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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Reactor Performance Improvement Options to Sustain High Flux Isotope Reactor Leadership into the Future

The mission of the Neutron Sciences Directorate (NScD) at the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) is the undertaking of high-impact research into the structure and properties of materials across the spectrum of biology, chemistry, physics, materials science, and engineering. NScD operates two world-leading neutron scattering facilities including the High Flux Isotope Reactor (HFIR) and the Spallation Neutron Source. HFIR achieved full power in 1966, and over a half century later, it continues to serve a variety of national missions. HFIR provides one of the highest steady-state neutron fluxes of any research reactor in the world to support scientific missions including cold and thermal neutron scattering, isotope production, and materials irradiation research. To sustain leadership in neutron sciences into the future, ORNL is exploring areas in which HFIR can be improved to enhance its performance. Many improvement areas are being explored such as upgrading the cold source and neutron scattering facilities; however, the improvement areas focused on in this paper include replacing the reactor pressure vessel, upgrading the neutron reflector, and converting from high-enriched uranium to low-enriched uranium fuel.

Chandler, David↗
DOE OSTI2020

Controlling surface adatom kinetics for improved structural and optical properties of high indium content aluminum indium nitride

A non-traditional, for AlInN, method of controlling adatom kinetics and a low temperature growth condition were employed to improve the quality of high indium content aluminum indium nitride films. Metal-rich surfaces were used to enhance adatom mobility and compensate for the low growth temperature (Tsub ≤ 400 °C) effect of reducing surface diffusion lengths. The metal-rich approach resulted in 12 times lower x-ray diffraction full-width at half-maximum rocking curve figures of merit when compared to literature. In addition to promising photoluminescence emission, these results indicate improved structural quality over other reported approaches. AlInN films with ∼70% indium content were characterized via x-ray diffraction, atomic force microscopy, and photoluminescence spectroscopy with each technique indicating an optimal growth temperature of 350 °C. Al0.3In0.7N grown above 400 °C exhibited phase separation and a reduction in quality, while samples grown colder were predominantly single-phase and displayed improved photoluminescence at ∼1.45 eV. The photoluminescence spectra suggest emission from quantum wire-like structures with dimensions ranging from 15 to 18 nm. These low-temperature, metal-rich findings for high indium content AlInN are promising for future long-wavelength III-nitride optical devices.

Engel, Zachary (ORCID:0000000205021981)↗
DOE OSTI2020

Toward a systematic improvement of the fixed-node approximation in diffusion Monte Carlo for solids—A case study in diamond

While Diffusion Monte Carlo (DMC) is in principle an exact stochastic method for ab initio electronic structure calculations, in practice, the fermionic sign problem necessitates the use of the fixed-node approximation and trial wavefunctions with approximate nodes (or zeros). This approximation introduces a variational error in the energy that potentially can be tested and systematically improved. Here, we present a computational method that produces trial wavefunctions with systematically improvable nodes for DMC calculations of periodic solids. These trial wavefunctions are efficiently generated with the configuration interaction using a perturbative selection made iteratively (CIPSI) method. A simple protocol in which both exact and approximate results for finite supercells are used to extrapolate to the thermodynamic limit is introduced. This approach is illustrated in the case of the carbon diamond using Slater–Jastrow trial wavefunctions including up to one million Slater determinants. Fixed-node DMC energies obtained with such large expansions are much improved, and the fixed-node error is found to decrease monotonically and smoothly as a function of the number of determinants in the trial wavefunction, a property opening the way to a better control of this error. The cohesive energy extrapolated to the thermodynamic limit is in close agreement with the estimated experimental value. Interestingly, this is also the case at the single-determinant level, thus, indicating a very good error cancellation in carbon diamond between the bulk and atomic total fixed-node energies when using single-determinant nodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Improvement in the spatial resolution of heavy ion beam probe measurements through application of ion optics

A technique for more accurately modeling and improving the spatial resolution of heavy ion beam probe measurements is described. We use a set of particle trajectories to numerically determine the focusing properties of a complicated three-dimensional magnetic field and characterize those properties with a transfer matrix. We then modify the transfer matrix approach of traditional ion optics to include a parameter that describes the ionization location of the detected ions. The ion optics model calculated using this technique enables a more accurate description of the particle trajectories than previously feasible. The model also allows one to easily determine an initial beam focus that could be used during experimental operation to optimize the spatial resolution of measurements. The technique has been applied to the design of a heavy ion beam probe diagnostic for the Wendelstein 7-X stellarator and improvements in the modeled spatial resolution by a factor of about 2 over previous estimates are possible. As a result, the improved spatial resolution will enable measurements of plasma fluctuations with smaller wavelengths than would otherwise be possible.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Fiber-optic silicon Fabry–Perot interferometric bolometer with improved detection limit for magnetic confinement fusion

