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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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We show that Quarkyonic Matter can mitigate the hyperon puzzle. The key observation is that the hyperon threshold is shifted to a higher density by a factor of constituent strange quark mass. We illustrate this effect by using the ideal dual Quarkyonic (IdylliQ) model with multiple flavors.
This paper presents a “hybrid” approach to direct drive inertial confinement fusion that can exploit a high-energy gas laser with two opposed beams. The target and driver are asymmetric, much like experiments performed on the National Ignition Facility, but have been designed to benefit from scale and their particular compatibility with a fusion power plant. The imploded masses (and areal densities) are increased by a factor of 12 (3) and provide a path to high-gain implosions that robustly ignite. The design also mitigates common concerns such as laser imprint and cross-beam energy transfer. We discuss the rationales for a hybrid target, the methods used to control implosion symmetry, and the implication(s) for inertial fusion energy.
The U.S. Department of Energy (DOE)’s Hanford Site in Washington State houses 177 underground storage tanks containing millions of gallons of nuclear and chemical waste with high aluminum content. Aluminum (Al) salt reactions with strong bases produce aluminum hydroxides, like boehmite (γ-AlOOH), which are main components of insoluble nuclear waste sludge. Understanding the morphology, crystallinity, and dissolution behavior of boehmite under waste tank conditions is necessary for improving waste management and mitigation strategies. The ORNL team uses in situ multimodal analysis strategy to study Al chemistry of simulated tank waste using microfluidic reactors and advanced chemical imaging and mass spectrometry (MS) techniques. Because the SALVI device is vacuum compatible and transferrable among different platforms, we can use it in scanning electron microscopy (SEM), vacuum ultraviolet single photon mass spectrometry (VUV SPI-MS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) to study the chemical speciation and colloid stability in liquids in this project. Additionally, ex situ transmission electron microscopy (TEM) and x-ray diffraction (XRD) spectroscopy can be used to verify particle phase to further the understanding of the Al-bearing phases. This report gives a summary of the technical progress of the ORNL tasks. A plan for year 2 performance is recommended in the summary.
On 2022 September 26, the Double Asteroid Redirection Test (DART) spacecraft intentionally collided with Dimorphos, the moon of the binary asteroid system 65803 Didymos. This collision provided the first full-scale test of a kinetic impactor for planetary defense. Images from DART’s DRACO camera revealed Dimorphos to be an oblate spheroid covered in boulders of varying sizes and shapes. Very little was known about Dimorphos prior to DART’s impact, including its shape, structure, and material properties. Approach observations and those following the DART impact have provided crucial knowledge that narrows the parameter space relevant to modeling the impact into Dimorphos. Here we present the results of a suite of hydrocode simulations of the DART impact on Dimorphos. Despite remaining uncertainties, initial models of DART’s kinetic impact provide important information about the results of DART (e.g., potential crater size and morphology, ejecta mass) and the properties of Dimorphos. Simulations here suggest that Dimorphos has near-surface strength ranging from a few Pascals to tens of kPa, which corresponds to crater sizes of ~40–60 m. Simulated crater sizes provide a crucial comparison metric for the European Space Agency Hera mission when it arrives at the Didymos system. Hera’s measurement of crater size in combination with measurement of Dimorphos’s mass will allow us to assess our simulations and provide the information needed to make the DART impact experiment both the first test of a planetary defense mitigation mission and the first full-scale planetary defense simulation validation exercise.
Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.
