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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Measurement of Photons Emitted by High-Energy Charged Particles as Background in Single-Photon Resolving Image Sensors

This work introduces an advanced technique optimized for detecting photons generated by charged particles, leveraging Skipper charge coupled device (Skipper-CCD) image sensors. By analyzing background sources and detection efficiencies, the technique achieves strong agreement between experimental results and Cherenkov-based simulations. It also provides a robust framework for investigating secondary photon production in environments with high fluxes of ionizing particles, such as those anticipated in space-based astronomical instruments. These secondary photons present a critical challenge as background noise for next-generation single-photon resolving imagers used to study faint celestial objects. Furthermore, the method exhibits significant potential for broader applications, including exploring photon generation in various substrate materials and examining their transport through multiple interfaces.

Fernandez Moroni, Guillermo [Fermilab; Chicago U.,↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗
DOE OSTI2022

Transverse oscillating bubble enhanced laser-driven betatron X-ray radiation generation

Abstract Ultrafast high-brightness X-ray pulses have proven invaluable for a broad range of research. Such pulses are typically generated via synchrotron emission from relativistic electron bunches using large-scale facilities. Recently, significantly more compact X-ray sources based on laser-wakefield accelerated (LWFA) electron beams have been demonstrated. In particular, laser-driven sources, where the radiation is generated by transverse oscillations of electrons within the plasma accelerator structure (so-called betatron oscillations) can generate highly-brilliant ultrashort X-ray pulses using a comparably simple setup. Here, we experimentally demonstrate a method to markedly enhance the parameters of LWFA-driven betatron X-ray emission in a proof-of-principle experiment. We show a significant increase in the number of generated photons by specifically manipulating the amplitude of the betatron oscillations by using our novel Transverse Oscillating Bubble Enhanced Betatron Radiation scheme. We realize this through an orchestrated evolution of the temporal laser pulse shape and the accelerating plasma structure. This leads to controlled off-axis injection of electrons that perform large-amplitude collective transverse betatron oscillations, resulting in increased radiation emission. Our concept holds the promise for a method to optimize the X-ray parameters for specific applications, such as time-resolved investigations with spatial and temporal atomic resolution or advanced high-resolution imaging modalities, and the generation of X-ray beams with even higher peak and average brightness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Hunting extreme BL Lacertae blazars with Fermi-Large Area Telescope

ABSTRACT The emission of very-high-energy (VHE) photons ($E\ \gt\ 100\ \mathrm{GeV}$) in blazars is closely connected to the production of ultra-relativistic particles and the role of these γ-ray sources as cosmic particle accelerators. This work focuses on a selection of 22 γ-ray objects from the 2BIGB catalogue of high-synchrotron-peaked sources, which are classified as blazar candidates of uncertain type in the 4FGL-DR2 catalogue. We study these sources by means of a re-analysis of the first 10 yr of γ-ray data taken with the Fermi-Large Area Telescope, including the attenuation by the extragalactic background light. Their broad-band spectral energy distributions are also evaluated, using multiwavelength archival data in the radio, optical, and X-ray bands, in terms of one-zone synchrotron-self-Compton models, adding an external Compton component when needed. Out of this analysis, we identify 17 new extreme high-synchrotron-peaked (EHSP) candidates and compare their physical parameters with those of prototypical EHSP blazars. Finally, the resulting models are used to assess their detectability by the present and future generation of ground-based Imaging Atmospheric Cherenkov Telescopes. We find two VHE candidates within the reach of the current and next generation of Cherenkov telescopes: J0847.0−2336 and J1714.0−2029.