Fiber-optic bolometers (FOBs) intended for plasma radiation measurement in magnetically confined fusion have been previously developed using a silicon pillar that functions as both a Fabry–Perot interferometer (FPI) for temperature measurement and an absorber for the radiation. We report an FOB design that can significantly improve the detection sensitivity over earlier designs by engineering the absorber of the FOB. Our design uses the fact that, compared with a silicon pillar, a gold film with the same x-ray absorption thickness will show a much higher temperature rise from a given power density of the radiation. Therefore, the responsivity of an FOB can be improved by attaching a large gold disk to the silicon FPI as the absorber. We have developed a fabrication method for FOBs of such design and obtained an FOB with a 4-µm-thick, 0.6-mm-diameter gold disk attached to a 200-µm-diameter, 100-µm-thick silicon FPI. We have characterized the noise, responsivity, response time, and noise-equivalent power density (NEPD) and compared these with the earlier design where the absorber is mainly the silicon FPI itself. The experimental result suggests that the FOB with the gold disk achieves a responsivity of ∼2.8 mK/(W/m2) and a noise-equivalent-power-density of 0.13 W/m2, which are, respectively, more than nine times larger and six times smaller compared to the FOB using a previous design. Improved NEPD and good absorption over a broad frequency range will make the FOB more attractive for applications in magnetic-confinement fusion devices.

Uddin, Nezam↗
DOE OSTI2023

Improved wall-plug efficiency of III-nitride tunnel junction micro-light-emitting diodes with AlGaN/GaN polarization charges

The electrical performances of III-nitride blue micro-light-emitting diodes (µLEDs) with different tunnel junction (TJ) epitaxial architectures grown by metalorganic chemical vapor deposition are investigated. A new TJ structure that employs AlGaN is introduced. The current density–voltage characteristic is improved by incorporating AlGaN layer above the n-side of the TJ layer, and the effects of the AlGaN/GaN superlattices is examined. Based upon the data from band diagram simulation, net positive polarization charge is formed at the AlGaN/GaN interface, which results in a reduction in tunneling distance and increase in tunneling probability. Moreover, similar electrical improvement is observed in various device dimensions and is independent of operating current density, suggesting that AlGaN/GaN biaxial tensile strain or current spreading is not the main contribution for the improvement. Finally, the effects on the efficiency performances are determined. While the maximum external quantum efficiency of the TJ devices remains identical, the wall-plug efficiency of µLEDs is enhanced significantly by the proposed AlGaN-enhanced TJ design. This work reveals the possibility of energy efficient TJ contact with high transparency in the visible wavelength range.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improved accuracy and robustness of electron density profiles from JET’s X-mode frequency-modulated continuous-wave reflectometers

JET’s frequency-modulated continuous wave (FMCW) reflectometers have been operating well with the current design since 2005, and density profiles have been automatically calculated intershot since then. However, the calculated profiles had long suffered from several shortcomings: poor agreement with other diagnostics, sometimes inappropriately moving radially by several centimeters, elevated levels of radial jitter, and persistent wriggles (strong unphysical oscillations). Here, in this research, several techniques are applied to the reflectometry data analysis, and the shortcomings are significantly improved. Starting with improving the equilibrium reconstruction that estimates the background magnetic field, adding a ripple correction in the reconstructed magnetic field profile, and adding new inner-wall reflection positions estimated through ray-tracing, these changes not only improve the agreement of reconstructed profiles to other diagnostics but also solve density profile wriggles that were present during band transitions. Other smaller but also persistent wriggles were also suppressed by applying a localized correction to the measured beat frequency where persistent oscillations are present. Finally, the burst analysis method, as introduced by Varela et al., has been implemented to extract the beat frequency from stacked spectrograms. Due to the strong suppression of spurious reflections, the radial jitter that sometimes would span several centimeters has been strongly reduced. The stacking of spectrograms has also been shown to be very useful for stacking recurring events, like small gas puff modulations, and extracting transport coefficients that would otherwise be below the noise level.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography

A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

High-density CRISPRi screens reveal diverse routes to improved acclimation in cyanobacteria

