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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 163 records · Page 9

Vehicular Re-Identification from Uncontrolled Multiple Views

Vehicle re-identification (re-ID) across disparate sensing modalities remains a fundamental challenge for transportation research. In this work, we introduce a deep multi-view vehicle re-ID framework that leverages Siamese networks to compare pairs of vehicle images and produce matching scores, enabling robust association across drastically different viewpoints such as those from UAVs, surveillance cameras, and ground sensors. The model exploits convolutional neural networks to learn features that remain discriminative under changes in angle, distance, and illumination, supporting more generalizable re-ID performance. As part of this effort, we also developed an automated pipeline to synchronize roadside and UAV video streams, producing a multi-perspective dataset that complements preexisting real collections and a synthetic dataset generated in this study. Together, these contributions advance the capability to re-identify vehicles across wide viewing baselines; establish a foundation for scalable, reproducible research in vehicle re-ID; and open pathways for future applications, such as inferring routine behaviors, movement patterns, and daily habits of the individual associated with the vehicle.

convolutional neural networks↗

Extraction of Vibration Data with Imaging

To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SpaceNet 9—Cross-Sensor Alignment of Optical and SAR Imagery

Precise registration of high-resolution synthetic aperture radar (SAR) and optical imagery is necessary for realizing the full potential and benefits of multimodal image analysis. However, two significant challenges presently exist. First, there is a lack of annotated datasets and benchmarks available for high-resolution SAR–optical image registration. Second, an assessment of efficient and reliable image registration methods that can precisely align these modalities is lacking. Here, we present a holistic description of the SpaceNet 9 Challenge and its results. We present a description of the dataset and baseline algorithm along with the results of the challenge, including a description of the winning algorithms. We release the SpaceNet 9 dataset along with open-sourcing the winning algorithms and baseline. The objective of SpaceNet 9 was to compute a dense displacement map that indicates the shift needed to align pixels in an optical image to the pixels in a SAR image. The challenge launched in April 2025 and was active for approximately two months. The top five solutions reduced image alignment error from approximately 34 m to under 13 m for public and private test data, with the best results obtaining a registration error of only 8.5 and 6.7 m on the public testing and private testing dataset, respectively. Usage of pretrained image matching models, robust outlier rejection with RANSAC, and estimating local displacement were common among the top solutions. The results of this challenge provide insight into high-resolution SAR–optical image registration and offer opportunities for future benchmarking in this domain. The baseline algorithm, winning solutions, and datasets are available at https://spacenet.ai/sn9-challenge/.

benchmark datasets↗

Functionalization of nitrogen vacancy-containing nanodiamonds with a metal-organic framework for quantum sensing applications

Nitrogen vacancy (NV)-containing nanodiamonds (NDs) are an important material in applications such as biological imaging, catalysis, and, in particular, quantum sensing. Careful manipulation of the surface coating on NV NDs is essential for both enhancing quantum sensor performance and for tuning selectivity towards specific sensing targets. Here, we demonstrate a simple synthetic approach for functionalizing NV NDs with the zeolitic imidazole framework-8 (ZIF-8) metal–organic framework (MOF), providing a well-ordered, porous scaffold for immobilizing target analytes near the NV ND surface. The composites were structurally characterized by x-ray diffraction, electron microscopy, and X-ray photoelectron spectroscopy, and these results were all consistent with NV NDs fully encapsulated by ZIF-8. Critically, the luminescent properties of the NV NDs, which are vital for quantum sensing experiments such as optically detected magnetic resonance (ODMR), are unchanged by the MOF coating. Moreover, spin relaxometry experiments indicate that the ZIF-8 coating significantly enhances the NV ND spin longitudinal relaxation time T1, a critical quantum parameter for sensing applications. Given the tremendous structural diversity of MOFs, the NV ND@MOF composites are an exciting material class with exciting implications for the development of high-performance quantum sensors.

Crawford, Scott↗

Gain characterization of LGAD sensors with beta particles and 28-MeV protons

Low Gain Avalanche Diodes, also known as LGADs, are widely considered for fast-timing applications in high energy physics, nuclear physics, space science, medical imaging, and precision measurements of rare processes. Such devices are silicon-based and feature an intrinsic gain due to a p + -doped layer that allows the production of a controlled avalanche of carriers, with multiplication on the order of 10–100. This technology can provide time resolution on the order of 20–30 ps, and variants of this technology can provide precision tracking too. The characterization of LGAD performance has so far primarily been focused on the interaction of minimum ionizing particles for high energy and nuclear physics applications. This article expands the study of LGAD performance to highly-ionizing particles, such as 28-MeV protons, which are relevant for several future scientific applications, e.g. in biology and medical physics, among others. These studies were performed with a beam of 28-MeV protons from a tandem Van de Graaff accelerator at Brookhaven National Laboratory and beta particles from a ^90Sr source; these were used to characterize the response and the gain of an LGAD as a function of bias voltage and collected charge. Here, the experimental results are also compared to TCAD simulations.

