PNNL

Abstract

Radioactive contamination in the environment, be it from accidental or intentional release, can create an urgent need to assess water and food supplies, the environment, and monitor human health. Alpha-emitting radionuclides represent the most ionizing, and therefore the most damaging, form of radiation when internalized. Additionally, because of its ease of energy attenuation in solids or liquids, alpha emissions cannot be reliably monitored using non-destructive means. In the event of such an emergency, rapid and efficient methods will be needed to screen scores of samples (food, water, and human excreta) within a short time window. Unfortunately, the assay of alpha-emitting radionuclides using traditional radioanalytical methods is typically labor intensive and time consuming. The creation of analytical counting sources typically requires a series of chemical treatment steps to achieve well performing counting sources. In an effort to devise radioanalytical methods that are fast, require little labor, and minimize the use of toxic or corrosive agents, researchers at PNNL have evaluated magnetite (Fe3O4) nanoparticles as extracting agents for alpha-emitting radionuclides from chemically unmodified aqueous systems. It is demonstrated that bare magnetic nanoparticles exhibit high affinity for representative a-emitting radionuclides (241Am and 210Po) from representative aqueous matrices: river and ground water. Furthermore, use of the magnetic properties of these materials to concentrate the sorbed analyte from the bulk aqueous solution has been demonstrated. The nanoparticle concentrate can be either directly dispensed into scintillation cocktail, or first dissolved and then added to scintillation cocktail as a solution for alpha emission assay by liquid scintillation analysis. Despite the extreme quench caused by the metal oxide suspensions, the authors have demonstrated that quench correction features available on modern liquid scintillation analyzers can be employed to assure that quench-induced analytical biases can be avoided.