Preceramic polymers (PCPs) offer advantages in producing ceramics due to their processability and ability to tailor the final chemistry of the produced material. However, challenges such as volumetric shrinkage and mass loss during pyrolysis often result in polymer-derived ceramics containing pores and cracks. PCP-grafted ceramic nanoparticles (PCPGNPs) have been proposed and studied as a route to mitigate the shrinkage issues associated with neat PCPs. Prior studies on PCPGNPs have principally focused on the synthesis and characterization of neat materials. Dispersing PCPGNPs in commercial preceramic polymer is another attractive, but underexplored, route to control the rheological and char yield properties of PCP systems. In this work, a systematic rheological study of commercial PCP (SMP-877) and PCPGNP (silica with poly(1,1-dimethylpropylsilane) corona) mixtures was executed to develop design rules for the processing of such systems. A rheological study demonstrated the effect of increasing particle concentration on network formation with percolation occurring between 50 and 60 wt %. Samples above the percolation threshold exhibited higher viscosities and rapid shear thinning thus demonstrating their direct-write printability. X-ray photon correlation spectroscopy (XPCS) corroborated the rheology and showed two diffusive modes when the material was above percolation. Mixtures of PCPGNPs and SMP-877 had synergistically higher char yields upon thermal treatment and pyrolysis. XPCS and rheological measurements during thermal treatment identified thermal jamming of the polymer grafts as a key factor in improving the char yield. In conclusion, with the insights gained here, we expect these mixed systems to provide attractive feedstocks for polymer-derived ceramics, with proof-of-principal application as feedstocks for direct ink write (DIW) additive manufacturing.
Perturbative quantum chromodynamics (pQCD), while valid only at densities exceeding those found in the cores of neutron stars, could provide constraints on the dense-matter equation of state (EOS). Here, in this work, we examine the impact of pQCD information on the inference of the EOS using a nonparametric framework based on Gaussian processes (GPs). We examine the application of pQCD constraints through a ``pQCD likelihood,'' and verify the findings of previous works; namely, a softening of the EOS at the central densities of the most massive neutron stars and a reduction in the maximum neutron-star mass. Although the pQCD likelihood can be easily integrated into existing EOS inference frameworks, this approach requires an arbitrary selection of the density at which the constraints are applied. The EOS behavior is also treated differently on either side of the chosen density. To mitigate these issues, we extend the EOS model to higher densities, thereby constructing a ``unified'' description of the EOS from the neutron-star crust to densities relevant for pQCD. In this approach the pQCD constraints effectively become part of the prior. Since the EOS is unconstrained by any calculation or data between the densities applicable to neutron stars and pQCD, we argue for maximum modeling flexibility in that regime. We compare the unified EOS with the traditional pQCD likelihood, and although we confirm the EOS softening, we do not see a reduction in the maximum neutron-star mass or any impact on macroscopic observables. Though residual model dependence cannot be ruled out, we find that pQCD suggests the speed of sound in the densest neutron-star cores has already started decreasing toward the asymptotic limit; we find that the speed of sound squared at the center of the most massive neutron star has an upper bound of $\sim 0.5$ at the 90% level.
While significant strides have been made to increase the coherence time of superconducting qubits, further advancements are essential for realizing scalable quantum computing. Decoherence is often a result of loss and noise stemming from two-level systems and excess quasiparticles, arising due to material defects, fabrication processes, and ambient exposure, particularly at surfaces and interfaces. Our recent efforts to mitigate these decoherence mechanisms have employed a variety of strategies, including low-loss surface encapsulation materials, advanced substrate preparation techniques, modifications to metal film growth, and the development of novel fabrication processes. The structural and chemical properties of materials, surfaces, and interfaces are studied using scanning probe microscopy, electron microscopy, photoelectron spectroscopy, mass spectrometry, and X-ray diffraction, which is correlated to device performance metrics, including superconducting resonator internal quality factor and qubit T1 time. This information is used to identify and understand material sources of loss and their origins in the device fabrication process. Through multi-institution efforts within SQMS we have identified the loss mechanism of interstitial hydrogen in niobium-based devices and shown how standard fabrication processes introduce these hydrides, developing strategies to mitigate their formation.1 Furthermore, we have characterized the metal-substrate interface, including the loss of niobium-silicides formed at that interface, and developed silicon surface treatments that reduce atomic scale roughness and oxygen content at the metal-substrate and Josephson junction interfaces.2-4 By developing the connection between materials properties and the overall performance of superconducting quantum circuitry, we can develop fabrication strategies to mitigate material losses, thus supporting the ongoing efforts to enhance coherence time in superconducting quantum devices. 1. Torres-Castanedo, C. G.*, Goronzy, D. P.*, et al., Adv. Funct. Mater., 2401365 (2024) 2. Lu, X., et al., Phys. Rev. Materials 6, 064402 (2022) 3. Berti, G., Appl. Phys. Lett. 122, 192605 (2023) 4. Kopas, C. J., Goronzy, D. P., et al., arXiv:2408.02863 (2024)
Significant vibration amplitudes and cycles can be produced when traffic signal structures with low inherent damping are excited near one of their natural frequencies. For the mitigation of wind-induced vibrations, dynamic vibration absorbers coupled to the structure are often used. Here, this research investigates the performance of a tapered impact damper, consisting of a hanging spring-mass oscillator inside a housing capable of reducing vibration amplitude over a broader frequency range than the conventional tuned mass damper. A nonlinear, two degree-of-freedom model is developed with coordinates representing the traffic structure and the tapered impact damper. This research focuses on the application of the harmonic balance method to approximate the periodic solutions of the nonlinear equations to compute the nonlinear dynamics of the damped traffic signal structure. After designing and manufacturing a tapered impact damper, the traffic signal structure is tested with and without the damper using free vibration snapback tests. The experimental frequency and damping backbone curves are used to validate the analytical model, and the effectiveness of the damper is discussed.
