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Special Issue "Measurements, Instrumentation, Sensing and Simulation Techniques for the Detection of Radiation"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 15 December 2022 | Viewed by 7999

Special Issue Editors

Prof. Dr. Andrea Malizia
E-Mail Website
Guest Editor
Assistant Professor in Nuclear Measures and Radiological Safety at the Department of Biomedicine and Prevention, University of Rome Tor Vergata
Interests: Nuclear Measures; Nuclear Instrumentations; Software for the prediction of radiological/nuclear dispersion/diffusion; CBRNe security and safety
Prof. Dr. Tzany Kokalova Wheldon
E-Mail Website
Guest Editor
Nuclear Physics Group, School of Physics and Astronomy, University of Birmingham, Birmingham, UK
Interests: experimental nuclear physics/astrophysics; implementation of novel detection and analysis techniques; medical isotopes production; applications and industry-related nuclear decommissioning

Special Issue Information

Dear Colleagues,

This special issues will host original papers on both, fundamental and applied research and reviews regarding the design, construction and use of instrumentation, methodologies and techniques for the detection of nuclear radiation generated by natural and artificial radionuclides or by nuclear reactions used in several application fields (energy, medicine, industry, security and safety).  The papers can be oriented also on: the design and construction of systems for innovative nuclear measurements; the measurements and instrumentation for nuclear plants; the measurements and instrumentation used in nuclear decommissioning or waste management; the applications of radioisotopes in industrial and non-industrial fields; the detection of environmental radioactivity and nuclear metrology; safety and protection from radiation (Radioprotection); the geological, archaeological and environmental dating; methods and detection techniques for radioecology; the sensing techniques and instrumentation for biomedical application; radiometric measurements including gamma and particle emissions; the development and performance of nuclear instrumentation including radiation spectrometry, dosimetry and novel counting systems.

Prof. Dr. Andrea Malizia and Prof. Dr. Tzany Kokalova Wheldon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

radiation measurement; detection; simulations; radionuclides; instrumentation; metrology; radioprotection; gamma and particle spectroscopy

Published Papers (8 papers)

