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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ultralight dark matter detection with levitated ferromagnets

Levitated ferromagnets act as ultraprecise magnetometers, which can exhibit high quality factors due to their excellent isolation from the environment. These instruments can be utilized in searches for ultralight dark matter candidates, such as axionlike dark matter or dark-photon dark matter. In addition to being sensitive to an axion-photon coupling or kinetic mixing, which produce physical magnetic fields, ferromagnets are also sensitive to the effective magnetic field (or “axion wind”) produced by an axion-electron coupling. While the dynamics of a levitated ferromagnet in response to a dc magnetic field have been well studied, all of these couplings would produce ac fields. In this work, we study the response of a ferromagnet to an applied ac magnetic field and use these results to project their sensitivity to axion and dark-photon dark matter. We pay special attention to the direction of motion induced by an applied ac field, in particular, whether it precesses around the applied field (similar to an electron spin) or librates in the plane of the field (similar to a compass needle). We show that existing levitated ferromagnet setups can already have comparable sensitivity to an axion-electron coupling as comagnetometer or torsion balance experiments. In addition, future setups can become sensitive probes of axion-electron coupling, dark-photon kinetic mixing, and axion-photon coupling, for ultralight dark matter masses m DM ≲ feV . Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Using kernel-based statistical distance to study the dynamics of charged particle beams in particle-based simulation codes

Measures of discrepancy between probability distributions (statistical distance) are widely used in the fields of artificial intelligence and machine learning. We describe how certain measures of statistical distance can be implemented as numerical diagnostics for simulations involving charged-particle beams. Related measures of statistical dependence are also described. The resulting diagnostics provide sensitive measures of dynamical processes important for beams in nonlinear or high-intensity systems, which are otherwise difficult to characterize. Here, the focus is on kernel-based methods such as maximum mean discrepancy, which have a well-developed mathematical foundation and reasonable computational complexity. Several benchmark problems and examples involving intense beams are discussed. While the focus is on charged-particle beams, these methods may also be applied to other many-body systems such as plasmas or gravitational systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Machine learning study of magnetism in uranium-based compounds

Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized f electrons and include unconventional superconductivity and magnetism, hidden order, and heavy-fermion behavior. Due to the strongly correlated nature of the 5f electrons, magnetic properties of these compounds depend sensitively on applied magnetic field and pressure, as well as on chemical doping. However, precise connection between the structure and magnetism in actinide-based materials is currently unclear. In this investigation, we established such structure-property links by assembling and mining two datasets that aggregate, respectively, the results of high-throughput density functional theory simulations and experimental measurements for the families of uranium- and neptunium-based binary compounds. Various regression algorithms were utilized to identify correlations among accessible attributes (features or descriptors) of the material systems and predict their cation magnetic moments and general forms of magnetic ordering. Descriptors representing compound structural parameters and cation f-subshell occupation numbers were identified as most important for accurate predictions. The best machine learning model developed employs the random forest regression algorithm. It can predict both spin and orbit moment size with root-mean-square error of 0.17 μ B and 0.19 μ B , respectively. Lastly, the random forest classification algorithm is used to predict the ordering (paramagnetic, ferromagnetic, and antiferromagnetic) of such systems with 76% accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Privacy Preservation from High-Performance Computing to Autonomous Science [Industrial and Governmental Activities]

High-Performance Computing (HPC) and Leadership-Class Supercomputing are driving forces behind scientific advancements, enabling researchers to tackle complex challenges in physics, chemistry, biology, and engineering. These systems power vast simulations and data analyses, fueling discoveries in fields ranging from materials science to climate modeling. However, their use often involves processing sensitive data—such as proprietary industry simulations, biomedical records, and national security computations—posing significant privacy concerns. In conclusion, this issue is amplified in collaborative environments like Department of Energy (DOE) user facilities, where HPC resources are shared across institutions to foster innovation.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2022

Technical and Economic Evaluation of the First Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska's North Slope via Machine Assisted History Matching

