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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Emergent Electronic and Dielectric Properties of Interacting Nanoparticles at Finite Temperature

Lead chalcogenide nanoparticle solids have been successfully integrated into certified solar cells and represent promising platforms for the design of novel photoabsorbers for photoelectrochemical cells. While much attention has been drawn to improving efficiency and device performance through altering the character of the individual nanoparticles, the role of interactions between nanoparticles is not yet well-understood. Using first-principles molecular dynamics and electronic structure calculations, we investigated the combined effect of temperature and interaction on functionalized lead chalcogenide nanoparticles (NPs). Here, we show that at finite temperature, interacting NPs are dynamical dipolar systems, with the average values of dipole moments and polarizabilities substantially increased with respect to those of the isolated building blocks. In addition, we show that the interacting NPs exhibit slightly smaller fundamental gaps that decrease as a function of temperature and that the radiative lifetimes of both the isolated NPs and the solids are greatly reduced at finite temperature compared to T = 0. Lastly, we present a critical discussion of various results reported in the literature for the values of dipole moments of nanoparticles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On the minimum number of radiation field parameters to specify gas cooling and heating functions

Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions in the presence of a generalized incident local radiation field computed by Cloudy. We characterize the radiation field through binned radiation field intensities instead of the photoionization rates used in our previous work. We find a set of 6 energy bins whose intensities exhibit relatively low correlation. We use these bins as features to train machine learning models to predict Cloudy cooling and heating functions at fixed metallicity. We compare the relative SHapley Additive exPlanation (SHAP) value importance of the features. From the SHAP analysis, we identify a feature subset of 3 energy bins (0.5-1, 1-4, and 13-16Ry) with the largest importance and train additional models on this subset. We compare the mean squared errors and distribution of errors on both the entire training data table and a randomly selected 20% test set withheld from model training. The machine learning models trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have comparable accuracy everywhere, with errors ≳10 times smaller than for the interpolation table of Gnedin and Hollon (2012). We conclude that 3 energy bins (or 3 analogous photoionization rates: molecular hydrogen photodissociation, neutral hydrogen HI, and fully ionized carbon CVI) are sufficient to characterize the dependence of the gas cooling and heating functions on our assumed incident radiation field model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Correction Scheme for Comparison of Computed and Experimental Optical Transition Energies in Functionalized Single-Walled Carbon Nanotubes

We present that covalent functionalization of single-walled carbon nanotubes (SWCNTs) introduces red-shifted emission features in the near-infrared spectral range due to exciton localization around the defect site. Such chemical modifications increase their potential use as near-infrared emitters and single-photon sources in telecommunications applications. Density functional theory (DFT) studies using finite-length tube models have been used to calculate their optical transition energies. Predicted energies are typically blue-shifted compared to experiment due to methodology errors including imprecise self-interaction corrections in the density functional and finite-size basis sets. Furthermore, artificial quantum confinement in finite models cannot be corrected by a constant-energy shift since they depend on the degree of exciton localization. Herein, we present a method that corrects the emission energies predicted by time-dependent DFT. Confinement and methodology errors are separately estimated using experimental data for unmodified tubes. Finally, corrected emission energies are in remarkable agreement with experiment, suggesting the value of this straightforward method toward predicting and interpreting the optical features of functionalized SWCNTs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On the minimum number of radiation field parameters to specify gas cooling and heating functions

Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions computed by Cloudy in the presence of a generalized incident local radiation field. We characterize the radiation field through binned radiation field intensities instead of the photoionization rates used in our previous work. We find a set of 6 energy bins whose intensities exhibit relatively low correlation. We use these bins as features to train machine learning models to predict Cloudy cooling and heating functions at fixed metallicity. We compare the relative SHapley Additive exPlanation (SHAP) value importance of the features. From the SHAP analysis, we identify a feature subset of 3 energy bins ($0.5-1, 1-4$, and $13-16 \, \mathrm{Ry}$) with the largest importance and train additional models on this subset. We compare the mean squared errors and distribution of errors on both the entire training data table and a randomly selected 20% test set withheld from model training. The machine learning models trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have comparable accuracy everywhere, with errors $\gtrsim 10$ times smaller than for the interpolation table of Gnedin and Hollon (2012). We conclude that 3 energy bins (or 3 analogous photoionization rates: molecular hydrogen photodissociation, neutral hydrogen HI, and fully ionized carbon CVI) are sufficient to characterize the dependence of the gas cooling and heating functions on our assumed incident radiation field model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0

Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exploring the dependence of gas cooling and heating functions on the incident radiation field with machine learning

