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At least 793 records · Page 44

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DOE OSTI2021

Engineering Thermal Transport across Layered Graphene–MoS 2 Superlattices

Layering two-dimensional van der Waals materials provides a high degree of control over atomic placement, which could enable tailoring of vibrational spectra and heat flow at the sub-nanometer scale. In this work, using spatially resolved ultrafast thermoreflectance and spectroscopy, we uncover the design rules governing cross-plane heat transport in superlattices assembled from monolayers of graphene (G) and MoS 2 (M). Using a combinatorial experimental approach, we probe nine different stacking sequences, G, GG, MG, GGG, GMG, GGMG, GMGG, GMMG, and GMGMG, and identify the effects of vibrational mismatch, interlayer adhesion, and junction asymmetry on thermal transport. Pure G sequences display evidence of quasi-ballistic transport, whereas adding even a single M layer strongly disrupts heat conduction. The experimental data are described well by molecular dynamics simulations, which include thermal expansion, accounting for the effect of finite temperature on the interlayer spacing. The simulations show that an increase of ~2.4% in the layer separation of GMGMG, relative to its value at 300 K, can lead to a doubling of the thermal resistance. Using these design rules, we experimentally demonstrate a five-layer GMGMG superlattice “thermal metamaterial” with an ultralow effective cross-plane thermal conductivity comparable to that of air.

2D materials↗

DOE OSTI2024

Inducing Circularly Polarized Single-Photon Emission via Chiral-Induced Spin Selectivity

One of the central aims of the field of spintronics is the control of individual electron spins to effectively manage the transmission of quantized data. One well-known mechanism for controlling electronic spin transport is the chiral-induced spin-selectivity (CISS) effect in which a helical nanostructure imparts a preferential spin orientation on the electronic transport. One potential application of the CISS effect is as a transduction pathway between electronic spin and circularly polarized light within nonreciprocal photonic devices. Here in this work, we identify and quantify the degree of chiral-induced spin-selective electronic transport in helical polyaniline films using magnetoconductive atomic force microscopy (mcAFM). We then induce circularly polarized quantum light emission from CdSe/CdS core/shell quantum dots placed on these films, demonstrating a degree of circular polarization of up to ~21%. Utilizing time-resolved photoluminescence microscopy, we measure the radiative lifetime difference associated with left- and right-handed circular polarizations of single emitters. These lifetime differences, in combination with Kelvin probe mapping of the variation of surface potential with magnetization of the substrate, help establish an energy level diagram describing the spin-dependent transport pathways that enable the circularly polarized photoluminescence.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Comparative Analysis of Report-Back of Research Results Strategies for Personal Chemical Exposure Data

Background. Report-back of research results (RBRR) is ethically supported and highly requested by participants yet lacks broadly transferable guidelines for RBRR. Effective RBRR must be responsive to target audience needs and may not be addressed by a ‘one-size-fits-all’ approach. Objective. Within a subset of our 19 studies on RBRR, we had the unique opportunity to carry out a comparative analysis of RBRR strategies across cohorts with similar development and evaluation methods, yet distinct in life stage, geography, number and type of chemicals assessed, and community contexts. Methods. We highlight key outcomes from three environmental health studies: an ongoing New York, NY cohort (Fair Start; n=486) and a Detroit, MI cohort (CLEAR; n=34) assessing exposure to ambient urban pollution during pregnancy, and a longitudinal cohort in Houston, TX (Houston-3H) following Hurricane Harvey (n=312). Focus group and survey data were analyzed to identify lessons learned and explore how RBRR supports understanding of environmental health. Results. Commonalities emerged in RBRR development, design, organization, and data visualization, as well as in how RBRR can contribute to an understanding of health-environment connections. Differences included preferences for individual versus community level findings, as well as distinguishable contextual considerations. For pregnancy cohorts, messaging was framed with cultural sensitivity, and to avoid unintended consequences of parental guilt due to prenatal exposures. In the post-disaster Houston-3H study, participants requested additional transparency regarding sampling design and study rationale. Significance. All RBRR case studies reported chemicals without known regulatory or health guidelines, so results were contextualized within the study population. Participants across cohorts requested multi-study comparisons to better understand their results beyond their communities. While foundational RBRR elements (e.g. plain language, graphic organizers) may supersede cohort-specific differences, RBRR should be personalized to encompass perceptions of health across different life-stage, cultural, and environmental contexts.

