The inorganic scintillator used in gamma spectroscopy must have good efficiency in converting the kinetic energy of charged particles into light as well as high light output and high light detection efficiency. Accordingly, various studies have been conducted to enhance the net-efficiency. One way to improve the light yield has been studied by coating scintillators with various nanoparticles, so that the scintillation light can undergo resonance on surface between scintillators and nanoparticles resulting in higher light yield. In this study, an inorganic scintillator coated with CsPbBr3 perovskite nanocrystals using dip coating technique was proposed to improve scintillation light yield. The experiment was carried out by measuring scintillation light output, as the result of interaction between inorganic scintillator coated with CsPbBr3 perovskite nanocrystals and gamma-ray emitted from Cs-137 gamma source. The experimental results show that the channel corresponding to 662 keV full energy peak in the Cs-137 spectrum shifted to the right by 14.37%. Further study will be conducted to investigate the detailed relationships between the scintillation light yield and the characteristics of coated perovskite nanoparticles, such as diameter of nanoparticles, coated area ratio and width of coated region.

In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

As the decommissioning and decontamination (D&D) of nuclear power plants (NPPs) has actively proceeded worldwide, the management of radiation exposure of workers has become more critical. Radioactive aerosol is one of the main causes of worker exposure, contributing to internal exposure by inhalation. It occurs in the process of cutting radioactive metal structures or melting radioactive wastes during D&D, and its distribution varies according to decommissioning strategies and cutting methods. Among the dominant radionuclides in radioactive aerosols, Fe-55 is known to be the most abundant. Fe-55, which decays by electron capture, is classified as a difficult-to-measure (DTM) radionuclide because its emitted X-rays have too low energy to measure directly from outside of the container. Generally, for measuring DTM nuclides, the liquid scintillation counting (LSC) method and the scaling factor (SF) method are used. However, these methods are not suitable for continuous monitoring of the D&D workplace due to the necessity of sampling and additional analysis. The radiation measurement system that can directly measure the radionuclides collected at the aerosol filter could be more useful. In this study, as preliminary research on developing the radioactive aerosol monitoring system, we fabricated a gamma-ray spectrometer based on a NaI (Tl) scintillator and measured the energy spectrum of Fe-55. A beryllium window was applied to the scintillator for X-ray transmission, and the Fe-55 check source was directly attached to the scintillator assuming that the aerosol filter was equipped. 5.9 keV photopeak was clearly observed and the energy resolution was estimated as 44.10%. Also, the simultaneous measurement with Cs-137 was carried out and all the peaks were measured.

Plastic scintillators can be used to find radioactive sources for portal monitoring due to their advantages such as faster decay time, non-hygroscopicity, relatively low manufacturing cost, robustness, and easy processing. However, plastic scintillators have too low density and effective atomic number, and they are not appropriate to be used to identify radionuclides directly. In this study, we devise the radiation sensor using a plastic scintillator with holes filled with bismuth nanoparticles to make up for the limitations of plastic materials. We use MCNP (Monte Carlo N-particle) simulating program to confirm the performance of bismuth nanoparticles in the plastic scintillators. The photoelectric peak is found in the bismuth-loaded plastic scintillator by subtracting the energy spectrum from that of the standard plastic scintillator. The height and diameter of the simulated plastic scintillator are 3 and 5 cm, respectively, and it has 19 holes whose depth and diameter are 2.5 and 0.2 cm, respectively. As a gamma-ray source, Cs-137 which emits 662 keV energy is used. The clear energy peak is observed in the subtracted spectrum, the full width at half maximum (FWHM) and the energy resolution are calculated to evaluate the performance of the proposed radiation sensor. The FWHM of the peak and the energy resolution are 61.18 keV and 9.242% at 662 keV, respectively.

