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Innovative Detection Techniques for Ionizing Radiation in Medicine, Environment and Space

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

Deadline for manuscript submissions: 1 June 2025 | Viewed by 1369

Special Issue Editors


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Guest Editor
1. Department of Physics “E. Pancini”, University of Naples Federico II, 80126 Naples, Italy
2. National Institute of Nuclear Physics (INFN), Naples Section, 80126 Naples, Italy
Interests: radiation; radon; dosimetry; passive radon detector; gamma spettroscopy; alpha spettroscopy; ionizing radiation; gamma radiation; electret
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. National Institute for Nuclear Physics (INFN), Via Cinthia ed. 6, 80126 Naples, Italy
2. Department of Physics “E.Pancini”, University of Naples Federico II, Via Cinthia ed. 6, 80126 Naples, Italy
Interests: environmental radioactivity; air quality; radiation measurements; radiation protection
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Physics, University J.J. Strossmayer of Osijek, Osijek, Croatia
Interests: radon–radiation measurements in air, water and soil; soil permeability; environmental radioactivity; neutron dosimetry

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Guest Editor
Department of Physics, Università degli Studi di Roma Tor Vergata, Rome, Italy
Interests: condensed matter; epithermal neutron instrumentation; neutron scattering

Special Issue Information

Dear Colleagues,

We are witnessing a growing interest in the monitoring of ionizing radiation both due to the legislative updates following the transposition of the European Directive 59/2013 EURATOM (which represents the Basic Safety Standards—BSS), and due to the increase in awareness of the risks associated with exposures. In addition to living and working environments, another frontier is the exposure of astronauts.

This Special Issue therefore aims to gather original research and review articles on recent advances, technologies, solutions, applications and new challenges in the field of detection techniques for ionizing radiation.

Potential topics include, but are not limited to, the following:

  • Passive detectors;
  • Active detectors;
  • Continuous monitoring;
  • Clinical dosimetry;
  • Space dosimetry;
  • Environmental dosimetry;
  • Monte Carlo simulation in clinical dosimetry.

This topic falls within the scope of Sensors as it concerns studies and progress in the development and use of sensory devices and their application in industry, the environment and, in particular, work and life scenarios.

Dr. Giuseppe La Verde
Prof. Dr. Mariagabriella Pugliese
Dr. Marina Poje Sovilj
Prof. Dr. Roberto Senesi
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 2600 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

  • ionizing radiation
  • radiation protection
  • radioactivity measurements
  • mapping
  • radiometric characterization
  • environment

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Published Papers (1 paper)

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Research

9 pages, 2029 KiB  
Article
Pulse Shape Discrimination of n/γ in Liquid Scintillator at PMT Nonlinear Region Using Artificial Neural Network Technique
by Eungyu Yun, Ji Young Choi, Sang Yong Kim and Kyung Kwang Joo
Sensors 2024, 24(24), 8060; https://doi.org/10.3390/s24248060 - 18 Dec 2024
Viewed by 885
Abstract
Reactor-emitted electron antineutrinos can be detected via the inverse beta decay reaction, which produces a characteristic signal: a two-fold coincidence between a prompt positron event and a delayed neutron capture event within a specific time frame. While liquid scintillators are widely used for [...] Read more.
Reactor-emitted electron antineutrinos can be detected via the inverse beta decay reaction, which produces a characteristic signal: a two-fold coincidence between a prompt positron event and a delayed neutron capture event within a specific time frame. While liquid scintillators are widely used for detecting neutrinos reacting with matter, detection is difficult because of the low interaction of neutrinos. In particular, it is important to distinguish between neutron (n) and gamma (γ) signals. The principle of the interaction of neutrons with matter differs from that of gamma rays with matter, and hence the detection signal’s waveform is different. Conventionally, pulse shape discrimination (PSD) is used for n/γ separation. This study developed a machine learning method to see if it is more efficient than the traditional PSD method. The possibility of n/γ discrimination in the region beyond the linear response limits was also examined, by using 10- and 2-inch photomultiplier tubes (PMTs) simultaneously. To the best of our knowledge, no study has attempted PSD in a PMT nonlinear region using artificial neural networks. Our results indicate that the proposed method has the potential to distinguish between n and γ signals in a nonlinear region. Full article
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