The orientation measurement of isotope neutron sources is critical in nuclear security and safeguards, particularly for monitoring special nuclear materials. This paper introduces a cubic neutron detection device composed of six planar neutron detectors for orientation detection in neutron source localization. A comprehensive Monte Carlo simulation was conducted, and the results showed that the planar neutron detector, based on CdTe/CdS semiconductor layers and 6Li foils, achieved a thermal neutron detection efficiency of approximately 50%. The orientation of the neutron source can be inferred from the relative neutron count ratios among the six planar detectors. To improve the measurement precision, machine learning algorithms, including k-nearest neighbor, random forest algorithm, and multi-layer perceptron were implemented using simulated data from a 252Cf neutron source at varying distances and orientations. The system achieved an angular classification accuracy exceeding 90% at 5-deg intervals, with regression-based models yielding a root-mean-square error of approximately 1 deg, confirming the effectiveness of the proposed approach for neutron source orientation detection.

Detection of special nuclear materials (SNMs) is of vitalimportancein the prevention of nuclear terrorism and to secure states’national security. Neutron detection is a particularly useful toolto identify SNM, and neutron-sensitive scintillators have many promisingproperties, such as ease of use, good time resolution, and high detectionefficiency. In this work, we develop highly stable, self-oriented,ultrafast 1D ZnO:Li (and codoped with Al, Ga, and In) nanorods (NRs)as thermal neutron-sensitive scintillators. Lithium-6 has high thermalneutron cross section for the (n, α) reactionin ZnO:Li scintillators which have a vertical nano array design greatlyincreasing the effective surface area and scintillation efficiency.Cost-effective low-temperature (95 °C) hydrothermal growth isused to obtain highly crystalline ZnO:Li nano scintillators by combiningnuclear range data and electron transport mechanisms. Among the studiesusing low-temperature hydrothermal synthesis and a relatively lowannealing temperature (≈350 °C) along with optimized NRs(length ≈ 5–8 μm, mean diameter ≈ 700 nm)for thermal neutron detection, this study reports the shortest scintillationdecay time (≈ 470 ps) so far to the best of our knowledge.This nano array scintillator combines the advantages of a low-costgrowth technique with environmentally friendly and widely availablematerials.

The capability to discriminate among nuclear fuel properties is essential for a successful nuclear safeguard and security program. Accurate nuclear material identification is hindered due to challenges such as differing levels of enrichments, weak radiation signals in the case of fresh nuclear fuel, and complex self-shielding effects. This study explores the application of supervised machine learning algorithms to digitized radiation detector data for classifying signatures of special nuclear materials. Three scintillation detectors, an EJ-309 liquid scintillator, a CLYC crystal scintillator, and an EJ-276 plastic scintillator, were used to measure gamma-ray and neutron data from special nuclear material at the National Criticality Experiments Research Center (NCERC) at the National Nuclear Security Site (NNSS), at Nevada, USA. Radiation detector pulse data was extracted from the collected digitized data and applied to three separate supervised learning models: Random Forest, XGBoost, and a feedforward Deep Neural Network, chosen for their wide-spread use and distinct data ingest and processing analytics. Through model refinement, such as adding an additional parameter feature, an accuracy of greater than 95% was achieved. Analysis on model parameter feature importance revealed Countrate, which is the overall gamma-ray and neutron incidents for each detector, was the most influential parameter and essential to include for improved classification. Initial model versions not including the Countrate parameter feature failed to classify. Supervised learning models allow for measured gamma-ray and neutron pulse data to be used to develop effective identification and discrimination between material compositions of different fuel assemblies. The study demonstrated that traditional pulse shape parameters alone were insufficient for discriminating between special nuclear materials; the addition of Countrate markedly improved model accuracy but all models were heavily dependent on this specific feature, thus illustrating the need for alternative, more distinct parameter features. The machine learning development framework captured in this study will be beneficial for future applications in discriminating between different fuel enrichments and additives such as burnable poisons.

