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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

Diamond magnetometer enhanced by ferrite flux concentrators

Magnetometers based on nitrogen-vacancy (NV) centers in diamond are promising room-temperature, solid-state sensors. However, their reported sensitivity to magnetic fields at low frequencies (≲1 kHz) is presently ≳10 pTs 1/2 , precluding potential applications in medical imaging, geoscience, and navigation. Here we show that high-permeability magnetic flux concentrators, which collect magnetic flux from a larger area and concentrate it into the diamond sensor, can be used to improve the sensitivity of diamond magnetometers. By inserting an NV-doped diamond membrane between two ferrite cones in a bowtie configuration, we realize a ~ 250-fold increase of the magnetic field amplitude within the diamond. We demonstrate a sensitivity of ~ 0.9pTs 1/2 to magnetic fields in the frequency range between 10 and 1000 Hz. This is accomplished using a dual-resonance modulation technique to suppress the effect of thermal shifts of the NV spin levels. The magnetometer uses 200 mW of laser power and 20 mW of microwave power. This work introduces a new degree of freedom for the design of diamond sensors by using structured magnetic materials to manipulate magnetic fields.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Quapprox: A Framework for Benchmarking the Approximability of Variational Quantum Circuit

Most of the existing quantum neural network models, such as variational quantum circuits (VQCs), are limited in their ability to explore the non-linear relationships in input data. This gradually becomes the main obstacle for it to tackle realistic applications, such as natural language processing, medical image processing, and wireless communications. Recently, there have emerged research efforts that enable VQCs to perform non-linear operations. However, it is still unclear on the approximability of a given VQC (i.e., the order of non-linearity that can be handled by a specified design). In response to this issue, we developed an automated tool designed to benchmark the approximation of a given VQC. The proposed tool will generate a set of synthetic datasets with different orders of non-linearity and train the given VQC on these datasets to estimate their approximability. Our experiments benchmark VQCs with different designs, where we know their theoretic approximability. We then show that the proposed tool can precisely estimate the approximability, which is consistent with the theoretic value, indicating that the proposed tool can be used for benchmarking the approximability of a given quantum circuit for learning tasks.

artificial intelligence↗
DOE OSTI2021

Cadmium Magnesium Telluride for Next Generation X-Ray Free Electron Laser, Synchrotron and Many Other Applications

We developed a picosecond photodetector based on our Bridgman grown and specially engineered Cadmium Magnesium Telluride (Cd 1-x Mg x Te) single crystal that is sensitive to both optical and x-ray pulses for coarse timing in Free Electron Laser applications. Cd 1-x Mg x Te is a wide bandgap semiconductor material with potential applications, not only in optoelectronics, but also in particle physics as an intense pulse radiation detector for bremsstrahlung, X-ray/gamma ray radiation, thermal neutrons, and medical imaging. However, for femtosecond optical and x-ray cross correlation, the material has to have very short lifetime, a condition that is opposite to that required for nuclear spectroscopy applications. At the same time, the material also needs to have very low bulk leakage current, in the 0.10 pA range. Hence the ability to tailor or engineer the material is very crucial. In this paper, picosecond response and the crystal growth of this specially engineered cadmium magnesium telluride material will be presented. Other material characterization and transient measurements will also be discussed along with room temperature semiconductor detector performance for other nuclear radiation detection applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Novel Cascaded Ring Oscillator Vernier based TDC Architecture with a Feedthrough Reference Oscillator

Time to digital converters (TDCs) are widely used in particle physics and medical imaging to determine the relative arrival time of photons and other particles. Vernier TDCs are one of the most attractive options due to their power consumption efficiency and their small footprint. We present our newly patented innovative architecture of cascaded-stage Vernier TDCs comprised of a feedthrough reference oscillator. This architecture lead to successive Vernier measurements that result in a lower number of cycles for the same dynamic range to resolution ratio (DRRR), leading to reduced conversion time and lower jitter. To validate this architecture, a test chip was designed in 0.18~μm TSMC technology, integrating four types of cascaded Vernier TDCs with an increasing number of stages. The performance of the chip was assessed using a modular test platform specifically designed for TDCs. For comparison purposes, all TDCs were tested with a LSB of 50~ps and a dynamic range of 20~ns. The experimental results demonstrate the highest gain in performance between 1 stage Vernier and 2 stage Vernier TDCs with the precision improved from 36~ps~rms to 21~ps~rms, the maximum number of turns from 110 to 25 counts and the dead time, from 266.4~ns to 76.5~ns, on average. Adding Vernier stages is only relevant for a higher DRRR. Therefore, the 3-stage and 4-stage Vernier TDCs also show performance gains, but improvements are limited.

