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DOE OSTI2014

First-principles high-pressure unreacted equation of state and heat of formation of crystal 2,6-diamino-3, 5-dinitropyrazine-1-oxide (LLM-105)

We report dispersion-corrected density functional theoretical calculations of the unreacted equation of state (EOS) of crystal 2,6-diamino-3, 5-dinitropyrazine-1-oxide (LLM-105) under hydrostatic compression of up to 45 GPa. Convergence tests for k-points sampling in the Brillouin zone show that a 3 × 1 × 2 mesh is required to reproduce the X-ray crystal structure at ambient conditions, and we confirm our finding with a separate supercell calculation. Our high-pressure EOS yields a bulk modulus of 19.2 GPa, and indicates a tendency towards anisotropic compression along the b lattice vector due to molecular orientations within the lattice. We find that the electronic energy band gap decreases from a semiconductor type of 1.3 eV at 0 GPa to quasi-metallic type of 0.6 eV at 45 GPa. The extensive intermolecular hydrogen bonds involving the oxide (–NO) and dioxide (–NO{sub 2}) interactions with the amine (–NH{sub 2}) group showed enhanced interactions with increasing pressure that should be discernible in the mid IR spectral region. We do not find evidence for structural phase transitions or chemically induced transformations within the pressure range of our study. The gas phase heat of formation is calculated at the G4 level of theory to be 22.48 kcal/mol, while we obtain 25.92 kcal/mol using the ccCA-PS3 method. Density functional theory calculations of the crystal and the gas phases provided an estimate for the heat of sublimation of 32.4 kcal/mol. We thus determine the room-temperature solid heat of formation of LLM-105 to be −9.9 or −6.5 kcal/mol based on the G4 or ccCA-PS3 methods, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

A comprehensive method of estimating electric fields from vector magnetic field and Doppler measurements

Photospheric electric fields, estimated from sequences of vector magnetic field and Doppler measurements, can be used to estimate the flux of magnetic energy (the Poynting flux) into the corona and as time-dependent boundary conditions for dynamic models of the coronal magnetic field. We have modified and extended an existing method to estimate photospheric electric fields that combines a poloidal-toroidal decomposition (PTD) of the evolving magnetic field vector with Doppler and horizontal plasma velocities. Our current, more comprehensive method, which we dub the 'PTD-Doppler-FLCT Ideal' (PDFI) technique, can now incorporate Doppler velocities from non-normal viewing angles. It uses the FISHPACK software package to solve several two-dimensional Poisson equations, a faster and more robust approach than our previous implementations. Here, we describe systematic, quantitative tests of the accuracy and robustness of the PDFI technique using synthetic data from anelastic MHD (ANMHD) simulations, which have been used in similar tests in the past. We find that the PDFI method has less than 1% error in the total Poynting flux and a 10% error in the helicity flux rate at a normal viewing angle (θ = 0) and less than 25% and 10% errors, respectively, at large viewing angles (θ < 60°). We compare our results with other inversion methods at zero viewing angle and find that our method's estimates of the fluxes of magnetic energy and helicity are comparable to or more accurate than other methods. We also discuss the limitations of the PDFI method and its uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Protein expression and gene editing in monocots using foxtail mosaic virus vectors

