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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Localization of fluorescent targets in deep tissue with expanded beam illumination for studies of cancer and the brain

Imaging fluorescence through millimeters or centimeters of tissue has important in vivo applications, such as guiding surgery and studying the brain. Often, the important information is the location of one of more optical reporters, rather than the specifics of the local geometry, motivating the need for a localization method that provides this information. We present an optimization approach based on a diffusion model for the fast localization of fluorescent inhomogeneities in deep tissue with expanded beam illumination that simplifies the experiment and the reconstruction. We show that the position of a fluorescent inhomogeneity can be estimated while assuming homogeneous tissue parameters and without having to model the excitation profile, reducing the computational burden and improving the utility of the method. We perform two experiments as a demonstration. First, a tumor in a mouse is localized using a near infrared folate-targeted fluorescent agent (OTL38). This result shows that localization can quickly provide tumor depth information, which could reduce damage to healthy tissue during fluorescence-guided surgery. Second, another near infrared fluorescent agent (ATTO647N) is injected into the brain of a rat, and localized through the intact skull and surface tissue. This result will enable studies of protein aggregation and neuron signaling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

High‐Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning‐Augmented Diffusion Model

Terahertz (THz) metamaterials with high‐figure‐of‐merit (high‐FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low‐asymmetry geometric structures, leaving high‐asymmetry designs largely unexplored due to the inefficiency of trial‐and‐error‐based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data‐driven nature. Here, a novel prior knowledge‐guided generative model augmented by a physics‐constrained active learning mechanism to design high‐asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high‐FoM THz resonance and generates new high‐asymmetry structures. To mitigate the limited number of classical structures, the generated high‐asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high‐asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning‐based metamaterial design. The proposed scheme for generating high‐asymmetry structures provides a new effective and efficient solution for high‐FoM resonance, expanding applications in high‐sensitivity THz metadevices.

diffusion model↗
DOE OSTI2020

IMAGINE: The neutron protein crystallography beamline at the high flux isotope reactor

IMAGINE is a high intensity, quasi-Laue neutron crystallography beamline developed at the 85 MW High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). This state-of-the-art facility for neutron-diffraction enables neutron protein structures to be determined at or near atomic resolutions from crystals with volumes of < 1 mm 3 and unit cell edges of < 150 Å. The beamline features include elliptical focusing mirrors that deliver neutrons into a 2.0 × 3.2 mm 2 focal spot at the sample position, and variable short and long wavelength cutoff optics that provide automated exchange between multiple wavelength configurations. The beamline is equipped with a single-axis goniometer, neutron-sensitive cylindrical image plate detector and room temperature and cryogenic sample environments. This article describes the beamline components, the diffractometer and the data collection and data analysis protocols that are used, and outlines the protein deuteration, crystallization and conventional crystallography capabilities that are available to users at ORNL's neutron facilities. We also present examples of the scientific questions being addressed at this beamline and highlight important findings in enzyme chemistry that have been made possible by IMAGINE.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Simulation study for particle identification with the dRICH of the ePIC experiment at the EIC

The dual-radiator Imaging Cherenkov detector (dRICH), employing an aerogel and a gas radiator, is a key component of the forward particle identification system for the ePIC experiment at the Electron-Ion Collider (EIC). This study evaluates the dRICH performance using Geant4 simulations in the context of the global ePIC simulation stack, focusing on the optimization of the aerogel radiator and the impact of sensor noise. We compare two aerogel configurations: the initial design (n = 1.019) and the current default (n = 1.026). The latter, characterized by improved optical properties and a higher refractive index, demonstrates enhanced π/K separation at high momenta, effectively extending the operational overlap with the C 2 F 6 gas radiator (n = 1.00076 at 25 °C, as implemented in the simulation software). Additionally, the study investigates the impact of Silicon Photomultiplier (SiPM) dark noise, showing that a 300 kHz noise rate per 3mm x 3mm channel leads to a moderate reduction (approximately 1.5 GeV/c) in the 3σ separation threshold. These results validate the current dRICH design and quantify the purity levels achievable for both radiators under expected experimental conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Scalable ultrafast epitaxy of large-grain and single-crystal II-VI semiconductors

A general problem for semiconductor applications is that very slow deposition on expensive single-crystal substrates yields high crystalline quality with excellent electro-optical properties, but at prohibitive costs and throughput for many applications. In contrast, rapid deposition on inexpensive substrates or nanocrystalline films yields low costs, but comparatively inferior crystallinity, carrier transport, and recombination. Here, we present methods to deposit single-crystal material at rates 2-3 orders of magnitude faster than state-of-the-art epitaxy with low-cost methods without compromising crystalline or electro-optical quality. For example, single-crystal CdTe and CdZnTe films that would take several days to grow by molecular-beam epitaxy are deposited in 8 minutes by close-spaced sublimation, yet retain the same crystalline quality measured by X-ray diffraction rocking curves. The fast deposition is coupled with effective n- and p-type in-situ doping by In, P, and As. The epitaxy can be extended to nanocrystalline substrates. For example, we recrystallize thin CdTe films on glass to deposit large grains with low defect density. The results provide new research paths for photovoltaics, detectors, infrared imaging, flexible electronics, and other applications.

