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At least 469 records · Page 26

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DOE OSTI2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Application of community data to surface complexation modeling framework development: Iron oxide protolysis

This study presents a comprehensive community data-driven surface complexation modeling framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources. Using this framework, the impact of surface complexation model type and surface site density (SSD) on the fit quality and protolysis constants can be readily evaluated. For example, the non-electrostatic model yielded a poor data fit compared to diffuse double layer model and constant capacitance models due to the absence of known surface charge effects. Regardless of the choice of iron oxide mineral, pK a1 decreased with increasing SSD while the opposite tendency was observed for pK a2 . This newly developed framework demonstrates a method to reconcile community data-wide potentiometric titration data using Findable, Accessible, Interoperable, Reusable data principles to produce mineral protolysis constants that improve robustness of surface complexation models for applications in metal sorption and reactive transport modeling. The framework is readily expandable (as community data increase) and extensible (as the number of minerals increase). The framework provides a path forward for developing self-consistent, comprehensive, and updateable surface complexation databases for surface complexation and reactive transport modeling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

High-pressure structural systematics of dysprosium metal compressed in a neon pressure medium to 182 GPa

Here we present an experimental and theoretical study of dysprosium metal compressed in the soft pressure transmitting medium Ne up to 182 and 300 GPa, respectively. Angle-dispersive x-ray powder diffraction data from each of the high-pressure polymorphs shows anisotropic compression behavior indicating changes to the electron density distribution throughout its polymorphic landscape. We compare the monoclinic (mC4) and orthorhombic (oF16) structures for the collapsed structure for Dy above 82 GPa and verify that the oF16 structure offers a better fit to our data than the previously reported mC4 structure. Further, we have found that the oF16 structure undergoes similar anisotropic compression of its lattice parameters, with a turning point above 160 GPa; suggesting a potential phase transition at pressures much higher than achieved in this study. Density functional theory calculations show the likely candidate for this new high-pressure phase is the isosymmetric oF8 structure, which is predicted to be lower in energy than the oF16 structure above 275 GPa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

A Double-Wiebe Function for Reactivity Controlled Compression Ignition Combustion Using Reformate Diesel

Abstract Reactivity controlled compression ignition (RCCI) combustion has previously been proposed as a method to achieve high fuel conversion efficiency and reduce engine emissions. A single-fuel RCCI combustion strategy can have decreased fuel system complexity by using a reformate fuel for port fuel injection and the parent fuel (diesel) for direct injection. This paper presents a one-dimensional computational model of a compression ignition engine with single-fuel RCCI. A Wiebe function is used to predict the combustion process by representing the mass fraction burned (MFB) on a crank angle resolved basis. One single-Wiebe function (SWF) and two double-Wiebe functions(DWFs) were fitted to experimentally derive MFB data using the least-square method. The fitted results were compared with MFBs calculated from experimental data to verify the accuracy. The SWF did not fully capture the MFB curve with high fidelity while the detailed DWF captured the MFB curve within a root mean square error of 1.4%. The reduced double-Wiebe function (RDWF) also resulted in a predicted combustion profile with similar accuracy. Hence, the RDWF was used in a GT-power thermodynamic study to understand the effects of the low-temperature heat release (LTHR) fraction and combustion phasing on combustion characteristics. At optimum phasing of 5–10 crank angle degree after the top dead center, increasing the LTHR fraction from 20% to 60% resulted in the fuel conversion efficiency increasing from 39.5% to 41.1%, thus suggesting that the reformate fuel-based RCCI strategy is viable to unlock improved combustion performance.