Polymer flooding has become globally established as a potential enhanced oil recovery method for heavy oils. To determine whether this technology may be useful in developing the substantial heavy oil resources on the Alaska North Slope, a polymer flood field pilot commenced at the Milne Point Unit in August 2018. This study seeks to evaluate the results of the field pilot on a technical and economic basis. A reservoir simulation model is constructed and calibrated to predict the oil recovery performance of the pilot through machine-assisted reservoir simulation techniques. To replicate the early water breakthrough observed during waterflooding, transmissibility contrasts are introduced into the simulation model, forcing viscous fingering effects. In the ensuing polymer flood, these transmissibility contrasts are reduced to replicate the restoration of injection conformance during polymer flooding. Transmissibility contrasts are later reinstated to replicate fracture overextension interpreted in one of the producing wells. The calibrated simulation models produced at each stage of the history matching process are used to forecast oil recovery. These forecasts are used as input for economic analysis, incremental to waterflooding expectations. The simulation forecasts indicate that polymer flooding significantly increases the heavy oil production for this field pilot compared to waterflooding alone, yielding attractive project economics. However, meaningful variations between simulation scenarios demonstrate that a simulation model is only valid for prediction if flow behavior in the reservoir remains consistent with that observed during the history matched period. Critically, this means that a simulation model calibrated for waterflooding may not fully capture the technical and economic benefits of an enhanced oil recovery process such as polymer flooding. Subsequently, the simulation model and economic model are used in conjunction to conduct a sensitivity analysis for polymer flood design parameters, from which recommendations are provided for both the continued operation of the current field pilot and future polymer flood designs. The results demonstrate that a higher polymer concentration can be injected due to the development of fractures in the reservoir. The throughput rate should remain high without exceeding operating constraints. A calculated point-forward polymer utilization parameter demonstrates the decreasing efficiency of the polymer flood at later times in the pattern life. Future projects will benefit from starting polymer injection earlier in the pattern life. A pattern with tighter horizontal well spacing will observe a greater incremental benefit from polymer flooding.

Keith, Cody↗
DOE OSTI2020

Optical follow-up of the neutron star–black hole mergers S200105ae and S200115j

LIGO and Virgo’s third observing run revealed the first neutron star–black hole (NSBH) merger candidates in gravitational waves. These events are predicted to synthesize r-process elements creating optical/near-infrared ‘kilonova’ emission. The joint gravitational wave and electromagnetic detection of an NSBH merger could be used to constrain the equation of state of dense nuclear matter, and independently measure the local expansion rate of the Universe. Here, we present the optical follow-up and analysis of two of the only three high-significance NSBH merger candidates detected to date, S200105ae and S200115j, with the Zwicky Transient Facility. The Zwicky Transient Facility observed ~48% of S200105ae and ~22% of S200115j’s localization probabilities, with observations sensitive to kilonovae brighter than –17.5 mag fading at 0.5 mag d –1 in the g- and r-bands; extensive searches and systematic follow-up of candidates did not yield a viable counterpart. We present state-of-the-art kilonova models tailored to NSBH systems that place constraints on the ejecta properties of these NSBH mergers. Here, we show that with observed depths of apparent magnitude ~22 mag, attainable in metre-class, wide-field-of-view survey instruments, strong constraints on ejecta mass are possible, with the potential to rule out low mass ratios, high black hole spins and large neutron star radii.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Detecting new physics as novelty — Complementarity matters

Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in ref. [1], between isolation-based and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a $t\bar{t}γγ$ final state: $t\bar{t}h$, giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, resulting in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for detecting both signals are found to be encouraging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Characterization of Aliased Noise in the Advanced ACTPol Receiver

