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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 415 records · Page 23

Unveiling Swift Heavy Ion Track Morphology in Sr-Based High-Entropy Perovskites

The incorporation of multiple cations on a single lattice site in the high-entropy oxides is considered the key driving factor for modifying the known atomic-level response to energetic ion irradiation due to the presence of structural disorder; however, these effects are not well-understood yet. In this work, we present atomic-level insight into irradiation-induced nanoscale phase transformations in a perovskite-structured high-entropy oxide, Sr(Zr 0.2 Sn 0.2 Ti 0.2 Hf 0.2 Nb 0.2 )O 3 (Sr(HE)O 3 ), subjected to 774 MeV swift Xe heavy ions, where damage is dominated by inelastic ion−lattice interactions. While these ions generally are known to create nanoscale disordered channels, “ion tracks”, along the penetration direction in the material, this study shows the formation of discontinuous and partially recrystallized ion tracks in Sr(HE)O 3 . Compared to SrTiO 3 irradiated under identical energy loss conditions, the ion tracks in Sr(HE)O 3 exhibit significantly reduced diameters and a markedly different interfacial structure. Notably, the crystalline−amorphous interface in Sr(HE)O 3 shows minimal lattice distortion, confined to approximately 2−3 monolayers, in contrast to the extended disordered shell commonly observed in SrTiO 3 . Using in situ atomic-resolution electron microscopy, we further demonstrate that the amorphous/disordered regions within Sr(HE)O 3 ion tracks remain highly stable under electron irradiation, whereas tracks in SrTiO 3 readily recrystallize. This enhanced stability is attributed to the dominance of structural and chemical complexity arising from multiple B-site cations, which suppress defect migration and templated recrystallization driven by electronic excitations and local heating. Overall, this study highlights how high-entropy oxide chemistry fundamentally reshapes irradiation damage evolution, offering insights into defect formation and phase stability under extreme conditions.

36 MATERIALS SCIENCE↗

Metal–Metal Bonding Influences Hydride Reactivity in [Sn–Rh] 3+ and [Sn–Ni] 2+ Bimetallics

Heavier group 14 metal hydrides serve as key intermediates in catalytic transformations, such as hydroboration. Regenerating such intermediates via a clean hydride source like dihydrogen could provide catalytic processes with a more economical alternative to, e.g., silanes and hydridoborane reagents. Herein, we report our efforts toward this goal using a [Sn–Rh] 3+ bimetallic system with the formal Rh I center acting as a potential dihydrogen activator. Targeting the introduction of a hydride ligand to the bimetallic core, our reactivity studies have revealed a preference for the hydride ligand to be bound to the Rh center, instead of the Sn center. Finally, examination of the electronic structures of these complexes via theoretical and experimental methods has revealed the electron acceptor nature of the Rh center within the bimetallic core and offers an explanation for the localization of the hydride between metal centers.

anions↗

In Crystallo Synthesis of a Triplet Silver Nitrene

Reactive silver nitrenes are proposed intermediates in Ag-catalyzed C–H amination and olefin aziridination reactions. Synthesis and characterization of reactive Ag nitrenes has proven elusive due to fleeting lifetimes and argentophilic aggregation of Ag(I) precursors in solution. Here, we report the templated in crystallo synthesis and characterization of a mononuclear Ag nitrene supported by a tris(pyrazolyl)borate ligand. Photolytic N 2 elimination from the corresponding aryl azide complex yields a structurally defined Ag nitrene with a triplet ground state, as confirmed by crystallography, magnetic measurements, and quantum chemical calculations. This species displays both stoichiometric and catalytic nitrene transfer reactivity, enabling carbazole and indole formation from organic azides. These results leverage in crystallo templating to isolate a mononuclear triplet nitrene of Ag and provide a platform to experimentally elucidate ligand-controlled selectivity in Ag-catalyzed nitrene transfer catalysis.

