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At least 91 records · Page 5

Photon strength functions and nuclear level densities: invaluable input for nucleosynthesis

The pivotal role of nuclear physics in nucleosynthesis processes is being investigated, in particular the intricate influence of photon strength functions (PSFs) and nuclear level densities (NLDs) on shaping the outcomes of the i-, r- and p-processes. Exploring diverse NLD and PSF model combinations uncovers large uncertainties for (p, γ ), (n, γ ) and ( α , γ ) rates across many regions of the nuclear chart. These lead to potentially significant abundance variations of the nucleosynthesis processes and highlight the importance of accurate experimental nuclear data. Theoretical insights and advanced experimental techniques lay the ground work for profound understanding that can be gained of nucleosynthesis mechanisms and the origin of the elements. Recent results further underscore the effect of PSF and NLD data and its contribution to understanding abundance distributions and refining knowledge of the intricate nucleosynthesis processes. This article is part of the theme issue ‘The liminal position of Nuclear Physics: from hadrons to neutron stars’.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Understanding the Role of Order in Y‐Series Non‐Fullerene Solar Cells to Realize High Open‐Circuit Voltages

Abstract Non‐fullerene acceptors (NFAs) as used in state‐of‐the‐art organic solar cells feature highly crystalline layers that go along with low energetic disorder. Here, the crucial role of energetic disorder in blends of the donor polymer PM6 with two Y‐series NFAs, Y6, and N4 is studied. By performing temperature‐dependent charge transport and recombination studies, a consistent picture of the shape of the density of state distributions for free charges in the two blends is developed, allowing an analytical description of the dependence of the open‐circuit voltage V OC on temperature and illumination intensity. Disorder is found to influence the value of the V OC at room temperature, but also its progression with temperature. Here, the PM6:Y6 blend benefits substantially from its narrower state distributions. The analysis also shows that the energy of the equilibrated free charge population is well below the energy of the NFA singlet excitons for both blends and possibly below the energy of the populated charge transfer manifold, indicating a down‐hill driving force for free charge formation. It is concluded that energetic disorder of charge‐separated states has to be considered in the analysis of the photovoltaic properties, even for the more ordered PM6:Y6 blend.

36 MATERIALS SCIENCE↗

Enhancing biomass flowability for entrained flow Gasification: The role of densification and torrefaction

Gasification presents a key strategy in addressing future energy demands while minimizing environmental impact. This has been recognized as a promising method to convert biomass to higher value products such as biofuels or hydrogen. Among gasification technologies, high-temperature and high-pressure reactors, particularly the R-GAS® system, emerge as an advanced option boasting superior conversion efficiency. However, akin to conventional high-temperature and high-pressure gasifiers, R-GAS® necessitates small particle sizes for optimal carbon conversion, a requirement yet to be fully explored for biomass. Hence, this study investigated the effectiveness of combined mechanical and thermal preprocessing techniques in modifying the physicochemical properties of biomass to suit gasification systems. Mechanical techniques including densification and pulverization, alongside thermal techniques such as torrefaction and steam explosion, were examined. The results demonstrate that torrefaction fosters producing of uniform granular material, enhancing flowability and reducing energy requirements for pulverization compared to steam explosion. Notably, torrefied corn stover exhibited lower internal friction angles and effective cohesion (40.09 ± 0.22° and 0.56 ± 0.01 kPa, respectively) compared to steam exploded corn stover (41.87 ± 0.65° and 0.83 ± 0.06 kPa, respectively), indicative of improved flowability. Additionally, pulverization of torrefied corn stover required approximately 16 % less energy than steam exploded corn stover and 91 % less energy than raw corn stover. Furthermore, the torrefaction-induced alterations in particle size, shape, and packing densities emphasize its potential to optimize flow and handling processes for gasification. These findings underline that densification followed by torrefaction effectively addresses biomass variability, leading to more efficient and sustainable energy conversion.

