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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Constraining neutron-star matter with microscopic and macroscopic collisions

Interpreting high-energy, astrophysical phenomena, such as supernova explosions or neutron-star collisions, requires a robust understanding of matter at supranuclear densities. However, our knowledge about dense matter explored in the cores of neutron stars remains limited. Fortunately, dense matter is not probed only in astrophysical observations, but also in terrestrial heavy-ion collision experiments. Here we use Bayesian inference to combine data from astrophysical multi-messenger observations of neutron stars and from heavy-ion collisions of gold nuclei at relativistic energies with microscopic nuclear theory calculations to improve our understanding of dense matter. We find that the inclusion of heavy-ion collision data indicates an increase in the pressure in dense matter relative to previous analyses, shifting neutron-star radii towards larger values, consistent with recent observations by the Neutron Star Interior Composition Explorer mission. Our findings show that constraints from heavy-ion collision experiments show a remarkable consistency with multi-messenger observations and provide complementary information on nuclear matter at intermediate densities. This work combines nuclear theory, nuclear experiment and astrophysical observations, and shows how joint analyses can shed light on the properties of neutron-rich supranuclear matter over the density range probed in neutron stars.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Mineral reaction kinetics constrain the length scale of rock matrix diffusion

Mass transport by aqueous fluids is a dynamic process in shallow crustal systems, redistributing nutrients as well as contaminants. Rock matrix diffusion into fractures (void space) within crystalline rock has been postulated to play an important role in the transient storage of solutes. The reacted volume of host rock involved, however, will be controlled by fluid-rock reactions. Here we present the results of a study which focusses on defining the length scale over which rock matrix diffusion operates within crystalline rock over timescales that are relevant to safety assessment of radioactive and other long-lived wastes. Through detailed chemical and structural analysis of natural specimens sampled at depth from an active system (Toki Granite, Japan), we show that, contrary to commonly proposed models, the length scale of rock matrix diffusion may be extremely small, on the order of centimetres, even over timescales of millions of years. This implies that in many cases the importance of rock matrix diffusion will be minimal. Additional analyses of a contrasting crystalline rock system (Carnmenellis Granite, UK) corroborate these results.

58 GEOSCIENCES↗
DOE OSTI2023

Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction

By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies have increased reconstruction speed, they sacrifice image quality. Furthermore, these methods’ demand for extensive labeled training data is experimentally burdensome. Here, we propose an unsupervised physics-informed neural network reconstruction method, PtychoPINN, that retains the factor of 100-to-1000 speedup of deep learning-based reconstruction while improving reconstruction quality by combining the diffraction forward map with real-space constraints from overlapping measurements. In particular, PtychoPINN gains a factor of 4 in linear resolution and an 8 dB improvement in PSNR while also accruing improvements in generalizability and robustness. This blend of performance and computational efficiency offers exciting prospects for high-resolution real-time imaging in high-throughput environments such as X-ray free electron lasers (XFELs) and diffraction-limited light sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Constraining the impact of chlorine as a neutron absorber in next-gen fast reactor designs

The role of chlorine as a neutron poison and as a seed for producing radioactive waste in nuclear systems has driven a renewed interest to improve its nuclear data uncertainties. Additionally, basic and applied science programs that use CLYC (Cs 2 LiYCl 6 :Ce) detectors for neutron spectroscopy and monitoring are also very sensitive to any change in chlorine nuclear data for simulations of the detector response. In this work, sensitivities relevant for these different applications are addressed through simulations of the efficiency of CLYC detectors in a fast fission spectrum when applying new chlorine nuclear data as input. These simulations are validated by an experimental measurement using CLYC detectors coupled to an ionization chamber loaded with a 252 Cf spontaneous fission source. The results are then used to obtain the first reliable direct measurement of the 35 Cl(n,p 0 ) and summed Cl(n,p+n,α) fission spectrum average cross sections, found to be 54.7(32) and 105.0(98) mb, respectively. The results are within uncertainty of calculated fission spectrum averaged cross sections based on recently re-evaluated chlorine nuclear data, which confirm recent impact studies performed for the Molten Chloride Reactor Experiment. Meanwhile, there currently exists only one published criticality benchmark experiment that is sufficiently sensitive to chlorine nuclear data. Discrepancies are found with this set of criticality safety benchmarks, which are more sensitive to thermal and epithermal neutron energies than the energies, above 100 keV, tested in this current work. Hence, there is still a need to re-evaluate the chlorine nuclear data at lower energies to assess these discrepancies. Interpretation of the data from future “faster” criticality benchmarks, which are needed for next-gen fast reactor designs, benefit from the improved constraints on the chlorine nuclear data validated in this work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

New formation and fate of Isoprene SOA markers revealed by field data-constrained modeling

