Classification of diverse materials is of considerable interest in many applications. Given the chemical information gained in classification, the subsequent ability to predict geospatial features is an elusive task. In this work, we report the use of principal component analysis (PCA) as well as multivariate curve resolution-alternating least squares (MCR-ALS) methods applied to Raman microspectroscopic mapping for the classification and geospatial estimation of titanium dioxide polymorphs. Grains recovered from four Neoarchean spherule layers of differing geolocations consisting of rutile, anatase, and a high-pressure polymorph of titanium dioxide with the α-PbO2 structure, TiO2-II, were utilized in this study. Classification of the respective titanium dioxide polymorph was investigated using PCA and MCR-ALS methods. Geospatial estimation and further classification of the titanium dioxide polymorphs with respect to location was also explored. MCR-ALS analysis was used to provide a resolved spectrum of the specific titanium dioxide polymorph being probed. Using this uncontaminated Raman spectrum of a respective titanium dioxide polymorph from a particular location, PCA was utilized in order to distinctly evaluate the geospatial classification and estimation capabilities of the three polymorphs—rutile, anatase, and TiO2-II.

Classification and geospatial estimation of titanium dioxide polymorphs using multivariate exploratory methods

251st National Meeting (2016)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.