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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Accelerating actinium-225 purification by high-pressure ion chromatography

Actinium-225 (t1/2 = 9.92 days) is an important radioisotope for targeted alpha therapy applications. The limited supply obtained through the decay of thorium-229 has motivated accelerator-based production routes, including irradiation of thorium targets. Irradiated targets can produce useful quantities of actinium-225, but the product requires final purification from chemically similar lanthanide contaminants. This work describes an automated high-pressure ion chromatography method for this final polishing step. The method uses a reusable strong-acid cation-exchange column bearing sulfonic acid functional groups. α-Hydroxyisobutyric acid (α-HIBA), adjusted to pH 4.3 with lithium hydroxide, complexes and elutes lanthanides, a dilute hydrochloric acid matrix-exchange step removes residual α-HIBA, and concentrated hydrochloric acid then elutes retained actinium(III). The protocol purified actinium-225 to >99% radiopurity across tracer-level samples and samples containing >150 µCi (5.6 MBq) of activity. A 10 min, 0.1 M hydrochloric acid matrix exchange substantially reduced organic eluent carryover, and in-line sodium iodide detection enabled real-time monitoring of actinium and lanthanide elution. The developed method can be completed in <1 h and provides a basis for automated purification workflows for accelerator-produced actinium-225.

Gaddis, Kevin [ORNL] (ORCID:0000000183398314)↗
DOE OSTI2020

Performance testing of a Compton suppressed coincidence measurements using the Advanced Radionuclide Gamma-spectrOmeter

A Compton-suppressed multidimensional gamma-spectrometer is being developed within the Shallow Underground Laboratory (SUL) at Pacific Northwest National Laboratory (PNNL, USA). This Advanced Radionuclide Gamma-spectrOmeter (ARGO) system consists of two Broad Energy Germanium (BEGe) detectors, surrounded by a 12-detector sodium iodide (NaI) Compton suppression shield, situated inside a low-background lead and copper shield, fitted with a cosmic veto background reduction system. The detector has advanced functionality, including operation in single or combined detector mode, coincidence mode and Compton suppressed coincidence mode. Validation experiments measuring a neutron irradiated natural uranium sample, have demonstrated order of magnitude improvements in gamma detection sensitivity compared to conventional gamma-spectrometry techniques. Importantly this research demonstrates the feasibility of processing and analysing high-multiplicity datasets and the potential for high-sensitivity radionuclide measurements.

ARGO↗
DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗
DOE OSTI2021

Advanced inorganic halide ceramic scintillators

Research in ceramic scintillators has steadily progressed alongside the research in bulk single crystal scintillator growth. As interest in faster scintillation material production with lower cost increases, more research on scintillating ceramics is needed. Research targeting optimization of optically transparent ceramics that can rival bulk-grown crystals grown may lower cost, increase yield, increase volume, and improve energy resolution in applications and systems currently using sodium iodide and alike. Ceramic scintillators that are dense (>5 g/cm 3 ), have high effective Z (>60), are bright (>40,000 photons/MeV), and are not sensitive to moisture as well as those that can be handled without protection are desired. Ultra-fast ceramic materials are also of interest. This paper presents an equipment design and technique to produce inorganic halide ceramic scintillators Cs 2 HfCl 6 (CHC) and Tl 2 HfCl 6 (THC). Improvements and optimization of CHC and THC ceramic scintillator fabrication are gauged by monitoring the energy resolution and peak position of 137 Cs full energy peak at 662 keV. With a 1-inch diameter CHC ceramic scintillator, energy resolution of 5.4% (FWHM) and light yield of 20,700 ph/MeV are achieved, while with a 16-mm diameter THC ceramic scintillator, energy resolution of 5.1% (FWHM) and light yield of 27,800 ph/MeV are achieved. Decay times of 0.6 μs (21%) and 3.0 μs (79%) are measured for CHC and 0.3 μs (13%) and 1.0 μs (87%) for THC. Here, both ceramic CHC and THC scintillators have similarly good proportionality data when compared to their single crystal counterparts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deep Learning enabled spectral energy conversion for in situ exposure measurements

