillation experiments. This Thesis reports an investigation of charged current $\bar{v}_\mu$ interactions in the predominantly hydrocarbon medium of the NOvA Near Detector. The data are obtained with the detector exposed to a narrow-band $\bar{v}$ µ beam with spectral peak at E ν = 2.0 GeV, for a total accumulated exposure of 12.5×10$^{20}$ protons-on-target. The analysis expresses results in terms of three-momentum transfer,|$\vec{q}$| and available hadronic energy, $E$ avail . These variables approximate (q 0 , |$\vec{q}$|), the variables used in theoretical treatments of lepton scattering on multi-nucleon systems such as in 2-particle-2-hole (2p2h) processes. This Thesis delivers two core measurements: Firstly, the double-dierential cross section $d^2σ/d|\vec{q} |dE_{avail}$ for inclusive CC $\bar{v}_\mu$ interactions in the NOvA detector medium is obtained. The cross section has a roughly gaussian shape in its projection onto |$\vec{q}$|, while being largely concentrated at values of $E$ avail below 100 MeV. The single diferential cross sections dσ/d|$\vec{q}$| and dσ/dE avail are also obtained, and are compared to predictions based on the GENIE v3.0.6 neutrino generator augmented with four different implementations of 2p2h reactions. Secondly, the contribution of 2p2h reactions to the CC inclusive scattering sample is determined. Its presence is characterized in terms of double-differential and single-differential cross sections. The latter 2p2h distributions are compared to predictions from two experimental data-tune models and from two theory-based models, namely the SuSAv2 model and the Valencia model. Both of the theoretical models underpredict the measured cross-section strength and predict a shape for dσ/d|$\vec{q}$| that is flatter than the measured shape. Thus, this Thesis provides new information about CC antineutrino-nucleus scattering in the vicinity of Eν ~ 2 GeV, and it highlights improvements that are needed in theoretical treatments of 2p2h reactions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A stress-sensitive precipitate nucleation model beyond classical nucleation theory

The dynamic evolution of precipitates and second phases dictates the strength and stability of most engineering alloys. By design, or as a consequence of thermo-mechanical aging, engineering metals and alloys often form precipitates of second phases when subjecting to diverse thermal and mechanical loads. Precipitation is governed by several factors, including the alloy’s composition, processing/operating temperature, and stresses — either as a result of external loads or from residual stresses. However, state-of-the-art models for precipitate nucleation (i.e., classical nucleation theory) typically lacks consistent method to capture the effects of externally applied and/or internal stresses on nucleation; thereby severely limiting the applicability of these models to complex materials systems and to representative loading scenarios. Here, in this work, we extend upon classical nucleation theory to account for the effect of stresses on precipitation kinetics and thermodynamics. This is achieved via the use of an Eshelbian micromechanics framework keeping track of (i) the stress build up resulting from second phase formation as a function of mechanical load and, (ii) the effects of dislocations on precipitate formation. This new model is applied to σ precipitate in Fe–Cr binary alloys and M 23 C 6 precipitate in 316H stainless steel (SS). Simulations demonstrate the important role of both the remotely applied loads and dislocation pile ups on precipitate nucleation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2020

Predicting Compliance in an Epidemiological Model: Constructs from Psychological Theory and Research

