The Gamma and Neutron Sensor System for Rapid Dose Rate Mapping in the CLEANDEM Project

• Main Authors: Cosentino, L. (Author), Finocchiaro, P. (Author), Longhitano, F. (Author), Minutoli, S. (Author), Musico, P. (Author), Osipenko, M. (Author), Poma, G.E (Author), Ripani, M. (Author), Rossi, F. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: Data acquisition; Decommissioning (nuclear reactors); Diodes; Dose rate; gamma detector; Gamma detectors; Human operator; Mapping; neutron detector; Neutron detectors; Neutron irradiation; Neutron sensors; Neutrons; nuclear accident remediation; Nuclear accident remediation; Nuclear accidents; nuclear decommissioning; Nuclear decommissioning; Nuclear installations; Nuclear reactor accidents; robotic radiological inspection; Robotic radiological inspection; Robotics; Sensor systems
• Online Access: View Fulltext in Publisher; View in Scopus

 The decommissioning of nuclear installations, as well as the possible necessary accident remediations, requires the physical presence of human operators in potentially radiologically hostile environments. The number of active nuclear reactors worldwide is greater than 400, and most of them are 40 to 50 years old, thus implying that soon they will have to be dismantled. In the framework of the H2020 CLEANDEM project, a small robotic vehicle is being developed that is equipped with a series of different sensors for areas that are significantly contaminated by radiation. In this work, we describe the MiniRadMeter system, a compact low-cost sensor capable of being used to perform quick gamma and neutron radiation field mapping of environments prior to the possible start of human operations. The miniature gamma sensor is a 1 cm3 scintillator counter with moderate spectroscopic features read out by means of a 6 × 6 mm2 SiPM, whereas neutrons are detected by means of a silicon diode coupled to a layer of 6LiF and placed inside a 6 × 6 × 6 cm3 polyethylene box. The front-end and data acquisition electronics were developed based on a Raspberry Pi4 microcomputer. In this paper, the system performance and the preliminary test results are described. © 2023 by the authors.