Advanced ACTPol is the second-generation polarization-sensitive upgrade to the 6m aperture Atacama Cosmology Telescope (ACT), which increased detector count and frequency coverage compared to the previous ACTPol receiver. Advanced ACTPol utilizes a new two-stage time-division multiplexing readout architecture based on superconducting quantum interference devices to achieve a multiplexing factor as high as 64 (rows), fielding a 2012 detector camera at 150/220 GHz and two 90/150 GHz cameras containing 1716 detectors each. In a time-domain system, aliasing introduces noise to the readout. Here in this work we present a figure of merit to measure this noise contribution and present measurements of the aliased noise fraction of the Advanced ACTPol receiver as deployed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Crystallization-induced emission enhancement of alkyl chain-dependent pyrene-based luminogens: Visual detection of nitro-explosives

In view of the widespread applications and urgent need for efficient blue emitting materials, the synthesis of such materials has seen renewed interest. In the present work, three alkyl chain-dependent blue emitting pyrene-based luminogens were synthesized and characterized. Here, the experimental results show that these luminogens exhibited high photoluminescence efficiency both in solution (>86%) and in the solid state (>35%). Interestingly, 1,3,6,8-tetrakis (pentylphenyl)pyrene (TPPy), as a photoelectric functional material, was endowed with higher quantum yield (72%) and competitiveness due to a remarkable crystallization-induced emission enhancement (CIEE) property. More importantly, TPPy exhibits high sensitivity and selectivity for ortho-nitroaniline (o-NA) with low limit of detection (9.99 × 10 -8 M), which is mainly due to the distinctive odd-even effects of the terminal alkyl chain. The results demonstrate that this type of luminogen can potentially be utilized for practical applications in the field of contaminant or explosive detection as a sensitive and portable fluorimeter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Transition from retrograde to prograde drift instabilities in a magnetron microdischarge

Rotating plasma structures, or “spokes,” in magnetized discharges characterized by perpendicular electric and magnetic fields have been seen in a growing number of studies and are believed to be the result of gradient-driven drift instabilities. Under certain conditions, we have shown the spoke’s rotation to be opposite to the E B direction, i.e., retrograde in its expected direction. Recently, we have linked such counter-intuitive rotation to a local inversion of the electric field. Here, we give further experimental evidence for this inversion and attempt to provide an explanation for a relatively distinct transition seen between retrograde and positive (prograde) drift. In the experiments, a partially magnetized plasma forms inside a magnetron device of mm radius operated with argon. Discharge current–voltage measurements are acquired for a range of argon fill pressure and inter-electrode spacing. We find two branches of operation—a low current branch of negative resistance, coinciding with the retrograde spoke rotation, and a higher current branch of positive resistance, coincident with prograde spoke rotation. We postulate that at low discharge currents, high magnetic field confinement leads to a large density gradient, causing more electron transport to the anode than that demanded by the external circuit. At higher currents, anomalous axial electron transport (across the magnetic field lines) becomes dominant, eliminating the conditions for field inversion. Here, the current thresholds for the field inversion are found to be sensitive to the argon pressure and inter-electrode spacing.

42 ENGINEERING↗
DOE OSTI2023

Thermal adaptation of soil microbial growth traits in response to chronic warming

ABSTRACT Adaptation of soil microbes due to warming from climate change has been observed, but it remains unknown what microbial growth traits are adaptive to warming. We studied bacterial isolates from the Harvard Forest Long-Term Ecological Research site, where field soils have been experimentally heated to 5°C above ambient temperature with unheated controls for 30 years. We hypothesized that Alphaproteobacteria from warmed plots have (i) less temperature-sensitive growth rates; (ii) higher optimum growth temperatures; and (iii) higher maximum growth temperatures compared to isolates from control plots. We made high-throughput measurements of bacterial growth in liquid cultures over time and across temperatures from 22°C to 37°C in 2–3°C increments. We estimated growth rates by fitting Gompertz models to the growth data. Temperature sensitivity of growth rate, optimum growth temperature, and maximum growth temperature were estimated by the Ratkowsky 1983 model and a modified Macromolecular Rate Theory (MMRT) model. To determine evidence of adaptation, we ran phylogenetic generalized least squares tests on isolates from warmed and control soils. Our results showed evidence of adaptation of higher optimum growth temperature of bacterial isolates from heated soils. However, we observed no evidence of adaptation of temperature sensitivity of growth and maximum growth temperature. Our project begins to capture the shape of the temperature response curves, but illustrates that the relationship between growth and temperature is complex and cannot be limited to a single point in the biokinetic range. IMPORTANCE Soils are the largest terrestrial carbon sink and the foundation of our food, fiber, and fuel systems. Healthy soils are carbon sinks, storing more carbon than they release. This reduces the amount of carbon dioxide released into the atmosphere and buffers against climate change. Soil microbes drive biogeochemical cycling and contribute to soil health through organic matter breakdown, plant growth promotion, and nutrient distribution. In this study, we determined how soil microbial growth traits respond to long-term soil warming. We found that bacterial isolates from warmed plots showed evidence of adaptation of optimum growth temperature. This suggests that increased microbial biomass and growth in a warming world could result in greater carbon storage. As temperatures increase, greater microbial activity may help reduce the soil carbon feedback loop. Our results provide insight on how atmospheric carbon cycling and soil health may respond in a warming world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Electron bunch position determination using a high frequency button beam position monitor in the AWAKE facility