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Research

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Communication
Real-Time Monitoring Method for Radioactive Substances Using Monolithic Active Pixel Sensors (MAPS)
Sensors 2022, 22(10), 3919; https://doi.org/10.3390/s22103919 - 22 May 2022
Viewed by 517
Abstract
This study presents a real-time monitoring technique for radioactive substances that meets safety management needs. We studied the accumulation characteristics of radiation response signals of monolithic active pixel sensors (MAPSs) based on their response and discrimination ability to gamma (γ) photon or neutron [...] Read more.
This study presents a real-time monitoring technique for radioactive substances that meets safety management needs. We studied the accumulation characteristics of radiation response signals of monolithic active pixel sensors (MAPSs) based on their response and discrimination ability to gamma (γ) photon or neutron radiation. The radiation status of the radioactive substances was determined by monitoring the accumulation data of radiation responses. As per the results, Am-Be and 252Cf radiation response signals are primarily concentrated in the range of 0–70 pixels. Response signals of 60Co and 137Cs γ-ray were concentrated in two regions; there was a peak in the region with a pixel value of less than 50, and a plateau in the region with a pixel value of more than 75. Therefore, the results are able to discriminate between spectra. Furthermore, we designed a radioactivity monitoring system that is able to examine multiple radioactive materials. Its working principle is that a change in the accumulation of radioactivity monitoring data indicates a radiation change during the last accumulation cycle. This study provides vital technical support for the long-term supervision of radioactive substances. Full article
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Article
GEANT4 Simulation for Radioactive Particle Tracking (RPT) Technique
Sensors 2022, 22(3), 1223; https://doi.org/10.3390/s22031223 - 05 Feb 2022
Viewed by 789
Abstract
In the past two decades, the radioactive particle tracking (RPT) measurement technique has been proven to visualize flow fields of most multiphase flow systems of industrial interest. The accuracy of RPT, and hence the data obtained, depend largely on the calibration process, which [...] Read more.
In the past two decades, the radioactive particle tracking (RPT) measurement technique has been proven to visualize flow fields of most multiphase flow systems of industrial interest. The accuracy of RPT, and hence the data obtained, depend largely on the calibration process, which stands here as a basis for two subsequent processes: tracking and reconstruction. However, limitations in the RPT calibration process can be found in different experimental constrains and in assumptions made in the classical Monte Carlo approach used to simulate number of counts received by the detectors. Therefore, in this work, we applied a GEANT4-based Monte Carlo code to simulate the RPT calibration process for an investigated multiphase flow system (i.e., gas–liquid bubble column). The GEANT4 code was performed to simulate the number of counts received by 28 scintillation detectors for 931 known tracer positions while capturing all the types of photon interaction and overcoming solids’ angle limitations in classical approaches. The results of the simulation were validated against experimental data obtained using an automated RPT calibration device. The results showed a good agreement between the simulated and experimental counts, where the maximum absolute average relative deviation detected was about 5%. The GEANT4 model typically predicted the number of counts received by all the detectors; however, it over-estimated the counts when the number of primary events applied in the model was not the optimal. Full article
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Article
A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
Sensors 2022, 22(3), 1078; https://doi.org/10.3390/s22031078 - 29 Jan 2022
Viewed by 905
Abstract
Since the Fukushima Daiichi Nuclear Power Plant accident in March 2011, the technology available for unmanned aerial vehicles (UAVs) for radiation monitoring has improved greatly. Remote access to radiation-contaminated areas not only eliminates unnecessary exposure of civilians or military personnel, but also allows [...] Read more.
Since the Fukushima Daiichi Nuclear Power Plant accident in March 2011, the technology available for unmanned aerial vehicles (UAVs) for radiation monitoring has improved greatly. Remote access to radiation-contaminated areas not only eliminates unnecessary exposure of civilians or military personnel, but also allows workers to explore inaccessible places. Hazardous levels of radioactive contamination can be expected as a result of accidents in the nuclear power industry or as a result of the intentional release of radioactive materials for terrorist purposes (dirty bombs, building contamination, etc.). The possibility to detect, identify, and characterize radiation and nuclear material using mobile and remote sensing platforms is a common requirement in the radiation sensing community. The technology has applications in homeland security and law enforcement, customs and border protection, nuclear power plant safety and security, nuclear waste monitoring, environmental recovery, and the military. In this work, the authors have developed, implemented, and characterized a gamma-ray detection and spectroscopy system capable of operating on a UAV. The system was mainly developed using open-source software and affordable hardware components to reduce development and maintenance costs and provide satisfactory performance as a detection instrument. The designed platform can be used to perform mapping or localization tasks to improve the risk assessment process for first responders during the management of radiological and nuclear incidents. First, the design process of the system is described; the result of the characterization of the platform is then presented together with the use of the prototype installed on a UAV in an exercise simulating a radiological and nuclear contamination scenario. Full article
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Article
Novel Algorithm for Radon Real-Time Measurements with a Pixelated Detector
Sensors 2022, 22(2), 516; https://doi.org/10.3390/s22020516 - 10 Jan 2022
Viewed by 650
Abstract
Nowadays, radon gas exposure is considered one of the main health concerns for the population because, by carrying about half the total dose due to environmental radioactivity, it is the second cause of lung cancer after smoking. Due to a relatively long half-life [...] Read more.