Energy & Fuels↗

DOE OSTI2021

Methods—Analyzing Electrochemical Kinetic Parameters in Deep Eutectic Solvents Using an Extended Butler-Volmer Equation

Deep eutectic solvents (DESs) are promising electrolytes for electrochemical redox reactions, which can be used in redox flow batteries (RFBs). However, in some systems like the Fe 2+/3+ redox reaction in ethaline, traditional Tafel-based kinetic analysis generates unreasonable kinetic parameters (i.e., large anodic/cathodic charge transfer coefficients ( α a , α c ) along with low exchange current densities ( i 0 )). This hinders a comprehensive kinetic and kinetic mechanism study. Here, we perform a detailed investigation of the Tafel analysis using a series of synthetic rotating disk electrode (RDE) data. We find the Tafel analysis only works well when i 0 < 0.57 i lim (limiting current density) in our scenario and leads to abnormal kinetic values once i 0 exceeds this limiting value. Thus, we propose an extended Butler-Volmer (ex-BV) analysis based on modern non-linear fitting techniques to obtain the actual kinetic parameters for such systems. The results show that this method fits the RDE data closely and generates reliable α a , α c and i 0 values, demonstrating that it is a good replacement for traditional Tafel analysis for kinetic studies in high-viscosity electrolytes such as DES systems.

Electrochemistry↗

DOE OSTI2024

Adsorption of terbium (III) on DGA and LN resins: Thermodynamics, isotherms, and kinetics

Two commercially available extraction chromatography (EXC) resins containing N,N,N’,N’-tetra-n-octyldiglycolamide (DGA Resin, Normal, 50 – 100 μm) and Bis(2-ethylhexyl) phosphate (LN Resin, 100 – 150 μm) were used as adsorbents to study fundamental adsorption properties such as thermodynamic values, equilibrium isotherms, and kinetic uptake models for terbium(III) adsorption. Weight distribution ratios (D w ) for terbium on DGA and LN resins were measured using a [ 160 Tb]Tb 3+ radiometric tracer in nitric acid as a function of acidity, temperature, initial analyte concentration, and equilibrium time. The D w values showed increasing binding affinity for DGA resin at high nitric acid concentrations and decreasing binding affinity for LN resins. Thermodynamic studies for DGA and LN resins revealed that the Gibbs free energy (ΔG) increased consistently with temperature. To model equilibrium data, increasingly higher parameter equilibrium isotherm models (Henry (1) < Langmuir, Freundlich (2) < Redlich-Peterson (3) < Fritz-Schluender (4)) were compared on their root mean squared errors (RMSE) and adjusted determination coefficients to determine the most applicable model. In all cases, the empirical four-parameter Fritz-Schluender isotherm demonstrated a superior fit. Similar comparisons for reaction-based kinetic models (Pseudo-first-order < Pseudo-second-order < Pseudo-n-order) revealed that the higher-order PNO model yielded a superior fit of kinetic data for both resins. Furthermore, in some cases, adsorption isotherms and kinetic models could also be modeled by a lower-order model with minimal change in error parameters. Weber-Morris plots revealed that two linear sections are observed for each resin, where the first linear segment is attributed to fast (film diffusion) adsorption of terbium, followed by slower intraparticle diffusion of terbium through the pores as the rate-limiting step. Based on the Weber-Morris plot, both film and intraparticle diffusion are involved in controlling the kinetic rate of adsorption for DGA and LN resins.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

Magneto-structural studies of an unusual [Mn III Mn II Gd III (OR) 4 ] 4– partial cubane from 2,2'-bis- p- t Bu-calix[4]arene