Nievas Rosillo, M. (ORCID:0000000283219168)↗
DOE OSTI2023

Equalizing the Pixel Response of the Imaging Photoelectric Polarimeter Onboard the IXPE Mission

The Gas Pixel Detector is a gas detector, sensitive to the polarization of X-rays, currently flying onboard the Imaging X-ray Polarimetry Explorer (IXPE)—the first observatory dedicated to X-ray polarimetry. It detects X-rays and their polarization by imaging the ionization tracks generated by photoelectrons absorbed in the sensitive volume, and then reconstructing the initial direction of the photoelectrons. The primary ionization charge is multiplied and ultimately collected on a finely pixellated ASIC specifically developed for X-ray polarimetry. The signal of individual pixels is processed independently and gain variations can be substantial, of the order of 20%. Such variations need to be equalized to correctly reconstruct the track shape, and therefore its polarization direction. The method to do such equalization is presented here and is based on the comparison between the mean charge of a pixel with respect to the other pixels for equivalent events. The method is shown to finely equalize the response of the detectors onboard IXPE, allowing a better track reconstruction and energy resolution, and can in principle be applied to any imaging detector based on tracks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A New Workflow of X-ray CT Image Processing and Data Analysis of Structural Features in Rock Using Open-Source Software

X-ray computed tomography (CT) images of rock specimens often contain artifacts which must be corrected before scientific analyses are performed. Here, we present a new workflow of automated image processing to utilize poor-quality X-ray CT scan images. The workflow runs on the open-source image analysis software and efficiently separates desired features from low-contrast scanned images. The new workflow is a two-step technique using contrast enhancement and automated feature segmentation to generate noise-free binary images. The results of binary images using the proposed workflow and using a conventional thresholding technique are analyzed to show the quality of the proposed method. The paper also presents a workflow of estimating the structural geometries of features in two and three dimensions. The results of the structural feature analyses and computational time were compared between the open-source (ImageJ) and commercial image analysis software (Bruker Computed Tomography Analyzer). The commercial software was more computationally efficient, but the task-specific macros in open-source software enabled the user-desired automation in image processing and data extraction of desired structural features of comparable quality.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Diffractive optical computing in free space

Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optical and x-ray characterization of the Daedalus ultrafast x-ray imager

The Daedalus ultrafast x-ray imager is the latest generation in Sandia’s hybrid CMOS detector family. With three frames along an identical line of sight, 1 ns minimum integration time, a higher full well than Icarus, and added features, Daedalus brings exciting new capabilities to diagnostic applications in inertial confinement fusion and high energy density science. In this work, we present measurements of time response, dynamic range, spatial uniformity, pixel cross-talk, and absolute x-ray sensitivity using pulsed optical and x-ray sources. Here, we report a measured 1.5 Me — full well, pixel sensitivity at 9.58 × 10 —7 V/e — , and an estimate of spatial uniformity at ~5% across the sensor array.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Li-ion battery design through microstructural optimization using generative AI

Lithium-ion batteries are used across various applications, necessitating tailored cell designs to enhance performance. Optimizing electrode manufacturing parameters is a key route to achieving this, as these parameters directly influence the microstructure and performance of the cells. However, linking process parameters to performance is complex, and experimental or modeling campaigns are often slow and expensive. This study introduces a fast computational optimization framework for electrode manufacturing parameters. A generative model, trained on a small dataset of microstructural images associated with different manufacturing parameters, efficiently generates representative microstructures for new parameters. This model is integrated into a Bayesian optimization loop that includes microstructure generation, characterization, and simulation, aiming to find optimal manufacturing parameters for a particular application. Significant improvement in the energy density of a 4680 cell is achieved through bespoke cell design, highlighting the importance of cell-scale normalization. The framework’s modularity allows its application to various advanced materials manufacturing scenarios.

batteries↗
DOE OSTI2022

Direct visualization and control of antiferromagnetic domains and spin reorientation in a parent cuprate

Here, we report magnetic optical second-harmonic generation (SHG) polarimetry and imaging on Sr 2 Cu 3 O 4 Cl 2 , which allows direct visualization of the mesoscopic antiferromagnetic (AFM) structure of a parent cuprate. Temperature- and magnetic-field-dependent SHG reveals large domains with 90° relative orientations that are stabilized by a combination of uniaxial magnetic anisotropy and the Earth's magnetic field. Below a temperature T R ~ 97 K, we observe an unusual 90° spin reorientation transition, possibly driven by competing magnetic anisotropies of the two copper sublattices, which swaps the AFM domain states while preserving the domain structure. This allows deterministic switching of the AFM states by thermal or laser heating. Near T R , the domain walls become exceptionally responsive to an applied magnetic field, with the Earth's field sufficient to completely expel them from the crystal. Our findings unlock opportunities to study the mesoscopic AFM behavior of parent cuprates and explore their potential for AFM technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Time and space resolved diagnostics for plasma thermal-chemical instability of fuel oxidation in nanosecond plasma discharges

