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Removal of Trace Elements by Cupric Oxide Nanoparticles from Uranium In Situ Recovery Bleed Water and Its Effect on Cell Viability
Published on: June 21, 2015
A review of uranium (U) elemental detection methods
Xiang Yu1,2,3, Xuebin Su2,3, Zhe Wang1
1State Key Laboratory of Power System Operation and Control, Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Institute for Carbon Neutrality, Department of Energy and Power Engineering, Tsinghua University, Beijing, 100084, China.
Abstract:
With the increasing demand for energy, nuclear energy has been developing rapidly. The quantitative detection and qualitative identification of uranium (U) are of great significance for the comprehensive and efficient use of U resources and the control of nuclear and radioactive substances. In this study, the detection of U is divided into liquid sample detection, solid sample detection, gas sample detection, and industrial detection from the perspectives of the sample state and detection environment. For liquid samples, the advantages and disadvantages of various detection methods are summarized. The application of different detection methods for different samples is also analyzed. For solid samples, the application of laser-induced breakdown spectroscopy and X-ray fluorescence in powder compaction and U-containing solid samples is presented. The problems of low instrumental resolution and poor accuracy of quantitative analyses are discussed, and methods to solve the above problems are proposed. For gas samples, UF6 can be analyzed for U isotopes using laser-induced breakdown spectroscopy. U abundance (235U/(235U + 238U)) is obtained quickly, directly and without the need for sample preparation. Finally, the application of remote detection and in situ detection techniques in industrial detection of U is also discussed in this study. In the future, rapid, high-precision and portable U detection methods can be developed by coupling multiple detection methods to meet the needs of different scenarios.
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