A new gamma-ray sensor, which could be employed in harsh underwater conditions, was developed using YAlO(Ce) single crystal and carbon nanotube reinforced polyetheretherketone (CNT/PEEK). The sensor is compact, highly sensitive and stable, by providing real-time gross counts and an accumulated spectrum for fresh, saline, or contaminated water conditions. The sensor was tested in a water tank for quantification of the limit of detections. The Φ51 × 51 mm YAlO(Ce) crystal exhibits a nearly perfect proportionality with a correlation of over 0.999 in terms of light yield per energy and possesses a high energy resolution. The chemically stable CNT/PEEK window material further enhances the detection efficiency by minimizing the background counts from penetrating gamma-rays. Data timeliness was obtained for regulation-based minimum detectable activity targets within 300 s. For a source-detector distance of up to 300 mm in water, the gross counts demonstrate the existence of radionuclides (Cs-137 and Co-60), owing to their higher efficiency (max. ~15 times) than those of the photopeak counts. Such differences between efficiency values are more likely in water than in air because of the high density of water, resulting in an increased build-up of scattered photons. The proposed sensor is suitable for autonomous underwater systems.
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http://dx.doi.org/10.3390/s21051606 | DOI Listing |
Med Phys
November 2024
Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia.
Sensors (Basel)
November 2024
School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea.
Sensors (Basel)
November 2024
Department of Computer Sciences, Northwestern University, Evanston, IL 60208, USA.
Sci Rep
November 2024
Exploration Division, Nuclear Materials Authority, Cairo, Egypt.
Alteration zones in the Eastern Desert are promising for minerals and geological resources exploration. Remote sensing and geophysical techniques offer cost-effective tools for identifying new exploration sites. Additionally, their use in mapping potential alteration zones is crucial for enhancing exploration.
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October 2024
School of intelligent manufacturing, Zhejiang Guangsha Vocational and Technical University of Construction, Jinhua, 322100, China.
In this study, a feasibility of γ radiation detection using complementary metal-oxide semiconductor (CMOS) image sensors with a neural network algorithm to extract the γ rays interacted pixels has been investigated. The responses characteristics of the CMOS imaging sensor to γ-ray is studied by placed in a γ fields produced by standardCo orCs isotope sources. The supported preview frame rate of the CMOS image sensor is 25 fps, establishing the functional relationship between the gray level histograms and the dose rate through the neural network, the high energy γ-ray fromCo andCs source radiation dose rate in µSv/h level can be detected using the CMOS imaging sensor.
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