A detector-specific deep learning (DL) approach is presented for spectra-to-exposure conversion using large-format sodium iodide (NaI(Tl)) detectors deployed for in situ environmental radiation measurements in emergency response scenarios. Accurate determination of exposure from NaI spectra is challenging due to poor energy resolution, partial energy absorption, and the strong sensitivity of traditionally deployed analytical conversion methods to calibrated source geometry and pre-deployment assumptions. Here, to address these limitations, a multi-layer perceptron model was trained on a hybrid in situ /Monte Carlo dataset constructed to span a broad range of photon energies, spatial extents, and realistic deployment variability, representative of general in situ emergency response conditions. The DL model was evaluated against commonly fielded analytical approaches under matched simulation conditions, including a single-factor method, a G-function method, and a modeled pressurized ion chamber (PIC) baseline. This study was intentionally computational in scope to enable controlled, like-for-like comparisons between conversion techniques while minimizing confounding real-world variability. Comparison to the modeled PIC provides contextual benchmarking and is not intended as a field inter-comparison with deployed instruments. Across the evaluated 20 keV to 3 MeV energy range, the DL approach consistently exhibited higher accuracy and reduced variance relative to the analytical methods against a deterministically calculated exposure. This may indicate improved robustness to spectral complexity without reliance on source-, geometric-, or spectral region-specific optimization. While results do not represent real-world validation, the presented work demonstrates that deep learning may effectively learn the nonlinear detector response-to-exposure relationship for asymmetric NaI(Tl) detectors and offers a promising pathway for improving in situ exposure estimation using spectroscopic systems already integrated into initial real-time emergency response operations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Detection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks

Source search campaigns involve measurements of background gamma-ray spectra with a mobile detector-spectrometer traveling along arbitrarily chosen trajectories over a wide screening area. Radiation counts are typically measured with a tellurium-doped sodium iodide [NaI(Tl)] scintillator detector-spectrometer in short acquisition intervals, usually 1 s. The objective is to detect orphan isotopes with half-lives shorter than those of the isotopes in the natural background. In principle, radioisotopes can be identified by their unique gamma emission spectrum. However, detecting orphan isotopes in search data is challenging because low counts measured in short acquisition intervals result in incomplete spectral lines. In this study, we investigate the performance of a Hopfield neural network (HNN) that implements an auto-associative memory for the detection of isotopes of interest in an urban search campaign. The HNN is trained on one example of gamma spectra with well-resolved spectral lines of each isotope of interest. During testing, the auto-associative memory implementation of the HNN processes low-count gamma spectra with partially complete isotopic lines by matching incoming measurements to the closest one of its memory-stored patterns. The testing database consisted of almost 10 000 1-s gamma spectra, including measurements of orphan isotopes 137 Cs, 241 Am, and 131 I, obtained during two urban search surveys with a NaI(Tl) detector. The performance of the HNN detection algorithm was evaluated using precision, recall, and F1 scores, and benchmarked with a multiple linear regression (MLR) identification algorithm. In conclusion, the test results demonstrate that HNN outperforms MLR in the detection of all the isotopes of interest.

Auto associative memory↗
DOE OSTI2024

Factors influencing quantum evaporation of helium from polar semiconductors from first principles

While there is much indirect evidence for the existence of dark matter (DM), to date it has evaded detection. Current efforts focus on DM masses over ~GeV—to push the sensitivity of DM searches to lower masses, new DM targets and detection schemes are needed. In this work, we focus on the latter—a novel detection scheme recently proposed to detect 10–100 meV phonons in polar target materials. Previous work showed that well-motivated models of DM can interact with polar semiconductors to produce an athermal population of phonons. This new sensing scheme proposes that these phonons then facilitate quantum evaporation of 3 He from a van der Waals film deposited on the target material. However, a fundamental understanding of the underlying process is still unclear, with several uncertainties related to the precise rate of evaporation and how it can be controlled. In this work, we use ab initio density functional theory calculations to compare the adsorption energies of helium atoms on a polar target material, sodium iodide, to understand the underlying evaporation physics. We explore the role of surface termination, monolayer coverage, and elemental species on the rate of He evaporation from the target material. Using this, we discuss the optimal target features for He-evaporation experiments and their range of tunability through chemical and physical modifications such as applied field and surface termination.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluation of camera-based freehand SPECT in preoperative sentinel lymph node mapping for melanoma patients