Reaction of 2,2'-bis- p - t Bu-calix[4]arene (H 8 L) with MnCl 2 ·4H 2 O, GdCl 3 ·6H 2 O and 2,6-pyridinedimethanol (H 2 pdm) affords [Mn III Mn II Gd III (H 3 L)(pdmH)(pdm)(MeOH) 2 (dmf)]·3MeCN·dmf ( 3 ·3MeCN·dmf) upon vapour diffusion of MeCN into the basic dmf/MeOH mother liquor. 3 crystallises in the tetragonal space group P 4 1 2 1 2 with the asymmetric unit comprising the entire cluster. The highly unusual core contains a triangular arrangement of Mn III Mn II Gd III ions housed within a [Mn III Mn II Gd III (OR) 4 ] 4– partial cubane. Magnetic susceptibility and magnetisation data reveal best fit parameters J Mn(II)–Mn(III) = +0.415 cm –1 , J Mn(III)–Gd(III) = +0.221 cm –1 , J Mn(II)–Gd(III) = –0.258 cm –1 and D Mn(III) = –4.139 cm –1 . Theoretically derived magnetic exchange interactions, anisotropy parameters, and magneto-structural correlations for 3 are in excellent agreement with the experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Constraining the $\overline{K}$N coupled channel dynamics using femtoscopic correlations at the LHC

The interaction of K – with protons is characterised by the presence of several coupled channels, systems like $\overline{K}$ 0 n and πΣ with a similar mass and the same quantum numbers as the K – p state. The strengths of these couplings to the K – p system are of crucial importance for the understanding of the nature of the Λ(1405) resonance and of the attractive K – p strong interaction. In this article, we present measurements of the K – p correlation functions in relative momentum space obtained in pp collisions at $\sqrt{s}$=13 Te, in p–Pb collisions at $\sqrt{s_{NN}}$= 5.02 Te, and (semi)peripheral Pb–Pb collisions at $\sqrt{s_{NN}}$= 5.02 Te. The emitting source size, composed of a core radius anchored to the K + p correlation and of a resonance halo specific to each particle pair, varies between 1 and 2 fm in these collision systems. The strength and the effects of the $\overline{K}$ 0 n and πΣ inelastic channels on the measured K – p correlation function are investigated in the different colliding systems by comparing the data with state-of-the-art models of chiral potentials. A novel approach to determine the conversion weights ω, necessary to quantify the amount of produced inelastic channels in the correlation function, is presented. In this method, particle yields are estimated from thermal model predictions, and their kinematic distribution from blast-wave fits to measured data. The comparison of chiral potentials to the measured K – p interaction indicates that, while the πΣ –K – p dynamics is well reproduced by the model, the coupling to the $\overline{K}$ 0 n channel in the model is currently underestimated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Separation, speciation, and mechanism of astatine and bismuth extraction from nitric acid into 1-octanol and methyl anthranilate

We report a detailed study of At and Bi extraction from nitric acid media into conventional solvents, namely 1-octanol and methyl anthranilate, has been performed. The analysis includes a mathematical modeling which allows the fitting of experimental data and determination of extraction constants of the two above mentioned elements. Also, this approach helped to estimate a stability constant of a weak AtO(NO3) complex along with thermodynamic constants describing the redox process of At species in the acidic solution and formation of an adduct of Bi in the presence of methyl anthranilate. The results of the fitting have been used to calculate corresponding separation factors of Bi and At as well. Moreover, a computational study has been performed to evaluate At interaction with the above mentioned solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Complete collision data set for electrons scattering on molecular hydrogen and its isotopologues: III. Vibrational excitation via electronic excitation and radiative decay

We present state-resolved cross sections for vibrational excitation via electronic excitation followed by radiative decay (ERD), for electrons scattering on all bound vibrational levels of the ground electronic state ($X\hspace{1.5 mm} ^1Σ_g^+$) of molecular hydrogen and its isotopologues (H 2 , HD, HT, D 2 , DT and T 2 ). We consider excitation of the singlet $\textit{n}$ = 2–3 states (where n refers to the united-atoms-limit principle quantum number) and account for all possible decay pathways back to the bound vibrational levels of the ground electronic state ($X\hspace{1.5 mm} ^1Σ_g^+$) to produce an estimate for the ERD cross sections. A selection of the results are presented in graphical form and a full data set is provided as both numerical values and analytic fit functions in supplementary data files. The uncertainty in the cross sections is estimated to be 11%, except for scattering on the highest bound vibrational level of HD, HT, D 2 , and DT, where the uncertainty is 21%. The data can be downloaded from the MCCC database at mccc-db.org.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗

