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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms

Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here, we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size: $\geqslant$ 2.54 cm) in the central US (CUS) over the 20-year period of 2001–20 using the machine learning (ML), Random Forest (RF), and Extreme Gradient Boosting (XGB) methods. The developed RF and XGB models demonstrate high accuracy (> 90%) and F1 scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (Wyoming, South Dakota, Nebraska, and Kansas). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). Importantly, the results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Advances and perspectives in discovery and functional analysis of small secreted proteins in plants

Small secreted proteins (SSPs) are less than 250 amino acids in length and are actively transported out of cells through conventional protein secretion pathways or unconventional protein secretion pathways. In plants, SSPs have been found to play important roles in various processes, including plant growth and development, plant response to abiotic and biotic stresses, and beneficial plant–microbe interactions. Over the past 10 years, substantial progress has been made in the identification and functional characterization of SSPs in several plant species relevant to agriculture, bioenergy, and horticulture. Yet, there are potentially a lot of SSPs that have not been discovered in plant genomes, which is largely due to limitations of existing computational algorithms. Recent advances in genomics, transcriptomics, and proteomics research, as well as the development of new computational algorithms based on machine learning, provide unprecedented capabilities for genome-wide discovery of novel SSPs in plants. In this review, we summarize known SSPs and their functions in various plant species. Then we provide an update on the computational and experimental approaches that can be used to discover new SSPs. Finally, we discuss strategies for elucidating the biological functions of SSPs in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of the MPACT 69-group Library for Magnox Reactor Analysis using CASL VERA

The Consortium for Advanced Simulation of Light Water Reactors (CASL) has developed the CASL toolset, Virtual Environment for Reactor Analysis (VERA), for pressurized water reactor (PWR) analysis. Recently the CASL VERA was improved for Magnox reactor analysis, which required the development of a new cross section library and new geometrical and thermal feedback capabilities for graphite-moderated Magnoxreactors. The MPACT neutronics module of the CASL core simulator is a 3D whole core transport code, which requires a new cross section library with a different energy group structure due to the different neutronic characteristics of Magnox compared with PWR. A new 69-group structure was developed based on the MPACT 51-group structure to have more thermal energy groups and to be a subset of the SCALE 252-group structure. The ENDF/B-VII.1 MPACT 69-group library was developed for Magnox reactor analysis using the SCALE/AMPX and VERA-XSTools for which a super-homogenization method was applied, and transport cross sections were generated for graphite using a neutron leakage conservation method. Benchmark results show that new MPACT 69-group library works reasonably well for Magnox reactor analysis.

Kim, Kang Seog↗
DOE OSTI2024

Data and Scripts associated with “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling.”

This data package is associated with the publication “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling” submitted to Geoscientific Model Development (Muller et al., 2024). In this manuscript, organic matter chemistry and thermodynamics are directly connected to reactive transport simulators through the newly developed Lambda-PFLOTRAN (Parallel Reactive Flow and Transport model) workflow tool that succinctly incorporates organic matter chemistry data generated from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) into reaction networks to simulate aerobic respiration of the organic matter and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow, executed through a Jupyter Notebook interface, that digests raw FTICR-MS data, develops a representative reaction network based on substrate-explicit thermodynamic modeling (also termed lambda modeling due to its key thermodynamic parameter λ used therein), and completes a biogeochemical simulation with the open source, reactive flow, and transport code PFLOTRAN. This data package contains Jupyter Notebook based workflows for two test cases for running biogeochemical simulations of organic matter oxidation identified by FTICR-MS. It contains four primary folders (workflow, data, src, and analysis), a file-level metadata file (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_flmd.csv) that lists all the files contained in this data package with a short description of each, and a data dictionary (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_dd.csv) file that describes the tabular column headers. The ‘workflow’ folder contains the Jupyter Notebook based workflows for running the lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘data’ folder contains the FTICR-MS data, initial conditions, and incubation data for test cases 1 and 2 in folders titled ‘WHONDRS’ and ‘Colloids’, respectively. The data folder also has a ‘Database’ folder containing a reaction network for bulk organic matter (assumed to be CH2O) and a general database for PFLOTRAN (hanford_rxn_network). The CH2O reaction network defines bulk organic matter oxidation. Biogeochemical simulations are completed for both the lambda binned organic matter and bulk organic matter reaction networks. The ‘hanford_rxn_network’ database includes information required for PFLTORAN simulations including ion size, molar mass, and charge of the aqueous species, gases, and minerals phases. The ‘src’ folder contains python source codes for performing lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘analysis’ folder contains outputs from the test cases 1 and 2 including lambda analysis, PFLOTRAN runs and the calibration results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Resistivity of Surface Steps in Bulk-Insulating Topological Insulators

