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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Environmental risk factors associated with low birth weight: The case study of the Haifa Bay Area in Israel

Highlights: • Environmental factors affecting low birth weight (LBW) rate are investigated. • Analysis is conducted by alternative techniques of geo-statistical analysis. • Proximity to petrochemical industries is found to increase LBW rate. • Seashore proximity and elevation above the sea level reduce the LBW rate. • We attribute the latter to climate-moderating effect of elevated seashore locations. Low birth weight (LBW) is known to be associated with infant mortality and postnatal health complications. Previous studies revealed strong relationships between LBW rate and several socio-demographic factors, including ethnicity, maternal age, and family income. However, studies of association between LBW rate and environmental risk factors remain infrequent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Physics-informed semantic inpainting: Application to geostatistical modeling

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. In this work, to overcome this limitation, we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Atom-at-a-Time Radioactive Molecule Identification: Looking toward Studies of Superheavy Elements

The chemical behavior of superheavy elements (SHEs, Z > 103) remains poorly understood. Their chemical properties are expected to deviate from established trends, challenging the predictive power of the periodic table. To investigate these elements experimentally, they must first be synthesized through nuclear reactions and then quickly subjected to chemical studies before they decay. Given the low production rates of these reactions and the need for measurements on an atom-at-a-time basis, innovative techniques are needed. Here, to address these challenges, a novel gas-phase chemistry method has been developed at Lawrence Berkeley National Laboratory, utilizing the Berkeley Gas-filled Separator and FIONA. This technique enables the production, identification, and study of molecular species formed by SHEs. As a proof of concept, we present measurements on the formation and identification of 151,152 HoO + molecules, demonstrating the capability to study the production of radioactive molecules under controlled conditions and directly identify them via their mass-to-charge ratio. These measurements validate the effectiveness of this technique for low-statistics SHE studies, highlighting the potential of this approach to ignite the next generation of experimental SHE chemistry research, offering a path to re-evaluate SHE placement on the periodic table.

Chemistry↗
DOE OSTI2025

How are Heterogeneous Nucleation Rate Observations Influenced by Instrument Resolution?

Experimental measurements of the heterogeneous nucleation rate rely on counting the number of nuclei with time. However, the size of a thermodynamically stable nucleus is often a few nanometers in diameter and is below the resolution of most (in situ) measurement techniques that provide a statistically valid sample. Due to the finite resolution of the instruments and analysis methods, it is challenging to capture the incipient nuclei and the subsequent evolution of nuclei density over time. In this work, we demonstrate the impact of instrument resolution on observed nuclei densities by comparing numerical modeling with experimental results. Further, to achieve this, we implemented heterogeneous nucleation within the pore-scale reactive transport modeling framework using classical nucleation theory (CNT). We compared the modeling results with nucleation rates measured using X-ray nanotomography (XnT) and evaluated how these impact the apparent values of the prefactor and interfacial energy based on CNT and the crystal growth rate. Specifically, we applied a resolution threshold (artificial resolution limit) in the model during nuclei counting to resemble an experimental resolution, ranging from 15 to 500 nm. The findings reveal that the instrument resolution significantly impacts the apparent prefactor and interfacial energy. Both apparent prefactor and interfacial energy decrease with a decrease in the instrument resolution. While deviation in the prefactor due to resolution is anticipated, those in the interfacial energy are unexpected. The approach described here allows one to correct apparent nucleation rates that depend on the instrument’s resolution to derive “intrinsic” CNT parameters for the prefactor and interfacial energy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Deciphering the source of primary biological aerosol particles: a pollen case study

Primary biological aerosol particles (PBAPs) are microscopic solids suspended in the atmosphere emitted by biological systems and play critical roles in the atmosphere and at the atmosphere-biosphere interface, impacting human health, climate, and the ecosystem function. Understanding the sources of PBAPs is necessary to decipher the mechanistic interactions between aerosols, climate, and distinct ecosystem components. However, the detection of specific PBAPs in complex ambient aerosol samples is challenging. We performed metabolomics analyses of pollen from three pollinating tree species and ambient samples collected during the peak pollination period of each species. Random Forest and sPLS-DA machine learning methods were employed to evaluate whether metabolic signatures of ambient samples can reveal the source of the main pollen particles present in the atmosphere. Our results suggest that atmospheric eco-metabolomics techniques combined with sophisticated statistical methods can decipher the origin of abundant PBAPs from complex ambient samples. Developing complete libraries containing high-resolution metabolomic fingerprints of the major PBAPs present in the atmosphere would significantly advance future research to accurately understand the role of PBAPs in the atmosphere, ecosystems and human health.