Particulate 2-methyltetrols (2-MT) and 2-methylglyceric acid (2-MG) are typically used to indicate the abundance of isoprene-derived secondary organic aerosols (SOA). However, their formation and fate are not fully understood. In this study, we showed that particulate 2-MT and 2-MG collected at multiple monitoring sites under a wide range of atmospheric and emission conditions, with concentrations spanning six orders of magnitudes, are well reproduced with an expanded isoprene-SOA scheme implemented into the Community Multiscale Air Quality (CMAQ) model. The scheme considers their three-phase (gas-aqueous-organic phase) partitioning, formation from acid-driven multiphase reactions, and degradation by OH radicals in the gas and aqueous phases. The model results reveal that a non-aqueous formation pathway or direct biogenic emission is needed to supplement the commonly assumed acid-driven multiphase reaction process to explain the observed 2-MT concentrations. This missing pathway contributes to 20–40% of 2-MT in areas with aerosol pH < 2 and more than 70% under less acidic conditions (pH~2–5), such as those encountered in the western US and China. The typical summertime gas-phase photochemical lifetimes of 2-MT and 2-MG are estimated to be 4–6 and 20–30 h, respectively, and their aqueous lifetimes are approximately 20–40 h. Our simulations show that predicted 2-MT is mainly influenced by its aqueous phase loss to OH, but 2-MG is more sensitive to gas phase OH loss due to the preferential partitioning of the two tracers in the aqueous and gas phases, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improved quantum yield in geometrically constrained tetraphenylethylene-based metal–organic frameworks

In this paper we report the synthesis of a novel, tetraphenylethylene-based ligand for metal–organic frameworks (MOFs). Incorporation of this ligand into a Zn- or Eu-based MOF increased the quantum yield (QY) by almost 2.5× compared to the linker alone. Furthermore, the choice of guest solvent impacted the QY and solvatochromatic response. These shifts are consistent with solvent dielectric constant as well as molecular polarizability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Localizing tetrahedral aluminum in nitrate-bearing gibbsite to constrain defect-impurity coupling

The enhanced radiolytic stability of gibbsite (α-Al(OH) 3 ) containing trace nitrate (NO 3 − ) is a phenomenon in nuclear waste management, but its structural origins remain unresolved. Motivated by the detection of minority tetrahedral aluminum (T d ) defects in synthetic gibbsite, we hypothesized that these sites may participate in NO 3 − retention or mediate H 2 suppression. To evaluate this, we combined orthogonal techniques comprised of spatially selective solid-state 27 Al MAS NMR, comparative spectroscopy, and density functional theory (DFT) modeling. Paramagnetic editing and dynamic nuclear polarization (DNP) MAS NMR confirm that T d defects are confined to the particle interior. DFT calculations reveal no energetic stabilization of NO 3 − near T d sites. Comparative NMR analysis shows that T d is also present in chloride-bearing gibbsite, which exhibits high radiolytic hydrogen yields. These three independent disqualifications rule out T d as a structural contributor to nitrate-mediated suppression and narrow the scope of defect-driven explanations. The findings redirect mechanistic attention away from coordination defects and toward redox-active impurity pathways, providing a refined foundation for understanding radiation tolerance in Al(OH) 3 .

Graham, Trent R. [Pacific Northwest National Labor↗
DOE OSTI2020

Constraining the masses of high-redshift clusters with weak lensing: Revised shape calibration testing for the impact of stronger shears and increased blending

Weak lensing measurements suffer from well-known shear estimation biases, which can be partially corrected for with the use of image simulations. Here we present an analysis of simulated images that mimic Hubble Space Telescope/Advance Camera for Surveys observations of high-redshift galaxy clusters, including cluster specific issues such as non-weak shear and increased blending. Our synthetic galaxies have been generated to have similar observed properties as the background-selected source samples studied in the real images. First, we used simulations with galaxies placed on a grid to determine a revised signal-to-noise-dependent (S/N KSB ) correction for multiplicative shear measurement bias, and to quantify the sensitivity of our KSB+ bias calibration to mismatches of galaxy or PSF properties between the real data and the simulations. Next, we studied the impact of increased blending and light contamination from cluster and foreground galaxies, finding it to be negligible for high-redshift (z > 0.7) clusters, whereas shear measurements can be affected at the ~1% level for lower redshift clusters given their brighter member galaxies. Finally, we studied the impact of fainter neighbours and selection bias using a set of simulated images that mimic the positions and magnitudes of galaxies in Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) data, thereby including realistic clustering. While the initial SExtractor object detection causes a multiplicative shear selection bias of –0.028 ± 0.002, this is reduced to –0.016 ± 0.002 by further cuts applied in our pipeline. Given the limited depth of the CANDELS data, we compared our CANDELS-based estimate for the impact of faint neighbours on the multiplicative shear measurement bias to a grid-based analysis, to which we added clustered galaxies to even fainter magnitudes based on Hubble Ultra Deep Field data, yielding a refined estimate of ~ –0.013. Our sensitivity analysis suggests that our pipeline is calibrated to an accuracy of ~0.015 once all corrections are applied, which is fully sufficient for current and near-future weak lensing studies of high-redshift clusters. As an application, we used it for a refined analysis of three highly relaxed clusters from the South Pole Telescope Sunyaev-Zeldovich survey, where we now included measurements down to the cluster core (r > 200 kpc) as enabled by our work. Compared to previously employed scales (r > 500 kpc), this tightens the cluster mass constraints by a factor 1.38 on average.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Challenges of standard halo models in constraining galaxy properties from cosmic infrared background anisotropies