47 OTHER INSTRUMENTATION↗

Glassy carbon formation from pyrolysis of polymeric coatings on fiber-optic sensors

Deploying fiber-optic sensors in nuclear reactors requires a detailed understanding of radiation effects on the fiber materials and the transmitted signals. Previous work has shown large wavelength shifts in the reflected spectra obtained from polymer-coated fiber-optic temperature sensors exposed to high neutron fluences. The sensor drift resulting from these wavelength shifts cannot be explained by radiation effects on fused silica glass. These shifts are hypothesized to be caused by the conversion of the polymeric fiber coating to a glassy carbon via radiolysis and/or pyrolysis and subsequent radiation-induced compaction. Here, thermal degradation of these polymeric coatings was studied to provide insight into the potential origins of the sensor drift phenomenon. Acrylate- and polyimide-coated fibers were heated under various temperatures (250–1300 °C) and environments (oxidative and inert), and the resulting coating products were characterized via mass-loss data, scanning electron microscope imaging, and Raman spectroscopy. Results suggest that the polymer decomposition product of both coating types, at least under inert conditions, is indeed a glassy carbon. Analytical models that account for radiation-induced glassy carbon coating compaction show significant compressive fiber strains and predicted wavelength shifts that agree well with experimental measurements, providing additional evidence that supports the hypothesized origins of the sensor drift.

36 MATERIALS SCIENCE↗

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗

HRPPD photosensors for RICH detectors with a high resolution timing capability

Recently, a new version of DC-coupled High Rate Picosecond Photodetectors (DC-HRPPDs) substantially re-designed for use at the Electron-Ion Collider (EIC) has been developed. A first batch of seven “EIC HRPPDs” was manufactured in early 2024. These HRPPDs are DC-coupled photosensors based on Micro-Channel Plates (MCPs) that have an active area of 104 mm by 104 mm, 32 × 32 direct readout pixel array at a pitch of 3.25 mm, peak quantum efficiency in excess of 30%, exceptionally low dark count rates and timing resolution of 15–20 ps for a single photon detection. As such, these photosensors are very well suited for Ring Imaging CHerenkov (RICH) detectors that can additionally provide high resolution timing capability, especially in a configuration where a detected charged particle passes through the sensor window, which produces a localized flash containing a few dozens of Cherenkov photons in it.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Spectral Performance of Multilayer Amorphous Selenium and Selenium–Tellurium Photodetectors

The ability to robustly and with scalability detect single photons in the visible spectrum with wavelength resolution would transform many imaging applications. Theoretical studies propose an array of carbon nanotubes (CNTs) functionalized with semiconductor quantum dots (QDs) as a physical realization of such photon sensors. In this work, we report approaches to synthesize these CNT-QD nanostructures using DNA as a smart glue to connect CNTs to QDs.

36 MATERIALS SCIENCE↗

Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗

Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗

Demonstration of a 1820 channel multiplexer for transition-edge sensor bolometers

The scalability of most transition-edge sensor arrays is limited by the multiplexing technology, which combines their signals over a reduced number of wires and amplifiers. Here, in this Letter, we present and demonstrate a multiplexer design optimized for transition-edge sensor bolometers with 1820 sensors per readout unit, a factor of two more than the previous state-of-the-art. The design is optimized for cosmic microwave background imaging applications, and it builds on previous microwave superconducting quantum interference device multiplexers by doubling the available readout bandwidth to the full 4–8 GHz octave. Evaluating the key performance metrics of yield, sensitivity, and crosstalk through laboratory testing, we find an end-to-end operable detector yield of 78%, a typical nearest-neighbor crosstalk amplitude of ∼0.4%, and a median white noise level of 83 pA/$\sqrt{\textrm{Hz}}$ due to the multiplexer, corresponding to an estimated contribution of 4% to the total system noise for a ground-based cosmic microwave background telescope. Additionally, we identify a possible path toward reducing resonator loss for future designs with reduced noise. We expect these developments to alleviate the system complexity, cryogenic requirements, and cost of future large arrays of low temperature detectors.

cosmic microwave background↗

2024 roadmap on magnetic microscopy techniques and their applications in materials science

Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.