Rivas-Ubach, Albert↗
DOE OSTI2019

Impacts of Observational Constraints Related to Sea Level on Estimates of Climate Sensitivity

Reduced complexity climate models are necessary tools for quantifying decision-relevant uncertainties, given their flexibility, computational efficiency, and suitability for large-ensemble frameworks necessary for statistical estimation using resampling techniques (e.g. Markov chain Monte Carlo—MCMC). Here we document a new version of the simple, open-source, global climate model Hector, coupled with a one-dimensional diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model; DOECLIM) and a sea-level change module (Building blocks for Relevant Ice and Climate Knowledge; BRICK) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. We apply a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters with prescribed radiative forcing, using observational information from global surface temperature, ocean heat uptake, and sea-level change. Furthermore, we find sea-level change as an observational constraint sharpens inference for the upper tail of posterior climate sensitivity estimates (the 97.5 percentile is tightened from 6.5 K to 5.3 K). The addition of sea-level change as an observational constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5 2100 temperature decreases 0.3 K) and sea-level rise (the 97.5 percentile for RCP8.5 2100 sea level decreases 0.5 m).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator

Downscaling methods are critical in efficiently generating high-resolution atmospheric data. However, state-of-the-art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high-resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero-shot super-resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high-resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution-invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings, and topographic features used in the WRF model at the same resolution. We incorporated a physics-constrained loss in FNO by using the Clausius-Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4-km resolution, the FNO shows comparable performance with a widely used convolutional network, U-Net, achieving averaged modified Kling-Gupta Efficiency of 0.88 and 0.94 on the test dataset for temperature and pressure, respectively. We then employed the FNO to produce 1-km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high-resolution dynamics. Our study demonstrates the potential of using FNO for zero-shot super-resolution in generating first-order estimation on atmospheric modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Abstract Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Enhanced legume root growth with pre-soaking in α-Fe 2 O 3 nanoparticle fertilizer

The rising demand for food and energy crops has triggered interest in the use of nanoparticles for agronomy. Specifically, iron oxide-based engineered nanoparticles are promising candidates for next-generation iron-deficiency fertilizers. We used iron oxide and hybrid Pt-decorated iron oxide nanoparticles, at low and high concentrations, and at varied pHs, to model seed pre-soaking solutions for investigation of their effect on embryonic root growth in legumes. This is an environmentally friendly approach, as it uses less fertilizer, therefore less nanoparticles in contact with the soil. Analysis from varied material characterization techniques combined with a statistical analysis method found that iron oxide nanoparticles could enhance root growth by 88–366% at low concentrations (5.54 × 10 -3 mg L -1 Fe). Hybrid Pt-decorated iron oxide nanoparticles and a higher concentration of iron oxide nanoparticles (27.7 mg L -1 Fe) showed reduced root growth. The combined materials characterization and statistical analysis used here can be applied to address many environmental factors to finely tune the development of vital nanofertilizers for high efficiency food production.

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

The miniJPAS survey: White dwarf science with 56 optical filters

We analyze the white dwarf population in miniJPAS, the first square degree observed with 56 medium-band, 145 Å in width optical filters by the Javalambre Physics of the accelerating Universe Astrophysical Survey (J-PAS), to provide a data-based forecast for the white dwarf science with low-resolution ($R$ ~ 50) photo-spectra. We define the sample of the bluest point-like sources in miniJPAS with $r$ < 21.5 mag, a point-like probability larger than 0.5, ($u$ - $r$)< 0.80 mag, and ($g$ - $i$)< 0.25 mag. This sample comprises 33 sources with spectroscopic information: 11 white dwarfs and 22 quasi-stellar objects (QSOs). We estimate the effective temperature ($T$ eff ), the surface gravity, and the composition of the white dwarf population by a Bayesian fitting to the observed photo-spectra. The miniJPAS data are sensitive to the Balmer series and the presence of polluting metals. Our results, combined with those from the Javalambre Photometric Local Universe Survey (J-PLUS) which has a lower spectral resolution but has already observed thousands of white dwarfs, suggest that J-PAS photometry would permit – down to $r$ ~ 21.5 mag and at least for sources with 7000 < $T$ eff < 22 000 K – both the classification of the observed white dwarfs into H-dominated and He-dominated with 99% confidence and the detection of calcium absorption for equivalent widths larger than 15 Å. The effective temperature is estimated with a 2% uncertainty, which is close to the 1% from spectroscopy. A precise estimation of the surface gravity depends on the available parallax information. In addition, the white dwarf population at $T$ eff > 7000 K can be segregated from the bluest extragalactic QSOs, providing a clean sample based on optical photometry alone. The J-PAS low-resolution photo-spectra would produce precise effective temperatures and atmospheric compositions for white dwarfs, complementing the data from Gaia. J-PAS will also detect and characterize new white dwarfs beyond the Gaia magnitude limit, providing faint candidates for spectroscopic follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Light scattering by epitaxial VO{sub 2} films near the metal-insulator transition point