14 SOLAR ENERGY↗
DOE OSTI2021

RapidEELS: machine learning for denoising and classification in rapid acquisition electron energy loss spectroscopy

Recent advances in detectors for imaging and spectroscopy have afforded in situ, rapid acquisition of hyperspectral data. While electron energy loss spectroscopy (EELS) data acquisition speeds with electron counting are regularly reaching 400 frames per second with near-zero read noise, signal to noise ratio (SNR) remains a challenge owing to fundamental counting statistics. In order to advance understanding of transient materials phenomena during rapid acquisition EELS, trustworthy analysis of noisy spectra must be demonstrated. In this study, we applied machine learning techniques to denoise high frame rate spectra, benchmarking with slower frame rate “ground truths”. The results provide a foundation for reliable use of low SNR data acquired in rapid, in-situ spectroscopy experiments. Such a tool-set is a first step toward both automation in microscopy as well as use of these methods to interrogate otherwise poorly understood transformations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High resolution >40 keV x-ray radiography using an edge-on micro-flag backlighter at NIF-ARC

Radiography of low-contrast features in high-density materials evolving on a nanosecond timescale requires a bright photon source in the tens of keV range with high temporal and spatial resolution. One application for sources in this category is the study of dynamic material strength in samples compressed to Mbar pressures at the National Ignition Facility, high-resolution measurements of plastic deformation under conditions relevant to meteor impacts, geophysics, armor development, and inertial confinement fusion. Here, we present radiographic data and the modulation transfer function (MTF) analysis of a multi-component test object probed at ~100 keV effective backlighter energy using a 5 μm-thin dysprosium foil driven by the NIF Advanced Radiographic Capability (ARC) short-pulse laser (~2 kJ, 10 ps). The thin edge of the foil acts as a bright line-projection source of hard x rays, which images the test object at 13.2× magnification into a filtered and shielded image plate detector stack. The system demonstrates a superior contrast of shallow (5 μm amplitude) sinusoidal ripples on gold samples up to 90 μm thick as well as enhanced spatial and temporal resolution using only a small fraction of the laser energy compared to an existing long-pulse-driven backlighter used routinely at the NIF for dynamic strength experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Characterisation of signal-induced noise in Hamamatsu R11265 Multianode Photomultiplier Tubes

Signal-induced noise is observed in Hamamatsu R11265 Multianode Photomultiplier Tubes, manifesting up to several microseconds after the single photoelectron response signal and localised in specific anodes. The mean number of noise pulses varies between devices, and shows significant dependence on the applied high-voltage. The characterisation of this noise and the mitigation strategies to perform optimal single-photon counting at 40 MHz, as required by the LHCb Ring-Imaging Cherenkov detectors, are reported.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

VitroJet: new features and case studies

Single-particle cryo-electron microscopy has become a widely adopted method in structural biology due to many recent technological advances in microscopes, detectors and image processing. Before being able to inspect a biological sample in an electron microscope, it needs to be deposited in a thin layer on a grid and rapidly frozen. The VitroJet was designed with this aim, as well as avoiding the delicate manual handling and transfer steps that occur during the conventional grid-preparation process. Since its creation, numerous technical developments have resulted in a device that is now widely utilized in multiple laboratories worldwide. It features plasma treatment, low-volume sample deposition through pin printing, optical ice-thickness measurement and cryofixation of pre-clipped Autogrids through jet vitrification. This paper presents recent technical improvements to the VitroJet and the benefits that it brings to the cryo-EM workflow. A wide variety of applications are shown: membrane proteins, nucleosomes, fatty-acid synthase, Tobacco mosaic virus, lipid nanoparticles, tick-borne encephalitis viruses and bacteriophages. These case studies illustrate the advancement of the VitroJet into an instrument that enables accurate control and reproducibility, demonstrating its suitability for time-efficient cryo-EM structure determination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

HSolo v.1.0

SAND2021-11023 O HSolo is a library for estimating similarity, affine, projective homographies from matches produced by image feature detectors such as SURF and SIFT. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Gonzales, Antonio↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2024

Quantum Vision Transformers for Quark–Gluon Classification

We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large Hadron Collider, presenting the architecture as a potential solution. In particular, we evaluate our method by applying the model to multi-detector jet images from CMS Open Data. The goal is to distinguish quark-initiated from gluon-initiated jets. We successfully train the quantum model and evaluate it via numerical simulations. Using this approach, we achieve classification performance almost on par with the one obtained with the completely classical architecture, considering a similar number of parameters.