The AWAKE facility uses novel proton beam-driven plasma wakefields to accelerate electron bunches over 10m of Rubidium plasma. Precise monitoring of 2 diverse beam types necessitates an electron beam position monitor (BPM) working in a frequency regime of tens of GHz. A high frequency conical button-style BPM with a working regime of up to 40 GHz has been investigated as a way to discriminate the electromagnetic fields of 19 MeV, 4 ps electron bunches propagating spatially and temporally together with a 400 GeV, 170 ps proton bunch in the AWAKE common beamline. The sensitivity of the HF BPM to the electron beam position is determined under various beam conditions, with both electrons and protons, and integration with a TRIUMF front-end is discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Non-contact based measurement of thin film coatings using eddy current measurements

When a time varying magnetic field attempts to penetrate a conductive surface, the surface will respond to that time varying field by the generation of circular currents around the flux lines. This effect is described by Faraday’s law of induction. An eddy current based measurement system attempts to discern the power loss incurred by these circulating currents within the conductive surface. The measurement system described here uses an Agilent E 4980A LCR meter to quantify the reaction of the film to the high frequency magnetic field lines which are forced into the film. The measurement metric is the meter’s interpretation of the power dissipation caused by the measured film. The meter is setup to use the Ls/Rs inductor model, and is run at 1 Mhz to bring the losses within the film as high as possible for sensitivity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Nonlinear X-ray Optics

Novel X-ray free-electron lasers (XFELs) are capable of producing radiation with unprecedented properties. Specifically, their ultrashort coherent X-ray pulses can generate extreme peak intensities where ordinary rules of light-matter interaction may no longer apply, and nonlinear processes start to become important. XFELs have made it possible to observe some of the most important coherent non-sequential nonlinearities, including X-ray-optical sum frequency generation (SFG), X-ray second harmonic generation (XSHG), two-photon absorption (TPA) and nonlinear two-photon Compton scattering (2PCS). In particular, the 2PCS experiment has led to unexpected results, namely the observation of a substantial anomalous red shift in the energy of photons generated by nonlinear two-photon X-ray Compton scattering in beryllium. The energy shift is in addition to the predicted nonlinear Compton shift and was neither expected from extrapolations from linear X-ray interactions nor from nonlinear effects at optical wavelengths. Our results suggest a novel nonlinear scattering mechanism that can only be observed at high fields at X-ray wavelengths, where the photon energy is a significant fraction of the electron rest mass (and thus a significant momentum transfer to the electron occurs during the scattering) and where the interaction is not dominated by the quiver motion of the electron in the field (ponderomotive potential) as is usually the case at longer (optical) wavelengths. The goal of this research project was the investigation of fundamental nonlinear X-ray-matter interactions. As these processes are typically weak, part of this research is the exploration of more efficient nonlinear X-ray processes by studying novel mechanisms including solid-state effects, the assistance of optical radiation and novel phase-matching techniques. These experiments require extremely large X-ray field strengths, such as produced by XFELs. Nonlinear X-ray effects can lead to important applications in many research fields. For example, it could lead to instantaneous plasma diagnostics for materials in extreme conditions, as a method for combining atomic-scale structural sensitivity with chemical specificity, or as a probe of electron dynamics in solids on the attosecond (1 as = 10 -18 s) time- and Ångstrom (10 -10 m) length scales. In addition, a detailed understanding of nonlinear X-ray matter interactions is crucial for understanding and extending the limits of structural determination at high intensity such as in serial femto-crystallography. The results could have a profound impact on advanced XFEL light sources such as the LCLS-II.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