Abstract Plant viruses can be engineered to carry sequences that direct silencing of target host genes, expression of heterologous proteins, or editing of host genes. A set of foxtail mosaic virus (FoMV) vectors was developed that can be used for transient gene expression and single guide RNA delivery for Cas9‐mediated gene editing in maize, Setaria viridis , and Nicotiana benthamiana . This was accomplished by duplicating the FoMV capsid protein subgenomic promoter, abolishing the unnecessary open reading frame 5A, and inserting a cloning site containing unique restriction endonuclease cleavage sites immediately after the duplicated promoter. The modified FoMV vectors transiently expressed green fluorescent protein (GFP) and bialaphos resistance (BAR) protein in leaves of systemically infected maize seedlings. GFP was detected in epidermal and mesophyll cells by epifluorescence microscopy, and expression was confirmed by Western blot analyses. Plants infected with FoMV carrying the bar gene were temporarily protected from a glufosinate herbicide, and expression was confirmed using a rapid antibody‐based BAR strip test. Expression of these proteins was stabilized by nucleotide substitutions in the sequence of the duplicated promoter region. Single guide RNAs expressed from the duplicated promoter mediated edits in the N. benthamiana Phytoene desaturase gene, the S. viridis Carbonic anhydrase 2 gene, and the maize HKT1 gene encoding a potassium transporter. The efficiency of editing was enhanced in the presence of synergistic viruses and a viral silencing suppressor. This work expands the utility of FoMV for virus‐induced gene silencing (VIGS), virus‐mediated overexpression (VOX), and virus‐enabled gene editing (VEdGE) in monocots.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Understanding and quantifying electron beam effects during in situ TEM nanomechanical tensile testing on metal thin films

Transmission electron microscopy (TEM) imaging relies on high energy electrons for atomic scale resolution, however, the electrons themselves interact with and may alter the material being imaged. Using an in situ TEM MEMS-based nanomechanical testing technique, the effect of the electron beam (e-beam) on the deformation behavior of nanocrystalline Al and ultrafine-grained Au is investigated and quantified. We show that the e-beam enhances plastic deformation, leading to an increase in plastic strain rate and a decrease in true activation volume V* in Al (28 to 21b 3 , with b being the Burgers vector length). The e-beam has a much weaker effect on Au. The e-beam effect is not caused by knock-on damage, but rather an effective temperature increase due to additional atomic fluctuations provided by the e-beam. The effective temperature increase is larger for Al than Au. This e-beam effect does not change the deformation mechanisms, but instead accelerates the stress-driven, thermally activated plastic deformation. These experiments provide insight into the effects of the e-beam on plastic deformation in different metals and underscore the importance of understanding and quantifying these effects for proper interpretation of measured mechanical properties during in situ TEM experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Deep Learning Estimation of Daily Ground–Level NO 2 Concentrations from Remote Sensing Data

The limited number of nitrogen dioxide (NO 2 ) surface measurements calls for the development of highly accurate approaches to estimating surface NO 2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO 2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep-CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO 2 estimation. Additionally, we leverage a novel approach, SHapley Additive exPlanations (SHAP), to describe how Deep-CNN understands each predictor variable. The SHAP results show that the Deep-CNN model has an advanced understanding of the dataset, revealing that TROPOMI closely captures levels of NO 2 . In addition, we show the superiority of our Deep-CNN model at estimating surface NO 2 over other well-known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO 2 concentrations, their accuracy is inferior to that of the Deep-CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO 2 estimation and ranks last among all models. Furthermore, testing the impact of sample size on model performance, we also show that, compared to other models, Deep-CNN needs more samples to trigger its strength at surface NO 2 estimation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Quantitative x-ray scattering of free molecules

Advances in x-ray free electron lasers have made ultrafast scattering a powerful method for investigating molecular reaction kinetics and dynamics. Accurate measurement of the ground-state, static scattering signals of the reacting molecules is pivotal for these pump-probe x-ray scattering experiments as they are the cornerstone for interpreting the observed structural dynamics. Here, this article presents a data calibration procedure, designed for gas-phase x-ray scattering experiments conducted at the Linac Coherent Light Source x-ray Free-Electron Laser at SLAC National Accelerator Laboratory, that makes it possible to derive a quantitative dependence of the scattering signal on the scattering vector. A self-calibration algorithm that optimizes the detector position without reference to a computed pattern is introduced. Angle-of-scattering corrections that account for several small experimental non-idealities are reported. Their implementation leads to near quantitative agreement with theoretical scattering patterns calculated with ab-initio methods as illustrated for two x-ray photon energies and several molecular test systems.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2017

Feature Selection and Inferential Procedures for Video Data [Slides]