Assessment of lymphatic status via sentinel lymph node (SLN) biopsy is an integral and crucial part of melanoma surgical oncology. The most common technique for sentinel node mapping is preoperative planar scintigraphy of an injected gamma-emitting lymphatic tracer followed by intraoperative node localization using a non-imaging gamma probe with auditory feedback. In recent years, intraoperative visualization of SLNs in 3D has become possible by coupling the probe to an external system capable of tracking its location and orientation as it is read out, thereby enabling computation of the 3D distribution of the tracer (freehand SPECT). In this project, the non-imaging probe of the fhSPECT system was replaced by a unique handheld gamma camera containing an array of sodium iodide crystals optically coupled to an array of silicon photomultipliers (SiPMs). A feasibility study was performed in which preoperative SLN mapping was performed using camera fhSPECT and the number of detected nodes was compared to that visualized by lymphoscintigraphy, probe fhSPECT, and to the number ultimately excised under non-imaging probe guidance. Among five subjects, SLNs were detected in nine lymphatic basins, with one to five SLNs detected per basin. A basin-by-basin comparison showed that the number of SLNs detected using camera fhSPECT exceeded that using lymphoscintigraphy and probe fhSPECT in seven of nine basins and five of five basins, respectively. (Probe fhSPECT scans were not performed for four basins.) It exceeded the number excised under non-imaging probe guidance for seven of nine basins and equaled the number excised for the other two basins. Freehand SPECT using a prototype SiPM-based gamma camera demonstrates high sensitivity for detection of SLNs in a preoperative setting. Camera fhSPECT is a potential means for efficiently obtaining real-time 3D activity distribution maps in applications such as image-guided percutaneous biopsy, and surgical SLN biopsy or radioguided tumor excision.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Replacing photomultiplier tubes with silicon photomultipliers for nuclear safeguards applications

Photomultiplier tubes (PMTs) have been used for decades as the default light detection technology for scintillator-based radiation monitors. PMTs come with a handful of disadvantages, however, including large volume usage, fragility, high-voltage requirements, and susceptibility to magnetic fields. Arrays of silicon photomultipliers (SiPMs) are a possible alternative to PMTs providing similar performance while offering improvements in the areas listed. We focus on straightforward “drop-in replacement” evaluations by imposing a single channel output of the SiPM signal and a backend electronics data acquisition and analysis routine identical to that used for PMTs. In the realm of nuclear safeguards, the primary performance metric for gamma spectroscopy is detector resolution, and for neutron detection it is pulse shape discrimination (PSD) to separate neutron and gamma signals. In this work we present the results of replacing a PMT with a 2” x 2” SiPM array mounted, in turn, to the same 2” x 2” cylindrical sodium iodide (NaI) crystal. The ultimate comparison for this gamma spectrometry is confirmation of uranium enrichment standards, ranging from depleted uranium to 93% highly enriched uranium (HEU), by analyzing the resulting spectra with both a simple integral scaling in key energy regions of the spectrum, as well as with the NaIGEM software package. In addition to the gamma spectroscopy work, we will present the latest results on comparing the PSD capabilities of a 4” x 4” SiPM array to a 5” PMT. As with the gamma spectroscopy evaluations, the light detectors are mounted in turn to the same liter-scale organic scintillators. This neutron-focused work includes evaluation of prototype SiPM readout boards with a single output signal summed over 256 individual SiPM pixels, and evident tradeoffs between PSD capability and fast response.

42 ENGINEERING↗
DOE OSTI2024

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

Source search campaigns involve the measurement of consecutive background gamma-ray spectra obtained with a mobile detector-spectrometer, such as tellurium-doped sodium iodide NaI(Tl) scintillator detector-spectrometer. Counts are measured with NaI(Tl) detector in short acquisition intervals, such as one second. Detection of isotopes in search data is challenging because of the low signal-to-noise ratio (S/N) of the spectral lines and highly varying background due to naturally occurring radioactive material (NORM). In this work, we investigate the performance of the detection of orphan Cs-137, Am-241, and I-131 isotopes in a gamma ray data set obtained in a search survey. Isotopic detection is performed with a Hopfield Neural Network (HNN), which implements an auto-associative memory by matching incoming measurements to the closest one of its memory-stored signature patterns. HNN is trained on examples of spectra of calibrated isotopic sources with well-resolved spectral lines. During testing, HNN processes spectra with partially complete or distorted isotopic lines. The performance of the HNN detection algorithm is benchmarked with Precision, Recall, and F1 scores. Test results demonstrate the potential of HNN to detect the isotope of interest with high accuracy (F1 > 90%) for all isotopes in this study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Improving to the neutron fluence rate monitor measurement system at the Advanced Test Reactor