Cyanobacteria are the oldest form of photosynthetic life on Earth and contribute to primary production in nearly every habitat, from permafrost to hot springs. Despite longstanding interest in the acclimation of these microbes, it remains poorly understood and challenging to rewire. Here, this study uses a high-density, genome-wide CRISPR interference screen to examine the influence of gene-specific transcriptional variation on the growth of Synechococcus sp. PCC 7002 under environmental extremes. Surprisingly, many partial knockdowns enhanced fitness under cold monochromatic conditions. Transcriptional repression of genes for core subunits of the NDH-1 complex, which are important for photosynthesis and carbon uptake, improved growth rates under both red and blue light but at distinct, color-specific optima. Most genes with fitness-improving knockdowns were distinct to each light color, and dual-target transcriptional repression produced nonadditive effects. Findings reveal diverse routes to improved acclimation in cyanobacteria (e.g., attenuation of genes involved in CO 2 uptake, light harvesting, translation, and purine metabolism) and provide an approach for using gradients in sgRNA activity to pinpoint biochemically influential transcriptional changes in cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

AI↗
DOE OSTI2022

Pressure Drop Correlation Improvement for the Near-Wall Region of Pebble-Bed Reactors

Packed beds play an important role in several engineering fields, with their applications in nuclear energy being driven by the development of next-generation reactors utilizing pebble fuel. The random nature of a packed pebble bed creates a flow field that is complex and difficult to predict. Porous media models are an attractive option for modeling pebble-bed reactors (PBRs), as they provide intermediate fidelity results and are computationally efficient. Porous media models, however, rely on the use of correlations to estimate the effect of complicated flow features on the pressure drop and heat transfer in the system. Existing correlations were developed to predict the average behavior of the bed, but they are inaccurate in the near-wall region where the presence of the wall affects the pebble packing. This work aims to investigate the accuracy of a porous media model using the Kerntechnischer Ausschuss (KTA) correlation, the most common pressure drop correlation for PBRs compared to the high-fidelity large eddy simulation (LES). A bed of 1568 pebbles is investigated at Reynolds numbers from 625 to 10 000. The bed is divided into five concentric subdomains to compare the average velocity, friction losses, and form losses between the porous media and LES codes. The comparison between the LES simulation and the KTA correlation revealed that the KTA correlation largely underpredicts the form losses in the near-wall region, leading to an overprediction of the velocity near the wall by nearly 30%. An investigation of the form losses across the range of Reynolds numbers in the LES results provided additional insight into how the KTA correlation may be improved to better predict these spatial effects in a pebble bed. These data suggest that the form coefficient near the wall must be increased by 48% while decreasing the form coefficient of the inner bulk region of the bed by 15%. The implementation of these improvements to the KTA correlation in a porous media model produced a radial velocity profile that saw significantly improved agreement with the LES results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Improving mechanical properties of carbon and tool steels via chromizing

Steels are commonly used in high-performance demanding applications due to their favourable mechanical properties. Various surface engineering techniques have been developed for steels, among which chromizing is an affordable high-throughput case-hardenig process for improved surface hardness and wear resistance while retaining the substrate ductility and toughness. In this work, tribological testing along with nano- and micro-indentation and morphological and composional characterisation were used to understand the effects of the chromizing process on the AISI 1095 carbon steel, 52100 bearing steel and A2, D2 and M2 tool steels. The results of this study demonstrate that the chromizing treatment of low-cost 1095 and 52100 steels significantly improves their wear and hardness properties to a level comparable to the more costly tool steels. While chromizing also increased the hardness of the tool steels, it had little improvement on the wear resistance for the D2 and M2 tool steels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improving luminescence response in ZnGeN 2 /GaN superlattices: defect reduction through composition control

Abstract Color-mixed (cm) light-emitting diodes (LEDs) are theoretically the most efficient white light emitters, projected to improve white light luminous efficacy by 34% compared to incumbent phosphor converted LEDs. Since white light technology is pervasive and essential, small improvements in LED technology can result in energy savings. However, cm-LEDs are not yet realized due to poor efficacy in green and amber emitting materials, a spectral region colloquially referred to as the Green Gap. ZnGeN 2 is nearly isostructural and closely lattice-matched to GaN and can be heteroepitaxially integrated with existing GaN devices; ZnGeN 2 /GaN hybrid structures are theorized to emit green (~530 nn) light with a spontaneous emission rate 4.6–4.9 times higher than traditional InGaN LEDs when incorporated into III-N LED structures. In this report we demonstrate the molecular beam epitaxy (MBE) growth of GaN and ZnGeN 2 superlattices, an important step towards realizing multiple quantum well structures required for efficient LEDs. Elemental analysis, including atom probe tomography, shows that Ga and Ge are observed in both ZnGeN 2 and GaN layers, degrading the structural uniformity. The lack of elemental abruptness also leads to increased defect luminescence and reabsorption of band edge luminescence. The source of unintentional Ga distributed throughout the ZnGeN 2 layers was identified as excess flux escaping from around the closed MBE shutter. The source of unintentional Ge, which tended to incorporate as a single delta-doped layer in GaN, was identified as Ge riding along the cyclical metal-rich Ga adlayer used for high quality GaN, incorporating during subsequent nitrogen-rich growth step. Modifying the growth strategy results in improved structural quality, elemental abruptness, and luminescence response. This realization of structurally and elementally abrupt interfaces demonstrates the potential of heteroepitaxially integrated binary and ternary nitrides for energy-relevant devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Methods of improving brain dose estimates for internally deposited radionuclides *