Vogel, Taylor J.↗

DOE OSTI2022

Step-by-step state-selective tracking of fragmentation dynamics of water dications by momentum imaging

Abstract The double photoionization of a molecule by one photon ejects two electrons and typically creates an unstable dication. Observing the subsequent fragmentation products in coincidence can reveal a surprisingly detailed picture of the dynamics. Determining the time evolution and quantum mechanical states involved leads to deeper understanding of molecular dynamics. Here in a combined experimental and theoretical study, we unambiguously separate the sequential breakup via D + + OD + intermediates, from other processes leading to the same D + + D + + O final products of double ionization of water by a single photon. Moreover, we experimentally identify, separate, and follow step by step, two pathways involving the b 1 Σ + and a 1 Δ electronic states of the intermediate OD + ion. Our classical trajectory calculations on the relevant potential energy surfaces reproduce well the measured data and, combined with the experiment, enable the determination of the internal energy and angular momentum distribution of the OD + intermediate.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2023

Cryo-EM structures of PP2A:B55–FAM122A and PP2A:B55–ARPP19

Progression through the cell cycle is controlled by regulated and abrupt changes in phosphorylation. Mitotic entry is initiated by increased phosphorylation of mitotic proteins, a process driven by kinases, whereas mitotic exit is achieved by counteracting dephosphorylation, a process driven by phosphatases, especially PP2A:B55. Although the role of kinases in mitotic entry is well established, recent data have shown that mitosis is only successfully initiated when the counterbalancing phosphatases are also inhibited. Inhibition of PP2A:B55 is achieved by the intrinsically disordered proteins ARPP19 and FAM122A. Despite their critical roles in mitosis, the mechanisms by which they achieve PP2A:B55 inhibition is unknown. Here, we report the single-particle cryo-electron microscopy structures of PP2A:B55 bound to phosphorylated ARPP19 and FAM122A. Consistent with our complementary NMR spectroscopy studies, both intrinsically disordered proteins bind PP2A:B55, but do so in highly distinct manners, leveraging multiple distinct binding sites on B55. Our extensive structural, biophysical and biochemical data explain how substrates and inhibitors are recruited to PP2A:B55 and provide a molecular roadmap for the development of therapeutic interventions for PP2A:B55-related diseases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Samarium: from a distorted-fcc phase to melting under dynamic compression using in-situ x-ray diffraction

Abstract Lattice and electronic structure interactions for f -electrons are fundamental challenges for lanthanide equation of state development. Difficulties in first-principles calculations, such as density functional theory (DFT), emphasize the need for well-characterized experimental data. Here, we measure in-situ x-ray diffraction of shocked samarium (Sm) and temperature along the Hugoniot for the first time, providing direct evidence for phase transitions. We report direct evidence of a distorted fcc (dfcc) phase at 23 GPa. Shocked samarium melts from the dfcc phase starting at 33 GPa (1333 K), with complete melt at 40 GPa (1468 K). Previous work indicated shock melt at 27 GPa (1200 K), underscoring the significance of x-ray measurements for detecting phase transitions. Interestingly, our observed melting is in sharp contrast with the melting reported by a diamond anvil cell study. These experimental data can tightly constrain first principles calculations and serve as key touchstones for equation of state modeling.

36 MATERIALS SCIENCE↗

DOE OSTI2024

SGD-OD: investigating the potential oxygen demand of submarine groundwater discharge in coastal systems

Submarine groundwater discharge (SGD) supplies nutrients, carbon, metals, and radionuclide tracers to estuarine and coastal waters. One aspect of SGD that is poorly recognized is its direct effect on dissolved oxygen (DO) demand in receiving waters, denoted here as SGD-OD. Sulfate-mediated oxidation of organic matter in salty coastal aquifers produces numerous reduced byproducts including sulfide, ammonia, dissolved organic carbon and nitrogen, methane, and reduced metals. When these byproducts are introduced to estuarine and coastal systems by SGD and are oxidized, they may substantially reduce the DO concentration in receiving waters and impact organisms living there. We consider six estuarine and coastal sites where SGD derived fluxes of reduced byproducts are well documented. Using data from these sites we present a semiquantitative model to estimate the effect of these byproducts on DO in the receiving waters. Without continued aeration with atmospheric oxygen, the study sites would have experienced periodic hypoxic conditions due to SGD-OD. The presence of H2S supplied by SGD could also impact organisms. This process is likely prevalent in other systems worldwide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗

DOE OSTI2021

A quantitative model for a nanoscale switch accurately predicts thermal actuation behavior

Manipulation of temperature can be used to actuate DNA origami nano-hinges containing gold nanoparticles. We develop a physical model of this system that uses partition function analysis of the interaction between the nano-hinge and nanoparticle to predict the probability that the nano-hinge is open at a given temperature. The model agrees well with experimental data and predicts experimental conditions that allow the actuation temperature of the nano-hinge to be tuned over a range of temperatures from 30 °C to 45 °C. Additionally, the model identifies microscopic interactions that are important to the macroscopic behavior of the system, revealing surprising features of the system. This combination of physical insight and predictive potential is likely to inform future designs that integrate nanoparticles into dynamic DNA origami structures or use strand binding interactions to control dynamic DNA origami behavior. Furthermore, our modeling approach could be expanded to consider the incorporation, stability, and actuation of other types of functional elements or actuation mechanisms integrated into nucleic acid devices.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A study of space charge induced non-linearity in the Single Line Of Sight camera

We report a new generation of gated x-ray detectors at the National Ignition Facility has brought faster, enhanced imaging capabilities. Their performance is currently limited by the amount of signal they can be operated with before space charge effects in their electron tube start to compromise their temporal and spatial response. We present a technique to characterize this phenomenon and apply it to a prototype of such systems, the Single Line Of Sight camera. Results of this characterization are used to benchmark Particle In Cell simulations of the electrons drifting inside the detector, which are found to well reproduce experimental data. These simulations are then employed to predict the optimum photon flux to the camera, with the goal to increase the quality of the images obtained on an experimental campaign while preventing the appearance of deleterious effects. They also offer some insight into some of the improvements that can be brought to the new pulse-dilation systems being built at Lawrence Livermore National Laboratory

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Scaling patch analysis of planar turbulent mixing layers

Proper scales for the mean flow and Reynolds shear stress in planar turbulent mixing layers are determined from a scaling patch analysis of the mean continuity and momentum equations. By seeking an admissible scaling of the mean continuity equation, a proper scale for the mean transverse flow is determined as V ref =(dδ/dx)U ref , where dδ/dx is the growth rate of the mixing layer width and U ref =U h –U l is the difference between the velocity of the high speed stream U h and the velocity of the low speed stream U l . By seeking an admissible scaling for the mean momentum equation, a proper scale for the kinematic Reynolds shear stress is determined as R uv,ref =U avg V ref =[$\frac{1}{2A_u}$$\frac{dδ}{dx}$]$U^{2}_{ref}$ where A u $_{=}^{def}$(U h –U l )/(U h +U l ) is the normalized velocity difference that emerges naturally in the admissible scaling of the mean momentum equation. Self-similar equations for the scaled mean transverse flow V* and Reynolds shear stress $R^{*}_{uv}$=R uv /R uv,ref are derived from the mean continuity and mean momentum equations. Finally, approximate equations for V* and $R^{*}_{uv}$ are developed and found to agree well with experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Hyperfine interactions for small systems including transition-metal elements using self-interaction corrected density-functional theory

The interactions between the electronic magnetic moment and the nuclear spin moment, i.e., magnetic hyperfine (HF) interactions, play an important role in understanding electronic properties of magnetic systems and in realizing platforms for quantum information science applications. We investigate the HF interactions for atomic systems and small molecules, including Ti or Mn, by using Fermi–Löwdin orbital (FLO) based self-interaction corrected (SIC) density-functional theory. We calculate the Fermi contact (FC) and spin-dipole terms for the systems within the local density approximation (LDA) in the FLO-SIC method and compare them with the corresponding values without SIC within the LDA and generalized-gradient approximation (GGA), as well as experimental data. For the moderately heavy atomic systems (atomic number Z ≤ 25), we find that the mean absolute error of the FLO-SIC FC term is about 27 MHz (percentage error is 6.4%), while that of the LDA and GGA results is almost double that. Therefore, in this case, the FLO-SIC results are in better agreement with the experimental data. For the non-transition-metal molecules, the FLO-SIC FC term has the mean absolute error of 68 MHz, which is comparable to both the LDA and GGA results without SIC. For the seven transition-metal-based molecules, the FLO-SIC mean absolute error is 59 MHz, whereas the corresponding LDA and GGA errors are 101 and 82 MHz, respectively. Therefore, for the transition-metal-based molecules, the FLO-SIC FC term agrees better with experiment than the LDA and GGA results. We observe that the FC term from the FLO-SIC calculation is not necessarily larger than that from the LDA or GGA for all the considered systems due to the core spin polarization, in contrast to the expectation that SIC would increase the spin density near atomic nuclei, leading to larger FC terms.

