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• W4376602318 endingPage "110853" @default.
• W4376602318 startingPage "110853" @default.
• W4376602318 abstract "Gamma detector detection technology based on NaI(Tl) scintillation crystal has become a popular research topic and has been applied in the field of marine radioactive environment automatic monitoring because of its advantages of low power consumption, low cost and strong environmental adaptability. However, insufficient energy resolution of the NaI(Tl) detector and great Compton scattering in the low-energy region caused by the abundance of natural radionuclides in seawater hinder the automatic analysis of radionuclides in seawater. This study adopts the combination of theoretical derivation, simulation experiment, water tank test and seawater field test, establishing an effective and feasible spectrum reconstruction method. The measured spectrum in seawater is regarded as the output signal formed by the convolution of the incident spectrum and the detector response function. The acceleration factor p is introduced to construct the Boosted-WNNLS deconvolution algorithm, which is used to iteratively reconstruct the spectrum. The analysis results of the simulation test, water tank test and field test meet the radionuclide analysis speed and accuracy requirements for the in-situ automatic monitoring of seawater radioactivity. The spectrum reconstruction method in this study converts the physical problem of insufficient detection accuracy of spectrometer in the practical application into a mathematical problem of deconvolution solution, restores the original radiation information in seawater, and improves the resolution of the seawater gamma spectrum." @default.
• W4376602318 created "2023-05-17" @default.
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• W4376602318 date "2023-08-01" @default.
• W4376602318 modified "2023-09-28" @default.
• W4376602318 title "Study on reconstruction and analytical method of seawater radioactive gamma spectrum" @default.
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• W4376602318 doi "https://doi.org/10.1016/j.apradiso.2023.110853" @default.
• W4376602318 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37216724" @default.
• W4376602318 hasPublicationYear "2023" @default.
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