Scarpellini, Robin↗
DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗
DOE OSTI2021

Efficient Prompt Scintillation and Fast Neutron-Gamma Ray Discrimination Using Amorphous Blends of Difluorenylsilane Organic Glass and in-situ Polymerized Vinyltoluene

High performance radiation detection materials are an integral part of national security, medical imaging, and nuclear physics applications. Those that offer compositional and manufacturing versatility are of particular interest. In this work, we report a new family of radiological particle-discriminating scintillators containing bis(9,9-dimethyl-9H-fluoren-2- yl)diphenylsilane (compound “P2”) and in-situ polymerized vinyltoluene (PVT) that is phase stable and mechanically robust at any blend ratio. The gamma-ray light yield increases nearly linearly across the composition range, to ~16,400 photons/MeV at 75 % wt. P2. These materials are also capable of γ/n pulse shape discrimination (PSD) and between 20-50 % P2 loading are competitive with the PSD quality of commercially-available plastic scintillators. The 137 Cs scintillation rise and decay times are sensitive to P2 loading and approach the values for “pure” P2. Additionally, the radiation detection performance of P2-PVT blends can be made stable in 60°C air for at least 1.5 months with the application of a thin film of poly(vinylalcohol) to the scintillator surfaces.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Cadmium Magnesium Telluride for Next-Generation X-Ray Free Electron Laser, Synchrotron, and Many Other Applications

We developed a picosecond photodetector based on our Bridgman-grown and specially engineered cadmium magnesium telluride (Cd 1–x MgxTe) single crystal that is sensitive to both optical and X-ray pulses for coarse timing in free-electron laser applications. Cd 1–x MgxTe is a widebandgap semiconductor with potential applications, not only in optoelectronics, but also in particle physics as an intense pulse radiation detector for bremsstrahlung, X-ray/gamma-ray radiation, thermal neutrons, and medical imaging. For femtosecond optical and X-ray crosscorrelation, the material must have a very short lifetime, a condition that is opposite to that required for nuclear spectroscopy applications. At the same time, the material also needs to have a very low bulk leakage current, in the 10–90 nA range for voltages to even 1000 V. Hence, the ability to tailor or engineer the material is very crucial. Picosecond response and the crystal growth of this specially engineered Cd 1–x MgxTe material are presented. Furthermore, other characterization and transient measurements are discussed along with room-temperature semiconductor detector performance for other nuclear radiation detection applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Proton Quenching in Rare-Earth Inorganic Scintillators: GAGG:Ce and YSO:Ce

Scintillator detectors are an integral component of radiation detection systems for a variety of applications such as medical imaging, accelerator diagnostics, and space science. Typically, a scintillator detector’s response is characterized using gamma sources to understand the detection response to different types of radiation, including charged particle detection. However, there exists a nonlinearity of the amount of light produced from an incident gamma ray of specific energy and the light produced from an incident charged particle of the same energy. This important effect, known as quenching, must be accounted for to interpret energies from charged particles incident on detectors. In this article, we present results of quenching parameterization for two types of cerium-doped inorganic scintillators, Y2SiO5:Ce (YSO:Ce) and Gd3Al2Ga3O12:Ce (GAGG:Ce). We measured the light output from incident proton energies from 1 to 25 MeV using a 3-MV tandem accelerator and two reactions: Au(p,p)Au and 3He(d,p)⁴He. Using gamma-ray sources to calibrate the detectors, we compared the measured electron-equivalent energy versus the incident energy expected. Using an adaptation of the Birks semi-empirical formula, we extracted the Birks parameter (kB) to understand quenching. For one of the GAGG:Ce samples, the kB parameter of 0.0072 [g cm-2 MeV-1] is comparable to a similar study where the value of kB was 0.0065 [g cm-2 MeV-1]. For YSO:Ce, no other kB values were found in the literature. Three different types of GAGG:Ce were used to collect measurements of kB as a function of dopant concentration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Meta-optic accelerators for object classifiers

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic–based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.

42 ENGINEERING↗
DOE OSTI2023

Nuclear quadrupole resonance spectroscopy with a femtotesla diamond magnetometer

Radio frequency (RF) magnetometers based on nitrogen vacancy centers in diamond are predicted to offer femtotesla sensitivity, but previous experiments were limited to the picotesla level. We demonstrate a femtotesla RF magnetometer using a diamond membrane inserted between ferrite flux concentrators. The device provides ~300-fold amplitude enhancement for RF magnetic fields from 70 kHz to 3.6 MHz, and the sensitivity reaches ~70 fT√s at 0.35 MHz. The sensor detected the 3.6-MHz nuclear quadrupole resonance (NQR) of room-temperature sodium nitrite powder. The sensor’s recovery time after an RF pulse is ~35 μs, limited by the excitation coil’s ring-down time. The sodium-nitrite NQR frequency shifts with temperature as -1.00±0.02 kHz/K, the magnetization dephasing time is T 2 *=887±51 μs, and multipulse sequences extend the signal lifetime to 332±23 ms, all consistent with coil-based studies. Our results expand the sensitivity frontier of diamond magnetometers to the femtotesla range, with potential applications in security, medical imaging, and materials science.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors

This software offers methods and functions for training calibrated out-of-distribution detectors for medical image classification tasks. It includes functionalities for training, synthesizing data augmentations, calibration, and out-of-distribution detection. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, VivekSivaraman↗
DOE OSTI2024