Theoretical time cost to distinguish special nuclear materials in different scenarios through MPRC-ToF based muon scattering tomography

This paper investigates the acoustic frequency response of a Magnox package with internal pressure ranging from below atmospheric pressure to 3 bar. Vibrational resonant frequencies are measured using electromagnetic acoustic transducers (EMATs) and a finite element (FE) model is used to aid in interpreting the experimental results. This work builds on two previous studies that explored the vibrational resonant frequency response of Magnox packages at internal pressures above atmospheric pressure and addresses a gap in the literature by extending the analysis over a wider pressure range than these packages. The results demonstrate a strong correlation between the measured and modelled frequencies, validating the accuracy of the FE model in predicting the vibrational behaviour of the Magnox package under different pressure conditions. This work provides deeper insights into the acoustic characteristics of Magnox packages, which are essential for their ongoing safe and effective management.

New Spectral and Textural Feature Combinations for Corrosion Detection in Hyperspectral Images of Special Nuclear Materials Packages

Isotopic identification of special nuclear materials based on delayed $$\gamma$$ rays from photofission fragments

Template matching authentication technology is a critical means of protecting sensitive information, with widespread applications in security monitoring and verification. Nevertheless, the intrusiveness of this technology risks the leakage of the verified party’s sensitive information. To address this issue, we propose a method combining nuclear resonance fluorescence (NRF) technology with template matching authentication technology. The template gamma spectrum derived from NRF-based template matching authentication contains no sensitive information and can accurately verify the authenticity of special nuclear materials. We mainly investigated the principles of encrypted authentication based on NRF and the complete verification of the experimental objects. Using Monte Carlo simulations, we generated the template gamma spectrum during the verification, as well as the experimental objects’ gamma spectrum. By identifying six experimental objects, we found that the NRF-based template matching authentication technology could correctly identify encrypted experimental objects’ authenticity, indicating the technology’s effectiveness.

Monte Carlo simulation of an accounting system for small amounts of special nuclear material using fast neutron multiplicity counting

Facilities that handle special nuclear materials must account for the entire mass of materials in their control. Should that material be diverted, it must be traced back to its origins. This work explores whether uranium oxide precipitation routes can be classified through quantified morphology without human interaction. The system to achieve this started with a program to generate quantified morphology for algorithmically segmented images. The quantified morphological attributes for each segment were averaged before being used to train and test various machine learning (ML) classifiers on a five-class problem. Seven processing factors were evaluated in a design of experiments (DOE) study to determine their impact on classification accuracy using analysis-of-variance techniques. These processing factors included process steps like denoising, normalization, and augmenting the ML data with the final oxide type (UO3, U3O8, or UO2) and the image pixel width. The factors that offered the best accuracy included dataset augmentation with the final oxide and pixel width. The most accurate standard ML classifiers were the support vector machine (SVM) and random forest. The most beneficial DOE factors were applied to the most accurate classifiers, which were subsequently retrained resulting in an accuracy as high as 89% with an SVM. These experiments were repeated after dropping two attributes called “grayscale mean” and “gradient mean” from the training data to check for bias in the data, which reduced the best accuracy to 85%. This paper also includes an experiment to automatically select the watershed segmentation threshold using clustering analysis techniques. Those techniques used a dimension reduction algorithm and a clustering algorithm with silhouette scoring to select the best segmentation threshold for each image in the dataset. However, this reduced classifier accuracy by an average of 17%. Regardless, this work demonstrates that uranium oxide precipitation routes can be classified using morphology without human interaction with up to 89% accuracy.

ABSTRACT A symmetric spherical design for a Fast Neutron Multiplicity Counting (FNMC) system is proposed by our group. Two systems made of 32 and 16 liquid scintillation detectors were simulated by Geant4, and the spatial uniformities of the detection efficiencies of the two systems were characterized. Compared with the system made of 16 liquid scintillation detectors, the detection efficiency of the system made of 32 liquid scintillation detectors is more uniform, due to the solid angle covered by 32 detectors is larger than that covered by 16 detectors. The relative deviations (RDs) of its detection efficiencies within a spherical radius of 6 cm in the system are less than ± 3.3%. Thus, a FNMC system made of 32 liquid scintillation detectors was built. Several 252Cf neutron sources were placed on the sample platform with different spatial distributions to characterize the spatial uniformity of the detection efficiencies of the system. And their spontaneous fission (SF) rates were calculated using the FNMC analytical equation. The results show that the RDs of the detection efficiencies within a radius of 6 cm in the system are less than ± 2%, and the RDs of the estimated SF rates of the neutron sources are less than ± 1.8%.

