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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leveraging Machine Learning Capabilities for the Characterization of Irradiated Uranium: A Case Study of Analysis Methods for Nuclear Safeguards and Nuclear Forensics

Nondestructively determining the initial enrichment of irradiated uranium is a complex and laborious multivariable problem due to the presence of fission products. This work demonstrates the capabilities of machine learning to analyze gamma-ray spectral data to determine initial enrichment without knowledge of the decay time of the sample. The approach developed is agnostic to the particular scenario and is applicable to a wide variety of applications in nuclear forensics and nuclear safeguards. We irradiated 5 mg uranium standard reference materials at discrete enrichment values ranging from 0.02% to 97% 235 U (weight percent) in UT Austin’s Nuclear Engineering Teaching Laboratory TRIGA Mark II 1.1 MW research reactor, allowed each to decay for 8 hours, and then measured each sample via gamma-ray spectrometry for 50 hours post-irradiation yielding 1,400 individual gamma-ray spectra discretized into 8,192 energy bins. We then trained decision trees models to analyze individual gamma-ray spectra and estimate the associated initial enrichment without knowledge of the time since end of irradiation. We evaluated the performance of the models with a reserved test set not used for training or calibrating the model. A decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 2.3% (weight percent 235 U). Next, we implemented a principal component analysis pre-processing routine of the gamma-ray spectrometry data to reduce the dimensionality of the dataset from 8,192 channels in the spectrum to 10 principal components while retaining over 99% of the inherent variance in the data. Decision tree models constructed with these data demonstrated decreased mean absolute error in enrichment determination, reduced computational time, and decreased complexity. A single decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 0.05% (weight percent 235 U). Furthermore, we analyzed these models with learning curves to ensure that overfitting did not occur. The capabilities provided by these models can be naturally extended to other application-focused measurements in the fields of nuclear safeguards, nuclear forensics, and nuclear non-proliferation.

Drescher, Adam↗
DOE OSTI2022

Device-Centric Ransomware Detection using Machine Learning-Based Memory Forensics for Smart Inverters

Ransomware attacks are the fastest-growing form of cyberattacks worldwide. Recently, ransomware attacks have targeted industrial control systems (ICSs), including power grids. Lessons learned from recent incidents in ICSs show that ransomware groups can deliver ransomware into not only the organization’s control servers, but also the operational technology (OT) devices such as smart inverters and smart grid devices. This paper proposes a machine learning (ML)- based memory forensics method enabling the detection of ransomware binaries stored in the memory of a commercial smart inverter. Device firmware binary files are extracted from a Serial Peripheral Interface (SPI) flash memory, and samples of both benign and ransomware binaries are generated by a binary manipulation method and a real-world ransomware encryption, separately. A deep transfer learning (DTL) method is used to retrain a convolutional neural network (CNN)-based ransomware detection algorithm using the generated samples. The experimental result validates that the proposed ML-based memory forensics method can accurately detect ransomware files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Machine Learning Method for the Forensics Attribution of Separated Plutonium

Plutonium (Pu) source attribution would be a powerful tool to support nuclear nonproliferation efforts. This capability to find the source of a Pu sample would act as a deterrent to smuggling efforts, and also help regulatory agencies verify declared nuclear activities. Work at Texas A&M University yielded a nuclear forensics methodology, which is capable of determining separated Pu’s reactor of origin, fuel burnup, and the time since irradiation (TSI)—three parameters of interest. The methodology used a set of ten intra-element isotopic ratios found in separated Pu, which was compared to a library of isotopic ratio values produced using neutronics simulations for reactors of interest. By calculating the probability that unknown Pu sample’s isotopic ratio set matched a set in the library, the methodology could predict the three parameters of interest of the sample. One shortcoming of this methodology was an inability to correctly attribute spoofed Pu, where Pu sourced from two different reactors or two different fuel burnup levels are mixed. A new methodology to rectify this vulnerability using machine learning (ML) technique is developed, instead of the maximum likelihood calculation previously used and the results are satisfactory. The ML approach leverages the existing simulated data for training the algorithm, but use them efficiently by only using intra-element isotope ratios that contribute to the attribution one of the three parameters at a time. Previously, all isotope ratios were used to attribute all three parameters together. The new methodology attributes the Pu parameters in three steps, one for each parameter, rather than resolving all of the three parameters simultaneously like the previous maximum likelihood approach. First, a support vector machine classifier with a set of seven isotopic ratios finds the reactor of origin and a set of regression models trained using gaussian process predicts the burnup with a different set of seven isotopes. Finally, TSI is calculated analytically using decay equations. Thus far, the new methodology is capable of attributing pure Pu samples and has been validated using experimental data. The next step will to be augment the classifier training data set with spoofed Pu data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A highly-enriched 244 Pu reference material for nuclear safeguards and nuclear forensics measurements