The halo model, combined with halo occupation distribution (HOD) prescriptions, is widely used to interpret cosmic infrared background (CIB) anisotropies and extract physical information about star-forming galaxies and their connection to large-scale structures. Recent CIB-specific implementations of the halo model have adopted more physical parameterizations. However, the extent to which these models can reliably recover meaningful physical parameters remains uncertain. We assessed whether the current parameterization of CIB halo models is sufficient to recover astrophysical quantities, such as star formation efficiency, η(M h , z), and halo mass at which the peak of star formation efficiency occurs, M max , when fit to mock data. We also assessed whether discrepancies arise from assumptions about galaxy emission (the HOD ingredients) or from more fundamental components in the halo model, such as bias and matter clustering. We fit the M21 CIB HOD model, implemented within the halo model framework, to mock CIB power spectra and star formation rate density (SFRD) data generated from the SIDES-Uchuu simulation, and compared the best-fit parameters to the known simulation inputs. We then repeated the analysis using a simplified version of the simulation (SSU), explicitly designed to match the HOD assumptions. A detailed comparison of model and simulation outputs was carried out to trace the origin of observed discrepancies. While the M21 HOD model provides a good fit to the mock data, it failed to recover the intrinsic parameters accurately, particularly the halo mass at which star formation efficiency peaks. This mismatch persists even when fitting data generated with the same model assumptions. We find strong agreement (within 5%) in the emission-related components (SFRD, emissivity), but observe a scale- and redshift-dependent offset exceeding 20% in the two-halo term of the CIB power spectrum. This likely arises from limitations in the treatment of halo bias and matter clustering within the linear approximation. Additionally, incorporating scatter in the SFR–halo mass relation and the spectral energy distribution (SED) templates significantly affects the shot noise (∼50%), but has only a modest impact (less than 10%) on the clustered component. These results suggest that recovering physical parameters from CIB clustering requires improvements to the cosmological ingredients of the halo model framework, such as adopting scale-dependent halo bias and nonlinear matter power spectra in addition to careful modeling of emission physics.

cosmic background radiation↗
DOE OSTI2017

Constraining nuclear data via cosmological observations: Neutrino energy transport and big bang nucleosynthesis

Here, we introduce a new computational capability that moves toward a self-consistent calculation of neutrino transport and nuclear reactions for big bang nucleosynthesis (BBN). Such a self-consistent approach is needed to be able to extract detailed information about nuclear reactions and physics beyond the standard model from precision cosmological observations of primordial nuclides and the cosmic microwave background radiation. We also calculate the evolution of the early universe through the epochs of weak decoupling, weak freeze-out and big bang nucleosynthesis (BBN) by simultaneously coupling a full strong, electromagnetic, and weak nuclear reaction network with a multi-energy group Boltzmann neutrino energy transport scheme. The modular structure of our approach allows the dissection of the relative contributions of each process responsible for evolving the dynamics of the early universe. Such an approach allows a detailed account of the evolution of the active neutrino energy distribution functions alongside and self-consistently with the nuclear reactions and entropy/heat generation and flow between the neutrino and photon/electron/positron/baryon plasma components. Our calculations reveal nonlinear feedback in the time evolution of neutrino distribution functions and plasma thermodynamic conditions. We discuss the time development of neutrino spectral distortions and concomitant entropy production and extraction from the plasma. These effects result in changes in the computed values of the BBN deuterium and helium-4 yields that are on the order of a half-percent relative to a baseline standard BBN calculation with no neutrino transport. This is an order of magnitude larger effect than in previous estimates. For particular implementations of quantum corrections in plasma thermodynamics, our calculations show a 0.4% increase in deuterium and a 0.6% decrease in 4 He over our baseline. The magnitude of these changes are on the order of uncertainties in the nuclear physics for the case of deuterium and are potentially significant for the error budget of helium in upcoming cosmological observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Constraining Fission Yields Using Machine Learning

Having accurate measurements of fission observables is important for a variety of applications, ranging from energy to non-proliferation, defense to astrophysics. Because not all of these data can be measured, it is necessary to be able to accurately calculate these observables as well. In this work, we exploit Monte Carlo and machine learning techniques to reproduce mass and kinetic energy yields, for phenomenological models and in a model-free way. Finally, we begin with the spontaneous fission of 252 Cf, where there is abundant experimental data, to validate our approach, with the ultimate goal of creating a global yield model in order to predict quantities where data are not currently available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