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Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy
Author(s) -
Luke R. Sadergaski,
David W. DePaoli,
Kristian Myhre
Publication year - 2020
Publication title -
applied spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.415
H-Index - 110
eISSN - 1943-3530
pISSN - 0003-7028
DOI - 10.1177/0003702820933616
Subject(s) - raman spectroscopy , dissolution , calibration , partial least squares regression , hot cell , analytical chemistry (journal) , aluminium , chemistry , materials science , optics , metallurgy , nuclear engineering , computer science , physics , statistics , mathematics , chromatography , machine learning , engineering
Chemical processing of highly radioactive materials commonly takes place in heavily shielded hot cells. The remote, real-time monitoring of chemical processing streams via optical spectroscopic techniques in hot cells may be particularly useful. Here, we describe the implementation of Raman spectroscopy and chemometric analysis to monitor the dissolution of aluminum-clad targets containing irradiated aluminum-neptunium oxide cermet pellets in caustic solutions in a hot cell environment. Partial least squares regression analysis was used to generate calibration models to quantify the concentration of dissolved aluminum, nitrate, and hydroxide in solutions within the radiochemical hot cell. This work explored a systematic approach to optimize a matrix of calibration standards using a D-optimal experimental design. The Design of Experiments-based regression model, in comparison to more traditional analytical approaches, was found to be the more practical method for building calibration models, with fewer samples, to obtain informative analytical data from Raman spectra.

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