Comajoan Cara, Marçal (ORCID:0009000126263752)↗
DOE OSTI2023

Advanced Mixed-Mode Pixel Array Detector for X-rays Above 20 keV (Final Report)

The objective of the work reported upon was to perform research and development and test advanced prototype imaging x-ray detectors needed to meet pressing scientific research needs at U.S. x-ray synchrotron user facilities. The high x-ray brightness of the Nation’s x-ray synchrotron sources in the DOE National Laboratories holds promise to advance science and technology in a broad range of fundamental and applied disciplines, including physics, chemistry, biomedical areas and engineering. At present, progress in many areas is being limited not by the x-ray source but by the lack of imaging x-ray detectors capable of meeting experimental needs. Specifically, there is pressing need for imaging detectors with high dynamic range, low noise, high frame rates and the ability to capture x-ray images with x-rays of energy > 20 keV. This Final Technical Report summarizes progress made at Cornell University on novel integrating x-ray detectors that address these characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Joint Roman Space Telescope and Rubin Observatory Synthetic Wide-Field Imaging Survey

We present and validate 20 deg(2) of overlapping synthetic imaging surveys representing the full depth of the Nancy Grace Roman Space Telescope High-Latitude Imaging Survey (HLIS) and 5 yr of observations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The two synthetic surveys are summarized, with reference to the existing 300 deg(2) of LSST simulated imaging produced as part of Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2). Both synthetic surveys observe the same simulated DESC DC2 universe. For the synthetic Roman survey, we simulate for the first time fully chromatic images along with the detailed physics of the Sensor Chip Assemblies derived from lab measurements using the flight detectors. The simulated imaging and resulting pixel-level measurements of photometric properties of objects span a wavelength range of similar to 0.3-2.0 mu m. We also describe updates to the Roman simulation pipeline, changes in how astrophysical objects are simulated relative to the original DC2 simulations, and the resulting simulated Roman data products. We use these simulations to explore the relative fraction of unrecognized blends in LSST images, finding that 20-30 per cent of objects identified in LSST images with i-band magnitudes brighter than 25 can be identified as multiple objects in Roman images. These simulations provide a unique testing ground for the development and validation of joint pixel-level analysis techniques of ground- and space-based imaging data sets in the second half of the 2020s - in particular the case of joint Roman-LSST analyses.

gravitational lensing↗
DOE OSTI2021

Detector thickness effects on nanosecond-gated imager response

Hybrid CMOS multi-frame imagers with exposure times down to ~2 ns have made significant impacts in high energy density physics and inertial confinement fusion research. The detector thickness is a key parameter in both detector quantum efficiency and temporal response. The Icarus hybrid CMOS imager has been fabricated with Si detector thicknesses of 8, 25, and 100 µm. The temporal response of imaging sensors with exposure time down to 2 ns has been examined and compared to directly measured photodiode current. The 100-μm thick variant displays extended features related to charge carrier collection and is more susceptible to field collapse. Here, we also demonstrate charge collection time effects on spatial response.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Gaia: segmented germanium detector for high-energy X-ray fluorescence and spectroscopic imaging

We present Gaia, a monolithic array of 96 high-purity germanium pixel detectors integrated with a custom low-noise application-specific integrated circuit (ASIC) and a field-programmable gate array (FPGA)-based data acquisition system. The sensor operates at ∼100 K using a commercial closed-cycle cryocooler, with the in-vacuum electronics thermally isolated from the cold finger to ensure thermal stability. The system demonstrates an average energy resolution of 711 eV at 122 keV, measured using a 57 Co source, and 253 eV at 5.89 keV, measured with 55 Fe across all channels. The readout architecture incorporates a high-performance FPGA paired with a dual-core ARM processor, forming a complete embedded Linux-based computing platform. Communication between the processor and FPGA is handled via memory-mapped I/O, and data are streamed over high-speed gigabit Ethernet. A full-scale 384-pixel Gaia detector, based on this 96-element module, is currently under fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Detector Tilt Considerations in Bragg Coherent Diffraction Imaging: A Simulation Study

This paper addresses the three-dimensional signal distortion and image reconstruction issues in X-ray Bragg coherent diffraction imaging (BCDI) in the event of a general non-orthogonal orientation of the area detector with respect to the diffracted beam. Growing interest in novel BCDI adaptations at fourth-generation synchrotron light sources has necessitated improvisations in the experimental configuration and the subsequent data analysis. One such possibly unavoidable improvisation that is envisioned in this paper is a photon-counting area detector whose face is tilted away from the perpendicular to the Bragg-diffracted beam during the acquisition of the coherent diffraction signal. We describe a likely circumstance in which one would require such a detector configuration, along with the experimental precedent at third-generation synchrotrons. Using physically accurate diffraction simulations from synthetic scatterers in the presence of such tilted detectors, we analyze the general nature of the observed signal distortion qualitatively and quantitatively and provide a prescription to correct for it during image reconstruction. Our simulations and reconstructions are based on an adaptation of the known theory of BCDI sampling geometry, as well as the recently developed projection-based methods of wavefield propagation. Such configurational modifications and their numerical remedies are potentially valuable in realizing unconventional coherent diffraction measurement geometries, eventually paving the way for the integration of BCDI into new material characterization experiments at next-generation light sources.

36 MATERIALS SCIENCE↗