09 - BIOMASS FUELS↗

Advanced manufacturing provides tailor-made solutions for crystallography with x-ray free-electron lasers

Serial crystallography at large facilities, such as x-ray free-electron lasers and synchrotrons, evolved as a powerful method for the high-resolution structural investigation of proteins that are critical for human health, thus advancing drug discovery and novel therapies. However, a critical barrier to successful serial crystallography experiments lies in the efficient handling of the protein microcrystals and solutions at microscales. Microfluidics are the obvious approach for any high-throughput, nano-to-microliter sample handling, that also requires design flexibility and rapid prototyping to deal with the variable shapes, sizes, and density of crystals. Here, we discuss recent advances in polymer 3D printing for microfluidics-based serial crystallography research and present a demonstration of emerging, large-scale, nano-3D printing approaches leading into the future of 3D sample environment and delivery device fabrication from liquid jet gas-dynamic virtual nozzles devices to fixed-target sample environment technology.

36 MATERIALS SCIENCE↗

Three-dimensional hard X-ray ptychographic reflectometry imaging on extended mesoscopic surface structures

Many nanodevices and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function–structure relationship. We thus developed hard x-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard x-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of x-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures, such as shapes and electron densities, but also yields statistical details, such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard x-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices, such as integrated nanocircuits and lithographic photomasks for microchip fabrications.

47 OTHER INSTRUMENTATION↗

Fixed Source Sensitivity Calculations for Inertial Confinement Fusion Applications

A numerical code library was developed for the radiation transport code MCNP6.3 to calculate generalized response sensitivity coefficients for fixed source neutron transport problems with applications to inertial confinement fusion (ICF) experiments. The new MCNP6.3 dependency is used to generate a novel time convolution response that represents a neutron time-of-flight (nToF) signal. The traditional suite of macroscopic cross-section sensitivities and constrained fixed source probability distribution sensitivities are available for both the standard and the new response tallies in this library. However, novel sensitivity coefficients for the constrained hyperparameters of analytic fixed source probability distributions are emphasized in this work for their connection to ICF neutron transport models. Particularly, advanced Monte Carlo methods are developed for calculating the sensitivity of a nToF signal to perturbations in an ICF plasma’s ion temperature and burn history as well as perturbations in the target liner mass density and the shape parameters of the nToF detector’s impulse response function. Together, these capabilities form an advanced suite of computational tools that can be used to analyze and extract information from any ICF experimental platform.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A Forward Analytic Model of Neutron Time-of-Flight Signals for Inferring Ion Temperatures from MagLIF Experiments

A forward analytic model is required to rapidly simulate the neutron time-of-flight (nToF) signals that result from magnetized liner inertial fusion (MagLIF) experiments at Sandia’s Z Pulsed Power Facility. Various experimental parameters, such as the burn-weighted fuel-ion temperature and liner areal density, determine the shape of the nToF signal and are important for characterizing any given MagLIF experiment. Extracting these parameters from measured nToF signals requires an appropriate analytic model that includes the primary deuterium-deuterium neutron peak, once-scattered neutrons in the beryllium liner of the MagLIF target, and direct beamline attenuation. Here, mathematical expressions for this model were derived from the general-geometry time- and energy-dependent neutron transport equation with anisotropic scattering. Assumptions consistent with the time-of-flight technique were used to simplify this linear Boltzmann transport equation into a more tractable form. Models of the uncollided and once-collided neutron scalar fluxes were developed for one of the five nToF detector locations at the Z-Machine. Numerical results from these models were produced for a representative MagLIF problem and found to be in good agreement with similar neutron transport simulations. Twenty experimental MagLIF data sets were analyzed using the forward models, which were determined to only be significantly sensitive to the ion temperature. The results of this work were also found to agree with values obtained separately using a zero scatter analytic model and a high-fidelity Monte Carlo simulation. Finally, inherent difficulties in this and similar techniques are identified, and a new approach forward is suggested.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Enhanced energy confinement after series of pellets in Wendelstein 7-X

A series of ice pellets was injected into the advanced stellarator Wendelstein 7-X (W7-X). Although the pellets were small and slow, deep and efficient particle fueling could be observed experimentally. The most striking feature appearing after the injection of the pellets, however, was a transient increase in the energy confinement time. This transient phase resembled in several aspects modes of enhanced confinement after gas-puff or pellet injection, as observed in other fusion experiments. All experimental attempts, to prolong this phase, failed. In this paper, discharges are described that show the enhanced energy confinement, and some conditions are summarized which seem to be essential in order to generate it. The focus here is on deep particle fueling by pellets, and shaping of the density profiles during and after the series of pellets. During this time, neutral gas particle re-fueling at the plasma edge is reduced, while density profile peaking and low impurity radiation losses are present.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Competition between energy- and entropy-driven activation in glasses

