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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 685 records · Page 38

Solvent Dynamics Are Critical to Understanding Carbon Dioxide Dissolution and Hydration in Water

Simulations of carbon dioxide (CO 2 ) in water may aid in understanding the impact of its accumulation in aquatic environments and help advance technologies for carbon capture and utilization (via, e.g., mineralization). Quantum mechanical (QM) simulations based on static molecular models with polarizable continuum solvation poorly reproduce the energetics of CO 2 hydration to form carbonic acid in water, independent of the level of QM theory employed. Only with density-functional-theory-based molecular dynamics and rare-event sampling, followed by energy corrections based on embedded correlated wavefunction theory (in conjunction with density functional embedding theory), can a close agreement between theory and experiment be achieved. Such multilevel simulations can serve as benchmarks for simpler, less costly models, giving insight into potential errors of the latter. The strong influence of sampling/averaging over dynamical solvent configurations on the energetics stems from the difference in polarity of both the transition state and product (both polar) versus the reactant (nonpolar). When a solute undergoes a change in polarity during reaction, affecting its interaction with the solvent, careful assessment of the energetic contribution of the solvent response to this change is critical. Here, we show that static models (without structural sampling) that incorporate three explicit water molecules can yield far superior results than models with more explicit water molecules because fewer water molecules yield less configurational artifacts. Static models intelligently incorporating both explicit (molecules directly participating in the reaction) and implicit solvation, along with a proper QM theory, e.g., CCSD(T) for closed-shell systems, can close the accuracy gap between static and dynamic models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Tetris-inspired detector with neural network for radiation mapping

Abstract Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Deciphering the Dynamic Balance Between Solvation Strength and Polysulfides Reaction Heterogeneity in Practical Lithium‐Sulfur Batteries

Achieving stable interfacial chemistry in lithium–sulfur batteries under practical conditions remains a key barrier to commercialization. Here, we demonstrate that interfacial dynamics can be effectively regulated by coupling solvation-power control with intrinsic heterogeneity of sulfur redox chemistry through the introduction of a weakly solvating fluorinated cosolvent, LIB 1200ET (1200ET). Compared with conventional fluorinated ethers, 1200ET efficiently shifts Li + solvation environment toward a more non-coordinated configuration at low volume fractions, enabling substantial solvation modulation without significantly impairing sulfur redox kinetics. This solvation transition weakens Li + –solvent interactions while strengthening Li + –anion and Li + –lithium polysulfide (LPS) coordination, suppressing LPS solubility and promoting reconstruction of solid–electrolyte interphase (SEI). Regulated LPS chemistry, together with 1200ET, leads to formation of a S 4+ -rich, LiF-reinforced SEI with enhanced ionic conductivity and mechanical robustness. Spatially resolved sulfur K-edge X-ray absorption spectroscopy on pouch cells reveals pronounced current-density-dependent chemical heterogeneity, distinguishing kinetically dominated and solvation-controlled regions. Under practical conditions (3.7 mg cm −2 sulfur loading, E/S = 6 µL mg −1 ), a single-layer pouch cell delivers 527 mAh g −1 over 200 cycles at C/3, while an Ah-level multilayer pouch cell achieves an energy density of 358 Wh kg −1 . These results establish non-coordinating cosolvent-driven solvation engineering as a scalable strategy for practical Li–S batteries.

36 MATERIALS SCIENCE↗

Structure and Flow-Viscosity of Filled-Polymer-Based 3D Printing Ink: Exploration through Coarse-Grained Molecular Dynamics

The addition of nanofiller particles to a polymer matrix has long been known to enhance or modify the composite’s mechanical and rheological properties. However, quantitatively capturing such changes with molecular level simulations remains computationally challenging. Toward that goal, we performed coarse-grained molecular dynamics of a nanocomposite system at a fixed (25 vol %) filler loading under nonspecific, weak polymer–filler interactions representative of a broad class of technologically important materials. We report several interesting results, including: (1) the equilibrium chain-configuration remains Gaussian-like as in an unfilled melt; (2) smaller filler particles display a stronger tendency to cluster; (3) larger fillers act as plasticizers by reducing the entanglement density and accelerating the chain mobility; and (4) fillers enhance the tensile response modulus, with the effect being stronger for larger particles. We also simulate cluster breakup, yielding, and elongational flow under an applied time-linear tensile strain and study the flow viscosity as a function of filler-size and chain-length.