With the rise of electronic and high-dimensional data, new and innovative feature detection and statistical methods are required to perform accurate and meaningful statistical analysis of these datasets that provide unique statistical challenges. In the area of feature detection, much of the recent feature detection research in the computer vision community has focused on deep learning methods, which require large amounts of labeled training data. However, in many application areas, training data is very limited and often difficult to obtain. We develop methods for fast, unsupervised, precise feature detection for video data based on optical flows, edge detection, and clustering methods. We also use pretrained neural networks and interpretable linear models to extract features using very limited training data. In the area of statistics, while high-dimensional data analysis has been a main focus of recent statistical methodological research, much focus has been on populations of high-dimensional vectors, rather than populations of high-dimensional tensors, which are three-dimensional arrays that can be used to model dependent images, such as images taken of the same person or ripped video frames. Our feature detection method is a non-model-based method that fusses information from dense optical flow, raw image pixels, and frame differences to generate detections. Our hypothesis testing methods are based on the assumption that dependent images are concatenated into a tensor that follows a tensor normal distribution, and from this assumption, we derive likelihood-ratio, score, and regression-based tests for one- and multiple-sample testing problems. Our methods will be illustrated on simulated and real datasets. We conclude this report with comments on the relationship between feature detection and hypothesis testing methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Pitch-angle scattering of energetic charged particles in nearly constant magnitude magnetic turbulence

We use a method developed by Roberts that optimizes the phase angles of an ensemble of plane waves with amplitudes determined from a Kolmogorov-like power spectrum, to construct magnetic field vector fluctuations having nearly constant magnitude and large variances in its components. This is a representation of the turbulent magnetic field consistent with that observed in the solar wind. Charged-particle pitch-angle diffusion coefficients are determined by integrating the equations of motion for a large number of charged particles moving under the influence of forces from our predefined magnetic field. We tested different cases by varying the kinetic energy of the particles (E {sub p}) and the turbulent magnetic field variance (σ{sub B}{sup 2}). For each combination of E {sub p} and σ{sub B}{sup 2}, we tested three different models: (1) the so-called “slab” model, where the turbulent magnetic field depends on only one spatial coordinate and has significant fluctuations in its magnitude (b=√(δB{sub x}{sup 2}(z)+δB{sub y}{sup 2}(z)+B{sub 0}{sup 2})); (2) the slab model optimized with nearly constant magnitude b; and (3) the slab model turbulent magnetic field with nearly constant magnitude plus a “variance-conserving” adjustment. In the last case, this model attempts to conserve the variance of the turbulent components (σ{sub Bx}{sup 2}+σ{sub By}{sup 2}), which is found to decrease during the optimization with nearly constant magnitude. We found that there is little or no effect on the pitch-angle diffusion coefficient D{sub μμ} between models 1 and 2. However, the result from model 3 is significantly different. We also introduce a new method to accurately determine the pitch-angle diffusion coefficients as a function of μ.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Identification of novel small molecule inhibitors against NS2B/NS3 serine protease from Zika virus

Zika flavivirus infection during pregnancy appears to produce higher risk of microcephaly, and also causes multiple neurological problems such as Guillain–Barré syndrome. The Zika virus is now widespread in Central and South America, and is anticipated to become an increasing risk in the southern United States. With continuing global travel and the spread of the mosquito vector, the exposure is expected to accelerate, but there are no currently approved treatments against the Zika virus. The Zika NS2B/NS3 protease is an attractive drug target due to its essential role in viral replication. We report our studies have identified several compounds with inhibitory activity (IC 50 ) and binding affinity (K D ) of ~5–10 μM against the Zika NS2B-NS3 protease from testing 71 HCV NS3/NS4A inhibitors that were initially discovered by high-throughput screening of 40,967 compounds. Competition surface plasmon resonance studies and mechanism of inhibition analyses by enzyme kinetics subsequently determined the best compound to be a competitive inhibitor with a $K_i$ value of 9.5 μM. We also determined the X-ray structure of the Zika NS2B-NS3 protease in a “pre-open conformation”, a conformation never observed before for any flavivirus proteases. This provides the foundation for new structure-based inhibitor design.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Stem cells with FGF4-bFGF fused gene enhances the expression of bFGF and improves myocardial repair in rats

