Detection of radium in water by x-ray fluorescence using Monte Carlo simulations. (February 2020)

• Record Type: Journal Article
• Authors: Burille, F.; Correa, J.J.M.; Zambianchi, P.; Zambianchi, J.K.; Antoniassi, M.
• Is Part Of: Radiation physics and chemistry. Volume 167(2020:Feb.)
• Publication Date: 2020-02
• Subjects: Radium -- X-ray fluorescence -- Monte Carlo Method; Radiation chemistry -- Periodicals; Radiometry -- Periodicals; Radiation -- Periodicals; Chimie sous rayonnement -- Périodiques; 539.2
• DOI: 10.1016/j.radphyschem.2019.108374
• Languages: English
• ISSNs: 0969-806X

Abstract

Abstract: The natural occurrence of radium in rocks, soil and water may pose a health hazard since radium is chemically similar to calcium. Thus, by the ingestion of food and drinking water, bones can accumulate this radioactive element and develop tumors. Therefore, the detection of radium and its concentration in drinking water is of interest to public health agencies. Due to its high sensitivity, X-ray fluorescence (XRF) can be used to detect the presence of trace elements (in the sub ppm range) such as radium in water. However, at these low concentrations, it is highly desirable to optimize the technique and Monte Carlo simulations are a powerful tool to carry it out. In this paper, we studied the detection of radium in water by x-ray fluorescence technique using Monte Carlo simulations. In the simulations, radiation of 19.3 k e V excited the L-absorption edge of radium and the fluorescence yield from samples of different thicknesses, at different geometries (90 °, backscatter and forward-scatter), was investigated. The respective detection limits were calculated for each geometry. Our results show that the highest photon count and small background is given by the 90 ° geometry. It also establishes the optimized values of detection angles and thicknesses for each geometry. Highlights: Detection of radium in water by x-ray fluorescence technique was carried out by Monte Carlo simulations. The simulations evaluated different geometries, angles of detection and sample thickness. Optimized values of geometry, detection angles and sample thicknesses were obtained.