Azides↗

The Role of the Toroidal Vortex in Cumulus Clouds' Entrainment and Mixing

Abstract Shallow convective clouds play a crucial role in Earth's energy budget, as they modulate the radiative transfer in the atmosphere and participate in the vertical transport of aerosols, energy, and humidity. The parameterizations representing these complex, vital players in weather and climate models are mostly based on a description of steady‐state plumes and are a source of major uncertainty. Recently, several studies have shown that buoyant thermals are inherent in atmospheric convection and contain a toroidal (ring) vortex. This work studies those vortices in growing shallow cumulus (Cu) clouds using high‐resolution (10 m) Large Eddy Simulations that resolve these vortices in much detail. Recent analysis of such data showed that small‐scale turbulent diffusion is unable to explain the large diluted portion of the cloud. Here we advocate for the important role of the Cu toroidal vortex (TV) in cloud dilution and present the complex dynamics and structure of a Cu TV. Nevertheless, since the vortex dominates the cloud's dilution, simplicity emerges when considering the cloud's lateral mass flux profile. The cloud mixing is quantified using direct flux calculations and Eulerian tracers. In addition, Lagrangian tracers are used to identify the origin of the entrained air and its thermodynamic properties. It shows that most of the air entrained by the vortex is not recycled by the vortex, yet is significantly more humid than the environment. We suggest that the development of new models describing thermals, together with their toroidal vortices, might improve cloud parameterizations in weather and climate models.

Meteorology & Atmospheric Sciences↗

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗

Leveraging public AI tools to explore systems biology resources in mathematical modeling

Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.

59 BASIC BIOLOGICAL SCIENCES↗

3D-printed micro ion trap technology for quantum information applications

Trapped-ion applications, such as in quantum information processing1, precision measurements, optical clocks and mass spectrometry, rely on specialized high-performance ion traps. The last three of these applications typically use traditional machining to customize macroscopic 3D Paul traps, whereas quantum information processing experiments usually rely on photolithographic techniques to miniaturize the traps and meet scalability requirements. Using photolithography, however, it is challenging to fabricate the complex 3D electrode structures required for optimal confinement. Here, in this work, we demonstrate a high-resolution 3D printing technology based on two-photon polymerization (2PP) that is capable of fabricating large arrays of high-performance miniaturized 3D traps. We show that 3D-printed ion traps combine the advantages, such as strong radial confinement, of traditionally machined 3D traps with on-chip miniaturization. We trap calcium ions in 3D-printed ion traps with radial trap frequencies ranging from 2 MHz to 24 MHz. The tight confinement eases ion cooling requirements and allows us to implement high-quality Rabi oscillations with Doppler cooling only. Also, we demonstrate a two-qubit gate with a Bell-state fidelity of 0.978 ± 0.012. With 3D printing technology, the design freedom is greatly expanded without sacrificing scalability and precision, so that ion trap geometries can be optimized for higher performance and better functionality.

quantum information↗

Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements

Understanding the 6-dimensional phase space distribution of particle beams is essential for optimizing accelerator performance. Conventional diagnostics such as use of transverse deflecting cavities offer detailed characterization but require dedicated hardware and space. Generative phase space reconstruction (GPSR) methods have shown promise in beam diagnostics, yet prior implementations still rely on such components. Here we present the first experimental implementation and validation of the GPSR methodology, realized by the use of standard accelerator elements including accelerating cavities and dipole magnets, to achieve complete 6-dimensional phase space reconstruction. Through simulations and experiments at the Pohang Accelerator Laboratory X-ray Free Electron Laser facility, we successfully reconstruct complex, nonlinear beam structures. Furthermore, we validate the methodology by predicting independent downstream measurements excluded from training, revealing the reconstruction closely resembling ground truth. This advancement establishes a pathway for predictive diagnostics across beamline segments while reducing hardware requirements and expanding applicability to various accelerator facilities.

Kim, Seongyeol [Pohang Univ. of Science and Techno↗

Growth Strategy of a Hybrid Yb3Rh4Sn13/LaRuSn3 Structure Type: Integrating Thermal Analysis, In Situ Diffraction, and Geometric Descriptors

Remeika phases form a versatile family of cage like intermetallics built from transition metal–centered trigonal prisms linked into three dimensional frameworks that generate two characteristic voids: a large [AX₁₂] cuboctahedral cage hosting the rare earth ion and an [XX′₁₂] icosahedral cage accommodating the tetrel atom. In this work, we present a tool driven approach for targeted solid state synthesis of Remeika phases, integrating differential scanning calorimetry, in situ neutron diffraction, and a geometric tolerance factor that quantifies rare earth size compatibility within these polyhedral cages. When combined with arc melting and metallic flux crystal growth, this framework enables prediction, verification, and isolation of specific Remeika phases. Applying this strategy, we obtain single crystals of the predicted pseudo-perovskite Fe based Remeika compound Yb0.9FeGe3.1. More broadly, this perspective highlights how coupling real time structural probes with simple geometric descriptors provides a general pathway for designing synthesis conditions and accessing potentially metastable structure types in complex intermetallic systems.