Materials science↗

Bayesian inference for plasmonic nanometrology

Here, we introduce a Bayesian method for the characterization of plasmonic nanoparticles, which is applicable to both near- and far-field problems. Designed to combine data generated from any photon-plasmon interaction experiment with physically motivated theoretical models, our approach leverages state-of-the-art Markov chain Monte Carlo sampling techniques and returns parameter estimates on nanometric scales. Simulated spectral data sets, describing resonant scattering of photons from ellipsoidal and toroidal nanoparticles, are explored as concrete examples of our approach, with the resulting Bayesian estimates showing excellent agreement with the ground truth, even under conditions of high statistical noise. By incorporating Bayes factors into the method as well, we reveal how model selection can determine which one of competing geometric shapes better explains the observed data. Our comprehensive nanometrology procedure can be tailored to a variety of light-particle interaction models, and its reliance on Bayesian inference furnishes automatic uncertainty quantification. In addition to applicability to a host of plasmonic configurations such as nanoparticle dimers, trimers, and array studies, it is proposed that the presented analysis can be extended to the quantum regime, where nonclassical photon statistics may provide additional insight for inference of scatterer properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

𝛽−decay of neutron-rich 45 Cl located at the magic number 𝑁 = 28

Results from the β-decay study of 45 Cl, produced in the fragmentation of a 140-MeV/u 48 Ca beam, are presented. The half-life of 45 Cl is measured to be 513(36) ms. β – and β – 1n decay populate excited states in 45 Ar and 44 Ar, respectively, and decay schemes for both daughter nuclei are established based on γ-ray singles and γ-γ coincidence data. Comparison with shell-model calculations using the FSU interaction shows that the low-lying negative-parity states in 45 Ar are well described by single-neutron configurations near the Fermi surface, whereas positive-parity states require neutron excitations across the N = 20 shell gap, consistent with allowed Gamow–Teller decay from 45 Cl. The strong β-feeding to the 5/2 + state in 45 Ar supports a 3/2 + ground-state assignment for 45 Cl over 1/2 + . Furthermore, the high Q β – value enables population of 1p1h states above the neutron separation energy in 45 Ar, leading to positive-parity states in 44 Ar through neutron emission. Tentative spin-parity assignments for excited levels in 44 Ar are made for the first time based on comparison with shell-model predictions, and the 2978-keV level is identified as an excited 0 + state with a configuration distinct from the ground state.

39 ≤ A ≤ 58↗

Dark energy with a triplet of classical U(1) fields

Here, we present a new mechanism for cosmic acceleration consisting of a scalar field coupled to a triplet of classical U(1) gauge fields. The gauge fields are arranged in a homogeneous, isotropic configuration, with both electric- and magneticlike vacuum expectation values. The gauge fields provide a masslike term via a Chern-Simons interaction that suspends the scalar away from its potential minimum, thereby enabling potential-dominated evolution. We show this mechanism can drive a brief period of acceleration, such as dark energy, without the need for fine-tunings. We obtain simple analytic results for the dark energy equation of state and dependence on model parameters. In this model, the presence of the gauge field generically leads to a suppression of long-wavelength gravitational waves, with implications for the experimental search for cosmic microwave background B modes and direct detection of a stochastic gravitational wave background.