The US National Council on Radiation Protection and Measurements (NCRP) convened Scientific Committee 6–12 (SC 6–12) to examine methods for improving dose estimates for brain tissue for internally deposited radionuclides, with emphasis on alpha emitters. This Memorandum summarises the main findings of SC 6–12 described in the recently published NCRP Commentary No. 31, ‘Development of Kinetic and Anatomical Models for Brain Dosimetry for Internally Deposited Radionuclides’. The Commentary examines the extent to which dose estimates for the brain could be improved through increased realism in the biokinetic and dosimetric models currently used in radiation protection and epidemiology. A limitation of most of the current element-specific systemic biokinetic models is the absence of brain as an explicitly identified source region with its unique rate(s) of exchange of the element with blood. The brain is usually included in a large source region called Other that contains all tissues not considered major repositories for the element. In effect, all tissues in Other are assigned a common set of exchange rates with blood. A limitation of current dosimetric models for internal emitters is that activity in the brain is treated as a well-mixed pool, although more sophisticated models allowing consideration of different activity concentrations in different regions of the brain have been proposed. Here case studies for 18 internal emitters indicate that brain dose estimates using current dosimetric models may change substantially (by a factor of 5 or more), or may change only modestly, by addition of a sub-model of the brain in the biokinetic model, with transfer rates based on results of published biokinetic studies and autopsy data for the element of interest. As a starting place for improving brain dose estimates, development of biokinetic models with explicit sub-models of the brain (when sufficient biokinetic data are available) is underway for radionuclides frequently encountered in radiation epidemiology. A longer-term goal is development of coordinated biokinetic and dosimetric models that address the distribution of major radioelements among radiosensitive brain tissues.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Improving constraints on models addressing the Hubble tension with CMB delensing

The Hubble Tension is a well-known issue in modern cosmology that refers to the apparent disagreement in inferences of the Hubble constant H 0 as found through low-redshift observations and those derived from the ΛCDM model utilizing early universe observations. Several extensions to ΛCDM have been proposed to address the Hubble Tension that involve new ingredients or dynamics in the early universe. Reversing the effects of gravitational lensing on cosmic microwave background (CMB) maps produces sharper acoustic peaks in power spectra and allows for tighter constraints on cosmological parameters. We investigate the efficacy of CMB delensing for improving the constraints on parameters used in extensions of the ΛCDM model that are aimed at resolving the Hubble Tension (such as varying fundamental constants, contributions from early dark energy, and self-interacting dark radiation). We use Fisher forecasting to predict the expected constraints with and without this delensing procedure. We demonstrate that CMB delensing improves constraints on H 0 by ~ 20% for viable models and significantly improves constraints on parameters across the board in the low-noise regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Improving constraints on inflation with CMB delensing

Abstract The delensing of cosmic microwave background (CMB) maps will be increasingly valuable for extracting as much information as possible from future CMB surveys. Delensing provides many general benefits, including sharpening of the acoustic peaks, more accurate recovery of the damping tail, and reduction of lensing-inducedB-mode power. In this paper we present several applications of delensing focused on testing theories of early-universe inflation with observations of the CMB. We find that delensing the CMB results in improved parameter constraints for reconstructing the spectrum of primordial curvature fluctuations, probing oscillatory features in the primordial curvature spectrum, measuring the spatial curvature of the universe, and constraining several different models of isocurvature perturbations. In some cases we find that delensing can recover almost all of the constraining power contained in unlensed spectra, and it will be a particularly valuable analysis technique to achieve further improvements in constraints for model parameters whose measurements are not expected to improve significantly when utilizing only lensed CMB maps from next-generation CMB surveys. We also quantify the prospects of testing the single-field inflation tensor consistency condition using delensed CMB data; we find it to be out of reach of current and proposed experimental technology and advocate for alternative detection methods.

Astronomy & Astrophysics↗
DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗
DOE OSTI2022

Improving the stellarator through advances in plasma theory

Improvements to the stellarator concept can be realized through advancements in theoretical and computational plasma physics. Herein, recent advances are reported in the topical areas of: (1) improved energetic ion confinement, (2) the impact of three-dimensional (3D) shaping on turbulent transport, (3) reducing coil complexity, (4) novel optimization and design methods, and (5) computational magnetohydrodynamic tools. Furthermore, these advances enable the development of new stellarator configurations with improved confinement properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