A highly-enriched 244 Pu isotope dilution reference material has been prepared and characterized for metrologically traceable measurements of very small quantities of plutonium. The amount of plutonium in samples associated with nuclear safeguards and nuclear forensic measurements can be significantly less than 1 ng. Accordingly, the ability to quantify the amount and isotopic composition of plutonium from a single mass-spectrometric analysis is particularly desirable. The highly-enriched 244 Pu reference material, described here, will minimize the magnitude of spike corrections necessary to obtain accurate information on plutonium isotopic composition from isotope dilution measurements.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Exploring the use of thorium isotope compositions and concentrations as nuclear forensic signatures for uranium ore concentrates

This interlaboratory study measured thorium concentrations and isotope compositions in uranium ore concentrates from different geographical locations to examine whether thorium impurities may be useful forensic signatures for uranium ore concentrates found out of regulatory control. Measured 230 Th/ 232 Th in fifteen uranium ore concentrates record over three orders of magnitude of compositional variation. Results demonstrate that 230 Th/ 232 Th used in combination with U/Th ratios resulted in a unique signature for individual uranium ore concentrates from different processing locations. Data presented here suggest potential for 230 Th/ 232 Th and U/Th to be used as comparative signatures to investigate the provenance of seized uranium ore concentrates.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Separation of protactinium from uranium-niobium alloys for 231 Pa– 235 U radiochronometry in nuclear forensic investigations

The isolation and purification of protactinium from uranium materials is essential for 231 Pa– 235 U radiochronometry, but separating Pa from uranium-niobium alloys, a common material in the nuclear fuel cycle, is challenging due to the chemical similarity of Pa and Nb. Here, in this work, we present three resin chromatography separation techniques for isolating Pa from U and Nb which were independently developed by three different laboratories through ad hoc adaptations of standard operating procedures. Our results underscore the need for and value of purification methods suitable for a diversity of uranium-based materials to ensure the operational readiness of nuclear forensics laboratories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

An introduction to the significance of sample size in particle analyses for nuclear forensics and radiological investigations

Particulate isotopic analysis in nuclear forensics has developed rapidly during the past two decades due to technical advances in determining the isotopic composition of individual particles. This paper introduces basic statistical concepts that can be applied by analysts to understand the importance of statistical adequacy when interpretating particle data. While these basic statistical methods provide a useful point-of-entry to particle data analysis, more sophisticated statistical and modeling approaches are needed to extract maximal information from such datasets in the future.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Nuclear forensic analysis on uranium ore concentrates: A multi-laboratory intercomparison study

A collaboration between the United States and Japan performed nuclear forensic analyses on a set of 7 uranium ore concentrate (UOC) samples with known origins to improve methodologies and best practices in the field. Overall, good agreement between the 4 participating laboratories was achieved for uranium assay, uranium isotope composition, strontium isotope composition, and trace element analysis. In conclusion, the combination of these four signatures is sufficient to distinguish the different origins among these studied UOCs.

and nuclear chemistry↗
DOE OSTI2025

Interlaboratory comparison of secondary ion mass spectrometry analysis results from the 7th international technical nuclear forensics working group collaborative material exercise

