Elemental characterization of nuclear materials using total reflection X-ray fluorescence spectrometry

Suitability and Applications of Total-Reflection X-Ray Fluorescence Spectrometry for Analytical Characterization of Nuclear Materials

The suitability and applications of Total reflection X-ray Fluorescence (TXRF) for characterization of nuclear materials are numerous. TXRF has been successfully applied for trace, minor and major determinations of constituents in nuclear materials such as fuel, clad, control rod, coolant, etc. The two major advantages of TXRF i.e. requirement of very small sample for analysis and non-requirement of matrix matched standards, make this technique further more attractive and suitable for nuclear industry. The applications of TXRF for trace analysis in nuclear materials such as fuel, clad, coolant and control rods are described in detail along with its applications for determination of major and speciation studies in TXRF mode.

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.