In contrast to the traditional perspective that thermal fluctuations are insignificant in surface dynamics, here we report their influence on surface reaction dynamics. Using real-time low-energy electron microscopy imaging of NiAl(100) under both vacuum and O 2 atmospheres, we demonstrate that transient temperature variations substantially alter the direction of atom diffusion between the surface and bulk, leading to markedly different oxidation outcomes. During heating, substantial outward diffusion of atoms from the bulk to the surface results in step growth. Conversely, cooling induces considerable inward diffusion of adatoms, producing a distinct oxide morphology. In both scenarios, initially formed oxide islands impede local atomic step mobility, thereby increasing step length due to mass transfer between the surface and bulk, with atomic steps acting as adatom sinks during heating and sources during cooling. Furthermore, we show that this pinning effect on atomic step mobility can be mitigated by applying persistent temperature fluctuations. As a result, understanding these nuances is vital for accurately predicting and dynamically manipulating the performance of active materials in various chemical processes under transient thermal conditions.
We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of 4.2 ±0.1 tonne-years from 280 live days of LZ operation, of which 3.3 ± 0.1 tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from 214 Pb 𝛽 decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of 124 Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the dataset to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses ≥9 GeV/𝑐 2 . The strongest SI exclusion set is 2.2×10 −48 cm 2 at the 90% confidence level and the best SI median sensitivity achieved is 5.1 ×10 −48 cm 2 , both for a mass of 40 GeV/𝑐 2 .
The Atmospheric Pressure Glow Discharge (APGD) is a relatively simple and versatile plasma source used in a wide range of applications. Active cooling of the cathode can effectively mitigate instabilities, leading to glow-to-arc transitions. This study investigates the effect of varying the degree of cathode cooling in APGD with a planar cathode in helium. The plasma flow model incorporates mass conservation, chemical species transport, momentum conservation, conservation of thermal energy of heavy species and of electrons, and electrostatics. The model is applied to time-dependent simulations through a three-dimensional computational domain describing the whole discharge, without geometric symmetry or steady-state assumptions. Simulations of an experimentally characterized APGD explore the effects of electric current and cathode cooling—ranging from thermally insulated to extreme convective cooling. Results show the formation of an annular region with high electric field over the cathode surface under conditions of high current and low cooling.
An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.
The ability for bipolar membranes (BPMs) to interconvert voltage and pH makes them attractive materials for use in energy conversion and storage. Reverse-biased BPMs, which use electrical voltage to dissociate water into acid and base, have become increasingly well studied. However, forward-biased BPMs (FB-BPMs), in which voltage is extracted from pH gradients through recombination, require further study. Here physics-based modeling elucidates how the complex coupling of transport and kinetics dictates the performance of FB-BPMs in electrochemical devices. Simulations reveal that the open-circuit potential of FB-BPMs is dictated by the balance of ion recombination and crossover, where recombination of buffering counter-ions attenuates the open-circuit potential. Counter-ion mass-transport limitations and uptake of ionic impurities limit achievable current densities by reducing the applied pH gradient or the available fixed-charge sites that mediate recombination. Further, the model highlights the importance of selective ion management in mitigating energy losses and provides insight into the rational material design of FB-BPMs for energy applications.