A review and interlaboratory comparison of secondary ion mass spectrometry (SIMS) data obtained by laboratories participating in the International Technical Nuclear Forensics Working Group (ITWG) 7th Collaborative Material Exercise (CMX-7) has been conducted. The analyzed materials were two uranium compounds in powder form and two pieces of uranium metal. The instruments used in this comparison were a small-geometry (SG) SIMS, CAMECA IMS 7f from the Research Centre Rez, and a large-geometry (LG) SIMS, CAMECA IMS 1280 from Los Alamos National Laboratory. Despite the differences in instruments and analytical procedures, e.g., sample preparation, SIMS setups, and data post-processing, there was good agreement for the 234 U/ 238 U, 235 U/ 238 U, and 236 U/ 238 U ratios in the analyzed materials. The main difference was in the precision, which was, as expected, higher for the LG-SIMS. In addition, a comparison between the laboratories was also made for the image processing algorithms applied to raw data acquired in automated particle measurement (APM) mode. In conclusion, the result of this comparison has led to identification of best practices for setting up parameters of the APM software.

and nuclear chemistry↗
DOE OSTI2022

Nondestructive and destructive assay for forensics characterization of weapons-grade plutonium produced in LEU irradiated in a thermal neutron spectrum

A post-irradiation examination (PIE) of an enriched uranium sample was performed to advance a nuclear forensics methodology, based on intra-elemental nuclide ratios. The PIE was carried out on a few milligrams of low enriched uranium dioxide (LEUO 2 ) sample, irradiated to a burnup of 1 GWd/MTU. The PIE of the LEUO 2 sample provided the concentration of selected plutonium and fission product nuclides ( 239 Pu, 240 Pu, 241 Pu, 91 Y, 95 Zr, 95 Nb, 103 Ru, 133 Cs, 134 Cs, 135 Cs, 137 Cs, 136 Ba, 138 Ba, 140 Ba, 140 La, 141 Ce, 144 Ce, 149 Sm, 150 Sm, 152 Sm, 153 Eu, and 154 Eu). The PIE supported validation of the MCNP (Monte Carlo N-Particle neutronics simulation code) predicted concentration of these nuclides in the irradiated LEUO 2 sample. This study showed that most of the MCNP predicted nuclide concentrations were within 15% and several within 10% compared to the PIE results. Furthermore, gamma and mass spectrometry of nuclides in irradiated LEUO 2 enabled the determination of uranium burnup and time since irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Planning, design and logistics of a decision analysis study: The FBI/Ames study involving forensic firearms examiners

This paper describes design and logistical aspects of a decision analysis study to assess the performance of qualified firearms examiners working in accredited laboratories in the United States in terms of accuracy (error rate), repeatability, and reproducibility of decisions involving comparisons of fired bullets and cartridge cases. The purpose of the study was to validate current practice of the forensic discipline of firearms/toolmarks (F/T) examination. It elicited error rate data by counting the number of false positive and false negative conclusions. Preceded by the experimental design, decisions, and logistics described herein, testing was ultimately administered 173 qualified, practicing F/T examiners in public and private crime laboratories. The first round of testing evaluated accuracy, while two subsequent rounds evaluated repeatability and reproducibility of examiner conclusions. This project expands on previous studies by involving many F/T examiners in challenging comparisons and by executing the study in the recommended double-blind format.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Review of multi-faceted morphologic signatures of actinide process materials for nuclear forensic science

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. In conclusion, this review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Measuring very low radiation doses in PTFE for nuclear forensic enrichment reconstruction

Every country that has made nuclear weapons has used uranium enrichment to do so. Despite the centrality of this technology to international security, there is still no reliable physical marker of past enrichment in the open literature that can be used to perform forensic verification of historically produced weapons on gas centrifuges. We show that the extremely low radioactivity from uranium alpha emissions during enrichment leaves detectable and irreversible calorimetric signatures in the common enrichment gasket material PTFE, allowing for historical reconstruction of past enrichment activities at a sensitivity better than one weapon’s quantity of highly enriched uranium. Fast scanning calorimetry also enables the measurement of recrystallization enthalpies of sequentially microtomed slices, confirming the magnitude and the type of radiation exposure while also providing detection of tampering and a method for analyzing field samples useful for treaty verification. Furthermore, this work opens the door for common items to be turned into precise dosimeters to detect the past presence of radioactivity, nuclear materials, and related activities with high confidence.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Samarium isotope compositions of uranium ore concentrates: A novel nuclear forensic signature