Highlights: • BFGF exists only in the cytoplasm of live cells. • BFGF cannot be secreted into the extracellular space to promote cell growth. • We combine the secretion-promoting signal peptide of FGF4. • We successfully modified BMSCs with the fused genes of FGF4-bFGF. • We promoted the therapeutic effects of transplanted BMSCs in myocardial infarction. - Abstract: The aim of this study was to investigate whether the modification of bone marrow-derived mesenchymal stem cells (BMSCs) with the fused FGF4 (fibroblast growth factor 4)-bFGF (basic fibroblast growth factor) gene could improve the expression and secretion of BFGF, and increase the efficacies in repairing infarcted myocardium. We used In-Fusion technique to construct recombinant lentiviral vectors containing the individual gene of bFGF, enhanced green fluorescent protein (EGFP), or genes of FGF4-bFGF and EGFP, and then transfected these lentiviruses into rat BMSCs. We conducted an in vitro experiment to compare the secretion of bFGF in BMSCs infected by these lentiviruses and also examined their therapeutic effects in the treatment of myocardial infraction in a rodent study. Sixty rats were tested in the following five conditions: Group-SHAM received only sham operation as controls; Group-AMI received only injection of placebo PBS buffer; Group-BMSC, Group-bFGF and Group-FGF4-bFGF received implantation of BMSCs with empty lentivirus, bFGF lentivirus, and FGF4-bFGF lentivirus, respectively. Our results found out that the transplanted FGF4-bFGF BMSCs had the highest survival rate, and also the highest myocardial expression of bFGF and microvascular density as evidenced by Western blotting and immunohistochemistry, respectively. As compared to other groups, the Group-FGF4-BFGF rats had the lowest myocardial fibrotic fraction, and the highest left ventricular ejection fraction. These results suggest that the modification of BMSCs with the FGF4-bFGF fused gene can not only increase the expression of bFGF but also improve its secretion. The FGF4-bFGF BMSCs thus can enhance the survival of the transplanted cells, diminish myocardial fibrosis, promote myocardial angiogenesis, and improve cardiac functions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

First Leptophobic Dark Matter Search from the Coherent–CAPTAIN-Mills Liquid Argon Detector

We report the first results of a search for leptophobic dark matter (DM) from the Coherent–CAPTAIN-Mills (CCM) liquid argon (LAr) detector. An engineering run with 120 photomultiplier tubes (PMTs) and 17.9 × 10 20 protons on target (POT) was performed in fall 2019 to study the characteristics of the CCM detector. The operation of this 10-ton detector was strictly light based with a threshold of 50 keV and used coherent elastic scattering off argon nuclei to detect DM. Despite only 1.5 months of accumulated luminosity, contaminated LAr, and nonoptimized shielding, CCM’s first engineering run has already achieved sensitivity to previously unexplored parameter space of light dark matter models with a baryonic vector portal. With an expected background of 115 005 events, we observe 115 005 + 16.5 events which is compatible with background expectations. For a benchmark mediator-to-DM mass ratio of m V B =m χ = 2.1, DM masses within the range 9 MeV ≲ m χ ≲ 50 MeV are excluded at 90% C. L. in the leptophobic model after applying the Feldman-Cousins test statistic. CCM’s upgraded run with 200 PMTs, filtered LAr, improved shielding, and 10 times more POT will be able to exclude the remaining thermal relic density parameter space of this model, as well as probe new parameter space of other leptophobic DM models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Futility

The Futility package contains the following: 1) Definition of the size of integers and real numbers; 2) A generic Unit test harness; 3) Definitions for some basic extensions to the Fortran language: arbitrary length strings, a parameter list construct, exception handlers, command line processor, timers; 4) Geometry definitions: point, line, plane, box, cylinder, polyhedron; 5) File wrapper functions: standard Fortran input/output files, Fortran binary files, HDF5 files; 6) Parallel wrapper functions: MPI, and Open MP abstraction layers, partitioning algorithms; 7) Math utilities: BLAS, Matrix and Vector definitions, Linear Solver methods and wrappers for other TPLs (PETSC, MKL, etc), preconditioner classes; 8) Misc: random number generator, water saturation properties, sorting algorithms.