Cryogenic calorimetric experiments to search for neutrinoless double-beta decay (0vββ) are highly competitive, scalable and versatile in isotope. The largest planned detector array, CUPID, is comprised of about 1500 individual Li 2 100 MoO 4 detector modules with a further scale up envisioned for a follow up experiment (CUPID-1T). In this article, we present a novel detector concept targeting this second stage with a low impedance TES based readout for the Li 2 MoO 4 absorber that is easily mass-produced and lends itself to a multiplexed readout. We present the detector design and results from a first prototype detector operated at the NEXUS shallow underground facility at Fermilab. The detector is a 2-cm-side cube with 21 g mass that is strongly thermally coupled to its readout chip to allow rise-times of ~0.5 ms. This design is more than one order of magnitude faster than present NTD based detectors and is hence expected to effectively mitigate backgrounds generated through the pile-up of two independent two neutrino decay events coinciding close in time. Together with a baseline resolution of 1.95 keV (FWHM) these performance parameters extrapolate to a background index from pile-up as low as 5 • 10 –6 counts/keV/kg/yr in CUPID size crystals. The detector was calibrated up to the MeV region showing sufficient dynamic range for 0vββ searches. In combination with a SuperCDMS HVeV detector this setup also allowed us to perform a precision measurement of the scintillation time constants of Li 2 MoO 4 , which showed a primary component with a fast O(20 μs) time scale. Similar content being viewe
We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.
High-salinity crude glycerol generated from biodiesel production poses significant challenges to microbial valorization due to inhibitory ingredients that severely limit microbial growth. This study identified and mitigated inhibitory substances contained in high-salinity glycerol sludge to enable its conversion to polyhydroxyalkanoates (PHAs) by the extreme halophilic archaeon Haloferax mediterranei. The long-chain fatty acids (LCFAs) were consistently identified as the primary inhibitors by liquid chromatography−mass spectrometry, Fourier transform infrared spectroscopy, and ultraviolet−visible spectroscopy. Acid precipitation at pH 2 efficiently removed these LCFAs, substantially reducing the required feedstock dilution from 23 to 3 times, improving PHA titer by 40%. Furthermore, this dilution reduction also increased the feedstock salinity utilization, achieving a 46% reduction in external salt supplementation for H. mediterranei growth. In contrast, overliming and arrested anaerobic digestion were confirmed to be ineffective in inhibitor removal. This study provides deep insights into inhibitor chemistry and presents acid precipitation as an effective pretreatment strategy for waste valorization of highsalinity crude glycerol.
Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a wide range of redshifts. In order to model the observations and address various systematic uncertainties, N-body simulations are routinely adopted, however, the number of large simulations with sufficiently high mass resolution is usually limited by available computing time. Therefore, achieving a simulation volume with the effective statistical errors significantly smaller than those of the observations becomes prohibitively expensive. In this study, we apply the Convergence Acceleration by Regression and Pooling (CARPool) method to mitigate the sample variance of the DESI-like galaxy clustering in the AbacusSummit simulations, with the assistance of the quasi-N-body simulations FastPM. Based on the halo occupation distribution (HOD) models, we construct different FastPM galaxy catalogs, including the luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars, with their number densities and two-point clustering statistics well matched to those of AbacusSummit. We also employ the same initial conditions between AbacusSummit and FastPM to achieve high cross-correlation, as it is useful in effectively suppressing the variance. Our method of reducing noise in clustering is equivalent to performing a simulation with volume larger by a factor of 5 and 4 for LRGs and ELGs, respectively. We also mitigate the standard deviation of the LRG bispectrum with the triangular configurations k 2 = 2k 1 = 0.2 h Mpc -1 by a factor of 1.6. With smaller sample variance on galaxy clustering, we are able to constrain the baryon acoustic oscillations (BAO) scale parameters to higher precision. The CARPool method will be beneficial to better constrain the theoretical systematics of BAO, redshift space distortions (RSD) and primordial non-Gaussianity (NG).