79 ASTRONOMY AND ASTROPHYSICS↗

Signatures of linearized gravity in atom interferometers: A simplified computational framework

We develop a general framework for calculating the leading-order, general relativistic contributions to the gravitational phase shift in single-photon atom interferometers within the context of linearized gravity. We show that the atom gradiometer observable, which only depends on the atom interferometer propagation phase, can be written in terms of three distinct contributions: the Doppler phase shift, which accounts for the tidal displacement of atoms along the baseline, the Shapiro phase shift, which accounts for the delay in the arrival time of photons at atom-light interaction points, and the Einstein phase shift, which accounts for the gravitational redshift measured by the atoms. For specific atom gradiometer configurations, we derive the signal and response functions for two physically motivated scenarios: (i) transient gravitational waves in the transverse-traceless gauge and, for the first time, in the proper detector frame, and (ii) transient massive objects sourcing weak and slow-varying Newtonian potentials. Here, we find that the Doppler contribution of realistic Newtonian noise sources (e.g., a freight truck or a piece of space debris) at proposed atom gradiometer experiments, such as AION, MAGIS, and AEDGE, can exceed the shot noise level and thus affect physics searches if not properly subtracted. Note: 44 pages including appendices, 4 figures; v2: different formatting, fixed typos, 28 pages including appendices, 5 figures, agrees with published version

Atom interferometry↗

Multiscale Machine-Learned Modeling Infrastructure RAS

MuMMI RAS is the application component of the Multiscale Machine-Learned Modeling Infrastructure (MuMMI). It simulates RAS protein interactions at three scales of resolution coupled with ML-based selection and in-situ feedback. MuMMI RAS is particularly configured for massive scale, running thousands of simultaneous jobs with several terabytes of data on Lassen and Summit.

Moon, JosephY.↗

Monte Carlo Driven MDI Optimization at a Muon Collider

A Muon Collider represents a very interesting possibility for a future machine to explore the energy frontier in particle physics. However, to reach the needed luminosity, beam intensities of the order of 10⁹–10¹² muons per bunch are needed. In this context, the Beam-Induced Background must be taken into account for its effects on magnets and detector. Several mitigation strategies can however be conceived. In this view, it is of crucial importance to develop a flexible tool that allows to easily reconstruct the machine geometry in a Monte Carlo code, allowing to simulate in detail the interaction of muon decay products in the machine, while being able to change the machine optics itself to find the best configuration. In this contribution, a possible approach to such a purpose is presented, based on FLUKA for the Monte Carlo simulation and on LineBuilder for the geometry reconstruction. Results based on the 1.5 TeV machine optics developed by the MAP collaboration are discussed, as well as a first approach to possible mitigation strategies.

43 PARTICLE ACCELERATORS↗

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗

Discrete Open-Shell Tris(bipyridinium radical cationic) Inclusion Complexes in the Solid State

In the solid state the properties of organic radicals depend heavily on radical-radical interactions that are influenced by the superstructure of the crystalline phase. Here, we report the synthesis and characterization of a substituted tetracationic cyclophane, cyclobis(paraquat-p-1,4- dimethoxyphenylene), and outline how its bisradical dicationic redox state associates with the methyl viologen radical cation (MV •+ ) to give a trisradical tricationic inclusion complex. The (super)structures of the reduced cyclophane and this 1:1 complex in the solid-state deviate from the analogous (super)structures observed for the reduced state of cyclobis(paraquat-p-phenylene) and that of its trisradical tricationic complex. Titration experiments with a range of guests reveal that the methoxy substituents on the para-xylenyl do not influence significantly of the binding affinity of the cyclophane for small neutral guests, whereas the affinity decreases by an order of magnitude for larger radical cationic guests such as MV •+ . X-Ray diffraction reveals that the solid- state structures of the tricationic trisradical complexes to be a discrete entity with weak intermolecular orbital overlap between the neighboring complexes. Transient nutation EPR experiment and DFT calculations confirm that the complex has a doublet spin configuration in the ground state as a result of the strong orbital overlap within the complex, while the quartet-state spin configuration is higher in energy and so is inaccessible at ambient temperature. The superconducting quantum interference device (SQUID) measurement reveals that trisradical tricationic complexes interact antiferromagnetically (J/k = - 2.33 K) and form a 1D Heisenberg antiferromagnetic chain along the a-axis in the crystal. Furthermore, these results offer new insights into the design and synthesis of organic magnetic materials based on host-guest inclusion complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Large eddy simulation of wind farm performance in horizontally and vertically staggered layouts