Nowadays, radon gas exposure is considered one of the main health concerns for the population because, by carrying about half the total dose due to environmental radioactivity, it is the second cause of lung cancer after smoking. Due to a relatively long half-life of 3.82 days, the chemical inertia and since its parent Ra-226 is largely diffuse on the earth’s crust and especially in the building materials, radon can diffuse and potentially saturate human habitats, with a concentration that can suddenly change during the 24 h day depending on temperature, pressure, and relative humidity. For such reasons, ‘real-time’ measurements performed by an active detector, possibly of small dimensions and a handy configuration, can play an important role in evaluating the risk and taking the appropriate countermeasures to mitigate it. In this work, a novel algorithm for pattern recognition was developed to exploit the potentialities of silicon active detectors with a pixel matrix structure to measure radon through the α emission, in a simple measurement configuration, where the device is placed directly in air with no holder, no collection filter or electrostatic field to drift the radon progenies towards the detector active area. This particular measurement configuration (dubbed as bare) requires an α/β-discrimination method that is not based on spectroscopic analysis: as the gas surrounds the detector the α particles are emitted at different distances from it, so they lose variable energy amount in air depending on the traveled path-length which implies a variable deposited energy in the active area. The pixels matrix structure allows overcoming this issue because the interaction of α, β and γ particles generate in the active area of the detector clusters (group of pixels where a signal is read) of different shape and energy dispersion. The novel algorithm that exploits such a phenomenon was developed using a pixelated silicon detector of the TimePix family with a compact design. An α (Am-241) and a β (Sr-90) source were used to calibrate the algorithm and to evaluate its performances in terms of β rejection capability and α recognition efficiency. Successively, the detector was exposed to different radon concentrations at the ENEA-INMRI radon facility in ‘bare’ configuration, in order to check the linearity of the device response over a radon concentration range. The results for this technique are presented and discussed, highlighting the potential applications especially the possibility to exploit small and handy detectors to perform radon active measurements in the simplest configuration. Full article
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Article
Discrete Convolution-Based Energy Spectrum Configuring Method for the Analysis of the Intrinsic Radiation of 176Lu
Sensors 2021, 21(21), 7040; https://doi.org/10.3390/s21217040 - 23 Oct 2021
Viewed by 929
Abstract
There has been considerable interest in inorganic scintillators based on lutetium due to their favorable physical properties. Despite their advantages, lutetium-based scintillators could face issues because of the natural occurring radioisotope of 176Lu that is contained in natural lutetium. In order to [...] Read more.
There has been considerable interest in inorganic scintillators based on lutetium due to their favorable physical properties. Despite their advantages, lutetium-based scintillators could face issues because of the natural occurring radioisotope of 176Lu that is contained in natural lutetium. In order to mitigate its potential shortcomings, previous works have studied to understand the energy spectrum of the intrinsic radiation of 176Lu (IRL). However, few studies have focused on the various principal types of photon interactions with matter; in other words, only the full-energy peak according to the photoelectric effect or internal conversion have been considered for understanding the energy spectrum of IRL. Thus, the approach we have used in this study considers other principal types of photon interactions by convoluting each energy spectrum with combinations for generating the spectrum of the intrinsic radiation of 176Lu. From the results, we confirm that the method provides good agreement with the experiment. A significant contribution of this study is the provision of a new approach to process energy spectra induced by mutually independent radiation interactions as a single spectrum. Full article
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Article
Convolutional Neural Networks for Challenges in Automated Nuclide Identification
Sensors 2021, 21(15), 5238; https://doi.org/10.3390/s21155238 - 03 Aug 2021
Cited by 2 | Viewed by 922
Abstract
Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods [...] Read more.
Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID. Full article
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Article
Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning
Sensors 2021, 21(3), 684; https://doi.org/10.3390/s21030684 - 20 Jan 2021
Cited by 4 | Viewed by 1072
Abstract
Although plastic scintillation detectors possess poor spectroscopic characteristics, they are extensively used in various fields for radiation measurement. Several methods have been proposed to facilitate their application of plastic scintillation detectors for spectroscopic measurement. However, most of these detectors can only be used [...] Read more.
Although plastic scintillation detectors possess poor spectroscopic characteristics, they are extensively used in various fields for radiation measurement. Several methods have been proposed to facilitate their application of plastic scintillation detectors for spectroscopic measurement. However, most of these detectors can only be used for identifying radioisotopes. In this study, we present a multitask model for pseudo-gamma spectroscopy based on a plastic scintillation detector. A deep- learning model is implemented using multitask learning and trained through supervised learning. Eight gamma-ray sources are used for dataset generation. Spectra are simulated using a Monte Carlo N-Particle code (MCNP 6.2) and measured using a polyvinyl toluene detector for dataset generation based on gamma-ray source information. The spectra of single and multiple gamma-ray sources are generated using the random sampling technique and employed as the training dataset for the proposed model. The hyperparameters of the model are tuned using the Bayesian optimization method with the generated dataset. To improve the performance of the deep learning model, a deep learning module with weighted multi-head self-attention is proposed and used in the pseudo-gamma spectroscopy model. The performance of this model is verified using the measured plastic gamma spectra. Furthermore, a performance indicator, namely the minimum required count for single isotopes, is defined using the mean absolute percentage error with a criterion of 1% as the metric to verify the pseudo-gamma spectroscopy performance. The obtained results confirm that the proposed model successfully unfolds the full-energy peaks and predicts the relative radioactivity, even in spectra with statistical uncertainties. Full article
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Review

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Review
Application and Development of Noncontact Detection Method of α-Particles Based on Radioluminescence
Sensors 2022, 22(1), 202; https://doi.org/10.3390/s22010202 - 28 Dec 2021
Viewed by 559
Abstract
The detection of α particles is of great significance in military and civil nuclear facility management. At present, the contact method is mainly used to detect α particles, but its shortcomings limit the broad application of this method. In recent years, preliminary research [...] Read more.
The detection of α particles is of great significance in military and civil nuclear facility management. At present, the contact method is mainly used to detect α particles, but its shortcomings limit the broad application of this method. In recent years, preliminary research on noncontact α-particle detection methods has been carried out. In this paper, the theory of noncontact α-particles detection methods is introduced and studied. We also review the direct detection and imaging methods of α particles based on the different wavelengths of fluorescence photons, and analyze the application and development of this method, providing an important reference for researchers to carry out related work. Full article
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