Accurately detecting nuclear materials concealed within the bulk of cargo containers is essential for establishing a robust defense against nuclear terrorism. Identifying such hidden materials can be achieved through imaging techniques that are ideally non-intrusive—meaning the container does not need to be manually opened—and capable of providing quick and precise identification of the contents. Muon tomography is one such effective imaging technique, utilized across various fields. This technique reconstructs cargo images using cosmic-ray muons, highly penetrative particles that reach the Earth's surface from the upper atmosphere and interact with materials primarily through Coulomb scattering. This study conducts a simulated examination of cosmic-ray muon tomography to detect special nuclear materials, specifically focusing on identifying uranium isotopes U-235 and U-238, as well as plutonium (Pu-239), hidden within large liquid freight containers. A series of muon simulations is performed using the Geant4 software platform to explore the potential for imaging small amounts of concealed nuclear materials within large-scale containers filled individually with water or oil. The results confirm the accurate detection and localization of illicit nuclear content within the substantial volume of liquid freight containers.

Detecting shielded special nuclear material, such as nuclear explosives, is a difficult challenge pursued by non-proliferation, anti-terrorism, and nuclear security programs worldwide. Interrogation with intense fast-neutron pulses is a promising method to characterize concealed nuclear material rapidly but is limited by suitable source availability and proven instrumentation. In this study we have pioneered a demonstration of such an interrogation method using a high-intensity, short-pulse, laser-driven neutron source that offers potential benefits compared to conventional neutron sources. The measurement results reported here represent the first experimental demonstration of this interrogation approach on enriched uranium items and demonstrate the feasibility of a precise measurement using realistic nuclear materials, representative of field scenarios, even with just a single laser-driven neutron pulse. Bright pulsed sources can overcome the nuisance background of items with strong internal neutron sources, improving analytical power, while single-shot assay is attractive in high-throughput situations where time is at a premium. The science and technology of this type of neutron production is developing rapidly, and we anticipate that practical mobile interrogation systems will become available based on the detection concepts demonstrated here to meet the growing measurement needs.

X-ray scanning is widely used by customs for visual inspection of cargo containers at border controls. When Special Nuclear Material (SNM) such as uranium or plutonium is suspected, an active non-destructive nuclear measurement method serves as a second-line tool. The photofission reaction, central to the Active Photon Interrogation (API) technique, enables the direct detection of SNM. This paper reviews European advances in API over the past decade through three Horizon 2020 projects. In C-BORD (2015–2018), Europe’s first photofission system was tested at the Maasvlakte terminal of the Port of Rotterdam (Netherlands), with experiments on depleted uranium and container mock-ups demonstrating integration into a stationary 9 MeV industrial X-ray scanning facility. The ENTRANCE project (2020–2023) evaluated a mobile 7 MeV X-ray scanner at the Škrljevo terminal of the Port of Rijeka (Croatia), investigating the 5–6 MeV actinide photofission threshold and assessing operational performance. The MULTISCAN 3D project (2021–2025) explored new approaches using high-energy photons from laser–plasma sources. Collectively, these developments have strengthened nuclear security and enhanced Europe’s capacity to counter illicit SNM trafficking.

The assay of Special Nuclear Materials (SNM) and Minor Actinides (MA) in highly-radioactive nuclear materials poses a significant challenge for nuclear non-proliferation and security. No single nondestructive method can provide acceptable and practicable results for such materials. Therefore, the Japan Atomic Energy Agency (JAEA) and the Joint Research Centre of the European Commission (EC-JRC) have been developing an active neutron nondestructive assay (NDA) system. We utilize four different NDA techniques, namely Differential Die-Away Analysis (DDA), Prompt Gamma-ray Analysis (PGA), Neutron Resonance Transmission Analysis (NRTA) and Delayed Gamma-ray Analysis (DGA). These are promising and effective active neutron techniques especially for nuclear material accountancy. The techniques offer complementary information and can enhance the accuracy and reliability of the assay. The project focused on the development of the integrated NDA system for highly-radioactive materials. The new project, which started in 2022, aims to develop a compact NRTA system, because a more versatile system is required for nuclear security applications, and NRTA is considered as one of the most accurate NDA techniques to determine the amount of SNM and MA and is suitable for measurements of highly-radioactive nuclear materials.