Experimental observation of metal-insulator transition in epitaxial films of vanadium dioxide is reported. Hemispherical angle-resolved light scattering technique is applied for statistical analysis of the phase transition processes on mesoscale. It is shown that the thermal hysteresis strongly depends on spatial frequency of surface irregularities. The transformation of scattering indicatrix depends on sample morphology and is principally different for the thin films with higher internal elastic strain and for the thicker films where this strain is suppressed by introduction of misfit dislocations. The evolution of scattering indicatrix, fractal dimension, surface power spectral density, and surface autocorrelation function demonstrates distinctive behavior which elucidates the influence of structural defects and strain on thermal hysteresis, twinning of microcrystallites, and domain formation during the phase transition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Accelerated nanoscale magnetic resonance imaging through phase multiplexing

We report a method for accelerated nanoscale nuclear magnetic resonance imaging by detecting several signals in parallel. Our technique relies on phase multiplexing, where the signals from different nuclear spin ensembles are encoded in the phase of an ultrasensitive magnetic detector. We demonstrate this technique by simultaneously acquiring statistically polarized spin signals from two different nuclear species ({sup 1}H, {sup 19}F) and from up to six spatial locations in a nanowire test sample using a magnetic resonance force microscope. We obtain one-dimensional imaging resolution better than 5 nm, and subnanometer positional accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Correlation between macro- and nano-scopic measurements of carbon nanostructured paper elastic modulus

Extensive work has been done in order to determine the bulk elastic modulus of isotropic samples from force curves acquired with atomic force microscopy. However, new challenges are encountered given the development of new materials constructed of one-dimensional anisotropic building blocks, such as carbon nanostructured paper. In the present work, we establish a reliable framework to correlate the elastic modulus values obtained by amplitude modulation atomic force microscope force curves, a nanoscopic technique, with that determined by traditional macroscopic tensile testing. In order to do so, several techniques involving image processing, statistical analysis, and simulations are used to find the appropriate path to understand how macroscopic properties arise from anisotropic nanoscale components, and ultimately, being able to calculate the value of bulk elastic modulus.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Absolute photoluminescence intensity in thin film solar cells

A calculation is presented for the direct conversion of a measured luminescence signal to the implied open circuit voltage. The effects of re-absorption, spectral dependence, and interference with front and back interfaces are all included, so long as the optical properties of the structure are known. The method is validated through a comparison of the terminal open-circuit voltage with the photoluminescence intensity of Cu(In,Ga)Se 2 solar cells, each as a function of illumination intensity.

14 SOLAR ENERGY↗
DOE OSTI2018

Machine learning for energy-water nexus: challenges and opportunities

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

VALIDATION OF 12 SMALL KEPLER TRANSITING PLANETS IN THE HABITABLE ZONE

We present an investigation of 12 candidate transiting planets from Kepler with orbital periods ranging from 34 to 207 days, selected from initial indications that they are small and potentially in the habitable zone (HZ) of their parent stars. Few of these objects are known. The expected Doppler signals are too small to confirm them by demonstrating that their masses are in the planetary regime. Here we verify their planetary nature by validating them statistically using the BLENDER technique, which simulates large numbers of false positives and compares the resulting light curves with the Kepler photometry. This analysis was supplemented with new follow-up observations (high-resolution optical and near-infrared spectroscopy, adaptive optics imaging, and speckle interferometry), as well as an analysis of the flux centroids. For 11 of them (KOI-0571.05, 1422.04, 1422.05, 2529.02, 3255.01, 3284.01, 4005.01, 4087.01, 4622.01, 4742.01, and 4745.01) we show that the likelihood they are true planets is far greater than that of a false positive, to a confidence level of 99.73% (3σ) or higher. For KOI-4427.01 the confidence level is about 99.2% (2.6σ). With our accurate characterization of the GKM host stars, the derived planetary radii range from 1.1 to 2.7 R {sub ⊕}. All 12 objects are confirmed to be in the HZ, and nine are small enough to be rocky. Excluding three of them that have been previously validated by others, our study doubles the number of known rocky planets in the HZ. KOI-3284.01 (Kepler-438b) and KOI-4742.01 (Kepler-442b) are the planets most similar to the Earth discovered to date when considering their size and incident flux jointly.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Additionally, Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.

79 ASTRONOMY AND ASTROPHYSICS↗