Chemistry↗

DOE OSTI2021

Predicting transcriptional responses to cold stress across plant species

Although genome-sequence assemblies are available for a growing number of plant species, gene-expression responses to stimuli have been cataloged for only a subset of these species. Many genes show altered transcription patterns in response to abiotic stresses. However, orthologous genes in related species often exhibit different responses to a given stress. Accordingly, data on the regulation of gene expression in one species are not reliable predictors of orthologous gene responses in a related species. Here, we trained a supervised classification model to identify genes that transcriptionally respond to cold stress. A model trained with only features calculated directly from genome assemblies exhibited only modest decreases in performance relative to models trained by using genomic, chromatin, and evolution/diversity features. Models trained with data from one species successfully predicted which genes would respond to cold stress in other related species. Cross-species predictions remained accurate when training was performed in cold-sensitive species and predictions were performed in cold-tolerant species and vice versa. Models trained with data on gene expression in multiple species provided at least equivalent performance to models trained and tested in a single species and outperformed single-species models in cross-species prediction. These results suggest that classifiers trained on stress data from well-studied species may suffice for predicting gene-expression patterns in related, less-studied species with sequenced genomes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Critical Assemblies: Dragon Burst Assembly and Solution Assemblies

This work reviews the historical literature associated with the Dragon experiment and water boiler reactors operated at Los Alamos during the Manhattan Project. Frisch’s invited talk given at the American Nuclear Society’s Fast Burst Reactor Conference held at the University of New Mexico in Albuquerque, New Mexico, in 1969 is quoted. From the literature review, basic models for the Dragon experiment and for a water boiler–type assembly (aqueous homogeneous reactor) were created that can be used for conducting multiphysics simulations for criticality excursion studies. This methodology utilizes the coupled neutronic-hydrodynamic method to perform a time-dependent dynamic simulation of a criticality excursion. MCNP® was utilized to calculate important nuclear kinetic parameters that were incorporated into the models. Simulation results compare reasonably well with historic data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Advanced Multiphysics Code Coupling for Cladding Surface Thermocouples During Two-Phase Heat Transfer from Nuclear Fuel

Transient testing of nuclear fuel involves the evaluation of fuel performance under off-normal and accident conditions and is essential for proving design performance. Instrumentation included in such experiments commonly includes thermocouples attached to the outer surface of the cladding to provide temperature measurements throughout the transient. However, the presence of thermocouples on the cladding surface can alter the local heat transfer characteristics with the surrounding coolant. These localized effects can influence the temperature of the nearby cladding surface and introduce uncertainties in interpreting the thermocouple data. Understanding the impact of thermocouples attached to the outer surface of the cladding is crucial for accurate data interpretation as well as its effect on the thermomechanical behavior of the cladding. This paper presents a novel methodology for simulating the impact of outer cladding thermocouples during transient testing of nuclear fuels. The simulation framework leverages the thermal-hydraulic capabilities of RELAP5-3D coupled to the BISON fuel performance code through the RELAPCouplingApp interface. The methodology is compared against Accident Tolerant Fuel Reactivity Initiated Accident-1-E experiment performed at Idaho National Laboratory. The results reveal approximately 100°C difference between thermocouple-altered temperature and virgin cladding surface. The model overpredicts surface rewet time due to conservative correlations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Resonance Scattering Treatment with the Windowed Multipole Formalism

A new method for directly sampling the resonance upscattering effect is presented. Alternatives have relied on inefficient rejection sampling techniques or large tabular storage of relative velocities. None of these approaches, which require pointwise energy data, are particularly well suited to the windowed multipole cross-section representation. The new method, called multipole analytic resonance scattering, overcomes these limitations by inverse transform sampling from the target relative velocity distribution where the cross section is expressed in the multipole formalism. The closed-form relative speed distribution contains a novel special function we deem the incomplete Faddeeva function, and we present the first results on its efficient numerical evaluation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