Developments in photofission prompt neutron detection are crucial in nuclear instrumentation, whether for waste characterization and security applications. Bremsstrahlung photons, emitted by a linear electron accelerator (linac), can induce photofission reactions when interacting with actinides. These reactions then emit prompt and delayed neutrons and delayed gammas. However, the intense photon flash from the linac saturates detection systems, commonly He-3 proportional counters, making prompt neutron instantaneous detection challenging. This study evaluates a photofission prompt neutron detection system coupled with a 9 MeV linac to investigate the use of prompt neutrons as a third signature, alongside delayed neutrons and gammas. This to reduce uncertainties in detecting, differentiate and quantify actinide despite an attenuated interrogating flux or absorbed photofission particles by the environment. First, with Monte Carlo codes PHITS and MCNP6 and their respective nuclear data libraries (ENDF/B-VIII.0 and JENDL-5), we assessed the parameter of a photofission setup which consists in detecting the delayed 6.131 MeV gamma radiation emitted by N-16 decay, resulting from (n, α) activation of F-19. This approach avoids the impact of the linac’s photon flash and minimizes the strong photoneutron background. Secondly, we conducted experimental tests with neutron sources and then with a 9 MeV electron accelerator, Linatron® M9A (Varex Imaging Corp.), at the SAPHIR platform (CEA Paris-Saclay, France), using machined polytetrafluoroethylene and a bismuth germanate scintillator to detect prompt neutrons from a depleted UMo sample. This work enhances prompt neutron detection developments and contributes to advancements in characterizing special nuclear materials (SNMs) by photofission for various applications.

A comprehensive analysis of a novel dual-mode radiation detection system developed for preventing illicit trafficking of special nuclear materials and radioactive sources is presented. The system combines EJ-200 and EJ-426HD plastic scintillators with silicon photomultipliers to simultaneously detect gamma/beta radiation and neutrons, designed for integration with unmanned aerial vehicles and handheld operation. Through extensive experimental validation and comparative analysis against established detection methods, the system demonstrates superior performance across multiple evaluation criteria including localization accuracy, detection time, and cost-effectiveness.

The international radiological and nuclear (RN) community recognizes improvised nuclear devices (INDs) as a significant security threat. To maintain border security, efficient and reliable detection solutions at points of entry are critical. Screening of containerized cargo for INDs and RN materials is primarily done using drive-through radiation portal monitors (RPMs). Globally, advanced computing algorithms, including machine learning and data analytics, are being developed and enhanced to improve the consistency and accuracy of RN material identification. However, these data analytics algorithms require augmentation with true positive scan data covering the full threat space, including scenarios involving INDs of varying intensities and shielding configurations. Due to the strict controls on large quantities of special nuclear materials in diverse geometries and isotopic compositions, many RPMs and portable detectors have been deployed without adequate testing against realistic IND threats. This has led to high false alarm rates, requiring time-consuming secondary screenings, and may also increase the probability of false negatives, allowing real threats to go undetected. Conversely, training algorithms using full-mass INDs introduce nuclear criticality risks. In this paper we present a 3D-printed radiation source designed to mimic high-mass solid INDs by distributing the radioactive material along a thin, hollow shell. Thanks to the self-shielding effect, the 3D-printed radiation source achieves radiological performance comparable to that of a solid high-mass source with less material. This approach offers a solution to challenges related to source availability and nuclear criticality in training environments. A scaled-down prototype was fabricated, and its radiological performance was experimentally evaluated.

North Korea’s nuclear armament has placed South Korea in a dilemma, as it can neither rely entirely on the United States’ nuclear extended deterrence (NED) nor pursue its own nuclear weapons development (the nuclear option). The reliability of the US NED has diminished, given North Korea’s ability to deploy intercontinental ballistic missiles capable of striking the US mainland. However, pursuing the nuclear option is fraught with difficulties due to the stringent restrictions imposed by the international non-proliferation regime. This article evaluates the feasibility of South Korea’s nuclear option using three factors: opportunity, willingness and the availability of special nuclear materials. The findings suggest that the feasibility of South Korea’s nuclear option is very low. Consequently, the article advises the South Korean public to acknowledge this reality rather than making emotional demands for the nuclear option. Furthermore, it calls upon the international community to engage in discussions and address the dilemma faced by non-nuclear US allies, such as South Korea, in light of the growing threat of nuclear attacks from nuclear-armed states like Russia and North Korea.

A matrix effect correction method for fissile nuclear material mass measurement by delayed neutrons