Electron transport in topological insulators usually involves both topologically protected surface states and trivial electronic states in the bulk material. The surface transport is particularly interesting; however, it is also susceptible to atomic defects on the surfaces, such as vacancies, impurities, and step edges. Experimental determination of scattering effects of these surface defects requires both nanoscale spatial resolution and the ability to decipher surface transport from bulk transport. Here we directly measure the resistivity of individual surface steps in the surface dominating transport process of topological insulator Bi 2 Te 2 Se. A variable probe-spacing transport spectroscopy with a multiprobe scanning tunneling microscope is used to differentiate the surface conductance from bulk conductance, allowing the identification of a surface dominant transport regime. The technique also reveals a deviation from ideal 2D transport at atomic steps. Then, a multi-probe scanning tunneling potentiometry is employed to visualize the electrochemical potentials across individual step edges. A quantitative analysis of the potential distributions enables us to acquire a resistivity of 0.530 mΩ · cm for the one quintuple-layer atomic step. The result indicates that atomic defects, despite preserving the time-reversal symmetry, can still significantly affect the transport in topological insulators.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A systems genetic analysis identifies putative mechanisms and candidate genes regulating vessel traits in poplar wood

Wood is the water conducting tissue of tree stems. Like most angiosperm trees, poplar wood contains water-conducting vessel elements whose functional properties affect water transport and growth rates, as well as susceptibility to embolism and hydraulic failure during water stress and drought. Here we used a unique hybrid poplar pedigree carrying genomically characterized chromosomal insertions and deletions to undertake a systems genomics analysis of vessel traits. We assayed gene expression in wood forming tissues from clonal replicates of genotypes covering dosage quantitative trait loci with insertions and deletions, genotypes with extreme vessel trait phenotypes, and control genotypes. A gene co-expression analysis was used to assign genes to modules, which were then used in integrative analyses to identify modules associated with traits, to identify putative molecular and cellular processes associated with each module, and finally to identify candidate genes using multiple criteria including dosage responsiveness. These analyses identified known processes associated with vessel traits including stress response, abscisic acid and cell wall biosynthesis, and in addition identified previously unexplored processes including cell cycle and protein ubiquitination. We discuss our findings relative to component processes contributing to vessel trait variation including signaling, cell cycle, cell expansion, and cell differentiation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Sensitivities of time-dependent temperature profile predictions for NSTX with the multi-mode model

The Multi-Mode Model (MMM) for turbulent transport was applied to a large set of well-analyzed discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate its sensitivities to a wide range of plasma conditions. MMM calculations were performed for hundreds of milliseconds in each discharge by performing time-dependent predictive simulations with the 1.5D tokamak integrated modeling code TRANSP. A closely related study (Lestz et al 2025 Plasma Phys. Control. Fusion 67 105029) concluded that MMM predicted electron and ion temperature profiles that were in reasonable agreement with NSTX observations, generally outperforming a different reduced transport model, TGLF. This finding motivates the more thorough investigation of the characteristics of the MMM predictions conducted in this work. The simulations with MMM have electron energy transport dominated by electron temperature gradient modes in the examined discharges with relatively low plasma β (ratio of kinetic plasma pressure to magnetic field pressure) and high collisionality, transitioning to a mixture of different modes for higher β and lower collisionality. The thermal ion diffusivity predicted by MMM is much smaller than the neoclassical contribution, in line with previous experimental analysis of NSTX. Nonetheless, the electron and ion temperature profiles are coupled via collisional energy exchange and thus sensitive to which transport channels are predicted. The time-dependent simulations with MMM are robust to the simulation start time, converging to remarkably similar temperature profiles later during the discharge. MMM typically overpredicts confinement relative to NSTX observations, leading to the prediction of overly steep temperature profiles. Plasmas with spatially broader temperature profiles, higher plasma β, and longer energy confinement times tend to be predicted by MMM with better agreement with the experiment. As a result, these findings provide useful context for understanding the regime-dependent tendencies of MMM in anticipation of self-consistent, time-dependent predictive simulations of NSTX-U discharges with these same modeling tools.