Collins, BenjaminS.↗

DOE OSTI2017

Futility

Collins, Benjamin S.↗

DOE OSTI2022

Muonic force behind flavor anomalies

We develop an economical theoretical framework for combined explanations of the flavor physics anomalies involving muons: (g - 2) μ , R K(*), and b → sμ + μ - angular distributions and branching ratios, that was first initiated by some of us in ref. [1]. The Standard Model (SM) is supplemented with a lepton-flavored U(1) X gauge group. The U(1) X gauge boson with the mass of O(0.1) GeV resolves the (g - 2) μ tension. A TeV-scale leptoquark, charged under the U(1) X , carries a muon number and mediates B-decays without prompting charged lepton flavor violation or inducing proton decay. We explore the theory space of the chiral, anomaly-free U(1) X gauge extensions featuring the above scenario, and identify many suitable charge assignments for the SM+3ν R fermion content with the integer charges in the range X F i $\in$ [-10, 10]. We then carry out a comprehensive phenomenological study of the muonic force in representative benchmark models. Interestingly, we found models which can resolve the tension without conflicting the complementary constraints, and all of the viable parameter space will be tested in future muonic resonance searches. Finally, the catalog of the anomaly-free lepton-non-universal charge assignments motivated us to explore different directions in model building. We present a model in which the muon mass and the (g - 2) μ are generated radiatively from a common short-distance dynamics after the U(1) X breaking. We also show how to charge a vector leptoquark under U(1) μ-τ in a complete gauge model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

Magnetic excitations in stripe-ordered La 1.875 Ba 0.125 CuO 4 studied using resonant inelastic x-ray scattering

The charge and spin correlations in La 1.875 Ba 0.125 CuO 4 (LBCO 1/8) are studied using Cu L 3 edge resonant inelastic x-ray scattering (RIXS). The static charge order (CO) is observed at a wave vector of ( 0.24 , 0 ) and its charge nature confirmed by measuring the dependence of this peak on the incident x-ray polarization. The damped spin excitation or “paramagnon” in LBCO 1/8 is then measured as it disperses through the CO wave vector. Within the experimental uncertainty no changes are observed in the paramagnon at that wave vector and the paramagnon seems to be similar to that measured in other cuprates, which have no static CO. Given that the stripe correlation modulates both the charge and spin degrees of freedom, it is likely that subtle changes do occur in the paramagnon due to CO. Consequently, we propose that future RIXS measurements, realized with higher-energy resolution and sensitivity, should be performed to test for these effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Control Strategies and Validation in the Hybrid Optimization and Performance Platform (HOPP)

The Hybrid Optimization and Performance Platform (HOPP) is a tool that simulates hybrid power plants in various configurations, and also calculates the financial feasibility of these plants. This report outlines an overview of HOPP and the energy storage dispatch strategies available. It then presents three case studies which demonstrate different applications of HOPP. The first case looks at the profitability of hybrid power plants in different locations in the USA. The second case examines the availability of hybrid power plants to provide energy reliability services. The third case presents a plant that produces both hydrogen and electricity, and demonstrates a dispatch strategy that chooses the most profitable energy vector based on price signals. The next section shows the validation of HOPP on operational data, using data from both unit-scale and utility-scale power plants. This validation process demonstrated that HOPP can simulate the power output of both wind and solar PV plants at both scales with comparable fidelity to an existing commercial software tool. Finally, HOPP is applied in a field test which applies an optimal dispatch strategy to a physical battery in a unit-scale hybrid plant at NREL. HOPP's optimal dispatch strategy, applied in a real-world setting, improved this hybrid plant's ability to meet a load signal while minimizing operational costs.

14 SOLAR ENERGY↗

DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