2D materials↗

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↗

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↗

Benchmarking of X‐Ray Fluorescence Microscopy with Ion Beam Implanted Samples Showing Detection Sensitivity of Hundreds of Atoms

Abstract Single impurities in insulators are now often used for quantum sensors and single photon sources, while nanoscale semiconductor doping features are being constructed for electrical contacts in quantum technology devices, implying that new methods for sensitive, non‐destructive imaging of single‐ or few‐atom structures are needed. X‐ray fluorescence (XRF) can provide nanoscale imaging with chemical specificity, and features comprising as few as 100 000 atoms have been detected without any need for specialized or destructive sample preparation. Presently, the ultimate limits of sensitivity of XRF are unknown – here, gallium dopants in silicon are investigated using a high brilliance, synchrotron source collimated to a small spot. It is demonstrated that with a single‐pixel integration time of 1 s, the sensitivity is sufficient to identify a single isolated feature of only 3000 Ga impurities (a mass of just 350 zg). With increased integration (25 s), 650 impurities can be detected. The results are quantified using a calibration sample consisting of precisely controlled numbers of implanted atoms in nanometer‐sized structures. The results show that such features can now be mapped quantitatively when calibration samples are used, and suggest that, in the near future, planned upgrades to XRF facilities might achieve single‐atom sensitivity.

36 MATERIALS SCIENCE↗

Quantum Frequency Combs with Path Identity for Quantum Remote Sensing

Quantum sensing promises to revolutionize sensing applications by employing quantum states of light or matter as sensing probes. Photons are the clear choice as quantum probes for remote sensing because they can travel to and interact with a distant target. Existing schemes are mainly based on the quantum illumination framework, which requires quantum memory to store a single photon of an initially entangled pair until its twin reflects off a target and returns for final correlation measurements. Existing demonstrations are limited to tabletop experiments, and expanding the sensing range faces various roadblocks, including long-time quantum storage and photon loss and noise when transmitting quantum signals over long distances. We propose a novel quantum sensing framework that addresses these challenges using quantum frequency combs with path identity for remote sensing of signatures (“qCOMBPASS”). The combination of one key quantum phenomenon and two quantum resources—namely, quantum-induced coherence by path identity, quantum frequency combs, and two-mode squeezed light—allows for quantum remote sensing without requiring quantum memory. The proposed scheme is akin to a quantum radar based on entangled frequency-comb pairs that uses path identity to detect, range, or sense a remote target of interest by measuring pulses of one comb in the pair that never traveled to the target but that contains target information “teleported” by quantum-induced coherence by path identity from the other comb in the pair that traveled to the target but is not detected. We develop the basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce the qCOMBPASS equation—a quantum analog of the well-known LIDAR equation in classical remote sensing. We also describe an experimental scheme to demonstrate the concept using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various applications in remote quantum sensing, imaging, metrology, and communications. These applications include detection and ranging of low-reflectivity objects, measurement of small displacements of a remote target with precision beyond the standard quantum limit (SQL), standoff hyperspectral quantum imaging, discreet surveillance from space with low detection probability (detect without being detected), very-long-baseline interferometry, quantum Doppler sensing, quantum clock synchronization, and networks of distributed quantum sensors. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

AC Magnetometry Using Nano-ferrofluid Cladded Multimode Interferometric Fiber Optic Sensors for Power Grid Monitoring Applications

The AC magnetic field response of the superparamagnetic nano-ferrofluid is an interplay between the Neel and Brownian relaxation processes and is generally quantified via the susceptibility measurements at high frequencies. The high frequency limit is dictated by these relaxation times which need to be shorter than the time scale of the time varying magnetic field for the nano-ferrofluid to be considered in an equilibrium state at each time instant. Even though the high frequency response of ferrofluid has been extensively investigated for frequencies up to GHz range by non-optical methods, harnessing dynamic response by optical means for AC magnetic field sensing in fiber-optic-based sensors-field remains unexplored. Instead, the incorporation of nano-ferrofluid as sensing materials has been only limited to DC magnetic field sensing, often citing their long response time as a limiting factor to AC field sensing. This work reports the finding of high frequency (up to 15 kHz) AC magnetic field sensing capability of nanomagnetic fluid as the cladding material of a fiber-optic multimode interferometry (MMI) structure optimized for the fourth self-imaging spectral response. The key parameter enabling high frequency response is the short response time (<1 ms) achieved by optimizing both the sensing structure and nano-ferrofluid solution. Focus has been imparted on 60 Hz line-frequency profiles of various current/magnetic fields to test the efficacy of these sensors in metering and monitoring current and current-induced magnetic fields in the electrical power grid systems. The magnetic field sensitivity of 240 mV/Gauss per dBm of transmitted power was achieved for 60 Hz field applied via Helmholtz coil, whereas the 60 Hz AC current sensitivity of 2.83 mV/A was measured due to magnetic field induced by current in a straight conducting wire.

42 ENGINEERING↗