MMM↗
DOE OSTI2023

Stacking disorder in α – RuCl 3 investigated via x-ray three-dimensional difference pair distribution function analysis

The van der Waals layered magnet α-RuCl 3 offers tantalizing prospects for the realization of Majorana quasiparticles. Efforts to understand this are, however, hampered by inconsistent magnetic and thermal transport properties likely coming from the formation of structural disorder during crystal growth, postgrowth processing, or upon cooling through the first order structural transition. Here, we investigate structural disorder in α-RuCl 3 using x-ray diffuse scattering and three-dimensional difference pair distribution function (3D-ΔPDF) analysis. We develop a quantitative model that describes disorder in α-RuCl 3 in terms of rotational twinning and intermixing of the high and low-temperature structural layer stacking. As a result, this disorder may be important to consider when investigating the detailed magnetic and electronic properties of this widely studied material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Microtearing instabilities and electron thermal transport in low and high collisionality NSTX discharges

Microtearing mode (MTM) real frequency, growth rate, magnetic fluctuation amplitude and resulting electron thermal transport are studied in systematic NSTX scans of relevant plasma parameters. Here, the dependency of the MTM real frequency and growth rate on plasma parameters, suitable for low and high collision NSTX discharges, is obtained by using the reduced MTM transport model. The plasma parameter dependencies are compared and found to be consistent with the results obtained from MTM using the Gyrokinetic GYRO code. The scaling trend of collision frequency and plasma beta is found to be consistent with the global energy confinement trend observed in the NSTX experiment. The strength of the magnetic fluctuation is found to be consistent with the gyrokinetic estimate. In earlier studies, it was found that the version of the Multi-Mode (MM) anomalous transport model, which did not contain the effect of MTMs, provided an appropriate description of the electron temperature profiles in standard tokamak discharges and not in spherical tokamaks. When the MM model, which involves transport associated with MTMs, is incorporated in the TRANSP code and is used in the study of electron thermal transport in NSTX discharges, it is observed that the agreement with the experimental electron temperature profile is substantially improved.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Summary of Expansions and Updates in GREET ® 2022

The GREET ® (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model has been developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE) and other federal agencies. GREET is a life cycle analysis (LCA) tool, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail) and other end-use sectors, and energy systems. Argonne has expanded and updated the model in various sectors in GREET 2022, and this report provides a summary of the release.

25 ENERGY STORAGE↗
DOE OSTI2025

Probing the role of local tunnel variations in early-stage lithiation of α-MnO₂ nanowires via in situ TEM

Understanding lithium-ion transport in tunnel-structured manganese oxides is essential for designing high-performance lithium-ion battery electrode materials. Here, we elucidate the early-stage lithiation mechanism of potassium-stabilized α-MnO 2 nanowires using in situ transmission electron microscopy (TEM) coupled with electron energy-loss spectroscopy (EELS), high-resolution TEM (HRTEM), and geometric phase analysis (GPA). Real-time TEM imaging reveals clear volume expansion at the reaction front, while EELS analysis uncovers lithium-ion diffusion far beyond this region, where no visible expansion is observed, indicating fast, defect-assisted transport. GPA and HRTEM analyses show that localized tensile and compressive strain fields, originating from pre-existing local tunnel structural variations, persist after lithiation. The tensile-strained regions enable lithium-ion insertion with minimal lattice distortion, offering additional free volume that facilitates rapid lithium-ion accommodation ahead of the structural transformation. Our results demonstrate a local tunnel variation-mediated fast diffusion pathway that precedes bulk reaction, underscoring the critical role of local strain in enabling early-stage lithium transport. Given the structural versatility of MnO 2 and its ability to accommodate diverse atomic arrangements beyond the well-known tunnel phases (β-, γ-, δ-, λ-, R-phases), our findings highlight the importance of understanding and engineering local structural environments. This work provides fundamental insights into the interplay between defects, strain, and ion dynamics, and presents defect engineering as a promising approach to enhance both rate performance and structural stability in manganese-based cathodes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Hanford Site Composite Analysis Data Package: Exposure Scenarios and Radionuclide Specific Dose Conversion Factors