Here, an instability in a nanosecond pulsed dielectric barrier discharge plasma occurring in methane–oxygen–argon mixtures is experimentally observed and measured by 1D time-resolved in situ electric field measurements. This instability, which seems to be created by the positive feedback between plasma kinetics and plasma-assisted low temperature fuel oxidation, is studied using electric field induced second harmonic generation and direct ICCD imaging. The rapid formation of streamers from an originally uniform discharge appears to be caused by the chemical kinetics of plasma-assisted low temperature methane oxidation, which may be resulting in a new type of plasma instability: a thermal-chemical instability. The results also revealed that the occurrence of this possible thermal-chemical instability in a reactive flow drastically changes the plasma properties by forming multiple secondary discharges and possibly leads to micron-sized non-uniform electric distributions. Single shot uncalibrated measurements of the electric field of the micron sized streamers appears to show much greater strengths than the average electric field. Furthermore, one-dimensional data analysis shows the positive feedback loop between the streamers and the low temperature plasma assisted oxidation chemistry in the plasma thermal-chemical instability. The present finding advances the understanding plasma instability growth and provides a new way to control plasma uniformity in plasma-assisted combustion and plasma fuel reforming.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Liquid Metals for Advanced Batteries: Recent Progress and Future Perspective

ABSTRACT The shift toward sustainable energy has increased the demand for efficient energy storage systems to complement renewable sources like solar and wind. While lithium‐ion batteries dominate the market, challenges such as safety concerns and limited energy density drive the search for new solutions. Liquid metals (LMs) have emerged as promising materials for advanced batteries due to their unique properties, including low melting points, high electrical conductivity, tunable surface tension, and strong alloying tendency. Enabled by the unique properties of LMs, four key scientific functions of LMs in batteries are highlighted: active materials, self‐healing, interface stabilization, and conductivity enhancement. These applications can improve battery performance, safety, and lifespan. This review also discusses current challenges and future opportunities for using LMs in next‐generation energy storage systems. image

Zheng, Tianrui [Materials Science and Engineering ↗
DOE OSTI2021

A microstructure-based modeling approach to predict the mechanical properties of Zr alloy with hydride precipitates

In nuclear reactors, hydrides can form in fuel cladding due to hydrogen absorption in ZIrcaloy and cause embrittlement. This work presents a microstructure-based finite element model to predict the stress-strain response of Zircaloy containing hydrides. Quantitative microstructural details extracted from scanning electron microscopy (SEM) images were used to generate heterogeneous microstructures including the morphology and spatial distribution of hydrides. The constitutive material model for zircaloy in this study is based on crystal plasticity theory which considers the hexagonal close-packed (HCP) atomic structure of Zircaloy material. The hydrides were modeled as brittle material along with a damage model. Hydride formation inside the zircoloy matrix results in residual stress. This phenomenon is also captured in this model. A parametric study has been conducted to understand the effect of volume fraction, orientation, and lamellae thickness of the hydride phase on the mechanical properties of the overall material.

Kulkarni, Shank S.↗
DOE OSTI2026

Microbial spies and bloggers: programming cells to convert environmental information into discernible signals

Microbes regulate their dynamic behaviors using the chemical and physical characteristics of their environment. The ability of microbes to continuously convert this physicochemical information into biochemical information and to use organic matter in the environment as a power source makes these organisms attractive as chassis for building sensors. However, most biosensors have severe limitations when considering applications in hard-to-image settings like soils, sediments, and wastewater. Emerging technologies at the interface of biomolecular design, microbiome engineering, and synthetic biology offer new tools to program cells and communities as biosensors for these settings. Here, in this review, we describe innovations in biosensor outputs that are enabling new applications in complex environments, including reporters that are read out using electrochemical, gas chromatography, hyperspectral imaging, and next-generation sequencing methods. We also discuss computational advances that are accelerating the diversification of sensing components by mining metagenomics data for new transcriptional regulators and by designing allosteric protein switches that directly regulate reporter outputs using analytes. We highlight emerging opportunities for programming undomesticated microbes in communities to function as distributed sensors in the environment. Finally, we discuss the need for responsible biosensor development and to modernize regulatory frameworks to support evidence-based assessment of environmental biosensors.