In simplified models of glasses we clarify the existence of two different kinds of coexisting activated dynamics, with one of the two dominating over the other. One is the energy barrier hopping that is typically used to understand activation, and the other, which we call entropic activation, is driven by the scarcity of convenient directions in phase space. When entropic activation dominates, the height of the energy barriers is no longer the primary factor governing the system's slowdown. In our analysis, dominance of one mechanism over the other depends on temperature and the shape of the density of states. Herein, we also find that at low temperatures a phase transition between the two kinds of activation can occur. Our observations are used to provide a scenario that can harmonize the facilitation and thermodynamic pictures of the slowdown of glasses into a single description.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗

SULI Research Report

Acoustic levitation allows for the containerless manipulation of small samples limited by density, size, and shape. Modern acoustic levitators are either constructed with Langevin horns, which need to be machined to high tolerance and require at least 100V, or ultrasonic transducers, which are robust to changes in temperature and require low voltage. We investigated the ability of the TinyLev, a commercially available single axis levitator, and prototyped a two-axis levitator. Both the TinyLev and our two-axis levitator employ phased arrays of transducers and operate at 40 kHz. Models produced with the Python toolbox Levitate showed the characteristic nodes of the TinyLev and found two previously unpublished “traps” (stable local pressure minima) of the twoaxis levitator. We plan to use these levitators at the Advanced Photon Source (APS) to study a variety of samples, via white beam diffraction and high speed imaging. It is our goal to calculate the radial distribution function with respect to temperature across phase transitions.

42 ENGINEERING↗

K-Modules

K-Modules are capacitive energy modules designed to be stacked together to form a capacitor bank (aka a Marx bank). Discharging the stack produces a single, high voltage, high current pulse that when coupled to an X-ray (or electron) tube, produces energetic X-rays (or an intense electron beam, E-beam). The energetic X-rays are used in Flash X-ray Radiography (FXR), a technique to image objects moving at extremely high speed to measure their speed, shape and internal density profiles. The images help validate computer models for materials behavior under extreme pressure and temperature. FXR is a technology critical to dynamic experiments for the national security mission, and most users are DOE and DOD labs. Febetrons, the X-ray generating devices, are used at these sites and rely on capacitive energy modules for their operation. A stack of the newly developed K-modules will significantly extend the capabilities of these Febetrons

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Defect Equilibria from First Principles: From Widegap Oxides to Topological Semimetals

Materials functionality and performance is rarely determined by the ideal crystal alone but is usually affected by formation of imperfections and the solution of impurities. In some applications, such as solar thermochemical hydrogen generation, defect formation is the fundamentally enabling mechanism of the desired functionality. In other cases, such as Cd3As2 topological semimetals, unintentional self-doping presents an obstacle to the access to the unique electronic properties. In either case, a quantitative understanding of the relevant defect mechanism is essential for developing design strategies. This presentation will touch upon numerous aspects in the computational simulation of defect equilibria, including non-equilibrium design strategies, the coupling of solid state and gas-phase reactions, dopant-defect and defect-defect interactions, both attractive and repulsive, the accuracy of total energy functionals and electronic structure methods, and the role of the shape of the density of states for the charge balance condition and Fermi level position, as well as machine-learning prediction of defect energies (1). Specific materials systems include Ga2O3 (2), Cd3As2 (3), and (Sr,Ce)MnO3 (4). (1) M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. 3, 675 (2023). (2) A. Goyal, A. Zakutayev, V. Stevanovic, S. Lany, J. Appl. Phys. 129, 245704 (2021). (3) C. Brooks, M. van Schilfgaarde, D. Pashov, J.N. Nelson, K. Alberi, D.S. Dessau, S. Lany, Phys. Rev. B 107, 224110 (2023). (4) A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024).