ABSTRACT Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped up by using interpolation tables of pre-computed values, at the expense of making significant and sometimes even unjustified approximations. Here, we explore the capacity of machine learning to approximate cooling and heating functions with a generalized radiation field. Specifically, we use the machine learning algorithm XGBoost to predict cooling and heating functions calculated with the photoionization code cloudy at fixed metallicity, using different combinations of photoionization rates as features. We perform a constrained quadratic fit in metallicity to enable a fair comparison with traditional interpolation methods at arbitrary metallicity. We consider the relative importance of various photoionization rates through both a principal component analysis (PCA) and calculation of SHapley Additive exPlanation (shap) values for our XGBoost models. We use feature importance information to select different subsets of rates to use in model training. Our XGBoost models outperform a traditional interpolation approach at each fixed metallicity, regardless of feature selection. At arbitrary metallicity, we are able to reduce the frequency of the largest cooling and heating function errors compared to an interpolation table. We find that the primary bottleneck to increasing accuracy lies in accurately capturing the metallicity dependence. This study demonstrates the potential of machine learning methods such as XGBoost to capture the non-linear behaviour of cooling and heating functions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Testing Surrogate-Based Optimization with the Fortified Branin-Hoo Extended to Four Dimensions

Some popular functions used to test global optimization algorithms have multiple local optima, all with the same value, making them all global optima. It is easy to make them more challenging by fortifying them via adding a localized bump at the location of one of the optima. In previous work the authors illustrated this for the Branin-Hoo function and the popular differential evolution algorithm, showing that the fortified Branin-Hoo required an order of magnitude more function evaluations. This paper examines the effect of fortifying the Branin-Hoo function on surrogate- based optimization, which usually proceeds by adaptive sampling. Two algorithms are considered. The EGO algorithm, which is based on a Gaussian process (GP) and an algorithm based on radial basis functions (RBF). EGO is found to be more frugal in terms of the number of required function evaluations required to identify the correct basin, but it is expensive to run on a desktop, limiting the number of times the runs could be repeated to establish sound statistics on the number of required function evaluations. The RBF algorithm was cheaper to run, providing more sound statistics on performance. A four-dimensional version of the Branin-Hoo function was introduced in order to assess the effect of dimensionality. Furthermore, it was found that the difference between the ordinary function and the fortified one was much more pronounced for the four-dimensional function compared to the two dimensional one.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Interpolating between small- and large-$\mathscr{g}$ expansions using Bayesian model mixing

Bayesian model mixing (BMM) is a statistical technique that can be used to combine models that are predictive in different input domains into a composite distribution that has improved predictive power over the entire input space. We explore the application of BMM to the mixing of two expansions of a function of a coupling constant $\mathscr{g}$ that are valid at small and large values of $\mathscr{g}$ respectively. This type of problem is quite common in nuclear physics, where physical properties are straightforwardly calculable in strong and weak interaction limits or at low and high densities or momentum transfers, but difficult to calculate in between. Interpolation between these limits is often accomplished by a suitable interpolating function, e.g., Padé approximants, but it is then unclear how to quantify the uncertainty of the interpolant. We address this problem in the simple context of the partition function of zero-dimensional Φ 4 theory, for which the (asymptotic) expansion at small $\mathscr{g}$ and the (convergent) expansion at large $\mathscr{g}$ are both known. We consider three mixing methods: linear mixture BMM, localized bivariate BMM, and localized multivariate BMM with Gaussian processes. Here, we find that employing a Gaussian process in the intermediate region between the two predictive models leads to the best results of the three methods. The methods and validation strategies we present here should be generalizable to other nuclear physics settings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Hydrogen adsorption induced antiferrodistortive distortion and metallization at the (001) surface of SrTiO{sub 3}

SrTiO{sub 3} (STO) is attracting lots of research interests due to the rich physics and properties presented at its surfaces and the interfaces of STO with other transition metal oxides. Based on density functional theory methods, we have investigated the influence of hydrogen (H) atoms adsorption at the (001) surface of STO on the geometrical and electronic structures of the surface. We find that H adsorption induces significant antiferrodistortive (AFD) distortion of TiO{sub 6} octahedra at the surface. By calculating H adsorption energy, we show that AFD distortion makes a significant contribution to the stability of H adsorbed STO surfaces. The calculated energy position of O-H bond states by hybrid functional method is 9.9 eV below Fermi level, in agreement with experimental value of 10 eV. The electrons donated by H atoms first occupy several d{sub xy} bands and then start to fill in degenerate d{sub yz}/d{sub xz} bands, indicating the metallization of initially insulating STO. The band splitting and occupy sequence calculated here are consistent with recent angle-resolved photoemission spectroscopy experiments. Our results reveal that H adsorption changes the atomic and electronic structures and thus induce fascinating properties at the surface of STO.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