This numerical investigation employs Large Eddy Simulation (LES) coupled with Actuator Disk Model (ADM) to evaluate wind farm layout optimization strategies. The study presents a systematic analysis of aligned, horizontal staggering, vertical staggering, and mixed (combination of horizontal and vertical) staggering configurations, aiming to establish optimal design parameters for enhanced power production. The investigation examines key performance metrics including mean velocity distributions, turbulence intensity characteristics, and power generation efficiency. Results demonstrate better performance of both horizontal and vertical staggering patterns compared to conventional aligned configurations, with horizontal staggering exhibiting notably higher power output than vertical arrangements. Our findings also suggest that mixed configurations, incorporating both horizontal and vertical staggering, can offer optimal performance characteristics. As a result, this research advances the understanding of wake interactions in complex wind farm layouts and provides design guidelines for maximizing wind farm power generation efficiency through strategic turbine positioning.

17 WIND ENERGY↗

Critical role of magnetic moments in heavy-fermion materials: Revisiting mysteries of SmB 6

Heavy-fermion family exhibits fascinating and often puzzling properties due to the presence of open-shell f ions and the complexity of the associated charge, orbital, and spin degrees of freedom. SmB 6 is a prototypical heavy-fermion compound that is electrically insulating but yet it displays quantum oscillations, which are a telltale signature of the metallic state. Adding to the enigma is the possibility that SmB 6 is a topological Kondo insulator. Here, by treating the spin degree of freedom on an equal footing with other degrees of freedom using the parameter-free strongly-constrained and appropriately-normed (SCAN) density functional, we explore the ground-state electronic structure of SmB6. A number of competing magnetic phases lying very closely in energy are found, indicating the key role of spin fluctuations in the material. The computed band structure, crystal-field splittings in the f-electron complex, the heavy effective electron mass at the Fermi energy, and the large specific heat are all in good agreement with the corresponding experimental results. In particular, our predicted FS explains the experimentally observed bulk quantum oscillations as well as the low electrical conductivity of SmB 6 . The topological Kondo state of SmB6 is shown to be robust regardless of its magnetic configuration. The excellent performance of SCAN in heavy-fermion systems is explained in terms of its ability to treat self-interaction errors and symmetry breaking within the framework of the density functional theory. Our study provides a new approach for modeling heavy-fermion materials

36 MATERIALS SCIENCE↗

Perspective of a Dual Energy Electron Storage Ring Cooler for a Hadron Beam Cooling

A dual energy electron storage ring cooler was proposed and explored to maintain a good hadron beam quality against intra-beam scattering and all heating sources in a collider. The unique configuration of this concept is that electron beam in the low energy section extracts heat away from the hadron beam through Coulomb interaction while electron beam in the high energy section releases heat through its intrinsic synchrotron radiation damping. The early design employs a magnetized electron beam for achieving a high cooling efficiency. Recently, a non-magnetized cooling electron beam was explored in this dual energy cooler. It could meet the same cooling requirements while lowering the risks. This paper presents study results on design and optimization of a dual energy storage ring cooler and describes the trade-offs between using magnetized and non-magnetized cooling for this approach.

Lin, Fanglei↗

ARM Trajectories Data Set Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s ARM Trajectories Data Set (ARMTRAJ) Value-Added Product (VAP) provides trajectory data sets initialized at ARM deployment coordinates and configured using ARM data sets. The six trajectory data sets support aerosol, cloud, planetary boundary layer, and related research (aerosol-cloud interactions, etc.), as well as studies using ARM Aerial Facility (AAF) and tethered balloon system (TBS) measurements. Trajectory calculations use the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation atmospheric reanalysis (ERA5) data set at its highest spatial resolution (~31 km). HYSPLIT also runs at multiple initial starting locations surrounding ARM deployments (in latitude/longitude and/or vertical coordinates), facilitating an ensemble for each sample in the data sets. The ensemble mean and variability reported in ARMTRAJ improve the fidelity and provide uncertainty estimates of trajectory coordinates, thermodynamic properties, and other output fields.

54 ENVIRONMENTAL SCIENCES↗