DOE OSTI2024

Age-specific case data reveal varying dengue transmission intensity in US states and territories

Dengue viruses (DENV) are endemic in the US territories of Puerto Rico, American Samoa, and the US Virgin Islands, with focal outbreaks also reported in the states of Florida and Hawaii. However, little is known about the intensity of dengue virus transmission over time and how dengue viruses have shaped the level of immunity in these populations, despite the importance of understanding how and why levels of immunity against dengue may change over time. These changes need to be considered when responding to future outbreaks and enacting dengue management strategies, such as guiding vaccine deployment. We used catalytic models fitted to case surveillance data stratified by age from the ArboNET national arboviral surveillance system to reconstruct the history of recent dengue virus transmission in Puerto Rico, American Samoa, US Virgin Islands, Florida, Hawaii, and Guam. We estimated average annual transmission intensity (i.e., force of infection) of DENV between 2010 and 2019 and the level of seroprevalence by age group in each population. We compared models and found that assuming all reported cases are secondary infections generally fit the surveillance data better than assuming all cases are primary infections. Using the secondary case model, we found that force of infection was highly heterogeneous between jurisdictions and over time within jurisdictions, ranging from 0.00008 (95% CrI: 0.00002–0.0004) in Florida to 0.08 (95% CrI: 0.044–0.14) in American Samoa during the 2010–2019 period. For early 2020, we estimated that seropositivity in 10 year-olds ranged from 0.09% (0.02%–0.54%) in Florida to 56.3% (43.7%–69.3%) in American Samoa. In the absence of serological data, age-specific case notification data collected through routine surveillance combined with mathematical modeling are powerful tools to monitor arbovirus circulation, estimate the level of population immunity, and design dengue management strategies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Amplitude analysis of the ${D}_s^{+}$ → π - π + π + decay

A Dalitz plot analysis of the ${D}_s^{+}$ → π - π + π + decay is presented. The analysis is based on proton-proton collision data recorded by the LHCb experiment at a centre of-mass energy of 8 TeV and corresponding to an integrated luminosity of 1.5 fb -1 . The resonant structure of the decay is obtained using a quasi-model-independent partial-wave analysis, in which the π + π - S-wave amplitude is parameterised as a generic complex function determined by a fit to the data. The S-wave component is found to be dominant, followed by the contribution from spin-2 resonances and a small contribution from spin-1 resonances. The latter includes the first observation of the ${D}_s^{+}$ → ω(782)π + channel in the ${D}_s^{+}$ → π - π + π + decay. The resonant structures of the ${D}_s^{+}$ → π - π + π + and ${D}^{+}$ → π - π + π + decays are compared, providing information about the mechanisms for the hadron formation in these decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Cosmological constraints using Minkowski functionals from the first year data of the Hyper Suprime-Cam

We use Minkowski functionals to analyse weak lensing convergence maps from the first-year data release of the Subaru Hyper Suprime-Cam (HSC-Y1) survey. Minkowski functionals provide a description of the morphological properties of a field, capturing the non-Gaussian features of the Universe matter-density distribution. Using simulated catalogues that reproduce survey conditions and encode cosmological information, we emulate Minkowski functionals predictions across a range of cosmological parameters to derive the best-fit from the data. By applying multiple scales cuts, we rigorously mitigate systematic effects, including baryonic feedback and intrinsic alignments. From the analysis, combining constraints of the angular power spectrum and Minkowski functionals, we obtain S8≡σ8Ωm/0.3=0.808−0.046+0.033 and Ωm=0.293−0.043+0.157⁠. These results represent a 40 per cent improvement on the S8 constraints compared to using power spectrum only. Minkowski functionals results are consistent with other two-point, and higher order statistics constraints using the same data, being in agreement with CMB results from the Planck S8 measurements. Our study demonstrates the power of Minkowski functionals beyond two-point statistics to constrain and break the degeneracy between Ωm and σ8⁠.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