The existing fluence monitor wire scanning system at the Advanced Test Reactor (ATR) was designed and installed for use in the Engineering Test Reactor (ETR) when it began operation in 1958. The wire scanner was operated in ETR for over 20 years until ATR began operation, when it was moved to the ATR west canal area in 1971 and subsequently moved to the west canal in 2006 where it presently resides. With a continued service life of 65 years the system is well beyond the typical design life of 20 years for these types of systems. The need to update the data acquisition and control system was identified, and the benefits of replacing the existing sodium iodide (NaI) detector with an electronically cooled high-purity germanium (HPGe) detector are discussed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

UCNProBe NaI Veto System Characterization and Simulation

This paper presents a summary of my research and technical contributions during a summer internship at Los Alamos National Laboratory, supporting the development of the active veto system for the UCNProBe experiment. UCNProBe aims to perform a state-of-the-art measurement of the free neutron lifetime, requiring low-background conditions facilitated by an array of sodium iodide (NaI) scintillators.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

SpecFIDLER User Manual: Software Version 2.4.0

The Spectroscopic Field Instrument for Detection of Low Energy Radiation (SpecFIDLER) allows response teams to detect and quantify plutonium contamination on the ground. Notional scenarios include dispersion from a weapon accident, or the launch failure of a space probe containing a radioisotope thermoelectric generator. Unlike other instruments, the thin-window sodium iodide detector is sensitive to the low-energy gamma rays emitted by plutonium isotopes. The system supports both mobile survey as well as stationary sampling. This manual provides information about installing, maintaining, and troubleshooting the SpecFIDLER. The scope of this document includes the physical hardware, software for data acquisition, and algorithms for data analysis. Recent changes to the software and algorithms aim to streamline the operation of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

SpecFIDLER User Manual (Software V.2.5.0)

The Spectroscopic Field Instrument for Detection of Low Energy Radiation (SpecFIDLER) allows response teams to detect and quantify plutonium contamination on the ground. Notional scenarios include dispersion from a weapon accident, or the launch failure of a space probe containing a radioisotope thermoelectric generator. Unlike other instruments, the thin-window sodium iodide detector is sensitive to the low-energy gamma rays emitted by plutonium isotopes. The system supports both mobile survey as well as stationary sampling. This manual provides information about installing, maintaining, and troubleshooting the SpecFIDLER. The scope of this document includes the physical hardware, software for data acquisition, and algorithms for data analysis. Recent changes to the software and algorithms aim to streamline the operation of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SpecFIDLER User Manual (Software V.2.6.0)

The Spectroscopic Field Instrument for Detection of Low Energy Radiation (SpecFIDLER) allows response teams to detect and quantify plutonium contamination on the ground. Notional scenarios include dispersion from a weapon accident, or the launch failure of a space probe containing a radioisotope thermoelectric generator. Unlike other instruments, the thin-window sodium iodide detector is sensitive to the low-energy gamma rays emitted by plutonium isotopes. The system supports both mobile survey as well as stationary sampling. This manual provides information about installing, maintaining, and troubleshooting the SpecFIDLER. The scope of this document includes the physical hardware, software for data acquisition, and algorithms for data analysis. Recent changes to the software and algorithms aim to streamline the operation of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

SpecFIDLER User Manual (Software V.2.3.0)

The Spectroscopic Field Instrument for Detection of Low Energy Radiation (SpecFIDLER) allows response teams to detect and quantify plutonium contamination on the ground. Notional scenarios include dispersion from a weapon accident, or the launch failure of a space probe containing a radioisotope thermoelectric generator. Unlike other instruments, the thin-window sodium iodide detector is sensitive to the low-energy gamma rays emitted by plutonium isotopes. The system supports both mobile survey as well as stationary sampling. This manual provides information about installing, maintaining, and troubleshooting the SpecFIDLER. The scope of this document includes the physical hardware, software for data acquisition, and algorithms for data analysis. Recent changes to the software and algorithms aim to streamline the operation of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