How do we establish cardiac sympathetic nervous system imaging with {sup 123}I-mIBG in clinical practice? Perspectives and lessons from Japan and the US

Cardiac denervation is associated with progressive left ventricular (LV) dysfunction, ventricular arrhythmias, and sudden cardiac death (SCD) in heart failure (HF). In this regard, it is important to evaluate cardiac-specific sympathetic nervous system (SNS) function. The radiotracer Iodine-123 meta-iodobenzylguanidine ({sup 123}I-mIBG) can noninvasively evaluate pre-synaptic SNS function. Recent multicenter trials have shown {sup 123}I-mIBG to have strong predictive value for fatal arrhythmias and cardiac death in HF. {sup 123}I-mIBG was initially developed in the USA in the 1970s. In 1992, the Japanese Ministry of Health and Labour approved {sup 123}I-mIBG for the assessment of cardiac function. Following approval, the Japanese nuclear cardiology community developed {sup 123}I-mIBG imaging services in various medical centers. Japanese groups have been trying to establish the clinical utility of {sup 123}I-mIBG and standardize parameters for data acquisition and image analysis. The US Food and Drug Administration (FDA) has approved clinical use of {sup 123}I-mIBG for cardiac and non-cardiac imaging. However, clinical use of {sup 123}I-mIBG in the US has been very limited. The number of {sup 123}I-mIBG studies in Japan has also been limited. There are similarities and differences between the two countries. To establish the clinical utility of {sup 123}I-mIBG in both countries, it is important to characterize the situations of {sup 123}I-mIBG in each.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Remote Density Measurements of Molten Salts via Neutron Radiography

With an increased interest in the use of molten salts in both nuclear and non-nuclear systems, measuring important thermophysical properties of specific salt mixtures becomes critical in understanding salt performance and behavior. One of the more basic and significant thermophysical properties of a given salt system is density as a function of temperature. With this in mind, this work aims to present and layout a novel approach to measuring densities of molten salt systems using neutron radiography. This work was performed on Flight Path 5 at the Los Alamos Neutron Science Center at Los Alamos National Laboratory. In order to benchmark this initial work, three salt mixtures were measured, NaCl, LiCl (58.2 mol%) + KCl (41.8 mol%), and MgCl2 (32 mol%) + KCl (68 mol%). Resulting densities as a function of temperature for each sample from this work were then compared to previous works employing traditional techniques. Results from this work match well with previous literature values for all salt mixtures measured, establishing that neutron radiography is a viable technique to measure density as a function of temperature in molten salt systems. Finally, advantages of using neutron radiography over other methods are discussed and future work in improving this technique is covered.

Long, Alexander M. (ORCID:0000000343009454)↗
DOE OSTI2016

Optical properties of nearly lattice-matched GaN/(Al,In)N quantum wells

We report a systematic study of the photoluminescence (PL) properties of a series of nearly lattice-matched (LM) GaN/(Al,In)N single quantum well (SQW) samples, with well thickness ranging from 1.5 to 5 nm, grown by metalorganic vapor phase epitaxy. Temperature dependent PL and time-resolved PL measurements reveal similar trends among the studied SQW samples, which also indicate strong localization effects. The observed PL energy behavior, akin to the S-shape, accompanied first by a narrowing and then a broadening of the PL line width with increasing temperature, closely resemble previous observations made on the more established (In,Ga)N/GaN QW system. The similar trends observed in the PL features of those two QW systems imply that the PL properties of LM GaN/(Al,In)N SQW samples are also governed by localized states. The effects of carrier transfer among these localization sites are clearly observed for the 3 nm thick QW, evidenced by an increasing PL intensity in the lower energy spectral window and a concomitant increase in the corresponding PL decay time. Time-resolved data corroborate the picture of strongly localized carriers and also indicate that above a well thickness dependent delocalization temperature carrier distribution across the localized sites reaches thermal equilibrium, as the PL decay times over different spectral regions converge to the same value. Based on the difference between the calculated QW ground state transition energy, obtained using the envelope wave function formalism, and the measured PL energy, a localization energy of at least a few hundreds of meV has been extracted for all of the studied SQW samples. This rather large value also implies that In-related localization effects are more pronounced in the GaN/(Al,In)N system with respect to those in the (In,Ga)N/GaN one for a similar In content.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Bootstrapping smooth conformal defects in Chern-Simons-matter theories

