Development and Implementation of a Multi-Element Assay Method Using Laser Ablation Coupled with ICP-MS for the Analysis of Powdered Solids
The activities of the host laboratory within the CEA’s Directorate of Military Applications (DAM) focus on the analysis of small quantities of nuclear material. This study aims to develop and implement a method for elemental analysis in powdered solids, based on the coupling of a laser ablation (LA) device with an inductively coupled plasma mass spectrometer (ICPMS). The method developed must cover a wide range of concentrations, from ultra-trace levels (ng/g) to minor and major elements. Sample preparation will be performed by alkaline fusion to form glass pellets, ensuring good spatial homogeneity of the elements to be analyzed. For certain applications, up to forty chemical elements will need to be analyzed.
Measurements will be performed using a 213-nm nanosecond UV laser and a high-resolution ICPMS (Thermo-Fisher “Element XR”) located in a controlled area. The target samples are labeled soils and uranium ore concentrates. Part of this work will be conducted in collaboration with the European Joint Research Center (JRC) in Karlsruhe, Germany, which is also equipped with a multi-collection LA-ICPMS system (Thermo-Fisher “Neoma”).
detection of multiplets and application to turkey-Syria seismic crisis of february 2023
The correlation technique, or template matching, applied to the detection and analysis of seismic events has demonstrated its performance and usefulness in the processing chain of the CEA/DAM National Data Center. Unfortunately, this method suffers from limitations which limit its effectiveness and its use in the operational environment, linked on the one hand to the computational cost of massive data processing, and on the other hand to the rate of false detections that could generate low-level processing. The use of denoising methods upstream of processing (example: deepDenoiser, by Zhu et al., 2020), could also increase the number of erroneous detections. The first part of the research project consists of providing a methodology aimed at improving the processing time performance of the multiplets detector, in particular by using information indexing techniques developed in collaboration with LIPADE (L-MESSI method , Botao Peng, Panagiota Fatourou, Themis Palpanas. Fast Data Series Indexing for In-Memory Data. International Journal on Very Large Data Bases (VLDBJ) 2021). The second part of the project concerns the development of an auto-encoder type “filtering” tool for false detections built using machine learning. The Syria-Turkey seismic crisis of February 2023, dominated by two earthquakes of magnitude greater than 7.0, will serve as a learning database for this study.