Gamma-ray spectroscopy, which is an appropriate method to identify and quantify radionuclides, is widely utilized in radiological leakage monitoring of nuclear facilities, assay of radioactive wastes, and decontamination evaluation of post-processing such as decommissioning and remediation. For example, in the post-processing, it is conducted to verify the radioactivity level of the site before and after the work and decide to recycle or dispose the generated waste. For an accurate evaluation of gamma-ray emitting radionuclides, the measurement should be carried out near the region of interest on site, or a sample analysis should be performed in the laboratory. However, the region is inaccessible due to the safety-critical nature of nuclear facilities, and excessive radiation exposure to workers could be caused. In addition, in the case of subjects that may be contaminated inside such as pipe structures generated during decommissioning, surveying is usually done over the outside of them only, so the effectiveness of the result is limited. Thus, there is a need to develop a radiation measurement system that can be available in narrow space and can sense remotely with excellent performance. A liquid light guide (LLG), unlike typical optical fiber, is a light guide which has a liquid core. It has superior light transmissivity than any optical fiber and can be manufactured with a larger diameter. Additionally, it can deliver light with much greater intensity with very low attenuation along the length because there is no packing fraction and it has very high radiation resistant characteristics. Especially, thanks to the good transmissivity in UV-VIS wavelength, the LLG can well transmit the scintillation light signals from scintillators that have relatively short emission wavelengths, such as LaBr3:Ce and CeBr3. In this study, we developed a radiation sensor system based on a LLG for remote gamma-ray spectroscopy. We fabricated a radiation sensor with LaBr3:Ce scintillator and LLG, and acquired energy spectra of Cs-137 and Co-60 remotely. Furthermore, the results of gamma-ray spectroscopy using different lengths of LLG were compared with those obtained without LLG. Energy resolutions were estimated as 7.67%, 4.90%, and 4.81% at 662, 1,173, and 1,332 keV, respectively for 1 m long LLG, which shows similar values of a general NaI(Tl) scintillator. With 3 m long LLG, the energy resolutions were 7.92%, 5.48%, and 5.07% for 662, 1,173, and 1,332 keV gamma-rays, respectively.

In this study, the positions of Cs-137 gamma ray source are estimated from the plastic scintillating fiber bundle sensor with length of 5 m, using machine learning data analysis. Seven strands of plastic scintillating fibers are bundled by black shrink tube and two photomultiplier tubes are used as a gamma ray sensing and light measuring devices, respectively. The dose rate of Cs-137 used in this study is 6 μSv·h−1. For the machine learning modeling, Keras framework in a Python environment is used. The algorithm chosen to construct machine learning model is regression with 15,000 number of nodes in each hidden layer. The pulse-shaped signals measured by photomultiplier tubes are saved as discrete digits and each pulse data consists of 1,024 number of them. Measurements are conducted separately to create machine learning data used in training and test processes. Measurement times were different for obtaining training and test data which were 1 minute and 5 seconds, respectively. It is because sufficient number of data are needed in case of training data, while the measurement time of test data implies the actual measuring time. The machine learning model is designated to estimate the source positions using the information about time difference of the pulses which are created simultaneously by the interaction of gamma ray and plastic scintillating fiber sensor. To evaluate whether the double-trained machine learning model shows enhancement in accuracy of source position estimation, the reference model is constructed using training data with one-time learning process. The double-trained machine learning model is designed to construct first model and create a second training data using the training error and predetermined coefficient. The second training data are used to construct a final model. Both reference model and double-trained models constructed with different coefficients are evaluated with test data. The evaluation result shows that the average values calculated for all measured position in each model are different from 7.21 to 1.44 cm. As a result, by constructing the double-trained machine learning model, the final accuracy shows 80% of improvement ratio. Further study will be conducted to evaluate whether the double-trained machine learning model is applicable to other data obtained from measurement of gamma ray sources with different energy and set a methodology to find optimal coefficient.

We describe recent observations and measurements realized with microdot (MDOT) detectors having 50 mm, 100 mm, and 200 mm pitches for 3.7 keV X-rays. A gas gain of 3.1×103 has been measured with 50 mm pitch MDOT at potential difference much lower than those usually obtained with MSGCs. The defocusing effect caused by the existence of the readout lines passing below the insulator layer has been investigated from the drift voltage dependence of the count rate variation and the electric field simulation for all the detectors. Results concerning the spatial resolutions are presented with collimated X-ray beams.