analyte↗
DOE OSTI2020

Physics-informed semantic inpainting: Application to geostatistical modeling

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. In this work, to overcome this limitation, we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Strong Correlation Between Superconductivity and Ferromagnetism in an Fe-Chalcogenide Superconductor

The interplay among topology, superconductivity, and magnetism promises to bring a plethora of exotic and unintuitive behaviors in emergent quantum materials. The family of Fe-chalcogenide superconductors FeTe x Se 1–x are directly relevant in this context due to their intrinsic topological band structure, high-temperature superconductivity, and unconventional pairing symmetry. Despite enormous promise and expectation, the local magnetic properties of FeTe x Se 1–x remain largely unexplored, which prevents a comprehensive understanding of their underlying material properties. Exploiting nitrogen vacancy (NV) centers in diamond, here we report nanoscale quantum sensing and imaging of magnetic flux generated by exfoliated FeTe x Se 1–x flakes, demonstrating strong correlation between superconductivity and ferromagnetism in FeTe x Se 1–x . The coexistence of superconductivity and ferromagnetism in an established topological superconductor opens up new opportunities for exploring exotic spin and charge transport phenomena in quantum materials. The demonstrated coupling between NV centers and FeTe x Se 1–x may also find applications in developing hybrid architectures for next-generation, solid-state-based quantum information technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Characterization of high-flux cadmium zinc telluride detectors coupled to MM-PAD readout ASICs for high-flux X-ray applications

High-flux cadmium zinc telluride (HF-CZT) is a promising material for next-generation X-ray imaging due to its high stopping power, wide bandgap, and improved resistance to radiation-induced polarization [1]. We bonded 2 mm thick HF-CZT sensors with Cornell's Mixed-Mode Pixel Array ASICs, which combine analog charge integration and in-pixel digital counting to achieve single X-ray sensitivity, large full well capacity, and frame rates up to 10 kHz [2,3]. Detector characterization showed stable electron collection above 150 V bias, while hole collection remained limited by trapping even at 700 V. Temporal studies revealed non-negligible afterglow, with an unexpected increase in afterglow at higher biases, which was reduced by infrared illumination, suggesting that injected carriers reduce trapping near contact interfaces. Polarization measurements confirmed improved stability compared to conventional CZT, though polarization effects persisted, particularly with hole collection, with up to 5% signal loss and spatial distortion at high doses.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Search for extended Lyman-α emission around 9k quasars at z = 2–3

ABSTRACT Enormous Lyα nebulae (ELANe) around quasars have provided unique insights into the formation of massive galaxies and their associations with super-massive black holes since their discovery. However, their detection remains highly limited. This paper introduces a systematic search for extended Lyα emission around 8683 quasars at z = 2.34–3.00 using a simple but very effective broad-band gri selection based on the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program. Although the broad-band selection detects only bright Lyα emission (≳ 1 × 10−17 erg s−1cm−2 arcsec−2) compared with narrow-band imaging and integral field spectroscopy, we can apply this method to far more sources than such common approaches. We first generated continuum g-band images without contributions from Lyα emission for host and satellite galaxies using r- and i-bands. Then, we established Lyα maps by subtracting them from observed g-band images with Lyα emissions. Consequently, we discovered extended Lyα emission (with masked area >40 arcsec2) for 7 and 32 out of 366 and 8317 quasars in the Deep and Ultra-deep (35 deg2) and Wide (890 deg2) layers, parts of which may be potential candidates of ELANe. However, none of them seem to be equivalent to the largest ELANe ever found. We detected higher fractions of quasars with large nebulae around more luminous or radio-loud quasars, supporting previous results. Future applications to the forthcoming big data from the Vera C. Rubin Observatory will help us detect more promising candidates. The source catalogue and obtained Lyα properties for all the quasar targets are accessible as online material.

79 ASTRONOMY AND ASTROPHYSICS↗