This data package summarizes the exposure assumptions, equations, and methods used to calculate radionuclide-specific unit dose factors and the radiological doses for both groundwater and atmospheric pathways as a part of the updated Hanford Site Composite Analysis (CA). An All Pathways Representative Person exposure scenario is considered to evaluate exposure via both groundwater and atmospheric transport pathways. The radiological dose assessments for both groundwater and atmospheric pathways are included in the performance assessments (PAs) for various Waste Management Areas at the Hanford Site. This data package calculates exposure route-specific and total unit dose factors for CA-specific radionuclides of concern based on the exposure assumptions used in the CA and PAs. This data package also presents the results and comparison of the radionuclide-specific unit dose factors based on the exposure assumptions used in the revised Hanford Site Composite Analysis and Hanford Site performance assessments for low-level waste disposal facilities.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Analysis of SCALE Criticality and Sensitivity Calculations for Reflected HEU Cylinders [Slides]

The SCALE code package offers multiple nuclear data libraries supporting Monte Carlo transport, with sensitivity and uncertainty methods derived from MC transport solutions. Several libraries are multigroup, which introduce bias differing by system. Previous work has shown poor S/U results in several reflector materials: ICSBEP benchmark HMF-084 was selected to analyze biases and S/U method applicability to a variety of reflectors. Prior and ongoing work found inaccuracies in CSAS and TSUNAMI results, which were further investigated utilizing the HEU-MET-FAST-084 ICSBEP critical benchmark, chosen for its geometrical simplicity and variety of reflector materials. Perturbation of reflector thickness across various reflector materials allowed for an assortment of materials is to be tested swiftly for each sequence and method. Observation was an increasing bias of MG $k_{eff}$ relative to CE, in both direction and magnitude. IFP produced extremely reliable results. >85% of CLUTCH cases were found satisfactory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Particle Sensitivity Analysis

We propose to develop a computational sensitivity analysis capability for Monte Carlo sampling-based particle simulation relevant to Aleph, Cheetah-MC, Empire, Emphasis, ITS, SPARTA, and LAMMPS codes. These software tools model plasmas, radiation transport, low-density fluids, and molecular motion. Our report demonstrates how adjoint optimization methods can be combined with Monte Carlo sampling-based adjoint particle simulation. Our goal is to develop a sensitivity analysis to drive robust design-based optimization for Monte Carlo sampling-based particle simulation - a currently unavailable capability.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Summary of Expansions and Updates in R&D GREET ® 2023

The Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model was developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE) and other federal agencies. R&D GREET is a life cycle analysis (LCA) model, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail), other end-use sectors, and energy systems. Argonne has expanded and updated the model in various areas in R&D GREET 2023. This report provides a summary of the expansions and updates.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Summary of Expansions and Updates in GREET ® 2021

The GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model has been developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE). GREET is a life-cycle analysis (LCA) tool, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail) and other end-use sectors, and energy systems. Within the transportation sector, GREET covers road, air, water, and rail transportation sub-sectors. Recently, GREET was expanded to cover the building sector. Historically, GREET includes LCA of various materials such as steel, aluminum, cement, and different plastic types. Argonne has expanded and updated the model in various sectors in GREET 2021, and this report provides a summary of the release.

09 BIOMASS FUELS↗