In Situ UV–vis Quantification of Chlorobasicity in Binary Molten Chlorides

Optical basicity measurements provide a basis for determining the relative chlorobasicity of a given molten salt composition. This study uses Bi3+ as a spectroscopic probe to investigate optical basicity across three binary molten chloride systems (LiCl-KCl, MgCl2–KCl, and ZnCl2–KCl) through in situ UV–vis spectroscopy measurements of the 1S0→3P1 transition. LiCl-KCl systems exhibit the highest optical basicity with minimal compositional dependence, while both divalent systems show stronger, yet nonlinear, dependence that is reminiscent of acid–base titration curves. The influence of cation electronegativity and polarizing power (Li+ < Mg2+ < Zn2+) directly reflects the basicity curves, with ZnCl2–KCl displaying the steepest gradients and lowest basicity values. Nonlinear dependencies in divalent systems indicate formation of chloride-bridged network structures with coordination states dictated by the amount of KCl in a salt composition, which was further investigated and validated by molecular dynamics simulations of polymeric structures formed in ZnCl2 and MgCl2-based salts using machine learning force fields. The temperature dependence of chlorobasicity in terms of optical basicity was investigated, and a loose, positive correlation between the two was revealed. These findings establish Bi3+ as a sensitive probe for understanding electronic environments in molten chloride systems and provide insights for predicting the chemical behavior of molten salts for use in high-temperature environments.

Buttice, Kailee [Department of Nuclear Engineering↗
DOE OSTI2021

Nanotesla Magnetometry with the Silicon Vacancy in Silicon Carbide

Silicon carbide is a promising host material for spin-defect-based quantum sensors owing to its commercial availability and established techniques for electrical and optical microfabricated device integration. The negatively charged silicon vacancy is one of the leading spin defects studied in silicon carbide owing to its near-telecom photoemission, high spin number, and nearly temperature-independent ground-state zero-field splitting. We report the realization of nanotesla shot-noise-limited ensemble magnetometry based on optically detected magnetic resonance with the silicon vacancy in 4H silicon carbide. By coarsely optimizing the anneal parameters and minimizing power broadening, we achieve a sensitivity of 50 nT/√Hz and a theoretical shot-noise-limited sensitivity of 3.5 nT/√Hz. This is accomplished without utilizing complex photonic engineering, control protocols, or applying excitation powers greater than a watt. This work demonstrates that the silicon vacancy in silicon carbide provides a low-cost and simple approach to quantum sensing of magnetic fields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

High-resolution magnetic microscopy applications using nitrogen-vacancy centers in diamond

Magnetic microscopy with high spatial resolution helps to solve a variety of technical problems in condensed-matter physics, electrical engineering, biomagnetism, and geomagnetism. In this work we used quantum diamond magnetic microscope (QDMM) setups, which use a dense uniform layer of magnetically-sensitive nitrogen-vacancy (NV) centers in diamond to image an external magnetic field using a fluorescence microscope. We used this technique for imaging few-micron ferromagnetic needles used as a physically unclonable function (PUF) and to passively interrogate electric current paths in a commercial 555 timer integrated circuit (IC). As part of the QDMM development, we also found a way to calculate ion implantation recipes to create diamond samples with dense uniform NV layers at the surface. This work opens the possibility for follow-up experiments with 2D magnetic materials, ion implantation, and electronics characterization and troubleshooting.

36 MATERIALS SCIENCE↗