Abstract The expectation value of a smooth conformal line defect in a CFT is a conformal invariant functional of its path in space-time. For example, in largeNholographic theories, these fundamental observables are dual to the open-string partition function in AdS. In this paper, we develop a bootstrap method for studying them and apply it to conformal line defects in Chern-Simons matter theories. In these cases, the line bootstrap is based on three minimal assumptions — conformal invariance of the line defect, largeNfactorization, and the spectrum of the two lowest-lying operators at the end of the line. On the basis of these assumptions, we solve the one-dimensional CFT on the line and systematically compute the defect expectation value in an expansion around the straight line. We find that the conformal symmetry of a straight defect is insufficient to fix the answer. Instead, imposing the conformal symmetry of the defect along an arbitrary curved line leads to a functional bootstrap constraint. The solution to this constraint is found to be unique.

Physics↗
DOE OSTI2016

Interdependence of spin structure, anion height and electronic structure of BaFe{sub 2}As{sub 2}

Superconducting as well as other electronic properties of Fe-based superconductors are quite sensitive to the structural parameters specially, on anion height which is intimately related to z{sub As}, the fractional z co-ordinate of As atom. Due to presence of strong magnetic fluctuation in these Fe-based superconductors, optimized structural parameters (lattice parameters a, b, c) including z{sub As} using density functional theory (DFT) under generalized gradient approximation (GGA) does not match experimental values accurately. In this work, we show that the optimized value of z{sub As} is strongly influenced by the spin structures in the orthorhombic phase of BaFe{sub 2}As{sub 2} system. We take all possible spin structures for the orthorhombic BaFe{sub 2}As{sub 2} system and then optimize z{sub As}. Using these optimized structures we calculate electronic structures like density of states, band structures etc., for each spin configurations. From these studies we show that the electronic structure, orbital order which is responsible for structural as well as related to nematic transition, are significantly influenced by the spin structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

The ages of A-stars. I. Interferometric observations and age estimates for stars in the Ursa major moving group

We have observed and spatially resolved a set of seven A-type stars in the nearby Ursa Major moving group with the Classic, CLIMB, and PAVO beam combiners on the Center for High Angular Resolution Astronomy Array. At least four of these stars have large rotational velocities (vsini ≳ 170 km s{sup −1}) and are expected to be oblate. These interferometric measurements, the stars’ observed photometric energy distributions, and vsini values are used to computationally construct model oblate stars from which stellar properties (inclination, rotational velocity, and the radius and effective temperature as a function of latitude, etc.) are determined. The results are compared with MESA stellar evolution models to determine masses and ages. The value of this new technique is that it enables the estimation of the fundamental properties of rapidly rotating stars without the need to fully image the star. It can thus be applied to stars with sizes comparable to the interferometric resolution limit as opposed to those that are several times larger than the limit. Under the assumption of coevality, the spread in ages can be used as a test of both the prescription presented here and the MESA evolutionary code for rapidly rotating stars. With our validated technique, we combine these age estimates and determine the age of the moving group to be 414 ± 23 Myr, which is consistent with, but much more precise than previous estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 °C (V c,max25 ) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Terrestrial biosphere models underestimate photosynthetic capacity and CO 2 assimilation in the Arctic

Terrestrial biosphere models (TBMs) are highly sensitive to model representation of photosynthesis, in particular the parameters maximum carboxylation rate and maximum electron transport rate at 25°C (V c,max.25 and J max.25 , respectively). Many TBMs do not include representation of Arctic plants, and those that do rely on understanding and parameterization from temperate species. We then measured photosynthetic CO 2 response curves and leaf nitrogen (N) content in species representing the dominant vascular plant functional types found on the coastal tundra near Barrow, Alaska. The activation energies associated with the temperature response functions of Vc,max and Jmax were 17% lower than commonly used values. When scaled to 25°C, Vc,max.25 and J max.25 were two- to five-fold higher than the values used to parameterize current TBMs. This high photosynthetic capacity was attributable to a high leaf N content and the high fraction of N invested in Rubisco. Leaf-level modeling demonstrated that current parameterization of TBMs resulted in a two-fold underestimation of the capacity for leaf-level CO 2 assimilation in Arctic vegetation. Our study highlights the poor representation of Arctic photosynthesis in TBMs, and provides the critical data necessary to improve our ability to project the response of the Arctic to global environmental change.

54 ENVIRONMENTAL SCIENCES↗