Improving Fallout Characterization by Using Multivariate Techniques to Determine Composition
Abstract
Multivariate statistical techniques were applied to un-mix nuclear fallout chemical data. This information is critical to characterization of fallout formation following a nuclear detonation. Understanding the correlation between environmental precursors and actinide concentrations in post-detonation nuclear fallout material aids in the determination of type, source and origin of nuclear fuel. This research examines 123 nuclear fallout samples from a historical nuclear test. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) are used to collect chemical compositions of the fallout samples. Principal component analysis (PCA) is applied to examine variation in elemental compositions and make estimates of environmental precursor compositions. Estimates are modeled using multivariate curve resolution-alternating least squares (MCR-ALS). The precursors exhibited on sample surfaces were spatially correlated with sample radioactivity using autoradiography imaging of the sample set. Results suggest that feldspar is correlated with a moderate level of radioactivity, samples with porous textures have unique compositions with a uniform surface radioactivity, and quartz is anticorrelated with radioactivity. Additionally, size, shape, and morphology each have a relationship with actinide concentration with large, homogeneous and spherical samples exhibiting the highest relative radioactivity.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 22, 2018
- Accession Number
- AD1056248
Entities
People
- Christopher R. Pitkins
Organizations
- Air Force Institute of Technology