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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Production of neutron-rich isotopes for 𝑍 ≥ 98 in the 238 U + 248 Cm reaction

Multinucleon transfer (MNT) reactions in actinide systems are a promising method to synthesize transuranium neutron-rich elements. Appropriate theoretical approaches are needed to understand the mechanisms behind MNT. We employ a microscopic approach to calculate neutron-rich isotope production in the reaction 238 U + 248 Cm system. Here, the stochastic mean-field (SMF) approach is used to calculate the primary cross sections in MNT reactions based on the quasifission and inverse quasifission processes, and a statistical de-excitation model with GEMINI ++ code to calculate the secondary fragment cross sections. The calculated cross sections using SMF and GEMINI ++ explain available experimental results for the 238 U + 248 Cm system at 𝐸 c.m. = 898.7 MeV energy. This shows the effectiveness and applicability of the quantal diffusion approach, based on the SMF theory, in heavy-ion collisions. Production of transuranium neutron-rich elements with a proton number up to 𝑍=101 is obtained with sizable cross sections. Theoretical results calculated for the 𝑍=102–105 region, for which there are no experimental data, show that the cross-section values would be lower than the microbarn level. SMF theory does not contain any adjustable parameters other than the standard parameters of the energy density functional used in the TDHF theory and is an important approach for the microscopic understanding of reaction mechanisms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Flux Hypothesis for Odd Transport Phenomena

Onsager's regression hypothesis makes a fundamental connection between macroscopic transport phenomena and the average relaxation of spontaneous microscopic fluctuations. This relaxation, however, is agnostic to odd transport phenomena, in which fluxes run orthogonal to the gradients driving them. To account for odd transport, we generalize the regression hypothesis, postulating that macroscopic linear constitutive laws are, on average, obeyed by microscopic fluctuations, whether they contribute to relaxation or not. From this "flux hypothesis," Green-Kubo and reciprocal relations follow, elucidating the separate roles of broken time-reversal and parity symmetries underlying various odd transport coefficients. As an application, we derive and verify the Green-Kubo relation for odd collective diffusion in chiral active matter, first in an analytically tractable model and subsequently through molecular dynamics simulations of concentrated active spinners.

Hargus, Cory↗
DOE OSTI2025

Exploring a New Regime of Molecular Orbital Physics in 4⁢d Cluster Magnets with Resonant Inelastic X-Ray Scattering

Molecular orbital systems with clusters of heavy transition metal (TM) ions are one of the most important classes of model materials for studying the interplay between local physics and effects of itinerancy. Despite a large number of candidates identified in the family of 4⁢d TM materials, an understanding of their physics from competing microscopic energy scales is still missing. Here, we bridge this gap by reporting the first resonant inelastic x-ray scattering (RIXS) measurement on a well-known series of Ru cluster magnets with a 6⁢H-perovskite structure Ba 3 ⁢M⁢Ru 2 ⁢O 9 (M 3+ = In 3+ , Y 3+ , La 3+ ) comprised of Ru dimers. In addition to providing a microscopic explanation for their anomalous magnetic properties, our RIXS measurements combined with theoretical modeling uncover a new regime of molecular orbital physics where the combined effect of large hopping and small spin-orbit coupling results in highly fragile electronic states in the Ru-dimer compounds directly manifested as an abrupt change in the RIXS spectrum accompanying a tiny change in the local structure tuned by the M-site ion. This unique combination of energy scales found only in the 4⁢d but not the 5⁢d cluster magnets highlights the value of these materials as a new platform for studying quantum phase transition involving molecular orbitals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Phases of Theories with ℤ 𝑁 1-Form Symmetry, and the Roles of Center Vortices and Magnetic Monopoles

We analyze the phases of theories having a microscopic ℤ 𝑁 1-form symmetry, starting with a topological BF theory and deforming it so that only the microscopic symmetry is preserved. These theories have a well-defined notion of confinement, prototypical examples being pure SU⁡(𝑁) and ℤ 𝑁 gauge theories in the continuum and on the lattice. Our analysis shows that the generic phases are in 𝑑 = 2, only the confined phase; in 𝑑 = 3, both the confined phase and the topological BF phase; and in 𝑑 = 4, the confined phase, the topological BF phase, and a Coulomb phase. We construct a ℤ𝑁 lattice gauge theory with a deformation that, surprisingly, produces up to (𝑁−1) photons. We give an interpretation of these findings in terms of the behaviors of two competing drivers of confinement—magnetic monopoles and center vortices—and conclude that proliferation of center vortices is necessary but insufficient for confinement, while proliferation of magnetic monopoles is both necessary and sufficient.