Vaccination and the alternative behavior, vaccine refusal, are a classic example of manifesting behaviors driven by social norms and norm violations. Establishing how norms emerge, and under what circumstances people choose to violate them are key issues to understand in modeling epidemics. Interactions between individuals can lead to large-scale patterning of behavior (emergent phenomena). As norm violations are revealed through human behavior, drawing on psychological theory and principles to predict those violations is a viable approach for more human-constrained epidemiological models. As an example of the implications at scale, vaccine refusal is correlated with the spread of mis/disinformation about vaccine side-effects. Considering the complexities of network dynamics, the downstream effects means that if even a small group within a population are persuaded against vaccination, there is a reservoir from which disease and disease outbreaks can propagate. This work will attempt to identify those psychological indicators, to define circumstances that predict health behaviors, and identify potentially modifiable antecedents of health behavior, and factors that influence changes toward health protective behaviors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks(MRNs) models proposed for new microorganisms with promising properties is an importantgoal for chemical biology. Objective: In principle, we can perform a hand-on checking (Manual Curation). However, this is achallenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions). Results: The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes(metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy,specificity, and sensitivity in the range of 85-100% in both training and external validation dataseries. Methods: In this work, we used Combinatorial Perturbation Theory and Machine Learning techniquesto seek a CPTML model for MRNs >40 organisms compiled by Barabasis’ group. First, wequantified the local structure of a very large set of nodes in each MRN using a new class of node indexcalled Markov linear indices fk. Next, we calculated CPT operators for 150000 combinationsof query and reference nodes of MRNs. Last, we used these CPT operators as inputs of differentML algorithms. Conclusion: Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and RandomForest algorithms were identified as the three best non-linear models with accuracy greaterthan 97.5%. The present work opens the door to the study of MRNs of multiple organisms usingPTML models.

Pharmacology & Pharmacy↗
DOE OSTI2023

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly successful applications of PnP algorithms include biomicroscopy, computerized tomography (CT), magnetic resonance imaging (MRI), and joint ptychotomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. Additionally, we also point the way toward further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Boundary theory of the X-cube model in the continuum

We study the boundary theory of the Z N X-cube model using a continuum perspective, from which the exchange statistics of a subset of bulk excitations can be recovered. We discuss various gapped boundary conditions that either preserve or break the translation/rotation symmetries on the boundary, and further present the corresponding ground state degeneracies on T 2 × I. The low-energy physics is highly sensitive to the boundary conditions: even the extensive part of the ground state degeneracy can vary when different sets of boundary conditions are chosen on the two boundaries. Here, we also examine the anomaly inflow of the boundary theory and find that the X-cube model is not the unique (3 + 1)-dimensional theory that cancels the 't Hooft anomaly of the boundary.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Electrochemical CO2 Reduction over Metal-/Nitrogen-Doped Graphene Single-Atom Catalysts Modeled Using the Grand-Canonical Density Functional Theory

Renewably driven, electrochemical conversion of carbon dioxide into value-added products is expected to be a critical tool in global decarbonization. However, theoretical studies based on the computational hydrogen electrode largely ignore the nonlinear effects of the applied potential on the calculated results, leading to inaccurate predictions of catalytic behavior or mechanistic pathways. Here, we use grand canonical density functional theory (GC-DFT) to model electrochemical CO2 reduction (CO2R) over metal- and nitrogen-doped graphene catalysts (MNCs) and explicitly include the effects of the applied potential. We used GC-DFT to compute the CO2 to CO reaction intermediate energies at -0.3, -0.7, and -1.2 VSHE catalyzed by MNCs each doped with 1 of the 10 3d block metals coordinated by four pyridinic nitrogen atoms. Our results predict that Sc-, Ti-, Co-, Cu-, and Zn-N4Cs effectively catalyze CO2R at moderate to large reducing potentials (-0.7 to -1.2 VSHE). ZnN4C is a particularly promising electrocatalyst for CO2R to CO both at low and moderate applied potentials based on our thermodynamic analysis. Our findings also explain the observed pH independence of CO production over FeN4C and predict that the rate-determining step of CO2R over FeN4C is not *CO2- formation but rather *CO desorption. Additionally, the GC-DFT-computed density of states analysis illustrates how the electronic states of MNCs and adsorbates change non-uniformly with applied potential, resulting in a significantly increased *CO2- stability relative to other intermediates and demonstrating that the formation of the adsorbed *CO2- anion is critical to CO2R activation. This work demonstrates how GC-DFT paves the way for physically realistic and accurate theoretical simulations of reacting electrochemical systems.

CO2 reduction↗