Chemometric Modeling of Select Lanthanides in Solution via Partial Least Squares Regression for Material Accountancy and Safeguarding
Utilizing nuclear energy to combat climate change is rapidly becoming necessary to mitigate major disruptive events. However, nuclear technology has an inherent dual use concern which creates a challenge to the legitimacy of some nuclear energy programs. Ergo, nuclear energy must be expanded with care of region and country-specific threats to maintain long-term prosperity. One such avenue is implementing spectroscopic on-line monitoring for tracking special nuclear material in close to real time. Using UV/Visible spectrum absorbance implements a barrier to nefarious tampering of a recycling system by utilizing physical properties unique from existing safeguards. In this thesis, optimization of a partial least squares regression model built on UV/Vis absorbance was explored, along with probing of a power analysis to better understand modeling parameters, and the speciation of neodymium with citrate as may be found in advanced nuclear fuel recycling. The models were optimized to maximize accuracy while minimizing susceptibility to deception. Ultimately, an accurate on-line model for special nuclear material will make nuclear recycling a more globally achievable goal, ergo increasing the prevalence of nuclear energy and contributing to climate change abatement.