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS,M↗

Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations

We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE)-originally designed for nonlinear dimensionality reduction-for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probability density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes.

deformation↗

Thermophysical properties of high-density, sintered monoliths of yttrium dihydride in the range 373–773 K

Yttrium dihydride is a promising nuclear reactor moderator for high-temperature, thermal micro-reactors due to its ability to retain hydrogen to high temperatures while having a relatively low impact on neutron economy. However, it is difficult to fabricate yttrium dihydride in high-density, near net-shape monoliths for moderator applications due to challenges associated with the hydrogen absorption process. Here, high-density monoliths of yttrium dihydride were prepared by powder metallurgical methods. The lattice strain and thermal expansion coefficient were measured using neutron diffraction of powders of directly-hydrided yttrium, while the molar heat capacity and thermal diffusivity of sintered yttrium dihydride were measured using differential scanning calorimetry (DSC) and laser flash analysis (LFA), respectively. The molar heat capacity of yttrium dihydride was also calculated using density functional theory (DFT) for comparison. These parameters were used to then calculate the thermal conductivity and resistivity of yttrium dihydride as a function of temperature. The thermophysical properties of materials produced by both methods were observed to be consistent with the values from literature for yttrium dihydride. Due to the novelty of producing yttrium dihydride by powder metallurgical methods, this result indicated that the sintered monoliths were of high quality and that powder metallurgy is a viable method for large-scale production of yttrium dihydride monoliths for nuclear reactor moderator applications.

36 MATERIALS SCIENCE↗

Semi-solid alkali metal electrodes enabling high critical current densities in solid electrolyte batteries

The need for higher energy density rechargeable batteries has generated interest in alkali metal electrodes paired with solid electrolytes. However, metal penetration and electrolyte fracture at low current densities have emerged as fundamental barriers. Here, we show that for pure metals in the Li-Na-K system, the critical current densities scale inversely to mechanical deformation resistance. Furthermore, we demonstrate two electrode architectures in which the presence of a liquid phase enables high current densities while preserving the shape retention and packaging advantages of solid electrodes. First, biphasic Na-K alloys show K + critical current densities (with K-β*-Al 2 O 3 electrolyte) that exceed 15 mA∙cm -2 . Second, introducing a wetting interfacial film of Na-K liquid between Li metal and Li 6.75 La 3 Zr 1.75 Ta 0.25 O 12 (LLZTO) solid electrolyte doubles the critical current density and permits cycling at areal capacities exceeding 3.5 mAh∙cm -2 . These design approaches hold promise for overcoming electro-chemo-mechanical stability issues that have heretofore limited performance of solid-state metal batteries.

25 ENERGY STORAGE↗

The effect of areal density asymmetries on scattered neutron spectra in ICF implosions

Scattered neutron spectroscopy is a diagnostic technique commonly used to measure areal density in inertial confinement fusion experiments. Deleterious areal density asymmetries modify the shape of the scattered neutron spectrum. In this work, a novel analysis is developed, which can be used to fit the shape change. This will allow experimental scattered neutron spectroscopy to directly infer the amplitude and mode of the areal density asymmetries, with little sensitivity to confounding factors that affect other diagnostics for areal density. The model is tested on spectra produced by a neutron transport calculation with both isotropic and anisotropic primary fusion neutron sources. Multiple lines of sight are required to infer the areal density distribution over the whole sphere—we investigate the error propagation and optimal detector arrangement associated with the inference of mode 1 asymmetries.

Crilly, A. J. (ORCID:0000000204299332)↗

Machine Learning of Performance Space Mapping for the DPD Simulation of Drug Delivery to Endothelial Cells

Despite huge effort over the years, the design of functionalised nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative particle dynamics (DPD) simulation is used to study the adhesion of NCs to endothelial cells under the influence of series of parameters such as shape, size, and ligand density of the NCs. However, preparing a performance space mapping that illustrates the penetration depths of NCs as a function of variations in their properties requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by leveraging a Gaussian process regression (GPR)-informed active learning strategy and an extensive exploration of numerous samples, each representing different properties of NCs. The performance space mapping reveals that NCs with rod and disc shapes exhibit superior penetration capabilities compared to those with a spherical shape. Furthermore, it demonstrates that smaller-sized rod-shaped NCs and larger-sized disc-shaped NCs tend to achieve better penetration. When considering smaller NCs, the influence of ligand density appears to be limited. On the contrary, for larger NCs, an increase in ligand density correlates with greater penetration depth, underscoring its substantial role in shaping their penetration capabilities.

36 MATERIALS SCIENCE↗