36 MATERIALS SCIENCE↗

Distinct vibrational motions promote disparate excited-state decay pathways in cofacial perylenediimide dimers

A complex interplay of structural, electronic, and vibrational degrees of freedom underpins the fate of molecular excited states. Organic assemblies exhibit a myriad of excited-state decay processes, such as symmetry-breaking charge separation (SB-CS), excimer (EX) formation, singlet fission, and energy transfer. Recent studies of cofacial and slip-stacked perylene-3,4:9,10-bis(dicarboximide) (PDI) multimers demonstrate that slight variations in core substituents and H- or J-type aggregation can determine whether the system follows an SB-CS pathway or an EX one. However, questions regarding the relative importance of structural properties and molecular vibrations in driving the excited-state dynamics remain. Here, we use a combination of two-dimensional electronic spectroscopy, femtosecond stimulated Raman spectroscopy, and quantum chemistry computations to compare the photophysics of two PDI dimers. The dimer with 1,7-bis(pyrrolidin-1′-yl) substituents (5PDI2) undergoes ultrafast SB-CS from a photoexcited mixed state, while the dimer with bis-1,7-(3′,5′-di-t-butylphenoxy) substituents (PPDI2) rapidly forms an EX state. Examination of their quantum beating features reveals that SB-CS in 5PDI2 is driven by the collective vibronic coupling of two or more excited-state vibrations. In contrast, we observe signatures of low-frequency vibrational coherence transfer during EX formation by PPDI2, which aligns with several previous studies. We conclude that key electronic and structural differences between 5PDI2 and PPDI2 determine their markedly different photophysics.

Chemistry↗

Real-time steerable frequency-stepped Doppler backscattering (DBS) system for local helicon wave electric field measurements on the DIII-D tokamak

A new frequency-stepped Doppler backscattering (DBS) system has been integrated into a real-time steerable electron cyclotron heating launcher system to simultaneously probe local background turbulence (f < 10 MHz) and high-frequency (20–550 MHz) density fluctuations in the DIII-D tokamak. The launcher allows for 2D steering (horizontally and vertically) over wide angular ranges to optimize probe location and wavenumber response. The vertical steering can be optimized during a discharge in real time. The new DBS system employs a programmable frequency synthesizer with adjustable dwell time as a source to launch either O or X-mode polarized millimeter waves. This system can step in real-time over the entire E-band frequency range (60–90 GHz). This combination of capabilities allows for the diagnosis of the complex internal spatial structure of high power (>200 kW) helicon waves (476 MHz) injected from an external antenna during helicon current drive experiments in DIII-D. Broadband density fluctuations around the helicon frequency are observed during real-time scans of measurement location and wavenumber during these experiments. Analysis indicates that these broadband high-frequency fluctuations are a result of backscattering of the DBS millimeter-wave probe beam from plasma turbulence modulated by the helicon wave. It is observed that background turbulence is effectively locally “tagged” with the helicon wave electric field, forming images of the turbulent spectrum in the overall density fluctuation spectrum that appear as high-frequency sidebands of the turbulence. These observations of background turbulence and high-frequency fluctuations open up the possibility of monitoring local helicon wave amplitude by comparing the high-frequency signal amplitude to the simultaneously measured background turbulence. In combination with the real-time measurement location and wavenumber scanning capabilities (offered by real-time frequency-stepping and steering), this allows rapid determination of the spatial distribution of the helicon wave power during steady-state plasma operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

New limit on dark photon kinetic mixing in the 0.2 – 1.2 μ eV mass range from the Dark E-field Radio experiment

We report new limits on the kinetic mixing strength of the dark photon spanning the mass range 0.21 − 1.24 μ eV corresponding to a frequency span of 50–300 MHz. The Dark E-field Radio experiment is a wideband search for dark photon dark matter. In this paper we detail changes in calibration and upgrades since our proof-of-concept pilot run. Our detector employs a wide-bandwidth E-field antenna moved to multiple positions in a shielded room, a low noise amplifier, wideband analog-to-digital converter, followed by a 2 24 -point fast Fourier transform. An optimal filter searches for signals with Q ≈ 10 6 . In nine days of integration, this system is capable of detecting dark photon signals corresponding to a kinetic mixing strength ε several orders of magnitude lower than previous limits. We find a 95% exclusion limit on ε over this mass range between 6 × 10 − 15 and 6 × 10 − 13 , tracking the complex resonant mode structure in the shielded room. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

