Plutonium (IV) Quantification in Technologically Relevant Media Using Potentiometric Sensor Array

Quantification of elements in spent nuclear fuel using intrinsic radioactivity for sample excitation and chemometric data processing

Quantitative analysis of spent nuclear fuel (SNF) is a very challenging task. High radioactivity, complex chemical composition and personnel safety requirements severely limit the number of analytical tools suitable for this problem. There is an urgent need for the methods that would provide for remote on-line quantification of elements in spent nuclear fuel and its reprocessing technological solutions. Here we propose a novel approach based on the registration of X-ray fluorescence radiation from SNF samples induced by fission products radioactivity. In this case the X-ray excitation conditions will obviously vary from sample to sample; moreover the resulting spectra will be a complex superposition of numerous signals from soft gamma emitters and X-ray fluorescence of various nature. These complex spectra can be effectively treated with chemometric data processing for quantification of particular elements. We have demonstrated the validity of this approach for direct analysis of U, Zr and Mo in SNF raffinate.

Nonlinear Multivariate Regression Algorithms for Improving Precision of Multisensor Potentiometry in Analysis of Spent Nuclear Fuel Reprocessing Solutions

Potentiometric multisensor systems were shown to be very promising tools for the quantification of numerous analytes in complex radioactive samples deriving from spent nuclear fuel reprocessing. Traditional multivariate calibration for these multisensor systems is performed with partial least squares regression—an intrinsically linear regression method that can provide suboptimal results when handling potentiometric signals from very complex multi-component samples. In this work, a thorough investigation was performed on the performance of a multisensor system in combination with non-linear multivariate regression models for the quantification of analytes in the PUREX (Plutonium–URanium EXtraction) process. The multisensor system was composed of 17 cross-sensitive potentiometric sensors with plasticized polymeric membranes containing different lipophilic ligands capable of heavy metals, lanthanides, and actinides binding. Regression algorithms such as support vector machines (SVM), random forest (RF), and kernel-regularized least squares (KRLS) were tested and compared to the traditional partial least squares (PLS) method in the simultaneous quantification of the following elements in aqueous phase samples of the PUREX process: U, La, Ce, Sm, Zr, Mo, Zn, Ru, Fe, Ca, Am, and Cm. It was shown that non-linear methods outperformed PLS for most of the analytes.

On the Radiolytic Stability of Potentiometric Sensors with Plasticized Polymeric Membranes

There is not much known on the stability of plasticized polymeric sensor membranes against ionizing radiation. While recent studies have indicated the applicability of potentiometric sensors with such membranes for quantification of actinides and lanthanides in spent nuclear fuel reprocessing solutions, the real industrial application of such sensors will require their stability in ionizing radiation fields. The present study explores this problem and evaluates the stability of potentiometric sensitivity towards lanthanides and actinides for a variety of plasticized polymeric membranes based on different neutral ligands. We demonstrate that most of the studied sensor compositions retain their sensitivity up to 50–100 kGy of the absorbed gamma radiation dose. The higher doses lead to the gradual loss of sensitivity due to the radiolysis of ligands and a polymer membrane matrix as confirmed by electrochemical impedance and nuclear magnetic resonance studies.