Color confinement↗
DOE OSTI2025

Weak Ferromagnetism in Altermagnets from Alternating 𝑔-Tensor Anisotropy

Altermagnets are magnetic materials with antiferromagnetic spin ordering but exhibit ferromagnetic properties. Understanding the microscopic origin of the latter is a central problem. Ferromagnetlike properties such as the anomalous Hall effect are linked with weak ferromagnetism, whose microscopic origin in altermagnets remains unclear however. We show theoretically that the alternating 𝑔-tensor anisotropy in altermagnets can induce weak orbital ferromagnetism even when the Dzyaloshinskii-Moriya interaction is forbidden. We demonstrate this mechanism to explain weak ferromagnetism for both collinear and noncollinear spin altermagnets. Our findings provide new insights into the origin of weak ferromagnetism and suggest orbital-based ways for manipulating magnetic configurations in altermagnets.

Altermagnetism↗
DOE OSTI2025

First-principles electron-phonon interactions and polarons in the parent cuprate La 2 CuO 4

Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon ( e -ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here, we study e -ph interactions and polarons in a prototypical parent (undoped) cuprate, La 2 CuO 4 (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with the band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e -ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e -ph coupling to hole states. These modes consist of bond stretching and bond bending in the Cu-O plane as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e -ph coupling, exhibit broad quasiparticle peaks with a small spectral weight ( Z ≈ 0.25 ) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e -ph coupling distribution function not explained by existing models. These results show that the universal strong e -ph coupling found experimentally in doped lanthanum cuprates is also present in the parent compound, and elucidate its microscopic origin. Published by the American Physical Society 2025

Chang, Benjamin K. (ORCID:0000000313049324)↗
DOE OSTI2024

Acceptor-Induced Bulk Dielectric Loss in Superconducting Circuits on Silicon

The performance of superconducting quantum circuits is primarily limited by dielectric loss due to interactions with two-level systems (TLSs). State-of-the-art circuits with engineered material interfaces are approaching a limit where dielectric loss from bulk substrates plays an important role. However, a microscopic understanding of dielectric loss in crystalline substrates is still lacking. In this work, we show that boron acceptors in silicon constitute a TLS bath that leads to an energy dissipation channel for superconducting circuits. We discuss how the electronic structure of boron acceptors leads to an effective TLS response in silicon. We sweep the boron concentration in silicon and demonstrate the bulk dielectric loss limit from boron acceptors. We show that boron-induced dielectric loss can be reduced in a magnetic field due to the spin-orbit structure of boron. This work provides the first detailed microscopic description of a TLS bath for superconducting circuits and demonstrates the need for ultrahigh-purity substrates for next-generation superconducting quantum processors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Thermal nature of confining strings

We investigate the quantum statistical properties of the confining string connecting a static fermion-antifermion pair in the massive Schwinger model. By analyzing the reduced density matrix of the subsystem located in between the fermion and antifermion, we demonstrate that as the interfermion separation approaches the string-breaking distance, the overlap between the microscopic density matrix and an effective thermal density matrix exhibits a pronounced, narrow peak, approaching unity at the onset of string breaking. This behavior reveals that the confining flux tube evolves toward a genuinely thermal state as the separation between the charges grows, even in the absence of an external heat bath. In other words, one cannot tell whether a reduced state of the subsystem arises from a surrounding heat bath or from entanglement with the rest of the system. The entanglement spectrum near the critical string-breaking distance exhibits a rapid transition from the dominance of a single state describing the confining electric string towards a strongly entangled state containing virtual fermion-antifermion pairs. Our findings establish a quantitative link between confinement, entanglement, and emergent thermality, and suggest that string breaking corresponds to a microscopic thermalization transition within the flux tube.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Spin Stripes and Superconductivity in Bilayer Nickelates

The bilayer nickelate La3Ni2O7 has recently emerged as a high-temperature superconductor with unusual spin stripe order in its ambient pressure phase. We propose a microscopic Hamiltonian that faithfully reflects the crystalline symmetry of this system, with the primary aim of addressing its unconventional magnetism at ambient pressure. Using state-of-the-art density matrix renormalization group calculations, we show that (π/2,π/2) spin stripe order arises in our model at sizable Hund's coupling JH from a hidden quasi-one-dimensionality and persists over a range of electron concentrations. In the more symmetric high-pressure regime, our model exhibits enhanced interlayer pairing tendencies when the interlayer antiferromagnetic coupling J⊥ becomes sufficiently large. Our results provide a microscopic origin of the diagonal spin stripes and identify Hund's coupling JH and interlayer coupling J⊥ as key ingredients governing magnetic order and pairing tendencies in La3Ni2O7.