High Pressure Synthesis of Rubidium Superhydrides

Through laser-heated diamond anvil cell experiments, we synthesize a series of rubidium superhydrides and explore their properties with synchrotron x-ray powder diffraction and Raman spectroscopy measurements, combined with density functional theory calculations. Upon heating rubidium monohydride embedded in H 2 at a pressure of 18 GPa, we form RbH 9 − I , which is stable upon decompression down to 8.7 GPa, the lowest stability pressure of any known superhydride. At 22 GPa, another polymorph, RbH 9 − II is synthesised at high temperature. Unique to the Rb-H system among binary metal hydrides is that further compression does not promote the formation of polyhydrides with higher hydrogen content. Instead, heating above 87 GPa yields RbH 5 , which exhibits two polymorphs ( RbH 5 − I and RbH 5 − II ). All of the crystal structures comprise a complex network of quasimolecular H 2 units and H − anions, with RbH 5 providing the first experimental evidence of linear H 3 − anions. Published by the American Physical Society 2025

Kuzovnikov, Mikhail A.↗

Magnetoelectric properties at the Co/AlN(0001) interface

AlN nitride is a large-band-gap polar material that has gained interest due to its ability to become ferroelectric and its compatibility with Si- and GaN-based technologies. This compound could, therefore, be a good candidate to design new artificial multiferroics if combined with a ferromagnetic electrode. For this work, we performed first-principles calculations to investigate the set up of the magnetoelectric coupling at the Co/AlN(0001) interface. Our results describe a complex interfacial atomic structure with variable local magnetic properties as a function of the atom alignment. We predict an average variation of interface spin magnetization, when the polarization is reversed, of 3.83 𝜇B nm −2 , close to the values already reported in the literature at metal/oxide interfaces. This confirms the potential of this AlN-based ferroelectric compound to be used in future voltage-controlled spintronic devices.

36 MATERIALS SCIENCE↗

Electromagnetic Modeling of Lattice Structures in Additively Manufactured Electric Machines

Recent advancements in metal additive manufacturing (AM) show great potential to revolutionize the design and manufacturing of electromagnetic components used in the field of electrical engineering. Lattice structures directly printed by AM processes typically offer better structural performance with reduced weight, such as high stiffness, surface area, elongation, energy absorption, and porosity, than the solid counterpart. This paper aims to study the electromagnetic modeling of lattice structures used in additively manufactured magnetic cores or windings for electric machines. Three dimensional (3D) electromagnetic finite element (FE) analysis with high performance computing (HPC) shows the highest fidelity in predicting the electromagnetic performance of designs with lattice structures by preserving complex geometry details. FE-based homogenization methods have also been explored to potentially speed up concept design. A case study based on an additively manufactured axial-flux permanent magnet machine with a Hilbert pattern stator validates the discussed electromagnetic modeling approaches.

Chung, Shi-Uk [ANSYS, Inc.]↗

Normalized Solar-Induced Fluorescence Responds Earlier Than Vegetation Indices to the 2019 North China Plain Drought

Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield ($Φ_F$) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and $Φ_F$ to drought need further study. This study normalized SIF as SIFn to account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFn anomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and $Φ_F$ during a 2019 drought in the North China Plain (NCP). Here, the results show SIFn provides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and $Φ_F$. In the first two weeks of drought, SIFn decreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFn outperformed other indicators, proving sensitive to early drought detection. SIFn was also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFn anomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and $Φ_F$ anomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll ($Φ_F$) are impacted by drought and influence SIF at different stages.

54 ENVIRONMENTAL SCIENCES↗

Large Language Models for the Creation and Use of Semantic Ontologies in Buildings: Requirements and Challenges

Semantic ontologies offer a formalized, machine-readable framework for representing knowledge, enabling the structured description of complex systems. In the building domain, the adoption of ontologies like the Brick schema has transformed how buildings and their systems are modeled by providing a standardized, interoperable language. However, the complexity and the steep learning curve involved in developing and querying semantic models present substantial challenges, often requiring a workforce with specialized expertise. This paper builds on our experience in investigating how Large Language Models (LLMs) can help address these challenges, focusing on their role in constructing and querying of semantic models, particularly using the Brick Schema. Our study outlines the requirements and metrics for evaluating the scalability and effectiveness of LLM-based tools, while also discussing the current challenges and limitations in developing such tools. Ultimately, this paper aims to orient research efforts as various groups experiment with diverse techniques, while enabling more effective comparison of emerging solutions and fostering collaboration across the field.

Mulayim, Ozan Baris↗

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Furthermore, although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, here we extract lessons learned and make recommendations for future research.

malware obfuscation↗