Uranium ore is mined and milled to produce uranium ore concentrate (UOC), a regulated product of the nuclear fuel cycle. Additionally, diversion of UOC from the fuel cycle into possible weapons production is a key concern in global nonproliferation efforts. As such, the ability to trace the origin of seized nuclear materials is imperative to law enforcement efforts. Although isotopic signatures of UOCs have proven fruitful to pinpoint sample provenance, new isotopic signatures are needed because most existing isotopic signatures are not indicative of the original ore body from which the U is derived. In this work, we developed a new method to separate samarium (Sm) from a U-rich sample matrix and report the first Sm isotope compositions of 32 UOCs derived from a variety of worldwide uranium mines. Relative to terrestrial standards, approximately half the UOCs have resolved and anticorrelated 149 Sm- 150 Sm isotope compositions, consistent with the capture of thermal neutrons by 149 Sm in the ore body. The UOCs with anomalous Sm isotope compositions tend to derive from older (~>1.5Ga) and higher-grade ore bodies, although other factors, such as the presence of neutron moderators like water, also play a role. Nonetheless, the Sm isotope compositions of UOCs directly reflects the neutron fluence over the history of the original ore body and can be used to discern different geologic conditions associated with that ore body. Overall, this work demonstrates the potential use of Sm isotopes as a novel nuclear forensics signature for origin assessment of UOCs.

58 GEOSCIENCES↗
DOE OSTI2020

Innovative high-temperature ammonium bifluoride fusion and rapid analysis of elements with nuclear forensic value

High-temperature ammonium bifluoride (ABF) fusions were evaluated for potential use in rapid dissolution of post-detonation nuclear debris. The ABF fusion was carried out in a Pt crucible which allowed evaluation of higher fusion and evaporation temperatures. The high-temperature ABF fusion dissolution method was evaluated using geological reference materials: USGS QLO-1a Quartz Latite, USGS SDC-1 Mica Schist, and NIST 278 Obsidian Rock. The optimized dissolution method involved a 10 min fusion at 540 °C, a 5 min reflux in 8 M HNO 3 , an evaporation at 300 °C and final dilution into 45 mL of 2% (v/v) HNO3. The final solution was filtered after heating at 105 °C using a hotblock. This dissolution method was simple, requiring only a hotplate or hotblock, filtered samples were available for ICP-MS analysis or radiochemical separation within 150 min, and was found to have high (>90%) recovery for many isotopes of interest in nuclear forensics applications. U and Pu in the dissolved material was separated using TEVA and UTEVA extraction chromatography columns, a process which resulted in >90% recovery. An irradiated U tracer was spiked into the material prior to dissolution and analyzed for recovery of major fission products and 239Np. The monitored radionuclides had recoveries of greater than 90%, except for the volatile radioiodine isotopes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Mechanistic Investigation of Co(II) Extraction by TODGA to Aid Nuclear Forensic Separations

Nuclear forensic (NF) analysis supports law enforcement inquiries by analyzing evidence tainted with radioactive substances. Separation techniques can be used to identify and quantify actinides and fission 15 products in post-detonation (PD) debris. Environmental transition metals, also present in PD residues, have been observed to impact critical isotope extractions. For example, radio stable cobalt (Co), ubiquitous in urban environments, particularly in corrosion-resistant alloys, paint-drying agents, dyes, and pigments, can impact the separation of important actinides and fission products. The presented work aims to elucidate the chemistry Q2 governing Co extraction in samples pertinent to PDNF. Chemistry between Co and N,N,N,‘N’-tetraoctyl diglyco- 20 lamide (TODGA), the ligand present in the commercial chromatographic resin diglycolamide (DGA), were studied via solvent and chromatographic extraction and spectroscopic analyses. These results indicate that a tetrahedral Co(II) species is extracted by TODGA from highly acidic (>5M HCl) solutions via a spontaneous entropy-driven reaction. Furthermore, extraction trends in varied acid concentrations are consistent between solvent extraction and chromatographic extraction methods.

activation product↗
DOE OSTI2021

Overview of Algorithms for Using Particle Morphology in Pre-Detonation Nuclear Forensics

A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling’s T 2 ), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