Toward “super-scintillation” with nanomaterials and nanophotonics

Abstract Following the discovery of X-rays, scintillators are commonly used as high-energy radiation sensors in diagnostic medical imaging, high-energy physics, astrophysics, environmental radiation monitoring, and security inspections. Conventional scintillators face intrinsic limitations including a low extraction efficiency of scintillated light and a low emission rate, leading to efficiencies that are less than 10 % for commercial scintillators. Overcoming these limitations will require new materials including scintillating nanomaterials (“nanoscintillators”), as well as new photonic approaches that increase the efficiency of the scintillation process, increase the emission rate of materials, and control the directivity of the scintillated light. In this perspective, we describe emerging nanoscintillating materials and three nanophotonic platforms: (i) plasmonic nanoresonators, (ii) photonic crystals, and (iii) high-Q metasurfaces that could enable high performance scintillators. We further discuss how a combination of nanoscintillators and photonic structures can yield a “super scintillator” enabling ultimate spatio-temporal resolution while enabling a significant boost in the extracted scintillation emission.

Carr Delgado, Hamish↗
DOE OSTI2025

An engineering perspective on evaluating mechanisms governing ductility in pure molybdenum

The United States lacks a stable domestic supply of 99 mTc, a critical medical imaging isotope generated from 99 Mo. Accelerator-based production using 100 Mo targets (aMo) introduces mechanical concerns due to the ductile-to-brittle transition temperature inherent to refractory metals like Mo. This study evaluates the tensile behavior of aMo targets from 25 to 1000 °C, compared to powder-metallurgy-processed natural Mo (PMo) and cast-and-rolled Mo (RMo), both as-received and after 5 ppm O 2 exposure in flowing He. RMo showed superior ductility (7.3% at 25 °C, 48% at 1000 °C) and strength, attributed to its fine, elongated grains and high geometrically necessary dislocation density. PMo exhibited variable ductility (up to 33%), while aMo remained brittle, with a maximum elongation of 10.4% at 600 °C. EBSD analysis revealed weak texture in PMo and aMo, but high defect density in aMo limited dislocation mobility. This work links processing, microstructure, and deformation mechanisms to guide fabrication of ductile refractory targets.

Hyer, Holden C. [Oak Ridge National Laboratory (OR↗
DOE OSTI2021

Parameters of fast and high-yield InAs/GaAs quantum dot semiconductor scintillator

InAs quantum dots (QDs) embedded into a waveguiding GaAs semiconductor matrix may produce scintillation detectors with exceptional speed and yield, making them valuable for nuclear security, medical imaging, and high energy physics applications. In this work, we developed thick (~25 μm) epitaxial heterostructres with high luminescence efficiency composed of self-assembled nano-engineered InAs QDs grown by molecular beam epitaxy. Here, the bulk GaAs acts as a stopping material for incident particles and as a waveguide when layer-transferred onto a low-index substrate. Waveguiding and self-absorption (< 1 cm –1 ) were studied using photoluminescence with scanning laser excitation and modeled with ray optics approximation and geometrical coupling of high-index waveguide to a collection fiber. Scintillating signals from α-particles were analyzed with an external photodiode (PD) and an integrated PD which provided an improved optical coupling. The mean charge collected by the integrated PD corresponded to 3 × 10 4 photoelectrons per 1 MeV of deposited energy, or ~13% of the theoretically achievable light yield. Combined with the previously measured QD scintillation time of 0.3-0.6 ns, this makes the InAs/GaAs QD heterostructures the fastest high yield scintillation material reported.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Improving radiograph analysis throughput through transfer learning and object detection

SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Design Optimization of A generic Fissile Solution for Mo99 production using Electron Beam-based Neutron Generator using MCNP+CFD

Molybdenum-99 ( 99 Mo) is a medical isotope used in 80% of all medical imaging procedures today. However, 99 Mo is not directly administered to patients for imaging; its decay product, technetium-99m ( 99 mTc), is a metastable isotope with a half-life of ~6 hours. Such a short half-life does not allow for production, separation, and application to a patient before decaying further. Therefore, the solution is to produce the precursor to 99 mTc, 99 Mo. 99 Mo has a half-life of 66 hours, long enough to produce the material and send it to the desired location before it fully decays. Under National Nuclear Security Administration’s (NNSA) highly enriched uranium (HEU) minimization mission, the Material Management and Minimization (M3) office leads the Molybdenum-99 (Mo-99) program. A major goal of the Mo-99 program is to develop methods of producing 99 Mo using low enriched uranium (LEU) opposed to the traditionally used HEU domestically in the U.S. Internationally, LEU is the new standard for use in research reactors and isotope production facility. In this study, we introduce a generic fissile solution system for 99 Mo production facility meeting the design requirement of 1) LEU usage as fissile material and 2) subcritical fissile system coupled with a horizontal electron beam (E-beam) accelerator. The focus of this research is to optimize a fissile solution configuration for maximum 99 Mo production yield using MCNP model, and ultimately implement the optimized design with computational fluid dynamics (CFD) model to understand thermal hydraulic behavior and solution convection characteristic. The report consists of two parts: 1) isotope yield calculation and criticality analysis with a wide range of parametric test matrix, and 2) thermal hydraulic analysis for solution mixing and cooling assessment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Artificial Intelligence for Accelerating Nuclear Applications, Science, and Technology

Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