DESI 2024: reconstructing dark energy using crossing statistics with DESI DR1 BAO data

Here, we implement Crossing Statistics to reconstruct in a model-agnostic manner the expansion history of the universe and properties of dark energy, using DESI Data Release 1 (DR1) BAO data in combination with one of three different supernova compilations (PantheonPlus, Union3, and DES-SN5YR) and Planck CMB observations. Our results hint towards an evolving and emergent dark energy behaviour, with negligible presence of dark energy at z ≳ 1, at varying significance depending on data sets combined. In all these reconstructions, the cosmological constant lies outside the 95% confidence intervals for some redshift ranges. This dark energy behaviour, reconstructed using Crossing Statistics, is in agreement with results from the conventional w 0 –w a dark energy equation of state parametrization reported in the DESI Key cosmology paper. Our results add an extensive class of model-agnostic reconstructions with acceptable fits to the data, including models where cosmic acceleration slows down at low redshifts. We also report constraints on H 0 r d from our model-agnostic analysis, independent of the pre-recombination physics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Hot-spot model for inertial confinement fusion implosions with an applied magnetic field

Imposing a magnetic field on inertial confinement fusion implosions magnetizes the electrons in the compressed fuel; this suppresses thermal losses, which increases temperature and fusion yield. Indirect-drive experiments at the National Ignition Facility with 12 and 26 T applied magnetic fields demonstrate up to 40% increase in temperature, 3× increase in fusion yield, and indicate that magnetization alters the radial temperature profile [Moody et al., Phys. Rev. Lett. 129, 195002 (2022); Lahmann et al., APS DPP (2022)]. In this work, we develop a semi-analytic hot-spot model, which accounts for the two-dimensional (2D) Braginskii anisotropic heat flow due to an applied axial magnetic field. First, we show that hot-spot magnetization alters the radial temperature profile, increasing the central peakedness, which is most pronounced for moderately magnetized implosions (with 8–14 T applied field), compared to both unmagnetized (with no applied field) and highly magnetized (with 26 T or higher applied field) implosions. This model explains the trend in the experimental data, which finds a similarly altered temperature profile in the 12 T experiment. Next, we derive the hot-spot model for gas-filled (Symcap) implosions, accounting for the effects of magnetization on the thermal conduction and in changing the radial temperature (and density) profiles. Using this model, we compute predicted central temperature amplification and yield enhancement scaling with the applied magnetic field. The central temperature fits the experimental data accurately, and the discrepancy in the yield suggests a systematic (independent of applied field) degradation, such as mix, and additional degradation in the reference unmagnetized shot, such as reduced laser drive, increased implosion asymmetry, or the magnetic field suppressing ablator mixing into the hot-spot.

Alpha particles↗

DOE OSTI2021

Re-evaluation of ortho-para- dependence of self pressure-broadening in the ν 1 + ν 3 band of acetylene

Optical frequency comb-referenced measurements of self pressure-broadened line profiles of the R(8) to R(13) lines in the ν 1 + ν 3 combination band of acetylene near 1.52 µ m are reported. The analysis of the data found no evidence for a previously reported systematic alternation in self pressure-broadened line widths with the nuclear spin state of the molecule. This work brought out the need for the use of an accurate line profile model and careful accounting for weak background absorptions due to hot band and lower abundance isotopomer lines. The data were adequately fit using the quadratic speed-dependent Voigt profile model, neglecting the small speed-dependent shift. Parameters describing the most probable and speed-dependent pressure-broadening, most probable shift, and the line strength were determined for each line. Detailed modeling of the results of Iwakuni et al. showed that their neglect of collisional narrowing due to the speed-dependent broadening term combined with the strongly absorbing data recorded and analyzed in transmission mode were the reasons for their results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