Wang, Hao-Xin [The Chinese University of Hong Kong↗
DOE OSTI2025

Machine-learning-informed scattering correlation analysis of sheared colloids

We have carried out theoretical analysis, Monte Carlo simulations and machine-learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particles from coherent scattering intensity. Both monodisperse and polydisperse dispersions of colloids were created and underwent a rearrangement consisting of an affine simple shear and non-affine rearrangement using the Monte Carlo method. We calculated the coherent scattering intensity of the dispersions and the correlation function of intensity before and after the rearrangement and generated a large data set of angular correlation functions for varying system parameters, including number density, polydispersity, shear strain and non-affine rearrangement. Singular value decomposition of the data set shows the feasibility of machine-learning inversion from the correlation function for the polydispersity, shear strain and non-affine rearrangement using only three parameters. A Gaussian process regressor is then trained on the data set and can retrieve the affine shear strain, non-affine rearrangement and polydispersity with relative errors of 3%, 1% and 6%, respectively. Altogether, our model provides a framework for quantitative studies of both steady and non-steady microscopic dynamics of colloidal dispersions using coherent scattering methods.

Gaussian process regression↗
DOE OSTI2024

Thermomechanical Degradation of Sintered Copper Under High-Temperature Thermal Shock

The need for reliable bonded interface materials is critical to realize the performance benefits of wide-bandgap devices in power electronics modules, especially in operating temperatures greater than 150 degrees C. In this paper, we investigate the thermomechanical performance of sintered copper (Cu) as a large-area attachment, bonded between Cu baseplates and active-metal-bonded substrates, under accelerated thermal shock (-40 degrees C to 200 degrees C) conditions. In the fabrication phase of the samples, we used different stencil patterns and found that the grid and stripe patterns resulted in a better outgassing of the residual organics during the sintering process, thereby ensuring a substantially improved bond quality compared to a full-area print. The paste consisted of Cu microflakes, and we performed sintering using a Budatec SP300 sintering press at 275 degrees C with 15 MPa of bonding pressure for 5 minutes in a nitrogen atmosphere. Under accelerated experiments, we monitored the degradation of the sintered Cu bond in the samples through C-mode scanning acoustic microscope (C-SAM) images. To quantify the defect percentage in C-SAM images, we investigated image denoising techniques to exclude the pattern prints. Finally, we cross-sectioned a sample and obtained scanning electron microscope images, which revealed adhesive fracture as the dominant failure mechanism.

ADVANCED PROPULSION SYSTEMS,INORGANIC, ORGANIC, PH↗
DOE OSTI2025

Real-Twin

Real-Twin is a unified, model-agnostic scenario generation tool designed to streamline and standardize the evaluation of emerging mobility technologies. It provides an end-to-end framework that includes robust workflows, integrated tools, and comprehensive metrics to generate, calibrate, and benchmark microscopic traffic simulation scenarios across multiple platforms. Key Features of Real-Twin include: - Unified Scenario Generation: generate transferable, simulation-ready scenarios from heterogeneous data sources using a consistent workflow. - Automated Calibration Workflow: bridges simulation and real-world data, minimizing manual effort and making traffic simulation more accessible to researchers and engineers. - Model-Agnostic Compatibility: supports SUMO, VISSIM, and AIMSUN for cross-platform scenario generation and benchmarking. Enables reliable comparisons and reproducibility across different simulation tools. - Consistent Scenarios across Different Simulators: generate comparable simulation scenarios across different microscopic traffic simulators, providing users the ability to conduct benchmarking and cross-validation that are crucial for ensuring the reliability and reproducibility of simulation results. - Emerging Technology Support: includes a scenario database and pipeline for studying autonomous vehicles (AVs), with planned extensions to CAVs, EVs, and other advanced technologies.

Wang, Chieh (Ross) [Oak Ridge National Laboratory ↗
DOE OSTI2024

Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to enhancements in image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Synthesis and characterization of properties of (Tb1/3Mo2/3)2AlC polycrystalline

When molecules, atoms, or ions are grouped in a highly ordered microscopic structure to form a crystal lattice that stretches in all directions, the result is a solid material known as a crystal or crystalline solid. They are arranged in a highly ordered microscopic structure to form a crystal lattice. The smallest group of particles in material that constitutes this repeating pattern is unit cell of the structure. The unit cell is a repeating unit formed by the vectors spanning the points of a lattice.

Bretana, Alex↗
DOE OSTI2024

Theory Manual for the Reaction Summary Module- SUMMAR

The post-processing module of DIF3D called SUMMAR was created to produce commonly used reactor engineering outputs. It is a optional output option for DIF3D and provides alternative summarizes of power, flux, and neutron balance over the user specified regions and areas. It can also provide isotope-wise microscopic and macroscopic reaction rates along with the effective one-group microscopic cross sections needed for solving the Bateman equations. This report serves as both the theory manual, user guide, and software verification of the SUMMAR module. The methodology and equations for all SUMMAR calculated parameters are presented here and discussed. Then the verification work, based upon simple DIF3D models, is presented to validate the derived equations. The SUMMAR inputs and outputs are detailed and examples are given to demonstrate it.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

FY25 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and image analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and cracks in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or, in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, the identification of potential cracks was prioritized for the past several years at the request of program leadership.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